International Journal of Medical Informatics最新文献

{"title":"Machine learning models to further identify advantaged populations that can achieve functional cure of chronic hepatitis B virus infection after receiving Peg-IFN alpha treatment","authors":"Wenting Zhong ,&nbsp;Che Wang ,&nbsp;Jia Wang ,&nbsp;Tianyan Chen","doi":"10.1016/j.ijmedinf.2024.105660","DOIUrl":"10.1016/j.ijmedinf.2024.105660","url":null,"abstract":"<div><h3>Objective</h3><div>Functional cure is currently the highest goal of hepatitis B virus(HBV) treatment.Pegylated interferon(Peg-IFN) alpha is an important drug for this purpose,but even in the hepatitis B e antigen(HBeAg)-negative population,there is still a portion of the population respond poorly to it.Therefore,it is important to explore the influencing factors affecting the response rate of Peg-IFN alpha and establish a prediction model to further identify advantaged populations.</div></div><div><h3>Methods</h3><div>We retrospectively analyzed 382 patients.297 patients were in the training set and 85 patients from another hospital were in the test set.The intersect features were extracted from all variables using the recursive feature elimination(RFE) algorithm, Boruta algorithm, and least absolute shrinkage and selection operator(LASSO) regression algorithm in the training dataset.Then,we employed six machine learning(ML) algorithms-Logistic Regression(LR),Random Forest(RF),Support Vector Machines(SVM),K Nearest Neighbors(KNN),Light Gradient Boosting Machine(LightGBM) and Extreme Gradient Boosting(XGBoost)-to develop the model.Internal 10-fold cross-validation helped determine the best-performing model,which was then tested externally.Model performance was assessed using metrics such as area under the curve(AUC) and other metrics.SHapley Additive exPlanations(SHAP) plots were used to interpret variable significance.</div></div><div><h3>Results</h3><div>138/382(36.13 %) patients achieved functional cure.HBsAg at baseline,HBsAg decline at week12,non-alcoholic fatty liver disease(NAFLD) and age were identified as significant variables.RF performed the best,with AUC value of 0.988,and maintained good performance in test set.The SHapley Additive exPlanations(SHAP) plot highlighted HBsAg at baseline and HBsAg decline at week 12 are the top two predictors.The web-calculator was designed to predict functional cure more conveniently(<span><span>https://www.xsmartanalysis.com/model/list/predict/model/html?mid</span><svg><path></path></svg></span> = 17054&amp;symbol = 317ad245Hx628ko3uW51).</div></div><div><h3>Conclusion</h3><div>We developed a prediction model,which can be used to not only accurately identifies advantageous populations with Peg-IFN alpha,but also determines whether to continue subsequent Peg-IFN alpha.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"193 ","pages":"Article 105660"},"PeriodicalIF":3.7,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OrthoMortPred: Predicting one-year mortality following orthopedic hospitalization","authors":"Filipe Ricardo Carvalho ,&nbsp;Paulo Jorge Gavaia ,&nbsp;António Brito Camacho","doi":"10.1016/j.ijmedinf.2024.105657","DOIUrl":"10.1016/j.ijmedinf.2024.105657","url":null,"abstract":"<div><h3>Objective</h3><div>Predicting mortality risk following orthopedic surgery is crucial for informed decision-making and patient care. This study aims to develop and validate a machine learning model for predicting one-year mortality risk after orthopedic hospitalization and to create a personalized risk prediction tool for clinical use.</div></div><div><h3>Methods</h3><div>We analyzed data from 3,132 patients who underwent orthopedic procedures at the Central Lisbon University Hospital Center from 2021 to 2023. Using the LightGBM algorithm, we developed a predictive model incorporating various clinical and administrative variables. We employed SHAP (SHapley Additive exPlanations) values for model interpretation and created a personalized risk prediction tool for individual patient assessment.</div></div><div><h3>Results</h3><div>Our model achieved an accuracy of 93% and an area under the ROC curve of 0.93 for predicting one-year mortality. Notably, ’EMERGENCY ADMISSION DATE TIME’ emerged as the most influential predictor, followed by age and pre-operative days. The model demonstrated robust performance across different patient subgroups and outperformed traditional statistical methods. The personalized risk prediction tool provides clinicians with real-time, patient-specific risk assessments and insights into contributing factors.</div></div><div><h3>Conclusion</h3><div>Our study presents a highly accurate model for predicting one-year mortality following orthopedic hospitalization. The significance of ’EMERGENCY ADMISSION DATE TIME’ as the primary predictor highlights the importance of admission timing in patient outcomes. The accompanying personalized risk prediction tool offers a practical means of implementing this model in clinical settings, potentially improving risk stratification and patient care in orthopedic practice.