BMJ Health & Care Informatics最新文献

{"title":"Leveraging real-world data for continuous evaluation of computational clinical practice guidelines.","authors":"Kees C W J Ebben, Cornelis D de Kroon, Channa E Schmeink, Olga L van der Hel, Thijs van Vegchel, Michèle Thissen, Ignace H J T de Hingh, Jurrian van der Werf","doi":"10.1136/bmjhci-2024-101333","DOIUrl":"10.1136/bmjhci-2024-101333","url":null,"abstract":"<p><strong>Objectives: </strong>There is a bidirectional interaction between clinical practice guidelines and clinical care, with each informing the other. Structural signalling of trends in guideline adherence in clinical practice is essential for advanced updates. Recent advances in computable care guidelines allow automated evaluation using real-world registry data. Here, we assess the feasibility by evaluating adherence to Dutch endometrial cancer (EC) guidelines.</p><p><strong>Methods: </strong>This retrospective cohort study uses real-world data of EC patients from the Netherlands Cancer Registry (NCR) between January 2010 and May 2022. The Dutch guideline for EC was parsed into clinical decision trees (CDTs). Primary outcome was guideline adherence for multiple (sub)populations, with secondary outcomes encompassing adherence trends, recommendation implementation pace, non-adherent treatment strategies and impact of additional non-guideline-based patient and tumour characteristics on adherence.</p><p><strong>Results: </strong>The Dutch EC guideline was parsed into 10 CDTs, revealing 22 patient and disease characteristics and 46 interventions. NCR data were mapped to CDT data items. Four CDTs were successfully populated with NCR data, and 21 602 cases were assessed. Adherence levels were computed, which showed a mean adherence of 82.7% (range 44-100%). Three statistically significant trends in adherence were identified: two increasing trends in the 'non-adherent' compared with the 'adherent' group, and one decreasing trend.</p><p><strong>Discussion: </strong>This study introduces a novel framework for continuously evaluating (non-)adherence to cancer guidelines. Future efforts should focus on the inclusion of health outcome measurements.</p><p><strong>Conclusion: </strong>Through the integration of real-world data with a computer-interpretable guideline, we effectively calculated various facets of adherence to guidelines for EC.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12083447/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143958384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital innovation in healthcare: quantifying the impact of digital sepsis screening tools on patient outcomes-a multi-site natural experiment.","authors":"Kate Honeyford, Alf Timney, Runa Lazzarino, John Welch, Andrew Jonathan Brent, Anne Kinderlerer, Peter Ghazal, Anthony C Gordon, Shashank Patil, Graham Cooke, Ceire E Costelloe","doi":"10.1136/bmjhci-2024-101141","DOIUrl":"https://doi.org/10.1136/bmjhci-2024-101141","url":null,"abstract":"<p><strong>Introduction: </strong>The National Health Service (NHS) 'move to digital' incorporating electronic patient record systems (EPR) facilitates the translation of paper-based screening tools into digital systems, including digital sepsis alerts. We evaluated the impact of sepsis screening tools on in-patient 30-day mortality across four multi-hospital NHS Trusts, each using a different algorithm for early detection of sepsis.</p><p><strong>Methods: </strong>Using quasi-experimental methods, we investigated the impact of the screening tools. Individual-level EPR data for 718 000 patients between 2010 and 2020 were extracted to assess the impact on a target cohort and control cohort using interrupted time series analysis, based on a binomial regression model. We included one Trust which uses a paper-based screening tool to compare the impact of digital and paper-based interventions, and one Trust which did not introduce a sepsis screening tool, but did introduce an EPR.</p><p><strong>Results: </strong>All Trusts had lower odds of mortality, between 5% and 12%, after the introduction of the sepsis screening tool, before adjustment for pre-existing trends or patient casemix. After adjustment for existing trends, there was a significant reduction in mortality in two of the three Trusts which introduced sepsis screening tools. We also observed age-specific effects across Trusts.</p><p><strong>Conclusion: </strong>Our findings confirm that patients with similar profiles have a lower mortality risk, consistent with our previous work. This study, conducted across multiple NHS Trusts, suggests that alerts could be tailored to specific patient groups based on age-related effects. Different Trusts may require unique indicators, thresholds, actions and treatments. Including additional EPR information could further enhance personalised care.