After the Revolution: DRGs at Age 30
Abstract
1 October 2013 marked 30 years since Medicare began paying hospitals by diagnosis-related group (DRG), arguably the most influential innovation in the history of health care financing. Initially developed as a tool for hospital management, DRGs became the basis of the inpatient prospective payment system that Medicare implemented in 1983. The strong incentives were revolutionary in their impact. Medicare spending growth slowed sharply, and, more remarkable, hospitals posted record profits. After the link between cost and payment was broken, hospitals moved quickly to cut costs. Nevertheless, a literature survey concluded that none of the worst fears about adverse effects on patients were realized. Diagnosis-related groups have also come to define "the product of a hospital" for purposes of benchmarking and risk adjustment. The acceptance of DRG algorithms owes much to their categorical approach, clinical focus, and transparency. The 2 most commonly used algorithms, Medicare DRGs and All Patient Refined (APR) DRGs, typically explain more than 40% of cost variance in inpatient stays, although with considerable range by care category. Because Medicare DRGs are unsuitable for obstetrics, pediatrics, and neonatology, some payers prefer APR DRGs. Diagnosis-related groups have proven to be a suitable basis for payment, as evidenced by widespread use. Common issues include mitigation of adverse incentives, appropriate payment for extremely costly stays, applicability to certain hospitals and care categories, and growing complexity. The DRG experience offers lessons about the effectiveness of financial incentives, the likelihood of adverse effects, the usefulness of case-mix measures, the risks of growing complexity, and the example that sensible policy need not be the domain of any one political party or other entity.
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Since its inception by Medicare in 1983, DRG has served as the foundation for the inpatient prospective payment system within the United States.
“Since its inception by Medicare in 1983, DRG has served as the foundation for the inpatient prospective payment system within the United States7.”
From DRG-LLaMA : tuning LLaMA model to predict diagnosis-related group for hospitalized patients (2024) · cites this paper as [7]
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DRG-LLaMA : tuning LLaMA model to predict diagnosis-related group for hospitalized patients
Wang, H., Gao, C., Dantona, C., Hull, B., & Sun, J. (2024) · npj Digital Medicine · doi:10.1038/s41746-023-00989-3 · Cited by 99 · see citing works
“Previous studies have underscored the effectiveness of employing diverse machine learning algorithms and deep neural networks for DRG prediction within healthcare systems outside the United States (Gartner et al. 2015; Islam et al. 2021).”
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- Authors
- Quinn, K.
- Affiliations
- Xerox (United States) (US)
- Corresponding author
- Quinn, Kevin
- ORCID
- Quinn, Kevin
- Venue
- Annals of Internal Medicine
- Publisher
- American College of Physicians
- Published
- 2014-03-18
- Volume
- 160
- Issue
- 6
- Pages
- 426-429
- ISSN
- 0003-4819 (ISSN-L)
- 1539-3704
- OpenAlex
- W2082432478
- Cited by
- 47
- References
- 16
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 7 · 2023: 2 · 2022: 2 · 2021: 3 · 2020: 4 · 2019: 5 · 2018: 3 · 2017: 2 · 2016: 4 · 2015: 5 · 2014: 1
- FWCI
- 9.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annals of Internal Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Healthcare Policy and Management
- Social Sciences › Economics, Econometrics and Finance › Finance › Healthcare Systems and Reforms
- Health Sciences › Health Professions › Health Information Management › Healthcare Quality and Management
- Keywords
- diagnosis-related groups, Medicare, prospective payment system, hospital payment reform, hospital cost containment, risk adjustment, benchmarking, financial incentives, hospital profitability, health care financing
- MeSH terms
- Diagnosis-Related Groups, Economics, Hospital, Medicare, Hospital Administration, United States, Efficiency, Organizational, History, 20th Century
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