This document summarizes a panel discussion on transforming patient-generated health data for wellness and biomedical research. The panelists were Susan Peterson, Katherine Kim, Fernando Martin-Sanchez, Cagatay Demiralp, and Pei-Yun Sabrina Hsueh (moderator). Peterson discussed using sensors and mobile apps to monitor cancer patients undergoing radiation therapy to detect early signs of dehydration. Kim discussed leveraging patient data for personalized care coordination. Martin-Sanchez discussed generating evidence from patient data to inform research. Demiralp discussed visualization of patient data. Overall the panel explored opportunities and barriers to using patient-generated data from behavioral sensing to clinical decision support.
To address family history collection, interpretation, and application in busy primary care practices, NCHPEG has collaborated collaborating with the March of Dimes, Genetic Alliance, Harvard Partners, and the Health Resources and Services Administration to develop and evaluate a novel family history tool that focuses on prenatal and neonatal health. The tool helps to improve health outcomes for the female patient, fetus, and family by providing clinical decision support and educational resources for risk assessment based on family history. A set of screenshots and an overview of the module can be reviewed via this downloadable ppt.
An integrated model of psychosocial cancer care: a work in progress…Cancer Institute NSW
Cancer patients are faced with a multitude of stressors, from diagnosis, through treatment, at recurrence, in the stages following treatment completion, and in the terminal phase. Psychosocial care has been highlighted as a critical aspect of providing comprehensive patient-focused care. Specifically, one of the goals of The NSW Cancer Plan 2011-2015 is to improve the quality of life of people with cancer and their carers. This project was initiated to improve the current psychosocial model of care at The Kinghorn Cancer Centre (TKCC), to better reflect an integrated, holistic and comprehensive model of patient-centred care.
Guidelines - what difference do they make? A Dutch perspectiveepicyclops
This lecture was given by Dr Raymond Ostelo of the EMGO Institute, VU University Medical Center, Amsterdam, to the North British Pain Association Spring Scientific Meeting in Edinburgh on Friday 18th May, 2007. His lecture forms part of a conference "Blurring the Boundaries - Managing Pain in Primary Care and Secondary Care".
DASH - does arthritis self-management help?epicyclops
This lecture was given by Dr Marta Buszewicz, General Practitioner from North London and Senior Lecturer in Community Based Teaching & Research at UCL, to the North British Pain Association Spring Scientific Meeting in Edinburgh on Friday 18th May, 2007. Her lecture forms part of a conference "Blurring the Boundaries - Managing Pain in Primary Care and Secondary Care".
Clinical Questions types .
A Hierarchy of Preprocessed Evidence.
EBM definition and value.
Knowledge and Skills Necessary for Optimal Evidence-Based Practice.
Basic computer and internet knowledge for electronic searching of the literature
To address family history collection, interpretation, and application in busy primary care practices, NCHPEG has collaborated collaborating with the March of Dimes, Genetic Alliance, Harvard Partners, and the Health Resources and Services Administration to develop and evaluate a novel family history tool that focuses on prenatal and neonatal health. The tool helps to improve health outcomes for the female patient, fetus, and family by providing clinical decision support and educational resources for risk assessment based on family history. A set of screenshots and an overview of the module can be reviewed via this downloadable ppt.
An integrated model of psychosocial cancer care: a work in progress…Cancer Institute NSW
Cancer patients are faced with a multitude of stressors, from diagnosis, through treatment, at recurrence, in the stages following treatment completion, and in the terminal phase. Psychosocial care has been highlighted as a critical aspect of providing comprehensive patient-focused care. Specifically, one of the goals of The NSW Cancer Plan 2011-2015 is to improve the quality of life of people with cancer and their carers. This project was initiated to improve the current psychosocial model of care at The Kinghorn Cancer Centre (TKCC), to better reflect an integrated, holistic and comprehensive model of patient-centred care.
Guidelines - what difference do they make? A Dutch perspectiveepicyclops
This lecture was given by Dr Raymond Ostelo of the EMGO Institute, VU University Medical Center, Amsterdam, to the North British Pain Association Spring Scientific Meeting in Edinburgh on Friday 18th May, 2007. His lecture forms part of a conference "Blurring the Boundaries - Managing Pain in Primary Care and Secondary Care".
DASH - does arthritis self-management help?epicyclops
This lecture was given by Dr Marta Buszewicz, General Practitioner from North London and Senior Lecturer in Community Based Teaching & Research at UCL, to the North British Pain Association Spring Scientific Meeting in Edinburgh on Friday 18th May, 2007. Her lecture forms part of a conference "Blurring the Boundaries - Managing Pain in Primary Care and Secondary Care".
Clinical Questions types .
A Hierarchy of Preprocessed Evidence.
EBM definition and value.
Knowledge and Skills Necessary for Optimal Evidence-Based Practice.
Basic computer and internet knowledge for electronic searching of the literature
Introduction of the NZ Health IT Plan enables better gout management - Reflections of an early adopter. Presented by Peter Gow, Counties Manukau DHB, at HINZ 2014, 12 November 2014, 11.37am, Plenary Room
The global precision medicine market has benefitted greatly from advancements in the life science industry. Although in its nascent stage, targeted therapies hold high chances of becoming a massive success in the coming years because of the potential to treat and cure chronic illnesses. The market is thus expected to expand at a compound annual growth rate (CAGR) of 11.60% between 2018 and 2023, generating USD 88.25 Bn in revenue by 2023.
Since time has changed, the rectification, upgrading and innovation through disruptive ways have become a part of every aspect of our lives. From automobiles to communication every other line of lifestyle has seen an upgrade and so does the medicine
Weitzman 2013: State Health Policy Initiatives as Drivers for Improving Care...CHC Connecticut
Sue Birch presents on State Health Policy Initiatives as Drivers for Improving Care Outcomes: Colorado's Accountable Care Collaborative at the 2013 Weitzman Symposium
The Importance of measuring outcomes, including Patient Reported Outcome Measures (PROMS)
BAOT Lifelong Learning Event
10 November 2010
Dr Alison Laver-Fawcett
Head of Programme, BHSC(Hons) Occupational Therapy
York St John University
UCSF Informatics Day 2014 - Keith R. Yamamoto, "Precision Medicine"CTSI at UCSF
Keith R. Yamamoto, PhD — Opening Remarks – Precision Medicine
Vice Chancellor for Research
Executive Vice Dean of the School of Medicine
Professor of Cellular and Molecular Pharmacology
UCSF
Systematic Use of STroke Averting INterventions (SUSTAIN) TrialUCLA CTSI
This study, which is also funded by the American Heart Association, will assess whether lifestyle group clinics, care managers and support from community health workers may reduce the risk of a second stroke in socioeconomically disadvantaged minority patients.
Introduction of the NZ Health IT Plan enables better gout management - Reflections of an early adopter. Presented by Peter Gow, Counties Manukau DHB, at HINZ 2014, 12 November 2014, 11.37am, Plenary Room
The global precision medicine market has benefitted greatly from advancements in the life science industry. Although in its nascent stage, targeted therapies hold high chances of becoming a massive success in the coming years because of the potential to treat and cure chronic illnesses. The market is thus expected to expand at a compound annual growth rate (CAGR) of 11.60% between 2018 and 2023, generating USD 88.25 Bn in revenue by 2023.
Since time has changed, the rectification, upgrading and innovation through disruptive ways have become a part of every aspect of our lives. From automobiles to communication every other line of lifestyle has seen an upgrade and so does the medicine
Weitzman 2013: State Health Policy Initiatives as Drivers for Improving Care...CHC Connecticut
Sue Birch presents on State Health Policy Initiatives as Drivers for Improving Care Outcomes: Colorado's Accountable Care Collaborative at the 2013 Weitzman Symposium
The Importance of measuring outcomes, including Patient Reported Outcome Measures (PROMS)
BAOT Lifelong Learning Event
10 November 2010
Dr Alison Laver-Fawcett
Head of Programme, BHSC(Hons) Occupational Therapy
York St John University
UCSF Informatics Day 2014 - Keith R. Yamamoto, "Precision Medicine"CTSI at UCSF
Keith R. Yamamoto, PhD — Opening Remarks – Precision Medicine
Vice Chancellor for Research
Executive Vice Dean of the School of Medicine
Professor of Cellular and Molecular Pharmacology
UCSF
Systematic Use of STroke Averting INterventions (SUSTAIN) TrialUCLA CTSI
This study, which is also funded by the American Heart Association, will assess whether lifestyle group clinics, care managers and support from community health workers may reduce the risk of a second stroke in socioeconomically disadvantaged minority patients.
