The Paradox of Readmission Prevention Interventions: Missing Those Most in Need
| Autor: | Daniel J. Brotman, Amy Deutschendorf, Rosalyn W. Stewart, Diane Lepley, Curtis Leung, Blair Golden, Erik H. Hoyer, Melissa Richardson, Geoff B. Dougherty |
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| Rok vydání: | 2021 |
| Předmět: |
Male
Patient Transfer medicine.medical_specialty Psychological intervention Aftercare 030204 cardiovascular system & hematology Logistic regression Patient Readmission Risk Assessment Odds 03 medical and health sciences Sex Factors 0302 clinical medicine Intervention (counseling) Preventive Health Services medicine Humans 030212 general & internal medicine Generalized estimating equation Socioeconomic status Aged Maryland business.industry General Medicine Continuity of Patient Care Patient Acceptance of Health Care Readmission rate Patient Discharge Socioeconomic Factors Emergency medicine Female business Medicaid |
| Zdroj: | The American Journal of Medicine. 134:1142-1147 |
| ISSN: | 0002-9343 |
| DOI: | 10.1016/j.amjmed.2021.04.006 |
| Popis: | Background Post-hospitalization transition interventions remain a priority in preventing rehospitalization. However, not all patients referred for readmission prevention interventions receive them. We sought to 1) define patient characteristics associated with non-receipt of readmission prevention interventions (among those eligible for them), and 2) determine whether these same patient characteristics are associated with hospital readmission at the state level. Methods We used state-wide data from the Maryland Health Services Cost Review Commission to determine patient-level factors associated with state-wide readmissions. Concurrently, we conducted a retrospective analysis of discharged patients referred to receive 1 of 3 post-discharge interventions between January 2013 and July 2019—a nurse transition guide, post-discharge phone call, or follow-up appointment in our post-discharge clinic—to determine patient-level factors associated with not receiving the intervention. Multivariable generalized estimating equation logistic regression models were used to calculate the odds of not accepting or not receiving the interventions. Results Older age, male gender, black race, higher expected readmission rate, and lower socioeconomic status were significantly associated with 30-day readmission in hospitalized Maryland patients. Most of these variables (age, sex, race, payer type [Medicaid or non-Medicaid], and socioeconomic status) were also associated with non-receipt of intervention. Conclusions We found that many of the same patient-level characteristics associated with the highest readmission risk are also associated with non-receipt of readmission reduction interventions. This highlights the paradox that patients at high risk of readmission are least likely to accept or receive interventions for preventing readmission. Identifying strategies to engage hard-to-reach high-risk patients continues to be an unmet challenge in readmission prevention. |
| Databáze: | OpenAIRE |
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