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We study the association between payment parity policies and telehealth utilization at community health centers (CHCs) before, during, and after the onset of the pandemic.

Recent policy changes have required health care delivery organizations provide patients electronic access to their clinical notes free of charge. There is concern that this could have an unintended consequence of increased electronic health record (EHR) work as clinicians may feel the need to adapt their documentation practices in light of their notes being accessible to patients, potentially exacerbating EHR-induced clinician burnout. Using a national, longitudinal data set consisting of [...]

Understanding public discourse on emergency use of unproven therapeutics is essential to monitor safe use and combat misinformation. We developed a natural language processing-based pipeline to understand public perceptions of and stances on coronavirus disease 2019 (COVID-19)-related drugs on Twitter across time.

The accuracy of artificial intelligence (AI) in medicine and in pathology in particular has made major progress but little is known on how much these algorithms will influence pathologists' decisions in practice. The objective of this paper is to determine the reliance of pathologists on AI and to investigate whether providing information on AI impacts this reliance.

While the use of medical scribes is rapidly increasing, there are not widely accepted standards for their training and duties. Because they use electronic health record systems to support providers, inadequately trained scribes can increase patient safety related risks. This paper describes the development of desired core knowledge, skills, and attitudes (KSAs) for scribes that provide the curricular framework for standardized scribe training.

To understand and synthesize factors influencing user acceptance of digital interventions used for antimicrobial prescribing and monitoring in hospitals.

Early identification of infection improves outcomes, but developing models for early identification requires determining infection status with manual chart review, limiting sample size. Therefore, we aimed to compare semi-supervised and transfer learning algorithms with algorithms based solely on manual chart review for identifying infection in hospitalized patients.

Warfarin anticoagulation management requires sequential decision-making to adjust dosages based on patients' evolving states continuously. We aimed to leverage reinforcement learning (RL) to optimize the dynamic in-hospital warfarin dosing in patients after surgical valve replacement (SVR).

Clinical dashboards used as audit and feedback (A&F) or clinical decision support systems (CDSS) are increasingly adopted in healthcare. However, their effectiveness in changing the behavior of clinicians or patients is still unclear. This systematic review aims to investigate the effectiveness of clinical dashboards used as CDSS or A&F tools (as a standalone intervention or part of a multifaceted intervention) in primary care or hospital settings on medication prescription/adherence and [...]

During the coronavirus disease-2019 (COVID-19) pandemic, the Centers for Disease Control and Prevention (CDC) supplemented traditional COVID-19 case and death reporting with COVID-19 aggregate case and death surveillance (ACS) to track daily cumulative numbers. Later, as public health jurisdictions (PHJs) revised the historical COVID-19 case and death data due to data reconciliation and updates, CDC devised a manual process to update these records in the ACS dataset for improving the [...]