The Use of Grade of Membership Analysis to Evaluate and Modify Diagnosis-related Groups
Bibliographic Data
| ID | 9101967 |
|---|---|
| Authors | Kenneth G Manton (Duke University, corresponding author), James C Vertrees |
| Year | 1984 |
| Volume | 22 |
| Issue | 12 |
| Pages | 1067-1082 |
| Publication date | 1984-12-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Medical Care (JOURNAL) |
| Journal identifiers | ISSN: 0025-7079 • E-ISSN: 1537-1948 |
| Publisher | Ovid Technologies (Wolters Kluwer Health) (PUBLISHER) |
| DOI | 10.1097/00005650-198412000-00002 |
| PMID | 6439954 |
| OpenAlex | W2013645367 |
| Language | EN |
| Citations received | 4 |
| References cited | 1 |
A classification methodology is presented that can be used to evaluate the heterogeneity of reimbursement categories and service groups in multivariate terms. This methodology, called Grade of Membership analysis, has several properties that are particularly important in such assessments. First, simultaneously with the determination of the multivariate profile of characteristics that describe a group, the methodology determines the degree to which each case is described by that profile, which means that the model can explicitly represent the heterogeneity of individual cases in any derived classification scheme. Second, the estimates of the model's parameters are produced by maximum likelihood procedures; hence, the classification and group descriptions generated by the model can be statistically evaluated. Third, because of the way the group profiles are constructed, the results of the analysis will be reasonably robust to the selection of new samples. The analysis is illustrated using data on hospital discharges for the state of Maryland in 1981. The purpose of the analysis is to examine the association between the patterns of clinical and service attributes identified by the procedures with DRG category assignments
Classification scheme · Data mining · Econometrics · Health care · Machine learning · Multivariate analysis · Multivariate statistics · Reimbursement · Selection (genetic algorithm) · Statistics · Computer Science · Healthcare Policy and Management · Mathematics · Medical Coding and Health Information
| Unique citing works | 4 |
|---|---|
| Citations per year | 0,1 |
| Citation span | 1985 - 2007 (23) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |