GOOD MORNING.
WELCOME TO OUR WORKSHOP.
TRANSFORMING HYPERTENSION
DIAGNOSIS AND MANAGEMENT, IN THE
ERA OF ARTIFICIAL INTELLIGENCE.
TODAY WE HAVE THREE SESSIONS
WITH EXCELLENT SPEAKERS UNTIL
4:00 P.M. EASTERN TIME.
TOMORROW WE BEGIN AT 11:00 A.M.
EASTERN TIME AND CONCLUDE WITH
SESSIONS 4 AND 5.
WE'LL HAVE A BREAK TODAY AFTER
SESSIONS 1 AND 2.
WE ASK THAT YOU TYPE YOUR
QUESTIONS IN THE CHAT BOX
ACADEMIC KEEP -- AND KEEP YOUR
VOLUME ON MUTE UNLESS SPEAKING.
WE ASK THAT YOU KEEP YOUR VIDEO
OFF UNLESS YOU ARE SPEAKING AS
WELL.
I'D LIKE TO BEGIN BY INTRODUCING
OUR FIRST SPEAKER TODAY, DR.
GOFF, DIRECTOR OF THE DIVISION
OF CARDIOVASCULAR SCIENCE NHLBI,
BEFORE THAT WAS DEAN AND
PROFESSOR OF EPIDEMIOLOGY,
COLORADO SCHOOL OF PUBLIC HEALTH
AND AT THE WAKE FOREST SCHOOL OF
MEDICINE.
HE RECEIVED AN M.D. FROM
UNIVERSITY OF NORTH CAROLINA,
Ph.D. IN EPIDEMIOLOGY FROM
UNIVERSITY OF TEXAS HOUSTON
SCHOOL OF PUBLIC HEALTH.
HE IS AN ELECTED MEMBER OF THE
AMERICAN EPIDEMIOLOGICAL
SOCIETY, FELLOW OF AMERICAN
COLLEGE OF PHYSICIANS, AMERICAN
HEART ASSOCIATION.
HE HAS PUBLISHED OVER 300
MANUSCRIPTS, BOOK CHAPTERS AND
SCIENTIFIC REPORTS.
PLEASE WELCOME DR. GOFF.
>> THANKS, I APPRECIATE THE KIND
INTRODUCTION.
I WANT TO WELCOME EVERYONE TO
THIS REALLY IMPORTANT WORKSHOP.
IT'S IMPORTANT FOR US AS AN
INSTITUTE AND CLOSE TO MY HEART.
I SEE LONGTIME FRIENDS AND
COLLEAGUES HERE.
WHEN HE WAS AT THE UNIVERSITY OF
TEXAS HOUSTON SCHOOL OF PUBLIC
HEALTH IN THE LAST MILL ENYUM, I
CUT MY TEETH THERE WITH THE
TRIAL.
IT'S BEEN A LONG JOURNEY TO TRY
TO GET TO THE POINT WHERE WE CAN
ELIMINATE HYPERTENSION OR
RELATED CARDIOVASCULAR DISEASES.
WHICH IS ONE OF THE PILLARS AND
STRATEGIC VISION AND
IMPLEMENTATION PLAN FOR THE
DIVISION OF CARDIOVASCULAR
SCIENCES AT NHLBI.
SO, ELIMINATION OF HYPERTENSION
OR HYPERTENSION-RELATED
CARDIOVASCULAR DISEASE IS A GOAL
FOR US.
WHY?
WELL, THE GLOBAL BURDEN OF
DISEASE PROJECT TELLS US THAT
HIGH BLOOD PRESSURE IS LEADING
CAUSE OF DEATH ON THIS PLANET.
I OFTEN SAY, WELL, THAT'S JUST
THIS PLANET BUT IT SHOULD COUNT
FOR SOMETHING.
SO FOCUSING ON PREVENTION,
DETECTION, TREATMENT, AND
CONTROL OF HIGH BLOOD PRESSURE
MUST BE ONE OF OUR HIGHEST
PRIORITIES, GIVEN THAT HIGH
BLOOD PRESSURE IS LEADING CAUSE
OF DEATH AND DISABILITY ON THE
PLANET.
SO, WHAT YOU'RE GOING TO BE
DOING OVER THE NEXT COUPLE DAYS
GIVING US INPUT AND IDENTIFYING
OPPORTUNITIES TO ADVANCE THIS
PART OF OUR STRATEGIC VISION IS
JUST CRITICALLY IMPORTANT.
WE THANK YOU VERY MUCH.
WE KNOW THERE ARE MANY THINGS
YOU COULD BE DOING, ESPECIALLY
THESE DAYS.
THE FACT THAT YOU CHOSE TO SPEND
TIME WITH US IS SOMETHING FOR
WHICH WE'RE GRATEFUL.
SO WE WANT TO MAKE GOOD USE OF
YOUR TIME AND INPUT.
HOW WILL WE DO THAT?
WELL, THERE'S SOME THINGS THAT
ARE STRAIGHTFORWARD.
WE'LL GET A REPORT ON OUR
WEBSITE.
WE HOPE TO GET THAT REPORT OUT
WITHIN THE NEXT MONTH OR SO, SO
THE INFORMATION IS TIMELY WHEN
WE SHARE IT WITH THE BIOMEDICAL
RESEARCH COMMUNITY.
FOR THAT WE'LL BE LEANING ON YOU
AND ESPECIALLY YOUR CO-CHAIRS,
WHO WILL BE WORKING WITH US AND
YOU TO GET THAT REPORT OUT
PROMPTLY.
WE ALSO ANTICIPATE THAT YOU MAY
WANT TO PUT TOGETHER A PAPER FOR
SUBMISSION FOR PUBLICATION TO A
PEER-REVIEWED JOURNAL, WE'LL
SUPPORT THAT IF THAT'S SOMETHING
YOU WOULD LIKE TO DO.
IT'S NOT A REQUIREMENT BUT IT'S
OFTEN A USEFUL WORK PRODUCT OUT
OF A WORKSHOP LIKE THIS.
IF YOU DO THAT WE HOPE YOU'LL DO
IT PROMPTLY AND GET THE
INFORMATION OUT WITHIN SIX
MONTHS OR SO AT THE LATEST SO
THAT AGAIN IT'S TIMELY FOR THE
BIOMEDICAL RESEARCH COMMUNITY TO
GET YOUR IMPORTANT INSIGHTS AND
INPUT.
WE ALSO HOPE THAT AT THE END OF
THIS YOU AND THE OTHERS WHO ARE
LISTENING WILL BE STIMULATED TO
SUBMIT INVESTIGATOR-INITIATED
APPLICATIONS TO THE INSTITUTE.
THE VAST MAJORITY OF THE
RESOURCES THAT WE HAVE ARE GIVEN
OUT EACH YEAR IN
INVESTIGATOR-INITIATED -- TO
INVESTIGATOR-INITIATED
APPLICATIONS.
WE HOPE THIS WILL STIMULATE
IDEAS THROUGH THAT PROCESS.
WE USE YOUR INPUT TO GUIDE
SELECT PAY.
WE HAVE A POT OF FUNDS THAT WE
RESERVE EACH YEAR TO BE ABLE TO
PICK UP APPLICATIONS THAT COME
IN THROUGH THE PEER-REVIEWED
PROCESS THAT ARE NOT QUITE ON
THE RIGHT SIDE OF THE PAYLINE
BUT ARE STRATEGICALLY ALIGNED.
GIVEN THE IMPORTANCE OF THIS
AREA TO OUR MISSION AND
STRATEGIC VISION, WE USE YOUR
INPUT TO HELP GUIDE OUR SELECT
PAY PROCESS.
NOW, THERE ARE SOMETIMES FUNDING
OPPORTUNITIES THAT ARE ANNOUNCED
IN ASSOCIATION WITH WORKSHOPS.
I WANT TO SET REASONABLE
EXPECTATIONS BECAUSE WE DO MORE
WORKSHOPS EACH YEAR THAN WE CAN
POSSIBLY PUT OUT WELL-FUNDED
FUNDING OPPORTUNITIES.
SO WHETHER A FUNDING OPPORTUNITY
GROWS OUT OF THIS WORKSHOP OR
NOT IS I THINK YET TO BE
DETERMINED.
IT'S A POSSIBILITY, BUT IT'S NOT
A DEFINITE.
DEPENDS HOW STRONG THE IDEAS ARE
AND HOW PRACTICAL THEY ARE AND
THE RESOURCES THAT WE HAVE TO BE
ABLE TO PUT BEHIND IDEAS.
BUT WE WILL TAKE YOUR INPUT VERY
SERIOUSLY.
THIS IS AN ISSUE THAT'S JUST
CRITICAL TO OUR MISSION.
AND IT'S, YOU KNOW, IF YOU LOOK
AT OUR STRATEGIC VISION
IMPLEMENTATION PLAN AND YOU LOOK
ACROSS OUR PILLARS, I MENTIONED
THIS IS ONE OF THE SIX PILLARS,
IT'S RELATED TO SEVERAL OTHERS.
ONE OF OUR PRIORITIES IS TO
ADDRESS SOCIAL DETERMINANTS OF
CARDIOVASCULAR HEALTH AND HIGH
BLOOD PRESSURE IS A MAJOR PART
OF THE HEALTH INEQUITY CHALLENGE
IN OUR COUNTRY.
SO ADDRESSING SOCIAL
DETERMINANTS OF HEALTH
INEQUITIES WILL HELP US ADDRESS
HYPERTENSION.
ANOTHER PILLAR IS PROMOTING
CARDIOVASCULAR ACROSS THE
LIFESPAN.
WE KNOW THAT BLOOD PRESSURE
TENDS TO GO UP WITH AGE IN
COUNTRIES IN WHICH A LOT OF
SODIUM IS CONSUMED.
IF WE'RE ABLE TO WORK OUT A WAY
TO PREVENT THE AGE-RELATED
INCREASE OR AGE-ASSOCIATED
INCREASE IN BLOOD PRESSURE, THAT
WILL ALSO GO A LONG WAY TOWARDS
ADDRESSING HEALTH DISPARITIES
AND PREVENTING
HYPERTENSION-RELATED
CARDIOVASCULAR DISEASES.
SO I'M LOOKING FORWARD TO YOUR
INPUT ON THE TOPICS YOU'RE GOING
TO BE DISCUSSING OVER THE NEXT
COUPLE DAYS TO REALLY HELP US
RETHINK HOW WE APPROACH THESE
PARTS OF OUR MISSION.
AND WITH THAT, I'M GOING TO PASS
THE BATON BACK TO DR. ITURRIAGA
AND THE CO-CHAIRS SO WE CAN GET
ON WITH THE WORK OF THE DAY.
THANK YOU VERY MUCH.
>> THANK YOU, DR. GOFF.
I'D LIKE TO INTRODUCE THE TWO
CO-CHAIRS, DOCTORS SHAH AND
SHIMBO.
DR. SHAH IS ASSOCIATE PROFESSOR
UNIVERSITY OF UTAH SCHOOL OF
MEDICINE, CLINICAL RESEARCH
SCIENTIST, OVERALL GOAL IS TO
IMPROVE GLOBAL CARDIOVASCULAR
HEALTH BY EMPHASIZING QUALITY
AND VALUE INSIDE AND OUTSIDE OF
TRADITIONAL HEALTH CARE DELIVERY
SYSTEM.
DR. SHAH'S WORK FOCUSES ON
EMPOWERING PEOPLE TO ACHIEVE
HEALTH BY BUILDING AND
DELIVERING NEW TOOLS AND
RESOURCES THAT WORK.
SHE HAS EXPERIENCE AS A HEALTH
SERVICES RESEARCHER WITH
EXPERTISE IN CLINICAL
INFORMATICS, COMPARATIVE
EFFECTIVENESS, OBSERVATIONAL
STUDY DESIGN.
ACADEMIC WORK INCLUDES CREATING
PHENOTYPES AND ANALYSIS-READY
DATASETS.
PREVIOUS ACHIEVEMENTS INCLUDE
NIH GRANTS, DORIS DUKE AWARD,
INAUGURAL WOMAN AS AN ESCALATOR
AWARD.
DR. SHIMBO IS A
PHYSICIAN-SCIENTIST, PROFESSOR
OF MEDICINE AT COLUMBIA
UNIVERSITY IRVING MEDICAL
CENTER.
HE RECEIVED A BACHELOR OF
SCIENCE FROM JOHNS HOPKINS
UNIVERSITY IN BIOMEDICAL
ENGINEERING, M.D. FROM ALBANY
MEDICAL COLLEGE.
AFTER COMPLETING AN INTERNAL
MEDICINE RESIDENCY HE WENT ON TO
COMPLETE CHIEF MEDICAL RESIDENCY
IN INTERNAL MEDICINE IN
CARDIOVASCULAR MEDICINE, MOUNT
SINAI HOSPITAL BEFORE JOINING
FACULTY AT COLUMBIA UNIVERSITY
IN 2003.
DR. SHIMBO IS DIRECTOR OF
COLUMBIA HYPERTENSION CENTER AND
LABORATORY, CONDUCTS RESEARCH
INTO THE BEHAVIORAL AND
BIOLOGICAL MECHANISMS THAT
UNDERLIE HYPERTENSION AND
CARDIOVASCULAR DISEASE.
HE IS AN EXPERT IN THE DIAGNOSIS
AND TREATMENT OF HYPERTENSION
AND HOW HYPERTENSION AND ITS
PHENOTYPES ARE RELATED TO
CARDIOVASCULAR DISEASE ONSET.
I'M NOW TURNING OVER THE
WORKSHOP TO OUR WONDERFUL
CO-CHAIRS.
>> OKAY.
GOOD MORNING HERE IN NEW YORK.
I AM FIRST GOING TO SAY A FEW
WORDS, AND THEN I'M GOING TO
HAVE DR. SHAH TAKE US THROUGH
THE AGENDA FOR DAY 1.
I'M GOING TO END BY GOING
THROUGH DAY 2.
YOU HEARD FROM DR. GOFF.
I'M A CARDIOLOGIST, AS WELL AS
DR. SHAH.
I'M FOCUSED ON HYPERTENSION, I
AM BIASED, IT'S BECOMING MORE
RELEVANT GIVEN IT'S THE NUMBER
ONE KILLER WORLDWIDE.
AND IN THE UNITED STATES,
UNFORTUNATELY, BP CONTROL RATES
HAVE WORSENED EVEN BEFORE THE
PANDEMIC, HAVE GOTTEN WORSE
DURING THE PANDEMIC.
GIVEN HOW IMPORTANT HYPERTENSION
IS, TO ME IT'S TIME TO HIT THE
RESET BUTTON AND IDENTIFY
PRIORITIES FOR BLOOD PRESSURE
CONTROL.
THIS SESSION IS FOCUSED ON
ARTIFICIAL INTELLIGENCE.
YOU HAVE SEEN EVERYWHERE
ALGORITHMS ARE EVERYWHERE, I
JUST PICKED UP THE JAMA LAST
NIGHT AND SAW TWO MORE
PUBLICATIONS ON A.I. AND MEDICAL
HEALTH.
AND WHAT'S CLEAR IS IT'S ALL
OVER SOCIAL MEDIA, AND AS WELL
AS NOT JUST JAMA BUT OTHER
PUBLISHED JOURNALS.
AND IT'S PRETTY CLEAR THAT
INDUSTRY PARTNERS ARE INCREASING
INVESTMENTS, FACEBOOK, GOOGLE,
MICROSOFT, REALLY DOUBLING DOWN
ON A.I. I PERSONALLY HAVE CHUTE
CHAT USED CHATGPT AND OTHERS AND
HAVE BEEN IMPRESSED.
I THINK THE CHALLENGES THAT A.I.
IS NOW BEING USED SOMEWHAT IN
SOME MEDICAL DISCIPLINES AND AS
WELL AS, FOR EXAMPLE,
HYPERTENSION, THERE'S BEEN SOME
CONCERNS THAT HAVE ARISEN.
SOME QUESTIONS THAT HAVE COME
ABOUT IS HOW GOOD ARE THE
ALGORITHMS, DO THEY REALLY HELP
HUMAN HEALTH, AND WHAT ARE THE
POTENTIAL RISKS AND DOWN SIDES.
SO, DR. SHAH, MYSELF, AND THE
ENTIRE NIH STAFF WHO PUT THIS
SESSION TOGETHER REALLY DESIGNED
THIS SESSION AROUND A.I. AND ITS
USE IN HYPERTENSION DIAGNOSIS,
IN MANAGEMENT.
THE WAY THE SESSION'S BEEN
DESIGNED, THE WORKSHOP, IS IT'S
CLEAR WITH SO MANY PEOPLE
INVOLVED, CLINICIANS, INDUSTRY
LEADERS, LEGAL AND REGULATORY
EXPERTS, PUBLIC HEALTH EXPERTS,
CLINICAL RESEARCHERS, DATA
SCIENTISTS, ET CETERA WE'RE
DEEPLY CONCERNED THAT WE'RE
SPEAKING DIFFERENT LANGUAGES.
AND I ALWAYS THINK OF THE
PICTURE OF THE BLIND MONKS
HOLDING DIFFERENT PARTS OF THE
ELEPHANT, DESCRIBING THE
ELEPHANT BUT OF COURSE
DESCRIBING DIFFERENT PIECES.
THE DESCRIPTIONS SOUND DIFFERENT
BUT THEY ARE THE SAME ELEPHANT
WHAT YOU'LL SEE FROM THE
DIFFERENT SESSIONS IS THAT WE
WANT TO EDUCATE EACH OTHER ON
THE DIFFERENT CONTENT IN THIS
AREA.
IF YOU'RE A HYPERTENSION EXPERT
AND LISTEN, YOU MIGHT SAY
THERE'S NOTHING NEW HERE BUT
REALLY THE TARGET IS THE OTHER
PEOPLE WHO DON'T KNOW.
AND SO WE HOPE THAT YOU LEARN
FROM THE AREAS THAT YOU'RE NOT
FAMILIAR WITH.
AND AT THE END OF THE DAY, THERE
WILL BE LOTS OF DISCUSSION ABOUT
THE BENEFITS AND DOWN SIDES TO
A.I. AND HYPERTENSION.
WITH THAT INTRODUCTION, I'LL
HAVE YOU TAKE US THROUGH DAY 1.
>> THAT SOUNDS GREAT.
THANK YOU FOR PARTICIPATING, AND
NIH STAFF FOR HELPING ORGANIZE.
THIS HAS GOT ME EXCITED BECAUSE
I LOVE HOW THIS GROUP OF
SESSIONS AND SPEAKERS SPANS THE
WHOLE RANGE OF WHAT WE NEED TO
REALLY ADVANCE THE FIELD, NOT
JUST FROM A SCIENCE STANDPOINT,
ACTUALLY GET SOMETHING DONE
STANDPOINT.
I'M EXCITED ABOUT THE AGENDA.
I'LL BRIEFLY GO OVER WHAT WE
HAVE FOR DAY 1 TODAY.
TODAY WE'LL HAVE THREE SESSIONS.
THE FIRST SESSION IS BRIDGING
THE COMMUNICATIONS GAP BETWEEN
POPULATION HEALTH, CLINICAL
MEDICINE, ENGINEERING.
AND AS DR. SHIMBO ALLUDED TO,
THIS CONCEPT THAT IN SOME WAYS
NOW I WORK A LOT WITH ENGINEERS
BUT WE SPEAK DIFFERENT LANGUAGES
AND HOW DO WE GET ON THE SAME
PAGE TO DO SOMETHING THAT
MATTERS AND DO IT FAST.
SECOND SESSION IS BLOOD PRESSURE
MEASUREMENT, HYPERTENSION RISK,
AND BLOOD PRESSURE CONTROL.
WE'LL LEARN ABOUT SORT OF THE
CURRENT STATE OF BLOOD PRESSURE
CONTROL AND TREATMENT AND
PREVALENCE AND AWARENESS, AGAIN
MAYBE WE HAVE ENGINEERS IN THE
AUDIENCE, GET EVERYBODY ON THE
SAME PAGE.
FINALLY TODAY WE HAVE MAKING A
DENT IN THE PROBLEM.
WE HAVE SCIENCE, TOOLS,
ENGINEERS, PHYSICIANS, HOW ARE
WE GOING TO MAKE A DIFFERENCE?
THEY BEGINNING WE'LL HAVE OUR
KEYNOTE SPEAKER WHO I WILL
INTRODUCE IN A MINUTE HERE.
THANK YOU.
>> THANK YOU, DR. SHAH.
DAY 2 WE START AT THE SAME TIME,
11 A.M.
AND WE WILL END AT 4:00 P.M.
TOMORROW.
AND DAY 2 WILL START OFF WITH
SESSION NUMBER 4, WHICH IS ON
REAL WORLD IMPLEMENTATION
CHALLENGES AND ISSUES.
THIS SESSION WILL FOCUS ON THE
REAL WORLD PRACTICALITY ISSUES
THAT ARE OCCURRING OR AFFECT
USING A.I. IN THE CLINICAL CARE
OF PATIENTS WITH HYPERTENSION.
SESSION 5 IS A DISCUSSION.
IT'S A GROUP DISCUSSION FROM 2
TO 3, INVITED SPEAKERS AND
MODERATORS WILL ACTUALLY GO INTO
A ROOM.
THIS WON'T BE AVAILABLE TO THE
PUBLIC.
WE'LL BE DISCUSSING BEHIND THE
SCENES EVERYTHING THAT WE
LEARNED FROM EACH OTHER AND
WE'RE GOING TO COME OUT WITH
ANSWERS TO THREE QUESTIONS.
RESEARCH GAPS AND HIGH PRIORITY
QUESTIONS, WHAT ARE BARRIERS TO
ADDRESSING GAPS AND QUESTIONS,
AND RECOMMENDATIONS HOW TO MOVE
THIS FIELD FORWARD.
AND THEN FROM -- AT 3:00 WE'LL
RETURN TO THE ROOM, AND THAT
WILL THEN BE OPEN TO THE PUBLIC.
AND WE'LL PRESENT EACH OF OUR
FINDINGS AND RECOMMENDATIONS AND
THOUGHTS TO THE GENERAL PUBLIC
REGARDING THE THREE QUESTIONS
THAT I JUST NAMED.
FINALLY AT THE END, WE'LL HAVE A
CLOSE-UP, 3:45 TO 4:00, AND AT
THAT POINT WE'LL END THE
SESSION.
BEFORE WE START WITH OUR KEYNOTE
SPEAKER, I WILL SAY I WANT TO
REITERATE TO THE AUDIENCE,
VIRTUAL AUDIENCE, PLEASE,
PLEASE, PLEASE TYPE YOUR
QUESTIONS IN THE CHAT BOX.
WE WILL BE MONITORING THOSE
QUESTIONS.
THE MODERATORS THAT HAVE BEEN
ASSIGNED TO EACH SESSION AS WELL
AS DR. SHAH AND MYSELF WILL USE
THESE QUESTIONS.
WE WILL CRAFT THESE QUESTIONS
THAT WE CAN ASK OUR SPEAKERS
BECAUSE THERE'S A LOT OF
BUILT-IN DISCUSSION TIME WITHIN
EACH SESSION.
SO, WITHOUT FURTHER ADO I'M
GOING TO JUST HAND IT BACK OVER
TO DR. SHAH WHO IS GOING TO
INTRODUCE OUR KEYNOTE SPEAKER.
>> EXCELLENT.
THANK YOU.
IT IS REALLY MY HONOR TO
INTRODUCE DR. ERICA SPATZ,
KEYNOTE SPEAKER, FOR TODAY.
SHE WILL BE TALKING ABOUT THE
BLOOD PRESSURE NEXUS.
DR. SPATZ IS AN ASSOCIATE
PROFESSOR OF CARDIOVASCULAR
MEDICINE EPIDEMIOLOGY AT THE
YALE SCHOOL OF MEDICINE.
AND SHE'S ALSO FOUNDER OF THE
YALE HEART AND VASCULAR CENTER
FOR THE DIGITAL HYPERTENSION
PROGRAM.
AS YOU CAN SEE HER EXPERTISE IS
PERFECT FOR TODAY.
ACCOLADES ARE NUMEROUS.
I COULD LIST A LOT BUT I WANT TO
TALK ABOUT WHY SHE'S THE PERFECT
PERSON FOR THIS KEYNOTE.
LOTS OF US DO REALLY EXCELLENT
SCIENCE, BUILD TOOLS, WRITE
CODE.
BUT PEOPLE WHO GET OUT INTO THE
COMMUNITY AND UNDERSTAND THE
PEOPLE AND THE CHALLENGES PEOPLE
IN COMMUNITIES FACE RELATED TO
HYPERTENSION, THOSE PEOPLE
REALLY UNDERSTAND THE PROBLEM
THAT NEEDS TO BE SOLVE AND THAT
IS DR. SPATZ.
WITHOUT FURTHER ADO, I HAND IT
OVER TO HER.
>> THANK YOU SO MUCH.
I'D LIKE TO THANK NHLBI AND
CO-CHAIRS WHO ARE COLLEAGUES AND
FRIENDS AND THOUGHT LEADERS IN
HYPERTENSION.
I'M PRIVILEGED TO TALK ABOUT THE
BLOOD PRESSURE NEXUS, REAL WORLD
CHALLENGES IN COMMUNITIES WITH
OR AT RISK FOR HYPERTENSION.
NEXT SLIDE.
THANK U YOU.
THESE ARE MY DISCLOSURES.
NEXT SLIDE.
I'M GOING TO TELL YOU ABOUT A
PATIENT JOURNEY.
YOU CAN ADVANCE.
SORRY.
YOU CAN ADVANCE ONE.
AND AGAIN.
SO THIS IS A STORY OF THE
42-YEAR-OLD BLACK MAN, WHO I MET
IN THE HOSPITAL IN 2017,
HOSPITALIZED WITH NEW ONSET
HEART FAILURE, EJECTION FRACTION
OF 15%, VERY LOW.
DIAGNOSED WITH A NON-ISCHEMIC
CARDIOMYOPATHY, CAUSE BEING
UNCONTROLLED BLOOD PRESSURE AT
THE AGE OF 42.
YOU CAN ADVANCE.
YOU CAN ADVANCE AGAIN.
I LOOKED BACK AND WONDERED HOW
COULD THIS BE THAT THIS OCCURRED
IN A 42-YEAR-OLD MAN AND FOUND
HE FIRST ENTERED OUR HEALTH
SYSTEM ONE YEAR PRIOR WITH
EMERGENCY ROOM VISIT FOR KNEE
PAIN.
HIS BLOOD PRESSURE WAS DETECTED
TO BE 200/120, STARTED ON
MEDICATION, GIVEN APPOINTMENT
WITH PRIMARY CARE PROVIDER.
HE SHOWED UP FOR THAT
APPOINTMENT AND WE LEARNED HE IS
A SINGLE DATA CARING FOR TWO
YOUNG CHILDREN, WIFE PASSED AWAY
OF BREAST CANCER, WORKING TWO
JOBS, OBESE, DIDN'T EAT WELL, NO
SYMPTOMS.
CAN YOU HEAR ME OKAY?
>> WE CAN HEAR YOU.
>> OKAY, GREAT.
SORRY.
HIS BLOOD PRESSURE WAS 172/90.
HE WAS TOLD TO COME BACK IN A
MONTH, YET NEVER SHOWED UNTIL
PRESENTATION FOR HEART FAILURE.
NEXT SLIDE.
WHEN I MET HIM IN THE HOSPITAL
AND YOU CAN ADVANCE ANOTHER TIME
ON THE SLIDE.
THANK YOU.
I WAS DETERMINED TO CHANGE THE
COURSE FOR THIS GENTLEMAN.
I MET HIM IN THE CARDIOLOGY
CLINIC, TRIED TO BUILD THAT
CONNECTION, AND BUILD A PLAN
TOGETHER TO ADVANCE GDMT.
I FAILED.
TWO YEARS LATER HE PRESENTS WITH
DISABLING STROKE FROM LEFT
VENTRICULAR THROMBUS, A TRAGIC
CASE THAT CONTINUES TO MAKE ME
EMOTIONAL BECAUSE I CONTINUE TO
CARE FOR HIM AND STRUGGLE WITH
LOU TO HAVE PREVENTED THIS
OUTCOME.
SOME MAY SAY THIS IS ON HIM.
HE DIDN'T FOLLOW UP.
BUT IS IT?
HOW ARE WE AS A LEARNING HEALTH
SYSTEM REFLECTING ON THESE CASES
AND USING THESE CASES TO
INNOVATE AND TO BE BETTER?
BLOOD PRESSURE NEXUS IS REALLY
ABOUT COMBINING THE MEDICAL,
SOCIAL, TECHNOLOGY, IN ORDER TO
CREATE BETTER OUTCOMES FOR
PATIENTS.
NEXT SLIDE.
ROOT CAUSE ANALYSIS WE NEED DO
THAT, RIGHT?
IT'S NOT JUST THAT WE UNDERSTAND
MR. GREEN, OUR PATIENT, AT THE
INDIVIDUAL LEVEL.
WE UNDERSTAND HIS STRUGGLES.
BUT IT GOES DEEPER, RIGHT?
MR. GREEN LIVED IN NEW HAVEN.
HE LIVES IN A NEIGHBORHOOD, I
SEE THE ARROWS ARE OFF, BUT AT
THE TOP OF THAT NEW HAVEN, NEW
HALLVILLE WITH SOME OF THE
HIGHEST RATES OF POVERTY, YET
LOWEST LIFE EXPECTANCY, UNDER 74
YEARS IN THE HIGHLY POPULATED
AREA.
THERE'S HIGHER E.D. VISITS FOR
HYPERTENSION, STROKE, A LOT OF
CRIME AND VIOLENCE.
THIS BOOK IS ABOUT THIS
NEIGHBORHOOD CALLED "THE OTHER
SIDE OF PROSPECT."
I PICTURE IS TAKEN FROM ACROSS
THE STRAIGHT FROM BARBER SHOPS
WE'RE SCREENING FOR
HYPERTENSION.
IT'S ABOUT A YOUNG BOY FALSELY
ACCUSED OF MURDER.
THE STRESSES OF GROWING UP IN
THIS ENVIRONMENT THAT HAS A LOT
OF CRIME AND LACK OF
OPPORTUNITY.
I THINK ABOUT THIS BEING THE
NEIGHBORHOOD WHERE MY PATIENT
AND SO MANY OTHER PATIENTS ARE
GROWING UP.
YET HERE WE ARE TRYING TO MAKE A
CONNECTION WITH A PATIENT, AND
MAYBE POTENTIALLY IGNORING OR
NOT EVEN VALIDATING WHAT THE
FACTORS ARE CONTRIBUTING TO
THEIR HIGH BLOOD PRESSURE.
NEXT SLIDE.
THIS IS NOT JUST A STORY ABOUT
POVERTY.
MANY HIGH PROFILE HIGHLY
EDUCATED SUCCESSFUL PEOPLE HAVE
ALSO SUFFERED FROM POOR
HYPERTENSION OUTCOMES, TRAGIC
OUTCOMES.
SHALON IRVING WAS A CDC
RESEARCHER, WHEN PREGNANT AND
HAD A TOUGH PREGNANCY, AFTER SHE
DELIVERED BABY SHE REPORTED NOT
FEELING WELL, HAVING A HEADACHE,
HIGH BLOOD PRESSURE.
TOLD TO GO HOME AND REST.
SHE DIED OF A STROKE.
JOHN SINGLETON, SAME DIRECTOR OF
BOYS IN THE HOOD, DIED AT 51 OF
A STROKE, BOTH FAMILIES WERE
ACTIVE WITH MULTIPLE SOCIETIES
TO GET THE WORD OUT AROUND
HYPERTENSION, AS YOU'VE HEARD
THE MOST COMMON AND DEADLY
KILLER ACROSS THE GLOBE FOR
PEOPLE THROUGHOUT.
WE KNOW THERE ARE SIGNIFICANT
DISPARITIES BY RACE, INCOME, AND
GEOGRAPHY.
NEXT SLIDE.
YOU CAN ADVANCE.
THANKS.
SO, BETWEEN 2000 AND 2018,
HYPERTENSION-RELATED DEATHS HAVE
INCREASED, STAGNATED, OR
DECELERATED.
IF YOU LOOK AT THE TOP TWO LINES
THEY REPRESENT BLACK MEN AND
BLACK WOMEN.
YOU CAN SEE THAT THEY HAVE A
TWO-FOLD HIGHER RATE THAN OTHER
RACE/ETHNICITY GROUPS ACROSS ALL
HYPERTENSION-RELATED
CARDIOVASCULAR DISEASE OUTCOMES.
THE PROBLEMS ARE ONLY GETTING
WORSE.
NEXT SLIDE.
THESE ARE DATA FROM NHANES,
COMMUNITY STUDY.
HERE WE'RE LOOKING AT
HYPERTENSION AWARENESS,
TREATMENT, CONTROL BY RACE AND
ETHNICITY.
WHAT I WANT TO BRING OUT HERE IS
THAT THERE ARE DIFFERENCES BY
RACE AND ETHNICITY, BUT ALL
GROUPS ARE NOT DOING WELL.
SO ONLY -- 1 IN 4 PEOPLE ARE
UNAWARE THEY HAVE HIGH BLOOD
PRESSURE.
1 IN 4 ARE NOT TREATED.
ONLY 1 IN 4 HAVE BLOOD PRESSURE
UNDER CONTROL.
YOU CAN ADVANCE.
THESE ARE ALSO DATA FROM NHANES.
HYPERTENSION CONTROL RATES
ACROSS THE LAST DECADE AND A
HALF ARE ACTUALLY GETTING WORSE.
STARTING IN 2013, WHICH
COINCIDES WITH WHEN THE JNC 8
GUIDELINES CAME OUT, FOR PEOPLE
ON TREATMENT WORSE CONTROL.
THIS SUGGESTS BIG GUIDELINES,
POLICY CHANGES CAN AFFECT BLOOD
PRESSURE OUTCOMES AND WE KNOW
THEY ARE DECLINING FURTHER WITH
COVID-19.
THE PROBLEM IS EVEN WORSE THAN
WE MIGHT IMAGINE.
I SHOWED A LOT OF DATA AROUND
HYPERTENSION THAT, 140/90 CUT
POINT.
NOW WE USE 130/80 BASED ON MOST
RECENT GUIDELINES.
THERE'S OTHER PHENOTYPES
INCLUDING MASKED WHEN NORMAL IN
OFFICE, ELEVATED IN OUTPATIENT
SETTINGS.
MASKED HYPERTENSION IS
ASSOCIATED WITH CARDIOVASCULAR
OUTCOMES, SORELY UNDERDETECTED.
IN THIS STUDY YOU CAN SEE AMONG
PEOPLE WITH NORMAL BLOOD
PRESSURE IN THE OFFICE, 13% HAD
MASKED HYPERTENSION, AMONG
PEOPLE WITH PRE-HYPERTENSION,
ELEVATED BLOOD PRESSURE IN THE
OFFICE, 36% HAD MASKED
HYPERTENSION.
YOU CAN ADVANCE.
THIS IS INTERESTING BECAUSE WHY
WOULD A PERSON HAVE HIGH BLOOD
PRESSURE ONLY OUTSIDE OF THE
OFFICE?
WE'VE GOT TO THINK ABOUT THE
STRESSORS THAT MIGHT OCCUR.
THIS GROUP FROM UNIVERSITY OF
ALABAMA STUDIED WHY SOME PEOPLE
HAVE HIGHER BLOOD PRESSURES
OUTSIDE OF THE OFFICE.
THEY LOOKED AT ALDOSTERONE
LEVELS AND COMPARED.
INTERESTINGLY ENOUGH, THEY FOUND
THAT THE LEVEL IN THE MASKED
GROUP WAS HIGHER IN OUTPATIENT
AS COMPARED TO GROUP WITH
CONTROLLED HYPERTENSION.
HOWEVER, IN THE CLINIC THE
ALDOSTERONE LEVELS WERE THE
SAME.
THEY HAVE POSTULATED OUTPATIENT,
WHEN PATIENTS WERE IN THEIR REAL
WORLD THOSE WITH MASKED
HYPERTENSION HAVE INCREASED
SYMPATHETIC RESPONSE.
THE WHOLE CASCADE OF STRESS
HORMONES AND ALDOSTERONE SYSTEM
IS ELEVATED IN PEOPLE WITH
MASKED HYPERTENSION.
IF WE'RE NOT DETECTING AND
CAPTURING, WE'RE MISSING AN
OPPORTUNITY TO IMPROVE OUTCOMES,
IN A SIGNIFICANT PROPORTION OF
PEOPLE.
NEXT SLIDE.
OUR PATIENT, MR. GREEN, AND MANY
OTHERS ARE IN THAT RED CIRCLE,
AND WE'RE WORKING WITH THEM IN A
20-MINUTE VISIT.
THAT'S OUR INTERACTION WITH
THEM.
BUT THEIR LIFE, THEIR WORLD IS
ALL OF THESE DIFFERENT LEVELS.
IT'S THEIR LIFESTYLE.
IT'S SOCIAL AND COMMUNITY
NETWORKS.
IT'S WHERE THEY WORK.
IT'S WHETHER THEY HAVE ACCESS TO
FOOD, OR SAFE WATER, OR GREEN
SPACES TO EXERCISE.
AND IT'S SOCIOECONOMIC CULTURAL
AND ENVIRONMENTAL CONDITIONS
THAT THEY ARE LIVING IN DAY IN
AND DAY OUT.
AND THAT WE NEED TO KIND OF
CAPTURE AND BE ABLE TO BRING
INTO OUR CARE PLANS IN ORDER TO
IMPROVE OUTCOMES.
NEXT SLIDE.
HOW DO WE DO THIS?
DIGITAL HEALTH REPRESENTS A PATH
FORWARD.
WE CAN BETTER CAPTURE OUR
PATIENTS' CONTEXT AND BUILD
CONNECTIONS WITH OUR PATIENTS.
DIGITAL HEALTH TECHNOLOGY IS
HELPING US WITH CAPTURING BLOOD
PRESSURE AND HEART RATE AND
SLEEP AND ACTIVITY.
WE'RE DOING GREAT AND EVEN ABLE
TO CAPTURE A LOT OF DAILY
LIFESTYLE BEHAVIORS LIKE
EXERCISE, NUTRITION, STRESS
LEVELS.
BUT HOW DO WE IMPLEMENT AT THE
BEDSIDE?
EVEN THE BEST DOCTORS AND NURSES
HAVING THESE CONVERSATIONS WITH
PATIENTS IN THE CLINIC ARE STILL
LIMITED AND PATIENTS ARE STILL
LEFT TO JUGGLE ALL
RECOMMENDATIONS ONCE THEY LEAVE
OUR OFFICE.
NEXT SLIDE.
SO I SUBMIT TO YOU THAT LAYING
EVEN THE BEST TECHNOLOGY ONTO
THIS INFRASTRUCTURE DOESN'T
WORK.
WE'RE STILL MOSTLY IN PERSON,
STILL PHYSICIAN BASED, STILL
RELYING ON SCHEDULED FOLLOW-UPS
AND AT BEST THOSE ARE AT 3, 6,
12 MONTHS.
WE DON'T PRESCRIBE HOME BLOOD
PRESSURE CUFFS.
WHEN DO WE DON'T TEACH HOW TO
USE IT, SYNC WITH RECORDS, SO
OFTEN BLAME THE THE PATIENT FOR
NOT CONFORMING WITH WHAT WE'VE
PRESCRIBED, WHICH IS UNTENABLE.
IT'S NOT PRACTICAL.
NEXT SLIDE.
SOCIETY BLOOD PRESSURE NEXUS IS
COMPLEX.
YOU CAN ADVANCE.
WE NEED TO THINK ABOUT
ENGAGEMENT.
WE NEED TO THINK ABOUT
MEASUREMENT, IMPLEMENTATION,
BLOOD PRESSURE OUTCOMES, BIG
HEALTH OUTCOMES AND HEALTH
EQUITY.
ADVANCE.
REGARDING ENGAGEMENT WE KNOW
HYPERTENSION IS A SILENT
CONDITION.
IT'S CREATING CHALLENGES WITH
INFLECTION POINTS WHERE BLOOD
PRESSURE IS HIGHER.
PEOPLE NEED TO BELIEVE THEY ARE
AT RISK.
IT CAN'T BE NORMALIZED.
PEOPLE NEED TO FEEL THEY HAVE
THE CAPACITY TO MAKE CHANGES TO
IMPROVE OUTCOMES.
WE NEED TO TAKE INTO
CONSIDERATION THE SOCIAL
STRESSORS, LIFESTYLE BEHAVIORS,
ENGAGE PEOPLE IN SHARED DECISION
MAKING SO THEY CAN HELP
CO-DESIGN PLANS.
WE NEED TO THINK ABOUT
MEASUREMENTS IN THE OFFICE AND
AT HOME, THINK ABOUT PHENOTYPES
OF MASKED HYPERTENSION, WHAT IS
THE LIFETIME OF EXPOSURE,
ESPECIALLY STRESSORS AT HOME AND
ACROSS THE LIFETIME.
HOW DO WE UNDERSTAND AND
INTERPRET BLOOD PRESSURE
LABILITY IN THE CONTEXT OF OTHER
COMORBIDITIES?
WE NEED TO THINK ABOUT ACCESS TO
BLOOD PRESSURE CUFFS, SYNCING
WITH EHR, CARE TEAM, WHAT DOES
THAT LOOK LIKE?