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"192 ","pages":"Article 105657"},"PeriodicalIF":3.7,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142481234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Social services and healthcare personnel’s digital competence profiles: A Finnish cross-sectional study","authors":"Minna Ylönen ,&nbsp;Panu Forsman ,&nbsp;Tapio Karvo ,&nbsp;Erika Jarva ,&nbsp;Teuvo Antikainen ,&nbsp;Petri Kulmala ,&nbsp;Kristina Mikkonen ,&nbsp;Tommi Kärkkäinen ,&nbsp;Raija Hämäläinen","doi":"10.1016/j.ijmedinf.2024.105658","DOIUrl":"10.1016/j.ijmedinf.2024.105658","url":null,"abstract":"<div><h3>Background</h3><div>Recent research has highlighted the deficiencies and variations in the digital competences of social services and healthcare personnel. Yet there is a shortage of data regarding how the personnel use digital devices and solutions and their attitudes towards digitalisation. Hence, a systematic investigation into digital devices and solutions in healthcare is warranted.</div></div><div><h3>Objectives</h3><div>This study aimed to analyse the similarities and differences in digital competences and organisational support among healthcare personnel, focusing on using digital applications and services. The primary research question was to investigate what kinds of digital competence profiles are identifiable through social services and healthcare personnel self-assessments.</div></div><div><h3>Methods</h3><div>The survey was conducted in the Wellbeing Services County of Central Finland at the end of 2023. It utilised validated self-assessment methods and garnered 643 responses from social services and healthcare professionals. Data analysis involved quantitative cluster analysis for grouping participants and qualitative content analysis for describing the clusters.</div></div><div><h3>Results</h3><div>The study resulted in a final model of seven clusters that presented distinct digital competence profiles with relatively even sizes. These clusters represented the different aspects of digital usage among social services and healthcare professionals. They could be categorised into three overarching profiles: 1) <em>Motivated digital experts</em>, 2) <em>Burdened digital users</em> and 3) <em>Frustrated survivors. Motivated digital experts</em> comprised up almost half of the respondents (45.1%). Still, the findings also facilitated identifying of a small group of <em>Frustrated survivors</em> (7.5%) who represented burdened and stressed digital users.</div></div><div><h3>Conclusions</h3><div>The results indicate significant variances in digital competence profiles among employees. Social services and healthcare personnel perceive the opportunities and challenges associated with digital applications and services differently. Further detailed research into the disparities between digital competence profiles is necessary, particularly regarding the types of support that benefit different profiles the most.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"193 ","pages":"Article 105658"},"PeriodicalIF":3.7,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FedDSS: A data-similarity approach for client selection in horizontal federated learning","authors":"Tuong Minh Nguyen ,&nbsp;Kim Leng Poh ,&nbsp;Shu-Ling Chong ,&nbsp;Jan Hau Lee","doi":"10.1016/j.ijmedinf.2024.105650","DOIUrl":"10.1016/j.ijmedinf.2024.105650","url":null,"abstract":"<div><h3>Background and objective</h3><div>Federated learning (FL) is an emerging distributed learning framework allowing multiple clients (hospitals, institutions, smart devices, etc.) to collaboratively train a centralized machine learning model without disclosing personal data. It has the potential to address several healthcare challenges, including a lack of training data, data privacy, and security concerns. However, model learning under FL is affected by non-i.i.d. data, leading to severe model divergence and reduced performance due to the varying client's data distributions. To address this problem, we propose FedDSS, Federated Data Similarity Selection, a framework that uses a data-similarity approach to select clients, without compromising client data privacy.</div></div><div><h3>Methods</h3><div>FedDSS comprises a statistical-based data similarity metric, a <em>N</em>-similar-neighbor network, and a network-based selection strategy. We assessed FedDSS' performance against FedAvg's in i.i.d. and non-i.i.d. settings with two public pediatric sepsis datasets (PICD and MIMICIII). Selection fairness was measured using <span><math><mi>e</mi><mi>n</mi><mi>t</mi><mi>r</mi><mi>o</mi><mi>p</mi><mi>y</mi></math></span>. Simulations were repeated five times to evaluate average loss, true positive rate (TPR), and <span><math><mi>e</mi><mi>n</mi><mi>t</mi><mi>r</mi><mi>o</mi><mi>p</mi><mi>y</mi></math></span>.