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Recent legislation ensuring patient access to their electronic health records represents a promising national commitment to patient empowerment. Access and interoperability rules seek to empower individuals as well as increase opportunities for data sharing by hospitals, apps, and other parties for research and innovation. However, there are trade-offs between data accessibility and oversight. Some third-party apps may not be covered by federal regulations, and receiving records directly from individuals may [...]

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.

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 [...]

Complex interventions with multiple components and behavior change strategies are increasingly implemented as a form of clinical decision support (CDS) using native electronic health record functionality. Objectives of this study were, therefore, to (1) identify the proportion of randomized controlled trials with CDS interventions that were complex, (2) describe common gaps in the reporting of complexity in CDS research, and (3) determine the impact of increased complexity on CDS effectiveness.

The predictive modeling literature for biomedical applications is dominated by biostatistical methods for survival analysis, and more recently some out of the box machine learning approaches. In this article, we show a presentation of a machine learning method appropriate for time-to-event modeling in the area of prostate cancer long-term disease progression. Using XGBoost adapted to long-term disease progression, we developed a predictive model for 118 788 patients with localized prostate cancer [...]

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