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| DRG | |
|---|---|
| Name | Diagnosis-Related Group |
| Caption | Hospital case-mix classification system |
| Introduced | 1980s |
| Developer | Harvard Medical School team; Medicare program |
| Based on | Patient diagnoses, procedures, age, comorbidity, discharge status |
| Type | Prospective payment system; case-mix grouping |
| Used by | National health services, insurers, hospitals |
DRG
Diagnosis-Related Group classification systems assign inpatient stays to categories intended to reflect similar resource use, clinical characteristics, and expected cost. Originating in the United States in the late 20th century, DRG frameworks have been adopted and adapted by numerous health systems, insurers, and research institutions worldwide. They intersect with hospital finance, clinical coding, health services research, and policy debates involving reimbursement, quality measurement, and casemix adjustment.
Diagnosis-Related Group schemes group inpatient episodes by principal ICD diagnoses, procedure codes such as CPT or ICD-9-CM, patient demographics, and comorbidity indices like the Charlson Comorbidity Index. Each DRG denotes a payment weight or tariff linked to typical resource consumption; this creates incentives for hospitals similar to those seen in prospective payment reforms advocated by figures associated with Prospective payment system policy reforms and analyses by researchers at Johns Hopkins University and RAND Corporation. Principle architects included clinicians and economists affiliated with Harvard Medical School, influencing federal programs under Medicare and policy advisers from OMB.
Development traces to efforts at Harvard Medical School and Yale University in the 1970s and early 1980s to measure hospital resource use and create case-mix systems; these efforts informed the implementation of DRG-based payment under Medicare in the early 1980s. Key milestones include adoption by national purchasers in Australia, United Kingdom, Germany, and Canada during the 1990s and 2000s, often following pilot projects led by institutions such as Australian Institute of Health and Welfare, NHS England, and the Robert Koch Institute. Influential policy debates involved actors like Tom Scully and analyses from Kaiser Family Foundation and World Health Organization advisers recommending casemix financing models.
Major families of grouping algorithms include the original Medicare DRGs, the refined APR-DRG developed with input from 3M Health Information Systems, and national variants such as G-DRG and the AR-DRG. Other systems include procedure-focused groupers used by specialized centers like Mayo Clinic and pediatric adaptations by Children’s Hospital of Philadelphia. Groupers differ by base casemix logic, whether severity-of-illness subclasses are present, and mapping to national versions of ICD-10 or legacy ICD-9. Classification developers frequently collaborate with companies and agencies such as Truven Health Analytics, CMS, and national health ministries.
Clinically, DRG data support benchmarking across hospitals such as Johns Hopkins Hospital and Cleveland Clinic, case-mix adjustment in outcomes research by teams at Harvard School of Public Health and Imperial College London, and planning of service lines in academic centers like Massachusetts General Hospital. Economically, DRG-based tariffs form the basis for prospective payment programs in systems managed by ministries such as Brazilian Ministry of Health and payers including NHS commissioning groups. DRGs also underpin performance contracting in integrated delivery networks like Kaiser Permanente and inform cost-effectiveness studies conducted by institutes such as Institute for Clinical and Economic Review.
Assignment algorithms use coded principal diagnosis, secondary diagnoses, and procedure codes drawn from standards such as ICD-10 and billing classifications like CPT. Grouper software applies logic trees incorporating exclusion and hierarchy rules developed by vendors and agencies (for example, 3M Health Information Systems and CMS). Payment weights derive from average cost studies using hospital accounting methods developed by bodies like Healthcare Cost and Utilization Project and statistical modeling methods advanced at University of California, Berkeley and University of Michigan health economics programs.
Countries adapt DRGs to local coding versions, cost structures, and policy goals: Germany uses G-DRG with annual tariff negotiation involving the German Hospital Federation, Australia maintains AR-DRG for activity-based funding overseen by IHPA, and France implemented GHM as part of national reform. Low- and middle-income countries have piloted simplified casemix systems with technical assistance from World Health Organization and bilateral agencies like USAID and DFID. Variations reflect differences in legal frameworks involving ministries such as China's National Health Commission and payer mixes exemplified by systems in Japan and South Korea.
Critiques arise from clinicians and policy analysts at institutions like The Lancet and New England Journal of Medicine concerning risk adjustment adequacy, upcoding incentives flagged by auditors at OIG and fraud investigations involving private providers, and potential impacts on quality documented by researchers at Yale School of Medicine. Limitations include granularity loss for complex cases, challenges mapping across ICD revisions (for example, ICD-9 to ICD-10 transitions), and the need for robust clinical coding infrastructure as emphasized by World Bank health system assessments.