MINTU TURAKHIA, MD MASCo-Director, Center for Digital HealthDepartment of MedicineStanford University
Director, Cardiac ElectrophysiologyVA Palo Alto Health Care System
[email protected] @leftbundle
Quantitating Health:The Outlook from Silicon Valley
mailto:[email protected]
Disclosures Research support AHA, VA, NIH Janssen, Medtronic, iRhythm, Gilead Sciences JT Stroke Shield Foundation Trial enrollment: Janssen, Boehringer Ingelheim
Advisor/Consultant St Jude Medical, Medtronic, Gilead Sciences, Zipline
Medical, Precision Health Economics, Cyberheart, Metrica Health, Angilytics, thryva
Lecture honorariaMedtronic, St Jude Medical
Disclaimer
?
Bubbles don’t pop; they shrink1994-2004 dot-com tech bubble
Digital health investment
In 2014, overtook medical device funding
Digital health accounts for 7% of all venture funding
Rock Health 2015 Report
Rock Health 2015 Report
Where did the money go
Rock Health 2015 Report
The Economist, Feb 26, 2015
Rock Health 2016 Report
11
Who Pays?-The spiraling costs of care to government and private payors is forcing the launch of new methods and models for payment of healthcare services and products.
Role of New Participants– The emergence of IT based tools and services is witnessing the rise of a new breed of competitors.
Six Big Themes for the New Healthcare Economy
Rethinking the Customer– Patients are no longer going
to be passive participants in the process.
Source: Frost & Sullivan analysis.
Companies Revamping Strategies– Many industry participants as currently structured can not maintain viability without significant changes to their business model.
New Partnerships– An industry that historically operated in distinct silos is now being forced to integrate, and thus leading to firms seeking new types of partnerships and collaborations.
Modernizing Care Delivery– Clinical practice is moving from intuition based decisions to more analytics and data based approaches.
Courtesy of Greg Caressi, Frost & Sullivan
überification of health care
Would you like a doctor with your pizza?
Niche product; unlikely to displace conventional
care models
The Need: Challenges to Widespread Adoption of Digital Health Technology
• When and how to use apps and wearables
• Choosing the right product or technology
• Trusting the device, data, process
Patient
ClinicianDigital Device
& App Companies
• Defining use case• Understanding
patient, provider preferences
• Incorporating into clinical care
• Can’t self-differentiate
• Understanding benefits• Stakeholder buy-in• Incorporating into
workflows• Trust in device, data,
process• Validation• Clinical studies
• IT implementation• Reimbursement or
incentive• No clinical training in
remote management
Case study:Atrial Fibrillation
Challenge 1: Making sure Big Data
is Better Data
4
Adapted from Go. JAMA. 2001;285:2370.
Atrial Fibrillation Most common sustained arrhythmia in clinical
practice 4% of the population over age 60; 10% over age 80
Miyasaka Y. Circulation 2006;114:119-125
0.00
1.75
3.50
5.25
7.00
2009: 2.23M1995:
1.8M
Projections of AF Prevalence in the USA
2030: 5M-12M2014:
(3-5M)
AF complications are costly
(Avalere report, 2009)
Complication Prevalence Incremental Costs/YrHeart Failure 37% vs 10% $12,117
Stroke 23% vs 13% $7,907
Chest Pain 23% vs 13% $5,776
Tachycardia 11% vs 2.5% $10,143
Palpitations 7.0% vs 2.6% $1,993
Acute MI 5.0% vs 2.0% $12,162
AF is the most expensive cardiac dx Direct annual cost age < 65: $6.65 billion Medicare spending for new AF: $15.7 billion Mainly due to complications (stroke, CHF, MI, tachycardia)
Direct and indirect cost of stroke: $58 billion
(Avalere report, 2009)
The Problems Episodic care See patient, get holter, change meds,
repeat cycle Incomplete data (exam not needed) No real-time management To do so requires staff ($$$); not
reimbursed No closed-loop feedback for patient or
clinician
21
Which of these are acceptable?
22
Delivery of care vs. duration of screen
Kiosk
Prescription
Community
Retail Purchase
ShortSingle
Episode
Continuous24hr Extended(7-21 days)
Intermittent
Invasive Procedure
Reimburse-ment gap
Tech
nolo
gy
Gap
None of these address
management(e.g. adherence)
How good are stroke risk scores?
