TY - JOUR
T1 - The Paradox of Readmission Prevention Interventions
T2 - Missing Those Most in Need
AU - Hoyer, Erik H.
AU - Golden, Blair
AU - Dougherty, Geoff
AU - Richardson, Melissa
AU - Lepley, Diane
AU - Leung, Curtis
AU - Deutschendorf, Amy
AU - Brotman, Daniel J.
AU - Stewart, Rosalyn W.
N1 - Funding Information:
Funding: The project described was supported by Grant Number 1C1CMS331053-01-00 from the U.S. Department of Health and Human Services, Centers for Medicare & Medicaid Services . The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of the U.S. Department of Health and Human Services or any of its agencies. The research presented was conducted by the awardee. Results may or may not be consistent with or confirmed by the findings of the independent evaluation contractor.
Publisher Copyright:
© 2021 Elsevier Inc.
PY - 2021/9
Y1 - 2021/9
N2 - 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.
AB - 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.
KW - 30-day readmission
KW - Care coordination
KW - Transition of care
UR - http://www.scopus.com/inward/record.url?scp=85108515052&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85108515052&partnerID=8YFLogxK
U2 - 10.1016/j.amjmed.2021.04.006
DO - 10.1016/j.amjmed.2021.04.006
M3 - Article
C2 - 33971167
AN - SCOPUS:85108515052
SN - 0002-9343
VL - 134
SP - 1142
EP - 1147
JO - American Journal of Medicine
JF - American Journal of Medicine
IS - 9
ER -