Presentation by Prof. Mendel Singer of Case Western Reserve University, on the issue of "big data" in health care and policy research. Presented at the Myers-JDC-Brookdale Institute in Jerusalem.

1. Mendel E. Singer, PhD MPH
Associate Professor
Vice Chair for Education
Dept. of Population and Quantitative Health Sciences
Case School of Medicine
Case Western Reserve University
Cleveland, Ohio, USA

2.  PhD in Operations Research
• (Applied Math/Stat)
 Biostatistics
 Health Services Research
 Community and Public Health
• Did MPH program
 Done mathematical modeling studies and
biostatistics to claims data and EMR to semi-
structured interviews and focus groups
 Heavy in education administration,
transitioning to more research

3.  It’s a lot easier to use with consumer
behavior
• Tons of data, all usable and well categorized
 Manufacturer, cost, color, size, type of goods, etc…
• Data is mostly objective and easily measured and
tracked
• Interest drives purchasing
• Distance from data to profit is short, and easily and
quickly tested
 Methods well developed because of
business applications

4.  Reportable diseases
• Infectious disease tracking
 Surveillance, Epidemiology and End Results (SEER)
Program
• Details about the cancer (e.g. tumor size, location, histology)
• Details about treatment and mortality
• Linked to Medicare claims data
 Justice system
 Census data – sociodemographics by neighborhood
 Death certificates
 Emergency department visits for drug overdose
 Medical Claims Data (Medicare/Medicaid,
commercial)

5.  If the rate of fatal opioid overdose in my
home county was applied to the USA, it
would be the 3rd leading cause of death.
 Medical Examiner investigates each
accidental overdose.
• Toxicology – what drugs in system
• Data on injury (e.g. others present), history
(rehabilitation, incarceration, medical reason for
drug) and sociodemographics
 Public data – but only for those who died
 How do we identify those at risk?

6.  State prescription database
• Need identifiers; download patient at a time
 State emergency dept. data for overdoses
• Need identifiers; download one patient at a time
• Where do identifiers come from?
 Incarceration data
• Access difficult for people alive
 Medicare/Medicaid claims data
• Long lag time; access issues to data
 Emergency medical services (EMS) data updated every 15
minutes
• Not available for researchers
 Toxicology data from medical examiner’s lab (untapped)
• Fatalities, police lab
• Potential for de-identified data
 Police records
• Also difficult for people who are alive

7.  ClearPath is the merging of electronic
health records from the 3 main
hospital/health systems in Northeast Ohio
• Cleveland Clinic
• University Hospitals
• MetroHealth Medical Center
 Nearly all hospitals in Northeast Ohio
(43/45)
 All integrated health care delivery systems
• Full service outpatient, urgent care, emergency,
hospital

8. Almost 5 years in ….
Data from one system is almost ready
Many complications in actually getting
the data, process dragging
• Lots of delays
• Many levels of approval (lots of legal folks
involved)
• Turnover at hospitals
• Concern over being compared to other hospitals
• Benefit to hospital not sufficiently clear

9. All hospitalizations for most of the
patients
• And lots of outpatient data
Out of system use
• Not all physicians in one of these systems
• Data on Rx’s written, but not Rx’s filled
Different electronic health record
systems means data not coded
consistently across the health systems

10.  Social Media
• Individual postings, blogs, organization page
 Internet Searching
• Tracking outbreaks
• Side-effects/complications of treatment
 Grocery store purchases
• Effectiveness of programs to increase fruit and vegetable
consumption
 E.g. changes in displays, in-store marketing
 Prescription produce programs
 Wearable electronic devices, e.g. FitBit
 Sensors- detect movement and electricity
 Continuous data often results in substantial
computing and storage issues

11.  Insurance billing has been the driving force
behind computerized medical records in the U.S.
• Ease of filing
• Maximizing reimbursement
• Minimizing time to reimbursement (e.g. minimizing
rejection of claims)
 Track utilization of services
 Link outpatient, inpatient, prescription drug data
 Diagnosis and procedure codes
 Reimbursement (proxy for cost)
 Data available for purchase

12.  Advantages – Know what is being done
• Record of service utilization, including type, location,
reimbursement
• Diagnosis codes – for creating and following cohorts
• Hospital discharge and DRG codes
 Disadvantages – Don’t know details, limited
outcomes
• Tests are done – don’t know the result
• No knowledge of physician exam
• Don’t know about symptoms
• Crude proxies for severity (e.g. hospitalization,
multi-drug therapy)

13.  Benefits
• Utilization
• Test results, not just those taken
• Tests ordered but not taken
• Private as well as public insurance
• Many hospitals use the same system
 E.g. Epic is a very popular system in US Hospitals and
health care systems
• MIMIC database – free to the public
 De-identified intensive care unit (ICU) records
 50,000 ICU stays over 12 years
 Has all chart events, test results
 Other data also available – physionet.org

14. Challenges
• Physician notes and text reports – other free text
fields
 Fields often avoided – but that’s the largely untapped
potential
• Information on Rx’s written, but not those filled
• Few HMO’s left in US
 Kaiser downsized tremendously
 HMOs in Israel, but lots of inpatient out-of-system use
• Lack of connectedness – out of system use
 Doctors in different health care systems (or private
practice)

