Applied Clinical Informatics最新文献

Electronic health records (EHRs) are widely implemented and consume nearly half of physicians' work time. Despite the importance of efficient data entry, physicians' typing skills-potential contributors to documentation burden-remain poorly studied.This study aims to evaluate the typing skills of physicians and their associations with demographic characteristics and professional roles.This cross-sectional pilot study included a convenience sample of physicians (residents, chief residents, and attending physicians) from the internal medicine division of an academic hospital. Participants completed a 1-minute typing test under supervised conditions. The primary outcome was raw typing speed, measured in words per minute (WPM). The secondary outcome was a performance score calculated by subtracting 50 points for each error from the total number of characters typed per minute.Participation rate was 100% (82/82 physicians). The mean age was 33.7 ± 7.3 years; 7.2 ± 7.1 years since graduation; and 45.1% female. The mean typing speed was 53.4 WPM (range: 31-91 WPM), with 57.3% (47/82) of participants exceeding 50 WPM, a threshold commonly considered professional. Bivariate analysis showed a significant negative association with age (Spearman's ρ = -0.281, p = 0.011), which was not sustained in the multivariable analysis. No significant association was observed with sex, country of diploma, or role. Upon multivariable analysis, performance score showed a significant negative association with age (β = -17.724, p = 0.009) but a positive association with years since graduation (β = 16.850, p = 0.021), suggesting a generation- and experience-related interaction.Nearly half of physicians exhibited professional-level typing skills, yet overall performance varied widely and was influenced by both generational factors and clinical experience. Given that documentation burden affects clinicians across all skill levels, both individual and systemic strategies-such as improved EHR design and alternative input methods-should be explored.

Pain medicine triage plays a crucial role in ensuring patients receive timely and appropriate care by scheduling them to the most suitable treatment path. However, the absence of standardized triage protocols in pain medicine often leads to inefficiencies, including delay of care and wastage of healthcare resources.This study aims to develop a rule-based automated referral triage system leveraging information from patients' medical notes for scheduling patients to specific procedures in the pain medicine department.The proposed triage system, grounded in the knowledge and expertise of clinical providers, processed referral order comments and referring provider notes by iteratively refining the Natural Language Processing (NLP) rules and post-processing rules through intensively reviewing 76 patients. A post-processing regression model was incorporated to further enhance the accuracy. To ensure alignment with real-world practices, the system was integrated into an electronic health record (EHR) platform for real-time application, streamlining scheduling workflows and enhancing usability in daily clinical settings.After three iterations, the proposed NLP and post-processing rules improved accuracy from 76.3 to 80.3% compared to machine learning (ML) approaches in the preliminary study. The post-processing model further increased accuracy to 84.2%. The implementation accuracy of 200 cases for the first 3 months was consistent with our prediction at 83.5%, which concluded that the improvement over ML models (p-value = 0.018) was statistically significant at 95% significance level.This study demonstrates the feasibility and benefits of a knowledge-driven approach to referral triage in specialized medical fields. It lays a foundation for others in building similar triaging solutions to other specialties.

Anticoagulation decisions in atrial fibrillation (AF) depend on balancing stroke and bleeding risk, often guided by CHA₂DS₂-VASc (a validated clinical score used to estimate stroke risk in patients with atrial fibrillation) and HAS-BLED (a validated clinical score used to estimate bleeding risk in patients treated with anticoagulation) scores. Manual calculation of these scores can be time-consuming and inconsistently performed.This study evaluated whether implementing real-time, electronic health record (EHR)-integrated alerts in a rural academic primary care clinic would influence physician and non-physician provider (NPP) behavior around anticoagulation management.A single-arm observational study was conducted from March 2024 to September 2025 at a West Virginia University (WVU) Family Medicine Clinic. A rules-based engine in Epic calculated risk scores using 1 year of structured data and displayed them within a non-interruptive "Our Practice Advisory" alert. Physician or NPP interaction-defined as initiation of anticoagulation, documentation of rationale, or adding exclusion diagnosis to problem list-was analyzed using chi-square testing.Among 313 patients triggering the alert, 53 (16.9%) were newly started on anticoagulation, 112 (35.8%) had a documented rationale for not initiating therapy, and 2 had the exclusion diagnosis added to their chart. In total, 50.5% of patients had a clinically meaningful interaction with the tool (χ²= 9.82, p = 0.0017). Across 2,447 encounters, the overall alert success rate was 19.8%, reflecting encounter-level engagement. Common acknowledgment reasons included corrective measures completed, high bleeding risk, recent procedures, and patient refusal. Physician and NPP comments informed iterative refinement, leading to expanded acknowledgment options.Real-time alerts displaying stroke and bleeding risk scores were associated with meaningful physician and NPP engagement, particularly for initiating anticoagulation in high-risk patients. While most interactions reflected review rather than treatment change, the tool appeared to support point-of-care decision-making. These findings support further investigation of EHR-based advisories to improve anticoagulation management in AF.

