Applied Clinical Informatics最新文献

Approximately 10% of patients have a documented penicillin "allergy"; however, up to 95% have subsequent negative testing. These patients may receive suboptimal antibiotics, leading to longer hospitalizations and higher costs, rates of resistant and nosocomial infections, and all-cause mortality. To mitigate these risks in children, we implemented an inpatient penicillin allergy delabeling protocol and integrated it into the electronic health record (EHR) through a mixed methods approach of clinical decision support (CDS).We describe our protocol implementation across three sequential phases: "Pilot," "Active Antimicrobial Stewardship Program (ASP)," and "Mixed CDS." We highlight several potential pitfalls that may have contributed to poor clinician adoption.Patients were risk-stratified as nonallergic, low-risk, or high-risk based on history. Process measures included: evaluation rate, oral challenge rate for low-risk, and allergy referral rate for high- or low-risk when oral challenge was deferred. The primary outcome measure was the penicillin allergy delabeling rate among low-risk or nonallergic. Balancing measures included the rate of epinephrine or antihistamine administrations.The pilot and ASP phases used clinician education and an order set, but were mostly manual processes. The mixed CDS phase introduced interruptive alerts, dynamic text in note templates, and patient list columns to guide clinicians, but little education was provided. The mixed CDS phase had the lowest evaluation rate compared with the pilot and active ASP phases (6.4 vs. 25 vs. 15%). However, when the evaluation was performed, the mixed CDS phase had the highest oral challenge rate (33 vs. 26 vs. 13%) and delabeling rate (43 vs. 33 vs. 27%). No adverse events occurred.CDS tools improve clinician decision-making and optimize patient care. However, relying on CDS for complex clinical evaluations can lead to failure when clinicians cannot find the tool or appreciate its importance. Person-to-person communication can be vital in establishing a process and educating intended users for successful CDS implementation.

In the emergency department-initiated buprenorphine for opioid use disorder (EMBED) trial, a clinical decision support (CDS) tool had no effect on rates of buprenorphine initiation in emergency department (ED) patients with opioid use disorder. The Agency for Healthcare Research and Quality (AHRQ) recently released a CDS Performance Measure Inventory to guide data-driven CDS development and evaluation. Through partner co-design, we tailored AHRQ inventory measures to evaluate EMBED CDS performance and drive improvements.Relevant AHRQ inventory measures were selected and adapted using a partner co-design approach grounded in consensus methodology, with three iterative, multidisciplinary partner working group sessions involving stakeholders from various roles and institutions; meetings were followed by postmeeting surveys. The co-design process was divided into conceptualization, specification, and evaluation phases building on the Centers for Medicare and Medicaid Services' measure life cycle framework. Final measures were evaluated in three EDs in a single health system from January 1, 2023, to December 31, 2024.The partner working group included 25 members. During conceptualization, 13 initial candidate metrics were narrowed to 6 priority categories. These were further specified and validated as the following measures, presented with preliminary values based on the use of the current (i.e., preoptimization) EMBED CDS: eligible encounters with CDS engagement, 5.0% (95% confidence interval: 4.3-5.8%); teamwork on ED initiation of buprenorphine, 39.9% (32.5-47.3%); proportion of eligible users who used EMBED, 58.3% (50.9-65.8%); time spent on EMBED, 29.0 seconds (20.4-37.7 seconds); proportion of buprenorphine orders placed through EMBED, 6.5% (3.4-9.6%); and task completion, 13.8% (8.9-18.7%) for buprenorphine order/prescription.A measurement science framework informed by partner co-design was a feasible approach to develop measures to guide CDS improvement. Subsequent research could adapt this approach to evaluate other CDS applications.

In dentistry, social determinants of health (SDoH) are potentially recorded in the clinical notes of electronic dental records. The objective of this study was to examine the availability of SDoH data in dental clinical notes and evaluate natural language processing methods to extract SDoH from dental clinical notes.A set of 1,000 dental clinical notes was sampled from a dataset of 105,311 patient visits to a dental clinic and manually annotated for information pertaining to sugar, tobacco, alcohol, methamphetamine, housing, and employment. Annotations included temporality, dose, type, duration, and frequency where appropriate. Experiments were to compare extraction using fine-tuned pretrained language models (PLMs) with a rule-based approach. Performance was measured by F1-score.For identifying SDoH, the best-performing PLM method produced F1-scores of 0.75 (sugar), 0.69 (tobacco), 0.67 (alcohol), 0.42 (housing), and 0 (employment). The rule-based method produced F1-scores of 0.70 (sugar), 0.69 (tobacco), 0.53 (alcohol), 0.44 (housing), and 0 (employment). The overall difference between PLMs and rule-based methods was F1-score of 0.04 (95% confidence interval -0.01, 0.09). SDoH were relatively rare in dental clinical notes, from sugar (9.1%), tobacco (3.9%), alcohol (1.2%), housing (1.2%), employment (0.2%), and methamphetamine use (0%).The main challenge of extracting SDoH information from dental clinical notes was the frequency with which they are recorded, and the brevity and inconsistency where they are recorded. Improved surveillance likely needs new ways to standardize how SDoHs are reported in dental clinical notes.