Week 5 EBP ProjectAppraisal of EvidenceCLC EBP Research .docxcockekeshia
Week 5 EBP Project/Appraisal of Evidence
CLC: EBP Research Table
Citation
Include the APA reference note.
Abstract/Purpose
Craft a 100-150 word summary of the research.
Research/Study
Describe the design of the relevant research or study in the article.
Methods
Describe the methods used, including tools, systems, etc.
Setting/Subject
Identify the population and
the setting in which the study was conducted.
Findings/Results
Identify the relevant findings, including any specific data points that may be of interest to your EBP project.
Variables
Describe the independent and dependent variables in the research/study.
Implication for Practice
Articulate the value of the research to the EBP project your group has chosen.
Independent Variable
Dependent Variable
King-Shier, K.M., Mather, C., &LeBlanc, P. (2013). Understanding the influence of urban or rural living on cardiac patients’ decisions about diet and physical activity: Descriptive decision modeling. International Journal of Nursing Studies, 50(11), 1513-1523. doi: 10.1016/j.ijnurstu.2013.03.003
This research aims to answer to better understand the decision-making process of eating a heart healthy diet and extent of physical activity. Also, are these decisions influenced by whether the subject lives in a rural or urban setting. The research proposal was the cultural issues effected participants decision making as well as place of residence. This research used a previous qualitative research design in which 42 cardiac patients (21 urban, and 21 rural) were interviewed about their diet and physical activity. The researchers then designed a model for interviewing regarding the decision-making process. The combination model was then given and tested with 647 cardiac patients (327 urban and 320 rural) from Canada. The results were based on 93.5% accuracy for diet and 97.5 % accuracy with physical activity. Results indicated that decision-making was less about place of residence and more about perception of control over health including time, effort, or competing priorities, receipt of appropriate clear information, and appeal of the activity.
A three-staged, multi-methods approach was used to develop and analyze the descriptive decision making model that patients use in making decisions regarding their cardiac lifestyle. A cross-sectional survey was used to interview patients one year post-cardiac catherization. These interviews were performed via telephone. A three stage decision tree model was then used to analyze the information offered. The stages were as follows: 1. Factors that were influential in decision making. 2. If and where failure had occurred for patients. 3. Did patients consistently, sometimes, or not at all engage in physical activity and a heart healthy diet. Results were then analyzed using statistical analysis.
Information was gathered from a previous series of qualitative interviews conducted with 42 cardiac patients (21 rural, 21 urban). Based on the infor.
HXR 2016: FAST TRACK: Prove It: The role of Evidence and Insights in Health I...HxRefactored
Health intervention design is a comprehensive process that is aiming to solve multifactorial problems. How to identify these factors and approach them? How to decide who will be the best target audience for the intervention? Where would these evidence and insights come from? During this session you will learn what are the must-haves of a health intervention, what are the most common pitfalls that can ruin your intervention and how you can enhance your health intervention design using insights from research.
Dr. Ostrovsky describes the promise and concerns surrounding the precision medicine initiative and the importance of taking into account all determinants of health.
With the upcoming move to ICD-10 Procedure Codes across the world, information flow will reach many new recipients to improve the world's health conditions!
MIE Medical Informatics in Europe: European Federation for Medical Informatics (EFMI) annual meeting
Worklshop: Addressing Patient Adherence Issues by Engaging Enabling Technologies
Chair: Pei-Yun Sabrina Hsueh (IBM T.J. Watson Research Center)
Pei-Yun Sabrina HSUEHa, , Marion BALL b,a, Michael MARSCHOLLEKc, Fernando J. MARTIN-SANCHEZd , Chohreh PARTOVIANa, and Vimla PATELe
aIBM T.J. Watson Research Center, NY, USA
b John Hopkins University, MD, USA
c Hannover Medical School, Germany
d Melbourne Medical School, Australia
e Center for Cognitive Studies in Medicine and Public Health, The New York Academy, USA
Abstract One of the well known issues providers have contended with for many years is the issue of patients’ adherence to their care plans and medications outside clinical encounters. In this workshop, we review proof of concept studies using technology at the point of care to assess patient literacy and self-efficacy to provide timely intervention, remedy, and improvements in cost and quality. We focus on patient-generated information, including patient reported data and measurements from devices and sensors, as key to improving patient safety, gaining “meaningful use” data, improving patient centric care, and assisting providers in learning more about their patient needs to improve outcomes. We look into barriers to adherence, basic understanding of the patients and providers roles in improving adherence, and the use of technology to assist patients in staying on track. The participants will address their findings in the integration of patient-generated information into everyday life and clinical practice and share lessons learned from implementing these designs in practice. This workshop aims to share requirements for the next-generation healthcare systems, especially in areas where the explosive availability of patient-generated data is expected to make impacts.
