amia2016 pghd-panel-v8

Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support

Transforming Patient-Generated Data for Wellness and Biomedical Research:

From Behavioral Sensing to Decision Support

AMIA 2016 Didactic Panel Nov 14 15:30 - 17:00 Salon A1

Panelists: Susan Peterson, PhD, MPH, Katherine Kim, PhD, MPH, MBA, F. Martin-Sanchez, PhD, FACMI, FACHI, Cagatay Demiralp, PhD

Discussant Summary & Panel Moderator: Pei-Yun Sabrina Hsueh, PhD


Didactic Panel - Transforming Patient-Generated Data for Wellness and Biomedical Research: From Behavioral Sensing to Decision Support

Susan Peterson, PhD, MPH

University of Texas MD Anderson Cancer Center

Katherine Kim, PhD, MPH, MBA

University of California Davis

Fernando Martin-Sanchez, PhD, FACMI, FACHI

Weill Cornell Medicine,

Cagatay Demiralp, PhD

IBM T.J. Watson Research Center Pei-Yun Sabrina Hsueh, PhD

(Chair/Moderator)

(IBM T.J. Watson Research, USA)


Pei-Yun (Sabrina) Hsueh, PhD

Research Staff MemberIBM Watson Research Health Informatics PIC co-ChairBehavioral Analytics Lead Computational Health Behavioral and Decision Science Group Center for Computational Health IBM T. J. Watson Research Center

AMIA CPHI WG SecretaryAMIA Consumer and Pervasive Health Informatics Work Group

Opening Remark

StratificationIdentify sub-populations at-risk for

unhealthy behaviors

PersonalizationTailor intervention strategies for

individuals

EngagementPromote healthy behaviors on a day-to-

day basis


Improving health is not about more medical care: From Precision Medicine to the use of PGHD for Science of Care

Source: Bipartisan Policy Center, “F” as in Fat: How Obesity Threatens America’s Future (TFAH/RWJF, Aug. 2013)

6

Medinfo 2013 panel

MIE 2014 workshopMEDINFO 2015 workshop

HEC/MIE 2016(IMIA Consumer Health informatics WG)

AMIA 2016 (AMIA Consumer and Pervasive Health Informatics WG pre-symposium & didactic panel)


Who is Oscar? Patient is not Just a Patient

• Our agenda for Oscar:– Medication adherence– Come to follow-up appointments– Improved self-monitoring– Participation in PT– Nutritious food choices and

increased calories– Living Will– Participate in Shared Decision-

Making• Oscar’s agenda for Oscar:

– Grieving for his wife– Transportation– Managing Rx side effects– Seeing his grandchildren– Reducing knee pain

5


6

Can data from EHR be the answer? Capturing Social/Behavioral Determinants from EHR

Institute of Medicine report (2016)


Health consumer data collection are emerging with high perceived value, but gaps persist.

67% up from 27% in 2014. More than 48% and 33% know they can access lab results

and physician notes.

Willing to wear technology for health tracking

% believe that using PGHD would be beneficial

Access to EHR

Scale> 50%

45%

78%

78% : 18%The proportion of patients believes in full access to health records v.s. the

proportion of physicians

Gap

IBM Confidential

Patient Participation

Source: J.P. Gownder, et al. Forrester research report 2015. PwC Strategy Report 2016.Catalyst for Payment Reform,, CPR, 2015.W. Lynch, B. Smith, and M. Slover, Altarum Institute Survey of Consumer Health Care Opinions 2012. E.O. Lee and E.J. Emanuel,, N Engl J Med 368 (2013), 6-8.

Already wearing or using apps

21-33% > 90%% willing to participate in shared decision making with clinicians

https://www.forrester.com/go?objectid=BIO1728


PGHD evidence is still being collected…• Some initial evidence points to different directions

– Not effective for improving diet or physical activity behavior for weight management over 24 months (IDEA trial, JAMA 2016)

• Interestingly, no difference of dietary and physical activity behavior between the standard intervention and the wearable group; yet outcome is different.