The purpose of this systematic literature review is to critically evaluate the use of mathematical and simulation models within emergency departments (EDs) and assess their potential to improve the quality of care. This review emphasizes the critical need for quality enhancement in health care systems, with a specific focus on EDs.This review incorporates studies that have investigated the quality of care provided in ED settings, employing assorted mathematical and simulation models for adult populations. Based on the selected studies, a narrative approach was used to synthesize the findings, focusing on outcome classification, simulation, and modelling. There are six outcome dimensions: safety, effectiveness, patient-centeredness, timeliness, efficiency, and equity.This review analyzed 112 studies, uncovering a distinct focus on a set of key performance measures within ED operations, accounting for 222 instances across these studies. Measures assessing timeliness were most frequent, occurring 111 times, indicative of a strong emphasis on operational efficiency aspects such as waiting times and patient flow. A total of 75 examinations were conducted on efficiency-related measures, with a specific focus on identifying and addressing operational bottlenecks and optimizing resource utilization. On the other hand, safety, patient-centeredness, and effectiveness were not as commonly represented, with only 3, 4, and 29 instances, respectively.This review highlights the considerable potential of mathematical and simulation models to enhance ED operations, particularly regarding timeliness and efficiency. However, aspects such as patient safety, effectiveness, and patient-centeredness were underrepresented, while equity was absent across the studies, indicating a clear need for further research. These findings emphasize the importance of adopting a more thorough approach to evaluating and improving the quality of emergency care. Future research should also concentrate on refining data management practices, incorporating observational studies, and exploring various simulation tools to develop a more balanced and inclusive understanding of these models' applications.

Emergency department (ED) handoff to inpatient teams is a potential source of error. Generative artificial intelligence (AI) has shown promise in succinctly summarizing large quantities of clinical data and may help improve ED handoff.Our objectives were to: (1) evaluate the accuracy, clinical utility, and safety of AI-generated ED-to-inpatient handoff summaries; (2) identify patient and visit characteristics influencing summary effectiveness; and (3) characterize potential error patterns to inform implementation strategies.This exploratory study evaluated AI-generated handoff summaries at an urban academic ED (February-April 2024). A Health Insurance Portability and Accountability Act-compliant GPT-4 model generated summaries aligned with the IPASS framework; ED providers assessed summary accuracy, usefulness, and safety through on-shift surveys.Among 50 cases, median quality and usefulness scores were 4/5 (standard error = 0.13). Safety concerns arose in 6% of cases, with issues including data omissions and mischaracterizations. Consultation status significantly affected usefulness scores (p < 0.05). Omissions of relevant medications, laboratory results, and other essential details were noted (n = 6), and emergency medicine clinicians disagreed with some AI characterizations of patient stability, vitals, and workup (n = 8). The most common response was positive impressions of the technology incorporated into the handoff process (n = 11).This exploratory provider-in-the-loop model demonstrated clinical acceptability and highlighted areas for refinement. Future studies should incorporate recipient perspectives and examine clinical outcomes to scale and optimize AI implementation.

Electronic health record (EHR) patient portal messaging has become an essential tool for patient-clinician communication by improving accessibility to primary care. While messaging is beneficial for patients, it can increase clinicians' workloads. Female clinicians receive a greater number of EHR messaging, resulting in an increased workload.This evaluation explores the factors in clinician gender disparity in EHR messaging burden.The first phase of the evaluation included a retrospective analysis of the messages to 267 primary care clinicians in the University of Vermont Health Network (UVMHN). The second phase analyzed patient demographics and panel complexity. Statistical analysis was performed across all categories of patient care-generated messages to primary care clinicians and subsequently on all messages across the UVMHN.Female clinicians received significantly more patient-initiated medical advice request messages than their male counterparts (68.28 vs. 49.22 messages/month, p = 0.005) and spent more time managing messages (1.85 vs. 1.35 minute/day, p = 0.006). Despite this increased workload, response times remained similar between genders. Female clinicians have a higher proportion of female patients, and analysis of all messages sent across the organization demonstrated that female patient care produces more messages than male patient care (59 vs. 52 messages/female vs. male, p = 0.001). Panels size and complexity were similar for both male and female providers.These findings highlight an unequal messaging burden for female clinicians in primary care specialties of internal and family medicine, largely due to patient demographics. Patient panel complexity as defined by UVMHN and clinician full-time equivalent were similar between genders. Disparities in message volumes appear to be driven primarily by patient communication behavior differences between genders rather than differences in workload allocation. These findings likely contribute to increased burnout risk among female clinicians. Addressing this imbalance through workflow optimization and artificial intelligence-driven message triage systems may help to mitigate the burden on female clinicians and promote greater equity in primary care.

