AIMC Topic: Medicare

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A Machine Learning Approach to Identify Predictors of Potentially Inappropriate Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) Use in Older Adults with Osteoarthritis.

International journal of environmental research and public health Dec 28, 2020
Evidence from some studies suggest that osteoarthritis (OA) patients are often prescribed non-steroidal anti-inflammatory drugs (NSAIDs) that are not in accordance with their cardiovascular (CV) or gastrointestinal (GI) risk profiles. However, no suc...
Retrospective Studies Female Aged Aged, 80 and over Medicare Osteoarthritis Humans Gastrointestinal Diseases Machine Learning Inappropriate Prescribing Male Cardiovascular Diseases United States Anti-Inflammatory Agents, Non-Steroidal
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Machine learning to predict mortality after rehabilitation among patients with severe stroke.

Scientific reports Nov 18, 2020
Stroke is among the leading causes of death and disability worldwide. Approximately 20-25% of stroke survivors present severe disability, which is associated with increased mortality risk. Prognostication is inherent in the process of clinical decisi...
Machine Learning Algorithms Clinical Decision-Making Female Humans Mortality Stroke Rehabilitation Logistic Models United States Stroke Aged Medicare Male Middle Aged ROC Curve
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Closing the Digital Health Evidence Gap: Development of a Predictive Score to Maximize Patient Outcomes.

Telemedicine journal and e-health : the official journal of the American Telemedicine Association Nov 10, 2020
Clinical studies of telemedicine (TM) programs for chronic illness have demonstrated mixed results across settings and populations. With recent uptake in use of digital health modalities, more precise patient classification may improve outcomes, eff...
Bayes Theorem Medicare Humans Machine Learning Retrospective Studies Aged Cohort Studies United States
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Economic Challenges of Artificial Intelligence Adoption for Diabetic Retinopathy.

Ophthalmology Jul 25, 2020
Artificial Intelligence Sensitivity and Specificity United States Ophthalmology Humans Diabetic Retinopathy Medicare Diagnostic Techniques, Ophthalmological Reimbursement Mechanisms
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Using machine learning to predict risk of incident opioid use disorder among fee-for-service Medicare beneficiaries: A prognostic study.

PloS one Jul 17, 2020
OBJECTIVE: To develop and validate a machine-learning algorithm to improve prediction of incident OUD diagnosis among Medicare beneficiaries with ≥1 opioid prescriptions.
Machine Learning Prognosis Female Aged United States Humans Medicare Male Opioid-Related Disorders Middle Aged Fee-for-Service Plans Risk Assessment Computational Biology
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A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers.

International journal of environmental research and public health May 24, 2020
Late-arriving patients have become a prominent concern in several ambulatory care clinics across the globe. Accommodating them could lead to detrimental ramifications such as schedule disruption and increased waiting time for forthcoming patients, wh...
Appointments and Schedules Ambulatory Care Machine Learning Female Aged Male Time Factors Logistic Models United States Medicare Humans Neural Networks, Computer
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Machine learning and the future of Medicare fraud detection.

Journal of the American Academy of Dermatology Mar 29, 2020
Fraud Financial Audit Data Mining Datasets as Topic Clinical Coding Administrative Claims, Healthcare Machine Learning United States Medicare
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Assessment of utilization efficiency using machine learning techniques: A study of heterogeneity in preoperative healthcare utilization among super-utilizers.

American journal of surgery Jan 27, 2020
INTRODUCTION: In the United States, 5% of patients represent up to 55% of all health care costs. This study sought to define healthcare utilization patterns among super-utilizers, as well as assess possible variation in patient outcomes.
Treatment Outcome Medicare Arthroplasty, Replacement, Knee Arthroplasty, Replacement, Hip Preoperative Period Patient Acceptance of Health Care Aged, 80 and over Male Cohort Studies United States Humans Machine Learning Female Aged
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Comparison of Machine Learning Methods With Traditional Models for Use of Administrative Claims With Electronic Medical Records to Predict Heart Failure Outcomes.

JAMA network open Jan 3, 2020
IMPORTANCE: Accurate risk stratification of patients with heart failure (HF) is critical to deploy targeted interventions aimed at improving patients' quality of life and outcomes.
Humans Machine Learning Logistic Models United States Medicare Heart Failure Aged, 80 and over Electronic Health Records Outcome Assessment, Health Care Male Female Comorbidity Hospitalization Cohort Studies Aged
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Prevalence of Financial Considerations Documented in Primary Care Encounters as Identified by Natural Language Processing Methods.

JAMA network open Aug 2, 2019
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.
Health Care Costs Cost of Illness Medicaid Aged Prevalence Medicare Humans Adult Health Expenditures Primary Health Care Intensive Care Units Male Ethnicity United States Female Middle Aged Income Natural Language Processing Hospitalization
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