Abstract
ObjectivesIdentifying patients at risk of a 30-day readmission can help providers design interventions, and provide targeted care to improve clinical effectiveness. This study developed a risk model to predict a 30-day inpatient hospital readmission for patients in Maine, across all payers, all diseases and all demographic groups.MethodsOur objective was to develop a model to determine the risk for inpatient hospital readmission within 30 days post discharge. All patients within the Maine Health Information Exchange (HIE) system were included. The model was retrospectively developed on inpatient encounters between January 1, 2012 to December 31, 2012 from 24 randomly chosen hospitals, and then prospectively validated on inpatient encounters from January 1, 2013 to December 31, 2013 using all HIE patients.ResultsA risk assessment tool partitioned the entire HIE population into subgroups that corresponded to probability of hospital readmission as determined by a corresponding positive predictive value (PPV). An overall model c-statistic of 0.72 was achieved. The total 30-day readmission rates in low (score of 0–30), intermediate (score of 30–70) and high (score of 70–100) risk groupings were 8.67%, 24.10% and 74.10%, respectively. A time to event analysis revealed the higher risk groups readmitted to a hospital earlier than the lower risk groups. Six high-risk patient subgroup patterns were revealed through unsupervised clustering. Our model was successfully integrated into the statewide HIE to identify patient readmission risk upon admission and daily during hospitalization or for 30 days subsequently, providing daily risk score updates.ConclusionsThe risk model was validated as an effective tool for predicting 30-day readmissions for patients across all payer, disease and demographic groups within the Maine HIE. Exposing the key clinical, demographic and utilization profiles driving each patient’s risk of readmission score may be useful to providers in developing individualized post discharge care plans.
Highlights
From 2007 to 2010, the national inpatient 30 day post discharge readmission rate remained relatively unchanged and included approximately 18 percent of Medicare patients
A risk assessment tool partitioned the entire Health Information Exchange (HIE) population into subgroups that corresponded to probability of hospital readmission as determined by a corresponding positive predictive value (PPV)
The risk model was validated as an effective tool for predicting 30-day readmissions for patients across all payer, disease and demographic groups within the Maine HIE
Summary
From 2007 to 2010, the national inpatient 30 day post discharge readmission rate remained relatively unchanged and included approximately 18 percent of Medicare patients. Causes of potentially preventable hospital readmissions have been consistently identified to include premature discharge from the hospital, lack of resources for post discharge treatment, and insufficient provider consultation [3]. The Centers for Medicare and Medicaid Services (CMS) established a Hospital Readmission Reduction Program that defines a readmission as an admission to the hospital within 30 days post discharge from any hospital [6, 7]. Under reimbursement programs established by CMS in 2012, hospitals with high readmission rates for selected chronic diseases are penalized a percentage of overall reimbursement [8]. In an effort to prevent unwanted and avoidable hospital readmissions, it is first necessary to develop tools for actionable risk assessment and prediction, such that accountable healthcare stakeholders can target resources to those populations likely to yield the most benefit
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