AIMC Topic: Health Care Costs
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Comparison of statistical and machine learning models for healthcare cost data: a simulation study motivated by Oncology Care Model (OCM) data.
BMC health services research
Apr 25, 2020
BACKGROUND: The Oncology Care Model (OCM) was developed as a payment model to encourage participating practices to provide better-quality care for cancer patients at a lower cost. The risk-adjustment model used in OCM is a Gamma generalized linear mo...
Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study.
The Lancet. Digital health
Apr 23, 2020
BACKGROUND: Deep learning is a novel machine learning technique that has been shown to be as effective as human graders in detecting diabetic retinopathy from fundus photographs. We used a cost-minimisation analysis to evaluate the potential savings ...
Telemedicine
Cost-Benefit Analysis
Diagnostic Techniques, Ophthalmological
Health Care Costs
Photography
Physical Examination
Ophthalmology
Singapore
Models, Biological
Retina
Diabetes Mellitus
Mass Screening
Decision Trees
Adult
Sensitivity and Specificity
Middle Aged
Image Processing, Computer-Assisted
Diabetic Retinopathy
Artificial Intelligence
Machine Learning
Aged
Humans
The Economic Impact of Artificial Intelligence in Health Care: Systematic Review.
Journal of medical Internet research
Feb 20, 2020
BACKGROUND: Positive economic impact is a key decision factor in making the case for or against investing in an artificial intelligence (AI) solution in the health care industry. It is most relevant for the care provider and insurer as well as for th...
Artificial intelligence outperforms human students in conducting neurosurgical audits.
Clinical neurology and neurosurgery
Feb 10, 2020
OBJECTIVES: Neurosurgical audits are an important part of improving the safety, efficiency and quality of care but require considerable resources, time, and funding. To that end, the advent of the Artificial Intelligence-based algorithms offered a no...
Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore.
BMJ open
Jan 6, 2020
OBJECTIVE: We aim to characterise persistent high utilisers (PHUs) of healthcare services, and correspondingly, transient high utilisers (THUs) and non-high utilisers (non-HUs) for comparison, to facilitate stratifying HUs for targeted intervention. ...
Digital Health Consumers on the Road to the Future.
Journal of medical Internet research
Nov 21, 2019
Digital health is uniquely positioned to transform health care. This viewpoint explores the enormous benefits for health consumers when digital-first health care is embraced. Also, it explores what risks exist if surveillance capitalism takes over he...
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.
Artificial Intelligence and Machine Learning in Lower Extremity Arthroplasty: A Review.
The Journal of arthroplasty
Jun 11, 2019
BACKGROUND: Driven by the rapid development of big data and processing power, artificial intelligence and machine learning (ML) applications are poised to expand orthopedic surgery frontiers. Lower extremity arthroplasty is uniquely positioned to mos...
Preoperative Prediction of Value Metrics and a Patient-Specific Payment Model for Primary Total Hip Arthroplasty: Development and Validation of a Deep Learning Model.
The Journal of arthroplasty
May 2, 2019
BACKGROUND: The primary objective was to develop and test an artificial neural network (ANN) that learns and predicts length of stay (LOS), inpatient charges, and discharge disposition for total hip arthroplasty. The secondary objective was to create...
Exploring the use of machine learning for risk adjustment: A comparison of standard and penalized linear regression models in predicting health care costs in older adults.
PloS one
Mar 6, 2019
BACKGROUND: Payers and providers still primarily use ordinary least squares (OLS) to estimate expected economic and clinical outcomes for risk adjustment purposes. Penalized linear regression represents a practical and incremental step forward that p...
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