(Fang M, JACC 2006)
c-statistic = 0.56-0.62
Before wearables, there were implantables
Remote monitoring is continuous, usually passive, and reimbursed
VA remote monitoring study 10,000 patients with ICD and
pacemakers linked to EMR, claims, lab, pharmacy data 22,000 person-years of daily AF burden Stroke rate 3.2%
23% had AT/AF in 30d on or preceding ischemic stroke
Turakhia M, et al. Circ Arrhythm Electrophysiol, 2015
Inpatient Claims
Outpatient Encounters
VA Claims (2002-present)
Laboratory Pharmacy
Fee-based care
Vital signs, wt, BMI
VA EMR
Death records
Medicare ClaimsPart A, B, D
Pacemaker/ICD Remote Monitoring
CareLink®
10,000 patients (16K now) with devicesProgramming settings,daily AF burden, arrhythmiaepisodes, shocks, device failure
Linkage to VA clinical data to CIED data
Among patients with stroke, OR of having AF proximal to stroke, but not remotely prior was 5.5
Threshold did not matter (30 sec to 6 hours) – risk pattern was the same
Exact timing of AF and risk
Turakhia M, et al. Circ Arrhythm Electrophysiol, 2015
Contribution to prediction (attributable risk) is low
What if we throw “big data tools” at the problem?
31
Han L / Turakhia M, HRS 2015
Machine learning discriminationc statistic = sensitivity / (1-specificity)
Challenge 2: Working with Tech
smbc-comics.com
http://smbc-comics.com
smbc-comics.com
Challenge 2: Working with Tech
http://smbc-comics.com
Disruption with impunitywill not sustain
Scripps “Wired for Health” Study
Bloss CS, et al. PeerJ, 2016
Bloss CS, et al. PeerJ, 2016
Potential explanations Trial too short No passive data collection
(implantables) Not integrated with care
coordination Engagement
Wrong patients (prior smartphone experience not required); highly self-selected
Chillmark Research, 2016
Source: Ofcom. From The Economist, Feb 26, 2015
41
UI/UX: Do patients (or clinicians) want to look at data like this?
Bloss CS, et al. PeerJ, 2016
Iterating on trial design Randomized trial of a mobile app for adherence n = 316
Newly-initiated NOACs for AF Must have a smartphone to participate Outcome 6-month NOAC adherence PDC from pill counts, refill records
6-month OAC persistence Minimal clinical touches “Let the app do the work”
SmartADHERE Trial; PI: Turakhia
#1: Incentives are aligning
Opportunities & Tailwinds
www.relatecare.com
Over 60 new telehealth startups
since then
www.mobihealthnews.com
Overuse of ER and Urgent Care by younger patients; created ClickWell Care
0 35070010501400
65+55-6445-5420-44
ClickWell and the Stanford Health Care App
Cheung L, Desai S, Harrington B, AHA 201549
Phone35%
Video65%
Visit Modality
In-Person
43%Phone
32%
Video25%
All Visits by Visit Modality
Same-day access increases adoption >50% of MD visits are same-day No copayment (vs. $20 for face-to-face) All visits billed Salaried physicians; no RVUs
ClickWell implementation
Mobile40%Desktop
60%
Platform Use
Cheung L, Desai S, Harrington B, AHA 2015
0%
25%
50%
75%
100%
18-30 31-40 41-50 51-64 65+
NPV
In-Person Phone Video
0%
25%
50%
75%
100%
18-30 31-40 41-50 51-64 65+
RPV
In-Person Phone Video
Older patients are more willing to engage in virtual visits, both new and return visits.
