Hello, I am Lacy Fabian, a Healthcare Evaluation Specialist.
Moving the needle toward higher quality healthcare at the national level brings with it tremendous amounts of data. The volume of data is beneficial for designing complex models; however, the downside is that the data are often processed manually and years old by the time they have moved through the proper channels and are ready for analysis. The wait for data presents a challenge for programs. The ideal is that, with advances in electronic reporting, we will achieve “real time” and “on demand” data, but in the meantime—How do we better our programs? How do we know what is working? What measure refinements are needed?
- Focus on process improvements within your program. An efficient program is not a one to one association with an effective program. However, if you do have undesirable findings with your data, you will be able to identify root causes right away, knowing that process issues are less likely.
Rad Resource: For a causal analysis consider a Fishbone Diagram as described recently on AEA365.
- Focus on rigorous measure development methods, so you have confidence in your measures. Document the rationale that guided the decision-making to create historical program knowledge as you look to recall the reasoning years later to determine if they are achieving the originally intended outcomes. CMS follows their Measures Management System Blueprint.
- Phase measure implementation by implementing measures with stronger psychometrics early and implementing measures still in need of refinement later. Yielding the greatest return on this approach will require thinking strategically about the measure to know what the expectations are for it as it is used for longer periods—like knowing when you will hit the tipping point to having enough data to examine trends.
Waiting on data may never be fun, but there are valuable pre-data activities to strengthen your programs and allow you to make the most of your data when they are available.
Note: The author is currently working for The MITRE Corporation. The author’s affiliation with The MITRE Corporation is provided for identification purposes only, and is not intended to convey or imply MITRE’s concurrence with, or support for, the positions, opinions or viewpoints expressed by the author.
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