HEC 2016 Panel: Putting User-Generated Data in Action: Improving Interpretability for Consumer...

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Putting User Generated Data in Action: Improving Interpretability for Clinical and Consumer Informatics

Pa#ent generated data – The transi#on from “more” to “be6er”

HEC 2016 Workshop

WS 884 Pu(ng User-‐Generated Data in Ac8on: Improving Interpretability for Clinical and Consumer Informa8cs

Aug 30 16:30 -‐ 18:00

Panelists: Thomas WETTER, Ying-‐Kuen CHEUNG, Sanjoy DEY , XinXin ZHU, Bian YANG

Moderator: Pei-‐Yun Sabrina Hsueh

Putting User Generated Data in Action: Improving Interpretability for Clinical and Consumer Informatics HEC/MIE 2016 Workshop: PuJng User-‐Generated Data in Ac#on: Improving Interpretability for Clinical and Consumer Informa#cs Katie Zhu, PhD MD

(IBM TJ Watson Research, USA)

Sanjoy Dey, PhD


Ken Cheung, PhD

(Columbia University, USA)

Bian Yang

(Norwegian University of Science of Technology, Norway)

Thomas Wetter, PhD (Panel Discussant)

(University of Heidelberg, Germany

University of Washington, USA)

Pei-Yun Sabrina Hsueh, PhD (Moderator)

(IBM T.J. Watson Research, USA)


Agenda •  16:30-‐16:40 Opening Remark by Dr. Sabrina Hsueh

•  EMERGING HEALTHCARE LANDSCAPE SHIFT WITH PATIENT-‐GENERATED DATA

•  16:40-‐17:20 Presenta#ons –  Dr. Xinxin Zhu: So we got sensor data, now what? –  Dr. Sanjoy Dey: Enhancing interpretability of computa#onal model

–  Dr. Ken Cheung: SMART-‐AR to evaluate health apps for outcome op#miza#on

–  Dr. Bian Yang: The need for addressing privacy issues with be6er interpretable rules •  17:20-‐18:00 Discussant summary presenta#on & Panel

discussion/audience Q&A –  Dr. Thomas We6er: Pa#ent generated data – The transi#on from “more” to “be6er”

–  Panel discussion (moderated by Dr. Sabrina Hsueh)


Summary on Workshop Theme (1)

•  (1) Iden#fy immediate ac#on items to start ini#a#ng proposal for enabling evidence-‐based conversa#on with pa#ents/physicians/providers in the loop



•  2. Implica#ons and lessons learned from the case studies -‐-‐ especially the gaps you perceived as barriers of entry



•  3. Requirements for successful redesign of healthcare systems to accommodate pa#ent-‐generated informa#on (with a sub-‐goal of iden#fying the areas where such informa#on can make most impacts).


Ques#ons •  1. What is the state-‐of-‐the-‐art? •  2. What are the benefits of improving interpretability in PGHD

in ac#on? •  3. What the key dimension of interpretability of PGHD? What

are the barriers? Technical/social? •  4. What is our defini#on of interpretability? What are the

likely measures? •  5. What is the opportunity area going forward? •  6. What are the likely ac#on items to be suggested to the

community to further the discussion about improving interpretability for PGHD? –  In the field of consumer health informa#cs or beyond?


INTRODUCTION

EMERGING HEALTHCARE LANDSCAPE SHIFT WITH PATIENT-‐GENERATED DATA


Pei-Yun (Sabrina) Hsueh, PhD

Wellness Analy8cs Lead Global Technology Outlook Healthcare Topic co-‐Lead Healthcare Informa8cs PIC co-‐Chair Computa8onal Behavioral and Decision Science Group Health Informa8cs Research Dept. IBM T. J. Watson Research Center •  Research focus: Pa8ent-‐genera8on info from wearables and biosensor

devices/implants, Personaliza8on analy8cs, Pa8ent engagement & Adherence risk mi8ga8on, Interpretable machine learning

Opening Remark


Source: Based on McGinnis et al, The Case for More Active Policy Attention to Health Promotion, Health Affairs, 2002.

