Do your predictive models or analytics test for patient outcomes or other indicators of quality of care? Here are a few examples of measures, and uses for them beyond just the numeric results. After all it’s not only the analytics you should care about, but also the business intelligence accompanying the analysis that provides the best value.
General Treatment Outcomes
Consider a child diagnosed with asthma and managed by a specialist. Are there asthma-related emergencies leading to additional services for that child? If yes, what are they and how severe? How does the initial treating provider rank on this and similar measures as compared to their peers?
With this information, it is possible to not only track these scores but to compare treatments including medication, dosages and other factors to determine if providers with poor outcomes scores are in line with treatment practices of those with higher scores. This analytic is not designed to tell doctors how to treat patients or in any way attempt to replace physician judgements...
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...specially if the review refers to the lack of cleanliness of the office or rudeness of the staff. But I am really looking to find out which practitioner to bring my child to when they sprain their ankle. Which doctor is most effective in getting them back to 100%? Which physical therapy center has the best after-surgery results? Which doctor has the best track record in preventing serious asthma incidents?
Roadmap to Better and More Informed Choices
Today we have at our fingertips a treasure trove of bits and bytes that can help paint a strategic picture for plans providers, and consumers. Of course, each group comes at data with a different set of questions. The secret sauce in the recipe is designing purpose-built analytics that provide useful information for healthcare practice management, increasing accuracy, efficiency and hopefully lowering costs for all.
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