Applied Clinical Informatics最新文献

Drive to Zero is a mobile health application (app) designed to identify and retain people with HIV (PWH) who have experienced challenges with achieving or maintaining viral suppression. The app targets PWH who have lacked documented HIV care in the past months and are experiencing medication adherence barriers. Features include an interactive chat for communicating with the study team and access to educational resources to support care engagement and health management.This usability study aimed to assess the Drive to Zero app's ease of use and interface design through expert heuristic evaluation and end-user testing.Usability was evaluated through two approaches: heuristic evaluations conducted by five informatics experts following Nielsen's usability principles, and end-user testing with 20 PWH using the validated Post-Study System Usability Questionnaire and qualitative interviews to collect feedback on app functionality and user experience.Heuristic experts and end-users demonstrated satisfaction with the app's appearance, reporting that it has a simple and intuitive interface for identifying and retaining PWH, which will assist them with study engagement and ultimately reengage with HIV care. However, participants highlighted areas needing improvement, suggesting better accessibility of "home" and "help" buttons to improve user control and a more detailed explanation of the incentive program to enhance user engagement and retention.Usability evaluations provided valuable insights into the Drive to Zero app's design. Areas for improvement were enhancing user controls and improving the readability of the incentive program. These findings will guide iterative refinements, ensuring that future versions of the app improve the usability and acceptability of its target audience.

Getting patients out of intensive care units (ICUs) is a major goal for acute care clinicians, as prolonged stays increase the risk of complications and strain critical resources such as staff, equipment, and beds. The ICU Liberation Bundle, or the ABCDEF (A-F) care bundle, is an evidence-based framework for improving outcomes in critically ill patients by addressing pain, sedation, delirium, mobility, and family engagement. However, variability in documentation and a lack of standardized data elements hinder effective implementation and evaluation of adherence to bundle components.This study aims to characterize data elements of the A-F liberation bundle using a large, single-center critical care database and to develop standardized bundle cards that map bundle components to controlled vocabularies.We conducted a retrospective analysis of data elements related to the A-F bundle using the MIMIC-IV database. Clinical concepts were mapped to standardized vocabularies and aligned with the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). Bundle cards were developed for each component to provide structured, accessible documentation of assessment tools, adherence criteria, and terminology mappings.Pain assessments were documented in over 11,000 patients, with a median of 23 assessments per day. Sedation levels for nearly 59,000 patients were evaluated, with 37.7% meeting the Society of Critical Care Medicine (SCCM) adherence criteria. Delirium assessments followed standardized protocols incorporating Richmond Agitation-Sedation Scale (RASS) and CAM-ICU scores. Components E and F lacked formal compliance specifications; bundle cards for these components identified key activities and highlighted gaps in standardized vocabularies. Adherence analyses revealed variability likely due to non-standardized documentation practices.We developed and validated six ICU Liberation Bundle cards that map bundle components to standardized vocabularies and CDMs, enabling retrospective adherence evaluation in real-world data. These information resources promote consistent documentation, support interoperability, and provide a foundation for prospective monitoring to enhance bundle implementation in critical care.

The integration of large language models (LLMs) into clinical diagnostics presents significant challenges regarding their accuracy and reliability.This study aimed to evaluate the performance of DeepSeek R1, an open-source reasoning model, alongside two other LLMs, GPT-4.1 and Claude 3.5 Sonnet, across multiple-choice clinical cases.A dataset of complex medical cases representative of real-world clinical practice was selected.For efficiency, models were accessed via application programming interfaces (APIs) and assessed using standardized prompts and a predefined evaluation protocol.The models demonstrated an overall accuracy of 77.1%, with GPT-4 producing the fewest errors and Claude 3.5 the most. The reproducibility analysis indicated that the tests were very repeatable: DeepSeek (100%), GPT-4.1 (97.5%), and Claude 3.5 Sonnet (92%).While LLMs show promise for enhancing diagnostics, ongoing scrutiny is required to address error rates and validate standard medical answers. Given the limited dataset and prompting protocol, findings should not be interpreted as broader equivalence in real-world clinical reasoning. This study demonstrates the need for robust evaluation standards, attention to error rates, and further research.