Health Determinants Mismatches Today’s Spending “We need to invest in addressing all determinants of health…”

BIG DATA Clinical + behavior

driven Wellness Management

It’s Big Data! It is also not just 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

Solutions Population Health

Management

Condition Specific Care

Health and Wellness

Social Programs

Discovery Solutions

Real World Evidence

Ecosystem Population Health

Management

Condition Specific Care

Health and Wellness

Social Programs

Discovery Solutions

Real World Evidence

Individual

Social Programs

Education

Governments

Home Health Agencies

Practitioners

Hospitals

Therapists

Health Plans

Family

Public Health

Medical Devicesand Diagnostics

Bio-Pharma

Employers

Payers

DataInsight

IBM Watson // ©2015 IBM Corporation

To tap into the potential of DTR in open deployment, accessing a vast amount of untapped data could have a great impact


14

PGHD: Beyond Capturing Social/Behavioral Determinants from EHR

Institute of Medicine report (2016)


15

•  R.W. White, R. Harpaz, N.H. Shah, W. DuMouchel, and E. Horvitz. Toward Enhanced Pharmacovigilance using Patient-Generated Data on the Internet, Nature CPT, April 2014.

Success Story: PGHD for Pharmacovigilance


Success Story: PGHD for Personalized Communication

Palmquist, A.E.L., Koehly, L.M., Peterson, S.K. et al. J Genet Counsel (2010) 19: 473. doi:10.1007/s10897-010-9299-8


Success Story: PGHD for Diagnosis

Identify the onset and progression of disease states e.g., depression, Parkinson’s, PTSD

Assist with decision making in ER (e.g., FITBIT CHARGE HR)

Source: 1. http://www.androidauthority.com/fibit-charge-hr-save-patient-685205/ 2. M. Sung, C. Marci, and A.S. Pentland, Objective Physiological and Behavioral Measures for Identifying and Tracking Depression State in Clinically Depressed Patients, MIT Technical Report, 595 (2005): 1-20. 3. S. Arora, V. Venkataraman, S. Donohue, K.M. Biglan, E.R. Dorsey, M.A. Little, High accuracy discrimination of Parkinson’s disease participants from healthy controls using smartphones, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2014), 3641–3644.


Success Story: PGHD for Care Coordination

IBM Taiwan Collaboratory


19

Promoting patient activation for behavioral change (Dietary intake: Burke et al., 05; Physical activity: Prestwich et al., 09; Michie et al., 09) Preventing lifestyle-related chronic diseases, e.g., Type II Diabetes

Helmrich et al, 1991;Bailey, 2001; Scottish Intercollegiate Guidelines Network, 2001; Finland National Type II Diabetes Prevention Programme, 2007; American Diabetes Prevention Program, 2008).

Increase awareness to self-monitoring (Prestwich et al., 09; Burke et al., 05)

Triggering reminders to care plans (Consolvo et al. 09; Hurling et al., 07) Personalizing communication messages and education materials (Thaler and Sustein, ‘08)

Making Sense of PGHD for Individuals

Nudge: Improving Decisions About Health

PERSONAL INFORMATICS TOOLS (auto PGHD capturing + manual input)


20

The Failure of Scripps Trial

Patients who monitored their health were less likely to attribute health outcomes to chance than those who didn’t monitor their health


Where do we meet in the middle?

???