DECISION SUPPORT, COMBINATION
AND POLY PILLS, ARE THEY
FEASIBLE AND ACCESSIBLE AND HOW
WE PUT OUT POPULATION HEALTH
MEASURES, CAN WE INTEGRATE HOME
BLOOD PRESSURE MONITORING
MEASUREMENTS, CAN WE INTERPRET
CUFFLESS DATA WHICH WE KNOW IS
SOON TO COME.
ARE WE IMPACTING ORGAN DAMAGE,
CARDIOVASCULAR EVENTS, AFFECTING
BRAIN HEALTH, AFFECTING
FUNCTIONAL STATUS?
AND ARE WE DOING THIS THROUGH A
LENS OF HEALTH EQUITY?
WE HAVE INEQUITIES IN BLOOD
PRESSURE CONTROL.
ARE WE MOVING THE NEEDLE ON
EARLY AGE OF ONSET AFFLICTING
MORE BLACK MEN AND WOMEN IN OUR
COUNTRY.
ARE WE MOVING THE NEEDLE ON
PREMATURE MORTALITY?
ARE WE LOOKING AT OUR HEALTH
SYSTEMS AND IMPACT OF
HYPERTENSION ON GENERATIONAL
HEALTH AND WEALTH WHICH AFFECTS
COMMUNITIES WRIT LARGE.
NEXT SLIDE.
THE NATIONAL INSTITUTE ON
MINORITY HEALTH AND HEALTH
DISPARITIES PROVIDES A HELPFUL
FRAMEWORK WHEN YOU'RE DESIGNING
STUDIES.
I ENCOURAGE US TO USE THIS, IT
FORCES YOU TO THINK ABOUT LEVELS
OF INFLUENCE AND DOMAINS OF
INFLUENCE AND HOW THOSE INTERACT
SO WE CAN DESIGN INTERVENTIONS
AND CAPTURE OUTCOMES THAT REALLY
GET TO THE COMPLEXITIES OF THIS
WORK.
NEXT SLIDE.
THE DESIGNING PROGRAMS THAT
LEVERAGE TECHNOLOGY, I PUSH YOU
TO USE WHAT'S OUT THERE.
WE HAVE SO MUCH GOOD BASIS FOR
ALL OF THE WORK THAT YOU'RE
GOING TO BE DOING.
ONE ARE PRINCIPLES FROM AMERICAN
HEART ASSOCIATION TARGET BM MAP,
MEASURE ACCURATELY, ACT RAPIDLY,
PARTNER WITH PATIENTS, THREE
PRINCIPLES TO GET US FAR IF WE
DO THAT WELL.
IT NEEDS TO BE DATA DRIVEN.
I DON'T HAVE TO TELL YOU THAT,
AND WE NEED TO USE
PATIENT-CENTERED APPROACHES,
THINK ABOUT MULTI-DISCIPLINARY
TEAMS AND
[AUDIO DISTORTION]
ABOUT REMOTE BLOOD PRESSURE
MANAGEMENT, COMBINATION PILLS,
MAKING IT EASY AND ADDRESS
SOCIAL DETERMINANTS OF HEALTH.
NEXT SLIDE.
WE DID PILOT WORK AT YALE WHERE
WE WERE LOOKING TO UNDERSTAND
FACILITATORS AND BARRIERS TO
DIGITAL HEALTH UPTAKE, STUDIES
200 LOW INCOME PATIENTS, HALF
SPANISH-SPEAKING ONLY.
WE HAD THEM REGISTER AND SYNC
RECORDS, TRACKERS, CUFFS, INTO A
SINGLE PLATFORM AND SURVEYED
OVER THE COURSE OF 12 WEEKS.
WE PROVIDED MOTIVATION AND
SUSTAINED ENGAGEMENT AND
ADDRESSED SOCIAL DETERMINANTS OF
HEALTH.
THESE RESULTS ARE REPORTED IN
THE STUDY BUT WHAT I WANT TO
HIGHLIGHT, YOU CAN ADVANCE ONE,
IT'S GOING TO HIGHLIGHT THE
PERSON IN THIS PICTURE, OUR
COMMUNITY HEALTH WORKER.
SHE WAS REALLY AT THE EPICENTER
OF PATIENTS AND THEIR
COMMUNITIES.
SHE WORKED WITH US AS CLINICIANS
AND INVESTIGATORS AND WORKED
WITH INDUSTRY, OUR TECH
COLLABORATORS, IN ORDER TO BRING
THIS TOGETHER.
ONE PERSON WAS ABLE TO WORK WITH
200 PEOPLE TO KEEP THEM ENGAGED,
AND REALLY SHOWED THEM HOW TO
USE THIS TECHNOLOGY TO IMPROVE
THEIR HEALTH.
I CAN'T TELL YOU THE VALUE OF
THIS COMMUNITY HEALTH WORKER IN
REALLY LEVERAGING THE TECHNOLOGY
THAT WE WERE TRYING TO INTRODUCE
IN THIS POPULATION.
NEXT SLIDE.
YALE, DR. SHAH MENTIONED, WE
HAVE A YALE DIGITAL HEALTH
PROGRAM.
WE RECEIVE REFERRALS FROM
PROVIDERS FOR PATIENTS WITH
POORLY CONTROLLED HYPERTENSION,
ALSO ON THE LABOR AND DELIVERY
FLOORS, USING OUR PROGRAM IN THE
POSTPARTUM POPULATION FOR WOMEN
WITH PRE-ECLAMPSIA, STAND OF
CARE.
WE'RE IN THE COMMUNITY IN
BARBERSHOPS, CHURCHES, TRYING TO
DETECT HYPERTENSION AND GET
PEOPLE INVOLVED IN REMOTE BLOOD
PRESSURE MONITORING PROGRAMS.
WE GIVE THEM A BLOOD PRESSURE
CUFF THAT'S BLUETOOTH ENABLED,
SYNC THEM WITH MEDICAL RECORDS,
AND WE HAVE DIVERSE TEAM OF
PHARMACISTS ABLE TO DO
TELEHEALTH VISITS, SEE PEOPLE ON
A WEEKLY OR BIWEEKLY BASIS,
TITRATE MEDICATIONS, USING AN
AGREEMENT BETWEEN PHYSICIANS AND
PHARMACISTS THAT ALLOWS THEM TO
PRESCRIBE AND ADDRESS BARRIERS
TO HEALTH.
YOU CAN ADVANCE.
WE'RE WORKING WITH PROJECT
ACCESS NEW HAVEN WHICH IS WHERE
THE COMMUNITY HEALTH WORKER
COMES FROM IN ORDER TO ADDRESS
SOCIAL DETERMINANTS OF HEALTH,
MANY TIMES THE BARRIERS ARE
IMPLEMENTATION BARRIERS THAT
COMMUNITY HEALTH WORKERS CAN
ADDRESS.
NEXT SLIDE.
SO WHAT ARE SOME LESSONS LEARNED
THAT MAYBE YOU COULD TAKE WITH
YOU AS YOU HEAR THE DIFFERENT
TALKS TODAY?
THERE IS HIGH SATISFACTION IN
OUR PROGRAM AMONGST CLINICIANS,
PHYSICIANS NEED TO WARM UP TO
THE IDEA OF LETTING GO OF THE
REINS AND NOW THEY ARE WELCOMING
THE HELP THEY ARE GETTING BUT IT
TOOK TIME.
A CULTURE SHIFT NEEDS TO HAPPEN.
PHARMACISTS AND COMMUNITY HEALTH
WORKERS PRACTICE TO THEIR
LICENSE HAVING THE IMPACT THEY
WERE TRAINED TO DO.
REMOTE PATIENT MONITORING NEEDS
A LOT OF WORK, LIKE IF WE'RE
GOING TO USE CUFFS WE NEED TO
MAKE SURE FIT IS GOOD AND
GETTING A GOOD FIT IS REALLY
HARD.
WE NEED TO GET VALID DEVICES,
FIGURE HOW TO ENCOURAGE
ADHERENCE TO OUTPATIENT BLOOD
PRESSURE MONITORING.
WE NEED TO THINK ABOUT THE DATA
ASCERTAINMENT AND AGGREGATION,
HOW DO WE MAKE IT SIMPLE?
WE NEED TO RELEARN DATA WE'RE
ACQUIRING, HOW DO WE USE DATA TO
INDIVIDUALIZE CARE AND KEEP
PEOPLE ENGAGED IN THE
LONGITUDINAL WAY ACROSS 12-WEEK
PROGRAM?
SHOULD IT BE ONE YEAR?
HOW DO WE INCORPORATE AND PIVOT
WHEN PEOPLE ARE HAVING STRESSFUL
EVENTS, COMPETING HEALTH OR
NON-HEALTH PRIORITIES?
WE NEED TO THINK ABOUT WHO WE'RE
NOT REACHING.
WHO ARE NO-SHOWS, WHO DON'T HAVE
A PRIMARY CARE PROVIDER, WHO ARE
WE ONLY SEEING IN THE EMERGENCY
DEPARTMENT OR HOSPITAL? WHO IN
THE FAMILY AND PEER GROUP, IN
THE COMMUNITY MIGHT ALSO BENEFIT
FROM THE INFORMATION THAT WE'RE
SHARING WHEN WE'RE INTERACTING
WITH PATIENTS?
THEN WE NEED TO THINK ABOUT
SUSTAINABILITY.
HOW DO WE COMPENSATE, HOW DO WE
PROVIDE INCENTIVES FOR
PHARMACISTS, COMMUNITY HEALTH
WORKERS TO BUILD OUT THAT
MULTI-DISCIPLINARY TEAM AND
ENCOURAGE HEALTH SYSTEMS TO DO
THE RIGHT THING.
NEXT SLIDE.
MR. GREEN, OUR PATIENT, IS ONE
OF MANY.
THAT'S OUR CHARGE.
NEXT SLIDE.
WE KNOW DIGITAL HEALTH IS
NEEDED.
WE KNOW IT CAN MAKE CARE
CONVENIENT AND EFFICIENT, IT CAN
REVEAL BLOOD PRESSURE PHENOTYPES
THAT MATTER, WE KNOW IT CAN
CAPTURE CONTEXTUAL FACTORS AND
HAVE THIS GREAT, GREAT GOAL THAT
WE CAN INDIVIDUALIZE CARE PLANS
IF WE UNDERSTAND MORE ABOUT
PEOPLE'S BLOOD PRESSURE AND MORE
ABOUT THE SOCIAL AND BIOLOGICAL
FACTORS THAT ARE CONTRIBUTING.
I KNOW WE CAN IMPROVE OUTCOMES.
NEXT SLIDE.
BLOOD PRESSURE IS A NEXUS, IT'S
GOING TO REQUIRE BOLD CHANGE.
WE NEED FUNDAMENTALLY CHANGE THE
DELIVERY SYSTEM TO DO THIS WELL.
WE NEED TO MOVE FROM OUR HEALTH
SYSTEMS SILOS TO COMMUNITY
PARTNERSHIP.
WE NEED TO MOVE FROM ONEROUS
MEASUREMENTS IN THE OFFICE.
WE NEED TO MOVE FROM BEING
REACTIVE TO PRO-ACTIVE, FROM
BEING PHYSICIAN CENTRIC TO TEAM
CENTRIC, FROM FEE FOR SERVICE TO
VALUE-BASED CARE.
BLOOD PRESSURE IS INDEED A VERY
COMPLEX NEXUS.
YOU CAN ADVANCE.
THE SURGEON GENERAL HAS THE CALL
TO ACTION TO CONTROL
HYPERTENSION, I ENCOURAGE YOU TO
READ THIS AMAZING RESPONSE THAT
DR. MENSAH LED.
IT'S WONDERFUL TO HAVE THE
SURGEONS GENERAL BEHIND THIS.
THE NHLBI AS YOU HEARD FROM DR.
GOFF IS DEDICATED TO FUNDING
STUDIES THAT ADVANCE
HYPERTENSION AND HEALTH EQUITY,
AND THIS IS AN ENORMOUS
OPPORTUNITY.
I WOULD LIKE TO IMAGINE, YOU CAN
ADVANCE, THAT OTHER KEY FUNDERS
LIKE PCORI AND AMERICAN HEART
ASSOCIATION WITH DOUBLING DOWN
ON STUDIES AND RESEARCH TO
ADVANCE HYPERTENSION IN OUR
PATIENT POPULATIONS AND IN OUR
COMMUNITIES AND GLOBALLY
WORLDWIDE.
WITH THAT I'D LIKE TO THANK YOU
SO MUCH FOR YOUR ATTENTION TODAY
AND AM THRILLED TO HEAR YOUR
TALKS AND BE CHARGED BY THE WORK
THAT IS GOING TO COME OUT OF
THESE TWO DAYS.
THANK YOU SO MUCH FOR HAVING ME.
>> WHAT AN INCREDIBLY
INFORMATIVE KEYNOTE, PERFECT TO
KICK OFF THE SESSIONS.
THANK YOU.
SO, DR. SHAH IS GOING TO
MODERATE THE NEXT SESSION.
BRIDGING THE COMMUNICATION GAP
BETWEEN POPULATION-HEALTH,
CLINICAL MEDICINE, ENGINEERING.
>> THANK YOU.
THAT WAS AMAZING, DR. SPATZ.
YOU COVERED STARTING FROM THE
PEOPLE TO THE END, THE WHOLE
SYSTEM.
THAT WAS AN AMAZING
PRESENTATION.
THANK YOU FOR PARTICIPATING.
SO, LET'S MOVE ON NOW TO OUR
FIRST SESSION.
BRIDGING THE COMMUNICATION GAP,
NEAR AND DEAR TO MY HEART, I
WORK WITH A LOT OF ENGINEERS.
WE'RE FROM DIFFERENT PLANETS.
A FEW PEOPLE HERE HAVE
SUCCESSFULLY DONE THIS.
SOME PHYSICIAN, SOME COMPUTER
SCIENTISTS AND ENGINEERS WHO
MADE THIS WORK.
I'M THRILLED TO PRESENT THE
GROUP LEER.
DR. BOBAK MORTAZAVI, TEXAS A & M
UNIVERSITY.
HE'S DONE WONDERFUL WORK
COLLABORATING WITH CARDIOLOGISTS
AND DEVELOPING NEW TECHNOLOGY IN
THE FIELD.
NEXT DR. ATTIA
CO-DIRECTOR OF ARTIFICIAL
INTELLIGENCE AT THE MAYO CLINIC.
HE ALSO HAS TRAINING IN
ELECTRICAL ENGINEERING, HAS DONE
AMAZING WORK LEVERAGING THE
MAYO'S HEALTH SYSTEM DATA TO
ADVANCE THE FIELD.
AND FINALLY DR. AHMAD,
PRACTICING PHYSICIAN, ADVANCED
HEART FAILURE PHYSICIAN, THE
ASSOCIATE DIRECTOR CENTER FOR
ARTIFICIAL INTELLIGENCE AT THE
BLOOM INSTITUTE.
I'LL MODERATE.
IF THINGS ARE GETTING CLOSE TO
TIME LIMIT I MIGHT STEP IN TO
ASK THE PRESENTERS TO MOVE
FORWARD.
AT THE END WE'LL HAVE A PANEL
DISCUSSION.
IF YOU PUT YOUR QUESTIONS IN THE
CHAT I'LL KEEP UP WITH THOSE.
AND I THINK IT'S GOING TO BE A
WONDERFUL SESSION.
WITH THAT, I WILL HAND IT OVER
TO DR. MORTAZAVI.
>> THANK YOU FOR THE
INTRODUCTION, I'M EXCITED TO
SPEAK WITH YOU TODAY.
I HAVE THE PLEASURE OF BEING
ABLE TO TALK ABOUT SOME OF THE
ENGINEERING TERMS THAT MAYBE
SHOULD BE HELPFUL FOR MERGING
WITH CLINICIANS, MYSELF, I
TRAINED WITH CARDIOLOGISTS AT
THE YALE SCHOOL OF MEDICINE,
HAVE HAD THE FORTUNE OF WORKING
WITH THEM CLOSELY, HAVE THESE
DISCLOSURES THAT ARE FUNDING
WORK IN THIS AREA.
SO, JUST TO GO THROUGH HERE,
IT'S GOING TO BE DIFFICULT TO
TALK ABOUT ALL OF THE TERMS IN
ENGINEERING THAT ARE NECESSARY
FOR HYPERTENSION MANAGEMENT BUT
I'M HOPING GOING THROUGH THIS
OUTLINE WE CAN TALK ABOUT
GENERALLY KEY MODELS AND TERMS
TO USE, LANGUAGE THAT NEEDS TO
BE CONSISTENT ACROSS ENGINEERS
AND CLINICIANS.
I WHEN I STARTED SPENT AN
EMBARRASSING AMOUNT OF TIME
FIGURING OUT DIFFERENCE BETWEEN
SOME MEASUREMENTS WE USE, LIKE
AREA UNDER THE ROC CURVE AND
STATISTICS AND LEARNING THESE
ARE THE SAME THINGS.
IT'S THINGS LIKE THIS THAT
CHALLENGE INTEGRATING BETWEEN
FIELDS BUT WE CAN UNDERSTAND
SORT OF WHAT ARE THE KEY KINDS
OF MODELS AND TECHNIQUES USED,
AND TALK ABOUT JUST SORT OF
ADVANCED SURVEY OF MODELS AND
SENSING AND THINGS AVAILABLE TO
US TO TRY TO PROMOTE NEW
SOLUTIONS HERE.
MOST OF THIS IS JUST A QUICK
SURVEY OF THINGS YOU MIGHT HEAR
IN DIFFERENT TALKS AS WE GO
THROUGH THE DAY AND HOPEFULLY
LOOK FAMILIAR TO SOME THINGS AND
EXCITES YOUR INTEREST IN
LEARNING ABOUT SOME THINGS THAT
DON'T.
TYPICALLY WHEN WE LOOK AT A
BUNCH OF THESE STUDIES AND
GATHER DATA WE LOOK AT
SUPERVISED MACHINE LEARNING
TECHNIQUES.
SUPERVISED MODELS HAVE DATA
POINTS AND DIAGNOSING SOMETHING
WITH HYPERTENSION OR NOT
DIAGNOSING CARDIOVASCULAR
DISORDER OR NOT BASED ON THESE
TECHNIQUES THAT INVOLVE GRABBING
DATA AND UNDERSTANDING WHETHER
YOU HAVE THESE THINGS OR WHAT
ARE THE TIMES FOR THESE EVENTS.
IT'S IMPORTANT AS WE'VE COVERED
UNDERSTANDING A SUITE OF
MEASUREMENTS TO UNDERSTAND
THINGS THAT ARE NOT -- WE WANT
TO DIAGNOSE HYPERTENSIVE STATUS
AND PREDICT THE RISKS OF
OUTCOMES.
IT ISN'T JUST BEING ABLE TO
DISCRIMINATE BETWEEN ONE CLASS
OR ANOTHER BUT UNDERSTAND THE
RANGE OF PROBABILITY, CHANGES
AND RISKS, AND
THRESHOLD-SPECIFIC THINGS.
AS WE MOVE FORWARD AND DISCUSS
THESE TECHNIQUES AND SOLUTIONS,
WE NEED A SUITE OF MEASUREMENTS.
ANY ONE NUMBER IS NOT GOING TO
BE DEFINITIVE UNTIL IT TELLS US
THOUSAND ADDRESS PROBLEMS AND
INTERVENE EARLY.
THIS INCLUDES CLASSIFICATION
TECHNIQUES, AND REGRESSION
MEASUREMENTS.
REGRESSION MEASUREMENTS ARIC
THATTING NOVEL SENSING
TECHNIQUES AND BEING ABLE TO
PREDICT BLOOD PRESSURE
MEASUREMENTS AS WE GO ALONG, SO
CORRELATION ROOT MEAN SQUARE
ERROR, MEAN ABSOLUTE DIFFERENCE,
AND WHAT THEY ARE TELLING YOU IS
THAT WHAT PERCENTAGE OF THE
TIME, HOW FAR OFF ARE THESE SORT
OF MAYBE COMMERCIALLY AVAILABLE
DEVICES PRESENTING DATA VERSUS
WHAT YOU MIGHT MEASURE IN THE
CLINIC.
IF WE HAVE A SUITE, WE CAN THINK
ABOUT THE ADVANCEMENTS IN
ENGINEERING THAT ALLOW US TO
POTENTIALLY HELP DEVELOP AND
TECHNOLOGY AND INTERVENTION TO
AID IN ADDRESSING BURDENS.
WHAT I'M EXCITING ABOUT IN
COMING TALKS, OTHER TERMS YOU
MIGHT HEAR FROM SENSORS, WE LIKE
TO TALK ABOUT SPECIFICALLY
WEARABLES, NEARABLES,
IMPLANTABLES, SENSING
TECHNOLOGIES ON THE PATIENT OR
NEAR THE PATIENT WITH THE
PATIENT, AND HAVE IT
CONTINUOUSLY MEASURE THINGS.
THEY CAN COME FROM MORE
CLINICALLY ORIENTED LIKE
AMBULATORY CUFF OR COMMERCIALLY
AVAILABLE TECHNIQUES THAT MAY
HAVE MORE LONGITUDINAL USE AND
MORE UPTAKE IN COMMUNITIES THAT
DON'T HAVE MAYBE BURDENS OF
USING SIGNIFICANT BULKY CLINICAL
DEVICES LIKE FITNESS TRACKERS,
CONTINUOUS GLUCOSE MONITORS
BROADENING ACCEPTABILITY ACROSS
PATIENT POPULATIONS, NOT JUST
TYPE 1 DIABETICS.
A TREND IS TAKE THESE
TECHNOLOGIES THAT USE SENSING
SOLUTIONS AND SEE IF WE IS
DEVELOP NEW TECHNIQUES WHILE
MEASURING BLOOD PRESSURE.
FOR EXAMPLE, ONE THAT I'M
INVOLVED WITH USES ELECTRICAL
IMPULSES TO IDENTIFY RADIAL
ARTERY AND TRACK PRESSURE FLOW.
IF YOU CAN DO THIS DATA CAPTURE,
THE BIGGEST CONCERN AS
ENGINEERS, WHAT DO YOU WITH SUCH
OVERWHELMING AMOUNTS OF DATA,
THESE THINGS ARE ALMOST --
DIFFICULT TO DEAL WITH.
WHAT WE'RE TALKING ABOUT MAY BE
A SINGLE MEASUREMENT AT A VISIT
OR PERIODIC, NOW I'M TALKING
ABOUT GIVING YOU A CONTINUOUS
SIGNAL SIMILAR TO SOMETHING YOU
WOULD SEE IN ELECTROCARDIOGRAM.
NOW LOOK AT ADVANCEMENTS IN
TECHNIQUES AVAILABLE TO LOOK AT
WAVE FORMS OF DATA, CONTINUOUS
CAPTURES OF BIOMETRIC SIGNALS
RATHER THAN MAYBE STANDARD
METRIC OR THRESHOLD DATES,
VALUES CAPTURED PERIODICALLY.
WE'LL DIVE INTO ANOTHER SET OF
TERMS, WE'LL DESCRIBE THEM AS A
HIGH LEVEL TUTORIAL HERE, ONE
THING PARTICULARLY POPULAR ARE
DEEP LEARNING TECHNIQUES.
WHAT THESE ARE, UNIVERSAL
ESTIMATORS THAT CAN LEARN AND
INFER PATTERNS FROM RAW DATA.
WHEREAS PREVIOUSLY I MIGHT NEED
AN ECG SIGNAL AND DETECT PEAKS
AND QRS COMPLEXES AND UNDERSTAND
AN AREA UNDER ELEVATION, I CAN
PROVIDE DEEP LEARNING TECHNIQUE
WITH RAW SIGNALS WITHOUT SUCH
ANNOTATIONS AND SEE IF IT CAN
LEARN SETS OF FEATURES AND
PARAMETERS AND RELATIONSHIPS
THAT IDENTIFY NEW THINGS,
PRESENT NEW KNOWLEDGE, AND
IMPORTANTLY THESE KIND OF
TECHNIQUES CAN BE CONTINUALLY
ADAPTED, CONTINUALLY UPDATED AND
LEARN MULTIPLE THINGS AT ONCE,
SO THINGS RELATED TO BLOOD
PRESSURE IF WE'RE CAPTURING
CHARACTERISTICS OVER STUDIES, WE
DON'T HAVE TO THINK ABOUT EACH
AS INDIVIDUAL THINGS, JUST
LOOKING AT THE SYSTOLIC OR JUST
LOOKING AT DIASTOLIC OR CHANGES
IN HEART RATE, BUT ACTUALLY CAN
CAPTURE A SET OF SENSING DATA ON
THE PARTICIPANT AND LEARN,
INTERNAL REPRESENTATIVE
REPRESENTATIONS AND
PREDICTING SYSTOLIC AND
DIASTOLIC, PERHAPS NOT RELATING
IN THE SAME FUNCTION, WE USE
MULTI-TASK LEARNING LENDING TO
THE THING THAT'S ALL OVER THAT
WE'VE BEEN HEARING ABOUT,
GENERATEIVE MODELS.
IF WE CAN TAKE THESE DEEP
LEARNING TECHNIQUES AND PROVIDE
ENOUGH DATA TO THEM, THEY BECOME
CAPABLE OF REPRESENTING WHAT A
PATIENT AND CURRENT PATIENT
STATE LOOKS LIKE, INDEPENDENT OF
TASK.
WHETHER IT'S TRYING TO MEASURE
SYSTOLIC BLOOD PRESSURE OR
CLASSIFY HYPERTENSION OR NOW YOU
WANT TO MODEL RISKS OF ACUTE
EVENT IN THE NEXT SIX MONTHS, 12
OR 18 MONTHS, RATHER THAN
BUILDING DATASETS AND
INDEPENDENT STUDIES, WHAT WE CAN
DO IS LOOK AT OBSERVATIONAL
DATA, COLLECT IT OVER LARGE
PERIODS OF TIME OVER LARGE
POPULATION SOURCES, AND SORT OF
START TO LEARN WHAT DATA
REPRESENTS DIFFERENT TASKS AND
THINGS ABOUT PATIENTS, AND WE
CAN GENERATE FROM THIS AND LEARN
ALL KINDS OF DIFFERENT TASKS
FROM THIS.
AND THIS IS, YOU KNOW, YOU HAVE
HEARD A LOT OF THESE THINGS
LATELY, GENERATIVE MODELS,
SPECIFICALLY LARGE LANGUAGE
MODELS THAT ENABLE THINGS LIKE
CHAT GPT AND SUCH, TO BE ABLE TO
CREATE EXPLANATIONS ANSWERS AND
DESCRIPTIVES BEHIND DATA TOO
COMPLEX TO UNDERSTAND ON ITS
OWN.
THESE ARE FEASIBLE IN RECENT
YEARS SPECIFICALLY BECAUSE OF
THE COMPUTATIONAL AND
ENGINEERING ADVANCEMENTS THAT
WE'VE HAD.
WHILE DEEP LEARNING HAS EXISTED
FOR A LONG TIME, IT'S COMING TO
THE FOREFRONT NOW BEING ABLE TO
USE THESE THINGS, UNDERSTAND AND
REPRESENT WHAT DATA LOOKS LIKES
AND TRANSFER ACROSS POPULATIONS.
WE MIGHT HAVE A STUDY LIKE THE
ONE DR. SPATZ MENTIONED AT YALE
LOOKING AT PATIENTS AND
COLLECTING DATA AROUND THE
CONNECTICUT AREA, AND SAY, OKAY,
WHAT ABOUT MY PATIENT POPULATION
IN TEXAS, DO THEY LOOK THE SAME,
DIFFERENT?
HOW MUCH DO I NEED REPLICATE TO
GENERATE DATA TO IMPLEMENT THESE
SOLUTIONS HERE AND TREAT
PATIENTS IN TEXAS SIMILAR TO HOW
SHE MIGHT BE DOING IN
CONNECTICUT.
THE BEAUTY IS ONCE I CAN GET
ENOUGH DATA TO SORT OF LEARNING
AND GENERATE WHAT I THINK
DIFFERENT PATIENTS AT DIFFERENT
STAGES OF THEIR CARDIOVASCULAR
HEALTH AND HYPERTENSION LOOK
LIKE, I CAN TAKE THESE MODELS
AND ONLY JUST SORT OF FINE TUNE
THEM AND SAY, LOOK, WE KNOW
CLINICALLY, PHYSIOLOGICALLY
THERE ARE THINGS GOING ON THAT
ARE GOING TO BE COMMON, AND I
NEED TO LEARN THE ACUTE
DIFFERENCES THAT COME IN
CAPTURING DATA AND THEN SEEING
WHAT OUTCOMES COME AND MIGHT
PATIENT POPULATIONS, PERHAPS MY
SORT OF RURAL UNDERREPRESENTED
AREAS IN SOUTHEAST TEXAS.
THAT RAISES A CHALLENGE,
HOPEFULLY YOU CAN LOOK AT LINKS
AND FOLLOW UP WITH ME, ONE KEY
IS GOING TO BE PRIVACY CONCERNS
THAT COME UP IN THESE KIND OF
THINGS.
FEDERATED LEARNING IS A TERM WE
ALL NEED TO BECOME FAMILIAR
WITH, WHICH SUGGESTS THAT
THERE'S A METHOD BY WHICH THESE
DEEP LEARNING TECHNIQUES, ONCE
WE CAN TRAIN THEM AND UNDERSTAND
WHAT GENERATES DIFFERENT PATIENT
STATES AND REPRESENTS PATIENTS
PROPERLY, I CAN SHARE THAT
INFORMATION WITHOUT SHARING
PATIENT DATA.
I CAN LOOK AT WHAT WE LEARNED
FROM DIFFERENT HOSPITALS AND
COMMUNITIES, DIFFERENT CENTERS
AROUND THE COUNTRY, AND THEN
LEARN HOW TO ADAPT WITHOUT
NEEDING MAYBE THE PHI THAT GOES
WITH COLLECTING AND TRAINING IN
CONNECTICUT.
SO I THINK WHILE REGULATORY
THINGS ARE A CONCERN, THIS IS
PERHAPS AN APPROACH WE CAN
FIGURE OUT HOW TO WORK TOGETHER
WITHOUT HAVING THE BURDEN OF THE
CHALLENGE OF DAILY USE WITH EACH
OTHER.
AND IF WE CAN DO ALL THESE
THINGS, THE LAST THING THAT
NEEDS TO BE IMPORTANT IS
EXPLAINABILITY AND
INTERPRETABILITY.
HOW DO YOU TRUST THAT IT'S
DEVELOPING SOMETHING CORRECTLY?
HOW DO YOU UNDERSTAND RISK WORKS
WELL, HOW DO YOU KNOW YOU'RE
MODELING CORRECTLY NOT DEALING
WITH BIAS?
THERE'S A SERIES OF
EXPLAINABILITY AND INTERPRETABLE
A I THAT NEEDS TO GO WITH
TECHNIQUES.
YOU NEED TO TRUST IDENTIFYING
THIS IS A HUSKY IN THE PICTURE
BECAUSE IT'S TAKING FEATURES OF
THE DOG AND NOT, SAY,
IDENTIFYING IN TRAINING SET ALL
THE FEATURES HAD SNOW IN THE
BACKGROUND.
IF WE DO THAT WE GET TO SOME
FURTHER ADVANCEMENTS THAT WE'LL
HEAR ABOUT LATER TODAY FROM
SPEAKERS ABOUT HOW WE CAN THEN
PHENOTYPE AND TREAT AND
INTERVENE.
IF WE CAN UNDERSTAND
REPRESENTATION, WE NO LONGER
HAVE TO LOOK AT TREATMENT AFFECT
ACROSS LARGE SCALES BUT WE CAN
IDENTIFY PHENOTYPIC
NEIGHBORHOODS AND REGIONS AND DO
UNSUPERVISED CLUSTERING.
WE TAKE THESE REPRESENTATIONS,
WITHOUT WORRYING ABOUT THE
DOWNSTREAM TASKS, SEEING WHO
LOOKS SIMILAR, WHO DOESN'T, AND
UNDERSTAND WHAT KIND OF
TREATMENTS SEEM TO BE SUCCESSFUL
OR NOT.
FROM THOSE, WE CAN THEN DO THE
LAST TERM THAT HAS NOT SEEN AS
MUCH IN THE CLINICAL FIELD AND
DOMAIN, NEED COME IN MORE AND
MORE, REINFORCEMENT LEARNING.
SO THIS IDEA THAT ENGINEERING
TECHNIQUES CAN BE TAUGHT WHEN
THEY ARE DOING WELL AND WHEN
THEY ARE DOING POORLY AND ADAPT
PROPERLY.
SO YOU TAKE THESE PHENOTYPIC
REGIONS, THE MODEL CAN LEARN THE
RIGHT SPHERE AROUND THE PERSON
TO LOOK AT TO UNDERSTAND
LOCALIZED TREATMENT EFFECT TO
THEN PROVIDE POTENTIAL CLINICAL
DECISION SUPPORT.
THAT'S A SIGNIFICANT NUMBER OF
TERMS.
I HOPE MOST ARE MORE FAMILIAR
THAN NOT GIVEN ADVANCES BUT IF
WE CAN LEARN FROM THESE
TECHNIQUES WE HAVE THE ABILITY
AND TOOL SETS NOW TO TRY TO
REALLY DEVELOP TECHNIQUES THAT
YOU ALL HAVE DONE OVER DECADES
BUT MAYBE HAVE NOT USED THE
TERMS WE HAVE WITH RESPECT TO
GENERATIVE MODELS AND
REPRESENTING STATES BEING THE
SAME AS PHENOTYPING AND
CLUSTERING, AND IF WE CAN LOOK
AT THAT WE ENABLE CONTINUOUS
MEASUREMENT ALSO, WE CAN RUN
INTO ADDITIONAL CHALLENGES OF,
YOU KNOW, CAUSAL INFERENCE
ISSUES, MISSING DATA, AND AT THE
END REALLY CHALLENGE OURSELVES
TO LOOK AT THE REGULATORY
HURDLES THAT COME ACROSS.
IF YOU CAN GET PATIENTS TO
GENERATE THIS DATA, WHO OWNS
DATA, HOW DO YOU SHARE, HOW DO
WE ENABLE SHARED DECISION MAKING
AND COMMUNITY LEARNING WITHOUT
HAVING PRIVACY AND ACCESS
ISSUES, WITHOUT POTENTIALLY
HAVING INSURANCE REGULATORY
ISSUES.
IN PARTICULAR I'M VERY EXCITED
TO HEAR LUCIA SAVAGE SPEAK
TOMORROW FROM THE LEGAL SIDE.
I LOOK FORWARD TO YOUR
QUESTIONS.
I'M EXCITED TO TALK ABOUT
SENSING AND MODELING TECHNIQUES
THROUGH THE COURSE OF THE NEXT
FEW DAYS.
>> THANK YOU SO MUCH.
THAT WAS AMAZING.
AS YOU CAN SEE, HE HAS A WIDE
RANGE OF EXPERTISE.
PLEASE PUT YOUR QUESTIONS IN THE
CHAT AND I'LL TRY TO KEEP TRACK
FOR OUR DISCUSSION AT THE END OF
THE THREE TALKS.
NEXT DR. ATTIA WILL TALK ABOUT
ENGINEERS ARE FROM EARTH,
CLINICIANS ARE FROM VENUS.
>> THANK YOU AGAIN FOR THIS
OPPORTUNITY TO TALK AND
PARTICIPATE IN THIS REALLY
EXCITING SESSION.
I DIDN'T CHANGE THE TOPIC BUT
I'M GOING TO ADDRESS AS AN
ENGINEER AS AN EXTENT, LOOKING
AT THE IDEA, AND WHAT WE NEEDED
TO DO TO OPEN DIVERSITY, TO
ACTUALLY IMPLEMENT THIS INTO
PRACTICE.
THESE ARE MY DISCLOSURES.
AGAIN, I'LL DISCUSS HOW THIS
ENGINEERS WORK TOGETHER WITH
PHYSICIANS.
BUT THE USE CASE I'M GOING TO
DISCUSS IS DETECTION OF LOW
EJECTION FRACTION USING APPLE
WATCH.
AND MAYO APPLE WATCH STUDY.
APPLE DID NOT SUPPORT THIS STUDY
IN ANY WAY.
WE USED THE APPLE WATCH BECAUSE
WE HAD ACCESS.
LEFT VENTRICULAR FUNCTION
AFFECTS 2% OF THE GLOBAL
POPULATIONS, HIGH RATE OF
CLINICAL HEART FAILURE,
HOSPITALIZATION AND DEATH.
AS WE'VE HEARD IN KEYNOTE, THE
MAIN ISSUE IS IDENTIFYING THESE
PATIENTS.
AND IDENTIFICATION IS EXTENSIVE,
NOT READILY AVAILABLE TESTS LIKE
ECHO CARDIOGRAM.
CAN WE USE SOMETHING THAT'S MORE
UBIQUITOUS AND CHEAPER TO USE?
WE TALKED ABOUT ECG, THE
REPORTING OF THE ELECTRICAL
ACTIVITY OF THE HEART.
IF YOU PUT A SENSOR ON EACH,
YOU'LL GET MORPHOLOGY AND ECG.
THE PROBLEM IS HUMANS CANNOT
DETECT LOW EJECTION FRACTURE
FROM ECG BUT MAY A.I. WOULD BE
ABLE TO DO IT AFTER SEEING
ENOUGH EXAMPLES.
WE STARTED WITH 600,000
PATIENTS, ECHO AND ECG PAIR,
AFTER DATA CLEANING, MAJORITY OF
WORK FOR MOST SCIENTISTS,
CREATING GOOD DATASETS, WE HAD
100,000 PATIENTS, USING A THIRD
FOR TRAINING, THE REST FOR
VALIDATION.
WE FEED IN INPUT, ALL ECG, WE
DID NOT FEET QT PR, WE FIT IN
ECG AS A WHOLE.
AND DOING TRAINING FOR TRAINING
SAID WE SHOWED EJECTION FRACTION
FROM THE ECHO.
WE ASKED IT TO FIND PATTERNS IF
THE EF IS BELOW 35% OR ABOVE IT.
WE WOULD LATER CHANGE IT TO 40
TO MATCH THE GUIDELINES, BUT
IT'S THE SAME CONCEPT.
SHOW ECG, ASK BINARY QUESTION
ABOUT EJECTION FRACTION, AGAIN
HUMAN CONSIST NOT DO, BUT
BECAUSE WE HAD MATCHED PATIENTS
WE COULD TRAIN THE NETWORK.
WE USED THE AREA UNDER THE CURVE
TO SELECT OUR BEST MODEL.
YOU SEE 0.5 IS RANDOM TEST, 1 IS
PERFECT.
WE HAD AUC OF 0.93, SPECIFICITY
86% COMPARING FAVORABLY TO OTHER
MEDICAL TESTS.
WE THEN KNEW WHY IT IS BETTER
THAN ECHO FOR SCALABILITY,
TAKING IT TO MOBILE PHONE
WEARABLES MAKE IT MORE SCALABLE
BUT WE DON'T HAVE SIGNALS IN OUR
HEALTH SYSTEM AND DIDN'T WANT TO
GIVE AWAY APPLE WATCHES SO
NEEDED TO FIND A WAY TO RECORD
PATIENTS AND GET SIGNALS WHILE
KEEPING THEM ENGAGED.
IF THE ECG IS GENERATED BY WATCH
WILL WORK AS WELL BECAUSE THERE
ARE A LOT OF DIFFERENCES.
LYING SUPINE, OBVIOUS POSITIONS,
SKIN IS PREPPED, QUALITY IS MUCH
BETTER.
WHEN I DO AN APPLE WATCH ON MY
WATCH, I MIGHT BE SITTING ON THE
COUCH, LAYING DOWN, SO ON.
THE FIRST AIM WAS TO DEVELOP
INFRASTRUCTURE TO RECORD
REMOTELY AND SECURELY, SECOND TO
ADAPT USING A SINGLE WATCH ECG.
WE DID WEARABLE STUDIES REMOTELY
AND DIGITALLY, SENT E-MAILS, NO
ONE WAS IN DIRECT CONTACT WITH
PATIENTS.
SINGLE STUDY SENT SURVEY WITH
THE MAYO APP AND ASKED TWO
QUESTIONS.
DO YOU HAVE AN APPLE WATCH AND
WOULD YOU BE WILLING TO
PARTICIPATE?
IF YES TO BOTH WE SEND THEM A
SHORT CONSENT FORM USING PTRAX
WHICH THEY SIGN USING DIGITAL
SIGNATURE.
THEY CONSENT AND RECEIVE A LINK.
THIS WAS CREATED INTERNALLY IN
MAYO CLINIC, SOFTWARE ENGINEERS
AND A LOT OF TECH.
THIS APP ALLOWS PATIENTS TO
UPLOAD ECGs TO UDP AND DIGITAL
DASHBOARD.
WE NEED TO SEE IF WE CAN RUN THE
A.I. MODEL USING SINGLE LEAD, WE
HAD TO RETRAIN.
YOU CAN SEE THIS IS A PATIENT
THAT HAS AN APPLE WATCH, WITHIN
A FEW MINUTES FROM EACH OTHER,
YOU CAN SEE EVEN THOUGH THE
MORPHOLOGIES ARE SIMILAR THERE
ARE BIG DIFFERENCES IN THE HIGH
FREQUENCY NOISE, IN THIS CASE,
SO WE HAD TO WEIGHT TO NOT USE
FEATURES NOT EXISTING EXISTINGN
APPLE WATCH.
