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Artificial intelligence (AI) and machine learning (ML) enabled healthcare is now feasible for many health systems, yet little is known about effective strategies of system architecture and governance mechanisms for implementation. Our objective was to identify the different computational and organizational setups that early-adopter health systems have utilized to integrate AI/ML clinical decision support (AI-CDS) and scrutinize their trade-offs.

The COVID-19 (coronavirus disease 2019) pandemic has expanded telehealth utilization in unprecedented ways and has important implications for measuring geographic access to healthcare services. Established measures of geographic access to care have focused on the spatial impedance of patients in seeking health care that pertains to specific transportation modes and do not account for the underlying broadband network that supports telemedicine and e-health. To be able to measure the impact [...]

To develop prediction models for intensive care unit (ICU) vs non-ICU level-of-care need within 24 hours of inpatient admission for emergency department (ED) patients using electronic health record data.

Clinical decision support systems, which provide automated reminders in electronic health systems, are designed to provide physicians and other health professionals support in clinical decision-making. New clinical guidance from the Advisory Committee on Immunization Practices on a new category of recommendations referred to as "shared clinical decision-making" have left providers struggling to interpret how to best implement recommendations for adult vaccines. The issue at hand is how to ensure that [...]

Mobile health (mHealth) applications have the potential to improve health awareness. This study reports a quasi-controlled intervention to augment maternal health awareness among tribal pregnant mothers through the mHealth application. Households from 2 independent villages with similar socio-demographics in tribal regions of India were selected as intervention (Village A) and control group (Village B). The control group received government mandated programs through traditional means (orally), whereas the intervention group received [...]

To determine user and electronic health records (EHR) integration requirements for a scalable remote symptom monitoring intervention for asthma patients and their providers.

In intensive care units (ICUs), a patient's brain function status can shift from a state of acute brain dysfunction (ABD) to one that is ABD-free and vice versa, which is challenging to forecast and, in turn, hampers the allocation of hospital resources. We aim to develop a machine learning model to predict next-day brain function status changes.

The purpose of the study was to explore the theoretical underpinnings of effective clinical decision support (CDS) factors using the comparative effectiveness results.

Healthcare is undergoing a digital transformation, and the Centers for Medicare & Medicaid Services (CMS) aims to help providers navigate the clinical quality improvement landscape. In December 2017, CMS launched the Electronic Clinical Quality Measure (eCQM) Strategy Project. This article consists of 2 parts. The first part describes stakeholder outreach aimed to identify burdens and recommendations related to eCQM implementation and reporting. The second part describes how these burdens were [...]