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Despite their potential, Clinical Decision Support (CDS) systems often lack alignment with clinicians' needs and are underutilized in practice. Pilot implementations can help to improve the fit between systems and local needs by engaging users in real-world testing and refinement. Although pilot implementations of CDS have been reported, limited evidence has explored the factors contributing to pilot success.This study aimed to explore the opportunities and challenges associated with the pilot [...]

Cancer staging is integral to ensuring cancer patients receive appropriate risk-adapted therapy. Discrete cancer staging using a structured staging form helps ensure accurate staging, provides a single source of truth for staging information, and allows for reporting to regulatory authorities. Our institution created pediatric oncology specific discrete staging forms that have been shared with the broader Epic community. By November 2023, baseline utilization of the staging form for patients with [...]

The patient-facing ASTHMAXcel mobile platform has been linked to improved asthma knowledge decreased asthma-related health care utilization (emergency department [ED] visits, hospitalizations), and reduced prednisone use among adult and pediatric patient populations.Given the upfront costs associated with developing mobile health platforms, this paper seeks to estimate the savings attributable to pediatric and adult users of the ASTHMAXcel platform through decreased hospitalizations, ED visits, and prednisone use.Forty adult patients and 39 [...]

Effective clinical decision support (CDS) interventions improve adherence to care guidelines, reduce prescribing errors, and, in some settings, decrease patient mortality. However, misalignment with the "Five Rights" framework, particularly regarding CDS timing in clinical workflows, can lead to implementation failures, alert fatigue, and physician burnout.This case series aimed to evaluate and redesign three interruptive CDS alerts at a large safety net health system to better align with clinician workflows, reduce [...]

We aim to leverage more comprehensive phenotypic and genotypic clinical data to enhance the treatment response predictions.

Clinicians currently make decisions about placing an intracranial pressure (ICP) monitor in children with traumatic brain injury (TBI) without the benefit of an accurate clinical decision support tool. The goal of this study was to develop and validate a model that predicts placement of an ICP monitor and updates as new information becomes available.

Emerging efforts to identify patients at risk of suicide have focused on the development of predictive algorithms for use in healthcare settings. We address a major challenge in effective risk modeling in healthcare settings with insufficient data with which to create and apply risk models. This study aimed to improve risk prediction using transfer learning or data fusion by incorporating risk information from external data sources to augment the data [...]

To synthesize knowledge on tensions characterizing large-scale electronic health record (EHR) implementations.

Machine learning algorithms can advance clinical care, including identifying mental health conditions. These algorithms are often developed without considering the perspectives of the affected populations. This study describes the process of incorporating end-user perspectives into the development and implementation planning of a prediction algorithm for new perinatal depression onset.

Screening Brief Intervention and Referral to Treatment (SBIRT) can reduce the health and social costs associated with unhealthy alcohol use (UAU). Electronic health records (EHRs) can support evidence-based screening practices for UAU and provide performance data needed for quality improvement. The objective of this study was to describe barriers faced by primary care clinics when using EHR systems to support UAU screening and delivery of recommended interventions.