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Large clinical databases are increasingly used for research and quality improvement. We describe an approach to data quality assessment from the General Medicine Inpatient Initiative (GEMINI), which collects and standardizes administrative and clinical data from hospitals.

The study sought to test the possibility of differentiating chest x-ray images of coronavirus disease 2019 (COVID-19) against other pneumonia and healthy patients using deep neural networks.

Prior research on health information exchange (HIE) typically measured provider usage through surveys or they summarized the availability of HIE services in a healthcare organization. Few studies utilized user log files. Using HIE access log files, we measured HIE use in real-world clinical settings over a 7-year period (2011-2017). Use of HIE increased in inpatient, outpatient, and emergency department (ED) settings. Further, while extant literature has generally viewed the ED [...]

Due to a complex set of processes involved with the recording of health information in the Electronic Health Records (EHRs), the truthfulness of EHR diagnosis records is questionable. We present a computational approach to estimate the probability that a single diagnosis record in the EHR reflects the true disease.

Qualitative methods are particularly well-suited to studying the complexities and contingencies that emerge in the development, preparation, and implementation of technological interventions in real-world clinical practice, and much remains to be done to use these methods to their full advantage. We aimed to analyze how qualitative methods have been used in health informatics research, focusing on objectives, populations studied, data collection, analysis methods, and fields of analytical origin.

Information gaps that accompany hurricanes and floods limit researchers' ability to determine the impact of disasters on population health. Defining key use cases for sharing complex disaster data with research communities and facilitators, and barriers to doing so are key to promoting population health research for disaster recovery.

To determine interest in and barriers to video visits in safety-net patients with diverse age, racial/ethnic, or linguistic background.

Hiring medical scribes to document in the electronic health record (EHR) on behalf of providers could pose patient safety risks because scribes often have no clinical training. The aim of this study was to investigate the effect of scribes on patient safety. This included identification of best practices to assure that scribe use of the EHR is not a patient safety risk.

We sought to examine co-design in 3 contrasting case studies of technology-supported change in health care and explain its role in influencing project success.

The lack of representative coronavirus disease 2019 (COVID-19) data is a bottleneck for reliable and generalizable machine learning. Data sharing is insufficient without data quality, in which source variability plays an important role. We showcase and discuss potential biases from data source variability for COVID-19 machine learning.