Data and Methods to Facilitate Delivery System Reform: Harnessing Collective Intelligence to Learn from Positive Deviance

摘要

Researchers often focus on the data and methods to assess policy changes, but data and methods can also be policy tools. To improve, health care systems need mechanisms and incentives for continually gathering, assessing, and acting on data. This requires (1) more comprehensive data, (2) converting data into information, and (3) incentives to apply that information. Restructured economic incentives can encourage clinicians to increase value (higher quality and/or lower cost) for their patients. While necessary, incentives are not sufficient—information is also needed. Incentives can lead clinicians to demand better information. Much of the necessary data is already used in patient care and billing; some additional variables will come directly from patients. The notion builds on two concepts: collective intelligence and positive deviance. The former characterizes knowledge gained from observing the behavior of many independent actors adapting to changing situations. Positive deviants are those who achieve far better results than expected. By rewarding positive deviants, rather than trying to identify and “correct” those who are problematic, providers will voluntarily identify themselves and their methods for achieving superior outcomes.