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 Choice architecture refers to the design of decision environments, which can influence decision-making in health care. Nudges are subtle adjustments in these environments that guide decisions toward desired outcomes. For example, computerized provider order entry within electronic health records (EHRs) recommends frequencies for interventions such as nursing assessments and medication administrations, but these can link to around-the-clock schedules without clinical necessity.

 High-value care aims to enhance meaningful patient outcomes while reducing costs and is accelerated by curating data across health care systems through common data models (CDMs), such as Observational Medical Outcomes Partnership (OMOP). Meaningful patient outcomes, such as physical function, must be included in these CDMs. However, the extent to which physical therapy assessments are covered in the OMOP CDM is unclear.

 Interruptive alerts are known to be associated with clinician alert fatigue, and poorly performing alerts should be evaluated for alternative solutions. An interruptive alert to remind clinicians about a required peripherally inserted central catheter (PICC) dressing change within the first 48 hours after placement resulted in 617 firings in a 6-month period with only 11 (1.7%) actions taken from the alert.

Hospital at Home (HaH) programs currently lack decision support tools to help efficiently navigate the complex decision-making process surrounding HaH as a care option. We assessed user needs and perspectives to guide early prototyping and co-creation of 4PACS (Partnering Patients and Providers for Personalized Acute Care Selection), a decision support app to help patients make an informed decision when presented with discrete hospitalization options.

Clinical note section identification helps locate relevant information and could be beneficial for downstream tasks such as named entity recognition. However, the traditional supervised methods suffer from transferability issues. This study proposes a new framework for using large language models (LLMs) for section identification to overcome the limitations.

Large language models (LLMs) have demonstrated remarkable success in natural language processing (NLP) tasks. This study aimed to evaluate their performances on social media-based health-related text classification tasks.

 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.

Various data quality issues have prevented healthcare administration data from being fully utilized when dealing with problems ranging from COVID-19 contact tracing to controlling healthcare costs.

Conduct a scoping review of research studies that describe rule-based clinical decision support (CDS) malfunctions.

The increasing prevalence of electronic health records (EHRs) in healthcare systems globally has underscored the importance of data quality for clinical decision-making and research, particularly in obstetrics. High-quality data is vital for an accurate representation of patient populations and to avoid erroneous healthcare decisions. However, existing studies have highlighted significant challenges in EHR data quality, necessitating innovative tools and methodologies for effective data quality assessment and improvement.