CONTEXT: Scrutiny of hospital readmissions has led to the development and implementation of policies targeted at reducing readmission rates. OBJECTIVE: To assess whether historic hospital readmission rates predict risk-adjusted patient readmission and to measure the costs of readmission, thus informing reimbursement policies under consideration by non-Veterans Health Administration payers. DESIGN, SETTINGS, AND PARTICIPANTS: Multivariable hospital-fixed effects regression analyses of patients admitted to 129 Veterans Health Administration hospitals between 2005 and 2009 for 3 common conditions, acute myocardial infarction (AMI), community-acquired pneumonia (CAP), and congestive heart failure (CHF). MAIN OUTCOME MEASURES: We examined whether previous hospital readmission rates predicted risk-adjusted readmission or 30-day episode cost of care for subsequent patients. We then examined the 30-day inpatient hospitalization episode cost differences between those who had a readmission in the episode and those who did not. RESULTS: Hospital readmission rates in the previous quarter are not predictive of individual patient risk-adjusted readmission or of patients' inpatient hospitalization episode cost in the subsequent quarter. Relative to those who were not readmitted within 30 days of index visit discharge, readmitted patients had 30-day episode costs that were 53.3% (P<0.001), 82.8% (P<0.001), and 79.8% (P<0.001) higher for AMI, CAP, and CHF hospitalization episodes, respectively. CONCLUSIONS: Previous hospital readmission rates are poor predictors of readmission for future individual patients, therefore, policies using these measures to guide subsequent reimbursement are problematic for hospitals that are financially constrained. Our findings indicate current diagnosis related group payments would need to be raised by 10.0% for AMI, 11.5% for CAP, and 16.6% for CHF if these are to become 30-day bundled payments.
CONTEXT: Scrutiny of hospital readmissions has led to the development and implementation of policies targeted at reducing readmission rates. OBJECTIVE: To assess whether historic hospital readmission rates predict risk-adjusted patient readmission and to measure the costs of readmission, thus informing reimbursement policies under consideration by non-Veterans Health Administration payers. DESIGN, SETTINGS, AND PARTICIPANTS: Multivariable hospital-fixed effects regression analyses of patients admitted to 129 Veterans Health Administration hospitals between 2005 and 2009 for 3 common conditions, acute myocardial infarction (AMI), community-acquired pneumonia (CAP), and congestive heart failure (CHF). MAIN OUTCOME MEASURES: We examined whether previous hospital readmission rates predicted risk-adjusted readmission or 30-day episode cost of care for subsequent patients. We then examined the 30-day inpatient hospitalization episode cost differences between those who had a readmission in the episode and those who did not. RESULTS: Hospital readmission rates in the previous quarter are not predictive of individual patient risk-adjusted readmission or of patients' inpatient hospitalization episode cost in the subsequent quarter. Relative to those who were not readmitted within 30 days of index visit discharge, readmitted patients had 30-day episode costs that were 53.3% (P<0.001), 82.8% (P<0.001), and 79.8% (P<0.001) higher for AMI, CAP, and CHF hospitalization episodes, respectively. CONCLUSIONS: Previous hospital readmission rates are poor predictors of readmission for future individual patients, therefore, policies using these measures to guide subsequent reimbursement are problematic for hospitals that are financially constrained. Our findings indicate current diagnosis related group payments would need to be raised by 10.0% for AMI, 11.5% for CAP, and 16.6% for CHF if these are to become 30-day bundled payments.
Authors: Melissa O'Connor; Christopher M Murtaugh; Shivani Shah; Yolanda Barrón-Vaya; Kathryn H Bowles; Timothy R Peng; Carolyn W Zhu; Penny H Feldman Journal: Med Care Res Rev Date: 2015-07-14 Impact factor: 3.929
Authors: Hyo Jung Tak; Andrew M Goldsweig; Fernando A Wilson; Andrew W Schram; Milda R Saunders; Michael Hawking; Tanush Gupta; Cindy Yuan; Li-Wu Chen Journal: J Gen Intern Med Date: 2021-05-13 Impact factor: 6.473