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New Bundled Payments Research: Identifying the Most Predictive Risk Factors for TJR Readmissions

By Bob Kelley /Thursday, June 2, 2016

As part of our continuing research into bundled payments for commercially insured total joint replacements (TJRs), we recently turned our focus to patient readmissions — which we already know vary in cost and occurrence by geographic area and type of post-acute care a patient receives.

Logical thinking tells us that if we could accurately identify TJR patients who are at high risk for readmission due to certain clinical factors, we could reduce or prevent readmissions. And any change in TJR readmissions could then impact TJR value-based bundled payments. The goal, of course, would be to identify at-risk patients early in the care process, so that they could be targeted for special care or intervention programs.

But can we identify the top clinical risk factors?

To find out, we used TJR claims data from the Truven Health MarketScan® Commercial Database to analyze 84,648 simulated bundles. Each bundle was characterized by more than 35 data attributes, including patient demographics, presence of readmission, post-acute care usage, payments by component service type, comorbidities, utilization of healthcare services in the six months prior to the surgery, and more.

 Across several models, we found that the most predictive risk factors for TJR patient readmissions were:

  • Hospitalizations in the six months prior to the TJR
  • Emergency room visits in the six months prior
  • Unique condition/disease diagnoses in the six months prior

More specifically, when we looked at odds ratios estimated in a logistic regression model, additional significant TJR readmission risk factors were:

  • Number of comorbid, high-stage chronic conditions
  • Number of high-stage acute episodes in prior six months
  • Comorbid cardiovascular disease
  • Comorbid cerebrovascular disease

In a future blog post, we’ll take a look at whether or not using these clinical insights to identify and target at-risk patients provides better results than a random selection of patients.

Full details on this study, and additional findings, are available by downloading our latest research brief, Total Joint Replacements in the Commercially Insured Population: Predicting Risk of Readmission.

Bob Kelley
Senior Research Fellow, Advanced Analytics

 

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