– Not effective for healthcare cost and utilization control over 6 months (PeerJ 2015)• Interestingly, patients who monitored their health were less likely to attribute health

outcomes to chance than those who didn’t monitor their health

• Some initial success in linking internal motivators– Improving adherence to medication and blood pressure monitoring (McGillicuddy et

al., 2013)– Promoting an individual’s sense of autonomy by helping them to focus on their own

reasons for increasing levels of physical activity and exercise (Riiser et al., 2014)

• More research on the horizon….– Motivational framework (Stanford), sensing making (Columbia U), adaptive

intervention (U Mich, Columbia U, IBM Research), social media for treatment affordance (Well-Corneil), to name a few, etc.











(Chair/Moderator)



Agenda • 15:30-16:30 Presentations

– Opening remark– Prof. Peterson: Using Patient-generated Health Data for Assessment and

Intervention in Cancer Survivors– Prof. Kim: Leveraging Patient-generated Health Data for Person-centered

Care Coordination– Prof. Martin-Sanchez: Evidence generation from Patient-generated Health

Data for informing biomedical research– Dr. Demiralp : PGHD & Visualization– Dr. Hsueh (Summary): EMERGING HEALTHCARE LANDSCAPE SHIFT WITH PATIENT-

GENERATED DATA

• 16:30-17:00 Panel discussion/audience Q&A – Highlighting the opportunities (use cases) – Identifying the bottlenecks and barriers of using patient generated health data – Potential solution to overcome the barriers identified


Using Patient-generated Health Data for Assessment and Intervention

in Cancer Survivors

Susan K. Peterson, PhD, MPHProfessor, Department of Behavioral Science

The University of Texas MD Anderson Cancer CenterHouston, Texas

AMIA 2016 Panel Nov 14 15:30 - 17:00


Susan K. Peterson, PhD, MPHProfessor of Behavioral ScienceDirector, Patient-Reported Outcomes, Survey & Population Research Shared ResourceThe University of Texas MD Anderson Cancer Center

Research interests• Development and evaluation of e-Health interventions for populations at risk for hereditary cancer and cancer survivors, including sensor-based and mobile technology applications for behavioral assessment and intervention• Psychosocial and behavioral outcomes of cancer genetic and genomic testing in cancer survivors and families, including:

- Decision-making about testing & receiving genetic test results- Psychological and behavioral impact of testing on quality of life and

related factors


Health information technology priorities for oncology

• Systems approach for greater and more rapid access to patient clinical information

• Better understand therapeutic responses, side effects, quality of life, general health status

• Identify trends important to prevention and survivorship

• Synthesize and distill large amounts of data• Identify earlier opportunities to manage side effects, complications, long-

term survivorship outcomes

• Personalized, relevant guidance driven by real-time data for smarter management decisions by patients, physicians

• Support activated and empowered individuals

Blueprint for Transforming Clinical and Translational Cancer Research, ASCO, 2011

President’s Cancer Panel, NCI, 2014-2015


CYCORE: CYberinfrastructure for cancer COmparative effectiveness Research

RC2 CA148263, R01 CA177914, R01 CA177996

System features:• Patient-accessible platforms for rapid, direct data

collection• Remote patient monitoring and management, away

from clinic setting• Sensors, ecological momentary assessment

(EMA), video interface• Interfaces accessible to patients, clinicians,

researchers• Ability to receive feedback, track data

(historical and real-time)

Patrick, Transl Beh Med, 2011; Hirsch, Ca J, 2011


U.S. cancer incidence & deaths

All Cancers 1,665,540585,720

Lung 224,210163,660

Breast 235,030 40,430

Prostate 233,000 29,480

Colorectal 144,040 51,260

Head & Neck 55,070 11,490

(e.g., oropharynx, nasopharynx, larynx, oral cavity, salivary)

American Cancer Society 2016

New cases Expected Deaths

Incidence of HPV-positive oropharyngeal cancers has > doubled in past 20 years, due to increasing incidence of HPV in U.S. population


Head & neck cancer treatment

• Nasopharynx• Oropharynx• Hypopharynx• Larynx• Oral cavity• Thyroid• Sinonasal• Salivary• Skin

Curative radiation therapy (RT)With/without chemotherapy

Curative surgeryWith/without radiation therapyWith/without chemotherapy

RT typically lasts 5 days/week for 6-7 weeks


Challenges in managing treatment-related side effects

• Significant side effects from RT + chemo for HNC• Mucositis resulting in swallowing problems,

pain, altered eating/drinking capabilities• Adherence to complex self-care regimen necessary

to reduce long-term morbidity– 67% non-adherent to swallowing exercises during RT (Shinn,