15.  Difficulty connecting better care to profits
• Value-based care a step in the right direction (fee for
service is bad business)
• Profit drives the software and analysis
• How to document/market better care and outcomes?
 Public hasn’t differentiated well between systems
 Failure of Report Cards
 Each clinical problem needs to be addressed
separately
• Can’t use a single algorithm with unique parameters,
like internet store
• Best data mining will be specific to institution
 The insurer is the one with the largest profit motive
 Better for HMO where insurer=provider

16. Data access
Variability across institutions
Inconsistency within systems

17.  Clinicians wary of computer guys treating their patients
 Americans and their docs don’t want anyone else telling
them how to practice
• “You need evidence, but I know” (a doc once told me this)
 Fear of being replaced by computers, not just aided by
• Some specialties are at risk of being downsized
• Image recognition very advanced - already being used for cancer
detection
 Many data scientists have no concept of clinical impact
• Get excited about incredibly small increase in accuracy
• My not make the effort to understand clinicians
 Data scientists tendency to believe you really don’t have
to know anything about the problem
• Data will tell us, without us getting in the way
• IBM Watson Health – algorithm is great, but need specific knowledge
for most applications. Layoffs.
• Algorithms without clinical input works for some things
• Often requires clinician conceptualization of variables
 Based on how they think in practice

19. Categorize data
• Effects often aren’t linear
• Easier to interpret and apply
 Buckets of patients by severity, risk
• Labels aligned to treatment guidelines
• Consistency across studies

20.  Categorize data
• Effects often aren’t linear
• Easier to interpret and apply
 Buckets of patients by severity, risk
• Labels aligned to treatment guidelines
• Consistency across studies
 Categories arbitrary, then evidence fixes the
categories (nearly forever)
• Data can guide the creation of categories
 Continuous data – no loss of information
• Think more in terms of probabilities, not risk group
• Ways of modeling non-linear effects
• Software handles this easily for years
 Done more in biological applications

21. Clinicians think binary
• Have disease or not
Continuous variables

22. Physician note fields largely useless
• Free text – not categorized
• Using it would be subjective
• Not practical – labor intensive
• Wouldn’t be reproducible
• Symptom not present OR not checked
• Wide practice variation in the visit

23.  Physician note fields largely useless
• Free text – not categorized
• Using it would be subjective
• Not practical – labor intensive
• Wouldn’t be reproducible
• Symptom not present OR not checked
• Wide practice variation in the visit
 Natural Language Processing (NLP)
• Despite above problems, it works great on
physician notes.
• Validates reasonably well across institutions
• Very well developed in English

24. Large number of missing values
makes variable useless
Might be systematically missing
Imputation methods help to a point

25.  Large number of missing values
makes variable useless
 Might be systematically missing
 Imputation methods help to a point
 Machine learning can deal with
missing values
 It can work in practice
 If variable reliably predicts bad
outcomes, who cares if it’s missing for
most?
• E.g. Rovsing’s sign in appendicitis
 If present, always appendicitis. But 1/10,000

26. Concern with overfitting models
Need many observations per
variable in the model

27. Concern with overfitting models
Need many observations per
variable in the model
• # Observations >> # Variables
Machine learning can work when
• #Variables > # Observations

28. Natural Language Processing for:
• Analyzing open-ended questions
• Analyzing text reports (police, criminal justice,
medical records)
Voice Assistant (Siri, Alexa) for
conducting interviews
• Trained to respond to voice commands
• Conduct semi-structured interviews!

29. Current Value
Mean
Minimum
Maximum
Range
Standard Deviation
Change from 1st value
# consecutive increases, decreases

30. MIMIC III – De-Identified ICU EMR
Patients admitted to hospital for kidney
injury/kidney failure
Data at 12 hours after admission to the
intensive care unit (ICU)
4 Clinical variables, age and sex
Outcomes:
• Mortality
• Dialysis
• Ventilator

31.  Compared Traditional Logistic Regression
with:
• Logistic with LASSO
• Classification Trees
• Random Forest
 Random Forest did much better than the
others – quite well with few measures
• Area Under Curve ~ .9
 Most common summary measures:
• Current Values
• # consecutive increases, decreases
 Some models used range, mean, std dev.,
change

32. Clearly Not Israelis

33. What data sources can work?
How do you identify drug addicts?
How do we know if they have mental
health challenges?
How can you determine if they have
received mental health treatment?
What are appropriate outcomes?
How can you determine their outcomes?
What are the obstacles and limitations?

34. What data sources can work?
How do you identify drug addicts?
• Police/criminal justice, rehabilitation center
How do we know if they have mental
health challenges?
• Electronic medical records, Prescriptions
• Psychiatric hospitals
How can you determine if they have
received mental health treatment?
• Health care claims data
• Electronic medical records, Prescriptions

35. What data sources can work?
 What are appropriate outcomes?
• Mental health visits
• Adherence with medication (Rx refills)
• Psychiatric hospitalization
• Suicide attempts
 How can you determine their outcomes?
• Electronic medical records
• Rx refills
 What are the obstacles and limitations?
• Access to various data sets
 Any data, getting identifiers
 Linking to HMO data
 Funding for study – including EMR data

36. Out of time ….

37. Questions?
Mendel Singer, PhD MPH
mendel@case.edu