WE LOWERED THE NUMBER OF LEADS.
RETRAINED THE NETWORK.
NOW WITH THE SYNTHETIC DATA, AUC
DROPPED TO 0.88 BECAUSE WE HAVE
SIGNIFICANTLY LESS DATA SO THAT
MAKES SENSE.
WE WERE ABLE TO COLLECT 125,000
ECGs FROM 2454 PATIENTS, 56%
WOMEN, FROM 21 TO 92, FROM 45
STATES, 11 COUNTRIES, IN LESS
THAN FIVE MONTHS.
WE LOOKED AT USE STATISTICS.
YOU HEAR A LOT IN THE NEXT TWO
DAYS, WE LOSE A LOT OF PATIENTS
BECAUSE NOT ALWAYS KNOW HOW TO
USE SYSTEM, HARDER AT HOME
VERSUS SOMEONE HELPING YOU IN
THE CLINIC.
WE LOOK HOW OFTEN ANT HOW LONG
THEY USE IT, THEY USE EIGHT
TIMES ON AVERAGE, WHICH MATCHES
UP WITH NOTIFICATIONS.
ALSO MOST PATIENTS USED UNTIL
THE LAST DAY OF THE STUDY,
NORMALIZED TIME FOR USE.
THEY WERE ENGAGED AND KEPT
SENDING DATA.
ANOTHER INTERESTING FINDING THAT
AS THE PATIENTS WERE OLDER THEY
USED THE APP MORE OFTEN, AND
WHICH WAS ACTUALLY SURPRISING.
WE WERE WORRIED THE ONLY
PATIENTS THAT WOULD USE IT WERE
YOUNGER PATIENTS BUT SEEMS LIKE
PATIENTS THAT ARE OLDER ACTUALLY
WERE MORE INTERESTED IN
CONTRIBUTING THEIR DATA TO THE
STUDY.
WE TOOK PATIENTS WITH CLINICALLY
INDICATED ECHO CARDIOGRAM AT THE
SAME TIME, WITHIN 30 DAYS, 421
PATIENTS WITH ECHO CAN APPLE
WATCH ECG AND TOOK ALL THE APPLE
WATCH ECG.
16 HAD EF LESS THAN 40.
DURING MODEL THAT WAS TRAINED ON
SYNTHETIC DATA, REAL DATA
COLLECTED BY PATIENTS IN THEIR
OWN HOME, AUC 0.885, SENSITIVITY
AND SPECIFICITY 81%, HUMANS
CANNOT DO LOOKING AT ECG.
WE COLLECTED DATA FROM 125,000
ECGs, 46 STATES, 11 COUNTRIES,
APPLIED MAYO A.I. TO APPLE
WATCH.
WE'RE DOING SIMILAR WORK FOR
ADDITIONAL DISEASES, AND PEOPLE
WHO ARE INTERESTED IN
HYPERTENSION WE'RE ACTIVELY
LOOKING AT, ONE OF THE POSTDOCS
PUBLISHED IN ACC, AND WORK
SHOWING WE CAN DETECT HISTORIC
DYSFUNCTION FROM AN ECG IN
SIMILAR WAY BUT THE MOST
IMPORTANT THING FOR TODAY IS
THAT THIS WORK REQUIRED A LOT OF
PEOPLE FROM DIFFERENT EXPERTISE,
ENGINEERS, COORDINATORS, APP
DEVELOPERS, ENGINEERS,
STATISTICIANS.
I WANTED TO SPEND THE LAST FEW
MINUTES HOW WE ALIGN THE STARS.
WE FOUND IN THE LAST EIGHT YEARS
SINCE WE STARTED A.I. GROUP THAT
LANGUAGE BARRIER IS THE MAIN
PROBLEM.
ENGINEERS DON'T UNDERSTAND
MEDICINE AND CLINICIANS DON'T
UNDERSTAND TECHNOLOGY AS WELL AS
ENGINEERS.
WE TRIED TO FIND WAYS TO
UNDERSTAND THE PROBLEM AND GET
THEM TO TALK TO EACH OTHER.
AND FOR ENGINEERS, FOR EXAMPLE,
THE BIGGEST PROBLEM IS THAT
NUMBERS DON'T ALWAYS TELL THE
WHOLE STORY.
CLINICAL CONTEXT IS IMPORTANT,
RIGHT?
WE DON'T WANT THESE MODELS TO BE
COMPLETELY AUTONOMOUS, LOOK AT
CONTEXT, SHOWING AN OUTCOME A
PHYSICIAN CAN'T DO ANYTHING OUT,
TO US SHOULD BE A LOW PRIORITY.
AND WE HAVE TO BUILD IN A WAY
BECAUSE WE DON'T WANT TO ADD TO
CLINICAL BURDEN OF CLINICIANS.
FOR CLINICIANS WE HAD TO TEACH
A.I. IS NOT A MAGIC BULLET, IF
NOT SUPPORTED BY BIOLOGY IT
WON'T WORK.
IT'S A BLACK BOX, NOT A MAGIC
BOX.
CAN ONLY SEE PATTERNS IN
BIOLOGY.
WHAT CLEARLY DRIVES THE ACCURACY
IS THE DATA, ACCURACY OF IT, HOW
LABOR IS ADJUDICATED, AND WE ASK
THE QUESTION, IS THERE A GOLD
STANDARD TO ANSWER.
SO, TO DO THAT AND ANSWER THESE
QUESTIONS, WE EMBEDDED
ENGINEERING IN THE PRACTICE,
PART OF THE CARDIOVASCULAR
MEDICINE DEPARTMENT, NOT FROM
ENGINEERING.
THEY SIT IN THE SAME PHYSICAL
SPACE.
THEY OBSERVE PROCEDURES.
AND EACH OF THEM HAVE DUAL
APPOINTMENTS.
ENGINEERS ARE PART OF THE A.I.
LAB, ALSO PART OF THE CATH,
ECHO, ECG LABS, THEY CAN ATTEND
EVENTS TO IMPROVE THOSE, AND
COMMUNICATION SKILLS.
AS YOU CAN SEE WE ACTUALLY ASK
QUESTIONS, WHY ARE YOU DOING
THIS AND THAT, WE HAVE A CME
ONCE A YEAR THAT WE INVITE
ENGINEERS AND PHYSICIANS TO
LEARN ABOUT WHAT EACH OTHER IS
DOING, AND IT'S IMPORTANT TO
UNDERSTAND THERE ARE MORE STARS
IN THE SOLAR SYSTEM, AS I
MENTIONED, BUT WE CAN ONLY DO IT
TOGETHER, THESE MODELS WILL
BE -- WILL HAVE TO BE TESTED IN
THE WILD.
DIGITAL STUDIES, TO DO THESE WE
NEED DIVERSE TEAMS.
TO SQUEEZE THAT SOLAR SYSTEM
ANALOGY, WE ALL UNDERSTAND IT'S
NOT THE PHYSICIAN OR NOT THE
ENGINEERS, THE PATIENT SHOULD BE
IN THE CENTER, I THINK WE CAN
ALL BE ALIGNED AND WORK TOGETHER
TO BENEFIT AND IMPROVE HUMAN
HEALTH.
THANK YOU FOR THIS OPPORTUNITY.
AND IF YOU HAVE ANY QUESTIONS.
>> WONDERFUL.
THANK YOU.
I CAN SEE YOU HAVE SUCCESSFULLY
TRAVERSED THIS COMMUNICATION
BARRIER.
I LIKE YOUR LAST LINE, THERE'S
MANY STARS IN THE SOLAR SYSTEM.
WE'LL BE HEARING ABOUT ALL THOSE
OR PLANETS, REGULATORY PRIVACY,
ALL THE OTHER PIECES OF THE
PUZZLE.
I APPRECIATE THAT GREAT TALK.
AND NOW LAST BUT NOT LEAST WE
HAVE DR. AHMAD FROM NORTHWESTERN
MEDICINE WHO WILL BE TALKING
ABOUT THE BLOOD PRESSURE DATA
PUZZLE PIECES, AND I THINK DR.
ATTIA ALLUDED TO THIS.
90% OF THE WORK IS FINDING AND
CLEANING UP THAT DATA.
LOOKING FORWARD TO IT.
THANK YOU.
>> GREAT, THANK YOU.
ONE SECOND.
OKAY.
CAN YOU SEE MY SLIDES?
>> WE DO.
>> GREAT.
THANK YOU.
THANK YOU TO THE ORGANIZERS FOR
INVITING ME, I'M EXCITED TO
COVER THIS TOPIC, DATA PUZZLE
PIECES.
THESE ARE MY DISCLOSURES.
SO WE'RE ASKED BY ORGANIZERS TO
HIGHLIGHT KEY TAKEAWAYS FROM THE
TALKS.
TO COVER THESE THREE QUESTIONS I
PLAN TO HIGHLIGHT SIX AREAS.
ONE IS HOW DO WE IMPROVE DIGITAL
INFRASTRUCTURE IN OUR HEALTH
CARE ECOSYSTEM, DATA ECOSYSTEM.
TWO, HOW DO WE INCREASE
EFFICIENCY OF PROCESSING ANNUAL
HARMONIZING COMPLEX DIVERSE
DATA.
THREE, HOW DO WE IMPROVE DATA
INTEGRATION, EHR USABILITY.
FOUR, HOW DO WE TEST DEVICES AND
DIGITAL HEALTH TOOLS, FIVE CROSS
TRAINING EXPERTS, AND SIX
ADVANCING EQUITY WITH AI/mL
DIGITAL HEALTH TOOLS THAT DR.
SPATZ SPOKE ABOUT EARLIER.
THIS IS FOUR MAIN AREAS, FIRST
AN OVERVIEW OF THE ECOSYSTEM FOR
HYPERTENSION, THEN THE ROLE OF
STANDARDS TO DRIVE HYPERTENSION
DIAGNOSIS IN THIS ERA.
THEN EXAMPLES OF INTEGRATION OF
HOME BP INTO HEALTH SYSTEMS AND
PROMISING AREAS AND CHALLENGES
IN THE ERA OF A.I.
AND SO AS WE'VE HEARD, WE'RE
LIVING IN A WORLD IN WHICH DATA
CONTINUES TO GROW EACH DAY.
NORMAL SCIENCE DATA IS GENERATED
THROUGH DAILY INTERACTIONS CAN
TECHNOLOGY ACROSS SOCIETY.
THERE'S BEEN GROWTH OF HEALTH
CARE DATA, OLDER DATA FROM 2020.
I CAN SEE THE AMOUNT CONTINUES
TO GROW.
AS I'VE MENTIONED THIS IS A
GROWING ECOSYSTEM OF DEVICES TO
MEASURE BLOOD PRESSURE, MOVING
BEYOND TRADITIONAL BLOOD
PRESSURE CUFF.
AND THIS IS A FIGURE FROM A NICE
PAPER HIGHLIGHTING NEED OF
CONSENSUS STANDARDS FOR ACCURACY
OF THESE DEVICES, WE'LL TALK
ABOUT THIS LATER TODAY.
IT'S IMPORTANT WE HAVE STANDARDS
WITH ACCURACY BECAUSE FOR THESE
GARBAGE IN REALLY MEANS GARBAGE
OUT.
IDEALLY WE TAKE THESE DATA FROM
WEARABLES AND MERGE WITH OTHER
DATA THAT WE KNOW ARE IMPORTANT
TO HEALTH CARE AND MANAGEMENT OF
PATIENTS INCLUDING OMICS DATA,
MICROBIOME DATA, EHR DATA, AND
IDEALLY WOULD HAVE A WAY TO
MERGE THESE DATA TOGETHER, FOR A
NUMBER OF OPPORTUNITIES TO
IMPROVE CARE, PHYSICIAN
STRATEGIES BASED ON BLOOD
PRESSURE PHENOTYPES, DIGITAL
CLINICAL TRIALS, HOSPITAL AT
HOME PROGRAM, VIRTUAL HEALTH
COACHING WHICH IS ESPECIALLY
EXCITING GIVEN EMERGENCE OF
LARGE LANGUAGE MODELS.
THESE WOULD SEND INFORMATION
COMMONS AND RUN MULTI-MODAL
MODELS THAT WERE TALKED ABOUT
EARLIER TODAY, AND DEVELOP
INFERENCE AND USE IN GROUP CARE.
THERE IS A DIFFERENCE BETWEEN
WHAT I THINK WE HOPE TO DO AND
WHAT IS IN THE CLINIC AND LIVES
OF OUR PATIENTS.
THIS IS ME IN THE CLINIC.
THIS IS MY EXPERIENCE.
PATIENTS BRING IN DATA, VERY
WELL DETAILED, HELPFUL TO HAVE
DATA.
IT'S A CHALLENGE HOW TO
INCORPORATE, HOW MUCH IS
DOCUMENTED INTO EPIC ITSELF, AND
HOW CAN I USE THIS IN THE BEST
WAY TO TAKE CARE OF MY PATIENTS?
IT'S A CHALLENGE THAT THERE ARE
A LOT OF MODELS, TRADITIONAL
MODELING TECHNIQUES AND DEEP
LEARNING.
THIS IS A FIGURE I LOVE TALKING
ABOUT HOW THERE ARE DIFFERENT
WAYS IN WHICH MODELS DO NOT
ULTIMATELY IMPACT CLINICAL CARE.
I DO THINK THIS IS A TIME FOR
HOPE IN THIS SPACE BECAUSE WE
HAVE AN OPPORTUNITY BECAUSE DATA
IS BEING -- THERE ARE A LOT MORE
OPPORTUNITIES TO INTEGRATE.
TO FIGURE OUT WHAT "STAR WARS"
MOVIE OF THIS WAS FROM, YODA DID
NOT ACTUALLY SAY THIS, THAT MAY
HAVE BEEN CHAT TV HALLUCINATING.
I'M EXCITED.
I THINK WE'VE BEEN SEEING MORE
AND MORE DATA LIBERATION FROM
ELECTRONIC HEALTH RECORD, AND
THE 21ST CENTURY CURES ACT
STARTS PENALIZING EHR VENDORS
AND HEALTH SYSTEMS FOR BLOCKING
INFORMATION, BUILDING ON A
DECADE OF LEGISLATION FROM A
VARIETY OF STAKEHOLDERS, MANY
LIKELY ON THIS CALL, TO DRIVE
INTEROPERABILITY AND DATA
EXCHANGE.
IT'S EXCITING NOW DATA --
PATIENTS CAN ACCESS VIA FHIR AND
IT'S STANDARDIZED, THROUGH
APIs FROM VENDORS.
NEXT I'LL TALK ABOUT ROLES OF
STANDARDS.
THIS IS A BRIEF STANDARDS 101
SUMMARY SLIDE THAT I TOOK FROM
THE GOVERNMENT.
THERE ARE DIFFERENT CATEGORIES
OF STANDARDS.
VOCABULARY AND CODE SETS,
SEMANTICS, THEIR FUNCTION IS
INFORMATION UNDERSTOOD, AND
TYPES CAN INCLUDE RxNORM OR
SNOMED, THERE'S SYNTAX STANDARDS
THAT ARE USED TO MAKE SURE
INFORMATION ITSELF IS IN AN
APPROPRIATE FORMAT.
CCDA IS TYPE OF STANDARD BUT
FHIR IS MORE COMMONLY USED.
THERE ARE STANDARDS FOR
TRANSPORT SECURITY AND SERVICES,
THESE ARE REALLY IMPORTANT FOR
ENABLING THIRD PARTY ACCESS TO
DATA VIA FHIR APIs.
I WANT TO HIGHLIGHT OTHER GROUPS
IN THIS SPACE.
FHIR IS A REALLY IMPORTANT
STANDARD THAT LIKELY A LOT OF
YOU HEARD ABOUT AND ARE USING IN
YOUR WORK.
SET OF RULES AND SPECIFICATIONS
FORE CHANGING HEALTH CARE DATA,
DESIGNED TO BE FLEXIBLE,
ADAPTABLE.
THE USCDI THAT VENDORS ARE
MANDATED TO MAP TO, THERE'S
DIFFERENT VERSIONS OVER TIME,
AND DRAFT VERSION 4 IN JANUARY
OF 2023.
OPEN mHEALTH IS USING
STAKEHOLDER-DRIVEN GROUP USING
STANDARDS FOR MOBILE HEALTH
DATA.
THIS IS AN EXAMPLE OF A FHIR
REPRESENTATION OF BLOOD
PRESSURE.
YOU CAN SEE HERE THERE'S A BLANK
CODE ASSIGNED, A VALUE, AND THIS
IS A NARRATIVE VERSION.
THERE'S ALSO MACHINE READABLE
VERSIONS OF THIS.
ONE OF THE REALLY AMAZING THINGS
ABOUT HAVING DATA MAPPED TO FHIR
IS THAT IT'S AN ECO-SYSTEM SMART
TOOLS LED BY PEOPLE OUT OF
BOSTON CHILDREN'S HOSPITAL AND
TOMORROW YOU'LL HEAR A TALK FROM
ONE OF THE LEADERS OF SMART ON
FHIR INTEGRATING TOOLS INTO
CLINICAL CARE, AFFORDING THE
OPPORTUNITY TO DEVELOP WORK
FLOWS AND APPS THAT CAN LEVERAGE
HOME BLOOD PRESSURE MONITORING
INTO CLINICAL CARE IN AN
EFFICIENT SCALABLE WAY.
THERE'S ALSO A COMMUNITY OF
FOLKS, CODEX, TRYING TO WORK ON
THE ISSUE OF SILOED HEALTH CARE
DATA.
THEY STARTED IN ONCOLOGY, ALSO
IN GENOMICS AND CARDIOVASCULAR,
TO HELP THINK ABOUT HOW DO WE
TAKE DATA GENERATED FROM PEOPLE,
AND USE THAT DATA ACROSS THE
PATIENT'S JOURNEY AND ENTIRE
HEALTH CARE ECOSYSTEM.
AND THESE ARE -- THIS SLIDE
SUMMARIZES WHAT CODEX DOES FROM
THEIR WEBSITE BELOW, HOW THEY
HAVE A SYSTEM TO PRIOR TOPICS,
EXECUTE PILOTS AT SCALE.
AS I MENTIONED THERE'S A CARDX,
THE GROUP WORKING ON
CARDIOVASCULAR DISEASE, FIRST IS
HYPERTENSION MANAGEMENT.
THESE ARE GOALS FROM THEIR SITE.
TO CREATE INTEGRATED AUTOMATED
VENDOR AGNOSTIC STANDARD, ENABLE
LIQUIDITY BETWEEN DEVICES,
HARMONIZE HYPERTENSION
VOCABULARY, AND DEVELOP DATAE
CHANGE STANDARDS FOR HOME BLOOD
PRESSURE DEVICE AND DATA
COMMUNICATION BETWEEN PATIENTS
AND CLINICIANS.
AND THIS IS A SUMMARY FROM THE
WEBSITE OF HOW THEY ENVISION
THIS ECOSYSTEM WORKING.
THE PATIENT AND PERSON HAS
DEVICES, THEY CAN SHARE VIA
FHIR, BLOOD PRESSURE DAD,
PERSONAL HEALTH, PATIENT
GUIDANCE ENGINES THAT WORK ON
TOP OF THE PERSONAL HEALTH AND
CAN BE SHARED VIA FHIR TO EHR,
WHICH CLINICIAN FACING PORTALS
CAN BE BUILT, INFORMED FROM THE
GUIDELINES.
EVENTUALLY YOU CAN ALSO THEN
SHARE INFORMATION, ALSO WITH
PAYERS TOO.
A KEY STEP IS TO MAKE SURE
GUIDELINES AND QUALITY MEASURES
CAN BE COMPUTABLE IN USING
DIGITAL HEALTH DATA, A LOT OF
WORK OPPORTUNITIES IN THAT
SPACE.
THERE'S ALSO A LOT OF INTEREST
IN OMICS, AND HOW OMICS,
DIFFERENT TYPES OF DATA CAN BE
USED TO PREDICT ADVERSE EVENTS.
AND THERE ARE GROUPS WORKING ON
THINKING ABOUT HOW DO WE
REPRESENT GENOMICS DATA WITH
FHIR, AND INTEGRATE THAT INTO
CLINICAL CARE, EMPOWER PATIENTS
WITH DATA TO BE USED FOR
RESEARCH IF THEY CHOOSE, AND
THIS IS AN IMPORTANT AREA OF
DEVELOPMENT ENABLED BY STANDARDS
AND THE CHEAPNESS AND
ACCESSIBILITY OF CLOUD COMPUTING
AND STORAGE.
THERE ARE A NUMBER OF COMMON
DATA MODELS ALSO FOR RESEARCH
AND SURVEILLANCE.
THESE INCLUDE MODELS LIKE
ODYSSEY, PCORnet AND OTHERS
SHOWN HERE, VERY IMPORTANT FOR
BOTH RESEARCH AND QUALITY
EFFORTS AND SURVEILLANCE
EFFORTS.
AND THERE'S A LOT OF ACTIVE WORK
ON HARMONIZING ACROSS DIFFERENT
DATA MODELS.
HERE I SHOW OMOP ON FHIR, REPORT
ON CROSSWALKS, A LINK FROM NIH
THAT HIGHLIGHTS IMPORTANT
RESOURCES FOR MAPPING FHIR TO
OTHER COMMON DATA MODELS.
AND THIS IS WORK FROM OUR GROUP,
AND IN THIS WORK WE USED A
LANGUAGE CQL, CLINICAL QUALITY
LANGUAGE TO FIND A PHENOTYPE OF
HEART FAILURE, RAN ACROSS OMOP
AND FHIR, DATA REPRESENTED IN OH
MOPE AND FIRE, USING THIS QUERY.
WITH THE GROWTH AND USE OF
STANDARDS, MORE AND MORE
OPPORTUNITIES WILL BE THERE TO
EFFICIENTLY DEVELOP PHENOTYPES
AND RUN THEM ACROSS MULTIPLE
TYPES OF DATA.
NEXT I WANT TO HIGHLIGHT HOW
HOME BP MEASUREMENT IS USED IN
HEALTH SYSTEMS.
THERE ARE A LOT THROUGH VENDORS
AND PLATFORMS.
HERE AT NORTHWESTERN THIS IS LED
BY DR. STEVE PERSE WILL,L.
WE PARTNERED WITH OMRON,
INTEGRATED INTO EPIC.
THIS IS WHAT IT LOOKS LIKE TO
THE CLINICIAN.
YOU CAN SEE HERE IT GOES IN THE
PATIENT FLOW SHEET.
YOU CAN VIEW STRUCTURED DATA
WITH MEDICATION IN THIS VIEW.
DIFFERENT VIEWS, CAN YOU SEE
GRAPHICALLY WITH MEDS.
HERE'S ANOTHER VIEW AS WELL.
THIS IS AN IMPROVEMENT TO GET A
BETTER VIEW OF A PATIENT'S BLOOD
PRESSURE, IT'S A LOT BETTER THAN
THE VERSION WE USED PREVIOUSLY,
STILL A LOT OF ROOM FOR
IMPROVEMENT.
INSTITUTE OF CHICAGO, LED BY DR.
BERNSTEIN, INFORMATICS FELLOW,
LEVERAGING EXISTING PLATFORMS BY
APPLE AND GOOGLE, AND USED THAT
TO WRITE BLOOD PRESSURE INTO
MYCHART AND EPIC, DEVELOPED A
WORKFLOW WITH CLINICAL TEAM TO
MAKE SURE PATIENTS DID NOT HAVE
OPTIMAL PROFESSIONAL MEASUREMENT
HAD TITRATION OF DRUGS, AND
TITRATION OF MEDICATION REGIMEN.
AND SO LASTLY I WANT TO TALK
ABOUT PROMISING AREAS AND
CHALLENGES IN THE ERA OF A.I.
THESE ARE MY SIX KEY TAKEAWAYS
TO TACKLE THE THREE QUESTIONS.
FIRST I WANT TO TALK ABOUT
PROMISING AREA OF IMPROVING
DIGITAL INFRASTRUCTURE IN HEALTH
ECOSYSTEM COURTESY OF
NORTHWESTERN MEDICINE.
WE'VE INVESTED TRYING TO DEVELOP
A DIGITAL PLATFORM, MOVING FROM
THE DATA WAREHOUSE TO THE CLOUD,
AND SO THIS IS OVER
ARCHITECTURE, AS YOU CAN SEE,
I'LL WALK THROUGH THESE.
YOU CAN IMAGINE THAT A PATIENT
HAS A WEARABLE AND THE WEARABLE
SENDS ELEVATED HEALTH
MEASUREMENT OF SOME KIND,
WHETHER BLOOD PRESSURE OR
SOMETHING ELSE, VIA MESSAGE TO
THE CLOUD.
INGESTED AND TRIGGERS EVENT
ENGAGE THAT PROCESS AND
PUBLISHED AS ELEVATED TOPIC,
STORED IN DATA WAREHOUSE.
I CAN HAVE A.I. MODEL THAT
SUBSCRIBES TO THAT EVENT ENGINE,
READ IT AND TRIGGER, IDEALLY
BASED ON FHIR, MORE SCALABLE.
AND DEPENDING ON OUTPUT AND YOU
WHO IT'S SET UP COULD SEND SCORE
AS A NEW EVENT TRIGGERS
NOTIFICATION TO THE PROVIDER OR
WHATEVER IS SET UP TO TAKE CARE
OF PATIENTS WITH THAT PARTICULAR
ALERT.
I THINK WE'RE IN THE PROCESS OF
A LOT OF HEALTH SYSTEMS AND IN
GENERAL EVOLVING OUR SYSTEMS TO
BETTER INCORPORATE THESE TYPES
OF DATA BUT WE STILL HAVE A WAYS
TO GO, BOTH HERE AT NORTHWESTERN
AND NATIONALLY.
I THINK ANOTHER PROMISING AREA
IN THIS SPACE IS HOW DO WE
PROCESS AND HARMONIZE COMPLEX
DIVERSE DATA.
LARGELY MODELS HAVE HUGE
POTENTIAL IN THIS AREA.
THIS IS A TWEET FROM JOSH MENDEL
WHO WORKS AT MICROSOFT, ONE OF
THE LEADING THINKERS IN SMART ON
FHIR AND TOOK COMPLEX
DOCUMENTATION FROM EPIC AND
QUICKLY CLEANED IT UP IN A WAY
THAT'S MORE USABLE IN JSON
FORMAT.
THIS COULD BE USEFUL AS WE LOOK
THROUGH THE EXPERTS FROM EHRs,
MY PRIOR WORK WE LOOKED AT THE
QUALITY OF MAPPING TO CCBA, I
SUSPECT, I HAVEN'T TAKEN A DEEP
DIVE YET BUT ISSUES WITH DATA
QUALITY, OPPORTUNITY TO AUTOMATE
THE WAY WE ANALYZE THESE
STANDARDS AND ULTIMATELY CLEAN
THEM UP AND MAKE THEM USABLE.
I THINK ANOTHER PROMISING AREA
IS HOW DO WE IMPROVE DATA
INTEGRATION AND EHR USABILITY.
I DESCRIBE USER-CENTERED DESIGN
PROCESS TO CREATE IDEAL WAY TO
VISUALIZE BLOOD PRESSURE FOR
CLINICIANS.
YOU CAN SEE THIS IS A MUCH
IMPROVED VERSION OF WHAT I
SHOWED EARLIER THAT WE'RE USING
IN EPIC AND OUR WORKFLOW HERE.
AND I THINK ANOTHER REALLY
PROMISING AREA IS USE OF LARGE
LANGUAGE MODELS FOR DATA
INTEGRATION, FROM THE MOVIE
"OFFICE SPACE" YEARS AGO, ONE OF
MY FAVORITE MOVIES FROM A LONG
TIME AGO.
THIS IS ABOUT DISGRUNTLED OFFICE
WORKERS WHO TAKE A FAX MACHINE
AND SPARK IT IN A FIELD.
I DO THINK THAT THERE'S
OPPORTUNITY WHERE WE START
INTERACTING WITH COMPUTERS IN A
MORE EFFICIENT WAY INSTEAD OF
HAVING COMPLICATED GRAPHICAL
USER INTERFACES, CLICK ALL THE
TIME, START USING NATURAL
LANGUAGE INTERFACE.
BILL GATES PUBLISHED A BLOG POST
ABOUT THIS TOO.
THAT CAN TRANSFORM OUR
INTERACTION AND HELP US BE MORE
EFFICIENT AND TAKE CARE OF
PATIENTS BETTER.
I ALSO THINK THERE'S A LOT OF
CHALLENGES ALONG THE WHOLE
PIPELINE OF USING -- INTEGRATING
PATIENT GENERATED HEALTH DATA.
THE FIGURE ON THE LEFT IS FROM A
REVIEW OF PATIENT-GENERATED
HEALTH DATA INTERVIEWING LEADERS
IN THE SPACE.
YOU CAN SEE EACH KEY THEME UNDER
CORE AREAS OF DATA CAPTURE,
TRANSFER, REVIEW.
AND ALSO I THINK THERE'S GOING
TO BE A LOT OF WORK THAT NEEDS
TO BE DONE THINKING ABOUT LOU DO
WE SURFACE MODELS, MACHINE
LEARNING MODELS TO CLINICIANS,
THE FIGURE ON THE RIGHT FROM
DUKE, A NICE EXAMPLE OF A FOOD
LABEL EQUIVALENT TO MACHINE
LEARNING MODELS TO HELP
CLINICIANS UNDERSTAND HOW THE
MODEL IS TRAINED AND HOW IT
SHOULD BE USED.
ANOTHER PROMISING AREA, TESTING
DEVICES IN PRAGMATIC TRIALS.
THE FIGURE ON THE LEFT IS FROM A
REVIEW THAT TALKS ABOUT
STRATEGIES TO DRIVE IMPROVEMENT
IN QUALITY OF CARE FOR PATIENTS
WITH HYPERTENSION.
THE RIGHT I THINK IS A FIGURE
THAT SHOWS SOME DIFFERENT
PHENOTYPES OF PATIENTS WITH
HYPERTENSION AND EVENTUALLY HAVE
POTENTIALLY TARGETED
NON-PHARMACOLOGIC OR
PHARMACOLOGIC THERAPIES BASED ON
PHENOTYPE IF WE HAVE A LOW
PROFILE WIDELY USED 24-HOUR
BLOOD PRESSURE MONITOR.
THIS IS TO LEVERAGE LATEST FROM
IMPLEMENTATION SCIENCE AND
DESIGN TRIALS THAT MEASURE
CLINICAL EFFECTIVENESS OF DEVICE
OR TOOL, ALSO HOW IT'S
IMPLEMENTED ITSELF AND
IMPLEMENTATION STRATEGIES.
AND IT'S IMPORTANT FOR ANY MLA
TRIAL TO ADHERE TO IMPORTANT
STANDARDS OUT THERE AND ON THE
RIGHT YOU CAN SEE CHART THAT
HIGHLIGHTS CURRENT mL CLINICAL
TRIALS AND SOME OPPORTUNITIES TO
IMPROVE REPORTING THEM.
ANOTHER PROMISING AREA RELATED
TO CLINICAL TRIALS HOW DO WE
BUILD UP THE INFRASTRUCTURE TO
EXECUTE THESE TRIALS AT SCALE.
AND NIH ALREADY SUPPORTS A
NUMBER OF INITIATIVES AND
CARDIOVASCULAR TRIAL AND AI/ ML
SPACE, BUT HELP IT WILL TO SCALE
AND TEST INTERVENTIONS EFFICIENT
LIP ACROSS DIVERSE POPULATIONS.
ANOTHER GAP TO TRAIN PEOPLE TO
SPEAK BOTH LANGUAGES.
WE STARTED A FELLOWSHIP THREE
YEARS AGO, TAKING FELLOWS, AND
EVEN FROM INTERNAL MEDICINE
RESIDENTS, SPEND A YEAR WITH
ENGINEERS, MASTER IN
ENGINEERING, RECRUIT THEM
INTERNALLY OR PUT THEM IN
DIFFERENT POSITIONS IN ACADEMICS
OR HEALTH TECH.
LASTLY I WANT TO BRIEFLY MENTION
IMPORTANCE OF THINKING OF
VIEWING TECHNOLOGY WITH EQUITY
LENS.
DR. SPATZ DISCUSSED THIS
EARLIER, AND IT WILL BE COVERED
TOMORROW.
IT'S REALLY IMPORTANT TO THINK
ABOUT HOW DO WE USE TOOLS TO
IMPACT MULTI-LEVEL AND
MULTI-FACETED FACTORS TO
CONTRIBUTE TO HEALTH INEQUITIES
RELATED TO HYPERTENSION.
THANK YOU FOR YOUR TIME AND
ATTENTION.
I LOOK FORWARD TO THE
DISCUSSION.
>> THANK YOU SO MUCH.
THAT WAS A TOUR DE FORCE IN
TERMS OF DATA INFRASTRUCTURE.
SO EVERYBODY, IF YOU HAVE
QUESTIONS PLEASE PUT THEM IN THE
CHAT, I'LL TRY TO KEEP AN EYE.
WE HAVE SOME TIME NOW FOR A
PANEL DISCUSSION SO WE CAN PICK
THE BRAINS OF THESE BRILLIANT
PEOPLE WHO HAVE CROSSED THIS
CHASM BETWEEN THE CLINICAL WORLD
AND ENGINEERING WORLD.
I'M GOING TO HIGHLIGHT A FEW
THEMES THAT CAUGHT MY ATTENTION
AND START WITH A FEW QUESTIONS.
SO SOME OF THE THEMES I HEARD IS
THAT WE HAVE A LOT OF MODELS AND
COMPUTATIONAL POWER.
AS FAR AS I CAN SEE THAT
INCREASE IN COMPUTE POWER IS NOT
STOPPING.
IT'S GOING TO KEEP GROWING.
AND ADVANCEMENT OF TECH AND
PARTICULARLY HOW HUMANS INTERACT
WITH TECH OR SOME COMPUTATIONAL
PLATFORMS IS ALSO GROWING FAST.
WE HAVE A TON OF DATA,
PARTICULARLY HEALTHCARE DATA,
AND DIGITAL INFRASTRUCTURE IS
ADVANCING TO BE ABLE TO
HARMONIZE AND AGGREGATE DATA IN
WAYS THAT MAKE IT MORE SCALABLE
FOR USE.
BUT WHEN CHALLENGED NOW WE
SEE -- ONE CHALLENGE IS WE NEED
TRUE INTEGGATION OF DIFFERENT
TYPES OF EXPERTISE AND HEARD
BOTH SIDES, WHERE AT MAYO
ENGINEERS ARE HANGING OUT WITH
CLINICIANS, AT NORTHWESTERN
ENGINEERS -- CLINICIANS ARE
HANGING OUT WITH ENGINEERS, GOOD
TWO-WAY ROADS, LEADING TO THE
IMPORTANT CONCEPT WE HEARD THAT
THE NUMBERS ALONE WILL NEVER
SOLVE THE PROBLEM.
IT'S THE CLINICAL CONTEXT THAT
MATTERS.
NUMBERS DO NOT TELL THE WHOLE
STORY.
THESE ARE SOME OF THE THEMES I
HEARD.
OH, AND 90% OF THE WORK IS
CREATING GOOD DATASETS.
IT'S HARD WORK.
IT'S TEDIOUS, IT'S NOT GLAMOROUS
BUT IT'S THE FOUNDATION AND
HOPEFULLY SOME STUFF FARAZ WILL
MAKE THAT WORK FASTER AND
EASIER.
THESE ARE SOME THEMES.
MAYBE I'LL START WITH A
QUESTION.
ALL OF YOU HAVE TAKEN SOME FORM
OF COMPUTE OR TECH AND SOLVED A
PROBLEM.
I'LL PUT ON MY SUBMISSION HAT
FOR A MINUTE.
WHEN YOU STARTED HOW WAS THAT
PROBLEM DEFINED?
A CLINICIAN HAS AN IDEA,
SOMETHING THEY WANT TO TACKLE
AND WORK WITH ENGINEERS OR
COMPUTER SCIENCE PEOPLE, WHERE
DID THE IDEA START?
>> IT NEEDS A VILLAGE.
WE'VE BEEN DOING IT, MANY
PROCEDURES WE ATTEND, WE ASK
CLINICIANS WHY ARE YOU DOING
THAT, RIGHT?
WHAT'S THE NEXT STEP?
MOST OF THE TIME THERE ARE
STRAIGHTFORWARD ANSWERS BUT
SOMETIMES -- ACTUALLY, I DON'T
KNOW, WE'VE ALWAYS DONE THAT,
RIGHT?
AND SOME OF THE QUESTIONS ARE
VERY NAIVE.
WE'VE DEVELOPED A MODEL THAT
LOOKS AT ATRIAL FIBRILLATION,
THAT STARTED AS A TECHNICAL
CHALLENGE.
WE DIDN'T KNOW WHICH PATIENTS,
MAYBE TRY TO PREDICT WHO HAS AS
SECOND MODEL.
CLINICIAN SAID THAT'S A USEFUL
WAY FOR US TO KNOW IF A
CRYPTOGENIC PATIENT HAS AF OR
NOT.
A LOT STARTS WITH SPONTANEOUS
DISCUSSIONS.
SOMETIMES WE HAVE BRAINSTORMING
SESSIONS THAT WE CAN SAY WE HAVE
THIS TEST, AND ESPECIALLY NOW
WITH FRAMEWORKS IN PLACE, A
BIGGER TEAM.
IT'S EASIER TO DO IT IN A
STRUCTURED WAY BUT ESPECIALLY
WITH A SMALL TEAM, ONE OR TWO
ENGINEERS, SPENDING A LOT OF
TIME WITH THEM WITH
AUTOMATICALLY CASCADE THE IDEAS,
RIGHT?
ASKING THE QUESTIONS, LOOKING AT
THE DATA, SEEING WHAT'S
AVAILABLE.
EVENTUALLY THAT'S THE MOST
IMPORTANT THING.
YOU HAVE TO LOOK AT WHAT YOU
HAVE AVAILABLE AND THEN START
FISHING EXPEDITION.
>> YES.
SOUNDS LIKE THE
CROSS-POLLINATION APPROVE.
IF YOU DO IT ENOUGH AND SPEND
TIME TOGETHER, SMART PEOPLE WILL
FIND THE RIGHT QUESTIONS.
I MADE A COUPLE RELATED
QUESTIONS IN THE CHAT.
ONE PERSON MADE A COMMENT, A.I.
CAN DIRECT OUR ATTENTION TO
VARIOUS SIGNALS BUT WE NEED TO
SELECT MEANINGFUL SIGNALINGS.
ANOTHER QUESTION, WHAT ROLE DO
YOU SEE A.I. PLAYING IN CLEANING
AND HARMONIZING DATA.
I THINK THOSE ARE SORT OF
RELATED BECAUSE MEANINGFUL
SIGNALS MEANS YOU CLEANED TO
SOME EXTENT.
AND THAT'S A TEDIOUS PROCESS.
SO SIGNAL DATA IS A GREAT
EXAMPLE.
MAYBE TONS AND TONS OF SIGNAL
DATA, HOW DO YOU DO THAT?
QUOTE/UNQUOTE USING A.I. TO DO
THAT CLEANING?
>> FANTASTIC QUESTION.
IT'S GOING TO BE THE MOST
IMPORTANT THING TO ADDRESS.
LUCKILY THERE ARE SOME MODELS
THAT CAN TEACH US WHAT SORT OF
CORE DATA SHOULD LOOK LIKE, WHAT
ENDS UP BEING COMMON, NOT THAT
IT ELIMINATES OUTLIERS BECAUSE I
THINK ONE OF THE THINGS WE CAN
GET AS WE COLLECT MORE DATA IS
THAT UNIQUE DATA SHOULDN'T BE
CONSIDERED AS RARE OR MISSING,
BUT ACTUALLY AS REPRESENTATIVE
OF SOMETHING DIFFERENT.
SO WHAT'S NEEDED ARE A SERIES OF
TECHNIQUES THAT CAN CLEAN AND
HARMONIZE DATA, AT LEAST BY SOME
MEANINGFUL METRIC, SOME CLINICAL
STANDARD, SO IT'S PROVIDING YOU
WITH EITHER STANDARDIZED SIGNALS
OR NEW SIGNALS THAT YOU CAN
UNDERSTAND WHY THEY ARE
OUTLIERS.
THERE ARE TECHNIQUES SORT OF
AUTOINCLUDING WITH A.I. MODELS
GIVE DATA AT INPUT, TELL IT TO
DO ITS MAGIC INTERNALLY, HAVE IT
GENERATE OUTPUT AND MATCH TO SEE
HOW FAR THAT OUTPUT IS FROM
RECONSTRUCTING THE INPUT.
AUTOENCODING TECHNIQUES ARE
REMARKABLY GOOD AT DE-NOISING
SIGNALS.
WHAT YOU CAN THEN DO IS USE
THESE TECHNIQUES TO SORT OF
BOUND REASONABLE SPACE AND
PROVIDE CLINICIAN A TOOL.
YOU'RE NOT THROWING OUT SIGNALS
BECAUSE I DON'T ACTUALLY KNOW IF
AN OUTLIER VALUE IS FEASIBLE OR
NOT, FOR EXAMPLE.