2010)• Climate for malnutrition and dehydration

• Physiological decline can rapidly occur between routine visits

• Increased ER visits, inpatient admissions, costs


Standard clinical care during radiation therapy

• Weekly visits with radiation oncologist• Weight, temperature, pulse• Blood pressure (sitting & standing)

– Orthostatic Hypotension: Decrease of 20 mmHg systolic or 10 mmHg diastolic on standing. Usually accompanied by an increase in heart rate

– Assess nutrition and hydration• IV fluids if dehydrated; GI consult for feeding tube

– Pain and symptom assessment• Pain control, supportive care

Limitations- Ability to assess patient only once/week in clinic- Rapid physiological changes can occur between visits- Home assessment historically limited to self-report


Opportunity: Using patient-generated data to prevent dehydration during RT

• Objective, frequent data collection on key physiological and behavioral outcomes

• Weight, hydration, BP, pulse, swallowing• Identify patients at high risk, reduce hospitalization, ER

visits• Identify need for IV hydration early• Better nutritional and pain support • Support adherence to self-care

• Provide decision support for clinician to optimize chances for rapid intervention, support


Monitoring dehydration risk in HNC patients during RT

Blood pressure + pulse

Data transmission to home-based hub

Scale to monitor weight loss

Interface with CI

Data available to researchers, clinicians

Patient-reported outcomes (symptoms, nutrition, fluid intake/output) via phone/tablet app


CYCORE web-based interface:Daily weight, BP, pulse

Enables clinician/researcher to view data, track trends in weight, pulse, BP

Orthostatic hypotension

+ Pulse

Dehydration risk

Early identification via CYCORE

Earlier IV fluid therapy

Improved outcome: reduction in ER visits, hospitalizations


CYCORE web-based interface:Patient-reported outcomes

Enables clinician/researcher to view data on daily patient-reported symptoms and medications


Preliminary data• 48/50 patients completed feasibility study; 98% of

assessments completed• 60% of patients had at least one dehydration-related

event during study» Orthostatic hypotension: ↓ systolic BP + ↑ HR 10

points, sitting vs. standing readings• Symptoms associated with dehydration-related

events– Nausea (p=0.004), vomiting (p=0.004) swallowing difficulty

(p=0.004)• High level of patient and clinician support and

satisfactionPeterson, Shinn, et al., JNCI Monogr 2013


HNC patients and dehydration risk: Satisfaction and usability ratings

Peterson, JNCI Monogr, 2013

Participants’ responses to post-study evaluation questionnaire regarding usability and acceptability of mobile devices for home monitoring of dehydration risk (Range: 0 = “not at all” to 10 = “extremely”), n=48

0123456789

10

BP Device

Weight Scale

Phone: Daily Symptoms Surveys

Home Health Hub


Sensor-assisted prevention of dehydration in head and neck cancer patients

Aims: 1. Evaluate efficacy of sensor-based home monitoring in reducing the prevalence of hospitalization and emergency room visits related to dehydration in HNC patients undergoing RT

- Compare hospital and ER admissions in CYCORE- assigned patients vs. usual care

2. Evaluate the efficacy of sensor-based home monitoring in reducing costs related to treating dehydration in HNC

patients undergoing RT

1R01CA177914-01 NCI/NIH (Pis: Peterson, Shinn, Beadle, Garden)


Eligibility- Bilateral RT for:

oropharyngeal, hypopharyngeal, nasopharyngeal, laryngeal, salivary gland, thyroid, oral cavity, unknown primary HNC with cervical metastasis

- Age > 18 yrs; English proficient; Zubrod <2

- No prior dysphagia

CYCORE Head & Neck Cancer RCT

September 2016


CYCORE (n=127)

Standard care (n=127)

Age, M (range) 62 (26-79) 61 (36-79)

Female, % 22% 22%

Race – White % Black Hispanic Asian Other

86%1741

79%9913

Married % 84% 84%

< HS graduateSome college/other> bachelor’s degree

17%2756

20%2554

CYCORE Head & Neck Cancer RCT Participants’ Demographic Characteristics


Using patient-generated health data:Barriers and solutions

Barriers• Need to balance utility of information vs. clinician, workflow, and

time burden• Ability to collect, administer, and communicate PGHD ≠ clinical

goals or needs• When and how to integrate PGHD into EHRsPossible solutions• Involve multidisciplinary stakeholder teams to identify use cases

w/ clear clinical need & technology solutions• Collect, administer, and communicate PGHD for the right patient,

the right problem, the right time• Optimize process automation and usable system interfaces to

integrate PGHD into EHRsPatrick, Transl Beh Med (2013); Harle, JAMIA (2016)


