| | Category | CS | P06 | Optimizing Health Care Resources Allocation Using Data Mining |
| | Approaches |
| | Abstract | The goal of my project is to use a systematic approach to segment patients |
| | based on predictive morbidity indices such as the Charlson Index. |
| | Individuals do not know their exact score on the Charlson Index and |
| | determining this score exactly can be very time consuming and resource |
| | intensive. Through data analysis and data mining techniques, I was able to |
| | determine some of the factors that can very accurately predict the |
| | patient’s score. The benefit is that scarce medical resources, such as |
| | vaccines that are in short supply and attention can be directed towards |
| | people with higher scores, which will ensure that such resources get |
| | applied effectively in preventing spread of epidemics. |
| | |
| | To perform data mining, I used a tool called SQL Server on data that was |
| | made available by a prime insurance company. The medical claims data |
| | gave information regarding member profiles, health indicators of members, |
| | and the member’s severity of illness at that point. All the medical conditions |
| | as well as their demographic profiles were used as features to predict |
| | the Charlson Index. |
| | |
| | Different data mining techniques were used in this process. After inputting |
| | the different features that determined the severity of illness, the data |
| | mining tool created a decision tree which predicted one or more discrete |
| | variables, based on the other attributes in the dataset. There were two |
| | different analyses done: one, using only health indicators to predict the |
| | severity of illness and another, using member demographic details, such |
| | as age and gender, as well as health indicators to predict the severity of |
| | illness. For each analysis a dependency network of attributes was |
| | created and decision tree evaluation was done. Accuracy charts and lift |
| | charts were created for validation. |
| | |
| | My project proves that a small number of specific health related data is a |
| | better predictor of the Charlson Score and that health care providers |
| | should use these elements rather that traditional demographic measures |
| | such as age and gender to make decisions regarding allocation of scarce |
| | health care resources. |
| | Bibliography |