AIMC Topic: Health Expenditures

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Role of the Health System in Combating Covid-19: Cross-Section Analysis and Artificial Neural Network Simulation for 124 Country Cases.

Social work in public health
In the fight against Covid-19, developed countries and developing countries diverge in success. This drew attention to the discussion of how different health systems and different levels of health spending are effective in combating Covid-19. In this...

Prediction of out-of-pocket health expenditures in Rwanda using machine learning techniques.

The Pan African medical journal
INTRODUCTION: in Rwanda, the estimated out-of-pocket health expenditure has been increased from 24.46% in 2000 to 26% in 2015. Despite the existence of guideline in estimation of out-of-pocket health expenditures provided by WHO (2018), the estimatio...

Medical Fraud and Abuse Detection System Based on Machine Learning.

International journal of environmental research and public health
It is estimated that approximately 10% of healthcare system expenditures are wasted due to medical fraud and abuse. In the medical area, the combination of thousands of drugs and diseases make the supervision of health care more difficult. To quantif...

Prevalence of Financial Considerations Documented in Primary Care Encounters as Identified by Natural Language Processing Methods.

JAMA network open
IMPORTANCE: Quantifying patient-physician cost conversations is challenging but important as out-of-pocket spending by US patients increases and patients are increasingly interested in discussing costs with their physicians.

Development and Validation of a Machine Learning Algorithm After Primary Total Hip Arthroplasty: Applications to Length of Stay and Payment Models.

The Journal of arthroplasty
BACKGROUND: Value-based payment programs in orthopedics, specifically primary total hip arthroplasty (THA), present opportunities to apply forecasting machine learning techniques to adjust payment models to a specific patient or population. The objec...

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

Biomedical engineering online
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.

Machine Learning and Primary Total Knee Arthroplasty: Patient Forecasting for a Patient-Specific Payment Model.

The Journal of arthroplasty
BACKGROUND: Value-based and patient-specific care represent 2 critical areas of focus that have yet to be fully reconciled by today's bundled care model. Using a predictive naïve Bayesian model, the objectives of this study were (1) to develop a mach...

Robust Machine Learning Variable Importance Analyses of Medical Conditions for Health Care Spending.

Health services research
OBJECTIVE: To propose nonparametric double robust machine learning in variable importance analyses of medical conditions for health spending.

The Economic Burden of ACPA-Positive Status Among Patients with Rheumatoid Arthritis.

Journal of managed care & specialty pharmacy
BACKGROUND: Anticitrullinated protein antibodies (ACPAs) are serological biomarkers associated with early, rapidly progressing rheumatoid arthritis (RA), including more severe disease and joint damage. ACPA testing has become a routine tool for RA di...