Big Data and Positive Deviance

The triple aim continues to be the focus of payment and delivery reform. Predictive analytics and big data are poised to make an impact in achieving the triple aim. Recently, there has been interest in connecting predictive analytics with other tools to amplify impact in lowering costs, improving quality, and improving population health. Furthermore, it is possible to add another aim and that is inclusion of the patient voice through positive deviance.

Predictive Analytics

For the individual patient being admitted to the hospital, predictive analytics has already started to impact on care. By identifying individual patients who are at higher risk for readmission in a timely manner, there are opportunities for intervention. . This invaluable insight enables care team members to create a more productive impact through the prioritization of those in greatest need. This notion has already played out at Parkland Hospital in Dallas, Texas, by Dr. Rubin Amarasingham and colleagues. They created a predictive analytics system called the Parkland Intelligent E-Coordination and Evaluation System, or PIECES, that has helped target individual patients to get the care they need. PIECES identifies high risk patients by analyzing information available through the hospital’s electronic health record. Once these higher risk patients were identified, they were assigned as high priority for the hospital’s case managing staff. Since PIECES has been running, Parkland hospital has cut its 30-day readmissions for Medicare patients with heart failure by 26 percent with no increase in staffing.

Positive Deviant Paradigm Shift

The lessons learned thus far show that even a small improvement in prediction can have a significant impact in how to allocate resources. What we have yet to explore is how that small improvement could be used to enhance the positive deviance approach for patient care. The use of positive deviance has been shown to have great potential for improving quality of care in the healthcare setting.6 The synergy between predictive analytics and positive deviance creates a new approach to healthcare delivery that is currently untapped. This approach could help open the door to explore how positive deviants traverse through the social determinants of health in order to sustain their well being. When studying social determinants of health, researchers have shown that healthcare accounts for only 10-25 percent of the variance in health over time.7 The two fields of predictive analytics and positive deviance may work hand in hand by providing an effective approach for studying and spreading local behaviors that have been proven to overcome social determinants of health.

As we move towards a value based healthcare system, the identification of agreed upon quality indices is paramount. Through this approach there may be opportunities to develop new quality indices based on these identified positive deviant behaviors. The behaviors identified could help customize quality metrics based on measurable outcomes that are contextually specific to each care system. As we move towards partnering with patients to create quality metrics, this could serve as a more evidence based approach to achieve this goal.

Conflict of Interest Disclosures: Neither Author has any conflicts of interests to disclose. The conflicts of interest would include but are not limited to financial interests, activities, relationships or affiliations.

References: 1) “What Is Positive Deviance?” Positive Deviance Initiative. Web. 09 Dec. 2013. 2) Nyce, Charles (2007), Predictive Analytics White Paper, American Institute for Chartered Property Casualty Underwriters/Insurance Institute of America, p. 1 3) United States. City of Chicago. Office of the Mayer. City of Chicago Launches Preventive Rodent Baiting Pilot. By Molly Poppe. Chicago: City of Chicago Office of the Mayer, 2013. Print. 3) Howard, Alex. “Predictive Data Analytics Is Saving Lives and Taxpayer Dollars in New York City.” Strata. O’Reilly: Strata Making Data Work, 26 June 2012. Web. 28 Sept. 2013. 5) Amarasingham R, Patel P, Toto K, et al. Allocating scarce resources in real-time to reduce heart failure readmissions: a prospective, controlled study. BMJ Qual Safdoi:10.1136/bmjqs-2013-001901 6) Bradley, E., Curry, L., Ramanadhan

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