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12035476/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143958381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Potential for near-term AI risks to evolve into existential threats in healthcare.","authors":"Vallijah Subasri, Negin Baghbanzadeh, Leo Anthony Celi, Laleh Seyyed-Kalantari","doi":"10.1136/bmjhci-2024-101130","DOIUrl":"https://doi.org/10.1136/bmjhci-2024-101130","url":null,"abstract":"<p><p>The recent emergence of foundation model-based chatbots, such as ChatGPT (OpenAI, San Francisco, CA, USA), has showcased remarkable language mastery and intuitive comprehension capabilities. Despite significant efforts to identify and address the near-term risks associated with artificial intelligence (AI), our understanding of the existential threats they pose remains limited. Near-term risks stem from AI that already exist or are under active development with a clear trajectory towards deployment. Existential risks of AI can be an extension of the near-term risks studied by the fairness, accountability, transparency and ethics community, and are characterised by a potential to threaten humanity's long-term potential. In this paper, we delve into the ways AI can give rise to existential harm and explore potential risk mitigation strategies. This involves further investigation of critical domains, including AI alignment, overtrust in AI, AI safety, open-sourcing, the implications of AI to healthcare and the broader societal risks.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12035420/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143959908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Designing an interoperable solution to support pharmacogenomic-guided prescribing in primary care: an implementer report.","authors":"Videha Sharma, John McDermott, Jessica Keen, Ben McIntyre, Scott Watson, William Newman","doi":"10.1136/bmjhci-2024-101163","DOIUrl":"10.1136/bmjhci-2024-101163","url":null,"abstract":"<p><strong>Study objectives: </strong>Describe the implementation of an interoperable solution to support pharmacogenomic-guided prescribing in primary care in the National Health Service, England.</p><p><strong>Methods: </strong>We used an iterative approach to software development going through clinical workflow mapping, architecture design and development, and pilot-testing.</p><p><strong>Results: </strong>We configured a commercial health data management platform to store pharmacogenomic results in a structured format and created a knowledge base of pharmacogenomic guidance. This solution was deployed 'as-a-service' using an open application programming interface (API) specification, allowing third parties to receive pharmacogenomic results and guidance by querying the service using a patient identifier and medicine code. This was integrated with existing clinical decision support tools and presented contextual information to prescribers within their native electronic health record (EHR).</p><p><strong>Discussion: </strong>Pharmacogenomic results and guidance will be used across care settings and have greatest utility at the point of prescribing. This requires a solution, which separates the data from the applications, allowing integration with different EHRs through APIs.</p><p><strong>Conclusions: </strong>A vendor-agnostic standards-based solution can support the implementation of pharmacogenomic-guided prescribing across primary care.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12082653/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143954017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prospective external validation of the automated PIPRA multivariable prediction model for postoperative delirium on real-world data from a consecutive cohort of non-cardiac surgery inpatients.","authors":"Kelly A Reeve, Nayeli Schmutz Gelsomino, Michela Venturini, Felix Buddeberg, Martin Zozman, Reto Stocker, Mary-Anne Kedda, Philipp Meier, Marius Möller, Simone Pascale Wildhaber, Benjamin T Dodsworth","doi":"10.1136/bmjhci-2024-101291","DOIUrl":"https://doi.org/10.1136/bmjhci-2024-101291","url":null,"abstract":"<p><strong>Objectives: </strong>Postoperative delirium (POD) is a common complication in surgical patients over 60, increasing morbidity, mortality and hospital stays. While international guidelines recommend risk screening, resource constraints limit implementation. This study externally validated the Pre-Interventional Preventive Risk Assessment (PIPRA) algorithm, a CE-certified tool for identifying high-risk patients to enable targeted prevention.