Collaborators

MD Anderson – Karen Basen-Engquist, PhD, MPH– Eileen Shinn, PhD– Beth Beadle, MD, PhD– Adam Garden, MD– Sanjay Shete, PhD– Chan Shen, PhD– Alex Prokhorov, MD, PhD– Stephanie Martch, MS, RD, LD

UC San Diego/Calit2– Kevin Patrick, MD– Emilia Farcas, PhD– Fred Raab, MS– Chaitanya Baru, PhD– Ingolf Krueger, PhD– Viswanath Nandigam, MS– Kai Lin, PhD– Yan Yan, MS

Univ. of Alabama-Birmingham– Wendy Denmark-Wahnefried, PhD











(Chair/Moderator)



Leveraging Patient-generated Health Data for Person-centered Care Coordination

Session S45 Didactic Panel Transforming Patient-generated Data for Wellness and Biomedical Research: From Behavioral Sensing to Decision SupportNov 14, 2016, 3:30-5:00

Katherine K. Kim, PhD, MPH, [email protected]


33

Challenges for individuals with complex chronic and co-morbid conditions


34

Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision SupportCare Coordination Challenge

35 35

HOSPITALSINPATIENT

COMMUNITYSERVICES

PERSON

CLINICS/

OFFICES

HOSPICE CARE

OUTPATIENTCARE

CAREGIVERS

RESPITE CARE

SOCIAL SERVICES

HOME CARE

PRIMARY CARE

SPECIALIST

SPECIALIST

SPECIALIST

PRIMARY CARE

SPECIALIST

SPECIALIST

FAMILY

Many touch points, multiple transitions, unclear accountability


Care Coordination Challenge

HOSPITALSINPATIENT

HOME CARE

PRIMARY CARE

SPECIALIST

SPECIALIST

COMMUNITYSERVICES

PATIENT

CLINICS/

OFFICES

HOSPICE CARE

OUTPATIENTCARE

CAREGIVERS

SPECIALIST

RESPITE CARE

SOCIAL SERVICES

FAMILYPRIMARY CARE

SPECIALIST

SPECIALIST

Many portals with incomplete information

Care Coordination Challenge


Community-Wide Care Coordination: A Framework in Process

“Care coordination is the deliberate synchronization of activities and information to improve health outcomes by ensuring that

care recipients’ and families’ needs and preferences for healthcare and community

services are met over time.”

(National Quality Forum, 2014)

37


38

Personal Health Network (PHN) for Chemotherapy Care Coordination• 2-arm feasibility RCT: care coordination with and

without PHN• 60 participants• User-centered design• Evaluation:

- Health technology acceptance and use- ED and inpatient utilization- Symptom severity- Workflow efficiency


39

Personal Health Network (PHN)

• Shared care plan• Patient education & information at the “point of need”• Person-generated data: symptoms, PROs, needs• Communication• HIPAA compliant mobile application

- Tiatros platform- iOS application - Browser application


40

Table 1: Baseline Socio-Demographic and Health Characteristics

Care Coordination

Plus PHN Care Coordination

Alone pn=35 n=26

Age in Years (SD) 59.00 (11.12) 59.17 (9.18) 0.95Female, % 75 75 1.00White non-Hispanic, % 89 73 0.12

Completed Education, % 0.68Employment, % 0.55Married/Partnered, % 58 79 0.19Family Income, % 0.24Health Status, % 0.32Cancer Stage, % 0.62I 14 8II 37 44III 26 16IV 23 32

Treatment Plan*Chemotherapy 100 96 0.24Radiation 31 34 0.79Other 20 8 0.18

* Treatment Plan variables do not add up to 100%; respondents could check more than one.


