Background:
Practitioners and policymakers have identified 30‐day readmissions as an important quality indicator to measure hospital care, with the premise that readmissions can be indicators of poor care or coordination of care. Among cancer centers, there is no benchmark data in terms of readmission rates or recommendations in terms of risk adjustment models for cancer patients admitted to cancer centers.
Purpose:
To estimate the baseline unplanned readmission rate among patients admitted to the General Internal Medicine Hospitalist Service at M.D. Anderson Cancer Center and to identify possible risk factors for readmission in our patient population. We intended to use this baseline data to implement interventions to decrease our readmission rate.
Description:
Unplanned readmission was defined as an emergent or urgent non‐elective readmission excluding admissions for chemotherapy, radiation, rehabilitation. We studied potential risk factors for readmission in our baseline cohort such as patient demographics and clinical and hospital encounter characteristics. There were 2 intervention phases. Phase I Intervention: Hospitalist service move to a single floor. Phase IIa Intervention: twice weekly interdisciplinary meetings on the unit. Phase IIb Intervention: Use of a risk assessment tool during the meetings– the Cancer Outcomes Augmented through Safe Transitionstool (figure 1) which was developed using known risk factors for readmission as well as those identified from our baseline data and unique subset of patients. During our 2‐month pilot, the COAST tool paper form transitioned to an online checklist to identify areas that could be addressed to reduce risk of readmissions.
Results:
The unplanned readmission rate was calculated to be22.8% at baseline. The 45‐65 age group, having Medicare insurance, and being discharged to hospice were protective of a readmission. Patients with distant metastases and more than 3 comorbidities were associated with increased risk for readmission. Readmitted patients have a greater length of stay (7 days) and a higher average cost of inpatient stay ($20.3K). During Phase I, our average readmission rate over 7 months was 22.75%. We plan on analyzing our post‐intervention data after 6 months and 1 year and further streamline our interventions based on the results.
Conclusions:
Our project has provided an insight into the rates and risk factors for readmission in an oncology hospitalist service in a tertiary cancer center and has guided the development of our interventions. The meetings have improved communication between the interdisciplinary staff and allowed us to identify and correct missed opportunities of care. Reducing the readmission rate could lead to reduced length of stay, reduced costs, and may improve patient outcomes. Due to the short follow up (2 months) we have limited post‐intervention results and these will be presented later as part of this ongoing project.
