Diagnosis-related groups (DRGs) are medico-economical classification systems of patients hospitalized for acute care that correlate common variables such as diagnoses, procedures and demographic characteristics with an expected consumption of hospital resources and length of stay (LOS). These systems seek to assign patients to different groups that are coherent and homogeneous in respect to the costs incurred by the provider. Cases grouped under a certain DRG have not only a similar resource consumption pattern, but they are also medically alike.    
DRGs are used throughout the world to address payment issues, constituting the foundation of the prospective payment system. Also, they were developed to be used as instruments in the evaluation of health care quality and to stimulate an increased transparency in hospitals.    DRG systems facilitate performance comparisons between providers and contribute to the reduction of hospital costs. Moreover, these classification systems can be used by hospitals to identify the types of patients attended, as well as to guide management decisions.   In order to achieve these objectives, the DRG method uses a combination of statistical and computational techniques, medical knowledge, terminology standards and healthcare processes. Groupers are computer programs that calculate the DRG assignment. 
DRGs guiding principals
• The variables used should be limited to data routinely collected in hospital discharges.
• There should be a manageable number of DRGs that includes all types of hospital admissions.
• Each group should be economically homogeneous, representing a similar resource consumption pattern.
The design and development of the DRGs began in the late 1960s, carried out by Robert B. Fetter and his team from Yale University. Their local university hospital encouraged them to create an "utilization review" program that would help to address the growing demand for healthcare quality evaluation, performance comparisons and cost assessment.    The Yale team pointed out that the main goal of the hospital is to provide patient care. Therefore, the analysis of the hospital product should be directly related to the patients attended. In addition, as the services provided differ according to certain patient characteristics such as age, comorbidities or complications, professor Fetter stated that the analysis of these attributes would be indispensable for understanding the cost variations among hospitals and among patients. These researchers developed a tool that could define the complexity of the provider’s nosological profile in terms of inpatient resources consumption. The first extensive use of a DRG system was in 1978 in New Jersey, as the cornerstone of a prospective payment system. In October of 1983, Medicare implemented a DRG-based hospital reimbursement system at a national level. Since its development, different versions of the initial system have been released in order to enhance its performance and uses. In the fourth version, ICD-9-CM was used as a terminology standard for the codification of the diagnoses and procedures. In 1984, Medicare, in association with Health Systems International, engaged in the task of developing a grouper software, publishing DRG manuals and releasing classification system revisions. In 1987, 3M developed a new DRG system called All Patient DRG (AP-DRG) that was incorporated by New York’s inpatient payment program for non-Medicare patients. Three years later, the same company released an All Patient Refined DRGs (APR DRGs), which took into account the severity of the disease as well as the mortality risk. Since the 1990s, the adoption of DRGs has expanded to the majority of high-income countries in America and Europe. Some nations, like Australia, have altered the structure of DRGs to adapt it to the local healthcare systems, creating their own DRG classifications. In recent decades, DRGs have also been gradually incorporated by low- and middle-income countries.
• Hospital reimbursement.
• Hospital cost assessment.
• Performance comparisons between hospital.
• Definition of the case-mix complexity of the hospital.
• Tool for management decisions.
Main characteristics of Medicare Severity-Diagnosis Related Groups (MS-DRG)
“DRGs are defined by grouping criteria in a comprehensive framework of rules. Specific figures and prices can be attributed to each DRG”. MS-DRG is a refined DRG system that was released in 2007. It has three severity levels, which are determined by the most severe secondary diagnosis. DRGs have a hierarchical structure, which includes a limited number of mutually exclusive principal diagnosis areas referred to as major diagnostic categories (MDCs) that represent mainly organ systems or etiologies. The version for fiscal year 2017 (version 34) contains 25 MDCs. There are 14 DRGs, called Pre-MDCs, which are not assigned to MDCs and that are directly grouped according to ICD-10-PCS procedure codes. This version consists of 756 MS-DRGs (defined within 335 base MS-DRGs). The following step in the hierarchy, classifies the case in surgical or medical. A further division, takes into account whether comorbidities or complications are present or absent. According to the level of severity or complexity, the possible alternatives for classification include:
• with major comorbidities or complications (MCC);
• with comorbidities or complications (CC);
• without comorbidities or complications.
The Centers for Medicare and Medicaid Services (CMS) assign a numeric value or weight to each DRG, which relates to the average level of resource consumption expected for that clinically homogenous group. The objective of the weights is to account for cost variations between different types of therapies. More expensive diseases correlate with higher DRG weights.
Main classification variables
• Principal diagnosis: coded using ICD-10-CM.
• Secondary diagnoses: coded using ICD-10-CM.
• Procedures: coded using ICD-10-PCS.
• Date of admission.
• Date of discharge.
• Type of discharge.
• Birth weight (for neonates).
Hospital acquired conditions
Hospital acquired conditions (HACs) include a selected subset of secondary diagnoses that were not present on admission. These complications are characterized by being expensive, CCs or MCCs and preventable by using current guidelines. The DRG method exclude HACs from the group assignment. Hence, if other diagnoses that are consider to be CCs or MCCs are not present, the DRG may change. For the fiscal year 2017, the list of HACs consists of the following items:
1) Foreign object retained after surgery.
