Thesis - Open Access
Master of Science (MS)
Electrical Engineering and Computer Science
Sung Y. Shin
database, disease management, healthcare, predictive analysis, risk assessment, SQL
Individuals with chronic conditions are the ones who use health care most frequently and more than 50% of top ten causes of death are chronic diseases in United States and these members always have health high risk scores. In the field of population health management, identifying high risk members is very important in terms of patient health care, disease management and cost management. Disease management program is very effective way of monitoring and preventing chronic disease and health related complications and risk management allows physicians and healthcare companies to reduce patient’s health risk, help identifying members for care/disease management along with help in managing financial risk. The main objective of this research is to introduce efficient and accurate risk assessment model maintaining the accuracy of risk scores compared to existing model and based on calculated risk scores identify the members for disease management programs using structured query language. For the experimental purpose we have used data and information from different sources like CMS, NCQA, existing models and Healthcare Insurance Industry. In this approach, base principle is used from chronic and disability payment system (CDPS), based on this model weight of chronic disease is defined to calculate risk of each patient. Also to be more focused, three chronic diseases have been selected as a part of analysis. They are breast cancer, diabetes and congestive heart failure. Different sets of diagnosis, electronic medical records, and member pharmacy information are key source. Industry standard database system have been in taken in consideration while implementing the logic, which makes implementation of model more efficient since data is warehoused where model is built. We obtained experimental result from total 4761 relevant medical records taken from Molina Healthcare’s data warehouse. We tested proposed model using risk score data from State of Illinois using multiple linear regression method and implemented proposed logic in health plan data, based on which we calculated p-value and confidence level of our variables and also as second validation process we ran same data source through original risk model. In next step we showed that risk scores of members are highly contributing in member selection process for disease management program. To validate member selection criteria we used fast and frugal decision tree algorithm and confusion matrix result is used to measure the performance of member selection process for disease management program. The results show that the proposed model achieved overall risk assessment confidence level of 99%, with R-squared value of 98% and on disease management member identification we achieved 99% of sensitivity, 89% of accuracy and 74% of specificity. The experimental result from proposed model shows that if risk assessment model is taken one step further not only risk of member can be determined but it can help in disease management approach by identifying and prioritizing members for disease management. The resulting chronic risk and disease management method is very promising method for any patient, insurance companies, provider groups, claims processing organizations and physician groups to more accurately and effectively manage their members in terms of member health risk and enrolling them under required care management programs. Methods and design used in this research contributes to business analytics approach, overall member risk and disease management approach using predictive analytics based on member’s medical diagnosis, pharmacy utilization and member demographics.
Includes bibliographical references (pages 63-68)
Number of Pages
South Dakota State University
In Copyright - Educational Use Permitted
Ojha, Mamata, "Chronic Risk and Disease Management Model Using Structured Query Language and Predictive Analysis" (2018). Electronic Theses and Dissertations. 2480.