Data and methods can be used as policy tools, according to this essay in Health Services Research in 2020, which advocates two systemic national policies: first, aggregating health and medical data on a broad scale to create “collective intelligence”; and second, using that collective intelligence to find and reward “positive deviance”—unexpectedly good outcomes—in the health care system.
- Given the benefits to society of having the best, most comprehensive health care and medical data available, all this data should be linked, decrypted of personal information, and aggregated, so that it can be shared in the public domain (with HIPAA-type protection.) Public funding should support the creation and maintenance of a highly functional and complete database that could be used—but regulated—for research purposes.
- “Collective intelligence” is what is learned by observing patterns in how a large group of actors independently adapt to their environment. “Positive deviants” are those actors who, through collective intelligence, can be identified as unusually successful.
- The author suggests policies reward positive deviants for their superior performances, rather than singling out poor performers.
- The author also suggests incentives could be used to encourage these superior (but anonymous) performers to step forward voluntarily to share their successful methodologies.
The author asserts more can be learned from positive deviance than from studying negative results or measures of quality focused on process. Further, the article says collecting and linking the data used in patient care and payment systems would require no central agency; to the contrary, this collective intelligence would reap better information if it is accessible by multiple analysts. The author calls for “legislation and leadership” by federal payers to create a system to enable consolidation and secure sharing of medical data so that data can be used to enhance care delivery.
- 1. Data Governance and Stewardship
- 2. Balancing Access to Health Data and Privacy
- 3. Health Services Research and Data Linkages
- 4. Data and Measurement Issues in the Analysis of Health Disparities
- 5. Viewing Health Care Delivery as Science
- 6. Multiple Chronic Conditions and Disabilities
- 7. Modeling Health Care Policy Alternatives
- 8. Improving Evaluations of Value-Based Purchasing Programs
- 9. Data and Methods to Facilitate Delivery System Reform