This study aimed to compare two ambient AI documentation tools, Dragon Ambient eXperience (DAX) and Abridge in the emergency department (ED), assessing perceived effects on work burden, usability, documentation quality, satisfaction, and overall preference.We conducted a single-site, prospective crossover study in an ED over 6 weeks, from April to June 2025. Out of 20 faculty enrolled, 18 completed both phases. Participants used both ambient AI scribe tools in alternating 3-week phases. Pre-tool, tool-specific, and post-tool surveys captured four domains: burden, usability, quality, and satisfaction. Adoption was the proportion of notes containing any ambient output. Paired Wilcoxon tests and linear mixed-effects models were used to compare tools, adjusting for order and adoption.DAX was associated with greater reduction in overall perceived work burden compared with Abridge (median: 1.5 vs. 2; p = 0.025). Usability was high and comparable between the tools (SUS medians: 73.5 vs. 73.5, p = 0.94; UMUX-Lite medians: 86 vs. 82.5, p = 0.079). Scores from a modified version of the Physician Documentation Quality Instrument (PDQI-9) favored DAX (median: 39 vs. 36.5; p = 0.011). DAX received higher satisfaction ratings (median likelihood-to-recommend: 9 vs. 7.5; p = 0.015), but adjusted models suggested these differences reflected order effects more than inherent tool differences. Post-pilot preferences showed no overall preference after accounting for order, with first-tool exposure significantly shaping ratings.In this 6-week crossover study in the ED, both ambient AI scribes were highly usable, and perceived to reduce documentation burden while preserving note quality. Findings support the feasibility and perceived value of ambient AI scribes in the ED and motivate larger, longer-duration, multi-site evaluations with objective outcomes.

Abandoned medication orders-those initiated but not signed-represent a potential safety risk and an indicator of electronic health record (EHR) inefficiency. This study explores inpatient medication abandonment across two large tertiary healthcare systems using different EHRs.Silent alerts were deployed to identify abandoned orders at Site 1 (June 2018-May 2019) and Site 2 (July 2020-May 2023). At Site 1, alerts triggered on all inpatient medication orders. At Site 2, alerts were part of a broader study implementing indication alerts; only orders for study medications triggered alerts. An abandoned order was defined as an order initiated but not signed within 24 hours of initiation. We calculated abandonment rates and rates of reorders, and performed regression to examine the association between abandonment and clinician, patient, and order characteristics. Exponential models were fit to characterize the chronology of reordering.Among 6.8 million medication orders, abandonment rates were 11.2% at Site 1 and 25.0% at Site 2. Due to fundamental differences in alert configuration and order capture, no direct statistical comparison of abandonment rates between the two sites was conducted. Over half of abandoned orders were reordered within 24 hours (65.3% at Site 1; 54.2% at Site 2). The chronology of reordering was similar at both institutions. Attendings, the most senior clinicians, had the lowest rates of abandonment. Abandonment rates decreased as clinicians placed more orders, but rose as clinicians ordered on more unique patients. Abandonments were higher when ordering for children compared with adults.Order abandonment is common and varies by patient's age, clinician type, and workload. Abandonment rates declined as house staff providers advanced in training, signifying clinical experience plays a role. Frequent reordering suggests that workflow interruptions or modifications, rather than intentional medication cancellation, may lead to a significant proportion of abandonments. Similarity in the timing of reordering between healthcare systems suggest common reordering processes across sites. Our findings demonstrate significant order abandonment rates, with the potential to use abandonment as a metric to improve computerized provider order entry (CPOE) functionality, clinicians' workflows, and patient safety.