Visit Modality By Age51
Virtual visits are more efficient
0
63
125
188
250
Min 1-5 Min 6-10 Min 11-15 Min 16-20 Min 21-25 Min 26-30 Min 31-35 Min 36-40 Min 41-45 Min 46-50 Min 51-55 Min 56-60 Min >60
Num
ber o
f Vis
its
Visit Duration (in minutes)
Video Phone In-Person
Estimated MD Hours Saved Over 9 Months: 155 hours
Estimated MA/RN Hours Saved Over 9 Months: 153 hours
Study in Spain (Xbox Kinect) reduced 52,000 hospital visits; 7% reduction in cost per patient
(Cheung L / Desai S, in preparation)
52
The evolution of academic collaboration
Version 1.0
Version 2.0 +
Version 3.0 +Similar pattern across manyinstitutions and stakeholders
BASELINE STUDYA collaboration among Duke, Stanford, and Google to develop an integrated understanding of human health
Omics- Genomics- Epigenomics- Transcriptomics- Metabolomics- Microbiome
Immune Status
EMR HistoryFamily TreeSurveys
Imaging- Echocardiography- Coronary CT- Whole Body MRI
Physical Exam
Standard Lab Tests- Blood work
Cohort Cancer Cardiovascular DiseaseCohort 1 Low Risk Low RiskCohort 2 High Risk High Risk
Cohort 3 High Risk for Recurrence High Risk for Recurrence
Making trials more efficient Recruitment / enrollment Site management
DATA INTEGRATION ANALYTICSPROCESS CHANGE
Hurdles to Reaching the Promise of Digital Health
Millions of data points from a wide variety of
sources
Data is in separate solutions.
Integration has pain points.
FREEING AND INTEGRATING DATA ARE KEY
Predictive analytics is here. May work
for population management, but
has not shown benefit at the
individual level
Culture change, behavior change, process changes
are difficult to accomplish
Source: Frost & Sullivan
Modified from Frost & Sullivan, 2015
The tapestry of data
Weber GW, JAMA. 2014
Epic-centered care
www.arstechnica.com; www.mediaite.com
Where would you want your PHI?
Summary Digital health is here to stay, but we are
still in beta testing Implementation, care delivery, and
workflow lags technical innovation of smartphones and sensors Disruption with impunity won’t work; Tech
and Investment community coming around Big Data for the Individual is a work in
progress
“Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it,
so everyone claims they are doing it.”
– Daniel Ariely, Ph.D.Duke University
Research Group Staff Jun Fan, Susan Schmitt, Mariam
Askari Graduate Students Lichy Han, Aditya Ullal, Claire Than
Residents Andrew Chang, Jessica Hellyer,
Andrew Cluckey, George Leef Postdocs/Fellows Daniel Kaiser, Alex Perino
Mentors Paul Heidenreich, Ken Mahaffey,
Bob Harrington
Thank you [email protected]@leftbundle
66
Quantitating Health:The Outlook from Silicon ValleyDisclosuresDisclaimerBubbles don’t pop; they shrinkDigital health investmentSlide Number 6Where did the money goSlide Number 8Slide Number 9Slide Number 10Slide Number 11Six Big Themes for the New Healthcare EconomySlide Number 13überification of health careWould you like a doctor with your pizza?The Need: Challenges to Widespread Adoption of Digital Health TechnologyCase study:Atrial FibrillationSlide Number 18AF complications are costlyAF is the most expensive cardiac dxThe ProblemsWhich of these are acceptable?Slide Number 23Delivery of care vs. duration of screenHow good are stroke risk scores?Before wearables, there were implantablesRemote monitoring is continuous, usually passive, and reimbursedVA remote monitoring studyLinkage to VA clinical data to CIED dataExact timing of AF and riskWhat if we throw “big data tools” at the problem?Machine learning discriminationc statistic = sensitivity / (1-specificity)Challenge 2: Working with TechChallenge 2: Working with TechDisruption with impunitywill not sustainScripps “Wired for Health” StudySlide Number 37Potential explanationsSlide Number 39Slide Number 41UI/UX: Do patients (or clinicians) want to look at data like this?Iterating on trial designSlide Number 44#1: Incentives are aligningSlide Number 46Slide Number 47When Stanford created an ACO, an unmet need emergedClickWell and the Stanford Health Care AppClickWell implementationVisit Modality By AgeVirtual visits are more efficientThe evolution of academic collaborationBASELINE STUDYSlide Number 57Making trials more efficientHurdles to Reaching the Promise of Digital HealthThe tapestry of dataEpic-centered careWhere would you want your PHI?SummarySlide Number 64Research GroupSlide Number 66
