Scalable and Locally Applicable Measures of Treatment Variation That Use Hospital Billing Data

Care variation studies often use large amounts of data but approaches developed for such research are either scalable but not locally applicable or locally applicable but not scalable. We present a method that is scalable and locally applicable while being statistically significant. Using a population of patients diagnosed with both congestive heart failure and myocardial infarction, we developed and tested measures of care variation on data derived from hospital billing records. Our metrics yielded statistically significant results. Computing time for the method was found to increase linearly allowing for the desired scalability. In the future, our care variation metrics be used to gain insight into local conditions that correlate with outcomes of interest like visit charges or morbidity rates.