Unbiased Analysis of Today's Healthcare Issues

What can you learn from quality or cost outliers?

Written By: Jason Shafrin - Jun• 13•17

Many researchers have pointed to (positive) cost or quality outliers and made the claims that if only all physicians, or hospitals, or regions could be like these high quality or low cost providers/regions, then the health care system would be much more efficient.  Research teams such as the Dartmouth Atlas are famous for finding these conclusions.  My own research has also investigated these questions (here, here and here).

Describing variation in quality or cost, however, does not mean that the solution is to make everyone like the best providers.  For instance, the Mayo clinic has some of the best physicians in the world.  It is not possible for all hospitals to hire the best physicians, since clearly there is a limited number of these “best” physicians.  However, oftentimes, you can learn from these outliers using qualitative data collection methodologies.

What people can learn from so-called “positive deviance” analysis is exactly the topic of a recent HSR study by Rose and McCullough (2017). Their study describes how to sample sites for positive deviance analysis, how many sites to examine, how to collect data, and many other practical challenges. For instance, the authors provide an example why surveying both positive and negative deviants is important.

…we had expected to find that the staff at the best-performing sites would be distinguished by their willingness to go “above and beyond” for their patients, but in fact, we found that this was equally true at the high- and the low-outlier sites…meaning that simply exhorting providers to go the extra mile for their patients would not be an effective approach.

On the other hand, if there are specific processes or organizational structures that can be identified by positive deviants, these could potentially be adapted by both negative deviants as well as the typical organizations. When idiosyncratic factors, talent, or effort are key drivers of variability, then policy and management interventions to improve outcomes and reduce cost at negative deviant sites are less likely to succeed.

The authors do a good job of showing both the utility of positive deviant analysis (e.g., looking for potential causes for improved outcomes) as well as its limitations (e.g., negative deviants may face resource, organizational or legal constraints and thus may not be able to implement the practices of positive deviants).

For more information on studies using the positive deviance approach, check out the Positive Deviance Initiative.


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