Identifying patient portals (PP) activation disparities, especially in electronic health record (EHR) activation workflows, can help facilitate equitable health care access.Our study aimed to assess whether the parent/guardian's preferred language was associated with being offered, activating, and using the PP and the methods used to offer activation codes.This retrospective cohort study examined PP offer, activation, and usage rates at a large freestanding children's hospital. Patients <12 years old with ambulatory visits from July 1, 2022, to June 30, 2023, without prior active proxy PP accounts were included. The primary independent variable was the self-reported parent/guardian preferred language (English/Spanish). Outcomes included the probability of being offered, overall and by specific offer methods, activation, and usage. Zou's modified multivariate Poisson regression models examined the association between preferred language and offer/activate/use status.Among 39,578 patients, 85.1% were patients with English as preferred language (PEPL) and 14.9% had Spanish as preferred language (PSPL). PSPL had a lower probability of being offered (adjusted relative risk ratio [aRR]: 0.65, 95% confidence interval [CI]: 0.63-0.67), activated (aRR: 0.72, 95% CI: 0.70-0.75), and used (aRR: 0.68, 95% CI: 0.65-0.72) a PP compared to PEPL. Specifically, PSPL had a lower probability of activating if ever offered via instant activation (aRR: 0.72, 95% CI: 0.69-0.75), parent/guardian with existing account (aRR: 0.73, 95% CI: 0.69-0.76), and had equal probability of activating if ever offered via letter (aRR: 0.42, 95% CI: 0.19-0.94) and clinician-assisted method (aRR: 0.99, 95% CI: 0.86-1.16), compared to PEPL.PSPL at a large, free-standing pediatric health system had a lower probability of PP offer, activation, and usage than PEPL. Activation methods were not universally effective across language groups, emphasizing the need for equitable workflow optimization. This study highlights an approach to analyzing health disparities in activation workflows to inform targeted interventions to improve equitable PP access.

The digital transformation of healthcare is reshaping care delivery among healthcare professionals, requiring nurses to develop digital competencies. These competencies are essential but often underdeveloped due to limited training and resources. Global initiatives emphasize integrating these competencies into nursing education, necessitating valid instruments to assess them.This systematic review aims to identify instruments measuring digital competence in nursing and to assess their measurement properties.This review was registered in PROSPERO (identifier: CRD42024522349) and conducted according to PRISMA guidelines. A systematic search was performed in CINAHL, PubMed/MEDLINE, and Scopus on instruments assessing digital competencies in nursing and reporting measurement properties. Measurement properties and their methodological quality were assessed using the COSMIN criteria, and the overall quality of the evidence was graded using a modified GRADE approach.A total of 27 instruments were identified, relating to three interconnected constructs: nursing informatics, digital health, and information and communication technology. Based on their measurement properties, the instruments were categorized into three groups (A, B, C) following the COSMIN methodology to support recommendations for use. Six instruments were classified under category A (recommended for use): the DigiHealthCom and DigiComInf instruments, the Turkish version of TANIC, the short version of ITASH, the Digital Competence Questionnaire, and the 30-item Arabic version of SANICS. Twenty instruments were categorized under category B (potentially recommendable, but further validation is needed). One instrument was placed in category C (not recommended for use).As digital competence becomes an increasing priority in education and public health, valid and reliable instruments are essential for assessing and monitoring these competencies. Such instruments support the identification of training needs, the evaluation of educational outcomes, and the integration of digital skills into nursing curricula and clinical practice, ultimately strengthening the digital readiness of the nursing workforce.

