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The change in performance of machine learning models over time as a result of temporal dataset shift is a barrier to machine learning-derived models facilitating decision-making in clinical practice. Our aim was to describe technical procedures used to preserve the performance of machine learning models in the presence of temporal dataset shifts.

The amount of time that health care clinicians (physicians and nurses) spend interacting with the electronic health record is not well understood.

Suicide risk prediction models have been developed by using information from patients' electronic health records (EHR), but the time elapsed between model development and health system implementation is often substantial. Temporal changes in health systems and EHR coding practices necessitate the evaluation of such models in more contemporary data.

Pilot-testing is important in standards development because it facilitates agile navigation of the gap between needs for and use of standards in real-world settings and can reveal the practicalities of implementation. As the implementation and use of health data standards are usually more complicated than anticipated, the Office of the National Coordinator for Health Information Technology (ONC) routinely oversees and organizes relevant pilot projects.

Expectations regarding use and potential benefits of telehealth (TH) in nursing homes (NHs) are high; however, unplanned and unexpected consequences can occur as a result of major policy and technological changes.

Long-term care (LTC) services are a professional service-driven (PSD) system; to deliver appropriate care services to residents, health care providers first need to collect appropriate patient data and make a professional assessment. A well-designed LTC information system should therefore consider the information requirements of multidisciplinary health care providers to adequately support their care services.

In the United States, attention has been focused on "open notes" and "open results" since the Office of the National Coordinator for Health Information Technology implemented the 21st Century Cures Act Final Rule on information blocking. Open notes is an established best practice, but open results remains controversial, especially for diseases associated with stigma, morbidity, and mortality. Coronavirus disease 2019 (COVID-19) is associated with all three of these effects and [...]

The dramatic increase in complexity and volume of health data has challenged traditional health systems to deliver useful information to their users. The novel coronavirus disease 2019 (COVID-19) pandemic has further exacerbated this problem and demonstrated the critical need for the 21st century approach. This approach needs to ingest relevant, diverse data sources, analyze them, and generate appropriate health intelligence products that enable users to take more effective and efficient [...]

This study investigated information security behaviors of professionals working in the public health sector to guide policymakers toward focusing their investments in infrastructure and training on the most vulnerable segments. We sought to answer the following questions: (1) Are certain professional demographics more vulnerable to cybersecurity threats? (2) Do professionals in different institution types (i.e., hospitals vs. primary care clinics) exhibit different cybersecurity behaviors? (3) Can Internet usage behaviors by [...]

A new development in the practice of medicine is Artificial Intelligence-based predictive analytics that forewarn clinicians of future deterioration of their patients. This proactive opportunity, though, is different from the reactive stance that clinicians traditionally take. Implementing these tools requires new ideas about how to educate clinician users to facilitate trust and adoption and to promote sustained use. Our real-world hospital experience implementing a predictive analytics monitoring system that uses [...]