Background: An estimated 1 in 5 patients are at risk for being readmitted to the hospital within 30 days of discharge. Readmissions put a large financial strain on the United States healthcare system and increase risk for poor patient care outcomes. The Centers for Medicare/Medicaid Services publicly report readmission data and have financial penalties for poor performance. Numerous validated risk calculators aim to identify patients at risk of 30-day readmission, yet their predictive value varies widely. Our university hospital uses a proprietary risk tool that stratifies patients into low, moderate, or high risk for readmission. We investigated the utility of this tool.
Methods: Using interviews and a Gemba walk, a process map was developed to look at the steps involved in utilizing the readmission risk tool. The tool was also closely examined to identify what variables went into calculating risk of readmission. Readmission data was extracted from Vizient for 19028 unique admissions over a 6 month period from January to June 2021. The encounters were categorized according to the index admission readmission risk score of low, moderate or high. These scores were then correlated with actual readmissions. The frequency of completion of different subfields of the risk assessment tool was also evaluated.
Results: The variables of the readmission risk assessment tool are reason for admission, hospital criteria, funding, age, disabilities, living situation, and psychosocial barriers each containing subfields to determine low, moderate, or high readmission risk. The tool was completed on 19,028 of the 19,882 unique admissions, with the subfields of two or more chronic conditions (40.6%), lack of advanced directive (35.4%) and estimated length of stay (LOS) >3 days (23.2%) being completed most often. 17.5% of the 5,949 high risk, 13% of the 3,858 moderate risk, and 9.6% of the 4,616 low risk categorized patients were actually readmitted within a 30 day period.
Conclusions: The current process and tool for screening readmission risk at our academic institution has many opportunities for improvement. Primarily, the variables of the tool were not being consistently filled out and important fields were often skipped. Nurse case managers complete the tool within 24 hours of admission by chart review and rarely conversations with patients or their families. Involving families and updating the tool closer to discharge could more clearly identify risk areas. After discharge, a high risk patient receives a follow up phone call from the care management team. Addressing high risk factors identified on the tool during hospitalization rather than after discharge may help decrease readmission risk. Next steps for this project include systematically identifying areas for improvement on the current process map, targeting areas on the tool that could trigger inpatient interventions, updating the tool to include other variables that are known to be predictors of readmission, and comparing the internal tool scores to a validated readmission risk tool. Preventable rehospitalizations are costly and put patients at high risk for poor outcomes. A validated tool to predict which hospitalized patients are at risk for readmission can help target increased interventions for prevention.