Applied Clinical Informatics最新文献

In recent years, there has been an expansion in the literature on the effects of computerized alerts on prescribing and patient outcomes. The aim of our study was to examine the impact of these systems on clinician prescribing and patient outcomes.We searched three databases (Medline, Embase, and PsychINFO) for studies that had been conducted since 2009 and included studies that examined the effects of alerts at the point of prescribing. We extracted data from 69 studies.Most studies reported a beneficial effect on prescribing of computerized alerts (n = 58, 84.1%), including all studies (n = 4) that used passive alerts. Seven of the 10 studies that reported on patient outcomes showed a beneficial effect. Both randomized controlled trials (RCTs) and non-RCTS showed beneficial effects on prescribing across a range of different types of alerts. In 43 studies, it was possible to ascertain the effects of different types of alerts; the interventions that were most frequently associated with improvements in prescribing were drug-laboratory alerts (9/11; 81.8%); dose range checking (6/7; 85.7%); formulary alerts (8/9; 88.9%), and drug-allergy alerts (4/4; 100%). However, most of the studies did not satisfy the quality criteria.Most of the studies found a beneficial effect of computerized alerts on prescribing. We have also shown that these benefits are apparent for a range of different types of alerts. These findings support the continued development, implementation, and evaluation of computerized alerts for prescribing.

The timely administration of postoperative antibiotics is crucial for preventing surgical site infections. Despite surgical ordering workflows designed to facilitate care across settings, delays in antibiotic administration posttransfer to the pediatric intensive care unit (PICU) were identified. We aimed to develop a clinical decision support (CDS) system to enhance timely order activation in a large pediatric health system. We hypothesized that the time to release signed and held orders by PICU nurses would decrease after implementation of an electronic health record alert, ultimately reducing time to antibiotic administration.This study aimed to describe the CDS design for the timely release of postoperative orders, evaluate its effectiveness, and share lessons learned from its implementation.Stakeholder interviews and a staged implementation approach were employed to develop and implement the CDS in one of the two PICUs. An interruptive alert was designed to prompt nurses to release specific signed and held orders. The study period spanned from January 2019 to August 2024, with pre- and postintervention comparisons of the mean time to release medication orders.The alert was used from May to December 2021, but was associated with increased time to release orders. Postintervention usability testing revealed confusion among nurses, leading to the alert's discontinuation. A post hoc analysis suggested that the observed delays might align with seasonal trends rather than the CDS intervention.The CDS implementation had unintended adverse effects on order release times, emphasizing the importance of monitoring and evaluating such systems postimplementation. Usability testing highlighted the complexity of the alert messaging and the importance of including end-users in the design phase. Extended evaluation periods are recommended to discern CDS impact accurately. The study also underscores the necessity of assessing whether a technological or workflow/process change is needed in response to safety reports.

Sleep quality critically influences recovery in neurological patients, yet its longitudinal monitoring during hospitalization remains limited. Nursing narrative notes offer an underutilized resource to track sleep trajectories objectively across time.To propose and apply a formal pipeline that integrates structured clinical data and unstructured nursing annotations to monitor sleep trajectories during post-acute inpatient neurorehabilitation, relying exclusively on free-to-use software tools and without increasing nursing workload.A total of 17,039 nighttime nursing annotations were extracted and categorized into four sleep quality states. Two expert raters manually labeled a training set of 2,000 annotations (κ = 0.84). A random forest classifier achieved 0.93 sensitivity and 0.94 specificity and was used to classify the remaining notes. Sleep sequences were constructed and clustered using sequence analysis (TraMineR) and hierarchical clustering (AGNES, Ward's method). The obtained clusters (silhouette = 0.40) were compared using non-parametric statistics across clinical, functional, and social variables in a cohort of 303 post-acute consecutive neurorehabilitation inpatients.Four distinct sleep trajectory clusters were identified, each characterized by unique functional and socio-environmental profiles. The first group (n = 102; 33.7%) combined high functional independence, strong social support, stable economy, short hospitalization, and favorable sleep quality. The second group (n = 76; 25.1%) presented moderate functional independence, precarious economic conditions, and the highest proportion of poor sleep quality. The third group (n = 76; 25.1%) exhibited severe functional impairment, long hospitalization, poor housing conditions, but paradoxically the highest proportion of good sleep quality. The fourth group (n = 49; 16.2%) showed profound disability, relatively favorable socio-economic conditions, and predominance of intermediate sleep quality, likely influenced by medication. Distinctive sets of social and functional keywords emerged for each cluster.This pipeline identified clinically meaningful sleep profiles from nursing notes, highlighting functional and social determinants' role in shaping neurorehabilitation sleep trajectories.

