Journal article

Hospital readmissions by variation in engagement in the healthcare hotspotting trial: A secondary analysis of a randomized clinical trial

Care management & redesign Data analysis & integration Strengthening ecosystems of care Measurement & evaluation Quality improvement

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This study, conducted by our research team in partnership with researchers from Kaiser Permanente, revisited the data used in the 2020 randomized controlled study of our Camden Core Model to determine if there would be a statistically significant treatment effect among those who were more likely to participate in the program. It was published in JAMA Network Open.

The team used the Kaiser Permanente researchers’ novel “distillation methodology” to determine which baseline characteristics were predictive of program participation, then applied that predictive model to both the intervention and control arms of the study. The methodology was awarded the 2023 James F. Burgess Methods Article-of-the-Year Award by Health Services Research.

When they compared outcomes within this “distilled” subset of patients, they found that patients in the intervention arm had 1) lower hospital readmission rates 30 and 90 days after discharge, and 2) fewer instances of hospital readmission at 30 and 180 days after discharge. They concluded that the Camden Core Model reduces hospital readmissions among a subset of patients who are more likely to participate in care management.

The findings of which baseline characteristics were predictive of program participation also suggest new avenues of inquiry into how to tailor our programs to better target those most likely to benefit, as well as how to address barriers to participation on an operational or programmatic level.

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