Ning Tang1, Judith H Maselli, Ralph Gonzales. 1. Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, California.
Abstract
BACKGROUND: Reducing hospital readmissions is a national healthcare priority. Little is known about how readmission rates vary across unique primary care practices. OBJECTIVE: To calculate all-cause 30-day hospital readmission rates at the level of individual primary care practices and identify factors associated with variations in these rates. DESIGN: Retrospective analysis SETTING: Seven primary care clinics affiliated with the University of California, San Francisco (UCSF). PATIENTS: Adults ≥18 years old with a primary care provider (PCP) at UCSF MEASUREMENTS: All-cause 30-day readmission rates were calculated for primary care clinics for discharges between July 1, 2009 and June 30, 2012. We built a model to identify demographic, clinical, and hospital factors associated with variation in rates. RESULTS: There were 12,564 discharges for patients belonging to the 7 clinics, with 8685 index discharges and 1032 readmissions. Readmission rates varied across practices, from 14.9% in Human Immunodeficiency Virus primary care and 7.7% in women's health. In multivariable analyses, factors associated with variation in readmission rates included: male gender (odds ratio [OR]: 1.21, 95% confidence interval [CI]: 1.05-1.40), Medicare insurance (OR: 1.31, 95% CI: 1.05, 1.64; Ref = private), Medicare-Medicaid dual eligible (OR: 1.26, 95% CI: 1.01-1.56), multiple comorbidities, and admitting services. Patients with a departed PCP awaiting transfer assignment to a new PCP had an OR of 1.59 (95% CI: 1.16-2.17) compared with having a current faculty PCP. CONCLUSIONS: Primary care practices are important partners in improving care transitions and reducing hospital readmissions, and this study introduces a new way to view readmission rates. PCP turnover may be an important risk factor for hospital readmissions.
BACKGROUND: Reducing hospital readmissions is a national healthcare priority. Little is known about how readmission rates vary across unique primary care practices. OBJECTIVE: To calculate all-cause 30-day hospital readmission rates at the level of individual primary care practices and identify factors associated with variations in these rates. DESIGN: Retrospective analysis SETTING: Seven primary care clinics affiliated with the University of California, San Francisco (UCSF). PATIENTS: Adults ≥18 years old with a primary care provider (PCP) at UCSF MEASUREMENTS: All-cause 30-day readmission rates were calculated for primary care clinics for discharges between July 1, 2009 and June 30, 2012. We built a model to identify demographic, clinical, and hospital factors associated with variation in rates. RESULTS: There were 12,564 discharges for patients belonging to the 7 clinics, with 8685 index discharges and 1032 readmissions. Readmission rates varied across practices, from 14.9% in Human Immunodeficiency Virus primary care and 7.7% in women's health. In multivariable analyses, factors associated with variation in readmission rates included: male gender (odds ratio [OR]: 1.21, 95% confidence interval [CI]: 1.05-1.40), Medicare insurance (OR: 1.31, 95% CI: 1.05, 1.64; Ref = private), Medicare-Medicaid dual eligible (OR: 1.26, 95% CI: 1.01-1.56), multiple comorbidities, and admitting services. Patients with a departed PCP awaiting transfer assignment to a new PCP had an OR of 1.59 (95% CI: 1.16-2.17) compared with having a current faculty PCP. CONCLUSIONS: Primary care practices are important partners in improving care transitions and reducing hospital readmissions, and this study introduces a new way to view readmission rates. PCP turnover may be an important risk factor for hospital readmissions.
Authors: Deniz Can Guven; Furkan Ceylan; Ibrahim Yahya Cakir; Engin Cesmeci; Basak Sayinalp; Berkay Yesilyurt; Gurkan Guner; Hasan Cagri Yildirim; Oktay Halit Aktepe; Zafer Arik; Alev Turker; Omer Dizdar Journal: Support Care Cancer Date: 2021-01-06 Impact factor: 3.603