Name
Abstract | ||
|---|---|---|
Healthcare administrators worldwide are striving to lower the cost of care whilst improving the quality of care given. Hospitalization is the largest component of health expenditure. Therefore, earlier identification of those at higher risk of being hospitalized would help healthcare administrators and health insurers to develop better plans and strategies. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population.We utilized a regression decision tree algorithm, along with insurance claim data from 242,075 individuals over three years, to provide predictions of number of days in hospital in the third year, based on hospital admissions and procedure claims data from the first two years. The proposed method performs well in the general population as well as in sub-populations. Results indicate that the proposed model significantly improves predictions over two established baseline methods (predicting a constant number of days for each customer and using the number of days in hospital of the previous year as the forecast for the following year). A reasonable predictive accuracy (AUC = 0:843) was achieved for the whole population. Analysis of two sub-populations - namely elderly persons aged over 63 years or older in 2011 and patients hospitalized for at least one day in the previous year - revealed that the medical information made more contribution to predictions of these two sub-populations, in comparison to the population as a whole. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/JBHI.2015.2402692 | IEEE J. Biomedical and Health Informatics |
Keywords | Field | DocType |
Australia,big data,health care,health insurance claims,hospitalizations,predictive modeling | Health care,Population,Diagnosis code,Demography,Regression,Pattern recognition,Health insurance,Artificial intelligence,Quality of care,Medical emergency,Medicine,Decision tree learning | Journal |
Volume | Issue | ISSN |
PP | 99 | 2168-2194 |
Citations | PageRank | References |
6 | 0.64 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Xie | 1 | 6 | 0.64 |
| Günter Schreier | 2 | 56 | 23.73 |
| David C. W. Chang | 3 | 6 | 0.64 |
| Sandra Neubauer | 4 | 6 | 0.64 |
| Ying Liu | 5 | 19 | 2.14 |
| Stephen J Redmond | 6 | 122 | 25.75 |
| nigel h lovell | 7 | 618 | 118.68 |