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Hospital Readmissions Reduction Program
Open accessAbstract
Hospital readmission measures have been touted not only as a quality measure but also as a means to bend the healthcare cost curve. The Affordable Care Act (ACA) established the Hospital Readmission Reduction Program (HRRP) in 2012. Under this program, hospitals are financially penalized if they have higher-than-expected risk-standardized 30-day readmission rates for acute myocardial infarction, heart failure, and pneumonia. The HRRP has garnered significant attention from the medical community, both positive and negative. Here, we describe the reasons the HRRP was implemented, the penalties levied, the impact it has had on transitional care and readmissions, the pros and cons of the policy, and its future. Hospital readmissions are associated with unfavorable patient outcomes and high financial costs.1,2 Causes of readmissions are multifactorial, and rates vary substantially by institution.3,4 Historically, nearly 20% of all Medicare discharges had a readmission within 30 days.1 The Medicare Payment Advisory Commission has estimated that 12% of readmissions are potentially avoidable. Preventing even 10% of these readmissions could save Medicare $1 billion.5 Therefore, reducing hospital readmissions has been made a national priority. In 2008, the Medicare Payment Advisory Commission recommended to Congress that the Centers for Medicare & Medicaid Services (CMS) begin confidentially reporting readmission rates and resource use to hospitals and physicians.6 In 2009, CMS began publicly reporting hospital-level readmission rates, which were added to the Hospital Compare Web site.7 Before 2012, hospitals had little direct financial incentive to reduce readmissions. For Medicare beneficiaries with inpatient stays, hospitals receive payment with the inpatient prospective payment system (IPPS). This payment, based on a diagnosis-related group (DRG), covers the inpatient stay and any outpatient diagnostic and admission-related outpatient nondiagnostic services provided by the institution on the date of the patient’s admission or within 3 days immediately …
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Cited by
Readmission prediction using deep learning on electronic health records
Ashfaq, A., Sant’Anna, A., Lingman, M., & Nowaczyk, S. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103256 · Cited by 194
McCants, K. M., Reid, K. B., Williams, I., Miller, D. E., Rubin, R., & Dutton, S. (2019) · Professional Case Management · doi:10.1097/ncm.0000000000000359 · Cited by 59
Coffey, J. D., Christopherson, L. A., Williams, R. D., Gathje, S. R., Bell, S. J., Pahl, D. F., Manka, L., Blegen, R. N., Maniaci, M. J., Ommen, S. R., et al. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.1052408 · Cited by 44
Alabdulhafith, M., Saleh, H., Elmannai, H., Ali, Z. H., El-Sappagh, S., Hu, J.-W., & El-Rashidy, N. (2023) · IEEE Access · doi:10.1109/access.2023.3312343 · Cited by 27
Improving hospital readmission prediction using individualized utility analysis
Ko, M., Chen, E., Agrawal, A., Rajpurkar, P., Avati, A., Ng, A., Basu, S., & Shah, N. H. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103826 · Cited by 15
Metadata
- Authors
- McIlvennan, C. K., Eapen, Z. J., & Allen, L. A.
- Venue
- Circulation
- Published
- 2015-05-19
- Volume
- 131
- Issue
- 20
- Pages
- 1796-1803
- ISSN
- 0009-7322 (ISSN-L)
- 1524-4539
- OpenAlex
- W2097548575
- Cited by
- 689
- References
- 0
- FWCI
- 121.73
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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