Health Insurance Membership and Claims Data Betsy Wasilevich, PhD, MPH Department of Epidemiology and Biostatistics Michigan State unive

1. Challenges of Big Data in
Health Care: Health Insurance
Membership and Claims Data
Betsy Wasilevich, PhD, MPH
Department of Epidemiology and Biostatistics
Michigan State University
Interdisciplinary Forum, 04.07.2016

2. Payor Data
 Membership
◦ Demographic characteristics and geographical location
◦ Enrollment
◦ Benefits
 Claims
◦ Facility
◦ Professional
◦ Pharmacy
◦ Vision
◦ Dental
 Provider
◦ Physician
◦ Practice
◦ Facilities

3. How Payor Data Is Used
 Ongoing surveillance
 Population health management
 Risk stratifying for targeting intervention
activities
 Predictive modeling to determine cost
drivers
 Incentive payment determination
 Clinical and public health program
evaluation
 Health services and epidemiology
research

4. Challenges
 Claims for billed services only
 Timeliness of claims (runout)
 Missing information
◦ Practice and member characteristics
◦ Clinical information: illness severity and health outcomes
 Lack of standardized methods
◦ Inability to test the validity of metrics
 Continuous enrollment requirement
 Small n for physician/practice level analysis
 Differences based on type/source of claim
 Availability
◦ Lack of multi-payor data systems
◦ Data sharing hurdles
 Generalizability

5. Interdisciplinary Collaboration
 Epidemiology
◦ Population level health and health care measurement
◦ Evaluate determinants of health status and health care utilization
 Health care practitioners
◦ Treatment and appropriateness of care
◦ Relevance of measurement priorities
◦ Downstream consequences of incentive/health care policy/intervention
 Medical and pharmacy billing
◦ Claims and coding practice
 Medical/Public health informatics
◦ Data management and communication
◦ Development and adoption of IT solutions
 Statistics/biostatistics
◦ Multilevel data approaches
◦ Handling missing data and small n
◦ Modeling techniques for complex, observational data
 Health care administration and policy
◦ Systems of care
◦ Benefit and reimbursement policy
 IT
◦ Data management and processing - development and support