Applied Clinical Informatics最新文献

Electronic patient-reported outcome measures (ePROMs) can improve care for people with cancer, but effectiveness hinges on well-supported integration in clinical settings.We evaluated clinician use of specific clinical decision support (CDS) tools in the electronic health record (EHR) designed to facilitate timely, clinically appropriate responses to ePROM scores for six symptoms commonly experienced by cancer patients.The parent pragmatic trial, which took place at Mayo Clinic (Rochester, Minnesota, United States) and its affiliated community health care system between March 2019 and January 2023, evaluated the population-level effectiveness and implementation of an ePROM surveillance and EHR-facilitated collaborative care symptom management intervention. The present evaluation used a case study approach with four data sources: (1) clinician interactions with CDS tools abstracted from the EHR; (2) clinician notes identified with an institution-specific textual search tool; (3) qualitative interviews and group discussions with care teams; and (4) administrative records reviewed to identify training and outreach to care teams.EHR metrics showed very low adoption of CDS tools including alerts and symptom-specific order sets, despite educational outreach and information technology support provided to clinical care teams. Qualitative findings revealed that CDS use was not easy to integrate into busy clinical workflows and highlighted clinician perceptions that the collaborative care intervention provided additional patient support that reduced clinicians' need to utilize CDS tools. They also highlight the importance of contextual factors, including institutional priorities and EHR changes.This pragmatic clinical trial case study found limited adoption of EHR CDS tools that had been developed to increase clinicians' awareness of and responses to ePROM data. Findings suggest the need to align clinician and organizational implementation strategies, simplify CDS tools to fit practice expectations, and identify and address contextual factors that could undercut strategies like education and peer support. This may be especially important for teams who aim to iteratively evaluate and refine CDS and implementation strategies for multicomponent interventions or introduce new strategies that are responsive to barriers while maintaining scalability.

This study aims to evaluate the impact of using a large language model (LLM) for generating draft responses to patient messages in the electronic health record (EHR) system on clinicians and support staff workload and efficiency.We partnered with Epic Systems to implement OpenAI's ChatGPT 4.0 for responding to patient messages. A pilot study was conducted from August 2023 to July 2024 across 13 ambulatory specialties involving 323 participants, including clinicians and support staff. Data on draft utilization rates and message response times were collected and analyzed using statistical methods.The overall mean generated draft utilization rate was 38%, with significant differences by role and specialty. Clinicians had a higher utilization rate (43%) than scheduling staff (33%). Draft message usage significantly reduced all users' message response time (13 seconds on average). Support staff experienced a more substantial and statistically significant time saving (23 seconds) compared to negligible time savings seen by clinicians (3 seconds). Variability in utilization rates and time savings was observed across different specialties.Implementing LLMs for drafting patient message replies can reduce response times and alleviate message burden. However, the effectiveness of artificial intelligence (AI)-generated draft responses varies by clinical role and specialty, indicating the need for tailored implementations. Further investigation into this variability, and development and personalization of AI tools are recommended to maximize their utility and ensure safe and effective use in diverse clinical contexts.

The current study examined the moderating effect of self-control on the relationship between attitudes toward cybersecurity and risky online behaviors among direct care nurses.A cross-sectional study collected data from 260 direct care nurses in Saudi Arabia using a self-reported questionnaire. Hierarchical multiple regression analysis and simple slope analysis examined the moderation effect of self-control on the relationship between attitudes toward cybersecurity and risky online behaviors.We found that a better attitude toward cybersecurity and greater self-control predicted lower risky online behaviors. Simple slope tests revealed a significant negative association between attitude toward cybersecurity and risky online behaviors at low levels of self-control, but this association disappears at high levels of self-control, meaning that high levels of self-control have a protective/moderating effect on the relationship between attitude toward cybersecurity and risky online behaviors.Self-control moderates the effect of attitudes on online practices. The negative attitudes' influence on risky online behaviors is stronger when self-control is low. On the other hand, at high levels of self-control, individuals may engage in safer practices regardless of their attitudes.