This study aimed to assess the effect of a passive, opt-in electronic health record (EHR)-based clinical decision support (CDS), the Chronic Pain OneSheet, on guideline-recommended chronic pain management in primary care.A pragmatic randomized controlled trial with a parallel group design was conducted between October 2020 and May 2022. Participants were 137 primary care clinicians (PCCs) treating qualifying patients with chronic pain at 25 primary care clinics within two academic health systems in the United States. PCCs were randomized in the EHR to have access to OneSheet or usual care. OneSheet aggregates guideline-relevant information in a single view and provides shortcuts to guideline-recommended actions (e.g., ordering urine drug screening [UDS] for patients prescribed opioids). We constructed five visit-level binary outcomes: (1) documenting pain-related goals; (2) documenting pain and function via Pain, Enjoyment of Life and General Activity (PEG) scale; (3) reviewing prescription drug monitoring programs (PDMPs); (4) ordering UDS; and (5) ordering naloxone. Analysis used generalized linear mixed models for each outcome.OneSheet access minimally increased rates of pain-related goal documentation (0.2 percentage point increase, p = 0.013), PEG scale documentation (0.7 percentage point increase, p < 0.001), and UDS orders (2.2 percentage point increase, p = 0.006). OneSheet access decreased the rate of ordering naloxone (0.5 percentage point decrease, p < 0.001). OneSheet access did not affect PDMP review rates (0.5 percentage point decrease, p = 0.382).OneSheet access did not result in clinically significant improvements in guideline-recommended management of chronic pain in primary care despite a robust user-centered design incorporating clinician input and EHR integration. Several factors likely limited OneSheet effectiveness, including limited ability to target certain patient visits, workflow limits on data collection and ordering, and evolving COVID-19 and opioid epidemic-related policies and procedures. These findings highlight specific limitations of OneSheet and the broader challenges of implementing effective EHR-based CDS in complex health care environments.

Quantify the effect of ambient artificial intelligence (AI) scribe technology on work experience, clinical operations, and patient experience in pediatric primary care.We conducted a 12-week study of 39 clinicians within a large pediatric primary care network. Clinician experience was measured using a custom survey instrument which included a combination of discrete and free-text responses. Qualitative analysis of free-text responses provided additional context and identified key facilitators and barriers to optimal usage. Proprietary electronic health record (EHR) efficiency measures and utilization data were used to further quantify clinician experience, adoption, and operational effects. Patient experience was measured using a vendor-supplied survey instrument.AI scribe technology was used in 32% of eligible encounters (6,249 of 19,264). Survey responses demonstrated significant heterogeneity in clinician experience. The most commonly reported benefits were reduction in self-perceived cognitive burden (21/39), ability to finish work sooner (18/39), and ability to enjoy clinical work more (18/39). No significant change in EHR efficiency measures around documentation time, afterhours EHR time, total EHR time, or visit closure rates were observed. Clinicians reported AI scribes were most helpful for urgent care visits and for summarizing the history of present illness. Areas of improvement specific to pediatric primary care include suboptimal performance in summarizing and organizing content relating to preventive and behavioral health visits. Patient survey responses showed no difference in Net Promoter Score and related patient experience questions between ambient and non-ambient encounters.A subset of clinicians reported self-perceived improvements in work experience despite unchanged EHR efficiency measures. Heterogeneity in clinician experience suggests that benefit from ambient technology likely depends on personal and contextual factors. Enhancements to note organization and facility with pediatric well child visit and behavioral health content could improve the utility of this tool for pediatric primary care.

The digital availability of health data not only improves processes in primary care, but it also facilitates the evaluation of healthcare delivery. Nevertheless, the preprocessing of data for secondary use is still time-consuming and expensive, particularly in head and neck cancer (HNC), where patients undergo complex multidisciplinary treatment trajectories. Therefore, we have looked further into the effects on data quantity and quality following the implementation of structured care pathways. Leveraging data extracted from these care pathways, we assessed the potential of real-time quality-of-care evaluation through dashboards, incorporating indicators such as a proposed "textbook process" model.Our mixed methods study assessed the value of a newly implemented structured HNC pathway and its effect on data quantity and quality through three processes: (1) A qualitative assessment of current barriers, data registration processes, and data-interpretation discrepancies with in-house data managers. (2) A prospective pilot (n = 41) in which patient data is registered both manually and semi-automatically. (3) An evaluation of the patient journey through dashboards with real-time indicators 1 year after go-live.During the iterative implementation phase of the structured care pathway, data completeness and correctness averaged 84.8 and 88.4%, respectively. The new method reduced registration time by 3.7 minutes per patient. A majority of 87.8% followed all four defined time points of the structured care pathway. One year after implementation and in-house validation, time-to-treatment intervals could be tracked, and processes could be adapted accordingly.A structured care pathway, followed by early implementation guided by a multidisciplinary team, forms the foundation for sustainable data capturing for multiple purposes, including quality registries. In-house dashboards further enhance data quality and process improvement.

