Diagnosis-based Cost Groups in the Dutch Risk-equalization Model
Effects of Clustering Diagnoses and of Allowing Patients to be Classified into Multiple Risk-classes
Bibliographic Data
| ID | 9100399 |
|---|---|
| Authors | Frank Eijkenaar (0000-0002-4471-7623, Ministry of Health), René C J A Van Vliet (Ministry of Health), Richard C van Kleef (0000-0002-8971-6341, Ministry of Health) |
| Year | 2018 |
| Volume | 56 |
| Issue | 1 |
| Pages | 91-96 |
| Publication date | 2018-01-01 |
| 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/mlr.0000000000000828 |
| PMID | 29068907 |
| OpenAlex | W2765358571 |
| Language | EN |
| Citations received | 3 |
| References cited | 12 |
BACKGROUND: The risk-equalization (RE) model in the Dutch health insurance market has evolved to a sophisticated model containing direct proxies for health. However, it still has important imperfections, leaving incentives for risk selection. This paper focuses on refining an important health-based risk-adjuster in this model: the diagnosis-based costs groups (DCGs). The current (2017) DCGs are calibrated on "old" data of 2011/2012, are mutually exclusive, and are essentially clusters of about 200 diagnosis-groups ("dxgroups"). METHODS: Hospital claims data (2013), administrative data (2014) on costs and risk-characteristics for the entire Dutch population (N≈16.9 million), and health survey data (2012, N≈387,000) are used. The survey data are used to identify subgroups of individuals in poor or in good health. The claims and administrative data are used to develop alternative DCG-modalities to examine the impact on individual-level and group-level fit of recalibrating the DCGs based on new data, of allowing patients to be classified in multiple DCGs, and of refraining from clustering. RESULTS: Recalibrating the DCGs and allowing enrolees to be classified into multiple DCGs lead to nontrivial improvements in individual-level and group-level fit (especially for cancer patients and people with comorbid conditions). The improvement resulting from refraining from clustering does not seem to justify the increase in model complexity this would entail. CONCLUSIONS: The performance of the sophisticated Dutch RE-model can be improved by allowing classification in multiple (clustered) DCGs and using new data. Irrespective of the modality used, however, various subgroups remain significantly undercompensated. Further improvement of the RE-model merits high priority
Actuarial science · Business · Cluster analysis · Econometrics · Machine learning · Modalities · Computer Science · Healthcare Policy and Management · Healthcare Systems and Reforms · Mathematics · Medicine · Primary Care and Health Outcomes
| Citations per year | 0.5 |
|---|---|
| Citation span | 2020 - 2025 (6) |
| Highly cited | No |