Unsustainable, ill-supported health consumers

Healthcare Triple aim


22

Reference Story: Kaiser Permanente – Improved Outcome and Reduced Co Individualized Guideline Improved Clinical Outcomes §  Reduce 5-year CVD risk 2.4 times more than EHR+panel support tool alone (≈ 13% absolute risk reduction)

§  ≈ 6,000 myocardial infarctions (MIs) and strokes prevented annually if applied throughout KP (≈43% increase over JNC7 guideline for the same cost)

Individualized Guideline Reduced Operational Costs §  ≈ $7,000 cost savings per MI and stroke §  ≈ $420M annual net savings if applied throughout KP

Source: Eddy, et al. (2011). Individualized Guidelines: The Potential for Increasing Quality and Reducing Costs. Annals of Internal Medicine, vol. 154, no. 9, p.627-634. http://www.annals.org/content/154/9/627.abstract

22


23

Kaiser Permanente – Improved Patient Motivation and Adherence, Increased Clinician Confidence

(Respondents were)“…more likely to report that they have been asked to change their medication, diet and exercise habits. ”

—Patient Survey

“…helped the doctor to motivate them and helped them participate in their treatment choices, i.e., making lifestyle changes and understanding the rationale for their suggested interventions.”

— Patient Focus Group

“All doctors agreed that it helps them to make the best clinical decisions for their patients.”

— Clinician Survey

23


Adding High Touch by Lay Care Guides

•  Parallel-group randomized trial (2010-2012). –  6 primary care clinics in Minnesota. –  Adults with hypertension, diabetes, or heart failure. –  Assigned in a 2:1 ratio to a care guide or usual care.

Putting User Generated Data in Action: Improving Interpretability for Clinical and Consumer Informatics HEC/MIE 2016 Workshop: PuJng User-‐Generated Data in Ac#on: Improving Interpretability for Clinical and Consumer Informa#cs Katie Zhu, PhD MD


Sanjoy Dey, PhD


Ken Cheung, PhD

(Columbia University, USA)

Bian Yang

(Norwegian University of Science of Technology, Norway)

Thomas Wetter, PhD (Panel Discussant)

(University of Heidelberg, Germany

University of Washington, USA)

Pei-Yun Sabrina Hsueh, PhD (Moderator)

(IBM T.J. Watson Research, USA)


Agenda •  16:30-‐16:40 Opening Remark by Dr. Sabrina Hsueh

•  EMERGING HEALTHCARE LANDSCAPE SHIFT WITH PATIENT-‐GENERATED DATA

•  16:40-‐17:20 Presenta#ons –  Dr. Xinxin Zhu: So we got sensor data, now what? –  Dr. Sanjoy Dey: Enhancing interpretability of computa#onal model

–  Dr. Ken Cheung: SMART-‐AR to evaluate health apps for outcome op#miza#on

–  Dr. Bian Yang: The need for addressing privacy issues with be6er interpretable rules •  17:20-‐18:00 Discussant summary presenta#on & Panel

discussion/audience Q&A –  Dr. Thomas We6er: Pa#ent generated data – The transi#on from “more” to “be6er”

–  Panel discussion (moderated by Dr. Sabrina Hsueh)


SO WE GOT SENSOR DATA, NOW WHAT?


•  MD (Anesthesiologist) from China Medical University

•  PhD in Biomedical Informa#cs from Columbia University

•  Past Experience –  Chief Medical Informa#on Officer at Kforce Government Solu#ons, U.S.A.

–  Associate Medical Director, Pfizer Health Solu#ons, U.S.A.

–  Senior Manager, Pfizer Health Solu#ons, U.S.A.

–  Clinical Program Manager, Philips North America Research Center, U.S.A.

–  Healthcare Informa#cs Subject Ma6er Expert, Veterans Affairs Medical Center, U.S.A.

Xinxin (Katie) Zhu •  Telehealth lead at IBM

Watson •  External Advisory Board

member to Columbia Univ. Center of Advanced Technology


So we got sensor data, now what?

•  What sensor data could help with care? •  How to determine when to use what? •  Are the sensor data reliable? •  What is the context when data were collected? •  How to interpret data in context? •  Clinicians’ concerns


What sensor data could help with care? Use case: stress management

Subjec#ve Stressors

Psychological Response

Physiological Response

Stress Hormones


Many sensors are out there… Tinke

31

Approach •  Plug into a smartphone •  Scan finger •  Provide stress/relax index

Data Tracked •  Heart rate variability •  Respiration rate •  Blood oxygen level

Tinke Website


Spire

32

Data Tracked •  Breathing pattern •  Steps

Approach •  Consistent breaths à Calmness •  Uneven breaths à Tension •  Fast and consistent breaths à Focus •  Guided meditation


Pip

33

Data Tracked •  Skin conductance (EDA)

Approach •  Hold device between the

thumb and index fingers •  Stress level via audio/

visual feedback


Many sensors are out there

Brain Wave (EEG sensor)

Skin Conductance (EDA sensor)

Blood Volume Pulse (PPG sensor)

Skin Temperature (Infrared Thermophile)

Heart Rate (PPG sensor)

Heart Rate Variability (ECG sensor)

Respiration Rate/Volume (RIP sensor)

RR Interval Distribution (ECG sensor)

Image Source: Neurosky, Empatica, Hexoskin


Hexoskin V.S. BioSens Holter ECG Valida#on


Brain Wave

36

Relaxed Reading a paper with a time limit

Delta - Adult slow wave sleep Theta - Drowsiness, idling, inhibition Alpha - Relaxed, reflecting Beta - Alert, busy, anxious, thinking Gamma - Short term memory usage Mu - Rest state motor neuron activity

-  Produced by electrical pulses from neuron communication

-  Frequency bands associated with different behaviors and emotions


How can people make sense of these?

37


Clinicians’ concerns

Information overload Unreliable data à false alarms

Clinical workflow Context, context, context!



•  (1) Iden#fy immediate ac#on items to start ini#a#ng proposal for enabling evidence-‐based conversa#on with pa#ents/physicians/providers in the loop



•  2. Implica#ons and lessons learned from the case studies -‐-‐ especially the gaps you perceived as barriers of entry



•  3. Requirements for successful redesign of healthcare systems to accommodate pa#ent-‐generated informa#on (with a sub-‐goal of iden#fying the areas where such informa#on can make most impacts).


Ques#ons (preliminary) •  1. What is the state-‐of-‐the-‐art? •  2. What are the benefits of improving interpretability in PGHD

in ac#on? •  3. What the key dimension of interpretability of PGHD? What

are the barriers? Technical/social? •  4. What is our defini#on of interpretability? What are the

likely measures? •  5. What is the opportunity area going forward? •  6. What are the likely ac#on items to be suggested to the

community to further the discussion about improving interpretability for PGHD? –  In the field of consumer health informa#cs or beyond?


ENHANCE INTERPRETABILITY WITH PRIOR KNOWLEDGE


Sanjoy Dey PhD.

Postdoctoral Research Scientist, Center of Computational Health, IBM T. J. Watson Research Center, Yorktown Heights, NY 10598

Sanjoy Dey’s research interests lie in the areas of health care informa#cs, data mining

and machine learning, especially in building interpretable models by integra#ng mul#ple healthcare datasets. . In par#cular, Sanjoy is interested in building models

which aim to incorporate domain knowledge at mul#ple stages of model development (e.g., feature selec#on, cohort selec#on and study design) so that these models can infer knowledge that are complementary to the already known clinical prac#ces and

guidelines. Prior to this posi#on, he earned his Ph. D. from the department of computer science at university of Minnesota.