41

Table 2: Health Technology Acceptance and Use (HTAU) at Baseline (n=33; Intervention Group Only)

Construct and Items Score* Mean SDPrice Value (3 PV Items) 4.40 0.12

Facilitating Conditions (4 FC Items) 4.48 0.48

Effort Expectancy (4 EE Items) 3.86 0.11

Social Influence (5 SI Items) 3.13 0.10

Performance Expectancy (8 PE Items) 3.10 0.40

Hedonic Motivation (3 HM Items) 3.30 0.30

Behavioral Intention (3 BI Items) 3.41 0.23

Habit (3 HT Items) 2.37 0.08

* Each item rated from 0 (not at all) to 6 (a great deal)


42

Implications

• One of the first examples of a technology-enabled care coordination intervention in oncology

• Early evaluation of usability has allowed for refinements and PHN v.2 to be rolled out to the same participants.

• Equal attention to person-generated and clinical data allows potential for person-centered care.

• Transparency supports collaboration: Data generated by patient and coordinator is open to each PHN’s members.

• Remaining challenges: connectivity, integration with the EHR for seamless adoption.

Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision SupportAcknowledgements UC Davis Collaborative Care Coordination Research Group (3CRG): Betty Irene Moore School of Nursing, School of Medicine, Comprehensive Cancer Center

• Jill Joseph• Janice Bell• Andra Davis• Sarah Reed• Rick Bold• David Copenhaver• Tom Semrad• Victoria Ngo• Robin Whitney• Joy Morgan• Wendy Wait• Chelsie Antonio-Gonzales• Ronald Grummer• Thuy Le

Funding• McKesson Foundation #201401953 (PI

Joseph) • NIBIB/Boston University Center for the

Future of Technology in Cancer Care, #U54-EB015403-04 (PI Kim, Joseph)

• Oncology Nursing Society• UC Davis Center for Health Policy

Research (PI Bell)• UC Davis Academic Senate grant for New

Research Initiatives and Collaborative Interdisciplinary Research (PI Bell)

• Gordon and Betty Moore Foundation grant to Betty Irene Moore School of Nursing


44

Summary• Opportunities for person-generated data

- Equal attention to person-generated and clinical data enables person-centered care.

- Transparency may be a prerequisite for collaborative care models• Identify bottlenecks and barriers for using person-generated data

- Ubiquitous persistent connectivity- Need enterprise level robustness and functionality with ease and

footprint of mobile app• Potential mechanisms for overcoming

- Innovations in application design for performance, scalability, integration


45

Thank You

Katherine [email protected]











(Chair/Moderator)



Fernando Martin-Sanchez PhD, FACHI, FACMI• Professor - Division of Health Informatics, Dept. of Healthcare Policy and

Research• Environmental and Participatory Health Informatics (ENaPHI) Research

Group• PhD in Informatics, PhD in Medicine, MSc in Knowledge Engineering, MSc

in Molecular Biology, BSc in Biochemistry• Joined Weill Cornell in December 2015

– Professor and Chair of Health Informatics, Melbourne Medical School (2011-2015)

– Director, Health and Biomedical Informatics Research Centre, (HaBIC) the University of Melbourne (2013-2015)

– Head of Dept. Medical Bioinformatics. National Institute of Health Carlos III of Spain. (1998-2010)

• Research interests: biomedical data integration, participatory health informatics, exposome informatics, precision medicine

Email: [email protected]: @fermarsan

mailto:[email protected]


ENaPHI@WeillCornellMedicine

Participatory Technologies (Digital Health)

QS, mHealth, SoMe, DTC …

Biomedical Research

Healthcare & Prevention

Environmental Health

Informatics

Participatory Health

Informatics

EXPOSOME • Ontologies• Resources• Expotyping

EVIDENCE GENERATION

• Therapeutic affordances of social media

• Essential characteristics of SQS

Precision Medicine Informatics


Availability of devices, sensors, apps, DTC services and Social Networks

Wearables

Sensors

DTC lab tests

Apps


• Digital health (participatory) technologies – smartphone apps, – personal sensing devices, – direct-to-consumer e-services – social media

• Health Informatics is moving into new territories, beyond provider-generated clinical data - PGHD (Patient-generated health data) 1. Monitoring of individual environmental health risk factors. Exposome2. Participatory Health

• These new sources of individual big and small data (continuous, comprehensive and personalized) pose great challenges for Health Informatics and will require new approaches to data collection, storage, standardisation, integration, analysis and visualisation.

accessible and affordable for

individuals


51

• Gregory Abowd (2011) predicted “within 5 years, the majority of clinically relevant data...will be collected outside of clinical settings.”