This study aimed to evaluate the effect of optimizing the ambulatory medication preference list on provider efficiency in medication ordering.Using electronic health record (EHR) vendor data, a multidisciplinary informatics team optimized the general ambulatory medication preference list to better align with providers' ordering patterns. We conducted a pre-postintervention analysis assessing time-in-orders per encounter and number of manual changes per order.Postintervention, average manual changes per order decreased from 4.12 to 3.00 (p < 0.01), and median time spent in the orders activity per encounter decreased from 3.1 to 2.3 minutes (p < 0.01).Optimizing the ambulatory medication preference list reduced time spent and clicks needed by providers when ordering medications. This is relevant to ongoing efforts to address EHR-related burden.

The adoption of electronic health records (EHRs) into clinical practice has changed clinical workflows and, in some cases, increased documentation burden and clinician burnout. Identifying factors associated with perceived EHR usability after the implementation of a new EHR may guide efforts to reduce burden and burnout.This study measured: (1) group-level perceptions of EHR usability pre- and postimplementation of a new EHR; (2) adaptation to the new EHR; and (3) the effects of clinical role, setting, and specialty on these measures.Pre- and postimplementation surveys were sent to clinical staff at two academic medical centers (AMC A and AMC B), each part of the same Northeast health system where one instance of a new EHR was implemented starting in 2020. The surveys measured constructs from the Health Information Technology Usability Evaluation Scale (Health-ITUES) and Health Information Technology Adaptation survey. Unpaired t-tests assessed changes in group-level scores from pre- to postimplementation, and multiway analyses of variance with post hoc pairwise t-tests with Bonferroni's correction were used to assess differences in scores by clinical role, setting, and specialty.Average Perceived Usefulness (PU) and adaptation scores were higher at AMC B than at AMC A, but similar pre- to postimplementation trends were observed at both sites. Perceptions of Quality of Work Life (QWL), PU, and User Control (UC) improved across both sites postimplementation, whereas Perceived Ease of Use and Cognitive Support and Situational Awareness declined. Ordering Providers, Registered Nurses, clinicians practicing in the Emergency Department setting, and Emergency Medicine, and Critical/Intensive Care specialists had statistically different scores across various constructs.After implementation of a new EHR system at two AMCs, clinical staff perceptions of quality of work life (QWL), perceived usefulness (PU), and user control (UC) generally improved, although perceptions of perceived ease of use and cognitive support declined.

Federated network studies allow data to remain locally while the research is conducted through the sharing of analytical code and aggregated results across different health care settings and countries. A large number of databases have been mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), boosting the use of analytical pipelines for standardized observational research within this open science framework. Transparency, reproducibility, and robustness of results have positioned federated analyses using the OMOP CDM within the European Health Data and Evidence Network (EHDEN) as an essential tool for generating large-scale evidence.We conducted large-scale federated analyses involving 52 databases from 19 countries using the OMOP CDM. In this State-of-the-Art/Best Practice article, we aimed to share key lessons and strategies for conducting such complex, large multidatabase analyses.Meticulous planning, establishing a strong community of collaborators, efficient communication channels, standardized analytics, and strategic division of responsibilities are essential. We highlight the benefits of network engagement, cross-fertilization of ideas, and shared learning. Further key elements contributing to the study's success included an inclusive, incremental implementation of the analytical code, timely engagement of data partners, and community webinars to discuss and interpret study findings.We received predominantly positive feedback from data custodians about their participation, and included input for further improvements for future large-scale federated network studies from this shared learning experience.

Sepsis is a heterogeneous syndrome with high morbidity and mortality. Despite extensive clinical trials, therapeutic progress remains limited, in part due to the absence of actionable sepsis subtypes.This study aimed to evaluate the feasibility of using HL7 Fast Healthcare Interoperability Resources (FHIR) for prerandomization sepsis subtyping to support clinical trial enrichment across multiple health systems.Data from 765 encounters at two academic medical centers were analyzed. FHIR-based resources were extracted from both research data warehouses (RDWs) and electronic health records (EHRs). A Python implementation of the Sepsis Endotyping in Emergency Care (SENECA) sepsis subtyping algorithm was developed to query and assemble FHIR resources for subtype classification.Open-source Python code for the SENECA algorithm is provided on GitHub. Experiments demonstrated: (1) successful sepsis subtyping across both health systems; (2) concordance between the original R implementation and the new Python implementation; and (3) discrepancies when comparing RDW-derived versus EHR-integrated FHIR APIs, primarily due to query and filtering limitations. Missing data were common and influenced by both clinical practice and FHIR API constraints. We provide five recommendations to address these challenges.FHIR can support multi-institutional sepsis subtyping and trial enrichment, though technical and governance challenges remain.