BUT YOU PROVIDE A THRESHOLD,
TUNEABLE KNOB THAT CAN BE USED
IN THE LOOP TO DECIDE I DON'T
WANT TO CONSIDER EXTREME
OUTLIERS, OR I DO, YOU CAN USE
TECHNIQUES TO SUMMARIZE
PLAUSIBLE AND UNCERTAIN RANGES.
FROM THERE I THINK IT BECOMES A
FUNCTION OF HOW MUCH MIGHT
PROVIDE VALUE.
A.I. IS PLAYING A KEY ROLE BUT
AS SORT OF A BOX YOU CAN PLAY
AROUND WITH, PROBABLY TIERS,
HERE IS DATA THAT'S OBVIOUSLY
GARBAGE, HERE IS DATA THAT SEEMS
CLEAN, AND HERE IS DATA IN THE
MIDDLE THAT YOU HAVE TO DECIDE
WHAT YOU'RE GOING TO DO, THAT
HAS VALUE TOO, I THINK.
>> WHAT YOU'RE SAYING IS THAT
HUMANS ARE STILL IN THE LOOP AND
THAT'S GOING TO BE THE CASE FOR
A LONG TIME?
>> I THINK SO.
AS THEY ARE ABLE TO GENERATE
THESE THINGS, THE HUMAN STILL
NEEDS TO BE IN THE LOOP BECAUSE
YOU HAVE TWO FACTORS.
YOU HAVE THE MODEL TO DECIDE
THINGS THAT YOU'RE ALREADY
FAMILIAR WITH AND HAVE
CONFIDENCE IN.
IF IT GENERATES SOMETHING NEW,
THERE'S A SERIES OF POTENTIALS
OF BIAS AND CONFOUNDING THINGS
WITH RESPECT TO WHAT DATA TRAINS
THE MODEL THAT YOU NEED SOME
GAME TO TRUST.
IT MAY BE GENERATING NEW
INFORMATION BUT THE MORE YOU'RE
INVOLVED YOU'LL UNDERSTAND WHY
IT'S BEING GENERATE AND TRUST IT
AND GO FROM THERE.
>> THAT TO ME MEANS THAT
BUILDING ON THIS IDEA OF
CROSS-POLLINATION AND PEOPLE
MOVING TO DIFFERENT FIELDS THEY
HAD NOT BEEN IN PREVIOUSLY,
PHYSICIANS HAVE TO GET OUT OF
THEIR COMFORT ZONE AND BE MORE
COMPUTATIONAL AND MAP SAVVY THAN
IN THE PAST TO PARTICIPATE IN
THESE HUMAN IN THE LOOP THINGS.
WHAT ARE THE STEPS TO CONSIDER
WHEN PROPOSING OR DEVELOPING
A.I. MODELS FOR DIAGNOSTIC
SUPPORT?
I'LL BUILD ON THAT ONE.
ONE PIECE OF THAT IS LET'S SAY
YOU DO BUILD SOMETHING FOR
DIAGNOSTIC OR ADDRESSING SOME
PROBLEM YOU'VE IDENTIFIED IN
YOUR HEALTH CARE DELIVERY, HOW
DO YOU KEEP TRACK OF THE FACT IT
WORKS?
DATASETS WILL CHANGE.
WHEN YOU GO LIVE YOUR DATASET
WILL CHANGE, YOU DON'T KNOW IF
IT'S WORKING THE WAY WITH THE
FIRST VALIDATION MODEL.
FARAZ, YOU IMPLEMENTED STUFF ON
HEART FAILURE SIDE.
DO YOU KEEP TRACK?
>> IT'S A GREAT QUESTION.
YEAH, WE DO.
WE HAVE THE COMMITTEE, A.I.
CATALYST COMMITTEE,
INTERDISCIPLINARY, WITH
CLINICIANS, ENGINEERS,
SCIENTISTS, HUMAN COMPUTER
EXPERTS, EVERYONE INVOLVED FROM
IDEA INCEPTION TO DEPLOYMENT,
POST DEPLOYMENT SURVEILLANCE.
SO AS WE GO THROUGH THE PROCESS
WE VALIDATE MODELS AS BEST WE
KNOW IN DIFFERENT POPULATIONS
OVER TIME.
ONCE DEPLOYED WE'RE WORKING ON
SETTING UP SYSTEMS WHICH WE
MODEL PERFORMANCE OVER TIME, SET
CHECK-INS, AND MAKE SURE IT'S
PERFORMING AS WE THINK IT WILL.
IDEALLY CAN RETAIN MODELS OVER
TIME TO IMPROVE QUALITY OF CARE.
THIS IS ALL VERY EARLY DAYS IN
THE WORK WE'RE IN, WE'RE STILL
LEARNING AS WE GO.
>> YES, JUST LOOKING AT SOME
QUESTIONS HERE.
ONE OF THOSE, HOW DO WE BUILD
TRUST IN MACHINE LEARNING
MODELS?
THAT'S LIKE A BIG, BIG QUESTION.
SO MAYBE IF I COULD TAKE OFF ONE
PIECE OF THAT AND GET YOUR
FEEDBACK.
AGAIN, LET'S SAY YOU HAVE A
MODEL, LET'S SAY HYPERTENSION,
SOME MODEL THAT USES A LOT OF
INFORMATION TO DIAGNOSE PRESENCE
OR ABSENCE.
THAT HAS TO GET REGULATORY
CLEARANCE OR PASS THE REGULATORY
THRESHOLD.
BUT IT'S SOLELY DEPENDENT ON THE
DATA ON WHICH YOU TRAIN THE
VALIDATOR, RIGHT?
SO IF YOU NEED TO DEVELOP TRUST
IN THAT KNOWING IT'S NOT TRAINED
ON THE FULL POPULATION, WHAT
WOULD YOU THINK OF THIS IDEA?
WHAT IF THERE WERE SOME
STANDARDIZED DATASET FOR OUTCOME
X, HYPERTENSION, LET'S SAY, AND
THAT ALL MODELS WERE TRAINED TO
GET WORK -- AND THIS IS DEFINED
BY PEOPLE WHO UNDERSTAND PEOPLE
LIKE DR. SPATZ WHO UNDERSTAND
THE COMMUNITIES AND NEEDS AND
PEOPLE, CONTEXT WHERE PEOPLE
NEED IT, THEY CAN SAY, OKAY, IF
YOU'RE GOING TO PUT THIS THING
ON THE MARKET, IT MUST WORK, SHE
KNOWS THAT POPULATION.
YOU HAD TO MEET THAT BAR.
WHAT DO YOU THINK OF SOMETHING
LIKE THAT?
STANDARDIZED SETS, YOU HAVE TO
PERFORM AGAINST?
>> I'D BE HAPPY TO TAKE THE
FIRST STAB.
SO, I THINK COLLEAGUES ARE DOING
EXCELLENT WORK WITH DIGITAL
CENTER NOW, AND ONE OF OUR
COLLEAGUES IS A CARDIOLOGIST
ACTUALLY, WE'RE IN THE PROCESS
OF IMPROVING MODELS NOW.
YOU SEE THEY PROPOSE WHAT YOU
PROPOSED.
BECAUSE THEY HAVE TO TAKE ONE
BENCH MARK FOR ALL STUDIES, ONE
FOR HYPERTENSION, USUALLY THESE
MODELS LOOK AT VERY SPECIFIC
OUTCOMES.
WE HAD TO GO TO DIFFERENT
HOSPITALS WITH DIFFERENT
DEMOGRAPHICS OF PATIENTS, AND
SHOW THAT THE WORKS ON ALL OF
THEM, RIGHT?
SO I THINK FDA ROLE IS CRUCIAL,
MAKING SURE MODELS HELP
PATIENTS, AND USUALLY NEED TO GO
ONE STEP FURTHER.
EXTERNAL VALIDATION IS THE
ABSOLUTE MUST, BUT HOW
CLINICIANS USING THE TOOL BEHAVE
IS ANOTHER VERY IMPORTANT TASK
BECAUSE MOST OF THESE MODELS ARE
GIVING THE CLINICIAN ONE
INSIGHT, RIGHT?
BUT CLINICIAN IS LOOKING AT THE
PATIENT, THERE'S A MILLION
THINGS THEY ARE CONSIDERING,
RIGHT?
WE NEED TO SEE IF THAT SPECIFIC
INSIGHT ACTUALLY IMPROVES THE
PATIENT CARE OVER THE LONG TERM.
THE MINIMUM IS AS YOU SUGGESTED
A BASELINE, A VERY
DEMOGRAPHIC-SPECIFIC ANALYSIS,
BUT I THINK WE HAVE TO HAVE A
HIGHER BAR AND HAVE PRACTICE
VALIDATION AT WORK.
>> THAT'S A REALLY EXCELLENT
POINT BECAUSE THERE'S UNKNOWN
EFFECTS ON JUST IMPLEMENTATION,
NOTHING TO DO WITH THE TECH,
SLOWLY ON IMPLEMENTATION SIDE,
UNINTENDED EFFECT YOU DIDN'T
THINK OF AND MAKE SURE THAT
WHATEVER YOU'RE IMPLEMENTING IS
STILL HAVING EFFECTIVENESS WITH
THOSE FACTORS INCLUDED.
ANOTHER GOOD QUESTION, WHAT
PRACTICAL ADVICE WOULD YOU GIVE
EARLY CAREER CLINICIAN AND
RESEARCHERS REACHING OUT TO A.I.
ENGINEERS?
>> IT'S A GREAT QUESTION.
I MEAN, WHEN I REACHED OUT TO
COLLABORATORS WHO WERE
ENGINEERS, REALLY FILLING THE
CLINICAL PROBLEM, EXPLAINING
THAT, GETS THEM EXCITED TO
PARTNER.
AND I THINK A LOT OF ENGINEERS
ARE EXCITED, HOW DO WE DEVELOP A
MODEL EMPLOYED IN PRACTICE AND
CHANGE CARE.
SELLING YOUR STRENGTHS AND THE
PROBLEM IS REALLY IMPORTANT.
BUILDING A BACKGROUND SO YOU
UNDERSTAND BASICS, A TON OF
SELF-LEARNING OPPORTUNITIES TO
BUILD UP A BASIC BACKGROUND IN
BIG DATA SCIENCE, THE SPACE, AND
SO I THINK LEARNING THE LANGUAGE
TOO IS REALLY IMPORTANT TO THOSE
COLLABORATIONS.
>> YEAH, I'LL DOUBLE ON THAT
ONE.
THAT SECOND ONE.
IF YOUR EARLY CAREER GETS INTO
THE WEEDS, ANYBODY CAN LEARN HOW
TO WRITE CODE THESE DAYS.
AND STATISTICS.
MOSTLY I TELL EARLY CAREER
PHYSICIANS, LEARN STATISTICS.
THAT'S REALLY IMPORTANT.
YEAH, HAVE YOU HAD GOOD
EXPERIENCE?
>> I MIGHT ECHO THAT SENTIMENT.
ON THE FLIP SIDE TOO AS YOU
START TO LEARN THESE THINGS I
THINK AS A RESULT YOU'LL THEN
QUICKLY BE ABLE TO TELL ON THE
FLIP SLIDE WHO ARE THE ENGINEERS
INTERESTED IN EMBEDDING WITHIN
MEDICINE AND LEARNING MORE,
RIGHT?
I CAN GO AND AT LEAST SPEAK TO
SOME EXTENT OF CARDIOVASCULAR
PROBLEMS AND CHALLENGES OF
COLLECTING DATA AND, YOU KNOW,
UNDERSTANDING THAT A
REINFORCEMENT LEARNING ALGORITHM
PLAYS A GAME AND FAILS A FEW
TIMES UNTIL IT LEARNS IS FINE
BUT YOU CAN'T INTENTIONALLY FAIL
50 TIMES WITH A PERSON.
YOU'LL START TO FIND MORE
ENGAGED COLLABORATORS THERE
EXCITED ABOUT TRYING TO SOLVE A
CLINICAL PROBLEM VERSE -- VERS
THE ONES WITH SOMETHING THEY CAN
RUNOFF LINE, THERE ARE DIFFERENT
LEVELS OF COLLABORATIONS BUT THE
MOST SUCCESSFUL WILL MAKE
GROUNDBREAKING SOLUTIONS, THAT
CAN GET BOTH SIDES INTO THE
NITTY-GRITTY SO THEY ARE WILLING
TO BE ENGAGED AT A FREQUENT
BASIS.
>> YEAH, IF WE BRING IT BACK TO
THE NIH AND NHLBI, THERE ARE
WAYS, MAYBE THERE ARE WAYS THAT
THE NATIONAL FUNDING
ORGANIZATIONS, YOU KNOW, CAN
SUPPORT THIS.
YOU HAVE TO REALLY GET OUT OF
YOUR COMFORT ZONE TO DO THIS
WORK AND OF COURSE THERE'S
WONDERFUL CAREER DEVELOPMENT
PROGRAMS AT THE NIH THAT CAN BE
GEARED TOWARDS THIS NEED WHICH I
THINK WE CLEARLY IDENTIFY HERE.
ALL RIGHT.
LET'S SEE.
I GUESS, DO YOU HAVE ANY
QUESTIONS?
WE HAVE A BREAK PLANNED HERE.
>> I DON'T KNOW IF YOU SAW --
YOU MIGHT HAVE ADDRESSED MY
QUESTION IN THE CHAT TALKING
ABOUT EARLY YEAR.
>> LET ME GO BACK HERE.
>> I CAN REITERATE.
I SEE THE VALUE OF A.I.,
CERTAINLY.
I'M THINKING FROM THE POINT OF
VIEW PREVENTIVE CARE, CLINICAL
CARE, WITH A YOUNG PHYSICIAN OR
MID-CAREER USING THESE, DO THEY
HAVE TO UNDERSTAND WHAT'S UNDER
THE HOOD IN ORDER TO IMPLEMENT?
I.E. UNDERSTAND ITS LIMITATIONS
AND STRENGTHS?
YOU'RE HANDING A TOOL TO
SOMEBODY, DO YOU SEE WHAT I'M
SAYING?
HOW MUCH DO THEY HAVE TO KNOW OR
SHOULD WE RELY JUST ON YOU ALL
WHO CROSS THE DIVIDE?
DID YOU UNDERSTAND WHAT I'M
SAYING?
THAT'S THE QUESTION, NOT A
COMMENT.
I DON'T KNOW WHAT THE ANSWER IS
BUT I THOUGHT IT WOULD POSE IT
TO YOU ALL.
>> I CAN START AND OTHERS CAN
JUMP IN.
IT DEPENDS ON USE CASE, SOME
CASES IN WHICH THEY DO NEED TO
HAVE SOME DEEPER UNDERSTANDING
WHAT'S UNDER THE HOOD.
OTHER USE CASES WHERE IF IT'S A
TOOL OR DEVICE TESTED IN A
RANDOMIZED CONTROLLED TRIAL AND
MEETS EVIDENCE BAR TO BE USED IN
PRACTICE THEY PROBABLY DON'T
NEED TO UNDERSTAND DETAILS OF
ALGORITHM BUT NEED TO UNDERSTAND
THE EVIDENCE THAT THE STUDY,
DETAILS OF THE STUDY THAT TEST
IT TO USE IT, HOW TO USE IT.
>> THE CURRENT GENERATION
WORKING ON THESE THINGS NEED TO
HAVE SOME EXPERIENCE, YOU KNOW,
SOME MERGED PROGRAM OF
ENGINEERING/MEDICINE TO HELP
DEVELOP, VALIDATE, DEVELOP TRUST
IN THE TECHNIQUES, UNTIL A POINT
IN WHICH THEY BECOME COMMON
ENOUGH THAT YOU CAN THEN
ABSTRACT DETAILS AND TRUST YOU
KNOW WHAT IT'S DOING AND WHY IT
PRODUCES WHAT IT PRODUCES
WITHOUT UNDERSTANDING WHAT IT'S
DOING UNDER THE HOOD.
>> I AGREE.
IF YOU WANT TO BE PART OF
BUILDING THE TOOL YOU GOT TO
UNDERSTAND A LOT MORE THAN IF
YOU'RE ON THE DELIVERY SIDE,
WHERE YOU HAVE TO UNDERSTAND THE
TYPICAL THINGS ABOUT
INTERVENTION, DOES THIS MATCH
THE POPULATION I'M TREATING,
WHAT'S THE TREATMENT EFFECT, DO
I HAVE TO THINK ABOUT PRE-TEST
PROBABILITY, THAT SORT OF THING.
IF YOU WANT TO BUILD TOOLS THEN
YOU HAVE TO GET INTO THE WEEDS I
THINK.
>> THANK YOU FOR ANSWERING MY
QUESTION.
>> DO YOU THINK WE SHOULD GO TO
BREAK?
>> YEAH, WHY DON'T WE TAKE A
BREAK HERE, BIO-BREAK, GRAB SOME
COFFEE, AND THEN AT 1:15 WE'LL
BE BACK FOR SESSION 2, BLOOD
PRESSURE MEASUREMENT,
HYPERTENSION RISK, BLOOD
PRESSURE CONTROL.
SO WE'LL TAKE A SHORT BREAK
WE'RE GOING TO KEEP MOVING
FORWARD HERE.
FIRST I WANT TO THANK THE
SPEAKERS FROM SESSION 1, REALLY
LAID SOME EXCELLENT GROUND WORK.
YOU KNOW, LAYING OUT THE STATE
OF THE FIELD FROM THE TECH AND
ENGINEERING SIDE TO MOVE FORWARD
ON THE NEXT SESSIONS.
THANK YOU TO ALL THREE OF YOU.
NOW TO SESSION 2, I'LL INTRODUCE
OUR MODERATOR, DR. KEITH
FERDINAND, ENDOWED CHAIR AT
PROFESSOR AT TULANE UNIVERSITY,
NATIONAL LEADER AND CHAMPION IN
PREVENTIVE CARDIOLOGY,
PARTICULARLY RACIAL AND ETHNIC
MINORITIES AND PUSHING US
FORWARD IN FOCUSING ON AND NOT
LOSE EVER SIGHT OF HEALTH EQUITY
THINGS WE NEED TO FOCUS ON.
IT'S MY PLEASURE TO INTRODUCE
KEITH FERDINAND AS THE MODERATOR
OF OUR NEXT SESSION.
>> THANK YOU FOR THE KIND
PRODUCTION.
SESSION 2 IS GOING TO LOOK AT
BLOOD PRESSURE MEASUREMENT,
HYPERTENSION RISK AND BLOOD
PRESSURE CONTROL.
WE HAVE EXCELLENT SPEAKERS,
DIVERSE IN TERMS OF GEOGRAPHY
AND INTEREST, STARTING WITH DR.
ABDULLAH, FROM COLUMBIA
UNIVERSITY IN NEW YORK,
DISCUSSING BLOOD PRESSURE
MEASUREMENT INSIDE AND OUTSIDE
OF THE OFFICE.
DR. ABDALLA, YOU MAY START.
>> THANK YOU SO MUCH.
I'M GOING TO DISCUSS DEVICES
USED FOR BLOOD PRESSURE
MEASUREMENT, GUIDELINE
APPROACHES, FOR FUTURE RESHAPED
BY CURRENT APPS IN LITERATURE.
BLOOD PRESSURE IS TYPICALLY
MEASURED IN THE OFFICE FROM THE
BRACHIAL ARTERY, THIS WAS DONE
USING MERCURY COLUMN DEVICE, NW
RECOMMENDING USE OF AUTOMATIC
DEVICE FOLLOWING A STANDARDIZED
PROCEED
PROTOCOL, SEATED FOR FIVE
MINUTES, NOT OFTEN DONE IN BUSY
OFFICE SETTINGS.
WE MEASURE IN OFFICE TO DIAGNOSE
WITH HYPERTENSION OR HIGH BLOOD
PRESSURE OR BLOOD PRESSURE
CONTROL.
BLOOD PRESSURE IN THE OFFICE HAS
MANY LIMITATIONS, FOR SOME IT IS
NOT REFLECTIVE OF THE OUTSIDE
ENVIRONMENT.
OUT OF OFFICE BLOOD PRESSURE CAN
PROVIDE MORE DATA POINTS,
COMPARED WITH THE SNAPSHOT
COMING INTO THE OFFICE WITH
CLINICIAN.
AND CAN TELL US REALLY HOW BLOOD
PRESSURE MAY VARY EITHER OVER
24-HOUR PERIOD OR SEVERAL DAYS
OR WEEKS.
MULTIPLE DATA HAS SHOWN IT MAY
BE ASSOCIATED WITH DISEASE
OUTCOMES COMPARED TO OFFICE
BLOOD PRESSURE, THE 2017 ACC/AHA
GUIDELINES AND 21 GUIDELINES
RECOMMEND OUT OF OFFICE BLOOD
PRESSURE FOR DIAGNOSTIC
CONFIRMATION.
THE 2017 SPECIFIC OUT OF OFFICE
FOR MEDS.
THERE ARE TWO WAYS BLOOD
PRESSURE IS TYPICALLY MEASURED
OUTSIDE THE OFFICE, USING
AUTOMATED.
ON THE LEFT SIDE WORN FOR 24 OR
48 HOURS, CAN MEASURE BLOOD
PRESSURE FREQUENTLY THROUGH THE
AWAKE OR DAYTIME PERIOD, OR
SLEEP NIGHTTIME PERIOD.
DOES NOT REQUIRE AN INDIVIDUAL
TO INITIATE BLOOD PRESSURE
READINGS.
THE OTHER IS HOME BLOOD PRESSURE
MONITORING, SELF-MEASURED BLOOD
PRESSURE.
WHICH DOES REQUIRE AN INDIVIDUAL
TO INITIATE BLOOD PRESSURE
READINGS, THE DEVICE IS USED
OVER SEVERAL DAYS OR WEEKS.
I'LL PROVIDE ADDITIONAL
INFORMATION ON EACH LATER.
FIRST I WANT TO TALK ABOUT THE
LIMITATIONS OF OFFICE BLOOD
PRESSURE.
IN PARTICULAR INCLUDING THE
INABILITY TO IDENTIFY CERTAIN
ABNORMAL BLOOD PRESSURE
PHENOTYPES.
TRADITIONAL DEFINITION OF
HYPERTENSION IS BASED ON OFFICE
BLOOD PRESSURE REPRESENTED HERE
ON THE LEFT SIDE.
WHEN YOU CROSS-CLASSIFY OFFICE
BLOOD PRESSURE WITH OUT OF
OFFICE, USING HOME BLOOD
PRESSURE MONITORING OR AWAKE OR
DAYTIME ABPM WE HAVE FOUR
PHENOTYPES.
TWO HIGHLIGHTED IN RED REPRESENT
A MISMATCH.
WHITE COAT HYPERTENSION, HIGH
OFFICE BLOOD PRESSURE BUT OUT OF
OFFICE IS NOT HIGHER THAN
NORMAL.
THE SECOND IS MASKED
HYPERTENSION, WHERE OFFICE BLOOD
PRESSURE IS NOT HIGH BUT OUT OF
OFFICE IS HIGH.
IT IS OFTEN MISSED IN THE OFFICE
BECAUSE OBVIOUSLY OFFICE BLOOD
PRESSURE IS NOT HIGH BY
DEFINITION BUT AN EXTREMELY
DANGEROUS PHENOTYPE ASSOCIATED
WITH CARDIOVASCULAR DISEASE
EVENTS.
COMPARED TO OFFICE BLOOD
PRESSURE, AMBULATORY MONITOR CAN
TAKE AUTOMATED READINGS EVERY 15
TO 30 MINUTES OVER A 24-HOUR
PERIOD.
ON THE RIGHT HIGHEST IN AWAKE OR
DAYTIME PERIOD, AND THAT DIP
DURING THE SLEEP PERIOD.
ADPM CAN IDENTIFY SEVERAL BLOOD
PRESSURE PHENOTYPES INCLUDING
ELEVATION IN BLOOD PRESSURE IN
SLEEP OR 24-HOUR PATTERN OF
BLOOD PRESSURE, INCLUDING
DIPPING.
I MENTIONED CAN DETECT WHITE
COAT AND MASKED, CAN BE USED TO
DIAGNOSE WITH POSTPRANDIAL,
POSTURAL OR DRUG INDUCED
HYPOTENSION.
AMBULATORY BLOOD PRESSURE HAS
STRONGER ASSOCIATION WITH
CARDIOVASCULAR DISEASE EVENTS
THAN OFFICE BLOOD PRESSURE.
THERE ARE SEVERAL LIMITATIONS.
SEVERAL STUDIES REPORTED POOR
PATIENT TOLERABILITY FROM THE
CUFF INFLATING SO FREQUENTLY, UP
TO HALF REPORTED SLEEP
DISRUPTION.
IT'S EXPENSIVE, COSTS MORE THAN
$2,000.
DEVICES HAVE TO BE PLACED AT
SPECIALIZED CENTERS WHERE DEVICE
MUST BE RETURNED, DATA
DOWNLOADED, PREPARED,
INTERPRETATION OF REPORTS DOES
REQUIRE EXPERTISE.
THE DEVICE ISN'T READILY
AVAILABLE.
OVERALL UTILIZATION OF ABPM IN
THE U.S. IS LOW DESPITE THE FACT
THAT CMS HAS BEEN REIMBURSING
ALBEIT AT LOWER RATES TO DETECT
WHITE COAT HYPERTENSION FOR
ALMOST TWO DECADES.
FROM 2007 TO 2010 ONLY 0.1% OF
MEDICARE PATIENTS,
BENEFICIARIES, ACTUALLY HAD ABPM
CLAIMS.
IN CONTRAST HOME OR
SELF-MEASURED IS MORE WIDELY
AVAILABLE, MAYBE A MORE
PRAGMATIC APPROACH IN CLINICAL
PRACTICE.
TO DO IT PROPERLY DOES REQUIRE
CERTAIN AMOUNT OF TRAINING OF
OUR PATIENTS.
BLOOD PRESSURE MEASUREMENT
SHOULD BE OBTAINED FOLLOWING
PROTOCOL SIMILAR TO WHAT SHOULD
BE PERFORMED IN THE OFFICE.
GUIDELINES SUGGEST INDIVIDUALS
PERFORM TWO READINGS IN THE
MORNING, TWO AT NIGHT, FOR SEVEN
DAYS.
THERE'S SOME DATA TO SHOW THAT
THREE DAYS OF READINGS ARE
ACCEPTABLE.
AND IN GENERAL PEOPLE THINK THIS
IS MUCH MORE FEASIBLE WHEN
COMPARED TO ABPM.
WHAT ARE THE LIMITATIONS OF HOME
BLOOD PRESSURE MONITORING?
UNLIKE ABPM MOST HOME BLOOD
PRESSURE DEVICES CAN'T MEASURE
BLOOD PRESSURE DURING SLEEP,
THOUGH THERE ARE NOCTURNAL
DEVICES, NOT WIDELY AVAILABLE,
OUR THE COUNTER DEVICES ARE NOT
VALIDATED, PATIENTS MAY PICK UP
SOMETHING THAT IS A DEVICE
THAT'S NOT CORRECT.
OFTEN THEY ONLY SUPPLY ONE CUFF
SIZE.
MANY ARE NOT USING THE CORRECT
CUFF SIZE.
BEST USUALLY WITH
CO-INTERVENTIONS.
AUTOMATIC RECORDING IS NOT
ALWAYS AVAILABLE, COPY OF A
PATIENT BRINGING IN BLOOD
PRESSURE RECORDINGS ON A PIECE
OF PAPER.
LONG-TERM ADHERENCE IS CHALLENGE
ASKING SOMEONE OVER DAYS OR
WEEKS OR MONTHS TO CONTINUE
CHECKING BLOOD PRESSURE TWICE IN
THE MORNING, TWICE AT NIGHT.
AGAIN, REIMBURSEMENT RATES ARE
LOW.
MAIN CHALLENGES IS BLOOD
PRESSURE THRESHOLDS, CHASE TO
UNDERSTAND WHAT MODALITY
PROVIDES THE BEST ESTIMATE OF
TRUE BLOOD PRESSURE.
TO QUOTE THE GUIDELINES PRO
SIGHS RELATIONSHIP BETWEEN
OFFICE READINGS, AMBULATORY AND
HOME READINGS IS UNSETTLED.
THERE'S GENERAL AGREEMENT THAT
OFFICE BLOOD PRESSURES ARE OFTEN
HIGHER THAN AMBULATORY AND HOME
BLOOD PRESSURES.
I HIGHLIGHT THE DIFFERENCES IN
BLOOD PRESSURE THRESHOLDS FOR
HOME BLOOD PRESSURE, DAYTIME,
SLEEP, 24 HOUR ABPM, AND OFFICE
BLOOD PRESSURE THRESHOLD OF
130/80 MILLIMETERS OF MERCURY,
AND 140/90 MILLIMETERS OF
MERCURY.
OFFICE THRESHOLD OF 130/80 DO
YOU HAVE THE SAME THRESHOLD FOR
HOME BLOOD PRESSURE AND DAYTIME
ABP M.
THIS IS NOT TRUE AT 140/90.
WHAT ARE THE BIGGEST UNANSWERED
QUESTIONS, WHICH DEVICE IS
SUPERIOR?
I MOST ASSOCIATE WITH EVENTS AND
MORTALITY.
WE CONDUCTED SYSTEMATIC REVIEW
OF MORE THAN A THOUSAND
ARTICLES, IDENTIFIED ONLY NINE
ARTICLES FROM SEVEN COHORTS
EXAMINING WHETHER BLOOD PRESSURE
HOME BLOOD PRESSURE MONITORING
IS MORE STRONGLY ASSOCIATED WITH
CARDIOVASCULAR EVENTS OR
MORTALITY.
WHEN AMBULATORY BLOOD PRESSURE
WAS ADJUSTED FOR HOME BLOOD
PRESSURE, SYSTOLIC BLOOD
PRESSURE ON ABPM WAS ASSOCIATED
IN TWO OF FOUR COHORTS, THREE OF
FOUR FOR DIE TOLIC.
WHEN HOME BLOOD PRESSURE WAS
ADJUSTED FOR AMBULATORY BLOOD
PRESSURE, SYSTOLIC BLOOD
PRESSURE ON HOME MONITORING IS
ASSOCIATED WITH OUTCOMES IN
ZERO, NO STUDIES REPORTING IT,
ONLY ONE OF TWO FOR DIASTOLIC
BLOOD PRESSURE.
WE STILL HAVE LACK OF DATA, AND
WE'LL DISCUSS THIS LATER, A
MAJOR BARRIER.
CLEARLY AN UNANSWERED QUESTION
GIVEN THE SMALL NUMBER OF
STUDIES IN THE LITERATURE.
NONETHELESS, DEPICTED ON THE
BOTTOM 2017 ACCAHA GUIDELINES,
UNTREATED USING DAYTIME ABPM IS
PREFERRED WITH HOME MONITORING
AS ALTERNATIVE.
FOR THOSE TREATED, I.E. ON
ANTI-HYPERTENSIVE MEDICATION FOR
EXAMPLE THE ALGORITHM SUGGESTS
USING HOME BLOOD PRESSURE
MONITORING, THE PREFERRED
APPROACH, SECONDARILY
ALTERNATIVE DAYTIME ABPM ONCE
HOME BLOOD PRESSURE MONITORING
IS DONE.
NOW, BESIDE LACK OF DATA ON
OUTCOMES, VALUES OBTAINED USING
HOME VERSUS AMBULATORY
MONITORING DO NOT ALWAYS AGREE.
WE EXAMINE ABPM ON MASKED
HYPERTENSION, THE DANGEROUS
PHENOTYPE, USING DATA FROM
IMPROVING DETECTION OF
HYPERTENSION COHORT.
THE IDL IS COMMUNITY-BASED STUDY
OF NEW YORK ADULTS WHO UNDERWENT
AMBULATORY AND HOME BLOOD
PRESSURE AND OFFICE BLOOD
PRESSURE MEASUREMENTS.
IN OUR SAMPLE, A LITTLE OVER 300
INDIVIDUALS, THE MAJORITY, 61%,
HAD MASKED HYPERTENSION ONLY
DETECTED BY ABPM.
30% HAD MASKED HYPERTENSION
DETECTED ON ABPM AND HOME
MONITORING, A SMALL MINORITY,
9%, MASKED HYPERTENSION DETECTED
ON HOME MONITORING BUT NOT
AMBULATORY.
IF YOU ONLY DO HOME, YOU
MISCLASSIFY MANY IF NOT THE
MAJORITY OF INDIVIDUALS WHO HAVE
DANGEROUS PHENOTYPE MASKED
HYPERTENSION AS YOU MIS60% ONLY
DETECTED ON ABPM.
IN THE LITERATURE ONE OF THE
GROUPS THAT'S MOST AT RISK ARE
BLACK ADULTS.
SO WE HAVE TO THINK ABOUT THIS
HEALTH INEQUITY PERSPECTIVE,
MISSING THIS DANGEROUS PHENOTYPE
USING HOME BLOOD PRESSURE
MONITORING ALONE.
NOW, INTERESTINGLY ENOUGH, HOME
BLOOD PRESSURE MONITORING,
PRELIMINARY DATA MAY BE MORE
RELIABLE THAN OFFICE BLOOD
PRESSURE AND ABPM, MORE STRONGLY
CORRELATED WITH ORGAN DAMAGE.
THIS IS DATA FROM THE IDH COHORT
COMPARING INDIVIDUALS ON OFFICE
BLOOD PRESSURE, HOME BP,
AMBULATORY BLOOD PRESSURE, WHEN
ALL WERE ASSESSED USING
GUIDELINE-RECOMMENDED
APPROACHES.
WE'VE COMPARED RELIABILITY OF
OFFICE IN THREE VISITS, THREE
READINGS, MEASURED BY ABPM EVERY
30 MINUTES, BLOOD PRESSURE
MEASURED BY HOME BLOOD PRESSURE
MONITORING USING TWO MORNING AND
TWO EVENING READINGS OVER A
WEEK.
WE LOOKED AT ASSOCIATIONS WITH
LESS VENTRICULAR MASS INDEX ON
2D ECHO.
THE COEFFICIENTS ARE REPRESENTED
ON THE FIRST TABLE.
FOR OFFICE BLOOD PRESSURE .9,
HIGHEST FOR HOME BLOOD PRESSURE
AT .94, AWAKE ON 24 HOUR.
THE MODEL BELOW WAS ADJUSTED,
THE ESTIMATED DIFFERENCE IN LEFT
VENTRICULAR MASS INDEX FOR
10-MILLIMETER HIGHER 3.2 GRAMS
FOR OFFICE BP, 4.04 HOME BLOOD
PRESSURE, AS WELL AS 3.71 GRAMS
PER METER SQUARED FOR
AMBULATORY.
FULLY ADJUSTED MODEL 3 INCLUDES
COVARIATES OF MODEL 2 AND ALL
THREE, ONLY HOME WAS ASSOCIATED
WITH HIGHER LEFT VENTRICULAR
MASS INDEX.
THIS WOULD SUGGEST WHEN BLOOD
PRESSURE IS ASSESSED, ABPM MAY
NOT BE IMMEDIATED, ESPECIALLY
WITH RESPECT TO DETECTION OF
ORGAN DAMAGE, MIGHT BE A
PARADIGM SHIFT.
IN SUMMARY PRESSING QUESTIONS,
STILL MUCH EXCITEMENT IN THE
FIELD, JUST PROVIDING A FEW
REFLECTIONS ON WHAT THE MOST
PROMISING AREAS ARE.
WE OBVIOUSLY KNOW WE'RE IN A
CONSUMER-DRIVEN MARKET, WE'VE
ALLUDED TO WRIST DEVICE, EMR
INTEGRATION, CUFFLESS WAS
ALLUDED TO ALREADY.
WHAT'S INTERESTING IS THE
CURRENT DEVICES DISTURB SLEEP.
WE DON'T HAVE A TRUE ESTIMATE OF
WHAT SLEEP BLOOD PRESSURE IS.
CUFFLESS MAY BE A WAY AROUND
THIS.
I WOULD CHALLENGE US TO SAY CAN
WE DISRUPT THE CONCEPT OF OFFICE
BLOOD PRESSURE, ONLY RELY ON OUT
OF OFFICE, MAYBE THAT'S THE
FUTURE IN PARTICULAR IN THE
PANDEMIC SHOWED MANY PEOPLE
COULDN'T COME TO THE CLINICIAN
OFFICE.
WHAT ARE THE GAPS?
I THINK MORE UNIFORM BLOOD
PRESSURE THRESHOLDS ACROSS
OFFICE AND OUT OF OFFICE
MEASUREMENTS AREN'T REALLY
UNDERSTOOD BY MANY IN TERMS OF
IMPLEMENTATION, CAN WE IMPROVE
UPON MEASUREMENT, THIS MIGHT BE
ANOTHER PARADIGM SHIFT, MAYBE WE
SHOULD DISRUPT AND IMPROVE THE
OFFICE MEASUREMENT, SOMETHING
CALLED UNATTENDED OFFICE BP
WHERE YOU TEACH PATIENTS TO
SELF-MEASURE BLOOD PRESSURE.
WE STILL DON'T KNOW OPTIMUM
METHODOLOGY AND FREQUENCY OF
HOME BLOOD PRESSURE MONITORING.
I'LL END WITH KEY CHALLENGES AND
BARRIERS.
I'VE DISCUSSED THE RELIABILITY
AND VALIDATION OF
NON-TRADITIONAL DEVICES IS HUGE.
THERE'S LACK OF DATA WITH OUT OF
OFFICE AND OFFICE BLOOD PRESSURE
CONDUCTED AT THE SAME TIME.
AND CANNOT HIGHLIGHT ENOUGH
THERE'S BARRIERS TO
IMPLEMENTATION AND ACCESS TO
CARE, IMPACT OF STRUCTURAL LEVEL
FACTORS, HEARKENING TO DR.
SPATZ'S KEYNOTE ADDRESS.
WE TALK ABOUT CURING OR BATTLING
HYPERTENSION BUT WE NEED TO
UNDERSTAND HOW DO WE EVEN GET
ACCESS TO CARE, TO OUR PATIENTS.
I'LL ANNOUNCE MY FUNDING SOURCES
AND SAY THANK YOU.
>> THANK YOU.
YOU COVERED A LOT OF GROUND.
IT'S IMPORTANT TO RECOGNIZE
HYPERTENSION IS THE MOST POTENT
AND PREVALENT CARDIOVASCULAR
RISK FACTORS, MEASUREMENT IS SO
HAPHAZARDLY.
WE'RE GOING TO HEAR ON
DETERMINING BLOOD PRESSURE USING
NOVEL TECHNOLOGIES VIA A.I. BY
DR. STERGIOU, UNIVERSITY OF
ATHENS IN GREECE.
WHILE WE'RE WAITING, I SHOULD
NOTE THERE ARE VALIDATED SOURCES
WHERE YOU CAN FIND MORE
INFORMATION ON MAKING SURE
PATIENT HAS BLOOD PRESSURE
DEVICE THAT ACTUALLY WORKS
CORRECTLY, VALIDATE BP, IN THE
CHAT, THOSE ARE IMPORTANT
BECAUSE MANY DEVICES JUST DON'T
WORK WELL.
ARE YOU HERE?
>> CAN YOU HEAR ME?
>> YES.
>> THANK YOU FOR ASKING ME TO BE
PREPARED FOR THIS TALK, WHICH
MEANS I HAVE LOOK AT NEW
INFORMATION IN MORE DETAIL.
THIS IS MY DECLARATION OF
POTENTIAL CONFLICT OF INTEREST.
NO RELATION TO THIS TALK.
I HAVE ALSO TO SAY THAT ALTHOUGH
I'M WORKING IN ATHENS, GREECE, I
COME FROM VENICE, LOOKING AT
WHAT PEOPLE MUST DO, AND WE'RE
INTERESTED IN WHAT THEY DO
BECAUSE THEY AFFECT US, AND WE
TRY TO TAKE ADVANTAGE FROM WHAT
THEY DO.
OF COURSE, WE'RE SKEPTICAL, AND
CAUTIOUS WITH WHAT THE ENGINEERS
DO.
AND WE WANT TO MAKE SURE THAT
WHAT THEY GIVE US IS A REAL
BENEFIT TO OUR PATIENTS.
CUFFLESS BLOOD PRESSURE
MEASUREMENT IS EXCITING TO
EVERYBODY, FROM EXPERTS TO
PRACTITIONERS AND TO PATIENTS,
TO PEOPLE.
IT IS A METHOD THAT CAN GIVE US
THE FULL PICTURE OF BLOOD
PRESSURE.
IF WE HAVE THAT, IF WE GET IT
RIGHT, OFFICE, HOME, AMBULATORY
DOESN'T EXIST.
WHITE COAT, MASKED DOESN'T
EXIST, BECAUSE WE HAVE THE FULL
PICTURE, ALL THE INFORMATION
ABOUT BLOOD PRESSURE PROFILE AND
BEHAVIOR OVER TIME.
IT'S SOMETHING IT WEAR ON THE
WRIST BUT COULD BE A RING, ON
YOUR GLASSES.
AND THIS IS SOMETHING YOU WEAR
ALL THE TIME.
BUT THIS IS ONE PICTURE OF
CUFFLESS MEASUREMENT POTENTIAL.
THERE'S ANOTHER EQUALLY
ATTRACTIVE AND QUITE DIFFERENT
POTENTIAL, AND IT IS THIS ONE.