• PGHD—health-related data created, recorded, or gathered by or from individuals to help address a health concern

•Individuals are responsible for recording data and decide how to share it (Personal Health Data)

Potential Benefits Challenges

• Personalized/ preventative medicine• Reduction of unnecessary patient visits

and/or hospital admissions•Easy and continuous monitoring•A cheap treatment for many chronic diseases

• Verifying measurement validity • Lack of standardization (interoperability)• Specific challenges for clinicians • Specific challenges for patients • Financial and technical• Legal

Mark Liber, 11


HealthInformatics

Bioinformatics

Proteomics and

MetabolomicsData

Geneexpression

Data

GenomicData

Patientgenerated

Data

PopulationData

ClinicalData


A different way to recruit research participants

• For a recent breast cancer study, epidemiologist Kathryn H. Schmitz of the University of Pennsylvania sent out 60,000 letters—and netted 351 women. Walking each participant through the paperwork took 30 minutes or more. Such inefficient methods of finding test subjects have been the norm for medical research.

• Apple, working with Stanford University School of Medicine, developed MyHeart Counts, an app for monitoring cardiac health. Within the first 24 hours, 10,000 participants signed up for the study.

• Kelton and Makovsky Health -fifth annual “Pulse of Online Health” found that 66 percent of Americans would use a mobile application to manage health-related issues.

• The patient’s voice has largely been missing from most of the design and the focus of clinical studies (Ken Mandl, Harvard).

• Citizen science, participatory health


Genotype * Expotype Phenotype

Coriell Personalised Medicine Collaborative

Marc Rubin,Nature 2015


Genome / GenotypeExposome / Expotype

Phenome / Phenotype

Biomarkers (DNA sequence, Epigenetics)

Environmental risk factors (pollution, radiation, toxic agents, …)

Anatomy, Physiological, biochemical parameters(cholesterol, temperature, glucose, heart rate…)

Social media / Personal health record / EMRs / Research Repository


Software Apple Google Cornell

Tech Samsung

DB smartphone HealthKit Google Fit S Health

Apps for researchers ResearchKit Study Kit

(Baseline)Research Stack

Apps for consumers CareKit OHMAGE-omh


EHR integration with HealthKit

• 2014 - Ochsner Health System in Louisiana• 2015 - Deaconess Health System in Indiana

integrates Fitbit into EHR portal • 2015 - Duke is using HealthKit to get patient-

generated data into the EHR.• 2015 - Cerner with Validic• 2015 - EPIC MyChart at Cornell Medicine has full

HealthKit functionality


mPower

• First six months of data from the app. mPower, a Parkinson's-focused app, one of the first five Apple ResearchKit studies

• Of the 12,000 mPower study participants, about 9,500 participants chose to share their data with all researchers.

• mPower stands for "mobile Parkinson’s observatory for worldwide, evidence-based research".

• The mPower app aims to help users track their symptoms using activities including a memory game, finger tapping, speaking, and walking. The app will also collect data from wearable devices


Jawbone and Bay Area Earthquake Sept 2014

https://jawbone.com/blog/napa-earthquake-effect-on-sleep/


ENTRAIN app


Health eHeart


Google Baseline


100K Wellness Project


N-of-1 studies• https://www.thegua

rdian.com/science/2016/jul/03/citizen-science-how-internet-changing-amateur-research?CMP=share_btn_fb

https://www.theguardian.com/science/2016/jul/03/citizen-science-how-internet-changing-amateur-research?CMP=share_btn_fb








US Precision Medicine Initiative Cohort Program


Summary: Use of PGHDChallenges / Limitations Possible solutions

Verifying validity of data Reproducibility exercises

Lack of standardization (interoperability) Standard reporting guidelines and templates

How to decide which data to use More research is needed

Lack of integration with clinical workflow

User centric design, involving clinicians

Insufficient training More emphasis in health professions curricula and outreach to society at large

Equity and access (economic barriers and digital gap)

Proper cost-efficiency evaluation and sponsorship by health insurers or providers











(Chair/Moderator)



Data Visualization

70

Çağatay Demiralp @serravisIBM Research


IBM T. J. WatsonStanford UniversityUniversity of WashingtonMicrosoft Research CambridgeBrown University

Çağatay Demiralp

CurrentPrevious

Data Visualization . Visual Analytics . HCI


IBM T. J. WatsonStanford UniversityUniversity of WashingtonMicrosoft Research CambridgeBrown University

Çağatay Demiralp

CurrentPrevious

Data Visualization . Visual Analytics . HCI

Extend the theoretical and perceptual foundations of data visualization Develop and automate interactive visual analysis tools Í

Current focus


What is visualization?


