Explore JAMA Network Open’s Health Informatics collection, including open access science about electronic health records, approaches to Big Data, and more.
This diagnostic study assesses the value of including neighborhood socioeconomic status in models that use electronic health record (EHR) data to predict health care use rates and mortality among adults in a large integrated care system.
This cohort study attempts to validate the Mental Health Research Network suicide risk–prediction model and estimate associated workloads.
This survey study describes social and behavioral determinants of health self-reported by Veterans Affairs patients who are at high risk for hospitalization and assesses whether adding these measures to a model based on electronic health record data improves the accuracy of estimates of 90-day and 180-day all-cause hospital admission risk.
This stepped-wedge cluster randomized clinical trial assesses the effect of a clinician-directed intervention combining machine learning mortality predictions with behavioral nudges vs usual care on motivating serious illness conversations between clinicians and patients with cancer.
This cross-sectional study uses a deep learning system to analyze the temporal trends in cervical spine curvature across sex and age groups in a population of South Korean adults.
This cross-sectional study uses data from the National Electronic Injury Surveillance System Firearm Injury Surveillance Study to test the ability of natural language processing combined with 4 machine learning models to predict the locations of nonfatal gunshot injuries.
This Viewpoint proposes creation of algorithmic stewardship programs at health systems, tasked similar to antibiotic stewardship committees with oversight of artificial intelligence and machine learning technologies to ensure they are used safely, effectively, fairly, and to the benefit of diverse patient populations.
This Viewpoint examines the association between the COVID-19 pandemic and health care–related data collection.
This prognostic study evaluates the concordance between blood pressures obtained in routine clinical practice and those obtained using the Systolic Blood Pressure Intervention Trial protocol and whether concordance varied by target trial blood pressure.
This diagnostic study examines the area under the curve, sensitivity, specificity, and positive predictive value of chest radiograph readings performed by an artificial intelligence algorithm vs by radiology residents.
This study evaluates an automated reporting checklist generation tool that uses natural language processing to improve adherence to the Consolidated Standards of Reporting Trials (CONSORT) reporting checklist for randomized clinical trials (RCTs).
This quality improvement pilot study evaluates whether an email intervention that communicates an oncologist’s performance in documenting cancer stage relative to that of peer physicians is associated with increased likelihood that stage is documented in the electronic health record (EHR).
This cross-sectional study assesses whether a model-to-data deep learning approach (ie, validation of the algorithm without any data transfer) can be applied in ophthalmology.
This case-control study of women screened at an academic hospital in Stockholm, Sweden, evaluates 3 commercially available artificial intelligence algorithms to assess whether they perform independently as well or better than radiologists in mammography screening assessment or improve the performance of radiologists.
This study investigates whether adversarial attacks can confuse deep learning systems based on imaging domains.
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