Improving Interpretability of Patients Generated Data

45

Dis

ease

H

ealth

y

Dataset 1 Dataset 2

Class label

Relation across the datasets

Analysing the obtained results from Complex Models •  Interpret the model parameters so that they

can be used to infer meaningful knowledge •  Visualize the obtained informa#on from a

model in a meaningful way

Taking prior knowledge into account

•  Many #mes, medical knowledge are available containing useful rela#onships among clinical events


Dataset

Interpre#ng Complex Computa#onal Models

•  Complex model parameters can be converted to metrics that are easily understandable by domain experts

–  Logis#c Regression –  LASSO with regulariza#on to perform

simultaneous variable selec#on

•  Logis#c loss func#on can be used as Log Odds, which can be converted to Odds Ra#o -‐ where β0 the log odds for smoking for men

•  Probabili#es of an event can be viewed as clinical uncertainty

Class Level

Liu et al. 2011, Jeiping Ye et al., 2012


Visualiza#on of the obtained model

Decision Boundaries of Logis#c Regression

Rule based representa#on of Decision Tree and Cart based Models

Graphical Models for Disease Models

Work Environment Gene

Disease

Symptoms

Westra et al. 2011, Manzi et al. 2013


Integra#ng Prior Knowledge with Mul#-‐source EHR data for Enhancing Interpretability

Diagnosis Codes (ICD-‐9)

Admission Assessment

Survey

Discharge Assessment

Survey

Home Healthcare

Dey et al. AMIA 13, Dey et. al., SDM 14, Westra et al. 11

Demographic, behavioral, pathological, psycho-social factors, outcome variables.

Problem Formulation:

48

260,000 patients

Data source: CMS OASIS dataset

Outcome prediction


Enhancing Interpretability of Patterns

49

Predictive Power

Inte

rpre

tabi

lity

•  Interpretability (Relevance) and predic#on power are different goals

•  Prior rela#onships present in the data can be incorporated into model

ICD-‐9 Group 1 ICD-‐9 Group 2 250.6: Neurological manifesta#on

401.1: Benign hypertension

290: Demen#a 838: Disloca#on of foot 331: Alzheimer’s disease 692.71: Sunburn 331.9: Cerebral degenera#ons

V58.42: Hip joint replacement

Neural disorders No common underlying disease

Interpretable Predic8ve

•  Which group of pa#ents are likely to improve ambula#on?

•  Are those factors clinically interpretable and make a homogeneous group?

G1

G2

Ideal


Proposed Approach Key Steps: •  Integrate both survey data from EHR and

ICD-‐9 diagnoses codes to predict the improvement of urinary incon#nence

•  Use clinical prior knowledge such as Clinical Classifica#on Sotware (CCS) into account to increase the interpretability

•  Develop a sta#s#cal technique called Sparse Hierarchical Canonical Correla#on Analysis (SHCCA) to address these challenges

50

X Y

Algorithm: •  Take the hierarchy of the CCS tree into account to

define a similarity matrix called H among the ICD-‐9 codes

•  Trade-‐off between the data-‐driven and prior knowledge driven similarity of ICD-‐9 codes using λh

•  Converted into convex formula#ons

•  Solve the final equa#on based on gradient descent formula#on

Prior Knowledge

λh trades off between domain-driven and data-driven knowledge

Dey et. al., SDM 14


Sparse Hierarchical CCA (SHCCA) Parameter Selec8on: •  Op#mize the parameters using cross-‐valida#on such that it

op#mizes the correla#on on valida#on data Evalua8on: Predic8on power: how well the selected group of ICD-‐9 codes can predict the improvement of outcome Interpretability: –  I-‐score based on the co-‐occurrences of the ICD-‐9 terms

belonging to a group C in PubMed ar#cles –  Domain knowledge by physicians and nurses