What is visualization? • “Transformation of the symbolic into the

geometric.”[McCormick et al. 1987]

• “... finding the artificial memory that best supports our natural means of perception.” [Bertin 1967]

• “The use of computer-generated, interactive, visual representations of data to amplify cognition.”

[Card, Mackinlay, & Shneiderman 1999]


Why create visualizations?


E.J. Marey’s sphygmograph [from Braun 83]


In 1854 John Snow plotted the position of each cholera case on a map [from Tufte 83]


Used map to hypothesize that pump on Broad St. was the cause

3472x————

3472x————

Mental Paper & Pencil

0

30

60

90

120

Tim

e (S

ecs)

682380 +————2448


New York weather [from NY Times 1981]


Bones in hand

Double helix [Watson and Crick 53]


Crimean War Deaths by Florence Nightingale,1856


Crimean War Deaths by Florence Nightingale,1856

“to affect thro’ the eyes what we fail to convey to the public through their word-proof ears”

Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision SupportWhy do we create visualizations? • Record information• Blueprints, photographs, seismographs, …

• Analyze data to support reasoning• Develop and assess hypotheses• Discover errors in data• Expand memory• Find patterns

• Communicate information to others• Share and persuade• Collaborate and revise


PGHD & Visualization


Usual story

Focus on data collection

Lack of tools for sense making


How effective are these?


What do they mean?


Challenges

Little known about effectivenessBehavior change, decision making, risk assessment, diverse users & tasks

Difficult to interpret & operationalizeNoisy, sparse and heterogenous

data


ChallengesLittle known about effectiveness

Behavior change, decision making, risk assessment, etc.

Difficult to interpret & operationalize

Noisy, sparse and heterogenous data

SolutionsSystematically evaluateDerive general principles Communicate risk & uncertainty PGHD-VisKit Integrative tools What-if scenarios Reproducible analysisAutomation











(Chair/Moderator)



Agenda • 15:30-16:30 Presentations

– Opening remark– Prof. Peterson: Using Patient-generated Health Data for Assessment and

Intervention in Cancer Survivors– Prof. Kim: Leveraging Patient-generated Health Data for Person-centered

Care Coordination– Prof. Martin-Sanchez: Evidence generation from Patient-generated Health

Data for informing biomedical research– Dr. Demiralp : PGHD & Visualization– Dr. Hsueh (Summary): EMERGING HEALTHCARE LANDSCAPE SHIFT WITH PATIENT-

GENERATED DATA

• 16:30-17:00 Panel discussion/audience Q&A – Highlighting the opportunities (use cases) – Identifying the bottlenecks and barriers of using patient generated health data – Potential solution to overcome the barriers identified


DISCUSSANT SUMMARY

EMERGING HEALTHCARE LANDSCAPE SHIFT WITH PATIENT-GENERATED DATA

It is not about Big Data

SOURCE: Barbara J. Sowada, A Call to Be Whole: The Fundamentals of Health Care Reform, July 30, 2003, Praeger.

IBM Watson // ©2015 IBM Corporation

NOISY, LARGE VOLUME, UNCONTROLLED

Need minimum description & quality/validity study


96

Can data from EHR be the answer? Capturing Social/Behavioral Determinants from EHR

Institute of Medicine report (2016)


Health consumer data collection are emerging with high perceived value, but gaps persist.

67% up from 27% in 2014. More than 48% and 33% know they can access lab results

and physician notes.

Willing to wear technology for health tracking

% believe that using PGHD would be beneficial

Access to EHR

Scale> 50%

45%

78%

78% : 18%The proportion of patients believes in full access to health records v.s. the

proportion of physicians

Gap

IBM Confidential

Patient Participation

Source: J.P. Gownder, et al. Forrester research report 2015. PwC Strategy Report 2016.Catalyst for Payment Reform,, CPR, 2015.W. Lynch, B. Smith, and M. Slover, Altarum Institute Survey of Consumer Health Care Opinions 2012. E.O. Lee and E.J. Emanuel,, N Engl J Med 368 (2013), 6-8.

