AIMC Topic: Medicaid

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Machine learning approaches for predicting high cost high need patient expenditures in health care.

Biomedical engineering online Nov 20, 2018
BACKGROUND: This paper studies the temporal consistency of health care expenditures in a large state Medicaid program. Predictive machine learning models were used to forecast the expenditures, especially for the high-cost, high-need (HCHN) patients.
Health Services Research Texas Medicaid Multimorbidity Health Expenditures Data Collection Economics, Medical Health Care Costs Humans Adolescent United States Chronic Disease Machine Learning Algorithms Female Aged Young Adult Adult Male Middle Aged
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Chronic Pain Prevalence, Opioid Use, and Primary Care Provider Opioid Prescription Patterns in the U.S. from 2017 to 2019 Derived from Medicaid Claims Data.

Studies in health technology and informatics Aug 7, 2025
Chronic non-cancer pain (CNCP) is a major health concern in the United States, incurring substantial healthcare costs and frequently requiring opioid therapy in primary care. This retrospective cross-sectional study used Medicaid claims data from six...
Cross-Sectional Studies Male Middle Aged Prevalence Aged Humans Young Adult Retrospective Studies Adult Female Practice Patterns, Physicians' Adolescent Drug Prescriptions Primary Health Care Chronic Pain United States Medicaid Analgesics, Opioid
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Identifying high-dose opioid prescription risks using machine learning: A focus on sociodemographic characteristics.

Journal of opioid management Jan 1, 2025
OBJECTIVE: The objective of this study was to leverage machine learning techniques to analyze administrative claims and socioeconomic data, with the aim of identifying and interpreting the risk factors associated with high-dose opioid prescribing.
Risk Factors Aged Young Adult Adult Humans Machine Learning Female Sex Factors Male United States Sociodemographic Factors Middle Aged Practice Patterns, Physicians' Medicaid Socioeconomic Factors Risk Assessment Drug Prescriptions Age Factors Analgesics, Opioid
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Machine learning for detection of heterogeneous effects of Medicaid coverage on depression.

American journal of epidemiology Jul 8, 2024
In 2008, Oregon expanded its Medicaid program using a lottery, creating a rare opportunity to study the effects of Medicaid coverage using a randomized controlled design (Oregon Health Insurance Experiment). Analysis showed that Medicaid coverage low...
Depression United States Medicaid Oregon Adult Male Middle Aged Insurance Coverage Humans Machine Learning Female Young Adult
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Drivers of Prolonged Hospitalization Following Spine Surgery: A Game-Theory-Based Approach to Explaining Machine Learning Models.

The Journal of bone and joint surgery. American volume Jan 6, 2021
BACKGROUND: Understanding the interactions between variables that predict prolonged hospital length of stay (LOS) following spine surgery can help uncover drivers of this risk in patients. This study utilized a novel game-theory-based approach to dev...
Spinal Diseases Game Theory Medicaid United States Elective Surgical Procedures Humans Critical Care Machine Learning Nervous System Diseases Computer Simulation Middle Aged Intensive Care Units Comorbidity Female Length of Stay Risk Factors Male
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Characterizing Subgroups of High-Need, High-Cost Patients Based on Their Clinical Conditions: a Machine Learning-Based Analysis of Medicaid Claims Data.

Journal of general internal medicine Aug 1, 2019
Humans Machine Learning Female Insurance Claim Review Adult United States Male Health Services Needs and Demand Middle Aged Health Care Costs Medicaid
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Using machine learning to examine medication adherence thresholds and risk of hospitalization.

Medical care Aug 1, 2015
BACKGROUND: Quality improvement efforts are frequently tied to patients achieving ≥80% medication adherence. However, there is little empirical evidence that this threshold optimally predicts important health outcomes.
Multivariate Analysis Adult Female Assessment of Medication Adherence Male Hypoglycemic Agents Hospitalization Risk Assessment Medicaid Middle Aged Humans Patient Compliance Aged Cohort Studies Artificial Intelligence Aged, 80 and over Diabetes Mellitus, Type 2 Quality Improvement Retrospective Studies United States
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