IMAGINE IF ALL PEOPLE WHO OWN A
SMARTPHONE WOULD PUT THEIR
FINGER ON THE SMARTPHONE AND USE
ITS SENSORS TO GET A RELIABLE
BLOOD PRESSURE MEASUREMENT.
OR LEAVE THE CAMERA OF THE
SMARTPHONE TO LOOK AT THE FACE
AND TAKE A SIGNAL AND MAYBE IT
CAN GIVE YOU A GOOD VALUE OF
BLOOD PRESSURE.
SUCH HUGE POTENTIAL AND
INTEREST, AND VARIOUS SIGNALS,
PPG IS THE MOST POPULAR, REALLY
MANY DEVICES USING PPG AS BASIS
TRYING TO PROVIDE MEASUREMENTS
OF BLOOD PRESSURE.
YOU CAN SEE STEEP INCREASE IN
PUBLICATIONS RELATED TO PPG
SIGNALS.
I THINK IT'S IMPORTANT TO START
WITH THIS SLIDE ABOUT HOW IT
WORKS.
ONE THING PPG SIGNAL, A LOOK AT
THE WAVEFORM, SOMETIMES COMBINED
WITH ECG TO GIVE US PULSE
ARRIVAL TIME, THE TRAVEL, SPEED
FOR WHICH THE PULSE RATE TRAVELS
ACROSS THE ARTERIAL TREE.
AND ALSO THESE DEVICES GET SOME
KIND OF INFORMATION FOR THE
INDIVIDUAL USER, AND THIS IS
PERSONAL BIOMETRIC INFORMATION,
COULD SHALL THE AGE, GENDER, THE
SIZE, BODY SIZE, HEIGHT AND
WEIGHT, OR COULD BE CLASSIC
CUFFLESS MEASUREMENT.
WHEN YOU HAVE CUFFLESS
ESTIMATION BY A NOVEL DEVICE,
YOU GET TWO DIFFERENT KINDS OF
DATA WHICH SHOWED THE OUTPUT.
ONE IS HEMODYNAMIC COMPONENT,
MEASUREMENT, PPG ALONE OR PLUS
PTT, BUT ALSO YOU HAVE A
PREDICTIVE COMPONENT.
AND WE KNOW THAT YOU CAN PRETTY
WELL PREDICT THE BLOOD PRESSURE
OF A POPULATION BASED ON THE
AGE, SEX, WEIGHT, HEIGHT, SO ON.
SO, THE OUTPUT OF THE CUFFLESS
DEVICE IS COMBINATION OF THE
TWO, AND IT'S IMPORTANT TO KNOW
IN WHAT PROPORTION EACH
CONTRIBUTES TO BLOOD PRESSURE
VALUE WE SEE.
SO, THIS IS THE PULSE WAVE, THEY
KNOW THE PULSE RATE, HOW IT GOES
UP, RIGHT SIDE OF THE PULSE
RATE, ENGINEERS HAVE A LOOK, CUT
IT INTO PIECES, THEY LOOK AT
SMALL FEATURES AND ANALYZE AND
GIVE OUT FEATURES FROM THE PULSE
WAVE, AND HAVE OTHER FEATURES
FROM THE RISK OF THE PPG SIGNAL
AND FROM THE DEMOGRAPHIC
EDUCATION AND OTHER FEATURES,
THEY CAME UP WITH ALMOST 100
FEATURES FOR SINGLE MEASUREMENT
THAT CAN'T INFERENCE THE OUTPUT.
SO, WHEN YOU HAVE CUFFLESS,
INTERPLAY OF REFERENCE BLOOD
PRESSURE MEASUREMENT WITH CUFF
DEVICE, IF IT IS USED.
DEMOGRAPHIC INFORMATION ABOUT
THE USER IF THEY ARE USED, AND
MEASUREMENT.
HEMODYNAMIC COMPONENT WHICH IS
PPG, PLUS OR MINUS PAT, THIS IS
AN INTERPLAY OF THESE FACTORS.
AND WHAT HAPPENS IS THAT AS
CLINICIAN, THE INVESTIGATORS
LOOK AT THE ASSOCIATIONS OF
THESE FEATURES WITH A CUFF BLOOD
PRESSURE MEASUREMENT.
AND REGRESSIONS.
THEY PUT THEM IN THE MODEL AND
TRIED TO PREDICT SYSTOLIC BLOOD
PRESSURE.
BUT THERE ARE MORE COMPLEX
MODELS.
YOU MIGHT ASK THE MODEL TO
EXTRACT THE FEATURES SO YOU
DON'T KNOW WHICH FEATURES ARE
TAKEN INTO ACCOUNT.
SO THIS IS AUTOMATED SYSTEM,
SEVERAL MODELS FOR DEEP LEARNING
ALONE.
WHETHER HAVE YOU DEMOGRAPHIC
DATA, WHETHER YOU HAVE IMAGE OR
SENSOR, SO ON, ASK PEOPLE.
WE HAVE DIFFERENT DEVICES.
WE HAVE THE PULSE WAVE ANALYSIS,
FACIAL VIDEO, OTHERS
OSCILLOMETRIC, ULTRASOUND
TECHNIQUES FOR CUFFLESS, VOLUME
CONTROL TECHNIQUES.
APART FROM THAT SOME DEVICES
REQUIRE INDIVIDUAL USER
CALIBRATION OR INITIATION, THE
DEVICE NEEDS TO KNOW ABOUT THE
USER OR MAY NOT REQUIRE PERIODIC
CALIBRATION.
THE OTHER PROBLEM WE FACE IS
THAT SOME OF THESE DEVICES HAVE
SOME REGULATORY APPROVAL.
THIS DOES NOT MEAN THEY ARE
CLINICALLY USEFUL FOR US.
I'M GOING TO SHOW YOU VERY
RECENT DATA IN THE LAST YEAR AND
O-NEGATIVE, SMALL EXPERIMENT,
USING FINGER PPG AND PAT, USED
DIFFERENT TECHNIQUES TO CHANGE
BLOOD PRESSURE, AND THESE
TECHNOLOGIES COULD NOT TRACK
CHANGES IN BLOOD PRESSURE.
THIS IS SAMSUNG DEVICE.
SAMSUNG SMARTWATCH WHICH HAS
IMPLEMENTED CUFFLESS TECHNOLOGY,
PPG, STUDY HAS BEEN PUBLISHED
AND SHOWS THAT THIS DEVICE
CANNOT ESCAPE TOO FAR, CANNOT
FALL CHANGING BLOOD PRESSURE
DURING 24-HOUR PERIOD.
THIS IS ANOTHER DEVICE WHICH
ATTRACTED A LOT OF ATTENTION,
STUDY NOW IN PRESS, JOURNAL OF
HYPERTENSION, SHOWING CAN TRACK
ONLY ONE-THIRD OF THE BLOOD
PRESSURE DROP DURING SLEEP.
AND THIS IS ANOTHER DEVICE WHICH
ALSO SUFFERS FROM THE SAME
PROBLEM.
IT TRACKS LESS THAN 50% OF THE
DROP OF BLOOD PRESSURE DURING
SLEEP.
BUT I'D LIKE TO BRING YOUR
ATTENTION IN THIS PROJECT, BY
MICROSOFT.
YOU KNOW MICROSOFT RESEARCH IS
AN OPEN SOURCE COMPANY, WHICH
MEANS WE KNOW WHAT THEY DO.
AND WHEN THEY FINISH THEY GIVE
THE DATA.
THEY ARE PUBLICLY AVAILABLE.
IT'S A HUGE PROJECT.
PAPER SIGNED BY 23
INVESTIGATORS, MORE THAN 1,000
PARTICIPANTS, PROBABLY TOOK
SEVERAL YEARS TO BE COMPLETED.
IT INCLUDES NOT ONLY LOW BLOOD
PRESSURE PEOPLE BUT ALSO
HYPERTENSIVE, NOT ONLY YOUNG
PEOPLE BUT ALSO OLD AND HAD SOME
REFERENCE.
THEY TESTED FOUR TECHNOLOGIES
FOR CUFFLESS MEASUREMENT AND
THEY HAD THE ADVANTAGE TO BE
ABLE TO IMPROVE TECHNOLOGY
DURING THE EXPERIMENTS.
THEY INVESTIGATED FOUR WAVEFORM
MODELS.
FOR EACH TWO COMPONENTS, ONE IS
HEMODYNAMIC COMPONENT,
MEASUREMENT BASED ON PPG, ECG,
OR COMBINATION, HAD PREDICTIVE
COMPONENT WHICH IS BASED ON
CALIBRATION, TIME OF DAY,
ESTIMATION.
AND IN THIS STUDY THEY ALSO
REPORTED BASELINE MODEL WHICH
MEANS WHAT DO YOU GET IF YOU
SKIP THE HEMODYNAMIC COMPONENT
AND PREDICT THE BLOOD PRESSURE
BASED OF THE CUFF BLOOD PRESSURE
CALIBRATION.
THIS IS WHAT THEY GOT.
YOU SEE THE BEST OF THE FOUR
WAVEFORM PICTURES, AND THIS WILL
BE COMPARED, PREDICTION ALONE.
YOU CAN SEE MEASUREMENT IN GREEN
GAVE NO ADDITIONAL INFORMATION
ON THE MEASUREMENT APART FROM
THAT PREDICTED MODEL WHICH MEANS
HEMODYNAMIC COMPONENT, THE
MEASUREMENT DID NOT HELP THE
ACCURACY OF THE DEVICE.
THERE ARE POSITIVE STUDIES WHICH
I WON'T PRESENT, STATIC
VALIDATION IMMEDIATELY POST
CALIBRATION, USELESS EXPERIMENT,
ALL DEVICES PASS.
THEY PROVIDE CORRELATION, HAVE
SMALL SAMP SAMPLE SIZE, YOUNG
PARTICIPANTS, LOW BLOOD
PRESSURE.
I'D LIKE TO TALK ABOUT HOW TO
TEST THESE AND WE'VE BEEN HAPPY
IN 2018 TO AGREE ON UNIVERSAL
PROTOCOL TO BE USED GLOBALLY.
WELL, THESE STANDARDS WHICH HAVE
BEEN DEVELOPED FOR CUFF DEVICES
IS ALMOST ZERO INFORMATION FOR
MOST OF CUFFLESS DEVICES
REQUIRING CALIBRATION.
CUFF OR DEMOGRAPHICS.
THE IEEE HAS DEVELOPED A
STANDARD IN 2014, AMENDMENT
LATER ON, BUT NOT A COOKBOOK ON
LOU TO EXACTLY DO THIS WORK.
THE STUDY PROTOCOL WHICH IS ONLY
FOR CONTINUOUS CUFFLESS DEVICES.
SO, HOW THESE DEVICES SHOULD BE
TESTED?
NOT IN THE WAY WE TEST CUFF
DEVICES.
THIS TESTING HAS TO BE MUCH MORE
COMPLEX.
FIRST, WE NEED TO HAVE REFERENCE
MANUAL AUSCULTATORY MEDICINE, TO
USE IN CLINICAL HYPERTENSION.
VALIDATION, WE NEED POST
CALIBRATION ASSESSMENT, MINIMAL
INFORMATION, ONLY USEFUL
PARTICULARLY FOR DEVICES THAT DO
NOT NEED INITIALIZATION OR
CALIBRATION.
THE MOST IMPORTANT PART OF THE
TEST IS TO TEST THE ABILITY OF
THIS DEVICE TO TRACK BLOOD
PRESSURE CHANGES.
THIS IS THE MOST WEAK POINT OF
THE TECHNOLOGIES.
WE NEED TESTING FOR
POSITION/MOTION, AND WE NEED TO
CHECK STABILITY OVER TIME FOR
THOSE DEVICES WHICH REQUIRE
PERIODIC CALIBRATION.
THE PROBLEM IS WE HAVE DIFFERENT
TECHNOLOGIES, DIFFERENT DEVICES
AND INTENDED USES, AND WE HAVE
FOR EACH ONE TO SEE WHETHER IT'S
WEARABLE, OR NOT, IF IT'S CUFF
INITIATED OR NOT, IF DEMOGRAPHIC
OR NOT, AUTOMATED OR OCCASIONAL,
PROVIDES CONTINUOUS MEASUREMENT,
MEASURES ALWAYS AT HEART LEVEL
AND WHEN YOU ANSWER ALL THESE
QUESTIONS ABOUT THE SINGLE
DEVICE, THEN YOU'LL BE ABLE TO
TAILOR WHICH PROTOCOL AND WHICH
TEST EACH DEVICE REQUIRES.
SO, ANSWERING TO THE THREE
QUESTIONS I WAS ASKED TO ANSWER,
WHAT IS THE MOST PROMISING
SCIENTIFIC AREAS, IT SEEMS
SENSORS NEED TO BE DEVELOPED
FURTHER AND PROBABLY PPG ALONE
MOST ENGINEERS SAY CANNOT
MEASURE PRESSURE.
SO MULTI-SENSORS COMBINING WITH
OTHER SENSORS MIGHT IMPROVE THE
ESTIMATION OF BLOOD PRESSURE.
AND I LED THAT THERE ARE MORE
MODELS FOR MACHINE LEARNING AND
DEEP LEARNING THAT CAN BE
INVESTIGATED.
AND CERTAINLY THE LARGER THE
DATASET, THE BETTER THE LEARNING
OF THE MODEL.
GAPS IN KNOWLEDGE, HOW TO MOVE
FORWARD, NEED IMPROVE MODEL
TRAINING, DATA QUALITY AND
DIVERSITY, WE NEED DATABASES TO
INCLUDE PEOPLE WITH HIGH AND LOW
BLOOD PRESSURE, YOUNG AND OLD,
WITH DIFFERENT SKIN PIGMENTATION
AND ANYTHING ELSE THAT MIGHT
AFFECT DIFFERENT SENSORS,
AFFECTED BY DIFFERENT
PARAMETERS.
WE NEED MORE DATA PER SUBJECT,
BETTER HAVE MORE INFORMATION PER
SUBJECT.
AND WE NEED TO VALIDATE AND TEST
THESE ALGORITHMS.
AND THE KEY CHALLENGES AND
BARRIERS HINDERING PROGRESS, IT
SEEMS THAT MY UNDERSTANDING IS
WE'RE NOT TOO CLOSE TO HAVE
MEASUREMENT WITH SENSORS,
CUFFLESS, IN OUR PEOPLE.
A PROBLEM IS THE HETEROGENEITY
OF TECHNOLOGY AND DEVICES WHICH
AS I SAID NEED DIFFERENT
TESTING.
WE CAN'T COME OUT WITH A SINGLE
STANDARD FOR ALL OF THEM.
AND CERTAINLY WE HAVE TO GO FOR
MORE COMPLEX VALIDATION
PROCEDURES WITH THESE DEVICES.
WE CAN'T GET IT SO EASY AS WE
DID FOR THE CUFF DEVICES.
WE STILL DO NOT HAVE UNIVERSALLY
ACCEPTED PROCEDURES FOR THESE
DEVICES.
MY FINAL SLIDE IS A STATEMENT WE
PUBLISHED LAST YEAR.
NO QUESTION THEY HAVE
CONSIDERABLE POTENTIAL FOR
CHANGING WHATEVER WE DO FOR
DIAGNOSING AND MONITORING OF
HYPERTENSION.
IF WE GET IT RIGHT WE DON'T NEED
OFFICE, HOME, AMBULATORY.
BUT WE DO HAVE FUNDAMENTAL
QUESTIONS ABOUT THE ACCURACY,
PERFORMANCE, AND IMPLEMENTATION.
AND ALL THESE HAVE TO BE
CAREFULLY ADDRESSED BEFORE WE
CAN RECOMMEND THEM FOR CLINICAL
USE.
THANK YOU.
>> THANK YOU FOR SUCH A
FORWARD-THINKING PRESENTATION,
THE BEDROCK OF THE PROMISE OF
CONTROLLING BLOOD PRESSURE HAS
NOT BEEN FULFILLED AT THE
PRESENT TIME.
WE'LL HEAR FROM DR. PAUL WHELTON
FROM TULANE SCHOOL OF UNIVERSITY
SCHOOL OF MEDICINE AND TROPICAL
MEDICINE, WHERE I AM, UPDATE ON
TREATMENT AND CONTROL OF HIGH
BLOOD PRESSURE.
PAUL?
>> THANK YOU.
A PLEASURE TO BE WITH EVERYONE
TODAY.
I'M GOING TO TALK ABOUT
HYPERTENSION PREVALENCE AND
BLOOD PRESSURE CONTROL.
I DO NOT HAVE ANY CONFLICTS OF
INTEREST THAT I'M AWARE OF.
LET ME JUST START BY SAYING MOST
OF THE REPORTS ON PREVALENCE AND
CONTROL OF HYPERTENSION COME
FROM NHANES, AND THAT'S
PERFECTLY SENSIBLE AND
UNDERSTANDABLE BECAUSE IT IS OF
COURSE NATIONALLY REPRESENTATIVE
DATA.
THERE ARE A LOT OF DATA POINTS
BECAUSE THE SURVEY IS DONE ON A
BIENNIAL BASIS.
AND THE BLOOD PRESSURE
MEASUREMENT METHODS ARE
RELATIVELY STABLE.
SINCE 1999, BLOOD PRESSURES HAVE
BEEN MEASURED ONLY AT MOBILE
EXAMINE CENTER, STANDARDIZED
PROTOCOL, GREAT DEAL OF
ATTENTION TO QUALITY CONTROL.
TYPICALLY THREE SYSTOLIC AND
DIASTOLIC MEASURES, A FOURTH IF
NECESSARY.
AND NHANES RECOMMENDS AVERAGING
THE LAST TWO PRESSURES TO GET
AVERAGE ESTIMATE, ALTHOUGH MANY
INVESTIGATORS AVERAGE ALL THREE
BECAUSE WE KNOW THAT IN THAT
NHANES DATASET THAT ACTUALLY HAS
PERFECTLY ACCEPTABLE.
BETWEEN 2009 AND 2018, THE
MEASUREMENTS WERE MADE WITH
AUSCULTORY METHODS, SYSTOLIC K
1, DIASTOLIC K 5.
IN 2017 USING 907, OVERSEEN BY
HOUSE TECHNICIAN.
IN THAT FIRST BIENNIAL SURVEY,
2017-2018, NHANES UNDERTOOK
RANDOMIZED COMPARISON OF
OSCILLOMETRIC AND AUSCULTORY.
OSCILLOMETRIC WERE 6.15
MILLIMETERS LOWER ON AVERAGE,
DIASTOLICS 1.3 HIGHER ON AVERAGE
COMPARED TO OSCILLOMETRIC
MEASUREMENTS.
WORTH BEARING IN MIND NHANES
DEFINITIONS OF HYPERTENSION
DIFFER FROM GUIDELINE
RECOMMENDATIONS FOR HOW IT
SHOULD BE DONE.
GUIDELINES I THINK ALMOST ALL
GUIDELINES RECOMMEND THAT
PRESSURES BE OBTAINED AT
REPEATED VISITS, GOING BACK TO
EARLY DAYS, THE TURN OF THE
1900s.
GNC AND HCC/AHA HAVE THE SAME
REPRESENT ATION, TWO OR MORE
ACCURATE MEASUREMENTS AND
FURTHER RECOMMENDATION THAT HIGH
OFFICE OR CLINIC BP BE CONFIRMED
WITH AN OUT OF OFFICE BLOOD
PRESSURE.
SO NHANES ESTIMATES OF
HYPERTENSION PREVALENCE WILL
OVERESTIMATE GUIDELINE
DEFINITION, DEFINED
HYPERTENSION.
THERE ARE LOTS OF PUBLICATIONS,
PROBABLY MORE THAN HERE, MANY
FROM MY OWN DEPARTMENT.
I'M GOING TO SHOW DATA FROM TWO.
FROM THE DORANS PAPER AND PAUL
MUNTNER AND COLLEAGUES.
OVERALL PATTERNS IN ALMOST ALL
OF THESE PUBLICATIONS ARE QUITE
SIMILAR, ALTHOUGH THE ABSTRACT
VALUES DO DIFFER A LITTLE BIT.
I'M GOING TO SHOW THE DORANS
PUBLICATION BECAUSE I THINK IT
GIVES A NICE SORT OF VIEW OF
MANY DATA POINTS.
IT PRECEDES OSCILLOMETRIC.
THE MUNTNER IS MORE RECENT, FROM
2009 THROUGH 2020.
AND IT PROVIDES A LOT OF DATA.
THERE'S A LOT OF DETAILED
INFORMATION, THE SUPPLEMENT
PROVIDES ESTIMATES THAT ARE
MODIFIED, THAT'S IN THE MAIN
PAPER, ALSO UNMODIFIED, AND A
LOT MORE.
SO, IN THIS PRESENTATION I'M
GOING TO SHOW WHAT'S IN THE MAIN
PAPER, THAT IS THE MODIFIED
READINGS, FOR OSCILLOMETRIC THAT
CAN BE COMPARABLE OVER TIME TO
THE OSCILLOMETRIC MEASUREMENTS.
AND IN MUNTNER BASED ON
READINGS.
YOU'LL SEE UP HERE FROM 1999
THROUGH 2016, A FEW THINGS, EACH
BIENNIAL SURVEYS HAS A PRETTY
GOOD SAMPLE SIZE, EVEN WHEN
LOOKING AT THIS MAJOR SUBGROUPS,
YOU STILL HAVE A GOOD DEAL OF
PRECISION.
I WANT TO POINT OUT THAT USE OF
BLOOD PRESSURE MEDS IN GENERAL
POPULATION INCREASED
PROGRESSIVELY THROUGH 2008,
2009, SORT OF STABLE EVER SINCE.
I WANT TO POINT OUT EVEN IN THE
GENERAL POPULATION, NOT
SELECTING OUT PATIENTS GOING TO
CLINICS BUT IN THE GENERAL
POPULATION, RISK RATE EQUAL OR
GREATER THAN 10%, ACCHA HIGH
RISK, QUITE HIGH, 20% OF THE
POPULATION HAD THAT.
MUCH HIGHER WHEN YOU GO TO
CLINIC SETTINGS.
AND YOU'LL SEE FOUR DEPICTIONS
OF TRENDS, SYSTOLIC.
I WON'T GO THROUGH IN DETAIL BUT
YOU'LL SEE THE GENERAL PATTERN,
ALL OF THESE SUBGROUPS THAT
THERE'S A PROGRESSIVE DECLINE,
AND UPTICK, CERTAINLY NO
IMPROVEMENT AFTER THAT.
I'M GOING TO CATEGORICAL DATA.
IN THE MUNTNER ET AL.
PRESENTATION FOR MOST OF THE
TIME FRAME THE 140/90 DEFINITION
WOULD HAVE APPLIED IN CLINICAL
PRACTICE.
THAT'S WHAT WAS RECOMMENDED..
THAT'S WHAT I'LL FOCUS ON,
SELF-REPORT OF TAKING
ANTI-HYPERTENSIVE MEDICATION.
HERE IS PREVALENCE.
YOU CAN SEE AT THE 140/90, A
LITTLE OVER 30%, MAYBE SLIGHT
TREND UPWARDS, IT'S NOT
STATISTICALLY SIGNIFICANT.
OBVIOUSLY IT'S AGE DEPENDENT.
YOU GET INTO THE UPPER AGE RANGE
OF 75+, YOU GET A VERY HIGH
PREVALENCE.
IF WE LOOK BY GENDER, RATE,
ETHNICITY, WHAT STANDS OUT IS
THAT NON-HISPANIC BLACKS,
PREVALENCE OF THAT HIGH CUT
POINT FOR DEFINITION OF
HYPERTENSION IS VERY, VERY HIGH.
MUCH HIGHER THAN ANY OF THE
OTHER RACE/ETHNICITY GROUPS IN
THE ITS.
I'M FOCUSING ON U.S. DATA
BECAUSE THIS IS AN NHANES
WORKSHOP.
I MEAN, AN NHLBI WORKSHOP.
LOOKING AT AWARENESS, YOU CAN
SEE OVERALL PATTERN ON THE LEFT
PANEL.
THERE'S A NOMINALLY SIGNIFICANT
DECLINE.
THAT'S TRUE FOR THE PATTERN --
THE PATTERN TRUE FOR ALL AGE
GROUPS, MORE SIGNIFICANT FOR
OLDEST AGE GROUP, SEEING DECLINE
IN AWARENESS.
IF WE LOOK AT DRUG TREATMENT,
THOSE AWARE THAT THEY HAD
HYPERTENSION, IT'S QUITE HIGH,
OVERALL ABOUT A LITTLE OVER 90%.
OF COURSE ESPECIALLY HIGH IN THE
OLDER AGE GROUP.
IF WE LOOK MORE GENERALLY AT
DRUG TREATMENT BROADLY, NOT JUST
IN THOSE BEING TREATED BUT IN
THE GENERAL POPULATION AS A
WHOLE, IT'S LOWER.
AND YOU'LL SEE THERE'S
PROGRESSIVE TENDENCY FOR A
DECLINE.
YOU'LL SEE THAT FOR THE LAST
TIME PERIOD, WE'RE LOOKING AT --
UPPER AGE RAGE, 50%.
THIS IS IN THOSE BEING TREATED.
THIS IS THE GENERAL.
AND PROGRESSIVE DECLINE,
SIGNIFICANT OVERALL, ESPECIALLY
SIGNIFICANT IN THE UPPER AGE
RANGE.
WHETHER THIS RELATES TO THE FACT
THAT ACCHA WE RECOMMEND 130/80,
LESS THAN 130, AND OLDER AGE
RANGE, A WAY TO COMPETING
GUIDELINE FROM ACP AND AAFP,
WHICH DURING THIS EARLY TIME
PERIOD IS DIFFERENT NOW, BUT
DURING THE SAME TIME PERIOD,
RECOMMENDED YOU COULD HAVE
SYSTOLIC AS HIGH AS 150 AND BE
COMFORTABLE UNLESS SOMEBODY HAD
VERY HIGH RISK.
THAT MAY HAVE HAVE SOMETHING TO
DO WITH IT, PERHAPS THE FACT
THAT HEDIS CONTINUES TO USE
140/90 MAY HAVE SOMETHING TO DO
WITH IT.
CAN'T BE SURE.
IF WE LOOK BY GENDER AND
RACE/ETHNICITY, NON-HISPANIC
BLACKS, WE NOT ONLY HAVE HIGHER
PREVALENCE, WE NOT ONLY HAVE
MORE OF ANY PARTICULAR LEVEL
COMPARED TO ANY OTHER GROUP,
ALSO HAVE LEAST EFFECTIVE
TREATMENT AND CONTROL THAN OTHER
GROUP, A DISMAL PICTURE.
I'LL LOOK AT 130/80, THIS WOULD
ONLY APPLY TO THE LAST OF THE
THREE PERIODS THAT I'M SHOWING
FROM THE MUNTNER PAPER.
HERE'S PREVALENCE, MUCH HIGHER
COMPARED TO 140/90, OBVIOUSLY.
ALMOST 50% OF THE POPULATION.
AND AGAIN VERY AGE DEPENDENT,
LIKEWISE SIMILAR TO 140/90,
STRIKINGLY DIFFERENT IN
NON-HISPANIC BLACKS, COMPARED TO
ALL THE OTHER RACE/ETHNICITY
GROUPS.
HERE'S AWARENESS.
AWARENESS IS CONSIDERABLY LOWER,
AND REALLY ONLY ABLE TO LOOK
REASONABLY AT THE LAST TIME
PERIOD TO SEE AWARENESS
IMPROVED, AND WHAT WE'RE SEEING
HERE IS, NO, AWARENESS DID NOT
IMPROVE DESPITE RECOMMENDATION
FROM ONE OF THE MAJOR GUIDELINES
WE SHOULD FOCUS ON 130/80, AND
PARTICULARLY SEEING DECLINE IN
OLDER AGE RANGES.
ANTI-HYPERTENSIVE DRUG TREATMENT
IN THOSE AWARE.
AGAIN, IT'S LESS SO THAN IN THE
140/90.
AND NO REAL SENSE THAT THERE'S
MUCH IMPROVEMENT IN DRUG
TREATMENT IN THE LAST TIME
PERIOD.
HERE IS CONTROL, IN THE GROUP
BEING TREATED.
AND NO EVIDENCE FOR IMPROVEMENT
IN CONTROL IN THE LAST TIME
PERIOD.
IF ANYTHING, IT'S DECLINING.
IT'S NOT STATISTICALLY
SIGNIFICANT BUT THE TREND
CERTAINLY IS IN THE WRONG
DIRECTION.
LOOKING AT CONTROL, AGAIN NO
EVIDENCE FOR IMPROVEMENT IN THAT
LAST TIME PERIOD.
SO, VERY DISTURBING PICTURE.
NOW, I WANT TO SHOW TWO OTHER
SLIDES AND THEN I'LL STOP.
I WANT TO SHOW A SLIDE OF
TEMPORAL TRENDS FROM SPRINT.
LEFT-HAND PANEL HAZARD RATIOS,
CARDIOVASCULAR MORTALITY.
AND AS YOU KNOW IN SPRINT, WE AS
INVESTIGATORS ENCOURAGE AND TALK
TO AND SO ON PRIMARY CARE
PHYSICIANS, STILL IN CHARGE OF
PATIENTS, BUT GAVE A LOT OF
ADVICE.
DURING THE TIME THE TRIAL IS
BEING DONE HAZARD RATIO WENT
DOWN PROGRESSIVELY, AND IN A
VERY NICE DIRECTION.
AND THEN TRIAL WAS STOPPED.
THERE'S A PERIOD AFTER THE TRIAL
WAS STOPPED WHERE WE STOPPED
GIVING ADVICE AND HANDED BACK
THE CARE OF THE PATIENTS TO
PRIMARY CARE PHYSICIANS.
THEY ALWAYS WERE IN CHARGE BUT
WE STOPPED GIVE ADVICE.
SORT OF A GENERAL HANDOVER
PERIOD, IT TAKES A WHILE TO DO
THAT.
THEN THERE'S A PERIOD AFTER
EVERYTHING IS COMPLETELY HANDED
BACK TO THE PRIMARY CARE
PHYSICIANS.
YOU SEE HAZARD RATIOS COMING
BACK UP TO WHERE IT STARTED,
MORE OR LESS.
WE CAN'T BE SURE TO UNDERSTAND
WHY THAT OCCURRED, BUT WE CAN
LOOK AT BLOOD PRESSURES.
IF YOU LOOK AT BLOOD PRESSURES,
DURING THE TRIAL, SHOWN HERE ON
THE TWO LINES, NICE SEPARATION.
WHEN WE LOOK AT ELECTRONIC
HEALTH RECORD PRESSURES, TAKEN
CONCURRENTLY FOR OFFICE VISITS,
NOT DONE AS WELL, OF COURSE, AS
YOU WOULD EXPECT.
THEY TEND TO OVERESTIMATE.
THERE WILL BE UNDERESTIMATION
BUT THEY TEND TO OVERESTIMATE SO
YOU GET HIGHER SYSTOLIC AND
DIASTOLICS WITH A NICE
SEPARATION NONETHELESS.
AS YOU FOLLOW OVER TIME WHAT
YOU'RE SEEING IS IN THE STANDARD
GROUP THERE'S NOT MUCH EVIDENCE
THAT THEY ARE GETTING MORE
INTENSIVE TREATMENT.
IN THE MORE INTENSIVELY TREATED
GROUP WHICH WE HOPE WOULD
CONTINUE TO HAVE FAIRLY
INTENSIVE TREATMENT, THEY ARE
PROGRESSIVELY GOING BACK UP AND
YOU'RE BACK TO BUSINESS AS USUAL
UP HERE.
SO, THIS IS DISTURBING.
IT'S NOT SURPRISING.
WE'VE SEEN THIS AFTER MANY
EFFICACY TRIALS.
WE DO THE EFFICACY TRIALS.
THEY DEMONSTRATE THE BENEFIT.
WHAT WE SEE IS IN PRACTICE THERE
ARE A LOT OF FACTORS THAT
INFLUENCE MANAGEMENT OF CARE,
AND WHAT WE'RE SEEING HERE IS
VERY POOR ADOPTION TO THE
FINDINGS OF THE SPRINT TRIAL.
NOW, ONE LAST DATA SLIDE HERE,
JUST TO SAY WE'RE DOING
TERRIBLY.
ON THE OTHER HAND, WE ABSOLUTELY
KNOW WHAT TO DO TO GET BLOOD
PRESSURES DOWN.
I WON'T GO INTO IT IN DETAIL BUT
THIS IS A GENERAL MODEL OF CARE
THAT HAS WORKED VERY WELL IN
HIGH-INCOME COUNTRIES, IT'S
WORKED VERY WELL IN LOW-INCOME
COUNTRIES.
STARTS WITH A COMMITMENT,
WHETHER IT'S AT THE PROVIDER
LEVEL, COUNTRY LEVEL, OR THE
INSTITUTION, OR HEALTH SYSTEM.
IT INVOLVES HAVING A BACKGROUND
OF HEALTH PROMOTION, SIMPLE
EVIDENCE-BASED PROTOCOLS FOR
COUNSELING PEOPLE FOR LIFESTYLE
IMPROVEMENT AND FOR
ANTI-HYPERTENSIVE DRUG
TREATMENT, RELIABLE ACCESS TO
MEDICATION IS VERY IMPORTANT.
COMMUNITY-BASED PATIENT-CENTERED
TEAM CARE, WITH ACCURATE BLOOD
PRESSURE MEASUREMENTS AND
INFORMATION TRACKING.
THIS IS THE MODEL USED IN W.H.O.
HEARTS PROGRAM, IN HYPERTENSION
LEAGUE, AND MOST RECENTLY THE
SORT OF MODEL USED FOR THE
COMMUNITY HEALTH CARE WORKER
TRIAL RURAL CHINA WHICH HAD
DRAMATIC DIFFERENCES IN SYSTOLIC
BLOOD PRESSURE BETWEEN THE
INTERVENTION VILLAGES AND
CONTROL VILLAGES.
IN FACT BIGGER DELTA IN BLOOD
PRESSURE THAN WE GOT IN SPRINT,
OVER 21 MILLIMETERS OF MERCURY
SYSTOLIC, AND YOU WOULD EXPECT
HUGE HEALTH BENEFITS BOTH
CARDIOVASCULAR AND ALL-CAUSE
MORTALITY.
IN SUMMARY MOST REPORTS OF
HYPERTENSION PREVALENCE AND
CONTROL ARE BASED ON NHANES,
MOST REPORTS FOR THE U.S.
THAT MAKES SENSE.
AND ADULTS WE'VE TENDED TO SEE
MEAN SYSTOLIC BLOOD PRESSURE
GOING DOWN TO 2018, 2010, SLIGHT
UPTICK, PREVALENCE 140/90,
STABLE AT 30%, AND DECLINES IN
AWARENESS, AND CERTAINLY DEFINES
AND CONTROL RATE.
IF WE LOOK AT 130/80, OBVIOUSLY
HIGHER PREVALENCE.
NO EVIDENCE IN MORE RECENT YEARS
THERE'S BEEN ANY IMPROVEMENT
COMPARED TO THE EARLIER PERIODS
AND ACHIEVING THE 130/80, IT WAS
MENTIONED EARLIER IN THE SESSION
LESS THAN A QUARTER OF THE U.S.
ADULT POPULATION WERE ACHIEVING
BLOOD PRESSURE GOAL OF LESS THAN
130/80, THAT'S OBVIOUSLY WORSE
IN SOME SUBSETS IN THE
COMMUNITY.
I THANK YOU FOR ALLOWING ME TO
PRESENT TODAY.
>> THANK YOU.
A LOT OF DATA, MUCH OF IT
SOBERING.
NEXT PRESENTATION TIES BACK INTO
THE CONCEPT OF HYPERTENSION AND
A.I.
DR. SONYA MAKHNI, USING A.I. TO
PRE-DIDN'T HYPERTENSION STATUS
AND BLOOD PRESSURE, COMES FROM
THE MAYO CLINIC ROCHESTER
MINNESOTA.
>> NICE TO MEET YOU ALL.
HOPEFULLY YOU CAN HEAR ME AND
SEE MY SLIDES OKAY.
>> YES.
>> I'M A CLINICAL INFORMATIST,
HOSPITALIST AT MAYO CLINIC,
MEDICAL DIRECTOR AT MAY O CLINIC
PLATFORM.
NO CONFLICTS OF INTEREST TO
REPORT.
NOT TO SPENT MUCH TIME ON THE
BACKGROUND, WE'VE HEARD
HYPERTENSION IS VERY COMMON, ONE
OF THE MOST IMPORTANT RISK
FACTORS FOR CARDIOVASCULAR
DISEASE.
INCREASES RISK OF MANY OTHER
CONDITIONS.
MANAGING THESE CONDITIONS IS NOT
ONLY CHALLENGING BUT COSTLY AS
WELL.
THERE'S AMPLE EVIDENCE TO
SUGGEST EARLY DETECTION OF
HYPERTENSION IN CONJUNCTION WITH
GOOD BLOOD PRESSURE CONTROL AND
CORRECTION OF LIFE HABITS CAN
FURTHER REDUCE PROGRESSION OF
THE DISEASE AND DEVELOPMENT OF
DOWNSTREAM CONSEQUENCES.
SO THIS UNDERSCORES THE
IMPORTANCE OF EARLY ACCURATE
DIAGNOSIS.
WE'VE HEARD MEASURING MEASURING
BLOOD PRESSURE IN AROUND OUT OF
THE OFFICE AND LIMITATIONS OF
CURRENT PRACTICES.
TO BRIEFLY RECAP OUR OUT OF
OFFICE MEASUREMENTS CAN BE
DIFFICULT TO OBTAIN DUE TO
AVAILABILITY OF EQUIPMENT, COST
AND LOGISTICAL ISSUES, AND IN
ABPM CAN BE DISRUPTIVE, IN THE
CASE OF HOME MEASUREMENT
ACCURATE BLOOD PRESSURE
MEASUREMENTS DEPENDS ON HAVING A
VALIDATED DEVICE AND CORRECT
USE.
THESE ARE OVER LIMITED
TIMEFRAMES.
IF HOME OPTIONS ARE NOT POSSIBLE
PATIENTS NEED TO DEMONSTRATE
CERTAIN NUMBER MUCH ELEVATED
MEASUREMENTS IN THE OFFICE AGAIN
OVER CERTAIN TIMES WHICH
REQUIRES MULTIPLE DOCTOR VISITS
AND AS WE HEARD THERE ARE MANY
FACTORS THAT MIGHT PREVENT
SOMEONE FROM BEING ABLE TO CARRY
THESE VISITS OUT.
AND IN GENERAL, OFFICE VISITS
HAVE ISSUES OF WHITE COAT
HYPERTENSION, MASKED
HYPERTENSION, GENERALLY NOT
REFLECTIVE OF VARIABILITY.
AND THEN EVEN IF YOU CAN GET
ACCURATE MEASUREMENTS, WORK
AROUND ALL THESE THINGS I
MENTIONED, OUR GUIDELINES ARE
ONE-SIZE-FITS-ALL.
WE DON'T HAVE A DEEP
UNDERSTANDING OF HOW SUBGROUPS
OF PATIENTS MAY DIFFER IN
OPTIMAL TARGETS, THERE ISN'T
MUCH PHENOACTIVIC
DIFFERENTIATION IN DIAGNOSING
WHEN WE TREAT IT.
CLEARLY THERE ARE SEVERAL
BARRIERS TO ATTAINING THOSE
RELIABLE MEASUREMENTS WHICH
COMPLICATES EARLY AND ACCURATE
DIAGNOSIS.
AND HETEROGENEITY OF PEOPLE MAY
GET POSSIBLE, IF NOT LIKELY
TARGETS ARE DIFFERENT ACROSS
SUBGROUPS, ONE-SIZE-FITS-ALL
APPROACH IS NOT ADEAL.
THERE'S A GREAT CASE FOR
LEVERAGING A.I. SOLUTIONS TO
HELP PREDICT HYPERTENSION IN
PATIENTS.
THE ABILITY TO ACCURATELY
PREDICT HYPERTENSION STATUS
WITHOUT INTERVENTION LIKE ABPM
ALLOW PCPs TO EFFECTIVELY AND
EFFICIENTLY DIAGNOSIS AND MANAGE
PATIENTS.
AS WELL AS MINIMIZING RISK OF
HARM FOR UNDERTREATMENT OR
OVERTREATMENT.
WE NEED TO BE ABLE TO UNDERSTAND
PATIENTS' CLINICAL STATES WHEN
NOT WITHIN OUR WALLS, WHICH IS
OVER 99% OF THEIR LIVED
EXPERIENCES.
AND WE NEED TO EXPAND OUR
EFFORTS, IF WE CAN SAFELY AND
APPROPRIATELY CREATE A.I.
SOLUTIONS TO HELP US WE CAN
POTENTIALLY EXPAND OUR REACH TO
THESE PATIENTS.
THERE'S TWO COMMON -- THIS IS A
SIMPLIFICATION, TWO COMMON
APPROACHES TO DETECTION USING
A.I.
IN CLASSIFICATION MODELS,
ALGORITHMS DETECT HYPERTENSION
AS A LABEL, TRAINED ON THE
TRAINED DATASET, BASED ON THAT
TRAINING CATEGORIZE DATA INTO
CLASSES.
