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 Multitasking, defined as performing two or more interventions simultaneously, increases the cognitive burden of clinicians. This may, in turn, lead to higher risk of medication and procedural errors. Time motion study (TMS) data for nurses in nursing homes revealed an extensive amount of multitasking while managing medications. Further investigation of multitasked nursing interventions will provide a foundation for optimizing medication management workflows.

Patient-reported outcomes (PROs) describe a patient's unique experiences with disease or treatment, yet effective use of this information during clinical encounters remains challenging. This project sought to build a PRO based dashboard within the electronic health record (EHR), prioritizing interpretability and utility of PROs for clinical decision-making.

Pharmacists in over half of the United States can prescribe contraceptives; however, low pharmacist adoption has impeded the full realization of potential public health benefits. Many barriers to adoption may be addressed by leveraging an electronic health records (EHR) system with clinical decision support tools and workflow automation. We conducted a feasibility study to determine if utilizing a contraceptive-specific EHR could improve potential barriers to the implementation of pharmacist-prescribed contraceptive [...]

To develop and validate a novel measure, action entropy, for assessing the cognitive effort associated with electronic health record (EHR)-based work activities.

To investigate the impact of missing laboratory measurements on sepsis diagnostic delays.

Delirium is a syndrome that leads to severe complications in hospitalized patients, but is considered preventable in many cases. One of the biggest challenges is to identify patients at risk in a hectic clinical routine, as most screening tools cause additional workload. The aim of this study was to validate a machine learning (ML)-based delirium prediction tool on surgical in-patients undergoing a systematic assessment of delirium.

Electronic health records (EHRs) provide opportunities for the development of computable predictive tools. Conventional machine learning methods and deep learning methods have been widely used for this task, with the approach of usually designing one tool for one clinical outcome. Here we developed PheW2P2V, a Phenome-Wide prediction framework using Weighted Patient Vectors. PheW2P2V conducts tailored predictions for phenome-wide phenotypes using numeric representations of patients' past medical records weighted based on [...]

Firearm injuries constitute a public health crisis. At the healthcare encounter level, they are, however, rare events.

To demonstrate that 2 popular cohort discovery tools, Leaf and the Shared Health Research Information Network (SHRINE), are readily interoperable. Specifically, we adapted Leaf to interoperate and function as a node in a federated data network that uses SHRINE and dynamically generate queries for heterogeneous data models.

 Generative artificial intelligence (AI) tools may soon be integrated into health care practice and research. Nurses in leadership roles, many of whom are doctorally prepared, will need to determine whether and how to integrate them in a safe and useful way.