2) Air embolism.
3) Blood incompatibility.
4) Stage III and IV pressure ulcers.
5) Falls and trauma.
6) Catheter-associated urinary tract infection.
7) Vascular catheter-associated infection.
8) Surgical site infection-mediastinitis following Coronary Artery Bypass Surgery.
9) Manifestations of poor glycemic control.
10) Deep vein thrombosis/pulmonary embolism following total knee or hip replacement.
11) Surgical site infection following bariatric surgery.
12) Surgical site infection following certain orthopedic procedures of spine, shoulder or elbow.
13) Surgical site infection following cardiac implantable electronic device procedures.
14) Iatrogenic pneumothorax with venous catheterization. 
Case mix complexity
Case mix is defined as the relative value assigned to groups of patients with similar characteristics that require similar procedures and treatments. Consequently, they will have a homogenous resource consumption pattern. This concept has been identified as a major factor in determining cost differences among providers and among individual patients. It allows to identify the hospital’s production. “Case mix complexity refers to an interrelated but distinct set of patient attributes that includes: severity of illness, risk of mortality, prognosis, treatment difficulty, need for intervention and resource intensity”.  The DRG systems objective is to correlate a hospital’s case mix to its resource consumption pattern. Thus, the number and types of patients attended by the hospital can be compared with its costs. A higher case mix means that the group of patients attended by that hospital have a “greater severity of illness, a greater treatment difficulty, a poorer prognosis or a greater need for intervention”.
Modes of DRG refinement
Refined DRG systems are characterized by increasing granularity in regards to severity of illness and mortality risk. Some examples of DRG refinement include MS-DRG and APR-DRG.
Benefits of DRG systems
Among the benefits of the DRG system, the literature mentions:
• Accurate cost assessment and control.
• Reduction of LOS.
• Increased transparency of provider’s services.
• Efficient use of health care resources.
• Evaluation and improvement of quality of care.
• Facilitation of performance comparisons and benchmarking.
• Incentive of efficient resource use.
• Data management.
Despite the benefits listed above, some concerns and challenges regarding DRG systems still need to be addressed. Some of theses systems do not accurately describe certain specific groups such as neonates. For example, MS-DRG has only 7 DRG for newborns and there is no division between medical and surgical DRGs in this MDC. Probably, this is because the focus population is mainly over 65 years old. Refined DRG systems have tried to fix these issues, such as the improved neonates DRGs from 3M APR-DRG system. Another essential aspect of the DRG system, is that they require appropriate clinical documentation and coding of discharge summaries and billing forms in order to adequately classify patients into appropriate groups. DRG systems may vary a lot between countries and even within the countries, which make comparisons sometimes difficult.
- Norohna, Marina F. et al.O desenvolvimento dos "Diagnosis Related Groups"- DRGs. Metodologia de classificação de pacientes hospitalares. Rev. Saúde Pública [online]. 1991, vol.25, n.3, pp.198-208. ISSN 1518-8787. http://dx.doi.org/10.1590/S0034-89101991000300007
- Mathauer I, Wittenbecher F. Hospital payment systems based on diagnosis-related groups: experiences in low- and middle-income countries. Bulletin of the World Health Organization. 2013;91(10):746-756A. doi:10.2471/BLT.12.115931.
- Fischer W. The DRG family. 2007. Available online at http://fischer-zim.ch/textk-pcs-en-pdf/DRG-family-0801.pdf
- Centers for Medicare & Medicaid Services (CMS). 2016. Available online at https://www.cms.gov/ICD10Manual/version34-fullcode-cms/fullcode_cms/Design_and_development_of_the_Diagnosis_Related_Group_(DRGs)_PBL-038.pdf
- Wang Z, Liu R, Li P, JiangIC, Hao M. How to Make Diagnosis Related Groups Payment More Feasible in Developing Countries- A Case Study in Shanghai, China. Iranian Journal of Public Health. 2014;43(5):572-578.
- Vozikis A; Xesfingi S; Moustaferi E; Balbouzis T; Rigatos T. The DRG-Based Hospital Prospective Payment System in Greece: An Assessment of the Reimbursement Rates Using Clinical Severity Classification. Modern Economy, 2016, 7, 1584-1600. ISSN Online: 2152-7261. DOI: 10.4236/me.2016.713141
- AHIMA. "Evolution of DRGs (2010 update)." Journal of AHIMA (Updated April 2010), web exclusive. Available online at http://library.ahima.org/doc?oid=106590#.WQScElN95AZ
- Busse R; Geissler A; Quentin W; Wiley M. Diagnosis-Related Groups in Europe. McGraw-Hill. 2011. ISBN-10: 0-33-524557-9 eISBN: 978-0-33-524558-1
- Paat-Ahi G, Aaviksoo A, Świderek M, on behalf of the EuroDRG group. Cholecystectomy and Diagnosis-Related Groups (DRGs): patient classification and hospital reimbursement in 11 European countries. International Journal of Health Policy and Management. 2014;3(7):383-391. doi:10.15171/ijhpm.2014.121.
Submitted by Guadalupe Caballero Escuti