Predicting the risk of hospital readmission

Objective: When effectively performed, transitions of care present an opportunity to decrease patient suffering, reduce complication and lower the cost of care. As part of an effort to reduce unnecessary readmissions, we initiated development of a formula predicting the risk for 30-day readmission to a 300-bed district hospital. The goal was to optimize use of limited transitional care resources by targeting interventions at high-risk patients. With limited resources, focusing discharge planning on high-risk patients offers maximum return on resources. Materials and methods: We prospectively collected information about 10,000 patients discharged from a general acute care district hospital. The process analyzed 25 characteristics of patients admitted to hospital using a likelihood ratio analysis as a diagnostic test for predicting readmission. Clinical characteristics were excluded. The elements included are available on the day following admission. A relational database stored and analyzed the data (FileMaker, Santa Clara, CA). For validation, the next 2500 patients' risk of readmission was stratified prospectively. Excluded from the data collection were patients who expired, were on the maternity ward, were transferred to a psychiatric facility or transferred to another acute care hospital. Results: The10,000 patients discharged had an overall readmission rate of 11.8 (CI, 11.0-12.8) %. A weighted formula identified characteristics associated with patients readmitted and included (weighting factor): age (1.5), previous hospital admission (2.8), disposition (home health care, skilled nursing facility, residential care: 1.8), presence of stroke (1.3), congestive heart failure (2.2), pneumonia (1.9), chest pain (1.3) and the absence of an identified primary care physician providing follow up in the medical record. The predictive value of the formula, when applied prospectively, showed a 1% readmission rate for low risk patients, a 15% risk for moderate risk and 24% for high-risk patients. Conclusions: With evidence suggesting that 20% of seniors are readmitted to the hospital within 30 days of discharge, intense focus on improving transitions from hospital revealed that there were insufficient resources and little benefit in providing in depth discharge planning for all patients. Tailoring the discharge process based on need was the strategy. The discharge process would start the day following admission to allow sufficient time for completion. Basic elements include disease education, symptom recognition with red flag alerts, phone follow-up, follow-up with the PCP and medication reconciliation for all patients. Patients identified as high-risk would have an individualized care plan created in addition to the basic program. To stratify patients, we developed a formula predicting risk for readmission, included the calculation in the electronic medical record and applied the process prospectively. During the first 6 months of the program, the all cause, 30-day readmission rate decreased for 11.8% to 10.6 (CI: 10.0-11.8%). Readmission from a nursing home within 7 days fell from 6 (4.3-6.9) % to 4.1 (3.4-4.7) %.