Aging populations strain health care systems. Assisted Living Technologies (ALTs) emerge as a potential solution for promoting independent living among older adults. However, the real-world effect of ALTs remains unclear.This study explores benefits and challenges (anticipated and unanticipated) of ALTs for older adults and informal caregivers across three aged care settings (residential aged care facilities [RACFs], retirement villages [RVs], and home-dwelling communities [HDCs]) in Australia.Three ALTs (fall detection sensors, sleep monitors, and smartwatches) were deployed across three settings. NASSS framework (Non-adoption, Abandonment, Scale-up, Spread, and Sustainability), informed by sociotechnical theories, guided analysis of the interplay between technology, user needs, and caregiving context in ALTs implementation. Semistructured interviews with 14 older adults and 9 caregivers from 19 households explored user experiences. Benefits and challenges of using ALTs for older adults and informal caregivers were categorized using the consequences framework.Setting-specific challenges alongside common benefits and challenges across care settings were revealed. The NASSS framework analysis showed how technology limitations, user needs, and caregiving context influenced these outcomes. In RACFs, where residents receive constant nursing assistance, informal caregivers faced uncertainty regarding who was responsible for monitoring residents. In RVs, with a strong sense of community, informal caregivers (often neighbors) were more prone to overreacting to false alarms. Shared sleeping arrangements among HDCs made interpreting sleep data challenging.Implementing ALTs in elderly care settings requires a context-sensitive approach. In RACFs, clear role definitions for informal caregivers and staff are essential. For RVs, design should support help-seeking aligned with residents' social and geographical contexts. Home-dwelling settings may benefit from advanced sleep monitoring tailored to shared living arrangements. Future ALTs development should focus on real-world contexts to promote successful aging in place.

This study aims to improve concordance between patient end-of-life preferences and code status orders by incorporating data from a state registry with clinical decision support (CDS) within the electronic health record (EHR) to preserve patient autonomy and ensure that patients receive care that aligns with their wishes.Leveraging a health information exchange (HIE) interface between the New York State Medical Orders for Life-Sustaining Treatment (eMOLST) registry and the EHR of our academic health system, we developed a bundled CDS intervention that displays eMOLST information at the time of code status ordering and provides an in-line alert when providers enter a resuscitation order discordant with wishes documented in the eMOLST registry. To evaluate this intervention, we performed a segmented regression analysis of an interrupted time series to compare the percentage of discordant orders before and after implementation among all hospitalizations for which an eMOLST was available.We identified a total of 3,648 visits that had an eMOLST filed prior to inpatient admission and a code status order placed during admission. There was a statistically significant decrease of discordant resuscitation orders of -5.95% after the intervention went live, with a relative risk reduction of 25% (95% CI: -9.95%, -1.94%; p = 0.009) in the pre- and post-intervention period. Logistic regression model after adjusting for covariates showed an average marginal effect of -5.12% after the intervention (CI: -9.75%, -0.50%; p = 0.03).Our intervention resulted in a decrease in discordant resuscitation orders. This study demonstrates that accessibility to eMOLST data within the provider workflow supported by CDS can reduce discrepancies between patient end-of-life wishes and hospital code status orders.

Online healthcare portals provide access to electronic health information and support clinical communication. Almost no studies have examined perspectives on parental portal access. We aimed to characterize parental and adolescent perspectives on parental portal access.Semi-structured interviews with 51 dyads of parents and adolescents (102 total interviews). We stratified sampling for equal proportions of adolescents with and without chronic illnesses. We analyzed interview transcripts using thematic analysis.Parents and adolescents identified several benefits of parental portal access: improving understanding and access to information; supporting parents in managing adolescents' health and logistics; and supporting parents in teaching adolescents about their health. Parents and adolescents identified the following problems: threatening the adolescent's privacy; creating or exacerbating tension within the family; struggling to understand medical information; and creating emotional distress for parents. Parents described the following barriers to portal use: difficulties with enrollment and maintaining access; interface challenges; lack of awareness; and lack of interest. Some parents preferred to maintain access after their child was legally an adult. Although the portal has the potential to support collaborative care management between parents and adolescents, few parents use this tool collaboratively with their adolescents. Parents and adolescents identified multiple benefits, problems, and barriers to parents accessing the adolescent portal. Parents need sufficient access to health-related information in the portal to help them manage their adolescent's health and illness, especially for adolescents with chronic illness. Future efforts could better leverage the portal as a way of supporting collaboration in care between parents and adolescents.Portals offer several potential benefits to parents and adolescents. However, these benefits are impeded by technological limitations and lack of engagement of the adolescent.

Improving early recognition and treatment of sepsis is key to decreasing patient mortality. A large academic health system implemented several quality improvement initiatives, yet monthly compliance with sepsis best practices remained low.Develop and evaluate an electronic health record (EHR)-embedded microlearning intervention to address suboptimal adherence to sepsis care best practices.We conducted a randomized stepped-wedge trial of our microlearning intervention with randomization done at the nursing block level. Antibiotic delay and secondary outcomes extracted from the EHR were analyzed using mixed models to account for intracluster correlation.The microlearning intervention did not reduce antibiotic delay (mean difference = 0.71 hours; p = 0.49). Despite the alert firing over 30,000 times during the study period, the microlearning intervention was viewed only a total of 30 times.Our microlearning intervention did not improve sepsis care outcomes. We believe that although the content addressed key knowledge gaps, delivering the intervention through disruptive EHR alerts was not an accessible delivery channel to the nursing staff we targeted.

