|Thomson Medstat Medical Episode Grouper|
1 Thomson - Medstat. Medstat Disease Staging Software Version 5.24. http://www.hcup-us.ahrq.gov/db/nation/nis/DiseaseStagingV5_25ReferenceGu...
2 Black L, Runken MC, Eaddy M, et al. Chronic Disease Prevalence and Burden in Elderly Men: An Analysis of Medicare Medical Claims Data. J Health Care Finance. 2007; 33(4):68–78.
|Sponsorship:||Thomson Medstat Inc.|
|Description:||The Thomson Medstata Medical Episode Grouper (MEG) is a grouping system that creates clinically homogenous and meaningful units (episodes) for analysis using inpatient, outpatient and pharmaceutical claims data. MEG allows for the analysis of a particular patient’s complete episode of care for a single illness or condition.|
|Purpose/Use:||MEG can be used to express a patient’s severity of illness at the time of hospitalization, to adjust for case-mix, or as a measure of a patient’s healthcare outcome.|
|Coding Family:||ICD-9 & ICD-10|
Using professional claims, facility claims, inpatient admission records, and pharmacy claims, The Thomson Medstat MEG categorizes diagnosis codes into 550 disease conditions. These disease conditions are then staged (stratified) using the Thomson Medstat Disease Staging Criteria. This set of criteria defines levels of biological severity for specific medical diseases, where severity is defined as the risk of organ failure or death. The following stages are as follows:
Lastly, the MEG groups claims into episodes according to disease condition and relative time between services to create an aggregate episode file. This methodology results in one hierarchical level of coding.
|Level of Diagnosis Aggregation:||Diagnosis codes are grouped into 550 disease conditions|
|Number of Codes Included:||Proprietary - Not Available|
|Number of Codes Excluded:||Proprietary - Not Available|
|Methodological Considerations:||The Thomson Medstat Episode Grouper is a clinically focused grouping system that is used by a number of health systems, health plans and provider organizations to conduct health services research on large databases.|
|Related Data Sources:||Claims data|
|Used in Disease Complexity Research:||Yes2|