</p><p><strong>Methods: </strong>A prospective validation study was conducted at a 335-bed Swiss hospital as part of a quality improvement initiative. Data from 866 patients aged ≥60 undergoing non-cardiac, non-intracranial surgery (May-June 2023) were analysed. The PIPRA model's performance was assessed on discrimination (Area Under the Receiver Operating Characteristic Curve (AUROC)) and calibration.</p><p><strong>Results: </strong>POD occurred in 11.5% (n=100) of patients. The PIPRA model showed good discrimination (AUROC=0.77, 95% CI: 0.72 to 0.82) and generally accurate calibration, though slightly overpredicting risk in high-risk patients. POD was associated with higher mortality, prolonged intensive care unit (ICU)/hospital stays and increased nursing care needs. The model effectively stratified patients for targeted interventions.</p><p><strong>Discussion: </strong>The PIPRA algorithm demonstrated robust performance in a real-world setting, affirming its utility for POD risk prediction. The study highlighted the model's applicability across diverse clinical environments, despite differences in patient populations and screening protocols.</p><p><strong>Conclusions: </strong>The PIPRA algorithm is a reliable tool for identifying surgical patients at risk of POD, supporting early intervention strategies to improve patient outcomes. Its integration into clinical workflows may enhance POD prevention efforts and optimise resource allocation in perioperative care.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11987130/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144061328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of an explainable prediction model for portal vein system thrombosis post-splenectomy in patients with cirrhosis.","authors":"Dou Qu, Duwei Dai, Guodong Li, Rui Zhou, Caixia Dong, Junxia Zhao, Lingbo An, Xiaojie Song, Jiazhen Zhu, Zong Fang Li","doi":"10.1136/bmjhci-2024-101319","DOIUrl":"https://doi.org/10.1136/bmjhci-2024-101319","url":null,"abstract":"<p><strong>Background: </strong>Portal vein system thrombosis (PVST) is a common and potentially life-threatening complication following splenectomy plus pericardial devascularisation (SPDV) in patients with cirrhosis and portal hypertension. Early prediction of PVST is critical for timely intervention. This study aimed to develop a machine learning-based prediction model for PVST occurrence within 3 months after splenectomy.</p><p><strong>Methods: </strong>392 patients with cirrhosis who underwent splenectomy at the Second Affiliated Hospital of Xi'an Jiaotong University between 1 July 2016 and 31 December 2022 were enrolled in this study and followed up for 3 months. The predictive model integrated 37 candidate predictors based on accessible clinical data, including demographic characteristics, disease features, imaging results, laboratory values, perioperative details and postoperative prophylactic therapies, and finally, eight predictors were selected for model construction. The five machine learning algorithms (logistic regression, Gaussian Naive Bayes, decision tree, random forest and AdaBoost) were employed to train the predictive models for assessing risks of PVST, which were validated using five fold cross-validation. Model discrimination and calibration were estimated using receiver operating characteristic curves(ROC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value and Brier scores. The outcome of the predictive model was interpreted using SHapley Additive exPlanations (SHAP), which provided insights into the factors influencing PVST risk prediction.</p><p><strong>Results: </strong>During the 3-month follow-up, a total of 144 (36.73%) patients developed PVST. The AdaBoost model demonstrated the highest discriminative ability, with a mean area under the receiver operating characteristic curve (AUROC) of 0.72 (95% CI 0.60 to 0.84). Important features for predicting PVST included albumin, platelet addition, the diameter of the portal vein, γ-glutamyl transferase, length of stay, activated partial thromboplastin time, D-dimer level and history of preoperative gastrointestinal bleeding, as revealed by SHAP analysis.</p><p><strong>Conclusions: </strong>The machine learning-based prediction models can provide an initial assessment of 3-month PVST risk after SPDV in patients with cirrhosis and portal hypertension. The AdaBoost model demonstrates moderate discriminative ability in distinguishing between high-risk and low-risk patients, with an AUROC of 0.72 (95% CI 0.60 to 0.84). By incorporating SHAP analysis, the model can offer transparent explanations for personalised risk predictions, facilitating targeted preventive interventions and reducing excessive interventions across the entire patient population.