Health informatics continues to be a continuously evolving discipline. As a result, faculty in health informatics training programs cover a broad range of topics and work in highly diverse academic contexts. This is a strength of the field, and also introduces challenges in understanding faculty salary ranges and assessing potential salary disparities across contexts. Although limited studies have been done on salary ranges in specific academic contexts, prior to this, no comprehensive salary survey had been performed on faculty in health informatics.The goal of this study was to obtain a preliminary understanding of the salary ranges for academic health informatics faculty and contextual factors that affect salary ranges in this field.A team of researchers affiliated with the American Medical Informatics Association (AMIA) Academic Forum collaboratively developed a survey focused on salary and factors that affect salary for health informatics faculty. The survey was distributed through official AMIA communication channels, including communications at the 2023 AMIA Symposium. Descriptive statistics were calculated, and an ordinal regression analysis was performed.Of 314 responses, 153 individuals employed by academic organizations reported their base salary information. A majority (61%) of these respondents reported working in a school of medicine, with PhD (59%) and MD (37%) degrees reported as the highest educational level for the majority of the sample. When adjusted for cost of living, there were statistically significant associations between salary and type of school/department, position/title, and highest degree. We also found that while salaries at the assistant professor level were between $120,000 and 159,999, those of associate and full professors were at or above $200,000.The survey provided preliminary baseline data on salary ranges in academic health informatics programs and factors leading to salary differences. More data are needed on focused topics to extend the impact of this type of survey.

Gather insights regarding the state of third-party access cybersecurity in healthcare delivery organizations (HDOs).An online multinational survey was deployed to eligible respondents to assess HDO third-party access, cybersecurity, and challenges.Of 209 respondents, only 51.1% reported having a comprehensive inventory of all third parties accessing their network. Sixty percent stated third-party access to sensitive/confidential information was not routinely monitored, despite 19% having more than 40, and 31% having 21 to 40 third parties with network access. Reasons included lack of resources (48%) and centralized control over third-party relationships (36%), complexity (28%), and frequent third-party turnover (22%). Confidence in third-party ability to secure information and their reputations was cited. More than half (56%) reported a breach involving a third party in the last 12 months, and two-thirds anticipate breaches increasing in the next 12 to 24 months. Most agreed breaches are a cybersecurity priority, a resource drain, and their weakest attack surface. Slight majorities indicated high perceived effectiveness in mitigating, detecting, preventing, and controlling third-party access risks and security/privacy regulatory compliance. Regarding existing solutions, roughly half (55%) ranked the effectiveness of vendor privileged access management (VPAM) and privileged access management (PAM; 49%) at ≤ 6 on a 10-point scale, respectively. Barriers to reducing access risks include lack of oversight/governance (53%) and insufficient resources (45%). Of those monitoring third-party access, 53% do so manually. Breach consequences include loss/theft of sensitive information (60%), regulatory fines (49%), severed relationships with third parties (47%), and loss of revenue (42%) and business partners (38%).HDOs recognize the increasing threat of third-party cyber breaches but are struggling to effectively address them. Lack of budget, expert resources, complexity, and third-party turnover are among the reasons why. Need exists for automated, cost-effective solutions to address the significant risks of third-party access with a consistent strategy that minimizes breach risk by securing remote access to privileged assets, accounts, and data.

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.

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.

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.

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.