Ensuring clinician safety in health care settings is critical, particularly regarding exposure to hazardous drugs and bodily fluids, which can be carcinogenic, teratogenic, genotoxic, or cause organ toxicity at low doses. At SickKids a safety issue arose when a clinician was unknowingly exposed to hazardous bodily fluids due to inadequate communication of a patient's hazardous medication status.This clinical decision support (CDS) redesign aimed to reduce alert fatigue while ensuring timely team awareness to minimize hazardous bodily fluid exposure risk. This case study aims to explore how redesigning a CDS system addressed the dual challenge of maintaining safety communication while minimizing alert fatigue and improving workflow integration.In 2018, a biohazardous bodily fluids alert was introduced within the hospital's electronic patient record (EPR) to raise awareness. However, its frequent and disruptive nature resulted in a 0% alert action rate and 89 unactionable clinician hours over a 90-day period. Feedback collected over 42 months revealed clinician frustration and desensitization due to the alert's timing and frequency. Using a human-centered design approach, the alert was redesigned from an interruptive pop-up to a passive notification embedded within the patient's storyboard.The redesigned alert allowed clinicians to review hazardous status information without immediate interruptions, reducing workflow disruption while maintaining its critical safety function. This approach effectively balanced safety communication with clinicians' need for efficient workflows, addressing the root cause of alert fatigue.This case study highlights the importance of ongoing CDS evaluation and redesign to enhance clinician safety, minimize alert fatigue, and improve workflow integration. Future evaluations will assess the redesign's effect on personal protective equipment compliance and clinician burnout.

Hypertension is a significant contributor to cardiovascular disease, yet evidence-based blood pressure (BP) control practices are inconsistently applied. The Collaboration Oriented Approach to Controlling High Blood Pressure (COACH) is a digital clinical decision support tool designed to improve BP self-management and support clinician workflows. While the patient perspective on COACH has been evaluated in a separate study, this study evaluates organizational readiness for COACH implementation across three health systems using the Consolidated Framework for Implementation Research (CFIR).This study aimed to assess preimplementation facilitators and barriers for COACH, focusing on organizational readiness and modifiable factors influencing scalability.Qualitative interviews were conducted with 72 care team members from nine primary care clinics across three health systems using Epic or Oracle electronic health records. Data were analyzed using CFIR domains: innovation, inner setting, outer setting, individuals, and implementation process. Subdomains were rated from -2 (barrier) to +2 (facilitator).Overall, 79% of CFIR domain scores were positive, suggesting strong readiness for COACH implementation. The innovation domain scored 80% positive, highlighting COACH's user-friendly design, robust evidence base, and perceived advantages over current workflows. The inner setting domain showed 85% positive scores, driven by strong leadership, established infrastructures for patient-centered care, and high motivation for quality improvement. The outer setting domain scored 70% positive, reflecting barriers such as reimbursement policies, resource limitations, and staffing shortages. Participants noted the importance of continued leadership engagement, team-based support, and addressing workload challenges for sustainable implementation.The study demonstrates high organizational readiness for COACH, with critical barriers in reimbursement and resources that must be addressed for successful adoption. Findings underscore COACH's potential to enhance clinical decision-making and patient engagement. Future research should explore long-term impacts on care delivery and outcomes, informing broader adoption of digital health interventions in clinical practice.

The Health Level 7 (HL7) Electronic Health Record Workgroup identified home medication list reconciliation as a prime opportunity to improve patient safety and reduce clinician burden. We developed a platform-neutral, Fast Healthcare Interoperability Resources (FHIR)-enabled reference model and demonstration wireframe to articulate the concepts of an interoperable, patient-centric home medication list management ecosystem.Four principal artifacts describe the reference model: (1) a conceptual (high-level) model, (2) a data architecture (detailed) model including representations of the interactions among actors, workflows, data, and functionality, (3) a functionality (style) guide describing expected system behaviors, and (4) a high-fidelity, end-to-end wireframe. The wireframe was constructed using JavaScript, Bootstrap Studio, and FHIR to maximize code modularity, device compatibility, and interoperability.The conceptual and architecture models capture the complex interplay of actors and data occurring among healthcare providers, information systems, and patients, positioning the patient at the center of home medication list management. The style guide reflects functionality requirements. The wireframe demonstrates the use of FHIR for data interoperability while representing patient and clinician interactions that reduce burden. The wireframe accesses standardized data elements via FHIR calls to an EHR sandbox and integrates RxNorm content to improve usability and associated medication metadata. Finally, the wireframe generates a FHIR patient-reconciled medication list data package and printable lists that can be shared with the clinician to facilitate outpatient medication reconciliation.This proof-of-concept highlights the potential of FHIR to facilitate patient-facing medication list management and provides a reference framework for developers.