Ambient artificial intelligence scribes have become widespread commercial products in the era of generative artificial intelligence. While studies have examined the effect of these tools on the experience of attending physicians, little evidence is available regarding their use by resident physician trainees.To assess trainee experience with an ambient artificial intelligence scribe using measures of usability, acceptability, and documentation burden.This prospective observational study enrolled 47 trainees in a 2-month pilot. Pre/postsurveys were conducted with the NASA Task Load Index (NASA-TLX, raw unweighted form, pre/post, for cognitive load during the documentation), the System Usability Scale (post; general usability), the Net Promoter Score (post; acceptability), and the AMIA TrendBurden Survey (pre/post; documentation burden). Electronic health record utilization metrics were obtained from Epic Signal for both the pilot period and a 6-month baseline.In total, 43/47 (91.5%) of participants adopted the intervention in practice. NASA-TLX scores improved from 56.3 to 43.3 (p < 0.001), and multiple items on the TrendBurden survey improved with high measures of acceptability. No significant difference in time spent on notes activity per note written was observed, with a median increase of 0.4 minutes (p = 0.568).Trainee use of an ambient artificial intelligence scribe was associated with improvements in documentation burden. Additional research on the effect of this technology on trainee learning and expertise development is needed.

Uncontrolled hypertension is common and frequently related to inadequate adherence to prescribed medications, resulting in suboptimal blood pressure control and increased healthcare utilization. Although healthcare providers have the opportunity to improve medication adherence, they may lack the tools to address adherence at the point of care. This study aims to assess the usability of a digital tool designed to improve medication adherence and blood pressure control among patients with hypertension who are not adherent to therapy. By evaluating usability, the study seeks to refine the tool's design, underscore the role of technology in managing hypertension, and provide insights to inform clinical decisions.We performed qualitative usability testing of an electronic health record (EHR)-integrated intervention with medical assistants (MAs) and primary care providers (PCPs) from a large integrated health system. Usability was assessed with these end-users using the "think aloud" and "near live" approaches. This evaluation was guided by two frameworks: the End-User Computing Satisfaction Index (EUCSI) and the Technology Acceptance Model (TAM). Interviews were analyzed using a thematic analysis approach.Thematic saturation was reached after usability testing was performed with 10 participants, comprising 5 PCPs and 5 MAs. The study identified several strengths within the content, format, ease of use, timeliness, accuracy, and usefulness of the tool, including the user-friendly content presentation, the usefulness of adherence information, and timely alerts that fit into the workflow. Challenges centered around alert visibility and specificity of information.Leveraging the two conceptual frameworks (TAM and EUCSI) to test the usability of the medication adherence tool was helpful. The tool's several strengths and opportunities for improvement were found. The resulting suggestions will be used to support the enhancement of the design for optimal implementation in a clinical trial.

Central line-associated bloodstream infections (CLABSIs) are associated with substantial pediatric morbidity and mortality. The capacity to predict which children with central lines are at greatest risk of CLABSI could inform surveillance and prevention efforts. Our team previously published in silico predictive models for CLABSI.To prospectively implement a pediatric CLABSI predictive model and achieve adequate performance in offline validation for implementation in clinical practice.Most performant predictive models were deep learning models requiring substantial pre-processing of many features into 8-hour windows including the current day and up to 56 days prior for the current admission. To replicate this pre-processing, we created a novel infrastructure to (1) organize current-day data for all the relevant features and (2) create a staged historical data store for those same features with application programming interfaces to connect the two. We compared predictive performance of these scores for CLABSI in the next 48 hours with two labels, one based on manual review of positive blood cultures in children with central lines and another based on positive blood culture and receipt of at least 4 days of new IV antibiotics.The area under the receiver-operating characteristic (AUROC) fell from 0.97 from retrospective data to <0.60 despite multiple iterations of troubleshooting. Primary root causes included train/serve skew, feature leakage, and overfitting. Hypothesized secondary drivers were complex model specification, poor data governance, inadequate testing, challenging feature translation between real-time and historical data models, limited monitoring and logging infrastructure for troubleshooting, and suboptimal handoff between the model development and deployment teams.Bridging the gap from predictive model development to clinical deployment requires early and close coordination between data governance, data science, clinical informatics, and implementation engineers. Balancing predictive performance with implementation feasibility can accelerate the adoption of predictive clinical decision support systems.