Converting physician performance data into behavior change...4 obstacles...4 solutions...Part 2: Spe
This is the second part of a four part series addressing 4 common obstacles encountered when healthcare organizations attempt to convert physician performance data into behavior change. In the first part we covered Data Credibility, in this part we will discuss Speed.
Data driven insights are a powerful tool to motivate physicians to take action. The more relevant to the individual, and the more impactful to what matters most to them, the greater the motivation you can tap into to drive behavior change. Most of the time behavior change requires some type of training, which is why insights and training work well together. The more time it takes to process group data to deliver individualized insights and training, the less relevant the data is to the individual’s current work. Even if the work hasn’t changed, they may question circumstances from the past period to justify scores.
Data flow to Meaningful Insight & Training
Data typically flows in the following sequence (see image). Even with the use of Business Intelligence tools, manual steps to process data are often still required. Quality specialists will review a dashboard to determine group trends. Quality specialists will distribute generic reports to individual physicians without any ability to measure individual engagement. The question often remains, did anyone even look at their scores? Of the physicians that do open the reports, they must then sort through these generic reports to distill out what is most meaningful to them. As a separate initiative, the same training curriculum is delivered to all physicians, who must individually determine where to invest additional time to learn based on their gaps in performance.
Think outside the box!
Create fully automated rules to determine what is most meaningful to each individual, connect that to training & send it out WITH TRACKING! For instance, at one healthcare organization we spent the initial phase of the project identifying critical quality measures. We then set up an interface engine to receive data from the organization's population health platform and process the data. The interface engine processed the following:
Relationships between metrics including their goals
Group average scores at the site and specialty level
Individualized prescriptive recommendations such as the # of patients needed to achieve goal on screening measures
Relationships between metrics and training modules, including peer-to-peer tips
Once the interface engine was set up, within 30 minutes of the latest data snapshot from the population health platform, all physicians are notified with personalized messaging. These messages leveraged natural language generation (NLG..different than NLP) to summarize what is most meaningful to each individual based on their performance and the performance of their peers. The messaging also included links to elearning, or to schedule one-on-one time for additional support. Every step in the process was trackable, providing insight to fine-tune the process and maximize impact over time. These messages were repeated monthly, with escalation paths for physicians who do not engage at any point of the process.
In the next part of this four part series, we will discuss the obstacle Evaluation, along with a solution.
About the Author Over the last decade, Dr. Andres Jimenez developed training curriculums impacting over 1/3rd of all US physicians. Over the past 5 years, physicians in over 1,000 hospitals and clinics have used training software he developed. Dr. Jimenez completed the Dartmouth/Brown medical program, and continued on to General Surgery residency at Emory. He completed a fellowship in Educational Leadership at USC School of Medicine, and a Masters Degree in Education, with a focus on Instructional Design for Online Learning.