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Researcher Dilemmas using Behavioral Big Data in Healthcare (INFORMS DMDA Workshop)

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Keynote by Galit Shmueli at 12th INFORMS Data Mining and Decision Analysis Workshop, Houston TX, Oct 21, 2017

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Researcher Dilemmas using Behavioral Big Data in Healthcare (INFORMS DMDA Workshop)

  1. 1. Researcher Dilemmas using Behavioral Big Data in Healthcare INFORMS Workshop on Data Mining & Decision Analysis Houston, TX, Oct 21, 2017 Galit Shmueli 徐茉莉 Institute of Service Science
  2. 2. What is Behavioral Big Data (BBD) Special type of Big Data • Behavioral: people’s measurable “everyday” behavior, interactions, self- reported opinions, thoughts, feelings • Human and social aspects: Intentions, deception, emotion, reciprocation, herding,… When aware of data collection -> modified behavior (legal risks, embarrassment, unwanted solicitation)
  3. 3. BBD vs. Inanimate Big Data Behavioral Big Data Researcher Human Subjects Research Question Inanimate Big Data Researcher Research Question 1. Aware, ongoing interaction with the BBD - “contaminate” BBD with intention, deception, emotion, herding… 2. Can be harmed by BBD
  4. 4. Figure 1: The types of physiological data points and the wearable sensors under development or on the market to monitor them. Elenko, Underwood & Zohar (2015), “Defining Digital Medicine”, Nature Biotechnology 33, 456-461 Physiological Big Data Human Subjects
  5. 5. BBD vs. Physiological Big Data • Physical/bio measurements • Data collection timing often set by medical system • Clinical trials: awareness & vested interest • People’s daily actions, interactions, self-reported feelings, opinions, thoughts (UGC) • Data generation timing often chosen by user • Experiments: users often unaware; goal not always in user’s interest Different research methods in life sciences and behavioral sciences • Measurement instruments • Models (latent variable models, social network analysis) • Human subjects risks
  6. 6. “Behavioral Health” Data vs. BBD • Behaviors: substance abuse & mental health • Population: patients with mental illness / substance abuse • Specific (defined) behavior of patient • “Big”? www.carolinashealthcare.org/medical-services/prevention-wellness/behavioral-health
  7. 7. BBD in Healthcare Research
  8. 8. Hospital Data on Patients, Staff, Assets Patients Personal info Medical history (visits, tests, medication, hospitalization...) Scheduled events, billing Physicians Scheduled + actual appointments, procedures, prescriptions,… Entries of patient info/data Nurses Location, work hours,… Pharmacy staff Speed of service Quality of service Lab staff Speed of service Quality of service Other staff Finance/accounting Cleaning Receptionists Volunteers Food court… Data Collection Technologies: • Medical devices • HIT systems (EHR, HR for Health Info System) --- Smart Hospital • Cameras • Sensors • GPS • IoT
  9. 9. Typical Research Fields using Hospital Data Operations Researchers and Industrial Engineers For: Hospital Management and Operations (staffing, scheduling,…) Medical/Healthcare Researchers & Clinicians For: Improved Medical Treatment (safety, effectiveness,…) Information Systems Researchers For: Improved Design & Use of Medical IS (value of IS, effectiveness, standardization,…)
  10. 10. Hospital Telemedicine / Telehealth Remote Patient Monitoring mHealth/ eHealth Health-”unrelated” behavior Health-related behaviorNew Medical BBD Directions
  11. 11. Behavioral big data also on… Interactions between Patients – doctors/nurses Doctors – other doctors Patients – other patients Patient family – hospital staff Patients – social network ”friends” ...
  12. 12. Health-related BBD: Online • Medical/health websites • Online forums • Social networks • Search engines Info voluntarily entered by users: personal details, photos, comments, messages, search terms, likes, payment information, connections with “friends” Passive footprints: duration on the website, pages browsed, sequence, referring website, Internet browser, operating system, location, IP address
  13. 13. Quantified “Self”
  14. 14. “Some hospitals are collecting new information from patients directly, while others have sought data from companies that sell consumer and financial information, or federal agencies that provide statistics on poverty, housing density and unemployment.” The big obstacle: access to the data. Doctors and nurses have limited time to collect new data and patients bombarded with questions about their lives may suffer “interview fatigue” Health-unrelated BBD
