Understanding the High Prevalence of Low-Prevalence Chronic Disease Combinations: Databases and Methods for Research. Johns Hopkins University Grouper Systems

09/20/2013

Adjusted Clinical Groups Case-Mix System (ACG)

References:

1 The Johns Hopkins ACG System. State of the Art Technology and a Tradition of Excellence in One Integrate Solution (2013). http://www.acg.jhsph.org/index.php?option=com_content&view=article&id=46...

2 Adams EK, Bronstein JM, & Raskind-Hood. Adjusted Clinical Groups: Predictive Accuracy for Medicaid Enrollees in Three States. Health Care Financ Rev. 2002;24:43–61

3 Garcia-Olmos L, Salvador CH, Alberquilla A, et al. Comorbidity in patients with chronic diseases in general practice. Plus One. 2012; 7(2):e32141

Sponsorship: Johns Hopkins University
Description: The Johns Hopkins ACG grouping system uses a “person- focused” approach to capturing the multidimensional nature of a patient’s health over time. The system uses diagnosis and/or pharmaceutical codes from insurance claims or medical records to examine constellations of morbidities, rather than individual conditions. This method of measuring morbidity is used to evaluate performance, forecast utilization and set payment rates for over 300 health plans and provider organizations.1 The ACG system is clinically-focused and was primarily designed for research purposes.
Purpose/Use: The ACG system measures morbidity burden based on disease patterns, age and gender. It is used to adjust for patient case- mix.
Coding Family: ICD-9-CM, ICD-9, ICD-10-CM, NDC, ATC, READ, CPT, & HCPCS
Grouping Methodology: Diagnosis codes are first grouped into 32 Aggregated Diagnosis Groups (ADGs) that are similar in terms of disease severity and the likelihood of persistence of the disease over time (utilization). The ACG system then groups individual patients into one of 102 discrete categories based on their ADGs, age and sex. Patients grouped into these categories are known to experience similar morbidity and healthcare utilization over a 1 year period of time.1 The ACG system is longitudinal in nature and relies on diagnosis codes from a look-back period. This methodology results in two hierarchical levels of coding.
Level of Diagnosis Aggregation: Diagnosis codes are grouped into 102 discrete categories.
Number of Codes Included: Proprietary - Not Available
Number of Codes Excluded: Proprietary - Not Available
Methodological Considerations: Evidence suggests that the ACG grouping system outperforms traditional age and sex adjustment, which is the traditional risk-adjustment mechanism used by many health insurance providers. The ACG system uses all available data, is stable over time, avoids basing complexity on specific procedures or hospitalizations, has strong predictive power, can describe the health status of population across a spectrum of disease conditions, and can represent clinical complexity more than summing codes.1 However, higher and unpredictable expenses of short-term beneficiaries (< 6 m) are known to moderate the predictive power of the ACG system in certain populations.2 This is true for States, such as Mississippi, that have large patient populations who are poor, underemployed and have severe health problems.
Related Data Sources: Claims data
Used in Disease Complexity Research: Yes3

 

Aggregated Diagnosis Groups (ADGs)

References:

1 The Johns Hopkins ACG System. State of the Art Technology and a Tradition of Excellence in One Integrate Solution (2013). http://www.acg.jhsph.org/index.php?option=com_content&view=article&id=46...

2 Austin PC. Using the John’s Hopkins Aggregated Diagnosis Groups (ADGs) to predict mortality in a general adult population cohort in Ontario, Canada. Medical Care. 2011; 49(10): 932–939.

3 Austin PC, Shar BR, Newman A, & Anderson GM. Using the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to predict 1-year mortality in population-based cohorts of patients with diabetes in Ontario, Canada. Diabet Med. 2012; 29(9):1134–1141.

4 Starfield B & Kinder K. Multimorbidity and its measurement. Health Policy. 2011; 103(1):3–8.

Sponsorship: Johns Hopkins University
Description: The ADG system, formerly known as the Ambulatory Diagnostic Groups, is part of the Johns Hopkins ACG case- mix system. However, it has also been used independently to group diagnosis codes.
Purpose/Use: The ADG system is a component of the Johns Hopkins ACG system and is used to group diagnosis codes into 32 categories that are similar in terms of disease severity and resource utilization.1 Separate for the ACG system, the ADG system has also been used to predict mortality in general adult populations.2
Coding Family: ICD-9, ICD-9-CM, ICD-10
Grouping Methodology:

The ADG system groups all ICD-9, ICD-9-CM, and ICD- 10CA diagnosis code assigned to a patient into one of 32 different categories based on the following clinical and expected utilization criteria:

1. Duration of the conditions (acute, recurrent, or chronic).

2. Severity of the condition ( e.g., minor and stable versus major and unstable).

3. Diagnostic certainty (symptoms focusing on diagnostic evaluation versus documented diseases focusing on treatment services).

4. Etiology of the condition (infectious, injury, or other).

5. Specialty care involvement (medical, surgical, obstetric, hematology, etc.).1

Level of Diagnosis Aggregation: Diagnosis codes are grouped into 32 discrete categories.
Number of Codes Included: Proprietary - Not Available
Number of Codes Excluded: Proprietary - Not Available
Methodological Considerations: Evidence suggests that the ADG system can be used to accurately predict one year mortality in general and specialty populations.1,2 However, it is most often used to group diagnosis codes in the first aggregation step of the Adjusted Clinical Groups Case-Mix System.
Related Data Sources: Claims data
Used in Disease Complexity Research: Yes4

 

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