Predicting the Cost of Health Care Services
A Comparison of Case-mix Systems and Comorbidity Indices That Use Administrative Data
Bibliographic Data
| ID | 9101996 |
|---|---|
| Authors | Xiaotong Huang (University of British Columbia, corresponding author), Sandra Peterson (0000-0001-6000-436X, University of British Columbia, corresponding author), Ruth Lavergne (Simon Fraser University), Megan Ahuja (0000-0002-4192-2429, University of British Columbia, corresponding author), Kimberlyn Mcgrail (0000-0002-9349-1915, University of British Columbia, corresponding author) |
| Year | 2020 |
| Volume | 58 |
| Issue | 2 |
| Pages | 114-119 |
| Publication date | 2020-02-01 |
| 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.0000000000001247 |
| PMID | 31688565 |
| OpenAlex | W2983331452 |
| Language | EN |
| Citations received | 4 |
| References cited | 23 |
BACKGROUND: Case-mix systems and comorbidity indices aggregate clinical information about patients over time and are used to characterize need for health care services. These tools were validated for their original purpose, but those purposes are varied, and they have not been compared directly in the context of predicting costs of health care services. OBJECTIVE: To compare predictions of next-year health care service costs across 4 tools, including: the Johns Hopkins Adjusted Clinical Groups (ACG), the Elixhauser Comorbidity Index, Charlson-Deyo Comorbidity Index, and the Canadian Institute for Health Information (CIHI) population grouper. METHODS: British Columbia administrative data from fiscal years 2012-2013 were used to generate case-mix variables and the comorbidity indices. Outcome variables include next-year (2013-2014) total, physician, acute care, and pharmaceutical costs, Outcomes were modeled using 2-part models. Performance was compared using adjusted R, root mean squared error, and mean absolute error using the predicted and the actual next-year cost. RESULTS: Models including the CIHI grouper (239 conditions) and ACG system had similar performance in most cost categories and slightly better fit than Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI). Adding a dummy variable for nonusers in the models for CCI and ECI increased R values slightly. CONCLUSIONS: All these systems have empirical support for use in predicting health care costs, despite in some cases being developed for other purposes. No system is particularly effective at predicting next-year acute care cost, likely because acute events are often by definition unexpected. The freely available ECI and CCI comorbidity indices implemented using the highest-performing methods developed here may be a good choice in many circumstances
Comorbidity · Context (archaeology) · Environmental health · Health care · Health economics · Index (typography) · Population · Population health · Psychiatry · Public health · Chronic Disease Management Strategies · Computer Science · Medical Coding and Health Information · Medicine · Nursing · Primary Care and Health Outcomes · Gerontology
Urban and Rural Population and Development Research on Medical Coordination
Examining the Urban and Rural Healthcare Progress in Big Cities of China
Variability in Healthcare Expenditure According to the Stratification of Adjusted Morbidity Groups in the Canary Islands (Spain)
Assessing the impact of day programs on individuals living with dementia and their family/friend caregivers (Aida-DemCare)
Epidemiology and impact of multimorbidity in primary care
Presentation adapting a clinical comorbidity index for use with ICD-9-CM administrative data
Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases
A new method of classifying prognostic comorbidity in longitudinal studies
Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data
Development and Application of a Population-Oriented Measure of Ambulatory Care Case-Mix
Performance of the ACG Case-Mix System in Two Canadian Provinces
Comorbidity Measures for Use with Administrative Data
Comparison of the Elixhauser and Charlson/Deyo Methods of Comorbidity Measurement in Administrative Data
A Modification of the Elixhauser Comorbidity Measures Into a Point System for Hospital Death Using Administrative Data
Comparing Population-based Risk-stratification Model Performance Using Demographic, Diagnosis and Medication Data Extracted From Outpatient Electronic Health Records Versus Administrative Claims
| Citations per year | 0.67 |
|---|---|
| Citation span | 2020 - 2025 (6) |
| Highly cited | No |