THERE'S OFTEN EXTRACTED DATA
FROM EHRs, FOR EXAMPLE, AND
OUTCOME IS CATEGORICAL, LEVELS,
YES OR NO, OR PRE-HYPERTENSION,
NO HYPERTENSION.
REGRESSION MODELS GENERATE
CONTINUOUS VALUES, THAT WOULD BE
A NUMERIC MEASUREMENT WHICH WE
ALSO GOT TO HEAR ABOUT AS WELL.
AS WE'VE HEARD THERE ARE OTHER
SOURCES THAT CAN BE USED, THE
RAW SIGNALS THEMSELVES OR
EXTRACTED FEATURES.
ULTIMATELY THEY OUTPUT A NUMERIC
VALUE.
THERE ARE DIFFERENT BUT SIMILAR
WAYS TO EVALUATE PERFORMANCE OF
THESE MODELS DEPENDING ON TYPE
VIEWS AS WELL.
PUT ANOTHER WAY, CLASSIFICATION,
FUNCTION TO DIVIDE DATASET INTO
CLASS, MAPPING INPUT TO DISCRETE
OUTPUT Y, REGRESSION FINDS
CORRELATION BETWEEN DEPENDENT
AND INDEPENDENT VARIABLES,
MAPPING TO CONTINUOUS VARIABLE
OUTPUT Y.
THERE HAVE BEEN MANY ALGORITHMS
OF TYPES, AND I'LL SUMMARIZE AT
HIGH LEVEL WHAT THOSE LOOK LIKE.
KEEP IN MIND THIS IS JUST A
SUBSET OF WHAT IS IN
PUBLICATIONS.
YOU HAVE YOUR -- ON THE LEFT
MORE REGRESSION.
CLINICAL DATA ON THE RIGHT.
CLASSIFICATION MODEL USE
CLINICAL AND SOCIODEMOGRAPHIC
DATA INCORPORATING AGE, BMI,
GENDER, SYSTOLIC BLOOD PRESSURE,
HEART RATE.
LESS FREQUENTLY ACTUALLY
INCORPORATE OTHER VERY USEFUL
FACTORS SUCH AS RACE, LIFE
HABITS, GEOGRAPHY, YOU CAN
IMAGINE PAPERS THAT SHOW HOW
CRIME IMPACTS HYPERTENSION,
THAT'S NOT TYPICALLY
INCORPORATED IN STUDIES.
AND AGAIN LIKE I MENTIONED
OUTCOME IS A CLASSIFICATION.
HYPERTENSION YES OR NO.
STAGE.
ONE-YEAR RISK, PRESENCE OR
ABSENCE OF HYPERTENSION.
THERE ARE MORE POSSIBILITIES.
SO THIS IS A SMALL SAMPLE OF
MANY CLASSIFICATION MODELS.
I WANTED TO HIGHLIGHT A COUPLE
THINGS.
MORE RELEVANT TO THE PROCESS,
WHICH WILL SORT OF PAVE THE WAY
FOR LIMITATIONS I'LL TALK ABOUT
LATER.
FIRST THE DATASET COLUMN.
I'VE INCLUDED SAMPLE SIZE OF THE
DATASET, NOT TO BE CONFUSED AS
ACTUAL SIZE OF THE COHORTS USED.
CLEARLY SMALLER BUT TYPICALLY
DATASETS WOULD NOT ONLY BE
NARROWED DOWN BASED OFF
INCLUSION AND EXCLUSION CRITERIA
BUT ALSO DUE TO DATA QUALITY
ISSUES.
WHICH I'LL TOUCH ON LATER BUT
THE DATA IS IMPORTANT AND CAN
IMPACT OVERALL PERFORMANCE AND
GENERALIZABILITY OF THE MODEL.
IF THE RIGHT COHORT ISN'T USED
OR IF THERE ISN'T SUFFICIENT
DIVERSITY IN THE DATASET, OR IF
DATASET ITSELF HARBORS BIAS,
THAT WILL IMPACT THE MODEL, IN
OTHER CONTEXT, IT'S NOT THAT OWN
DATASET.
NEXT YOU CAN SEE THE USE OF THE
COMMON VARIABLES I MENTIONED,
YOUR MODEL TAKES DIFFERENT
VARIABLES INTO CONSIDERATION,
THIS CAN BE DUE TO FUNDAMENTAL
MODEL DESIGN, AVAILABILITY OF
DATA OR OTHER FEATURE SELECTION
METHODS THAT MAY LEAD TO THAT
PARTICULAR COMBINATION OF
PREDICTORS.
AGAIN, THERE'S MANY ML ALGORITHM
TIMES, A LOT COMPARE MODELS AND
USE THE BEST PERFORMING ONES.
THAT PERFORMANCE IS JUDGED USING
METRICS.
SENSITIVITY, SPECIFICITIES,
ACCURACY.
IT'S INTERESTING TO THINK ABOUT
METRICS IN MEANINGFUL CLINICAL
TERMS.
NOT ALL STUDIES CAN EXPLAIN FOR
EXAMPLE PREDICTION ACCURACY AS
PERFORMANCE METRIC IN THAT
MEANINGFUL CONTEXT.
IF WE'RE SAYING HYPERTENSION IS
UNDERDIAGNOSED AND USING A
DATASET THAT DOESN'T ACTUALLY
REFLECT THE TRUE PREVALENCE OF
HYPERTENSION, ACCURACY OF 99.5%
HELPFUL, PREDICTIVE?
ALSO IS THAT MAYBE AN OVERFIT
ALGORITHM, USING TOO MANY
FACTORS, BUT IT'S A COMPLEX
QUESTION BUT ALL OF THESE SORTS
OF ASPECTS OF ALGORITHM
DEVELOPMENT ARE IMPORTANT.
THIS TRANSITIONS TO THE NEXT
MODEL TYPE WE TALKED ABOUT,
REGRESSION PLOD ALSO THAT INTAKE
SIGNALS TO OUTPUT A NUMBER.
WE HAD THE OPPORTUNITY TO
DIVERSITY DEEPER.
THE ADVANTAGE IS THEY CAN SERVE
AS PROXY, BE CONTINUOUS,
NON-INVASIVE, WITH ECG AND PPG
BEING MOST COMMONLY USED
PREDICTORS, OTHER SIGNALS USED
AS WELL.
AGAIN, THERE ARE MANY MODELS
USING A COMBINATION OF SIGNAL
DATA, PERFORMANCE METRICS I HAVE
HERE MEAN ABSOLUTE ERROR CAN
VARY.
ERROR OF 11 IS MAYBE NOT HELPFUL
PRACTICALLY SPEAKING BUT 2 COULD
BE MORE USEFUL.
IT'S INTERESTING TO NOTE THAT
SOME OF THESE DATASETS
INCORPORATE MORE OF THE EHR TYPE
DATA, SOME LESS.
AND IDEALLY WE WOULD LOVE RICH
DATASETS THAT HAVE ALL DIFFERENT
TYPES OF DATA TYPES.
SIMILARLY THERE ARE HANDFULS OF
MOVED ALSO THAT ARE VERY
INTERESTING WHEN WE THINK OF
HYPERTENSION DETECTION AND
DIAGNOSIS.
THERE ARE MODELS THAT USE
UNSUPERVISED LEARNING TO PREDICT
DIFFERENT HYPERTENSION
PHENOTYPES.
OTHERS BETWEEN PRIMARY AND
SECONDARY HYPERTENSION, FURTHER
DISTINGUISH BETWEEN ETIOLOGY OF
ENDOCRINE HYPERTENSION, FOR
EXAMPLE.
THERE ARE ALGORITHMS THAT USE
OMIC DATA TO PREDICT
HYPERTENSION THROUGH VARIOUS
MODELS, LOOKING AT GENES
SPECIFICALLY.
AND OF COURSE THERE ARE MODELS
IN RESEARCH TO IDENTIFY VARIOUS
RISK FACTORS THAT WE MIGHT BE
MISSING.
AND THESE TOPICS I MENTION
REPRESENT SOME OF THE MORE
INTERESTING SCIENTIFIC AREAS OF
STUDY WITHIN HYPERTENSION.
IF WE CAN EFFECTIVELY INTEGRATE
MULTIOMIC DATA FROM DIFFERENT
SOURCES LIKE EHR, GENOME
DATABASES, IMAGING,
CARDIOVASCULAR IMAGING, WEARABLE
DEVICE, EVEN ENVIRONMENTAL AND
BEHAVIORAL DATA WE CAN LEVERAGE
TO ELUCIDATE EVEN DEEPER
INSIGHTS.
AND THOSE INCLUDE THINGS I
MENTIONED.
UNCOVERING NEW RISK FACTORS,
SHIFTING DIAGNOSIS AND TREATMENT
TO BE MORE PRECISE AND
PERSONALIZED ESPECIALLY, WITH
CUTOFFS FOR DIAGNOSIS.
WE CAN BETTER UNDERSTAND FACTORS
THAT CONTRIBUTE TO TREATMENT
SUCCESS AND THE HOPE IS THE
FUTURE INVESTIGATIONS WILL BE
ABLE TO EXPAND UPON AND VALIDATE
ABILITY OF A.I. TO DIAGNOSE
HYPERTENSION AND PREDICT BLOOD
PRESSURE.
THERE ARE MANY GAPS THAT NEED TO
BE FILLED, MANY ARE NOT JUST
SPECIFIC TO HYPERTENSION.
I TOOK THE BROAD APPROACH OF
WHAT IS HINDERING FROM
PERSPECTIVE THAT COULD BE
APPLIED TO OTHER DISEASES AND
CONTEXTS.
WE'VE BEEN ABLE TO TALK ABOUT
SOCIAL DETERMINANTS OF HEALTH,
NEEDS THAT THESE ARE EXPLORED IN
CONJUNCTION WITH CONTRIBUTING
FACTORS.
THERE'S A HUGE NEED FOR NEW
DATASETS THAT INCORPORATE SDOH
DATA.
IT'S VERY COMPLEX BUT IMPORTANT
TO ADDRESS SO WE CAN RECTIFY THE
PROBLEMS OF UNDERDIAGNOSIS IN
OUR DATA, FOR EXAMPLE.
THERE ARE ALSO DOZENS OF
STUDIES, FEW EXTERNALLY
VALIDATED THROUGH CLINICAL
PERSPECTIVE STUDIES EVEN AT
DIFFERENT SITES.
WE NEED TO UNDERSTAND HOW THESE
MODELS PERFORM IN THE REAL
WORLD.
AND MODELS DO NOT INCLUDE BIAS
ANALYSIS WHICH MAKES THEM
DIFFICULT TO UNDERSTAND IN THE
CONTEXT OF CERTAIN SUBGROUPS.
IF WE DON'T UNDERSTAND HOW
MODELS PERFORM WITHIN DIVERSITY
WENT GROUPS WE CAN POTENTIALLY
DELIVER WORSE CARE AND CAUSE
HARM.
ALSO CHALLENGES, TECHNICAL
LIMITATIONS IN THAT THESE
DATASETS ARE LIMITED.
REPRESENT A SINGULAR POPULATION,
UNCLEAR DATA QUALITY.
NOT MANY WAYS TO ADDRESS DATA
QUALITY CURRENTLY.
ALSO THESE ALGORITHMS ARE
TRAINED ON RETROSPECTIVE DATA
WHICH HAS ITS OWN ISSUES.
THEY ARE DIFFICULT TO REPRODUCE,
REQUIRES SHARING A LOT OF
INFORMATION WHICH IS DIFFICULT
TO DO AND THAT'S HOW FEDERATED
NETWORKS ARE INTERESTED.
FINALLY MANY ADOPTION
CHALLENGES, TRANSPARENT AND
EXPLAINABILITY MOST SIGNIFICANT,
A LOT OF INTERESTING WORK GOING
ON IN HOW WE CAN ADDRESS THESE
NEEDS BUT IT'S VERY COMPLEX.
THE MORE COMPLEX THE MODEL, THE
MORE DIFFICULT TO EXPLAIN.
AS A CLINICIAN, IT'S HARD.
AND WORKFLOW INTEGRATION ISSUES
WE'LL TALK ABOUT LATER.
MAYO CLINIC PLATFORM IS TRYING
TO ADDRESS CHALLENGES.
SOME DATA CHALLENGES THROUGH
DISTRIBUTIVE DATA NETWORK WHERE
WITH COMMON DATA MODEL AND
QUALITY CONTROL MECHANISMS, AND
SOLUTION DISCOVERY, OPENING UP
THESE DATA SOURCES BY
DE-IDENTIFYING, SO ALLOWING
OTHER PEOPLE TO TEST AND
INNOVATE.
VALIDATION IS SOMETHING WE'RE
TRYING TO TACKLE, NOT ONLY
MATHEMATICALLY BUT IN THE LENS
OF BIAS AND CLINICAL
INTERPRETATION.
SO WHAT DOES IT MEAN FOR CARE?
AND WORKFLOW AND DEPLOYMENT, HOW
DO WE GET THE RIGHT PREDICTION
AT THE RIGHT TIME AND KEEP
INVENTORY OF HOW THAT MODEL IS
PERFORMING.
SO, THAT'S ALL I HAVE.
THANK YOU SO MUCH FOR INVITING
ME TO SPEAK.
I LOOK FORWARD TO FURTHER
DISCUSSION.
>> THANK YOU VERY MUCH.
WE'RE RUNNING BEHIND BUT WE'LL
HAVE TIME FOR A FEW QUESTIONS.
THOSE WHO NEED A BREAK, FEEL
FREE.
WE'LL BE STARTING AGAIN IN
APPROXIMATELY 10 TO 15 MINUTES.
WE KNOW THAT WE HAVE TO
COMMUNICATE WITH PATIENTS TO
MAKE SURE THE BLOOD PRESSURES
THAT ARE DONE OUTSIDE THE OFFICE
ARE INCORPORATED.
HAVE YOU FOUND TRYING TO
INTEGRATE DIRECTLY INTO THE
HEALTH RECORD WORKS, THAT'S
SOMETHING THAT'S BEING DONE?
>> YEAH, IT DEPENDS ON THE
HEALTH CARE SYSTEM, RIGHT?
SO FOR SOME LARGE ACADEMIC
CENTERS, YES, THAT'S THE ABILITY
TO INTEGRATE THEM IN.
I THINK DATA WAS PRESENTED.
OTHER ACADEMIC CENTERS, IT'S NOT
POSSIBLE, BECAUSE OF THE IP
SECURITY CONCERNS OF PARTNERING
WITH PARTICULAR DEVICE
COMPANIES, ET CETERA.
IT'S BEEN A CHALLENGE, IN MY OWN
EXPERIENCE.
IN NEW YORK CITY IT'S BEEN
DIFFICULT TO GET THE FULL
INTEGRATION IN THE WAY ONE WOULD
WANT IT TO BE.
>> UNDERSTOOD.
YOU MENTIONED SOME OF THE
DEVICES HAVE BEEN STUDIED AND
APPROVED, NOT NECESSARILY VALID.
IS THERE ANYTHING ON THE MARKET
RIGHT NOW THAT'S BOTH APPROVED
AND VALID IN TERMS OF MEASURING
BLOOD PRESSURE?
AND WHILE YOU'RE -- ARE YOU
THERE, DR. STERGIOU?
>> THERE'S NO DEVICE THAT
FULFILLS THE REQUIREMENT FOR
CLINICAL USE.
THE MAIN PROBLEM IS THEIR
ABILITY TO TRACK CHANGES IN
BLOOD PRESSURE.
I HAVEN'T SEEN ANY DATA TOWARDS
TECHNOLOGY, BLOOD PRESSURE WITH
TREATMENT IS GOING TO TRACK
BLOOD PRESSURE CHANGE IN THESE
PATIENTS, IN WHOM THERE'S
NOTHING ELSE CHANGED ABOUT THE
BLOOD PRESSURE, AND THESE ARE
THE PROBLEMS WITH PREDICTING THE
BLOOD PRESSURE BECAUSE WE CHANGE
BLOOD PRESSURE WEEKEND NOTHING
ELSE.
I HAVEN'T SEEN DEVICES WHICH CAN
TRACK APPROPRIATELY NOCTURNALLY,
THIS IS WHAT WE DO IN CLINICAL
PRACTICE.
>> DR. WHELTON, WE'VE HAD
PERSONAL DISCUSSIONS IN THE
CHAT, MULTIPLE CONVERSATIONS,
WHY PERSISTENT POOR CONTROL IN
BLACK AMERICAN POPULATION,
DOESN'T SEEM TO BE GOING AWAY,
PERSISTENT FOR DECADES, WE DON'T
SEE IMPROVEMENT.
WHAT'S THE REASON?
>> YOU'RE MORE OF AN EXPERT THAN
ANYBODY ON THIS, CERTAINLY THAN
ME ON THIS CALL.
ON THE ONE HAND WE HAVE SEEN
SOME IMPROVEMENTS, YOU CAN TAKE
SOME CREDIT, WE SAW IMPROVEMENTS
IN AWARENESS, TREATMENT.
BUT STILL VERY, VERY POOR.
AWARENESS GOING DOWN.
TREATMENT POOR CONTROL, VERY
SUBSTANDARD.
PROBABLY HAS A LOT DO WITH THE
SOCIAL DETERMINANTS OF HEALTH.
IT REALLY UNDERLIES THE PROBLEM.
IT IS A VERY DIFFICULT AREA TO
TACKLE, AS YOU WELL KNOW.
I THINK THE SAD THING IS WE HAVE
MODELS THAT WE KNOW WORK.
THEY WORK IN NON-HISPANIC
BLACKS, JUST AS WELL AS IN
WHITES.
THEY WORK ALL OVER THE WORLD.
AND TRICK REALLY IS NOT ONLY TO
UNDERSTAND THAT MODEL BUT TO
IMPLEMENT IT.
THAT'S WHAT HAPPENS IN PRACTICE.
THERE'S NOT PROPER
IMPLEMENTATION OF WHAT WE KNOW
WORKS.
AND THAT'S THE FRUSTRATION OF
DOING ANY OF THE TRIALS.
YOU AND I GO BACK TO THE DAYS OF
BAHAD, WE'VE SEEN MANY EFFICACY
TRIALS, MOST RECENTLY SPRINT
THAT DEMONSTRATED WITHOUT
QUESTION THE BENEFIT OF A
TREATMENT, AND WE'VE SEEN THAT
LARGELY IGNORED IN CLINICAL
PRACTICE, VERY FRUSTRATING AND
WORSE OBVIOUSLY IN
HISPANIC/BLACK COMMUNITY THAN IN
OTHER RACE/ETHNICITY GROUPS IN
THE U.S.
>> THIS IS NOT JUST A NUMBER.
IT HAS A POWERFUL EFFECT ON
CARDIOVASCULAR OUTCOMES, MORE
STROKE, EARLIER HEART ATTACK,
MORE HEART FAILURE, MORE CHRONIC
KIDNEY DISEASE.
SO THIS IS NOT JUST LOOKING AT
THE PERCENTAGE OF POPULATION
THAT HAS CONTROL, IT TRANSLATES
INTO MORBIDITY AND MORTALITY.
DR. MAKHNI, VARIABILITY OF THE
HUMAN EXPERIENCE, HOW CAN YOU
PREDICT BASED ON CHANGES?
HUMANS CHANGE BEHAVIOR ALL THE
TIME.
WHATEVER DATA YOU'RE PUTTING IN,
TOMORROW MAY NOT BE VALID.
HOW DO YOU ADDRESS THAT?
>> COMPLEX QUESTION.
I THINK, YOU KNOW, WE MIGHT NOT
REALLY BE CLOSE TO FULLY BEING
ABLE TO ADDRESS THAT CONCERN.
BUT I THINK THERE ARE WAYS THAT
WE CAN BE MORE CREATIVE AND
CLEVER HOW WE INTEGRATE DATA
SOURCES.
SOME WORK GOING ON IN CHICAGO
THAT I WAS FORTUNATE TO BE
INVOLVED IN INVOLVED CONCEPT OF
SOCIO-DATA, DATA FROM OUR LIVED
ENVIRONMENT THAT, YOU KNOW,
INCLUDES NOT JUST CRIME BUT YOU
CAN GET DATASETS ON TRAFFIC AND
AIR POLLUTION.
AND THERE ARE SCENARIOS YOU CAN
IMAGINE HOW THAT SOCIAL AND
ENVIRONMENTAL DATA IN TANDEM
WITH CLINICAL AND OTHER SIGNAL
DATA POINTS WHERE MAYBE YOU CAN
CREATE A LONGITUDINAL
EXPERIENCE, AND KIND OF CREATE
PROXIES FOR CERTAIN ACTIVITIES,
SO MAYBE THERE'S A HIGHER BLOOD
PRESSURE LEVEL AT NIGHT WHEN A
CERTAIN PERSON GETS HOME FROM
WORK, IT'S A TOUGH AREA.
YOU CAN EXTRAPOLATE VARIOUS
INFERENCES ON THAT LIVED
EXPERIENCE.
>> A QUESTION IN THE CHAT, I'M
NOT SURE WHO MAY GIVE AN ANSWER.
ASKED ABOUT VALIDITY OF
ELECTRONIC EQUIPMENT USED BY
I-HEALTH.
DOES ANYONE KNOWN ABOUT I-HEALTH
AND THE EQUIPMENT?
NO ONE?
DR. ABDALLA, ONE THING WE DO IN
NEW ORLEANS, WE HAVE PILOT
STUDIES, NOT A BIG STUDY,
SELF-DEFINED AFRICAN AMERICANS
GAVE THEM VALID BLOOD PRESSURE
DEVICES AND NOT CALLING BUT
TEXTING, SOME MACHINES CAN
BLUETOOTH BLACK TO THE
TECHNICIAN, A MEDICAL STUDENT, I
DIDN'T DO A THING, SHOWED
INCREASE IN INHERENCE AND
CONTROL WITHOUT EVEN CALLING OR
TALKING TO THE PATIENT.
DO YOU THINK THAT'S GOING TO BE
A VALID WAY OF COMMUNICATIONS OF
PATIENTS?
>> MAYBE.
IT'S INTRIGUING DATA.
WE HAVEN'T DISCUSSED DIGITAL
DEVICE.
SOMEONE IN THE CHAT TALKED ABOT
THIS.
SOMETIMES THROWING TECHNOLOGY IS
PART OF THE SOLUTION BUT DOESN'T
GO BACK TO STRUCTURAL ISSUES.
ONLY A SMALL PROPORTION MAY BE
ABLE TO GET ACCESS OR BE
INVOLVED IN RESEARCH STUDIES BUT
REALLY THE QUESTION FOR ALL OF
US ON THIS CALL IS HOW DO WE
SCALE THIS AT LARGE, RIGHT?
NOT JUST IN, YOU KNOW, OUR U.S.
COMMUNITIES BUT ALSO GLOBALLY.
HYPERTENSION IS STILL THE
LEADING CAUSE OF MORTALITY
WORLDWIDE.
>> I HAVE A BIOWATCH ON NOW, I
USE IT FOR STEPS, HOW MANY STEMS
YOU TAKE -- HOW MANY STEPS YOU
TAKE A DAY.
YOU PUT COLD WATER ON THE
SELF-MONITORING BLOOD PRESSURE
BUT IS IT USEFUL, IS THERE ANY
DEVICE FOR BLOOD PRESSURE WE CAN
USE EVEN NOT VALIDATED
COMPLETELY?
>> I CANNOT FIND ANY POSITIVE
FEEDBACK FOR THE PRESENT TIME
ABOUT SOME PROMISES.
REALLY DEVICES CAN CHANGE
EVERYTHING.
REALLY, EVERYTHING.
AND CAN BE A GREAT IMPROVEMENT.
THAT'S WHY EVERYBODY LIKES THEM.
BUT IT DOESN'T SEEM THAT WHAT WE
HAVE FOR THE MOMENT CAN DO ANY
HELP.
THERE'S A DISCUSSION ABOUT THE
TRENDING DEVICES, THESE DEVICES
CANNOT MEASURE, IN THE WAY YOU
HAVE SET UP CLINICAL, I DON'T
SEE HOW THEY CAN BE USEFUL
UNFORTUNATELY.
MAYBE PREDICTION WHICH WE'RE NOT
FAMILIAR WITH THAT, WE WANT TO
MEASURE, NOT PREDICT, BUT MAYBE
PREDICTION IS MORE FEASIBLE FOR
SCREENING, BUT THE SENSORS BEING
ABSOLUTE MEASUREMENTS FOR US TO
USE FOR DECISION MAKING, IT
DOESN'T SEEM TO BE EASY, OR TOO
CLOSE.
>> THANK YOU.
I'M SMILING BECAUSE IN THE CHAT
I SPOKE ABOUT OUR VERY NICE
STUDY WHICH WE GAVE PATIENTS
DEVICES, SOMEBODY SAID TEXTING
IS NOT FOR EVERYBODY.
THERE ARE POSTPARTUM WOMEN THAT
GOT TIRED AND ASKED THEM TO
STOP.
THE HUMAN EXPERIENCE JUST
VARIES, DEPENDING ON THE
POPULATION, WHO YOU'RE STUDYING.
PAUL, YOU'LL HAVE THE LAST THREE
MINUTES, MAYBE TAKE TWO AND IF
ANYONE ELSE HAS SOMETHING ELSE
THEY CAN CHIP IN.
I'M STILL PERPLEXED, YOU HAVE
BLOOD PRESSURE CONTROL RATES
THAT IMPROVE THROUGHOUT THE
WORLD.
YOU SAID WORLDWIDE YOU MENTIONED
THE CHINA EXPERIENCE,
IMPROVEMENTS IN CONTROL.
WHY ARE WE NOT DOING IT IN THE
UNITED STATES?
I DON'T GET IT.
>> IT'S FRUSTRATING.
I WILL SAY ONE THING.
I DO A LOT OF GLOBAL HEALTH WORK
AROUND BLOOD PRESSURE.
AND ONE THING THAT YOU FIND IN
ALMOST EVERY REMOTE AREA, IT'S
NOT UNIVERSAL, BUT IT'S AN
iPHONE OR EQUIVALENT, YOU GO
INTO THE FLOATING VILLAGES IN
LAKE TITICAK A, THEY HAVE A SOLR
PANEL, TV, iPHONES, EVEN IN
SUB-SAHARAN AFRICA.
IN THE FUTURE IF THERE'S A WAY
TO REALLY GET iPHONES TO WORK,
I THINK THAT HAS MUCH BROADER
APPLICABILITY THAN WRIST WATCHES
WHICH PEOPLE AREN'T GOING TO BUY
BUT WILL BUY A MOBILE PHONE.
IT'S VERY FRUSTRATING.
THE QUESTION IS SO FRUSTRATING
BECAUSE WE KNOW WHAT WORKS.
WE HAVE THE EFFICACY STUDIES.
AND IT'S BEEN DARN DIFFICULT TO
IMPLEMENT.
WE KNOW WHEN IT'S DONE, IT'S NOT
THE MODEL.
THE MODEL WORKS.
WE KNOW WHEN IT'S DONE WITH A
HIGH LEVEL OF EXECUTION, IT
WORKS WONDERFULLY WELL.
THE CHINA STUDY WAS A GREAT
EXAMPLE, MORE THAN 21
MILLIMETERS OF MERCURY SYSTOLIC
DIFFERENCE, HUGE CARDIOVASCULAR
BENEFITS.
THE MOST RURAL OF RURAL AREAS.
AND WE'VE SEEN THE SAME THING IN
HIGH-INCOME COUNTRIES.
IT'S VERY FRUSTRATING TO SEE
THAT IT'S NOT HAPPENING,
FRUSTRATING TO SEE NO INTEREST
IN QUALITY OF MEASUREMENTS,
HEDIS WILL NOT TOUCH IT OR GET
OFF THE 140/90 GUIDELINE.
EVERYONE IS RECOMMENDING A LOWER
TARGET.
WE HAVE TO BE PERSISTENT.
>> THANK YOU TO THE PRESENTERS.
I'LL TURN IT BACK OVER TO DR.
SHIMBO.
>> THANK YOU.
WONDERFUL DISCUSSION.
WE HAVE RUN A LITTLE BIT BEHIND.
SO WE'RE GOING TO ASK PEOPLE TO
COME BACK, WE'LL TAKE A BREAK,
ASK PEOPLE TO COME BACK SHARP AT
2:45 EASTERN STANDARD TIME.
AT WHICH TIME WE'LL BEGIN
SESSION 3, THE FINAL SESSION
THIS AFTERNOON.
I THINK WE SHOULD GET
STARTED.
I'D LIKE TO OPEN SESSION 3,
MAKING A DENT IN THE PROBLEM.
WE STARTED WITH TECHNOLOGY AND
A.I. FOCUS, SESSION 2 SLANTED
TOWARD HYPERTENSION AND BLOOD
PRESSURE.
NOW WE ARE WELL EDUCATED ABOUT
A.I. AND HYPERTENSION, THE
PROBLEMS THERE.
THIS SESSION IS GOING TO FOCUS
ON A.I., INTERSECTION OF A.I.
AND HYPERTENSION.
I'D LIKE TO ASK OUR FIRST
SPEAKER, DR. JANE LEOPOLD TO
PRESENT.
ASSOCIATE PROFESSOR AT HARVARD
MEDICAL SCHOOL, DIRECTOR OF
CARDIOVASCULAR RESEARCH CORE OF
BRIGHAM AND WOMEN'S HOSPITAL.
WELCOME.
USING A.I. FOR PRIMARY
PREVENTION OF CV DISEASE WITH A
FOCUS ON HYPERTENSION, PRECISION
PUBLIC HEALTH.
THANK YOU AND WELCOME.
>> THANK YOU FOR THE KIND
INVITATION, TALKS SO FAR HAVE
BEEN FANTASTIC, I HOPE MY
DISCUSSION HERE ABOUT PRECISION
PUBLIC HEALTH WILL TIE IN WELL
WITH EVERYTHING.
THESE ARE MY DISCLOSURES.
AND SO IF WE'RE GOING TO TALK
ABOUTPRECISION MEDICINE WE HAVE
TO DEFINE IT.
IN OUR CURRENT TREATMENT SCHEME
MOST THERAPIES ARE DESIGNED FOR
THE AVERAGE PATIENT BASED ON
RESULTS FROM CLINICAL TRIALS.
HOWEVER, THIS ONE-SIZE-FITS-ALL
APPROACH MAY WORK FOR SOME, NOT
OTHERS.
IF YOU LOOK ATPRECISION MEDICINE
MED ASSIGN -- MEDICINE YOU
ACCOUNT FOR DIFFERENCES
REQUIRING DEEP PHENOTYPING.
WE WANT TO GET THE RIGHT
TREATMENT OR INTERVENTION TO THE
RIGHT PATIENT AT THE RIGHT TIME
FOR PRECISION PUBLIC HEALTH WE
WANT TO FOCUS ON THE HEALTHY
INDIVIDUALS AND THOSE AT RISK
FOR DISEASE.
THERE'S A CONTINUUM OF DISEASE,
INDIVIDUALS ARE EITHER IN IDEAL
HEALTH, DEVELOP RISK FACTORS FOR
A DISEASE OR ACTUALLY HAVE
ESTABLISHED DISEASE, AND THERE'S
VARYING GRADIENTS OF DISEASE
RISK OR SEVERITY WITHIN EACH OF
THE GROUPS, IT'S A SPECTRUM, BUT
THERE'S PLASTICITY IN THE
PHENOTYPES SO INDIVIDUALS CAN
TRANSITION BETWEEN GROUPS, AND
THIS CAN OCCUR EITHER OVER TIME
AND THIS WAS ALLUDED TO EARLIER,
OR IT CAN OCCUR IF WE CHANGE
DEFINITION OF WHAT CONSTITUTES
DISEASE AND HEALTH.
AS YOU CAN RECALL, THIS ACTUALLY
HAPPENED WHEN WE CHANGED THE
DEFINITION OF WHAT CONSTITUTES
HYPERTENSION, AND THE SAME THING
HAPPENED WHEN WE CHANGED
DEFINITION OF WHAT CONSTITUTES
HYPERCHOLESTEROL EMIA.
WE HAVE TO RECOGNIZE A LOT OF
HETEROGENEITY IN CARDIOVASCULAR
DISEASES.
SO HISTORICALLY WE USED TO THINK
THAT -- AND THIS IS QUITE A
WHILE AGO, THAT A DISEASE WAS
CAUSED BY A SINGLE GENE VARIANT
THAT MAY HAVE BEEN RESPONSIBLE
FOR MULTIPLE DIFFERENT TYPES OF
PHENOTYPES, AND THAT THIS IS
REALLY HOW WE ENDED UP WITH A
COMPLEX CARDIOVASCULAR
PATHOPHENOTYPE.
BUT THERE'S A LOT OF
HETEROGENEITY IN DISEASE.
IF YOU LOOK AT THESE
CARDIOVASCULAR PHENOTYPES, HERE
I HIGHLIGHTED ACUTE MYOCARDIAL
INFARCTION, CARDIOMYOPATHIES,
ALL OF THESE HAVE GENE VARIANTS
ASSOCIATED WITH THEM.
WE KNOW WHAT THE GENE VARIANTS
DO.
BUT IF YOU LOOK AT REALLY HOW
THEY EXPRESS THEMSELVES THEY CAN
HAVE DIFFERENT OUTWARD
PHENOTYPES, MORE COMPLICATED
THAN FOCUSING ON GENETICS ALONE.
WE TEND TO THINK OF THIS,
INCORPORATING THINGS THAT WE'VE
HEARD SEVERAL TIMES BEFORE
TODAY, THAT THERE'S BIOLOGICAL
RISK, THERE ARE POSSIBLE
ACQUIRED RISK FACTORS RELATED TO
OUR EXPOSURES, AND THERE'S
GENETIC VARIANTS THAT REALLY
COME TOGETHER TO CREATE
DIFFERENT ENDOPHENOTYPES,
INFORMING CARDIOVASCULAR
PATHOPHENOTYPE.
FOR ANYONE THAT DOESN'T BELIEVE
IN THE INTERACTION BETWEEN, SAY,
LIFESTYLE, EXPOSURES, GENETICS
AND HOW THEY MODIFY EACH OTHER,
THERE WAS A REALLY NICE ANALYSIS
DONE WHERE THEY POOLED TOGETHER
RESULTS FROM 50,000 PARTICIPANTS
FROM THREE STUDIES AND DEVELOPED
A POLYGENIC RISK SCORE FROM 50
SNPs ASSOCIATED WITH CORONARY
ARTERY DISEASE AND HAD DATA ON
LIFESTYLES, SO LOOKED AT TOBACCO
USE, WEIGHT, BMI, PATIENT DIET
AND PHYSICAL ACTIVITY, AND
CONSTRUCTED WHAT THEY CALLED A
LIFESTYLE SCORE.
EITHER FAVORABLE, INTERMEDIATE
OR UNFAVORABLE.
IN EACH OF THE WELL KNOWN
STUDIES, INDIVIDUALS WHO HAD
KNOWN GENETIC RISK ACTUALLY
COULD HAVE THAT GENETIC RISK
MODIFIED BY LIFESTYLE FACTORS.
SO, ALL OF THESE THINGS ARE
IMPORTANT AND THEY ALL COME INTO
PLAY.
IF YOU LOOK AT PRECISION
MEDICINE WE RECOGNIZE THAT AGAIN
PATIENTS WITH OUTWARD PHENOTYPES
SUCH AS HYPERTENSION WHO
APPEARED THE SAME ARE ACTUALLY A
MIXED GROUP OF INDIVIDUALS.
AND IF WE GO FORWARD AND WE DO
DEEP PHENOTYPING AND COLLECT A
LOT OF DATA, WE CAN THEN ANALYZE
IN MANY WAYS AND, AGAIN, THIS IS
JUST SHOWING A NETWORK ANALYSIS
BUT WE CAN USE THIS TO
SUBCATEGORIZE THE PATIENTS INTO
DIFFERENT CLUSTERS MORE ALIKE,
THIS MAY BE HELPFUL IN TERMS OF
IDENTIFYING BIOMARKERS FOR
MEDICATION DISTRIBUTION OR FOR
TAILORING DIFFERENT EXPOSURES
FOR THE PATIENTS.
THEY ALSO WILL HELP INFORM
ENROLLMENT IN CLINICAL STUDIES.
THE ISSUE THAT COMES UP WHEN YU
TALK ABOUT PRECISION PUBLIC
HEALTH DESPITE HAVING CLUSTERED
PHENOTYPES, WE REALLY WANT TO
KNOW WHAT CAN WE FIND OUT ABOUT
AN INDIVIDUAL PATIENT OR PERSON,
SO WHAT CAN WE FIND OUT ABOUT
THEIR INDIVIDUAL PHENOTYPE FROM
ALL OF THIS DATA TO HELP US DO
SOMETHING FOR THEM THAT WILL
PREVENT THEM FROM DEVELOPING
HYPERTENSION.
AND SO, YOU KNOW, ALONG WITH
DATA COLLECTION, AND ALL OF THE
WAYS THAT WE CAN GO FORWARD, AND
ANALYZE ALL THESE DATA, THERE'S
REALLY INTERESTING AND NEAT WAYS
TO PUT IT TOGETHER.
I KNOW THAT EARLIER WE TALKED A
LOT ABOUT DIFFERENT A.I. MODELS.
I ALSO PUT A PLUG IN HERE FOR
NETWORK MEDICINE WHICH USES SOME
SAME METHODOLOGY BUT LINKS
TOGETHER ALL OF THESE FACTORS.
THIS LOOKS LIKE REALLY AN IMAGE
OF HAIRBALLS THESE ARE
INDIVIDUAL PERSON-SPECIFIC
NETWORKS.
YOU CAN SEE HOW THEY LOOK
DIFFERENT.
THIS IS FROM 18 PATIENTS WHO ALL
HAVE THE SAME OUTWARD PHENOTYPE,
IN THIS CASE HYPERTROPHIC
CARDIOMYOPATHY.
NONE OF THEM HAD ANY OF THE
KNOWN MUTATIONS THAT WERE
ASSOCIATED WITH THE DISEASE.
AND IF YOU LOOK AT THE NETWORKS,
THEY ALL LOOK QUITE DIFFERENT.
SO, THERE'S VERY INTERESTING
WAYS TO FIND OUT THINGS ABOUT
INDIVIDUALS.
NOW, WE TALKED EARLIER ALSO
ABOUT DATA COLLECTION.
WHEN WE SEE OUR PATIENTS, WE
COLLECT A CERTAIN AMOUNT OF
DATA, AND THIS MAY BE COLLECTED
EITHER AT THE INITIAL
PRESENTATION OR OVER TIME, BUT
YOU CAN EITHER LOOK AT THIS
SAYING WE DO SEVEN OR EIGHT
TESTS, HISTORY AND PHYSICAL,
BLOOD WORKS, OR HAVE SEVEN OR
EIGHT TESTS, APPROXIMATELY 2,000
VARIABLES TO TRY TO UNDERSTAND
WHO IS AT RISK FOR DEVELOP
HYPERTENSION.
WE SEND ROUTINE LAB WORK, BUT
THE ROUTINE LAB WORK CAN
ACTUALLY, YOU KNOW, ALSO BE
INFORMATIVE, AND THIS CAN
ACTUALLY SERVE AS A LIQUID
BIOPSY.
SO, WHEN YOU CAN'T GET TISSUE,
WHEN YOU NEED TO PREDICT
SOMETHING THAT WILL HAPPEN IN
THE FUTURE, YOU CAN ACTUALLY USE
THESE SAMPLES FOR OMICS, WHICH
WAS AGAIN DISCUSSED EARLIER, AND
THESE TYPES OF STUDIES ARE
BECOMING MORE PREVALENT OWING TO
THE EASE AND ACCESSIBILITY OF
THE PLATFORMS, THE DECREASE IN
COST OVER TIME, INCREASE IN
SENSITIVITY, AND THE FACT THAT
THEY ARE BECOMING MORE
REPRODUCIBLE.
THERE'S AN IDEA HERE THAT
THERE'S A LOT OF DATA BUT WE
DON'T KNOW THE MINIMUM AMOUNT OF
DATA THAT WE NEED TO ACTUALLY
GET THE INFORMATION WE WANT.
AND THAT IS TO BE ABLE TO
PREDICT WHO WILL DEVELOP
INCIDENT HYPERTENSION, AND
SPECIFICALLY WHICH INDIVIDUALS
ARE AT RISK FOR IT.
SO WHILE WE'RE TALKING ABOUT
USING A.I. TO, YOU KNOW, EITHER
PREDICT WHO WILL DEVELOP
DISEASE, WHO HAS DISEASE, ET
CETERA, WE ALSO NEED TO
RECOGNIZE THAT OUR PATIENTS ARE
ACTUALLY AT HOME DOING EXACTLY
THE SAME THING, BUT IN A
DIFFERENT WAY.
AND THIS WAS AN INTERESTING
STUDY THAT WAS DONE IN DECEMBER
OF LAST YEAR, WHERE A NUMBER OF
INVESTIGATORS PUT TOGETHER A
25-QUESTION ASSESSMENT, LOOKING
AT TYPICAL QUESTIONS PATIENTS
COULD ENTER AT -- ONLINE TO GET
INFORMATION ABOUT PREVENTING
CARDIOVASCULAR DISEASE.
THEY ASKED THESE QUESTIONS OF A
CHATBOT, THREE DIFFERENT TIMES,
AND HAD CARDIOLOGISTS SCORE THE
ANSWERS.