51

ti is the set of articles found with ICD-9 code i

I-‐score(C)=∑𝑖ϵ𝐶↑▒∑𝑗ϵ𝐶↑▒| 𝑡↓𝑖  ⋂ 𝑡↓𝑗 |/| 𝑡↓𝑖  ∪ 𝑡↓𝑗 |   


Results •  SHCCA has similar performances as

the baseline methods, but with fewer components

•  It enhances the interpretability significantly

Predictive power of SHCCA

52


Components from SHCCA

Survey data 1 ICD-‐9 codes 1 Survey data 2 ICD-‐9 codes 2

Age, Prior Memory Loss,

Poor Speech, Poor Cogni8ve

Func8on, High Confusion,

Memory Deficiency, Frequent Behavioral

Problem

Demen8as, Persistent

mental disorders, Alzheimer's disease, Cerebral

degenera8ons

Surgical Wound, Fully

granulated Surgical Wound

Acercare for healing fracture of hip,

Knee joint replacement, Hip joint replacement,

Acercare following surgery of the musculoskeletal system, Acercare following joint

replacement, Acercare following surgery for

neoplasm, Acercare following surgery of

the circulatory system 53

Component relevant to Mental health

Component relevant to surgical treatment


Summary & Limita#ons •  Summary

–  Predic#on power and interpretability are two different goals, which are oten hard to achieve by computa#on models simultaneously

–  Predic#ve models can be post-‐processed and visualized to make them more

interpretable

–  Leveraging clinical prior knowledge such as Clinical Classifica#on Sotware (CCS) into account can increase the interpretability substan#ally

•  Limita#ons –  The defini#on of interpretability is oten subjec#ve and oten requires domain

exper#se

–  Prior knowledge about a par#cular problem is not oten readily available in many clinical applica#ons

–  Use of prior knowledge into the model op#miza#on is oten not straighvorward

54


SMART-‐AR to evaluate health apps for outcome op#miza#on

Ken Cheung Columbia University


Ying Kuen (Ken) Cheung

•  PhD in Sta#s#cs (U Wisconsin, Madison WI, USA)

•  Professor of Biosta#s#cs, Columbia University, New York NY, USA

•  General interest: Transla#onal research in all phases •  Specific areas

•  Dose and treatment selec#on in adap#ve clinical trials •  Op#mal behavioral interven#on for secondary stroke preven#on •  Analysis of high-‐dimensional physical ac#vity data •  N-‐of-‐1 trial designs •  Evalua#on and dissemina#on of mobile technologies for mental

health


Objec#ve & Reinforcement Learning

•  Data sequence: (X, A1, U1, A2, U2, …, AK, Y) –  X = Individual characteris#cs –  At = Apps downloaded (Ac#on) at #me t –  Ut = response and use pa6ern between At and At+1

–  Y = Final outcome (depression reduc#on)

•  Objec#ve: Iden#fy the sequence At based on X and Ut so as to maximize Y (on average)

•  Reinforcement learning: Q-‐learning, OWL, etc.


SMART Design

•  SMART (Sequen#al Mul#ple Assignment Randomized Trial)

App 1

App 2

Ac#ve use

Ac#ve use

Non-‐use

Non-‐use

App 1 + App 3

App 2

App 2 + App 3

App 2 + reminder

App 1

App 1 + reminder

App 2

App 3

Depression reduc8on at 6 months, Y

Enrichment based on intermediate use paeern, U

P = 2/3

P = 1/3

P = 0.3

P = 0.7

P = 0.6

P = 0.4

P = 0.6

P = 0.4

P = 0.3

P = 0.7


SMART-‐AR Design (Cheung et al, 2015 Biometrics)

•  SMART Design ü  Allows learning ✗  No feedback to system ✗  Curse of dimensionality: many apps

in prac#ce

•  SMART-‐AR •  AR = Adap#ve randomiza#on •  Assign more users to more

promising branches •  Curse of dimensionality: Sot

elimina#on of poor performing apps à Improve signal-‐noise ra8o, hence interpretability of the recommender

