Explore JAMA Network Open’s Health Informatics collection, including open access science about electronic health records, approaches to Big Data, and more.
This quality improvement study evaluates whether an electronic health record add-on app for neonatal bilirubin management is associated with clinician time savings and improvements in patient care.
This Users’ Guide to the Medical Literature discusses the use of machine learning models as a diagnostic tool, and it explains the important steps needed for making these models and the outcomes they derive clinically effective.
This Viewpoint proposes a template for thinking about physician malpractice liability arising from use of artificial intelligence (AI) in clinical practice, and identifies scenarios with and without injury and liability vs based on alignment of AI recommendations with standards of care, AI accuracy, and physician decisions in response.
This diagnostic study describes a novel attention-based deep neural network framework for classifying microscopy images to identify Barrett esophagus and esophageal adenocarcinoma.
This cross-sectional study develops and validates a machine learning method for collecting and classifying data from opioid-related postings on a social media platform.
This randomized clinical trial examines the effects of virtual reality education before chest radiography on anxiety and distress during the procedure among pediatric patients.
This meta-analysis evaluates the accuracy of computerized systems in the diagnosis of melanoma in patients with skin lesions.
This study uses machine learning to evaluate medical records from a database of 2 million randomly selected patients to develop a prediction model for incident nonmelanoma skin cancer from multidimensional nonimaging medical information.
This qualitative study analyzes submissions to a popular machine learning for health workshop to assess the current state of health research, including areas of methodologic and clinical focus, limitations, and underexplored areas.
This cohort study develops, validates, and compares machine learning algorithms that use structured electronic health record data before a clinic visit to predict mortality among patients with cancer.
This cohort study develops and assesses electronic surveillance definitions for nonventilator hospital-acquired pneumonia using clinical data routinely recorded in electronic health records.
This diagnostic study determines the accuracy of an artificial intelligence algorithm in identifying melanoma in dermoscopic images of lesions taken with smartphone and digital single-lens reflex (DSLR) cameras.
This Viewpoint reviews conventional ways of assessing performance of machine learning models to diagnose or predict outcomes, but emphasizes that if machine learning is to improve patient care the models must be evaluated for their utility in improving clinical decisions taking into account the range of decisions clinicians can take, the cost and efficacy of those options, and the likelihood that patients will follow the recommended decisions.
This qualitative analysis using interviews with physicians determines barriers and facilitators in electronic health record inbox management and suggestions for improvement.
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