  15. 15. Research Using New Medical BBD: Challenges Behavioral Big Data Researcher Human Subjects Research Question Scientific vs. Clinical vs. Commercial Explain vs. Predict Different (conflicting) Goals: Unit of analysis vs. Unit of measurement Under/over- coverage New risks (privacy, liability, security, HIPAA compliance) New ethical challenges: Generalization Challenges: Acquire + analyze data Users (self-selection, spill-over, knowledge of allocation, network) Company algorithms Average effect vs. individual effect Data contaminated by:New modes of connection & information (social networks, forums, IoT) ATE vs. Individual Technical expertise
  16. 16. Sample Behavioral Healthcare-Related BBD Studies Vocal Minority and Silent Majority: How Do Online Ratings Reflect Population Perception of Quality? Gao et al. (MISQ 2015) Outcomes matter: estimating pre-transplant survival rates of kidney-transplant patients using simulator-based propensity scores Yahav & Shmueli (Annals of Oper. Research, 2014) Emotional Contagion in Social Networks Kramer et al. (PNAS, 2014) Detecting influenza epidemics using search engine query data Ginsberg et al. (Nature, 2009)
  17. 17. Emotional Contagion in Social Networks Kramer et al. (2014) Proceedings of the National Academies of Sciences • Can emotional states be transferred to others via emotional contagion? • BBD from large-scale experiment run by FB, manipulating users’ exposure level to emotional expressions in their Facebook News Feed • No IRB “[The work] was consistent with Facebook’s Data Use Policy, to which all users agree prior to creating an account on Facebook, constituting informed consent for this research.” • PNAS editorial Expression of Concern • Varied response from public, academia, press, ethicists, corporates
  18. 18. Behavioral Big Data Researcher Human Subjects Research Question Scientific vs. Clinical vs. Commercial Explain vs. Predict Different (conflicting) Goals: Unit of analysis vs. Unit of measurement Under/over- coverage New risks (privacy, liability, security, HIPAA compliance) New ethical challenges: Generalization Challenges: Acquire + analyze data Users (self-selection, spill-over, knowledge of allocation, network) Company algorithms Average effect vs. individual effect Data contaminated by:New modes of connection & information (social networks, forums, IoT) ATE vs. Individual Technical expertise
  19. 19. Behavioral Healthcare-Related BBD Study: Example #2
  20. 20. Detecting influenza epidemics using search engine query data Ginsberg et al. (2009), Nature • “Up-to-date influenza estimates may enable public health officials and health professional to better respond to seasonal epidemics” • Researchers from Google and CDC • BBD: automated search results for 50M keywords on Google.com (2003- 2007). For each query, collected {query text, IP address} • Analysis: Fit 450M different models, correlating each query text with CDC data; Combined 45 queries with highest correlation
  21. 21. Researchers: epidemiologists + data science academics Dalton et al. (2016), “Flutracking weekly online community survey of influenza-like illness annual report, 2015” Communicable diseases intelligence quarterly report Challenge: Acquire data
  22. 22. • The algorithm detects “flu” or “winter” • Persistent over-estimation • Performs worse than lagged CDC 3-week-old data • Never released 45 terms used • Changes made by Google’s search algorithm to display potential diagnoses + recommend search for treatment (more advertising) -> increased search • Lazer et al. recommend combining/ calibrating GFT with CDC data
  23. 23. Behavioral Big Data Researcher Human Subjects Research Question Scientific vs. Clinical vs. Commercial Explain vs. Predict Different (conflicting) Goals: Unit of analysis vs. Unit of measurement Under/over- coverage New risks (privacy, liability, security, HIPAA compliance) New ethical challenges: Generalization Challenges: Acquire + analyze data Users (self-selection, spill-over, knowledge of allocation, network) Company algorithms Average effect vs. individual effect Data contaminated by:New modes of connection & information (social networks, forums, IoT) ATE vs. Individual Technical expertise
  24. 24. Telemedicine / Telehealth Remote Patient Monitoring mHealth/ eHealth Health-”unrelated” behavior Health-related behaviorNew healthcare BBD offers new research opportunities
  25. 25. … and new challenges Behavioral Big Data Researcher Human Subjects Research Question Scientific vs. Clinical vs. Commercial Explain vs. Predict Different (conflicting) Goals: Unit of analysis vs. Unit of measurement Under/over- coverage New risks (privacy, liability, security, HIPAA compliance) New ethical challenges: Generalization Challenges: Acquire + analyze data Users (self-selection, spill-over, knowledge of allocation, network) Company algorithms Average effect vs. individual effect Data contaminated by:New modes of connection & information (social networks, forums, IoT) ATE vs. Individual Technical expertise
  26. 26. Anal yt ics Humanit y Responsibil it y Galit Shmueli 徐茉莉 Institute of Service Science

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