BACKGROUND: Interventions designed to improve care and reduce costs for patients with the highest rates of hospital utilization (super-utilizers) continue to proliferate, despite conflicting evidence of cost savings. METHODS: We evaluated a practice transformation intervention that implemented team-based care and risk-stratification to match specific primary care resources based on need. This included an intensive outpatient clinic for super-utilizers. We used multivariate regression and a difference-in-differences approach to compare changes in mortality, utilization, and charges between the intervention group and a historical control. Sensitivity analyses tested the robustness of findings and revealed the inherent challenges associated with quasi-experimental designs. RESULTS: Observed charges for the intervention group were significantly lower than expected charges as derived by the trend of the historical control (p<0.04) resulting in total charge avoidance of approximately $26 million. While inpatient admissions were significantly higher (p<0.01), charges associated with total inpatient (p=0.01), intensive-care unit (p<0.05, not robust to sensitivity analyses), and surgery (p<0.01) were significantly lower than expected in the intervention group. One year mortality was significantly less in the intervention group (12.6% vs 11.5%, p<0.01). CONCLUSIONS: The use of tailored services, including a dedicated intensive outpatient clinic, for super-utilizers within a larger primary care practice transformation reduced mortality and provided significant savings, even while total hospitalizations increased. These savings were achieved through a reduction in the intensity of inpatient services. The unexpected finding of a reduction in ICU charges deserves further exploration. IMPLICATIONS: These findings suggest that intensity of inpatient service, and not merely volume of services, should be considered a focus for future intervention design and evaluated as an outcome. LEVEL OF EVIDENCE: Level III (Quasi-Experimental Design).
BACKGROUND: Interventions designed to improve care and reduce costs for patients with the highest rates of hospital utilization (super-utilizers) continue to proliferate, despite conflicting evidence of cost savings. METHODS: We evaluated a practice transformation intervention that implemented team-based care and risk-stratification to match specific primary care resources based on need. This included an intensive outpatient clinic for super-utilizers. We used multivariate regression and a difference-in-differences approach to compare changes in mortality, utilization, and charges between the intervention group and a historical control. Sensitivity analyses tested the robustness of findings and revealed the inherent challenges associated with quasi-experimental designs. RESULTS: Observed charges for the intervention group were significantly lower than expected charges as derived by the trend of the historical control (p<0.04) resulting in total charge avoidance of approximately $26 million. While inpatient admissions were significantly higher (p<0.01), charges associated with total inpatient (p=0.01), intensive-care unit (p<0.05, not robust to sensitivity analyses), and surgery (p<0.01) were significantly lower than expected in the intervention group. One year mortality was significantly less in the intervention group (12.6% vs 11.5%, p<0.01). CONCLUSIONS: The use of tailored services, including a dedicated intensive outpatient clinic, for super-utilizers within a larger primary care practice transformation reduced mortality and provided significant savings, even while total hospitalizations increased. These savings were achieved through a reduction in the intensity of inpatient services. The unexpected finding of a reduction in ICU charges deserves further exploration. IMPLICATIONS: These findings suggest that intensity of inpatient service, and not merely volume of services, should be considered a focus for future intervention design and evaluated as an outcome. LEVEL OF EVIDENCE: Level III (Quasi-Experimental Design).
Authors: James E Bailey; Satya Surbhi; Jim Y Wan; Kiraat D Munshi; Teresa M Waters; Bonnie L Binkley; Michael O Ugwueke; Ilana Graetz Journal: J Gen Intern Med Date: 2019-07-03 Impact factor: 5.128
Authors: Erin Yildirim Rieger; Josef N S Kushner; Veena Sriram; Abbie Klein; Lauren O Wiklund; David O Meltzer; Joyce W Tang Journal: BMJ Open Date: 2021-12-01 Impact factor: 2.692
Authors: Conor Grant; Colm Bergin; Sarah O'Connell; John Cotter; Clíona Ní Cheallaigh Journal: Open Forum Infect Dis Date: 2020-01-31 Impact factor: 3.835