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11987112/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143969770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cracking the code: a scoping review to unite disciplines in tackling legal issues in health artificial intelligence.","authors":"Sophie Nunnelley, Colleen M Flood, Michael Da Silva, Tanya Horsley, Sarathy Kanathasan, Bryan Thomas, Emily Ann Da Silva, Valentina Ly, Ryan C Daniel, Mohsen Sheikh Hassani, Devin Singh","doi":"10.1136/bmjhci-2024-101112","DOIUrl":"https://doi.org/10.1136/bmjhci-2024-101112","url":null,"abstract":"<p><strong>Objectives: </strong>The rapid integration of artificial intelligence (AI) in healthcare requires robust legal safeguards to ensure safety, privacy and non-discrimination, crucial for maintaining trust. Yet, unaddressed differences in disciplinary perspectives and priorities risk impeding effective reform. This study uncovers convergences and divergences in disciplinary comprehension, prioritisation and proposed solutions to legal issues with health-AI, providing law and policymaking guidance.</p><p><strong>Methods: </strong>Employing a scoping review methodology, we searched MEDLINE (Ovid), EMBASE (Ovid), HeinOnline Law Journal Library, Index to Foreign Legal Periodicals (HeinOnline), Index to Legal Periodicals and Books (EBSCOhost), Web of Science (Core Collection), Scopus and IEEE Xplore, identifying legal issue discussions published, in English or French, from January 2012 to July 2021. Of 18 168 screened studies, 432 were included for data extraction and analysis. We mapped the legal concerns and solutions discussed by authors in medicine, law, nursing, pharmacy, other healthcare professions, public health, computer science and engineering, revealing where they agree and disagree in their understanding, prioritisation and response to legal concerns.</p><p><strong>Results: </strong>Critical disciplinary differences were evident in both the frequency and nature of discussions of legal issues and potential solutions. Notably, innovators in computer science and engineering exhibited minimal engagement with legal issues. Authors in law and medicine frequently contributed but prioritised different legal issues and proposed different solutions.</p><p><strong>Discussion and conclusion: </strong>Differing perspectives regarding law reform priorities and solutions jeopardise the progress of health AI development. We need inclusive, interdisciplinary dialogues concerning the risks and trade-offs associated with various solutions to ensure optimal law and policy reform.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11987151/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143972138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AIAIAI: AI insights on amassing influence in AI-related publications - an AI-assisted retrospective analysis into AI-related publication.","authors":"Rotem Lahat, Noa Berick, Majd Hajouj, Tali Teitelbaum, Isaac Shochat","doi":"10.1136/bmjhci-2024-101244","DOIUrl":"10.1136/bmjhci-2024-101244","url":null,"abstract":"<p><strong>Objectives: </strong>This study analyses the trend of artificial intelligence (AI)-related publications in the medical field over the past decade and demonstrates the potential of AI in automating data analysis. We hypothesise exponential growth in AI-related publications, with continuous growth in the foreseeable future.</p><p><strong>Methods: </strong>Retrospective, AI-assisted analysis was conducted using the OpenAI application programming interface for data collection and evaluation. Publications from the top 50 medical journals (Web of Science, Journal Citation Report, 2022) covering 2014 to June 2024. A total of 315 209 papers were initially retrieved with 212 620 remaining after filtering. The outcomes were the total number and percentage of AI-related publications per year, with future trends prediction using statistical models.</p><p><strong>Results: </strong>AI-related publications increased from approximately 500 in 2014 to over 1000 in 2022, with the percentage rising from 2.5% to over 6% in 2024. The analysis identified cardiology and oncology as leading in AI adoption. Predictive models forecast that AI-related publications could reach 10% by 2030 with long-term projections suggesting potential dominance of AI presence by the mid-22nd century.</p><p><strong>Discussion: </strong>The study highlights the significant growth and integration of AI in medical research, with cardiology and oncology at the forefront. AI-assisted data analysis proves efficient and scalable but requires human oversight to maintain credibility.</p><p><strong>Conclusions: </strong>The trajectory of AI-related publications indicates substantial growth and future integration across medical disciplines. Ongoing evaluation of AI's reliability and applicability in medical research remains essential.