AND SO WHAT THEY FOUND WHEN THEY
DID THIS IS 85% OF THE TIME THE
RESPONSES WERE APPROPRIATE, BUT
THAT MEANS 15% OF TIME OUR
PATIENTS WEREN'T GETTING THE
RIGHT INFORMATION.
OR AT LEAST WHAT WE THOUGHT WAS
NOT THE RIGHT INFORMATION.
WE NEED TO REMEMBER THAT WE'RE
NOT THE ONLY ONES USING THESE
TOOLS TO UNDERSTAND HOW TO
PREVENT DISEASE OR GET
INFORMATION ABOUT IT.
NOW, WHEN TALKING ABOUT USING
A.I. TO PREDICT INCIDENT
HYPERTENSION, PRECISION PUBLIC
HEALTH APPROACH TO HYPERTENSION,
THIS IS A VERY INTERESTING AREA
BECAUSE THERE HASN'T BEEN A LOT
OF WORK DONE HERE.
AND THIS IS A TERRITORY WIDE
OPEN FOR FURTHER INVESTIGATION.
SO AMONG, YOU KNOW, ONE OF THE
FEW STUDIES RELATED TO THIS AREA
WAS A STUDY THAT WAS DONE IN
JAPAN, WHERE ALL EMPLOYERS
ACTUALLY HAVE TO GIVE FREE
ANNUAL PHYSICALS TO ALL OF THE
EMPLOYEES.
THESE INVESTIGATORS TOOK 20,000
PATIENTS, AND THEY LOOKED AT
BLOOD PRESSURE LEVELS AT THE
INITIAL EXAM, AND THEN THEY
LOOKED TWO YEARS OUT.
AND SO THEY CREATED A MODEL TO
FIGURE OUT WHO WAS GOING TO
DEVELOP INCIDENT HYPERTENSION.
SO, IN THEIR DERIVATION COHORT
THEY HAD A VERY NICE AREA UNDER
THE CURVE.
IN THE VALIDATION COHORT, NOT
QUITE AS HIGH.
BUT WHEN THEY WENT TO LOOK AT
PREDICTORS OF WHAT -- FROM ALL
THE DATA THEY ENTERED, WHAT WERE
THEIR TOP PREDICTORS OF BLOOD
PRESSURE, IT TURNED OUT THAT
SYSTOLIC BLOOD PRESSURE WHEN
THEY WERE MAKING MEASUREMENTS
FOR AORTIC STIFFNESS, A YEAR
BEFORE, WAS REALLY THE BEST
PREDICTOR.
KEEP THAT IN MIND AS WE TALK
ABOUT OTHER STUDIES.
ANOTHER ONE LOOKED AT ELECTRONIC
HEALTH REPORT DATABASE FROM THE
STATE OF MAINE.
AND THIS WAS INTERESTING BECAUSE
THEY HAD ACCESS TO ABOUT 900,000
CASES WITH DATA.
THEY USED THIS TO DEVELOP A
MODEL THAT INCLUDED OVER 1500
VARIABLES, AND AMONG THESE
VARIABLES HAD SOCIOECONOMIC
INDICATORS, WHICH I THOUGHT WAS
INTERESTING.
AND THEN THEY CREATED A
PROSPECTIVE COHORT OF
INDIVIDUALS AND THEY FOLLOWED
THEM TO CLASSIFY THEM AS BEING
AT VERY LOW RISK FOR DEVELOPING
HYPERTENSION WITHIN A YEAR UP TO
BEING VERY HIGH RISK FOR
DEVELOPING HYPERTENSION.
SO WHEN THEY TESTED THEIR MODEL
THEY FOUND OUT FOR THE VERY LOW
RISK PATIENTS, ONLY ABOUT 1
PERCENT OF THOSE INDIVIDUALS
WERE MISCLASSIFIED AND DID GO ON
TO DEVELOP HYPERTENSION, AND
AMONG INDIVIDUALS IN THE VERY
HIGH RISK GROUP ABOUT 50% OF
THOSE INDIVIDUALS DID DEVELOP
HYPERTENSION WITHIN A YEAR.
SO THE MODEL TO --
[ NO AUDIO ]
TO PREDICT INCIDENT HYPERTENSION
IN THE LOW RISK GROUP AND HIGH
RISK GROUP, IF YOU LOOK AT THE
VERY LOW RISK GROUP, WHEN THEY
LOOKED AT INDIVIDUALS AND
STRATIFIED THEM BY PREEXISTING
CONDITIONS, SO WHAT THEY CALL
PRE-HYPERTENSION, DIABETES, EVEN
MENTAL ILLNESS, ABOUT 85% OF
INDIVIDUALS DID NOT DEVELOP
HYPERTENSION WITHIN THE COURSE
OF THE NEXT YEAR.
WHEN YOU LOOK AT THOSE IN THE
VERY HIGH RISK CATEGORY, WITH
ALL OF THESE OTHER CONDITIONS,
ABOUT 55% OF THEM DEVELOPED
HYPERTENSION WITHIN A YEAR.
SO REALLY HAVING ONE COMORBID
CONDITION IN THE HIGH RISK
CATEGORY WAS IMPORTANT.
WHEN THEY ACTUALLY DIVIDED THIS
UP INTO INDIVIDUALS WHO HAD AT
LEAST ONE CHRONIC DISEASE SHOWN
ON THE RIGHT VERSUS NONE AND
STRATIFIED BY MALE VERSUS
FEMALE, AGE, ET CETERA, YOU CAN
SEE THAT AMONG PATIENTS WHO HAD
NO CHRONIC DISEASES, YOU KNOW,
THEY TENDED TO DO FAIRLY WELL
OVER THE COURSE OF THE YEAR.
REALLY WITH THE EXCEPTION OF
INDIVIDUALS WHO HAD DEPRESSION,
OR ADVANCING AGE.
SO, WE ALREADY KNOW AGE FACTORS
IN.
BUT THE FACT THE DEPRESSION
DOUBLES YOUR RISK OF DEVELOPING
HYPERTENSION WAS INTERESTING.
AMONG PATIENTS WHO HAD AT LEAST
ONE CHRONIC -- ONE DIAGNOSIS OF
CHRONIC DISEASE THEIR RISK OF
DEVELOPING HYPERTENSION OVER THE
COURSE OF THE YEAR REGARDLESS OF
THE MALE/FEMALE AND AGE LEVEL
WAS STILL PRETTY HIGH.
WE CANNOT FORGET ABOUT THE
SOCIAL DETERMINANTS OF HEALTH
BECAUSE USING THE SPEARMAN RANK
CORRELATION, THEY WERE ALSO ABLE
TO SHOW EDUCATION, INSURANCE
COVERAGE, SALARY, AND
AVAILABILITY OF LOCAL MARKETS
AND STORES AND GREEN SPACES FOR
EXERCISE ALL WERE IMPORTANT FOR
DETERMINING THE RISK TO INCIDENT
HYPERTENSION.
AND FINALLY, ANOTHER GROUP
ACTUALLY ASKED THE QUESTION THAT
I POSED EARLIER, WHICH IS WHAT
CAN YOU FIND OUT WHEN YOU HAVE
JUST A MINIMAL AMOUNT OF
INFORMATION THAT YOU'RE PUTTING
INTO YOUR MODEL?
AND SO IN THIS STUDY THEY TOOK A
NUMBER OF INDIVIDUALS, THIS WAS
DONE IN BEIJING, AND, YOU KNOW,
ASKED JUST QUESTIONS ABOUT THE
CLINICAL VARIABLES ON THE LEFT.
SO TYPICAL AGE, BMI, WAIST
CIRCUMFERENCE, FAMILY HISTORY,
HOW MUCH STRESS YOU'RE UNDER.
WHEN YOU LOOK AT THE PREDICTION
OF INCIDENT HYPERTENSION THEY
RAN THIS THROUGH FOUR MODELS AND
GOT DIFFERENT PERFORMANCE
RESULTS FOR EACH ONE WHICH I
THOUGHT WAS, AGAIN, ALSO
INTERESTING AND SPEAKS TO THE
FACT THAT WE NEED TO REALLY PAY
ATTENTION TO METHODOLOGY.
BUT THEY ALSO WERE ABLE TO SHOW
IN THIS STUDY THAT WHEN YOU
LOOKED AT THE FEATURE IMPORTANCE
FOR RANKING VARIABLES BMI CAME
OUT HIGHER THAN AGE, YOU KNOW,
FAMILY HISTORY, WAIST
CIRCUMFERENCE AND OTHER THINGS
WE KNOW ARE RELATED TO RISK FOR
INCIDENT HYPERTENSION.
SO NOW WE'VE GOT, YOU KNOW,
SEVERAL STUDIES THAT TELL US
SOMETHING A LITTLE BIT DIFFERENT
WITH EACH STUDY.
SO, THIS ALSO SPEAKS TO THE IDEA
OF REPRODUCIBILITY, WHAT WENT
INTO THE MODEL, HOW GOOD WERE
THE MODELS, AND WHAT CAN WE
LEARN FROM THEM.
WE HAVE REALLY PROMISING SCIENCE
IN THIS AREA, AND THIS CONCEPT
OF MAKING PROGRESS IN DEEP
PHENOTYPING AND FINDING WAYS TO
INCLUDES ALL OF THESE DATA,
INCLUDING NETWORK MEDICINE FOR
ANALYSIS, IS REALLY PROGRESSING.
THE FACT THAT PEOPLE ARE
FOCUSING ON INCIDENT DISEASE AND
DISEASE INCEPTION AS OPPOSED TO
JUST TREATING ESTABLISHED
DISEASE IS ALSO INCREDIBLY
IMPORTANT.
THEN MANY OF MY OTHER COMMENTS
HERE, WHAT DO WE NEED TO MOVE
THE FIELD FORWARD AND WHAT'S
HINDERING US ARE COMMON THEMES
THAT PEOPLE HAVE SPOKEN ABOUT
BEFORE.
I WOULD JUST ADD HERE THAT ONE
OF THE THINGS WE NEED TO FIGURE
OUT IS WHEN -- WHAT IS REALLY
THE SO-CALLED GO/NO-GO TIME
POINT FOR DISEASE INCEPTION
PROCESS WHERE WE CAN'T INTERVENE
AND CAN WE INTEGRATE ALL THESE
THINGS TOGETHER, PRECISION
PUBLIC HEALTH, NETWORK MEDICINE,
A.I. FOR RISK PREDICTION, AND
THEN KEY CHALLENGES AND BARRIERS
REALLY COME DOWN TO WIDESPREAD
ACCEPTANCE, STANDARDIZING DATA
COLLECTION, CURATION, SECURITY,
PRIVACY, AND DEVELOPING THE
SOLID PARTNERSHIPS BETWEEN THE
STAKEHOLDERS AND REALLY GETTING
BACK TO PUBLIC ACCEPTANCE,
ACCEPTANCE FROM, YOU KNOW, THE
PATIENTS THAT WE WOULD LIKE TO
BE WORKING WITH, TO HELP IRON
THIS OUT AND BRING PRECISION
PUBLIC HEALTH TO THE FOREFRONT.
SO THANK YOU VERY MUCH.
>> THANK YOU, DR. LEOPOLD.
I HAVE SOME QUESTIONS FOR THE
DISCUSSION PERIOD.
NEXT SPEAKER IS DR. BULL,
MASTER'S IN PUBLIC HEALTH,
TULANE UNIVERSITY SCHOOL OF
PUBLIC HEALTH AND ENTREPRENEUR
-- TROPICAL
MEDICINE, Ph.D. IN SOCIOLOGY.
I FORGOT TO UNMUTE MYSELF.
I'LL START OVER.
I'D LIKE TO INTRODUCE DR. BULL,
WHO RECEIVED MASTER'S IN PUBLIC
HEALTH, TULANE UNIVERSITY,
Ph.D. IN SOCIOLOGY FROM
GEORGIA STATE UNIVERSITY.
SHE IS A FOUNDING FACULTY MEMBER
IN THE COLORADO SCHOOL OF PUBLIC
HEALTH, 2006.
CURRENTLY HOLDS POSITION OF
PROFESSOR OF COMMUNITY AND
BEHAVIORAL HEALTH.
WELCOME.
YOU'RE GOING TO TALK ABOUT
A.I.-ENABLED HEALTHBOTS.
>> CAN YOU HEAR ME AND SEE MY
SLIDES?
>> I CAN HEAR YOU.
I SEE YOUR SLIDES AT THE MOMENT.
>> GREAT.
>> IT'S IN PRESENTER MODE FYI.
>> OKAY.
>> THERE'S AN ICON, BOTTOM
RIGHT.
>> HOW'S THAT?
STILL PRESENTER?
I'M GOING TO TRY DOING A PDF.
HOW'S THAT?
>> LOOKS GOOD TO ME.
>> I THINK SOMETHING PUT THEM
UP, AND MIGHT ADVANCE THEM FOR
ME.
IS THAT CORRECT?
>> THAT'S CORRECT.
WE'LL SHARE THE SLIDES FOR YOU.
JUST TELL US NEXT SLIDE PLEASE.
>> GREAT, THANK YOU.
IT'S LOVELY TO BE HERE TO TALK
ABOUT HEALTHBOTS TO SUPPORT
PATIENT SELF-MANAGEMENT.
DR. LEOPOLD SET ME UP NICELY FOR
THIS TALK.
BEFORE I GO ON, I WANTED TO MAKE
ACKNOWLEDGMENTS.
SOME WORK IS FUNDED BY AN NHLBI
STUDY, THE NUDGE STUDY, ALL THE
FOLKS LISTED ON THIS SLIDE ARE
COLLABORATORS ON THAT STUDY.
SOME WORK I'M TALKING ABOUT IS
FUNDED BY THE COLORADO
DEPARTMENT OF PUBLIC HEALTH.
TODAY TALKING ABOUT LEVERAGING
TO MANAGE CHRONIC CARDIOVASCULAR
CONDITIONS, ALSO CHALLENGES THAT
WE FACE IN THE NEAR TERM FOR
LEVERAGING A.I. TO SUPPORT
PATIENTS.
FINALLY ENDING WITH OPTIMIZING
OPPORTUNITIES FOR OUR
A.I.-ENABLED CHAT BOTS.
NEXT SLIDE PLEASE.
I WON'T SPEND A LOT OF TIME.
EVERYONE KNOWS THIS CONTEXT BUT
I WANT TO UNDERSCORE THREE
POINTS.
WE'VE HEARD A LOT TODAY ABOUT
THE PROBLEM OF CARDIOVASCULAR
ILLNESS.
WE'VE HEARD ABOUT DISPARITIES
AND THE NEED TO PAY GREATER
ATTENTION TO THOSE.
WE'VE ALSO HEARD ABOUT THE
OPPORTUNITIES THAT WE HAVE FOR
PATIENTS TO ENGAGE IN
SELF-MANAGEMENT, STRONGLY
EVIDENCE-BASED, AND EFFECTIVE.
WHAT IS THE OPPORTUNITY?
WE HAD DISCUSSION ON EARLIER
PANEL ABOUT THINGS LIKE ACCESS.
SO I WILL SHARE THAT, YOU KNOW,
EVERYONE KNOWS THE AVAILABILITY
OF MOBILE PHONES AND POPULARITY
OF TEXT MESSAGING IS BECOMING
ALMOST UNIVERSAL.
DESPITE LIMITATIONS IN PEOPLE'S
ENTHUSIASM FOR TEXT MESSAGING,
WE'VE BEEN ABLE TO SEE THAT IT'S
A VERY EFFECTIVE WAY TO REACH
OUT TO PEOPLE AND MEETS THEM
WHERE THEY ARE.
THEY ARE USED TO USING THESE
SYSTEMS.
MORE PATIENTS HAVE ACCESS TO THE
INTERNET AND USE PATIENT
PORTALS.
THIS IS AN AREA WHERE I DO
RECOGNIZE THERE'S STILL
REMAINING A DECENT LEVEL OF
DIGITAL DIVIDE, ALTHOUGH THAT IS
SHRINKING, EVEN IN LOWER AND
MIDDLE INCOME SETTINGS PEOPLE
USE PHONES TO ACCESS
COMMUNICATION THROUGH THE
INTERNET.
AND THIRD, PEOPLE ARE WELCOMING
FOR FAMILIAR, SEEING THEM WHEN
THEY DO SHOPPING ONLINE, SEEING
THEM IN PROVIDER PORTALS.
THIS IS AN EXAMPLE OF A TEXT
MESSAGE THAT'S GENERIC, THAT CAN
BE SENT OUT.
AND THROUGH A TELEPHONE, AND WE
HAVE GOOD EVIDENCE FROM RESEARCH
THAT THIS APPROACH DOES WORK TO
IMPROVE MEDICATION ADHERENCE, OR
PATIENT CONSISTENCY KEEPING
APPOINTMENTS, FOR EXAMPLE.
NEXT SLIDE PLEASE.
SO, OUR OWN WORK IN THE NUDGE
STUDY LOOKED AT WHETHER OR NOT
WE COULD OPTIMIZE MESSAGES AND
MAKE THEM MORE BIDIRECTIONAL AND
ENGAGING.
SO THIS IS AN EXAMPLE OF A FIRST
GENERATION BOT, NOT AN
A.I.-ENABLED BOT, BUT FROM OUR
PRELIMINARY ANALYSES OF THE
NUDGE DATA WE SEE THIS APPROACH,
THE INTERACTIVE FEATURE, AS WELL
AS OPTIMIZED MESSAGE CONTENT
I'LL TALK ABOUT IN A MOMENT,
THOSE TWO APPROACHES ARE
SIGNIFICANTLY SUPERIOR TO THE
GENERIC MESSAGING FOR INCREASING
ADHERENCE TO MEDICATION REFILL
FOR CARDIOVASCULAR MEDICATIONS.
AND SO THEREFORE WE CAN SEE
HIGHER PROPORTION OF DAYS
COVERED, FOR MEDICATIONS, AND
IMPROVED PATIENT SELF-MANAGEMENT
BEHAVIORS.
NEXT SLIDE PLEASE.
NOW WE HAVE THE OPPORTUNITY TO
LEVERAGE NATURAL LANGUAGE
PROCESSING AS WELL AS MACHINE
LEARNING FOR MORE ENGAINING AND
DYNAMIC EXPERIENCE, SO PEOPLE
AREN'T FORCED INTO
FIRST-GENERATION TYPE BOTS.
THIS IS AN EXAMPLE OF HOW THAT
MIGHT WORK.
IT USES SYSTEM DIALOGUE
CLASSIFICATION, SOMETHING WE'RE
USING RIGHT NOW.
AND THE PERSON CAN EITHER
RESPOND TO A PUSH NOTIFICATION
THAT THEY GET, OR THEY CAN
INITIATE A CONVERSATION.
AND WHAT HAPPENS FIRST IS THEY
GET TO USE THEIR OWN WORDS AND
THEIR OWN -- AND TALK ABOUT
THEIR OWN PREFERRED TOPIC.
THE SYSTEM CAN USE NATURAL
LANGUAGE PROCESSES TO CLASSIFY
AND PUT BACK A RESPONSE THAT
MATCHES THE INTENT OF THEIR
QUESTION.
THE MACHINE LEARNING COMES ABOUT
WHEN THAT CLASSIFICATION DOESN'T
HAPPEN CONSISTENTLY, WE CAN
RECLASSIFY SO THE NEXT TIME
SOMEBODY ASKS A QUESTION IN THAT
VEIN IT CAN BE RECLASSIFIED SO
THAT IT IS CONTINUALLY UPDATED
WITH MORE USERS.
NEXT SLIDE PLEASE.
SO, THIS IS FROM A STUDY THAT
CAME OUT LAST YEAR, AROUND THIS
TIME, LOOKING AT A REVIEW OF
CONVERSATIONAL AGENTS, CHAT
BOTS, IN PERSON-CENTERED CARE.
IN GENERAL FOUND THAT VERY FEW
OF THESE ARE TRULY OPTIMIZED
WITH THE FEATURES THAT WE KNOW
CAN WORK.
NEXT SLIDE PLEASE.
THEY LOOKED AT 78 DIFFERENT
CHATBOTS, PRIMARY CARE AND
MENTAL HEALTH.
FEWER THAN 10% HAD THEORETICAL
FRAMEWORK.
WE USED THESE TO IMPROVE EFFECT
SIZE AND HAVE GREATER PRECISION
IN TARGETING HEALTH BEHAVIORS
AND OUTCOMES.
ONLY 12% WERE HIPAA COMPLIANT,
LIKELY A NON-STARTER FOR
PROVIDERS AND PROVIDER CARE
SYSTEMS.
AND ONLY A FEW ARE TRULY USING
MACHINE LANGUAGE AND -- MACHINE
LEARNING AND NATURAL LANGUAGE
PROS EGGS.
I'LL TALK ABOUT GAPS WE HAVE IN
OUR KNOWLEDGE FOR A.I. HEALTH
BOTS, THREE I WANT TO FOCUS ON.
THE FIRST IS RELATED TO OUR
TECHNOLOGY, INFRASTRUCTURE AND
SYSTEM.
ONE ISSUE THEY WOULD LIKE TO SEE
SOME INTEGRATION WITH THEIR
SYSTEMS TO HELP WITH WORKFLOW.
WE NEED TO HAVE PROVIDER AND
PATIENT-FACING SYSTEMS.
AND THEN THE THIRD WE'VE TALKED
ABOUT SEVERAL TIMES TODAY,
INTEROPERABILITY, FHIR-ENABLED
TO LINK TO OTHER CHAT BOTS
SYSTEMS ARE WORKING WITH.
NEXT SLIDE PLEASE.
SO IN OUR OWN SYSTEM WHAT WE'RE
SEEING, WHAT WE'RE USING RIGHT
NOW, ADDRESSES ALL OF THESE.
ON THE LEFT WHAT YOU SEE THERE
IS THE USER COULD INITIATE A
QUESTION, WE'RE USING THE
MACHINE LANGUAGE PROCESSING TO
GET A RESPONSE.
ALL OF THOSE COMMUNICATIONS ARE
DOCUMENTED IN A LOG.
HERE IS WHERE HIPAA COMPLIANCE
COMES IF.
IF WE'RE COLLABORATING WITH A
HEALTH CARE DELIVERY SYSTEM, FOR
EXAMPLE, AND IT'S GOING THROUGH
A TEXT MESSAGE ON THE PHONE,
WHICH MIGHT BE PREFERRED FOR THE
PATIENT, THAT TELEPHONE NUMBER
IS PHI, PROTECTED HEALTH
INFORMATION.
SO THAT NEEDS TO EITHER BE --
MAKE SURE THE LOG IS STORED
BEHIND A FIRE WALL, AND THE
TELEPHONE NUMBERS CAN GET
ENCRYPTED OR THERE COULD BE DATA
USE AGREEMENTS, BUT THERE'S WAYS
TO ADDRESS HIPAA COMPLIANCE.
YOU HAVE TO PAY CLOSE ATTENTION
TO THEM.
THE SECOND ISSUE OF PATIENT AND
PROVIDER-FACING CONTENT, SO WHEN
WE WORK WITH PROVIDERS THEY TELL
US AGAIN AND AGAIN THEY DO NOT
WANT TO BE THE PERSON WHO LOOKS
THROUGH THE LOG AND SEES EVERY
SINGLE EXCHANGE WITH THE
CHATBOT.
THAT'S WAY TOO MUCH FOR THEM.
THEY WANT THESE THINGS TO HELP
THEM WITH WORKFLOW, NOT IMPEDE
THEIR WORKFLOW.
SO, THERE'S TWO WAYS THAT WE'RE
WORKING ON THAT.
THE FIRST IS TO IDENTIFY WHAT
ARE KEY PIECES OF INFORMATION
THAT PROVIDERS DO WANT, AND
GENERATE AN ALGORITHM TO PUSH
OUT THAT INFORMATION WHEN IT
BECOMES APPARENT.
SUCH AS WHEN WE KNOW THAT A
PATIENT HAS NOT FILLED OR IS
INDICATING THAT THEY DON'T PLAN
TO.
OR THEY HAVE GONE TO AN URGENT
CARE FACILITY.
ON THE OTHER THING ON THE TOP
RIGHT IS GENERATION OF
DASHBOARDS THAT ARE REALTIME
THAT PROVIDERS CAN TAKE A LOOK
AT TO SEE HOW MANY PEOPLE ARE
USING THE SYSTEM, WHAT ARE THE
PRIMARY QUESTIONS THAT PEOPLE
HAVE, WHAT ARE THE TIMES OF DAY
AND DAYS OF THE WEEK, WHEN THE
SYSTEM USES HIGHEST VOLUME.
THE THIRD ISSUE FOR
INFRASTRUCTURE IS RELATED TO
INTEROPERABILITY.
LOTS OF IDEAS CAME THROUGH THE
TALKS TODAY.
IF SOMEBODY IS WEARING A
WEARABLE DEVICE AND INFORMATION
SUGGESTS THAT THEY NEED TO BE
GIVEN AN ALERT, THAT COULD BE
PUSHED TO THEM THROUGH A CHATBOT
AND THEY COULD ENGAGE WITH THE
SYSTEM TO GET THEIR QUESTIONS
ANSWERED.
THIS IS ALSO AN EXAMPLE OF
INTEROPERABILITY WITH
SCHEDULING.
THEY WOULDN'T HAVE TO LEAVE THE
CHATBOT IF THE SYSTEM COULD
IDENTIFY IMMEDIATELY PLACES THY
COULD GET AN APPOINTMENT OR
MEDICATION.
THE OTHER IS RELATED TO CONTENT.
WE'VE HEARD ABOUT SYSTEMS WHERE
YOU COULD HAVE COMMUNICATION,
NOT JUST WITH THE A.I. BUT ALSO
WITH TEXT MESSAGING PROGRAMS OR
OTHER HEALTH BEHAVIOR TYPES OF
INITIATIVES WE'VE LEARNED FROM
OUR OWN RESEARCH AND RESEARCH
ACROSS MANY COLLEAGUES IN THE
FIELD THAT WHEN YOU HAVE
OPTIMIZED AND THEORY BASED
MESSAGING THAT USES HEALTH
COMMUNICATION AND BEHAVIOR
CHANGE THEORY WE CAN DO BETTER
AND CAN IMPACT NOT JUST HEALTH
BEHAVIOR BUT HEALTH OUTCOMES.
SO CHANGES IN THINGS LIKE
COMPLIANCE WITH CHILDHOOD
VACCINES OR HIV TESTING, FOR
EXAMPLE.
WE NEED TO MAKE SURE THAT
MESSAGES ARE CULTURALLY RELEVANT
AND TAILORED, AND THEY FOCUS ON
DISPARITIES.
I'M GOING TO COME BACK TO THAT.
PLEASE GO TO THE NEXT SLIDE AND
I'LL SET THE STAGE FOR THAT.
WE'VE HEARD NOT JUST TODAY BUT
AM AP LOT -- A LOT ABOUT CHAT
GPT.
A GROUP DID THIS LAST MONTH,
LOOKED AT CHAT GPT RESPONSES
AROUND CARDIOVASCULAR DISEASE
PREVENTION RECOMMENDATIONS.
THEY FOUND THAT THE SYSTEM DOES
HAVE PROMISE, IT LACKS PRECISION
AND CORRECTNESS IN 16% OF
RESPONSES WHICH COULD BE
PROBLEMATIC.
I THINK THAT MIGHT BE A
NON-STARTER FOR MANY.
AND THEY ALSO HAVE LIMITED
READABILITY AND NO CONSIDERATION
OF DIVERSITY.
NEXT SLIDE PLEASE.
I'M GOING TO COME BACK TO THAT
ISSUE OF CULTURAL
APPROPRIATENESS AND DISPARITIES.
WE'VE HEARD LOTS TODAY ABOUT
SOCIAL DETERMINANTS OF HEALTH.
HERE'S WAYS THAT WE'RE TRYING TO
ADDRESS THAT IN A WAY TO MAKE
SURE THAT MESSAGES CAN RESONATE
AND PEOPLE WILL STAY ENGAGED
WITH SYSTEMS.
SO WE'VE GOT EXAMPLES HERE OF
WHAT WE'RE TRYING TO DO WHICH IS
TO PUSH OUT MESSAGES AND ASSESS
WHAT ARE SPECIFIC SOCIAL
DETERMINANTS OF HEALTH THAT
PEOPLE ARE STRUGGLING WITH SAY
IN AREAS OF HOUSING OR FOOD OR
TRANSPORTATION.
AND THIS TOP LEFT MESSAGE
SUGGESTS THAT THE USER MAY HAVE
DIFFICULTY WITH FOOD, PROMPTING
REFERRAL TO FOOD BANK OR
RESOURCES FOR LOW COST FOOD.
WE SEE AN EXAMPLE WHERE THE
PATIENT MIGHT EXPLAIN THAT
MEDICATION COST IS AN ISSUE.
AND WE CAN GIVE THEM REFERRALS
TO GET LOWER COST MEDICATIONS.
AND THEN FINALLY ANOTHER EXAMPLE
OF INTEROPERABILITY, WE CAN LINK
PATIENTS TO SCHEDULE AN
APPOINTMENT TO TALK TO A
PROVIDER OR HAVE A TELEHEALTH
VISIT.
OR PHONE CALL THAT ALLOWS THEM
IN THIS CASE TO TALK TO A
PHARMACIST.
SO THAT THEY CAN IMMEDIATELY GET
SUPPORT FOR WHAT THE THINGS THAT
THEY NEED.
NEXT SLIDE PLEASE.
THIRD AREA OF GAP IN KNOWLEDGE
IS RELATED TO TEAM SCIENCE, ALSO
CAME UP TODAY.
NEED FOR INTEGRATED TEAMS, NOT
JUST COMPUTER SCIENTISTS AND
CLINICIAN EXPERTS BUT ALSO
SOCIAL SCIENTISTS FOR CONTENT
DESIGN AND THEN THINK MORE
BROADLY ABOUT HEALTH ECONOMISTS,
WHO CAN TALK ABOUT COSTS, AND
COLLECTIVELY THESE GROUPS SHOULD
BE WORKING ON QUESTIONS THAT ARE
EXPLORING SCALABILITY AS WELL AS
IMPLEMENTATION AND ON HEALTH
OUTCOMES AT THE POPULATION
LEVEL.
NEXT SLIDE PLEASE.
IN CONCLUSION I'M GOING TO
SUGGEST PROMISING AREAS FOR
ADVANCEMENT WOULD BE TO DEVELOP
A.I. HEALTH BOTS THAT HAVE THESE
SIX FEATURES UTILIZE NATURAL
LANGUAGE PROCESSING, WIDELY
ACCESSIBLE ACROSS DIFFERENT
PLATFORMS.
THEY NEED TO BE HIPAA COMPLIANT.
IT'S NOT A BIG HURDLE.
THEY SHOULD OFFER LINKAGE TO
CARE AND INTEROPERABILITY WITH
OTHER TYPES OF SYSTEMS THAT
WE'VE TALKED ABOUT TODAY.
THEY SHOULD OFFER PERSUASIVE
COMMUNICATIONS AND WE CAN
CONTROL COMMUNICATION WITH THE
CONTENT THAT WE KNOW WILL BE
EFFECTIVE IN IMPACTING HEALTH
BEHAVIOR AND HEALTH OUTCOMES.
FINALLY THEY SHOULD BE
ADAPTABLE.
IF YOU WOULD BE INTERESTED IN
COLLABORATING ON DEVELOPING OR
EVALUATING THOSE I WOULD LOVE TO
HEAR FROM YOU.
THANKS VERY MUCH FOR YOUR TIME.
>> THANK YOU VERY MUCH, DR.
BULL.
APPRECIATE IT.
I'VE BEEN MONITORING THE CHAT.
THERE'S A LOT OF GOOD DISCUSSION
FOR THE DISCUSSION PANEL.
I'D LIKE TO INTRODUCE DR. KHERA
FROM YALE UNIVERSITY, LEADS THE
CARDIOVASCULAR DATA SCIENCE LAB.
ALSO CLINICAL DIRECTOR FOR
CENTER FOR HEALTH INFORMATICS
AND ANALYTICS AT YALE CENTER FOR
OUTCOMES RESEARCH AND
EVALUATION.
HE WILL TALK ABOUT A.I. TOP
INDIVIDUALIZE TREATMENT RESPONSE
IN HYPERTENSION.
WELCOME.
>> I'M GOING TO SHARE MY OWN
SLIDES.
>> NO PROBLEM.
>> THANK YOU.
>> OKAY.
>> PERFECT.
THANKS SO MUCH.
CAN YOU SEE MY SLIDES?
>> YES, I CAN SEE THEM, VERY
CLEAR.
>> PERFECT.
THANKS SO MUCH.
THANKS FOR HAVING ME HERE.
THANKS FOR THE KIND
INTRODUCTION.
EXCELLENT MORNING OF TALKS.
I'M PRIVILEGED TO PRESENT SMALL
COMPONENT OF WHAT WE CAN OFFER
THROUGH A.I. IN THE TREATMENT
RESPONSE OPTIMIZATION
HYPERTENSION.
THE TALK IS ABOUT
INDIVIDUALIZING TREATMENT
RESPONSE IN HYPERTENSION.
I HAVE SOME DISCLOSURES.
KEY IS NOTE IS ANY ALGORITHM I
DISCUSS IN THIS PRESENTATION IS
ONLY RESEARCH USE, NOT FOR
CLINICAL USE YET.
FOCUS MAY BE ABOUT HYPERTENSION
BUT LARGELY ABOUT PRECISION
HEALTH CARE.
THAT MEANS FOR A GIVEN
INDIVIDUAL HOW DO WE PROVIDE
MANAGEMENT THAT IMPROVES THEIR
HEALTH AND BASE THE DECISION ON
THEIR UNIQUE FEATURES.
GIVEN A LARGE NUMBER OF BIAS AND
CONFOUNDING IN REAL WORLD DATA
FINDING SIGNATURES OF RESPONSE
AND SOLUTIONS IS CHALLENGING.
RANDOMIZED TRIALS REPRESENT
LARGE STUDIES, THAT TAKE AWAY
CHALLENGES, SPECIFICALLY AROUND
BIAS AND CONFOUNDING BY
INDICATION.
THE WAY TO LEVERAGE DATA ARE
LIMITED.
FOR EVERY DISEASE, ESPECIALLY
HYPERTENSION A PROPORTION GET
ENROLLED, AND THE FINDING,
POSITIVE OR NEGATIVE, NEEDS TO
APPLY UNIFORMLY TO ALL, TAKING
AWAY DIFFERENCES.
HOWEVER THIS APPROACH IS
IMPRECISE.
MORE SPECIFICALLY, RCTs FOCUS
ON AVERAGE DIFFERENCE BETWEEN
TWO GROUPS IN THE INTERVENTION
AND CONTROL ARMS, AND THIS FORM
OF INTERPRETATION IS BEDROCK OF
SCIENCE BUT HAS LIMITATIONS.
WE HAVE TO DISCARD INFORMATION
FROM LARGE HUMAN EXPERIMENT WITH
LARGE RESOURCES INVESTED, DATA
ARE DISRECORDED.
WHAT REALLY HAPPENS IN A
CLINICAL TRIAL, SOME INDIVIDUALS
MAY BENEFIT FROM THERAPY, OTHERS
MAY NOT.
AND BY CHANCE MANY WILL
EXPERIENCE AN ADVANTAGE IN EACH
STUDY ARM, MANY HAVE TRIED TO
ADDRESS THIS IN DIFFERENT WAYS.
THE MOST COMMON AGE-OLD APPROACH
IS SUBGROUP COMPARISONS.
INDIVIDUAL SUBGROUPS CONTINUE TO
BE CLINICALLY HETEROGENEOUS
AMONG THEMSELVES, THEORETICAL
UNTIL A PUSH TO SHARE DATA LED
BY NHLBI.
MAJOR ADVANCES FROM CLINICAL
TRIALS HAVE BEEN PROMPTED BY
DATA SHARING A BioLINCC, 218
SUBMISSIONS, VARIOUS ASPECTS OF
INTERPRETING CLINICAL TRIAL DATA
AND INFERENCE BASED ON
PARTICIPANT LEVEL DATA.
MANY STUDIES SUBMITTED DURING
THE CHALLENGE SPECIFICALLY
FOCUSED ON HETEROGENEITY
ESTIMATES INCLUDING BY OUR
GROUP, MANY FOUND COMMON
PREDICTORS OF TREATMENT EFFECTS.
USING SPRINT DATA CONTINUES TO
BE OF INTEREST WITH PUBLICATIONS
WITH VARIOUS NOVEL AND EMERGING
STRATEGIES.
WHAT IS THE DIFFERENCE BETWEEN
APPROACHES TO INDIVIDUALIZING
TREATMENT EFFECT ESTIMATES?
IT CAN BE CLASSIFIED A
PROGNOSTIC VERSUS PREDICTIVE.
STUDIES I MENTIONED HAVE LARGELY
FOCUSED A PROGNOSTIC.
CENTRAL ASSUMPTION IS LARGEST
DRIVER OF EFFECT DIFFERENCES
BETWEEN INDIVIDUALS IS BASELINE
ABSOLUTE RISK FOR EFFICACY OR
SAFETY OUTCOME.
THERE ARE DIFFERENCES IN
ABSOLUTE RISK REDUCTION ACROSS
INDIVIDUALS.
THE ASSUMPTION THAT INDIVIDUALS
WITH GIVEN CHARACTERISTICS HAVE
THE SAME RISK REDUCTION MAY NOT
BE COMPLETE AND COMPREHENSIVE.
SEVERAL HIGH RISK INDIVIDUALS WE
HAVE NON-MODIFIABLE RISK BASED
ON UNIQUE CHARACTERISTICS,
PRESENTED IN THE SCHEMATIC ON
THE RIGHT LEADING TO VARIABLE
ABSOLUTE RISK REDUCTION FOR
HIGHER BASE THAN ABSOLUTE RISK.
THE VALUE CAN BE BUILT TO
IDENTIFY RISK FACTORS
INDEPENDENT OF STUDY ARM USING
THAT APPROACH TO DEFINE HIGH
POPULATIONS.
ADDITIONAL LAYER IS ADDED,
INTERVENTION AND CONTROL ARMS
ARE BUILT, DIFFERENCE IS
QUANTIFIED, ASSESSING EVEN
INTERACTION EFFECTING ON
RELATIVE RISK SCALE BUT
OPPORTUNISTIC, WHAT COMES OUT OF
DATA AND ANALYSIS, BUT NOT
MODELS.
PREDICTIVE MODELS ARE EFFECT
MODELING APPROACH EXAMINE
WHETHER A PARTICIPANT RECEIVING
INTERVENTION HAD A GREATER
REDUCTION IN THEIR ADVERSE EVENT
PROFILE THAN RECEIVING STANDARD.
WHAT WE MODEL IS EFFECT ESTIMATE
OR EFFECT SIZE.
IF WE COULD DEFINE THIS BASED ON
PATIENT CHARACTERISTICS, WE HAVE
POTENTIAL OF GENERALIZING
OBSERVATION TO PEOPLE NOT
ENROLLED?
TRIALS.
I WANT TO USE THE EXAMPLE OF THE
RECENT STUDY TO DEFINE APPROACH
OF PERSONALIZING BENEFIT AND
RISK OF TREATMENT STRATEGIES IN
HYPERTENSION.
WE'VE DONE A FEW.
HERE ADDRESSING BLOOD PRESSURE
TREATMENT USING SPRINT AND
ACCORD DATA.
I'LL LEAVE THE QR CODE UP FOR
ONE SECOND.
PERFECT.
ON THE LEFT INCLUDES PATIENTS
WITHOUT DIABETES OR PRIOR
STROKE, LARGE REDUCTION IN MAJOR
ADVERSE CARDIOVASCULAR EVENTS,
INTENSIVE VERSUS STANDARD BLOOD
PRESSURE LOWERING, SYSTOLIC OF
120 OR 140.
THE ACCORD TRIAL WITH DIABETES
OR SAME INTERVENTION, ON THE
RIGHT.
WE CAN ATTRIBUTE TRIAL
DIFFERENCES, TRIALS WERE JUST
DIFFERENT.
DOES SPRINT SUGGEST PROFILES
WHICH CAN IDENTIFY THOSE IN
ACCORD THAT BENEFIT FROM BLOOD
PRESSURE LOWERING SUCH THERE'S
INDIVIDUALIZED PROFILE TO
INTENSIVE BLOOD PRESSURE
LOWERING.
WHO DO WE STUDY HETEROGENEITY?
UNLIKE CASE CONTROL STUDIES
PEOPLE RESEMBLE EACH OTHER, A
DISTRIBUTION OF FEATURES, SO,
FOR EXAMPLE, A WOMAN WITH CKD,
140/95, A MAN WITH CKD, SIMILAR
CREATININE, LOWER BLOOD
PRESSURE, THIRD PERSON A WOMAN
WITHOUT CKD, LOWER CREATININE,
SIMILAR BLOOD PRESSURE, AND SO
FORTH ON ALL COMBINATIONS OF
FEATURES.
WHILE I SHOW THIS IN TWO
DIMENSION, IT CAN BE DONE IN ANY
DIMENSION.
A PATIENT WITH ALL
CHARACTERISTICS HAS MULTIPLE
SHARED FEATURES WITH SOME
PEOPLE, CLOSED IN THE
MULTI-DIMENSIONAL PLANE, AND
FARTER FROM OTHERS WITH FEWER
SHARED CHARACTERISTICS.
