AIMC Topic: Health Expenditures

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Machine-Learning Algorithms to Code Public Health Spending Accounts.

Public health reports (Washington, D.C. : 1974) Mar 31, 2017
OBJECTIVES: Government public health expenditure data sets require time- and labor-intensive manipulation to summarize results that public health policy makers can use. Our objective was to compare the performances of machine-learning algorithms with...
Health Expenditures Algorithms Humans Public Health Machine Learning
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The impact of intelligent devices utilization on household medical expenditure of older adults in China.

Scientific reports Jul 2, 2025
With the rapid development of artificial intelligence, there is an increasing utilization of intelligent devices by older adults. The relationship between the utilization of intelligent devices and household medical expenditure has garnered widesprea...
Aged Aged, 80 and over Male Artificial Intelligence Female Longitudinal Studies Family Characteristics Health Expenditures Middle Aged Humans China
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Determining health care cost drivers in older Hodgkin lymphoma survivors using interpretable machine learning methods.

Journal of managed care & specialty pharmacy Apr 1, 2025
BACKGROUND: The cost of health care for patients with Hodgkin lymphoma (HL) is projected to rise, making it essential to understand expenditure drivers across different demographics, including the older adult population. Although older HL patients co...
Retrospective Studies Female Aged Humans Machine Learning SEER Program Health Care Costs Hodgkin Disease Cancer Survivors United States Medicare Health Expenditures Aged, 80 and over Male
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Novel Machine Learning Approach to Identify Preoperative Risk Factors Associated With Super-Utilization of Medicare Expenditure Following Surgery.

JAMA surgery Nov 1, 2019
IMPORTANCE: Typically defined as the top 5% of health care users, super-utilizers are responsible for an estimated 40% to 55% of all health care costs. Little is known about which factors may be associated with increased risk of long-term postoperati...
Health Expenditures Preoperative Care Patient Acceptance of Health Care Elective Surgical Procedures Medicare Aged Male United States Postoperative Care Risk Assessment Female Risk Factors Machine Learning Retrospective Studies Humans
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Bundled Care for Hip Fractures: A Machine-Learning Approach to an Untenable Patient-Specific Payment Model.

Journal of orthopaedic trauma Jul 1, 2019
OBJECTIVES: With the transition to a value-based model of care delivery, bundled payment models have been implemented with demonstrated success in elective lower extremity joint arthroplasty. Yet, hip fracture outcomes are dependent on patient-level ...
Machine Learning Female Male Middle Aged Aged Humans Health Expenditures Hip Fractures Health Care Costs Arthroplasty, Replacement, Hip Bayes Theorem Medicare Length of Stay Patient Care Bundles United States
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Identifying Specific Combinations of Multimorbidity that Contribute to Health Care Resource Utilization: An Analytic Approach.

Medical care Mar 1, 2017
BACKGROUND: Multimorbidity affects the majority of elderly adults and is associated with higher health costs and utilization, but how specific patterns of morbidity influence resource use is less understood.
Male Comorbidity United States Aged Aged, 80 and over Age Factors Medicare Self Report Health Behavior Socioeconomic Factors Health Status Humans Health Expenditures Retrospective Studies Activities of Daily Living Female Machine Learning
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