0 20 40 60 80 100

910

1112

1314

15

Enrollment number

BD

I red

uctio

n

o o o o o o o o o o o o

+ + + + + + + + + + + +

p p p p p p p p p p p p

m m m m m m m m m m m m

Scenario 1

CODIACSBalanced

Application in conventional depression program

Cheung et al, 2015 Biometrics


Some Simula#on Illustra#on Non-‐adap8ve SMART SMART-‐AR

Balanced randomiza#on

CODIACS randomiza#on

Scenario 1

Probability of iden#fying the op#mal sequence

0.91 0.94 0.95

Expected adjusted value* 0.98 0.99 0.99

Variance of adjusted value 3.1 2.3 1.3

Scenario 3

Probability of iden#fying the op#mal sequence

0.53 0.51 0.51

Expected adjusted value* 0.95 0.95 0.96

Variance of adjusted value 8.5 11.0 7.4


Health informa#cs support issues

•  SMART-‐AR requires real #me transmission between data site, apps cura#on site, compu#ng site –  Large volume: Use data pre-‐processing

–  Privacy & security •  Health outcomes

–  Valida#on of outcomes

0 10 20 30 40

Days since download first app

Ap

p ID

12

34

56

78

910

11

12

13

14


ADDRESSING PRIVACY/SECURITY CONCERN WITH INTERPRETABILITY


Bian’ intro goes here….


- February 4, 2015 -  Hacker broke into the medical

insurance database -  80 million records stolen in

plaintext -  Insurance company’s database

are not required to be encrypted by HIPAA

-  administrator's credentials were compromised


Now … the hot term for 2016 – «ransomware»

-  More than half of hospitals (in US) hit with ransomware in last 12 months

(HealthcareITNews, April 07, 2016)

-  Good business model for the hackers -  Low risk -  Good cost-benefit efficiency -  Easy to build "reputation" for

the service –

(https://www.theguardian.com/technology/2016/feb/17/los-angeles-hospital-hacked-ransom-bitcoin-hollywood-presbyterian-medical-center)


More A6ack vectors to Pa#ent Data Security

More attacking vectors opened due to … the shifts of healthcare patterns - now and future

•  hospital -> home / cyber space (telemedicine, IoT, mobile technologies, care research)

•  in-hospital treatment -> prevention (big data, health analytics, health electronics, e-drugs)

•  doctor-centered -> patient-centered (telemedicine, big data, machine intelligence, cloud storage and computing)

•  health care organizations -> associated business partners in liability (law and regulations, e.g., HIPAA -> HITECH (2009))

•  Local service -> global service (service across the borders) •  the “SafeHarbor” agreement •  Facebook fined by the Belgian court


What does security mean for eHeath/mHealth’s future

-  new breaches and "business models for hacking" would continue to come… (but take it easy)

-  More liability to IT tech enablers and business associates (e.g, HIPAA ->HITECH)

-  cloud / SDN makes "security as a service" that can be outsourced

-  IT Tycoons (Microsoft, IBM, Google, etc.) could finally take it over (capable to take risk, more resources, global threat intelligence, etc.)


Future Solu#ons

-  Data ownership re-definition -  Generating incentives for industry to migrate from

data silos to data sharing -  Patients’ awareness of their interests in their own

data -  Patients’ convenience in accessing their own data -  Legal support -  Technology: security / privacy by design


DISCUSSANT SUMMARY PRESENTATION


Prof. Dr. Thomas Weeer •  MSc / PhD in mathema8cs from Aachen Technical U,

Germany •  PostDoc with IBM Scien#fic Center Heidelberg, Germany •  Since 1997 Prof. of Medical Informa8cs, Heidelberg U

–  Interna8onal assignments to Boca Raton (FL), Aus#n (TX), Salt Lake City (UT), Sea6le (WA)

–  Affil. Faculty with Dept. BIME, U of Washington, Sea6le –  Author of textbook

Consumer Health Informa#cs: New Services, Roles and Responsibili#es; Heidelberg (Springer) 2015 (eBook) resp. 2016 (Hardcover)


Pa#ent generated data – The transi#on from “more” to “be6er”

Thomas We6er

May be obsolete here with the title slide already

using this paraphrase of the workshop title


Growth is everywhere •  More modali#es to collect and store data are offered •  More communica#on media carry health info •  More condi#ons suggest to be monitored •  More ins#tu#ons consider usage •  More consumers buy in

•  Does this make sense? •  How can we move towards meaningful ac#on? •  How can we protect against unethical exploita#on?