CRISPR-Cas9, a revolutionary gene-editing tool, holds immense potential to reshape medicine, agriculture, and our understanding of life. But like any powerful tool, it comes with ethical considerations.
Unveiling CRISPR: This naturally occurring bacterial defense system (crRNA & Cas9 protein) fights viruses. Scientists repurposed it for precise gene editing (correction, deletion, insertion) by targeting specific DNA sequences.
The Promise: CRISPR offers exciting possibilities:
Gene Therapy: Correcting genetic diseases like cystic fibrosis.
Agriculture: Engineering crops resistant to pests and harsh environments.
Research: Studying gene function to unlock new knowledge.
The Peril: Ethical concerns demand attention:
Off-target Effects: Unintended DNA edits can have unforeseen consequences.
Eugenics: Misusing CRISPR for designer babies raises social and ethical questions.
Equity: High costs could limit access to this potentially life-saving technology.
The Path Forward: Responsible development is crucial:
International Collaboration: Clear guidelines are needed for research and human trials.
Public Education: Open discussions ensure informed decisions about CRISPR.
Prioritize Safety and Ethics: Safety and ethical principles must be paramount.
CRISPR offers a powerful tool for a better future, but responsible development and addressing ethical concerns are essential. By prioritizing safety, fostering open dialogue, and ensuring equitable access, we can harness CRISPR's power for the benefit of all. (2998 characters)
Deep Leg Vein Thrombosis (DVT): Meaning, Causes, Symptoms, Treatment, and Mor...The Lifesciences Magazine
Deep Leg Vein Thrombosis occurs when a blood clot forms in one or more of the deep veins in the legs. These clots can impede blood flow, leading to severe complications.
How many patients does case series should have In comparison to case reports.pdfpubrica101
Pubrica’s team of researchers and writers create scientific and medical research articles, which may be important resources for authors and practitioners. Pubrica medical writers assist you in creating and revising the introduction by alerting the reader to gaps in the chosen study subject. Our professionals understand the order in which the hypothesis topic is followed by the broad subject, the issue, and the backdrop.
https://pubrica.com/academy/case-study-or-series/how-many-patients-does-case-series-should-have-in-comparison-to-case-reports/
CHAPTER 1 SEMESTER V - ROLE OF PEADIATRIC NURSE.pdfSachin Sharma
Pediatric nurses play a vital role in the health and well-being of children. Their responsibilities are wide-ranging, and their objectives can be categorized into several key areas:
1. Direct Patient Care:
Objective: Provide comprehensive and compassionate care to infants, children, and adolescents in various healthcare settings (hospitals, clinics, etc.).
This includes tasks like:
Monitoring vital signs and physical condition.
Administering medications and treatments.
Performing procedures as directed by doctors.
Assisting with daily living activities (bathing, feeding).
Providing emotional support and pain management.
2. Health Promotion and Education:
Objective: Promote healthy behaviors and educate children, families, and communities about preventive healthcare.
This includes tasks like:
Administering vaccinations.
Providing education on nutrition, hygiene, and development.
Offering breastfeeding and childbirth support.
Counseling families on safety and injury prevention.
3. Collaboration and Advocacy:
Objective: Collaborate effectively with doctors, social workers, therapists, and other healthcare professionals to ensure coordinated care for children.
Objective: Advocate for the rights and best interests of their patients, especially when children cannot speak for themselves.
This includes tasks like:
Communicating effectively with healthcare teams.
Identifying and addressing potential risks to child welfare.
Educating families about their child's condition and treatment options.
4. Professional Development and Research:
Objective: Stay up-to-date on the latest advancements in pediatric healthcare through continuing education and research.