Real-world data from vendor-aggregated health information exchanges represent a powerful resource for retrospective clinical research. Epic's Cosmos platform, a large-scale centralized data warehouse, enables querying deidentified electronic health record data.We describe the iterative development of a generalizable clinical informatics workflow for retrospective studies using Cosmos.We applied the Plan-Do-Study-Act cycle to iteratively refine a collaborative research process using Cosmo's SlicerDicer interface, focusing on setting, team, technical preparation, and workflow.We identified multiple areas of improvement to facilitate streamlined collaborative studies. Our institutional review board confirmed Cosmos studies as nonhumans subject research. We expanded collaborations across institutions. We identified key insights for team definition across clinical, research, and skill domains. We expanded our team based on technical skills, domain expertise, and educational aims. We optimized query building and cohort construction, data analysis and validation, and communication processes. We clarified and optimized a collaborative workflow across clinical and informatics expertise.Our collaborative approach to secondary data analysis in Cosmos supports the development of meaningful clinical evidence to support high-quality, well-evidenced patient care. This study demonstrates the application of the Plan-Do-Study-Act cycle to a collaborative workflow for clinically focused secondary data analysis. Our approach allows rapid, reproducible cohort construction and analysis, is adaptable across clinical domains, and scales to multiorganizational collaboration. This approach offers a model for others seeking to develop key clinical insights via retrospective studies within the Cosmos data aggregation tool.

Ambient listening tools utilize generative artificial intelligence (AI) to create clinical notes from real-time conversations between clinicians and patients during an encounter. One of the potential benefits of ambient listening tools is an improvement in reported patient experience.This study aimed to compare the patient experience of an outpatient visit during which an ambient listening tool is used with a standard visit and to quantify any perceived improvements in care.Patients completed a targeted survey following outpatient clinic visits across all departments at a large academic institution. We conducted ordered logistic regression analyses to examine the association between ambient scribe use and patient satisfaction across six survey domains: provider communication, provider attention, perceived time spent with the provider, overall interaction with the provider, understanding of health information, and quality of the after-visit summary.Our analysis included 8,120 patients who submitted a survey following their outpatient visit in February to April 2025. Patients whose provider used an ambient scribe had higher odds of reporting satisfaction with the perceived duration of time spent with the provider (OR = 1.13, 95% CI: 1.01-1.26, p = 0.033).In this observational study, use of an ambient AI scribe was associated with a small improvement in one patient-reported satisfaction domain of perceived attention from the provider and no detectable differences across other domains assessing patient experience. These findings suggest that, in early real-world implementation, ambient AI documentation tools may be acceptable to patients and do not appear to adversely affect perceived visit quality.

Learning about critically ill children's outcomes after transfer to the pediatric intensive care unit (PICU) can help emergency department (ED) physicians improve future performance. However, there are no standard processes in place to systematically provide this information; thus, most ED physicians obtain inconsistent feedback.We aimed to determine the effect of delivering patient outcome feedback through the electronic health record (EHR) on the frequency of ED physicians' re-access of patients' EHRs after PICU transfer.We performed a retrospective cohort study at an academic tertiary referral hospital before and after implementing an EHR-based system delivering individual patient outcome feedback to ED physicians who admitted children from the ED to the same institution's PICU (2019-2021).A total of 180 patients transferred to the PICU by 30 unique ED physicians were included (100 pre- and 80 postintervention). After implementing the feedback system, the proportion of patients for whom ED physicians re-accessed the EHR increased from 26% preintervention to 80% postintervention (p < 0.001). Propensity score-adjusted multivariable modeling accounting for patient, clinician, encounter, and diagnostic covariates showed a significant association between receipt of patient outcome feedback reports and ED physicians' EHR re-access, with the rate of EHR re-access 2.58 times higher in the postintervention cohort (p < 0.001). The estimated marginal means, which provide an adjusted average outcome for each cohort, showed a significantly higher number of EHR re-access episodes per patient postintervention (0.44 [95% CI: 0.3, 0.66] pre- vs. 1.14 [95% CI: 0.86, 1.51] postintervention, p < 0.001).Receipt of consistent patient outcome feedback increased ED physicians' re-access of patients' EHRs after PICU transfer, potentially allowing them to obtain information that can be used to improve future clinical performance. Further study is needed to determine the effectiveness of feedback systems in improving clinician practice and outcomes of critically ill children.