More data is more op#ons

.


More data is more op#ons

Period


But beware

Date are not the world; data map the world – truthfully? What is the ci#zen‘s contribu#on to the mapping? •  Carrier of implanted sensors •  Operator of a6ached and mobile sensors •  Witness of health signs •  Interpreter of health signs •  Self therapist •  Health plan contractor


More data is more tempta#ons

Ci#zens •  Trust more than warranted •  Shit focus from senses to data Clinicians •  Shit focus from senses to data Researchers, public health •  Urge to find something Big business •  More big business


More data is more tempta#ons: Ci#zens

Percep#on of the presumably unfailable objec#ve givens as proxy for truth •  Mental fixa#on on data •  Unwarranted trust as decision aid •  Adverse reac#on to contradictary data •  Overreac#on upon alarming data


More data is more tempta#ons: Researchers, Public health Percep#on that regarding the massive volume of data there cannot be no effects •  Do the big data mechanics •  Spot peculiari#es •  Publish results Knowing that 5% of significant studies are not substan#ated through an effect

Curb: Complexity reduction –

Sanjoy Rey, Ken CheungRisk: Blindfolded actionism

Curb: Plausibility, context – Katie ZhuRisk: Funding agency expectations Curb: Research ethics


More data is more expecta#ons

If scien#st dispose of more data their methods are challenged: •  Profound interpreta#on and predic#on – Sanjoy Dey •  Parsimony, wise selec#on – Sanjoy Dey •  Secure storage/communica#on – Bian Yang •  Insight – Ken Cheung, Sabrina Hsueh


More is more expecta#ons

If ci#zens volunteer their data, they expect services: •  Serious PGHD into PHR into EHR integra#on •  No data leakage •  Explana#ons of the unexplainable •  Emergency rescue in response to alarming data


More data can be the hays#ck

•  where we don‘t find the needle •  while being distracted by – hay •  but someone needs the needle – now


Claude Shannon 1948 1)

„Informa#on is that which reduces uncertainty“ Which the needle in the hays#ck does not do

1)  A mathematical theory of communication Bell Systems Technical Journal


Bring forth the signal from the noise

•  Concentrate trials on treatments with emerging posi#v prognosis (Ken Cheung)

•  Select data with high interpreta#ve or predic#ve power (Sanjoy Dey)

•  Regard context to detect noise (Ka#e Zhu)


Are we achieving quality that sa#sfies doctors?

•  Not a ma6er of taste •  Doctors‘ code of conduct regulates that when trea#ng diagnosed pa#ents he –  assumes responsibility for correct recordings of devices he hands to the pa#ents

–  has to waive liability for data generated through other pa#ent solicited devices

while, when coaching for healthy lifestyle –  anything goes


Are we achieving quality that sa#sfies doctors?

•  Under a treatment contract a doctor is held responsible for medical errors.

•  Morally, he cannot be held responsible for decisions based on false/faked data from outside his control

•  Pa#ents want their data used •  They cannot guarantee correct data

•  A classical gridlock 1)

1) In NY/NY en.wikipedia.org/wiki/Gridlock


Who can do what

to solve the gridlock?


Are we achieving full transparency? Do we want it?

Imagine that a certain set of sensor data is so characteris#c of you that you need not register, just

deliver a sample and they know who you are.


Are we suppor#ng personalized medicine?

•  If the wealth of our data is so large that we can iden#fy data-‐twins –  A treatment for the second twin should work if it did for the first

–  The end of clinical trials


Thank You

Merci Grazie

Gracias

Obrigado Danke

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