Objective. The overall goals of this study are to test the validity of the ACG (Adjusted Clinical Group) case-mix system in explaining healthcare costs, resource allocation, and predictive modeling using Taiwan's National Health Insurance (NHI) data, and to empirically identify morbidity trajectories among the population and assess the effects of including morbidity trajectories into risk adjustment models.
Data. A 1% random sample of NHI enrollees continuously enrolled in 2002 was used for concurrent analyses and evaluation of resource allocation (n=173,234). A 2002 to 2003 cohort (n=164,562) and a 2002 to 2005 cohort (n=147,892) were used for prospective and longitudinal analyses.
Methods. For concurrent analyses, health measures derived from 2002 diagnoses were used to explain 2002 resource utilization; for prospective analyses and predictive modeling, the outcome was 2003 resource utilization. For longitudinal analyses, health measures derived from 2002 to 2004 diagnoses were used to explain 2005 resource utilization. For evaluation of resource allocation, age/sex-adjusted self-reported health status, mortality rates, and the ACGs-based index in 2002 at district level were compared.
Results. Comprehensive models performed better in explaining resource utilization. Adjusted R2 of total costs in concurrent/prospective analyses were 4%/4% in the demographic model, 15%/10% in the ACGs or ADGs model, and 40%/22% in the models containing EDCs. The ACGs morbidity index could accurately reflect geographic differences in morbidity burden given its high correlation with both self-reported health status and mortality rates at district level. In terms of predictive modeling, both diagnosis-based and prior cost models performed much better than the demographic model. Over a three-year period, most people's morbidity levels tended to stay constant. There were significant differences in medical utilization across six morbidity trajectory groups. The effect of adding trajectory indicators differed substantially by risk adjustment models: the increase in adjusted R2 ranged from 0.3% in EDCs model to 5.7% in demographics model.
Conclusions. Given the wide-scale availability of claims data and the superior performance, Taiwan and other managers of health insurance plans should consider claims-based models for policy-relevant applications, such as cost prediction, resource allocation, predictive modeling, and quality improvement.
|School:||The Johns Hopkins University|
|School Location:||United States -- Maryland|
|Source:||DAI-B 70/04, Dissertation Abstracts International|
|Subjects:||Public health, Health care management|
|Keywords:||ACGs, Morbidity trajectory, Predictive modeling, Resource allocation, Risk adjustment, Taiwan|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
The supplemental file or files you are about to download were provided to ProQuest by the author as part of a
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
Copyright of the original materials contained in the supplemental file is retained by the author and your access to the
supplemental files is subject to the ProQuest Terms and Conditions of use.
Depending on the size of the file(s) you are downloading, the system may take some time to download them. Please be