THIS HAS BEEN CHALLENGING FOR A
LONG TIME.
IF YOU TAKE THIS TRIAL AND MAKE
A PROJECTION IN ALL PHENOTYPIC
CHARACTERISTICS, FOCUS ON THE
LOCATION OF A SINGLE PERSONAL IN
THE PHENOTYPIC SPACE THEY WILL
BE SURROUNDED BY OTHER
INDIVIDUALS LIKE THEM, HALF
WOULD HAVE RECEIVED THE
INTERVENTION, THE OTHER HALF
CONTROL.
GIVEN FACT RANDOMIZED TRIAL,
RANDOM ALLOCATION OF EFFECT.
FARTHER YOU GO, LESSER LOOKS
LIKE INDEX PERSON.
THIS CAN BE USED TO INFER
TREATMENT VERSUS CONTROL BY HOW
FAR THE TRIAL IS FROM THE INDEX
PATIENT.
AND WHETHER THEY RECEIVE
INTERVENTION OR CONTROL, YOU CAN
CONDUCT EQUIVALENT FOR THAT
INDEX PERSON.
AND WEIGH THEM BY DISTANCE AND
REPEAT THIS FOR EVERY PERSON IN
THE TRIAL WHO MAY HAVE A
DIFFERENT ESTIMATE AND REPEATED
THROUGHOUT -- FOR THE ENTIRE
ACTIVATION WHICH ALLOWS AN
INDIVIDUALIZED HAZARD ESTIMATE
FOR EACH PERSON'S UNIQUE
NEIGHBORHOOD.
THIS IS WHAT WE DID IN SPRINT,
TOOK ALL PRE-RANDOMIZATION
FEATURES, AND INCLUDED ALL
CONTINUOUS CATEGORICAL FEATURES,
LABS, ANYTHING IN TABLE 1.
ASSESSED SIMILARITY, USING A
METRIC FROM 0 TO 1, DISTANCE IS
0 BETWEEN TWO INDIVIDUALS,
MATCHED ON CHARACTERISTICS
PERFECTLY.
1 REPRESENTS MATCHING ON NONE OF
THE CHARACTERISTICS, DIGITALIZED
USING UMAP FOR INTERPRETATION.
UMAP'S 2D REPRESENTATION HAS
PROJECTION AND COMPLEX
ASSOCIATIONS, CONSTRUCTING A
MODEL THAT FINDS EFFECTS LIKE I
SHOWED AND OPERATIONALIZE FOR
ALL SPRINT TRIAL PARTICIPANTS.
THE OUTPUT OF THAT IS THIS
OUTCOME MAP.
EACH DOT IS A PERSON.
COLOR AND DEPTH INDICATES LOG
HAZARD RATIO FOR THE PRIMARY
OUTCOME OF SPRINT.
ON THE RIGHT NET CLINICAL
BENEFIT, PRIMARY OUTCOME OR ANY
ADVERSE EVENT.
AS YOU CAN SEE, MOST PATIENTS
BENEFITED FROM THE PRIMARY
OUTCOME IN SPRINT.
BUT DEGREE OF BENEFIT VARIES.
WHILE THIS PROCESS WAS
COMPUTATIONAL INTENSIVE WE
FURTHER BUILT LAYER ON TOP,
IDENTIFYING MINIMUM PATIENT
FEATURES PRESENT ACROSS MULTIPLE
NEIGHBORHOODS AND CONSISTENTLY
ASSOCIATED WITH BENEFIT FOR
MAJOR CARDIOVASCULAR EVENT FROM
INTENSIVE BLOOD PRESSURE
LOWERING, AND SO WE COULD BUILD
PREDICTIVE MODEL FOR
INDIVIDUALIZED EFFECT ESTIMATE
FOR MAJOR CARDIOVASCULAR EVENTS.
THIS MODEL WITH EIGHT TOP
FEATURES, COMBINATION SUFFICIENT
TO IDENTIFY MAJORITY OF
RESPONSE.
BASED ON SHAP VALUES, THE
FEATURES ARE MARKED HERE,
SIMILAR TO OTHER TRIAL STUDIES.
THESE WERE THEN USED TO MAKE A
CALCULATOR, ONLINE CALCULATOR
THAT CAN PREDICT HAZARD RATIOS
AND FOR THE CALCULATOR, FEATURES
WERE BROKEN DOWN, COMPLEX
FEATURES COMBINATIONS OF OTHER
FEATURES, WE TAKE NINE INPUTS
AND GIVE YOU THE PREDICTED
EFFECT ESTIMATE FOR A PERSON.
CAN YOU ACCESS THIS ON THE WEB
PAGE.
WE HAVE THIS MODEL, AND THIS
TOOL, CAN HOST IT, IT CAN BE
EMBEDDED IN EHR, BUT WHAT IF
WE'RE FINDING SOMETHING SPURIOUS
IN SPRINT?
WE SAID IF THERE WAS A PROFILE
WE EXPECTED IT TO FOLD OVER TO
ACCORD, IT'S HARD TO BELIEVE
PATIENTS WITH DIABETES WITH
FUNDAMENTALLY DIFFERENCE.
USING ACCORD, ADVERSE
CARDIOVASCULAR EVENTS STILL,
CALCULATING EFFECT ESTIMATE FOR
BENEFIT, USING OUR TOOL, IN THE
SPRINT TRIAL APPLIED TWO
PATIENTS IN THE ACCORD TRIAL,
FOUND A SIGNIFICANT QUANTITATIVE
INTERACTION SUCH THAT
INDIVIDUALSIZED PROFILE BASED ON
SPRINT IDENTIFIED PATIENTS WHO
BENEFITED FROM INTENSIVE BLOOD
PRESSURE LOWERING IN ACCORD WITH
A 30% RELATIVE RISK REDUCTION IN
THOSE WITH HIGH EFFECT ESTIMATE,
DEFINED AS LOW MEDIAN RISK, BUT
IN THE PLOT ON THE RIGHT.
WE'RE ASSESSING WHAT SURROGATES
WE CAN USE TO DEFINE.
WE DON'T HAVE TO CONDUCT
FIVE-YEAR STUDY TO EVALUATE OUR
TOOL.
AS I MENTIONED, WE HAVE SEVERAL
APPLICATIONS AND VARIATIONS, IN,
DIAGNOSTICS, DIABETES
MANAGEMENT, SO FORTH, THAT BUILD
ON HOW APPROACH EVOLVED OVER
TIME, ALL AVAILABLE AS RESEARCH
TOOLS.
KEY TAKEWAYS, CLINICAL DATA
SHARING IS INSTRUMENTAL.
APPLICATIONS OF mL/AI CAN
OPERATIONAL MODELS.
THERE ARE CHALLENGES.
WHAT IS THE GOLD STANDARD?
THERE IS NONE.
WE'RE BUILDING COMPLEX TREATMENT
EFFECTS, RIGOROUS EVALUATION OF
EXISTING MODELS TO ASSESS
DETECTION OF THESE
INDIVIDUALIZED EFFECTS.
HOW DO TRIALS WORK?
REPRESENTATION IN TRIALS HAS
BEEN AN ISSUE FOR A LONG TIME.
AS I MENTIONED, VALIDATION
REQUIRES US TO DO PRAGMATIC
DESIGNS AND BUILD NETWORKS TO
ASSESS SUFFICIENTLY.
THANK YOU.
ALL THANKS TO MY AMAZING LAB
MEMBERS FOR THE WORK I
PRESENTED, ESPECIALLY THE
CARDIOLOGY FELLOW AT YALE, FIRST
AUTHOR ON THE WORK IN THE
CLINICAL TRIALS I PRESENTED
TODAY.
THANK YOU.
>> THANK YOU.
APPRECIATE IT.
LAST SPEAKER IS DR. CHANG,
ASSOCIATE PROFESSOR IN
DEPARTMENT OF POPULATION SCIENCE
CO-DIRECTOR CENTER FOR KIDNEY
HEALTH RESEARCH, NEPHROLOGIST AT
GEISINGER.
IF WE COULD ADD 15 MINUTES TO
NOW, EXPECTING THAT YOUR TALK
ABOUT END AT 3:54, AND THE
SESSION ENDS AT 4:00.
I'M GOING TO EXTEND THIS AND LET
THE AUDIENCE KNOW THAT WE'RE
GOING TO GO THAT LONG.
IF YOU HAVE TO LEAVE AT 4:00
THAT'S PERFECTLY FINE.
DR. CHANG, YOU MAY PROCEED.
>> THANK YOU SO MUCH.
THANKS TO THE NHLBI AND AMAZING
PRESENTERS WITH TALKS TODAY.
MY DISCLOSURES.
SO, I ABSORBED A LOT OF GREAT
INFORMATION FROM EVERYTHING THAT
PEOPLE HAVE SAID BEFORE ME.
AND THE COMMENTS.
AND SO I'M PRESENTING THIS FROM
VIEW OF CLINICIAN-SCIENTIST, AND
TRYING TO UNDERSTAND ALSO HOW
WOULD YOU ACTUALLY USE THESE
TOOLS IN CLINICAL PRACTICE.
WE'RE OFTEN WORKING ON QUALITY
IMPROVEMENT WITH OUR POPULATION
HEALTH GROUP, AND A LARGE HEALTH
SYSTEM, AND WE'RE OFTEN SENSING
INFORMATION ABOUT A.I. TOOLS AND
PEOPLE WANTING TO ADOPT
DIFFERENT THINGS.
I'M GOING TO DESCRIBE EXISTING
LITERATURE ON USE OF A.I. AND
PREDICTING HYPERTENSIVE OR
SEQUELAE, CHRONIC KIDNEY DISEASE
AS EXAMPLE CONDITION, DESCRIBE
UNMET NEEDS AND POTENTIAL FOR
A.I. TO IMPROVE EARLY DIAGNOSIS
AND MANAGEMENT.
AND DESCRIBE KEY CHALLENGES AND
BARRIERS USING A.I. IN THIS
CONTEXT.
WE'VE HEARD WILL BLOOD PRESSURE
AND -- ABOUT BLOOD PRESSURE AND
END ORGAN DAMAGE, HOW CAN WE
PREDICT BETTER, FIT PREDICTION
MODEL INTO EXISTING CLINICAL
WORKFLOW, WHICH IS OFTEN VERY
CHALLENGING.
AND DO WE HAVE EVIDENCE THAT USE
OF A.I. PREDICTION MODELS CAN
HELP OUTCOMES.
THIS IS WAYS THAT IT COULD BE
USED, POPULATION OR HEALTH
SYSTEM LEVEL WHERE YOU CAN
IDENTIFY HYPERTENSIVE PATIENTS
AT PARTICULARLY HIGH RISK FOR
END ORGAN DAMAGE, AND MAYBE
THERE'S SOME NEED FOR SCREENING
OF HYPERTENSION-RELATED
CONDITIONS.
YOU CAN ALSO POTENTIALLY USE
RISK THRESHOLDS, AS PEOPLE
MENTIONED, THERE'S CLINICIAN
INERTIA.
AT THE INDIVIDUAL LEVEL, CAN WE
INCORPORATE, CLINICIANS IN
SHARED DECISION MAKING WITH
PATIENTS SO THEY UNDERSTAND
RISKS AND THAT MAY ACTUALLY HELP
DRIVE DECISION MAKING AND
ADDRESS CLINICAL INERTIA.
HEART FAILURE, I'M TOUCHING
BRIEFLY.
APOLOGIES TO THOSE THAT STUDY
EXTENSIVELY.
1 TO 2%, 4% ABOVE 65 TO 70 YEARS
OF AGE, ARE MORTALITY IS HIGH.
IN TERMS OF A.I. LITERATURE,
REVIEWS SHOWING THAT ARE IN
VARIOUS STUDIES THERE IS A LOT
OF POTENTIAL BIAS IN VALIDATION
PROCEDURES, LACK OF ADDRESSING
CALIBRATION WAS COMMON IN A LOT
OF PUBLISHED PAPERS.
AND NO EVIDENCE OF SUPERIORITY
OVER LOGISTIC REGRESSION FOR
PREDICTING HEART FAILURE
REHOSPITALIZATION, MAYBE SOME
IMPROVEMENT FOR PREDICTING
MORTALITY WAS SHOWN.
AND OF COURSE THERE'S TONS OF
HETEROGENEITY BETWEEN THE
STUDIES AND POPULATION.
THINKING ABOUT OTHER
COMPLICATIONS, MILD COGNITIVE
IMPAIRMENT AND DEMENTIA AFFECTS
LOTS OF PEOPLE IN OUR SOCIETY
GETTING ORDINARY, UP TO 1/3 OF
OLDER ADULTS.
INTENSIVE BLOOD PRESSURE CONTROL
MAY PREVENT COGNITIVE IMPAIRMENT
FROM SPRINT MIND.
AND THERE HAVE BEEN VARIOUS RISK
PREDICTIONS DEVELOPED USING
VARIOUS CLINICAL FACTORS AND A
LOT HAVE BEEN VALIDATED IN OTHER
COHORTS.
THERE'S OTHER SOURCES OF DATA
THAT ARE USUALLY IN SMALLER
STUDIES LOOKING AT BRAIN
IMAGING, GENETICS, INFORMATION
ON TOP OF RISK SCORES, VOICE
DATA, COGNITIVE FUNCTION TESTS,
BIOSAMPLES, A LOT OF RESEARCH
AND POTENTIAL WITH ANIMAL MODELS
THAT CAN IDENTIFY FACTORS THAT
ARE NOT EASY FOR HUMANS TO SEE
EASILY.
I'M POINTING OUT ONE PARTICULAR
STUDY, U.K. BIOBANK, RESEARCHERS
HAD USED DATA FROM THIS TO LOOK
AT 366 CANDIDATE VARIABLES TO
PREDICT DEMENTIA.
U.K. BIOBANK THEY HAVE REGULAR
CLINICAL PREDICTORS, ALSO
ADDITIONAL TESTS, THEY HAVE
APOE4, A GENETIC RISK FACTOR, A
GAME WHERE THEY HAVE TO MATCH
PAIRS OF CARDS, OTHER THINGS.
THEY HAD A MUCH BETTER AUC THAN
CLINICAL RISK SCORES, AND GOOD
CALIBRATION.
THIS WILL NEED EXTERNAL
VALIDATION.
IN TERMS OF CHRONIC KIDNEY
DISEASE, THIS AFFECTS 1 IN 7
U.S. ADULTS, COMMON WITH
HYPERTENSION, MOST COMMON RISK
FACTORS DIABETES, HYPERTENSION,
YOU CAN CONSIDER CONSIDER CKD
AND IT'S IMPORTANT TO POINT OUT
PATIENTS HAVE NO IDEA THEY HAVE
CHRONIC KIDNEY DISEASE.
THIS IS THE SCHEMA THAT WE USE
TO CLASSIFY KIDNEY DISEASE BY
ESTIMATED GLOMULAR FILTRATION
RATE, OR A URINE TEST FOR
PATIENTS AT RISK, YOU CAN USE
THAT TO PREDICT RISK IN BOTH OF
THOSE THINGS ON TOP OF EACH
OTHER CAN DRIVE DECISION MAKING
IN TERMS OF HOW AGGRESSIVE YOU
TREAT AND REFERRAL PATTERNS.
THIS HAS BEEN INTEGRATED INTO A
RISK EQUATION SCORE, WORK DONE
WITH MANY COHORTS AROUND THE
WORLD.
SOURCE DATA FROM CANADA.
THEY HAD EXCELLENT
DISCRIMINATION AUC 0.9
PREDICTING KIDNEY FAILURE,
CALIBRATION CAN BE AN ISSUE
SOMETIMES, ESPECIALLY FOR THE
NON-NORTH AMERICAN COHORTS
INCLUDED IN THE STUDY.
SO THIS IS A STANDARD -- SOME
HAS FLOWED INTO ELECTRONIC
HEALTH RECORDS.
IT'S NOT UNIVERSAL QUITE YET BUT
THIS REALLY IS PROBABLY ONE OF
THE MAIN THINGS WE WILL BE USING
TO DRIVE DECISION MAKING, WE'D
LIKE TO KEEP ROLLING OUT.
ONE OF THE PROBLEMS, YOU NEED
ALBUMIN CREATININE RATIO TO HELP
RISK STRATIFY KIDNEY DISEASE,
AND YOU CAN SEE AMONG PATIENTS
WITH DIABETES OR HYPERTENSION AT
RISK, VERY FEW END UP GETTING
ALBUMINEREA SCREENING, 5% FOR
HYPERTENSION, THAT RESULTS IN
UNDERDIAGNOSIS OF CHRONIC KIDNEY
DISEASE AND PROBABLY CONTRIBUTES
TO AWARENESS PROBLEM.
AND ALSO THERE'S A LOT OF
REASONS WE'RE NOT OPTIMIZING USE
OF MEDICATIONS AND GETTING BLOOD
PRESSURE UNDER CONTROL BUT
THAT'S DEFINITELY CONTRIBUTING
TO THAT.
IMAGINE AN A.I. RISK SCORE
THAT'S ABLE TO STRATIFY
HYPERTENSION PATIENTS INTO
HIGHER OR LOWER BUCKETS OF CKD
RISK, YOU CAN IMPLEMENT
POPULATION LEVEL STRATEGY TO
IMPROVE ALBUMINEREA SCREENING
THROUGH NUDGES, IMPROVE
ADHERENCE, AMONG THOSE WHO HAVE
KIDNEY DISEASE OR COULD QUALIFY
FOR TREATMENT, THAT MAY INFORM
CLINICAL DECISION MAKING AND
IMPROVE THE USE OF CARDIO RENAL
RISK REDUCING TREATMENTS.
SO I THINK THERE'S BEEN QUITE A
FEW STUDIES ON USE OF A.I. CKD.
I HAVE A COUPLE.
BOTTOM LINE, THERE'S A LOT OF
STUDIES, SOME HAVE EXTERNAL
VALIDATION, SOME DON'T.
DIFFERENT OUTCOMES.
SOME USE OTHER BIOMARKERS NOT
REGULARLY COLLECTED IN CLINICAL
PRACTICE, AND OF COURSE WILL
IMPROVE THE RISK PREDICTION.
THESE ARE TWO STUDIES THAT I'M
GOING FOCUS ON MORE HERE, SO
THIS FIRST ONE WAS A NICE STUDY
DONE USING ADMINISTRATIVE DATA
FROM CANADA, MANITOBA, SO THEY
HAD 77,000 IN THE COHORTS,
VALIDATED RESULTS IN EXTERNAL
ALBERTA, CANADA, COHORT.
AND THIS ONE REQUIRED THEM TO
HAVE EGFR GREATER THAN 10.
AND THEY HAD TO HAVE THE RATIO,
THAT URINE TEST, AVAILABLE.
THEY LOOKED AT OTHER COMMONLY
MEASURED LABS THAT COME UP IN
CLINICAL PRACTICE.
AND THEN THE OUTCOME WAS 40%
DECLINE IN EGFR, KIDNEY FAILURE,
AN ACCEPTED OUTCOME FOR KIDNEY
PROBLEMS.
AND THEY USED RANDOM FORCE.
AND SO OVERALL NOT SURPRISING
THEY WERE ABLE TO PREDICT KIDNEY
FAILURE AND EGFR DECLINE, ABLE
TO COMPARE IT TO SORT OF THE
PREEXISTING MODEL THAT I
MENTIONED SO HEAT MAP MODEL
BASED OFF EGFR AND ACR CATEGORY,
AND SHOW THERE'S SIGNIFICANT
IMPROVEMENT IN THE AUC COMPARED
TO THAT, AND THROW IN OTHER
CLINICAL FACTORS LIKE DIABETES,
HYPERTENSION, STROKE, ET CETERA,
WE'RE ABLE TO PREDICT BETTER.
YOU CAN SEE SOME OF THE FACTORS
THAT WERE IMPORTANT BEYOND ACR
AND EGFR, UREA, HEMATOCRIT AND
GLUCOSE.
THAT WAS A NICE STUDY.
THEY HAVE PRETTY GOOD
CALIBRATION THAT THEY SHOW HERE.
THERE'S EXTERNAL VALIDATION,
COMMONLY MEASURED LAB DATA USING
REAL EHR DATA, APPLICABLE.
YOU REQUIRE PATIENTS TO HAVE ACR
DATA AND A LOT ARE NOT TESTED
APPROPRIATELY.
SO, THIS IS ANOTHER STUDY, THE
EUROPEAN DATABASE, ALSO INCLUDES
OTHER COUNTRIES.
TRY TO PREDICT IF THEY ARE GOING
TO END UP WITH END STAGE KIDNEY
DISEASE.
WHAT THEY FOUND WAS THAT THE
KFRE, COMPARED TO THE KIDNEY
FAILURE RISK EQUATION, PREDICTED
SIMILAR AUC AS ALGORITHM.
AND HOWEVER THE OTHER
INTERESTING THING IS THAT YOU
DON'T REQUIRE THE ACR TO USE THE
ALGORITHM THAT THEY DEVELOPED,
THEY ARE ABLE TO GENERATE
PREDICTION ON ALL THE PATIENTS,
WHEREAS THE ALBUMINEREA WAS
AVAILABLE BE ON 30% OF SIX
MONTH, 16% OF 24-MONTH COHORT,
IF YOU'RE SEEING A PATIENT IN
CLINIC THIS MIGHT BE ABLE TO
GENERATE SOMETHING CLINICALLY
USEFUL, THIS IS FROM THE PAPER
OF RISK REPORT, SHOWING THE RISK
PROFILE OVER TIME, WHICH FACTORS
ARE PROTECTIVE VERSUS NOT A RISK
FACTOR, AND THEN THE OVERALL
RISK, AND WHAT MISSING
INFORMATION IS NOT THERE THAT
COULD BE INFORMATIVE AS WELL.
THIS IS A SIMULATION THAT THEY
DID WHERE THEY TOOK SEVERAL
NEPHROLOGISTS AND HAD THEM
EVALUATE CASES, ACTUAL CASES,
AND PROVIDE CLASSIFICATION
WHETHER PATIENTS WERE HIGH RISK
OR NOT.
AND THEN COMPARED TO WHAT IF
THEY USED THEIR ALGORITHMS AND
COMPARED TO THE CLINICIAN, YOU
CAN SEE ON THE PART B SECTION
HERE THAT THEY ARE ABLE TO HAVE
A GREATER IMPACT ON A
HYPOTHETICAL SITUATION WHERE YOU
TAKE THOSE PATIENTS THAT ARE
HIGH RISK AND GIVE THEM
SOMETHING TO REDUCE RISK OF
KIDNEY FAILURE COMPARED TO THE
EXPERT OPINION.
SO, THIS WOULD BE A GOOD
APPROACH WHERE YOU COULD TRY TO
IDENTIFY THOSE AT HIGHEST RISK
AND THEN HEALTH CARE IS A
LIMITED RESOURCE, SO YOU DRIVE
IT TOWARDS THE HIGHEST RISK
PATIENTS.
THAT'S ONE OF THE SCIENTIFIC
AREAS TRYING TO IMPLEMENT A
TOOL, WHETHER CKD OR HEART
FAILURE, OR DEMENTIA, TRY TO
IMPACT SHARED DECISION MAKING
AND IMPROVE CLINICAL CARE, MAYBE
USE IT TO DETECT EARLY
MODIFIABLE DISEASE AT THE
POPULATION LEVEL, AND THEN ALSO
WHAT HAPPENS WHEN YOU
INCORPORATE ALL THESE OTHER DATA
SOURCES, WEARABLES, EKG,
BIOMARKERS AS WELL.
OTHER GAPS IN KNOWLEDGE, DO WE
KNOW THAT OUR PATIENTS AND
PROVIDERS ARE GOING TO ACCEPT
THE A.I.-BASED ALGORITHMS?
CAN WE ACTUALLY SHOW, WE NEED TO
SHOW YOU CAN GET IMPROVEMENT,
SOME COMMENTS IN THE CHAT,
MAKING THINGS MORE COMPLICATED
MAY NOT BE IN THE BEST INTEREST,
IF THE SIMPLE ANSWER IS HARD
ENOUGH ITSELF.
WE NEED TO GENERATE EVIDENCE TO
SHOW ALL THIS.
SOME OTHER KEY CHALLENGES AND
BARRIERS, INTEROPERABILITY, DATA
SHARING AS MENTIONED BEFORE, AND
PRIVACY ISSUES AROUND THAT.
LACK OF VALIDATION OF THE
COHORTS, AND SOME OF THAT
PROBABLY IS RELATED TO
DIFFICULTIES OF SHARING DATA AND
THERE'S A LOT OF TALK ABOUT
FEDERATED MODELS.
EXPLAINABILITY OF A.I. IS VERY
IMPORTANT IN UNDERSTANDING, YOU
KNOW, THE DIFFERENT SUPERVISOR
VERSUS UNSUPERVISED LEARNING,
AND THEN OF COURSE ON THE
MANUFACTURERS, REGULATORY
CONSTRAINTS, LACK OF
REIMBURSEMENT FOR A.I. AND SOME
OF THE BIGGER CHALLENGES THAT
HAVE BEEN DISCUSSED SO FAR ON
THE UNIQUE PROPERTIES OF THESE
AND HOW FDA IS ADAPTING TO
FIGURE OUT THESE PROBLEMS.
THANK YOU VERY MUCH FOR YOUR
ATTENTION AND STAYING LATER.
THIS IS JUST A PHOTOGRAPH OF
ABIGAIL GEISINGER WHO FOUNDED
OUR HOSPITAL SYSTEM AT THE AGE
OF 85 IN 1915, AND I THINK IT'S
SOMETHING WE LIKE TO REFLECT,
IMAGINE WHAT SHE WOULD THINK
ABOUT WHAT'S GONE ON FROM THIS
HUMBLE HEALTH SYSTEM SHE BUILT
TO NOW WE'RE TALKING ABOUT
INCORPORATING A.I. INTO CLINICAL
DECISION MAKING AND CARE.
SO THANK YOU VERY MUCH FOR YOUR
ATTENTION.
>> THANK YOU, DR. CHANG.
I THINK WE'LL QUICKLY TRANSITION
INTO DISCUSSION PERIOD.
WE'RE GOING TO GO A LITTLE BIT
BEYOND 4:00.
I HAVE A QUESTION.
I'VE BEEN MONITORING THE CHAT.
I TWO MAIN QUESTIONS FOR OUR
GROUP OF SPEAKERS FOR THIS
SESSION.
SO, AS I WAS LISTENING TO YOUR
TALKS, MONITORING THE CHAT, I
THINK THERE'S A PRESSURE POINT
BETWEEN WHAT I CALL THE LAW OF
AVERAGES, WHICH ARE THE THINGS
THAT WE ASSESS IN CLINICAL
TRIALS.
RIGHT?
ONE GROUP RANDOMIZED, AND THEN
THE CONTROL, AND ON AVERAGE THE
GROUP DOES BETTER WITH REGARDS
TO BLOOD PRESSURE REDUCTION AND
CARDIOVASCULAR EVENTS THAN THE
CONTROL.
OF COURSE, IN CLINICAL CARE WE
FOCUS ON THE INDIVIDUAL.
I NOTICE THIS KIND OF BALANCE
BETWEEN USING EHR ALERTS,
PERHAPS DRIVEN BY A.I. BOTS,
CHAT BOTS, WHO WERE IDENTIFYING
PEOPLE THAT SHOULD BE SCREENED
FOR HYPERTENSION, OR HAVE THEIR
HYPERTENSION TREATMENT
INTENSIFIED BECAUSE THEY ARE
POORLY CONTROLLED, AND SO IT'S
ALMOST LIKE A DECISION SUPPORT.
AND THAT MODEL EVERYONE GETS THE
SAME SORT OF STEPPED
INTERVENTION.
HEY, YOU'RE NOT WELL CONTROLLED.
YOU SHOULD DO DIET AND EXERCISE,
AFTER THREE MONTHS YOU SHOULD
MOVE TO MEDICATION, THAT'S
SOMETHING I SAW IN THE CHAT, ONE
SIZE FITS ALL.
THEN ON THE OTHER HAND I KNOW
FROM THE TRIALS HUGE
HETEROGENEITY IN HOW PEOPLE
RESPOND.
SO, FOR EXAMPLE, SHOULD WE BE
USING A.I. RISK SCORE TO
DETERMINE WHO SHOULD BENEFIT
FROM DIET, EXERCISE, VERSUS A
LIFE COACH, WHO HAVE YOU GET A
CALCIUM CHANNEL BLOCKER VERSUS
ACE INHIBITOR, ONE PERSON MAY
BENEFIT OVER ANOTHER.
SO I WANTED TO ASK THIS GROUP, I
KNOW YOU'RE ALL IN THE A.I.
FIELD.
WHAT DO YOU THINK ABOUT THAT
SORT OF BALANCE BETWEEN
TREATMENT FOR ALL VERSUS
TREATMENT FOR AN INDIVIDUAL?
CAN THAT BE PERSONALIZED?
ANYONE CAN ANSWER.
>> I SEE MYSELF ON THE RIGHT.
I'M NEXT TO YOU.
>> GO FOR IT.
>> I THINK THIS IS OF COURSE
AGE-OLD TENSION BETWEEN PUBLIC
HEALTH AND PRECISION MEDICINE.
AND SOME OF THE GOALS OF THE
MEETING TODAY, I HOPE, WERE
ASPIRATIONAL RATHER THAN
FOUNDATIONAL.
>> RIGHT.
>> WHAT IS HAPPENING IN REALITY.
SO FOR MANY PATIENTS THESE
INDIVIDUALIZED STRATEGIES ARE
PROBABLY NOT SUITABLE FOR
MAJORITY OF OUR SOCIETY RIGHT
NOW.
AND I FEEL LIKE THE HOPE, I MEAN
THE GOAL OVERALL IS METHODS, AS
METHODS EMERGE, YOU'RE SEEING
THIS PATIENT, WHO YOU HAVE
ACHIEVED THAT STATE WHERE
PATIENTS ARE ALL SCREENED,
DIAGNOSED, THEY GET TREATED,
COME TO YOUR PRACTICE, ARE THERE
WAYS YOU CAN OPTIMIZE NET
CLINICAL BENEFIT, THAT'S WHEN
ALL STRATEGIES ARE UNDEFINED
RISK OR COMMUNICATING AND SO
FORTH.
>> GOOD COMMENT.
DO YOU HAVE ANYTHING TO ADD?
>> SURE.
I MEAN, I WOULD JUST ADD THAT
THE ANSWER IS WE HAVE A LOT OF
GREAT TOOLS THAT NEED TO BE
DEVELOPED, BUT WE ACTUALLY DON'T
KNOW THE ANSWER TO THE QUESTION.
BECAUSE WE NEED TO ACTUALLY
EVALUATE THIS IN A ROBUST WAY,
AND FIND OUT.
AND IT GOES TO A COMMENT THAT
WAS IN THE CHAT BEFORE.
DO WE NEED THE CADILLAC OR VW
BEETLE?
WE DON'T KNOW.
>> I WOULD ADD ONE THING, THAT,
YOU KNOW, IT COULD BE A SHARED
DECISION MAKING PART OF THE
PROCESS.
IT CAN BE BOTH, AND, WHERE YOU
MIGHT WANT TO SAY YOUR
PRESCRIPTION IS MORE PHYSICAL
ACTIVITY BUT THAT PERSON MAY NOT
HAVE ANY DESIRE OR INCLINATION
TO DO THAT ARE.
SO IT WOULD BE BETTER TO START
CAN SOMETHING THEY CAN BE
SUCCESSFUL BEFORE MOVING ON.
IT HAS TO BE A PARTNERSHIP WHERE
THE PERSON HAS AGENCY AND OF IN
DECIDING WHAT THEY CAN TAKE ON
AS WELL.
>> THAT MAKES SENSE.
THANK YOU.
DR. CHANG, ANYTHING TO ADD?
I WANT TO MAKE SURE EVERYONE IS
HEARD.
>> YEAH, I THINK IT'S -- MAYBE
IT'S NOT EITHER/OR, FROM AN
NHLBI FUNDING PERSPECTIVE, OF
COURSE, THE MONEY IS, YOU KNOW,
FUNDING HAS TO BE ALLOCATED A
CERTAIN WAY.
YEAH, I THINK YOU HAVE TO
ADDRESS BOTH SIMULTANEOUSLY, YOU
GOT TO THINK BEYOND, WITH THE
PATIENTS WE HAVE A PATIENT
ADVISORY GROUP MEETING, AND I
DISCUSSED SOME OF THE A.I.
CONCEPTS WITH THEM, JUST TO GET
ANY KIND OF SENSE.
AND I MEAN IT SEEMS FROM A SMALL
SAMPLE, PATIENTS WANT TO KNOW
MORE INFORMATION, SO IF THERE IS
A WAY TO EFFECTIVELY PERSONALIZE
CARE WITH SOME OF THESE TOOLS,
THE PATIENTS DO WANT TO KNOW,
WHETHER IT WORKS OR NOT, THAT'S
THE NEXT STEP.
>> THAT'S A GREAT POINT.
GREAT POINT.
HERE IS MY SECOND QUESTION.
I'M LISTENING TO YOUR TALKS.
DR. LEOPOLD, YOUR TALK MADE ME
THINK.
FOR SECONDARY PREVENTION, A LOT
OF EVIDENCE WITH REGARDS TO
HYPERTENSION MANAGEMENT, YOU'VE
BEEN DIAGNOSED ALREADY, REALLY
SEEMS LIKE IT'S CENTERED IN THE
EHR.
AND OBVIOUSLY SOMEPLACE LIKE DR.
CHANG, GEISINGER HAS A HUGE
DATABASE, VERY EFFECTIVE.
BUT FOR PREVENTION HOW DO YOU
DEAL WITH THE 40-YEAR-OLD MALE,
MAKING IT, WHO HAS NEVER SEEN A
DOCTOR, NO TOUCH POINT, THEY ARE
NOT IN A STUDY.
IS A.I. OFF LIMITS TO THAT
PERSON?
WHERE I'M GOING WITH THIS, NOT
TO PUT YOU ON THE SPOT, WE KNOW
THAT SOME SOCIAL MEDIA
TECHNOLOGY COMPANIES ARE USING
ONLINE BEHAVIOR TO PREDICT WHAT
PEOPLE ARE GOING TO PURCHASE OR
EVEN WHAT MEDICAL CONDITION THEY
HAVE.
RIGHT?
SO, I WANTED TO ASK YOU, DR.
LEOPOLD, WHEN IT COMES TO
PRECISION HEALTH, I KNOW THERE'S
AN ASPIRATIONAL LIST OF DATA
INPUTS, CURRENTLY IN MY MIND
THOSE DON'T EXIST FOR PEOPLE WHO
ARE NOT ACCESSING RESEARCH STUDY
OR CLINICAL CARE, OR MAYBE I'M
WRONG.
>> NO, YOU'RE ABSOLUTELY RIGHT.
I MEAN, THIS IS WHERE THIS WHOLE
CONCEPT OF YOUR ENVIRONMENT,
YOUR EXPOSURES HAS TO BE
REALLY -- YOU HAVE TO START
THINKING OUTSIDE THE USUAL
MEASURES FOR THIS.
SO, YOU KNOW, IS IT SOMETHING
WHERE USING A.I. YOU FIND OUT
THAT THERE'S JUST A LINK
BETWEEN, SAY, PURCHASES OF
TYLENOL BECAUSE YOU HAVE A
HEADACHE AND THE HEADACHE IS
REALLY ATTRIBUTABLE TO
HYPERTENSION, OR CAN YOU -- YOU
KNOW, IS THERE DATA WE CAN
COLLECT ELSEWHERE, OUTSITE OF
THE EHR THAT POINTS US TOWARDS
SOMEONE WHO IS EITHER AT RISK OR
WHO IS ABLE TO -- WHO IS GOING
TO DEVELOP DISEASE?
AND, AGAIN, WE HAVE TO INTERACT
WITH, SAY, PEOPLE WHO UNDERSTAND
HUMAN BEHAVIOR, AND WHAT SHOULD
BE COLLECTED, WHICH ONES SHOULD
WE COLLECT.
>> THAT MAKES SENSE.
ANYONE ELSE HAVE ANY FEEDBACK
ABOUT MY QUESTION?
NOT MEANT TO PICK ON DR.
LEOPOLD.
ANYTHING ELSE?
>> I AGREE COMPLETELY.
THE ONLY CONCERN WOULD BE
PRIVACY, AND HOW APPROPRIATE IT
IS TO IDENTIFY THIS FEATURE, AND
FLAG SOMEONE, ESPECIALLY WITH
MISTRUST OF THE HEALTH SYSTEM OF
SOCIAL MEDIA ORGANIZATIONS.
>> THAT MAKES SENSE.
>> WE HAVEN'T EXPLOITED SOCIAL
NETWORKS.
THE PATIENT MIGHT HAVE PEOPLE IN
THEIR PERSONAL NETWORKS THEY
COULD INFLUENCE.
SO YOU MIGHT NOT BE TALKING
ABOUT
[AUDIO DISTORTION]
BUT YOU COULD REACH OUT TO HAVE
THEM INFLUENCE OTHER PEOPLE.
>> MAKES SENSE.
OKAY.
IF ANYONE HAS OTHER COMMENTS?
ONE MORE QUESTION.
THIS GOES BACK TO A QUESTION
ASKED IN AN EARLIER SESSION BUT
IT WAS LEFT UNANSWERED.
I'M NOT A DATA SCIENTIST, I'M A
REGULAR HYPERTENSION RESEARCHER.
THIS IS A MASSIVE AMOUNT MUCH
DATA, DATA INPUTS.
WE TALKED ABOUT GENETICS,
EPIGENETIC, INSIDE AND OUTSIDE
THE OFFICE BLOOD PRESSURE,
PREDICTOR, SO FORTH.
IT SEEMS OVERWHELMING.
WITH ALL THAT DATA, THAT WE HAVE
TO GO THROUGH, HOW DO WE
INTEGRATE?
WE DON'T HAVE A SYSTEM SET UP TO
INTEGRATE IT RIGHT NOW.
BUT IF YOU HAD ALL THE MONEY IN
THE WORLD FROM NIH, HOW WOULD
YOU IMPLEMENT SOMETHING?
WHAT DOES THE IDEA LOOK LIKE?
I'M THINKING 20 OR 30 YEARS IN
THE FUTURE.
JUST TRYING TO THINK PIE IN THE
SKY.
DO HAVE YOU ANY COMMENTS ABOUT
THAT?
>> I THINK SOME STUFF, SPOT ON
ABOUT RELATION OF EHR.
IF A CHATBOT DOES EXIST FOR
PATIENT INTERACTION, MEASUREMENT
CAN BE DONE FOR AMBULATORY BLOOD
PRESSURE MEASUREMENT AND
SYNTHESIZES, NOT JUST
AGGREGATES, CAN BE USEFUL.
IT JUST MEANS IT HAS TO BE
INDEPENDENTLY VALIDATED.
>> WE ALSO NEED STANDARDS.
STANDARDIZATION.
>> MAKES SENSE.
I DON'T KNOW HOW YOU WORK WITH
EHR DATA.
I'M NOT A DATA SCIENTIST.
DR. SPATZ MENTIONED SEEMS LIKE A
BIG EFFORT IS TO CLEAN IT FIRST.
ONE FINAL QUESTION FROM DR. FINE
FROM NHLBI.
DR. LEOPOLD INDICATES WHETHER
IMPROVEMENT CARES WOULD BE
HELPFUL FOR EVENTS YEARS IN THE
FUTURE, REQUIRING LARGE SAMPLES,
RANDOMIZATION OR EQUIVALENT AND
LONG PERIODS OF OBSERVATION.
WHAT ARE SOLUTIONS TO THE COST
OF ROBUST VALIDATION, WHAT ARE
NEXT STEPS?
HOW DO WE VALIDATE?
WE'RE NOT DOING A HUGE
RANDOMIZED TRIAL, I GUESS.
MAYBE, I DON'T KNOW.
>> NO.
BUT I THINK WE REALLY DO NEED AN
ENORMOUS SAMPLE SIZE, AND REALLY
TO FIGURE OUT THE DURATION OF
TIME THAT'S CONSIDERED
ACCEPTABLE AND WHAT DATA TO
COLLECT.
AND I THINK CLUES ARE GOING TO
COME FROM THESE VERY LARGE
LONGITUDINAL STUDIES, WHAT COMES
TO MIND IS FRAMINGHAM WHICH HAS
COLLECTED DATA OVER A VERY LONG
TIME.
AND, YOU KNOW, A LOT OF THESE
OTHER OBSERVATIONAL STUDIES THAT
ARE -- THAT HAVE DONE
OUTSTANDING JOBS PHENOTYPING
PATIENTS, I THINK THERE'S CLUES
WE HAVEN'T CONSIDERED YET.
>> THANK YOU.
>> FIRST AND FOREMOST I'M A
CARDIOLOGIST.
BUT I KNOW ABOUT THE TECH WORLD.
THIS IS WHERE THIS ACADEMIC
INDUSTRY COLLABORATION COULD
REALLY HELP, WHAT IS TECH GOOD
AT?
SCALE THINGS AND DRIVING DOWN
COST.
IS THERE INCENTIVE TO DO STUDIES
IN A MORE SCALABLE LOWER COST
WAY?