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11973779/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143787564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Value of a Nationwide University Network in scaling up telemonitoring: a qualitative study.","authors":"H J Harm Gijsbers, S Azam Nurmohamed, Linda W Dusseljee-Peute, Marlies P Schijven, Tom H van de Belt","doi":"10.1136/bmjhci-2024-101320","DOIUrl":"10.1136/bmjhci-2024-101320","url":null,"abstract":"<p><strong>Objectives: </strong>The adoption and subsequent implementation of telemonitoring across university hospital settings is a challenging task. This study provides insight into the perceived value of using a nationwide network to support scaling up telemonitoring in university hospitals.</p><p><strong>Methods: </strong>A qualitative approach was used to evaluate the role of the National eHealth network 'Citrien eHealth programme Implementation and Upscaling (Citrien-2)'. In phase 1, an inventory questionnaire was used to identify successes and lessons learnt. Phase 2 consisted of a semi-structured group interview to develop a deeper understanding about the potential value of the network. Subsequently, we conducted a qualitative content analysis and results were organised into key themes of the non-adoption, abandonment, scale-up, spread and sustainability framework.</p><p><strong>Results: </strong>In total, 20 participants responded to our questionnaire, and 7 participants participated in our semistructured group interview. Qualitative analysis revealed 28 themes. The network's key value is the collaboration and structured approach it promotes. This serves as a foundation for exchanging ideas, identifying both temporary and sustainable funding, and establishing a robust stakeholder position, all of which serve to act as a catalyst for implementation and scaling up of telemonitoring.</p><p><strong>Discussion: </strong>Our findings align with known barriers to digital innovation, such as funding and legal issues. Our study shows the value of a nationwide network in overcoming these barriers.</p><p><strong>Conclusions: </strong>The Citrien-2 nationwide network contributes to scaling up telemonitoring across university settings. Therefore, we recommend that governments and their funding agencies recognise and embrace the power of these nationwide networks in scaling up digital initiatives.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11967004/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143771354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Usability of an electronic health record 6 months post go-live and its association with burnout, insomnia and turnover intention: a cross-sectional study in a hospital setting.","authors":"Signe Lohmann-Lafrenz, Sigmund Østgård Gismervik, Solveig Osborg Ose, Lene Aasdahl, Hilde Brun Lauritzen, Arild Faxvaag, Ellen Marie Bardal, Eivind Schjelderup Skarpsno","doi":"10.1136/bmjhci-2024-101200","DOIUrl":"10.1136/bmjhci-2024-101200","url":null,"abstract":"<p><strong>Objective: </strong>The aim of this study was to assess how different groups of health professionals evaluated the usability of a new electronic health record (EHR) and to investigate the association between the usability and burnout, insomnia and turnover intention.</p><p><strong>Methods: </strong>This cross-sectional study included 1424 health professionals who worked at a Norwegian University Hospital. The usability was measured with the System Usability Scale (SUS) 6 months after the previous electronic record was replaced with a more comprehensive, sector-wide, patient-centred EHR in 2022.</p><p><strong>Results: </strong>The median SUS score was 25 (IQR 12.5-37.5) out of 100 and ranged from 15 (IQR 7.5-25.0) among medical doctors to 40 (IQR 27.6-55.0) among laboratory technicians. Nurses reported a score of 25 (IQR 12.5-40.0). In clinical contexts, the median SUS score ranged from 15 (IQR 10.0-30.0) within radiology to 27.5 (IQR 15.0-42.5) within internal medicine, whereas laboratory medicine reported a score of 37.5 (IQR 27.5-55.0). In multivariable analyses using health professionals in the highest quarter of the SUS as the reference, those in the lowest quarter were more likely to report burnout (OR 3.05, 95% CI 1.86 to 5.00), insomnia (OR 1.72, 95% CI 1.18 to 2.50) and turnover intention (OR 2.35, 95% CI 1.53 to 3.64).</p><p><strong>Conclusion: </strong>Most health professionals across all occupational groups and clinical contexts reported low usability of a new EHR 6 months after go-live. Those who reported the lowest usability were more likely to report burnout, insomnia and turnover intention.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11956351/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143742212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}