Objective: Contribute to improving the quality of care for children by participating in research initiatives.
This includes tasks like:
Attending workshops and conferences on pediatric nursing.
Participating in clinical trials related to child health.
Implementing evidence-based practices into their daily routines.
By fulfilling these objectives, pediatric nurses play a crucial role in ensuring the optimal health and well-being of children throughout all stages of their development.
The dimensions of healthcare quality refer to various attributes or aspects that define the standard of healthcare services. These dimensions are used to evaluate, measure, and improve the quality of care provided to patients. A comprehensive understanding of these dimensions ensures that healthcare systems can address various aspects of patient care effectively and holistically. Dimensions of Healthcare Quality and Performance of care include the following; Appropriateness, Availability, Competence, Continuity, Effectiveness, Efficiency, Efficacy, Prevention, Respect and Care, Safety as well as Timeliness.
India Clinical Trials Market: Industry Size and Growth Trends [2030] Analyzed...Kumar Satyam
According to TechSci Research report, "India Clinical Trials Market- By Region, Competition, Forecast & Opportunities, 2030F," the India Clinical Trials Market was valued at USD 2.05 billion in 2024 and is projected to grow at a compound annual growth rate (CAGR) of 8.64% through 2030. The market is driven by a variety of factors, making India an attractive destination for pharmaceutical companies and researchers. India's vast and diverse patient population, cost-effective operational environment, and a large pool of skilled medical professionals contribute significantly to the market's growth. Additionally, increasing government support in streamlining regulations and the growing prevalence of lifestyle diseases further propel the clinical trials market.
Growing Prevalence of Lifestyle Diseases
The rising incidence of lifestyle diseases such as diabetes, cardiovascular diseases, and cancer is a major trend driving the clinical trials market in India. These conditions necessitate the development and testing of new treatment methods, creating a robust demand for clinical trials. The increasing burden of these diseases highlights the need for innovative therapies and underscores the importance of India as a key player in global clinical research.
Telehealth Psychology Building Trust with Clients.pptxThe Harvest Clinic
Telehealth psychology is a digital approach that offers psychological services and mental health care to clients remotely, using technologies like video conferencing, phone calls, text messaging, and mobile apps for communication.
The Importance of Community Nursing Care.pdfAD Healthcare
NDIS and Community 24/7 Nursing Care is a specific type of support that may be provided under the NDIS for individuals with complex medical needs who require ongoing nursing care in a community setting, such as their home or a supported accommodation facility.
ICH Guidelines for Pharmacovigilance.pdfNEHA GUPTA
The "ICH Guidelines for Pharmacovigilance" PDF provides a comprehensive overview of the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines related to pharmacovigilance. These guidelines aim to ensure that drugs are safe and effective for patients by monitoring and assessing adverse effects, ensuring proper reporting systems, and improving risk management practices. The document is essential for professionals in the pharmaceutical industry, regulatory authorities, and healthcare providers, offering detailed procedures and standards for pharmacovigilance activities to enhance drug safety and protect public health.
1. 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
2. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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)
3. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Pei-Yun (Sabrina) Hsueh, PhD
Research Staff Member
IBM Watson Research Health Informatics PIC co-Chair
Behavioral Analytics Lead
Computational Health Behavioral and Decision Science Group
Center for Computational Health
IBM T. J. Watson Research Center
AMIA CPHI WG Secretary
AMIA Consumer and Pervasive Health Informatics Work Group
Opening Remark
Stratification
Identify sub-populations at-risk for
unhealthy behaviors
Personalization
Tailor intervention strategies for
individuals
Engagement
Promote healthy behaviors on a day-to-
day basis
4. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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 workshop
MEDINFO 2015 workshop
HEC/MIE 2016
(IMIA Consumer Health
informatics WG)
AMIA 2016 (AMIA
Consumer and Pervasive
Health Informatics WG
pre-symposium & didactic
panel)
5. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
6
Can data from EHR be the answer?
Capturing Social/Behavioral Determinants from EHR
Institute of Medicine
report (2016)
7. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
8. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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.
9. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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)
10. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
11. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Using Patient-generated Health Data for
Assessment and Intervention
in Cancer Survivors
Susan K. Peterson, PhD, MPH
Professor, Department of Behavioral Science
The University of Texas MD Anderson Cancer Center
Houston, Texas
AMIA 2016 Panel
Nov 14 15:30 - 17:00
12. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Susan K. Peterson, PhD, MPH
Professor of Behavioral Science
Director, Patient-Reported Outcomes, Survey & Population Research Shared Resource
The 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
13. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
14. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
16. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
U.S. cancer incidence & deaths
All Cancers 1,665,540 585,720
Lung 224,210 163,660
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
17. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Head & neck cancer treatment
• Nasopharynx
• Oropharynx
• Hypopharynx
• Larynx
• Oral cavity
• Thyroid
• Sinonasal
• Salivary
• Skin
Curative radiation therapy (RT)
With/without chemotherapy
Curative surgery
With/without radiation therapy
With/without chemotherapy
RT typically lasts 5 days/week for 6-7 weeks
18. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
19. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
20. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
21. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision 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
22. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
23. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
CYCORE web-based interface:
Patient-reported outcomes
Enables clinician/researcher to view data on daily patient-
reported symptoms and medications
24. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
satisfaction
Peterson, Shinn, et al., JNCI Monogr 2013
25. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
0
1
2
3
4
5
6
7
8
9
10
Helpfulness
of baseline
training
session
Clarity of
printed
instructions
you used at
home
Ease of
device use
at home
Confidence
in ability to
use device
at home
Usefulness
of automatic
data
provision to
doctor
Importance
of seeing
device
reading at
home
Concern
about the
data privacy
Satisfaction
with use of
each device
BP Device
Weight Scale
Phone: Daily
Symptoms
Surveys
Home Health Hub
26. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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)
27. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
28. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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%
1
7
4
1
79%
9
9
1
3
Married % 84% 84%
< HS graduate
Some college/other
> bachelor’s degree
17%
27
56
20%
25
54
CYCORE Head & Neck Cancer RCT
Participants’ Demographic Characteristics
29. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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 EHRs
Possible 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 EHRs
Patrick, Transl Beh Med (2013); Harle, JAMIA (2016)
30. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
31. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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)
32. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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 Support
Nov 14, 2016, 3:30-5:00
Katherine K. Kim, PhD, MPH, MBA
kathykim@ucdavis.edu
33. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
33
Challenges for
individuals with
complex chronic and
co-morbid conditions
35. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Care Coordination Challenge
35 35
HOSPITALS
INPATIENT
COMMUNITY
SERVICES
PERSON
CLINIC
S/
OFFICE
S
HOSPICE CARE
OUTPATIENT
CARE
CAREGIVERS
RESPITE CARE
SOCIAL SERVICES
HOME CARE
PRIMA
RY
CARE
SPECIAL
IST
SPECIAL
IST
SPECIAL
IST
PRIMARY CARE
SPECIALI
ST
SPECIAL
IST
FAMILY
Many touch points, multiple transitions, unclear accountability
36. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Care Coordination Challenge
HOSPITALS
INPATIENT
HOME CARE
PRIMARY
CARE
SPECIALIS
T
SPECIALIS
T
COMMUNITY
SERVICES
PATIENT
CLINIC
S/
OFFICE
S
HOSPICE CARE
OUTPATIENT
CARE
CAREGIVER
S
SPECIALIS
T
RESPITE CARE
SOCIAL SERVICES
FAMILYPRIMARY CARE
SPECIALIS
T
SPECIALIS
T
Many portals with incomplete information
Care Coordination Challenge
37. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
38
39. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
39
40. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
40
Table 1: Baseline Socio-Demographic and Health Characteristics
Care Coordination
Plus PHN
Care Coordination
Alone p
n=35 n=26
Age in Years (SD) 59.00 (11.12) 59.17 (9.18) 0.95
Female, % 75 75 1.00
White non-Hispanic, % 89 73 0.12
Completed Education, % 0.68
Employment, % 0.55
Married/Partnered, % 58 79 0.19
Family Income, % 0.24
Health Status, % 0.32
Cancer Stage, % 0.62
I 14 8
II 37 44
III 26 16
IV 23 32
Treatment Plan*
Chemotherapy 100 96 0.24
Radiation 31 34 0.79
Other 20 8 0.18
* Treatment Plan variables do not add up to 100%; respondents could check more than
one.
41. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
41
Table 2: Health Technology Acceptance and Use (HTAU) at Baseline (n=33;
Intervention Group Only)
Construct and Items Score*
Mean SD
Price 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. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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.
42
43. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Acknowledgements
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. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
44
45. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Thank You
Katherine Kim
kathykim@ucdavis.edu
45
46. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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)
47. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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: fem2008@med.cornell.edu
Twitter: @fermarsan
48. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
49. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Availability of devices, sensors, apps, DTC services and Social Networks
Wearables
Sensors
DTC lab tests
Apps
50. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
• 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. Exposome
2. 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. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
52. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Health
Informatics
Bioinformatics
Proteomics
and
Metabolomics
Data
Gene
expression
Data
Genomic
Data
Patient
generated
Data
Population
Data
Clinical
Data
53. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
54. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Genotype * Expotype Phenotype
Coriell Personalised Medicine Collaborative
Marc Rubin,
Nature 2015
55. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
56. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
57. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
58. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
59. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Jawbone and Bay Area Earthquake
Sept 2014
https://jawbone.com/blog/napa-earthquake-effect-on-sleep/
60. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
ENTRAIN app
61. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
ENTRAIN app
62. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Health eHeart
63. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Google Baseline
64. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
100K Wellness Project
66. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
N-of-1 studies
• https://www.thegua
rdian.com/science/
2016/jul/03/citizen-
science-how-
internet-changing-
amateur-
research?CMP=sha
re_btn_fb
67. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
US Precision Medicine Initiative Cohort Program
68. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Summary: Use of PGHD
Challenges / 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
69. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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)
70. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Data
Visualization
70
Çağatay Demiralp @serravis
IBM Research
71. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
IBM T. J. Watson
Stanford University
University of Washington
Microsoft Research
Cambridge
Brown University
Çağatay Demiralp
Current
Previous
Data Visualization . Visual Analytics . HCI
72. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
IBM T. J. Watson
Stanford University
University of Washington
Microsoft Research
Cambridge
Brown University
Çağatay Demiralp
Current
Previous
Data Visualization . Visual Analytics . HCI
Extend the theoretical and perceptual foundations of
data visualization
Develop and automate interactive visual analysis tools Í
Current focus
73. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
What is
visualization?
74. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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]
75. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Why create
visualizations?
76. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
E.J. Marey’s sphygmograph [from Braun 83]
77. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
In 1854 John Snow plotted the
position of each cholera case
on a map [from Tufte 83]
78. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Used map to hypothesize that pump on
Broad St. was the cause
81. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
New York weather [from NY Times 1981]
82. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Bones in
hand
Double helix [Watson and
Crick 53]
83. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Crimean War Deaths by Florence Nightingale,1856
84. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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”
85. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Why 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
86. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
PGHD &
Visualization
87. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Usual story
Focus on data
collection
Lack of tools for
sense making
88. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
How effective
are these?
89. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
What do they mean?
90. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Challenges
Little known about effectiveness
Behavior change, decision making, risk
assessment, diverse users & tasks
Difficult to interpret & operationalize
Noisy, sparse and heterogenous data
91. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Challenges
Little known about
effectiveness
Behavior change,
decision making,
risk assessment, etc.
Difficult to interpret &
operationalize
Noisy, sparse and
heterogenous data
Solutions
Systematically evaluate
Derive general principles
Communicate risk &
uncertainty
PGHD-VisKit
Integrative tools
What-if scenarios
Reproducible analysis
Automation
92. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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)
93. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
94. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
DISCUSSANT SUMMARY
EMERGING HEALTHCARE LANDSCAPE SHIFT WITH
PATIENT-GENERATED DATA
96. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
9
6
Can data from EHR be the answer?
Capturing Social/Behavioral Determinants from EHR
Institute of Medicine
report (2016)
97. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
98. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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)
99. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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.
100. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
101. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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
102. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Integrating with Best Practice in Clinical Flow
Includes medication,
access, utilization, self-care.
Inferred from the assigned
interventions (enrollment,
goal, tasks).
Disease Burden
Includes 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?
103. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
What do you need to know about
these individuals?
103
104. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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)
105. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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)
106. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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?
107. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
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?
108. Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Thank You
Merci
Grazie
Gracias
Obrigado
Danke
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