Already wearing or using apps

21-33% > 90%% willing to participate in shared decision making with clinicians

https://www.forrester.com/go?objectid=BIO1728


HETEROGENEOUS SOURCES OF PGHD POSE CHALLENG TO HIT: PATIENT-CENTEREDNESS

EHR Administrative Data/Claims

PHR

Mobile PHR/Ecological Momentary

Assessment

Patient-Reported Outcome

Ref: Wu, A. W., et al. (Journal of Clinical Epidemiology 2013)


PGHD evidence vary• Some initial evidence points to different directions

– Not effective for improving diet or physical activity behavior for weight management over 24 months (IDEA trial, JAMA 2016)

• Interestingly, no difference of dietary and physical activity behavior between the standard intervention and the wearable group; yet outcome is different.

– Not effective for (PeerJ 2015)• Interestingly, patients who monitored their health were less likely to attribute health

outcomes to chance than those who didn’t monitor their health

• Some initial success in linking internal motivators– Improving adherence to medication and blood pressure monitoring (McGillicuddy et

al., 2013)– Promoting an individual’s sense of autonomy by helping them to focus on their own

reasons for increasing levels of physical activity and exercise (Riiser et al., 2014)

• More research on the horizon….– Motivational framework (Stanford), sensing making (Columbia U), adaptive

intervention (U Mich, Columbia & IBM Research), social media for treatment affordance (Well-Corneil), to name a few, etc.


Who is Oscar? Patient is not Just a Patient

• Our agenda for Oscar:– Medication adherence– Come to follow-up appointments– Improved self-monitoring– Participation in PT– Nutritious food choices and

increased calories– Living Will– Participate in Shared Decision-

Making• Oscar’s agenda for Oscar:

– Grieving for his wife– Transportation– Managing Rx side effects– Seeing his grandchildren– Reducing knee pain

100


More Patient-centered Comparative Effectiveness Research are needed

Provide outcome indicators.

Provide quality assessment and

improvement measures.

Serve as a basis for population health campaign tools.

Improve Clinical Care & Quality

Collect evidence for comparing

intervention options with similar efficacy.

Drive patient-centered CER and

identify personalization

factors.

Comparative Effectiveness


Integrating with Best Practice in Clinical Flow

Includes medication, access, utilization, self-care.

Inferred from the assigned interventions (enrollment, goal, tasks).

Disease BurdenIncludes job family,

lifestyle.

Inferred from patient profiling.

Life Demand

Ref: Mayo Clinic: Minimally Disruptive Medicine: Kerunit model.

Instrument for Patient Capacity Assessment (ICAN)What are you doing when you are not sitting here with me?Where do you find the most joy of your life?What’s on your mind today?Are these areas of your life a source of satisfaction, burden, or both?What are the things that your doctors or clinic have asked you to do to care for your health?Do you feel that they are a help, a burden, or both?


What do you need to know about these individuals?

103


Improving health is not about more medical care: From Precision Medicine to the use of PGHD for Science of Care

Source: Bipartisan Policy Center, “F” as in Fat: How Obesity Threatens America’s Future (TFAH/RWJF, Aug. 2013)

6

Medinfo 2013 panel

MIE 2014, MEDINFO 2015 workshop

HEC/MIE 2016(IMIA Consumer Health informatics WG)

AMIA 2016 (AMIA Consumer and Pervasive Health Informatics WG pre-symposium & didactic panel)











(Chair/Moderator)



Key Questions for PGHD transformation

• What is the opportunity area going forward for PGHD transformation?

• What is our definition of PGHD? What are the likely sources?• What the key dimension of PGHD for value evaluation of data

transformation? What are the barriers? More technical or social?

• What are the likely action items to be suggested to the community to further the discussion about transforming PGHD in biomedical and wellness research? Is there a filed difference to be addressed here?


Key Questions for PGHD transformation (2)

• How can PGHD contribute to successful provider-patient communications, risk reduction, and increase in early interventions?

• Can PGHD support shared decision making or help calibrate just-in-time intervention to patient’s values?

• Do the providers’ and patients’ beliefs and support of PGHD and approaches affect patient usage?

• Can dynamically configured healthcare IT help improve healthcare quality and patient behavior using a scalable technology-enabled platform?


Thank You

MerciGrazie

Gracias

Obrigado Danke

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