Date of Award

Summer 8-23-2025

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computational Analysis and Modeling

First Advisor

Pradeep Chowriappa

Abstract

Management of diabetes or heart disease may be uniquely challenging for older individuals with multiple chronic conditions[1]. Chronic Care Management would target patients living with comorbidities in Louisiana, who are not receiving sufficient healthcare services and help them receive the care they deserve[2]. This study is aimed at comparing the results of rural Louisiana Medicaid recipients with comorbidities to the National Averages. We will also use Machine Learning to predict Churn and Medical Costs. To help us identify these patients with comorbidities, we decided to use NCQA Quality Measures. We identified several measures from NCQA that we wanted to use, but we narrowed it down to five[3], [4], [5], [6], [7]. We acquired the blinded data from the Louisiana Department of Health (LDH) Medicaid data. This data is in the form of claims received from hospitals and doctors’ offices for patients in the Medicaid program[8]. After calculating our measures, we used the data we have created to predict the churn of patients in and out of Medicaid using Logistical Regression and Random Forest Classifier models. Finally, we used the initial data and machine learning to predict future costs of the rural patients using the ARIMA model. This study shows that rural communities in Louisiana have much lower rates of adherence to doctor visits and medication therapy than the national average. However, these rates are improving and have improved significantly over the six-year period of which this study investigated. We had positive results for both machine learning models in churn, but the best results came from not only the Random Forest Classifier but also from the inclusion of the data from the measures. The cost of medical care is increasing gradually on a monthly basis, but it still remains on a slow steady pace. Chronic Care Management systems would help improve patient outcomes, by helping patients manage treatment and doctor/hospital visits. By getting the patients the care they need more promptly, we can stave off worsening conditions and lower the overall costs of patient care.

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