Clinic level variation in emergency department and inpatient utilization in a community setting.

Abstract

226 Background: Early studies of the oncology medical home suggest that intensive outpatient care (e.g. 24-hour phone triage, same-day infusion) reduces emergency department (ED) and inpatient (IP) use during cancer treatment. Little is known about which services are most cost-effective. One strategy is to measure observed variation in ED and IP rates to pinpoint care features associated with low-use clinics. This study examined clinic-level variation in ED and IP use in a community setting. Methods: Cancer registry records for Western Washington from 2011 to 2015 were linked with claims from two regional commercial insurers. Included patients were diagnosed with breast, lung, colorectal, or prostate cancer and treated with chemotherapy or radiation. All ED and IP use was tracked 1 year after treatment start using claims data. Observed clinic rates were measured as the percentage of patients with 1 or more visits. Expected clinic rates were determined from regional average rates weighted by clinic’s cancer-specific stage mix. Observed-to-expected clinic ratios were calculated and the Wilson Score test (95% CI) was used to determine statistically different rates. Results: The 18 clinics included 4,558 eligible patients (median 196 pts/clinic; range: 35-859). Unstaged lung patients had the highest ED rates (38.5%); unstaged breast had the lowest (13.3%). The highest IP rate was among unstaged colorectal (66.7%); the lowest in local breast (11.1%). One clinic had an observed rate that was significantly above its expected rate in both ED only and ED to IP. One clinic was significantly below its expected rate in both ED to IP and IP only. Conclusions: Even after adjusting for cancer-specific stage, there was sizable clinic-level variation in the percentage of patients visiting the ED or IP. Investigation into care delivery features and practice characteristics, along with further risk adjustment, may yield insights into best practices and identify clinics for intervention. [Table: see text]