</div></div><div><h3>Results</h3><div>In i.i.d setting on PICD, FedDSS achieved a higher TPR starting from the 9th round and surpassing 0.6 three rounds earlier than FedAvg. On MIMICIII, FedDSS's loss decreases significantly from the 13th round, with TPR &gt; 0.8 by the 2nd round, two rounds ahead of FedAvg (at the 4th round). In the non-i.i.d. setting, FedDSS achieved TPR &gt; 0.7 by the 4th and &gt; 0.8 by the 7th round, earlier than FedAvg (at the 5th and 11th rounds). In both settings, FedDSS showed reasonable fairness (<span><math><mi>e</mi><mi>n</mi><mi>t</mi><mi>r</mi><mi>o</mi><mi>p</mi><mi>y</mi></math></span> of 2.2 and 2.1).</div></div><div><h3>Conclusion</h3><div>We demonstrated that FedDSS contributes to improved learning in FL by achieving faster convergence, reaching the desired TPR with fewer communication rounds, and potentially enhancing sepsis prediction (TPR) over FedAvg.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"192 ","pages":"Article 105650"},"PeriodicalIF":3.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of artificial intelligence on the knowledge, attitude, and practice of pharmacists across diverse settings: A cross-sectional study","authors":"Deema Jaber ,&nbsp;Hisham E. Hasan ,&nbsp;Rana Abutaima ,&nbsp;Hana M. Sawan ,&nbsp;Samaa Al Tabbah","doi":"10.1016/j.ijmedinf.2024.105656","DOIUrl":"10.1016/j.ijmedinf.2024.105656","url":null,"abstract":"<div><div>The pharmacy practice landscape is undergoing a significant transformation with the increasing integration of artificial intelligence (AI). As essential members of the healthcare team, pharmacists’ readiness and willingness to adopt AI technologies is critical. This cross-sectional study explores pharmacists’ knowledge, attitudes, and practices (KAP) regarding AI in various practice settings. Utilizing a descriptive survey methodology, we collected data through a structured questionnaire targeting pharmacists across diverse working environments. Statistical analyses were conducted to calculate KAP scores. Results revealed that 44.8 % of participants possessed a moderate level of knowledge about AI, while 49.1 % expressed positive attitudes toward its potential applications in pharmacy. However, their current practices related to AI were rated as adequate (57.3 %). Notably, a significant association was found between knowledge, attitudes, and practices (<em>p</em> &lt; 0.001). This study provides valuable insights into pharmacists’ readiness to incorporate AI into their practice, emphasizing the need for targeted educational interventions to enhance knowledge and promote positive attitudes. Furthermore, efforts must be directed towards facilitating the integration of AI into pharmacy workflows to fully leverage this transformative technology and improve patient care outcomes.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"192 ","pages":"Article 105656"},"PeriodicalIF":3.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142481235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Autocorrelation of daily resting heart rate: A novel metric of postoperative recovery","authors":"Michela Carter ,&nbsp;Rui Hua ,&nbsp;Megan K. O’Brien ,&nbsp;J. Benjamin Pitt ,&nbsp;Soyang Kwon ,&nbsp;Arun Jayaraman ,&nbsp;Hassan MK Ghomrawi ,&nbsp;Fizan Abdullah","doi":"10.1016/j.ijmedinf.2024.105655","DOIUrl":"10.1016/j.ijmedinf.2024.105655","url":null,"abstract":"<div><h3>Purpose</h3><div>Resting heart rate (RHR) is a sensitive indicator of an individual’s physiologic condition. However, its use in clinical practice has been limited due to the wide variation in baseline RHR based on multiple factors, including age, sex, cardiovascular fitness, and comorbidities. The study aims to develop a novel, clinically meaningful metric that is applicable across these conditions, based on day-by-day changes in RHR—the difference in autocorrelation of daily RHR (ACΔ-RHR). We present ACΔ-RHR in the context of monitoring post-discharge recovery for pediatric appendectomy patients.</div></div><div><h3>Methods</h3><div>Children 3–17 years old who underwent laparoscopic appendectomy for complicated appendicitis from 2019 to 2022 at a tertiary children’s hospital wore a Fitbit for twenty-one postoperative days (POD). Patients without complications were included to describe normative recovery. Using RHR on POD 1–3 as the baseline, autocorrelation of daily RHR was calculated (fixed lag = 1) for POD 3–21. Then, daily ACΔ-RHR was determined by subtracting autocorrelation values between the current and previous day. Means and standard deviations were calculated for daily RHR to estimate on which POD ACΔ-RHR stabilized at 0, representing general RHR stability and recovery from surgery for all patients. Subgroup analyses were performed by age (3–10 years old vs 11–17 years old) and sex.</div></div><div><h3>Results</h3><div>Thirty-one patients were included (58.1 % 3–10 years old, 41.9 % female, 67.7 % Hispanic). Whereas the mean daily RHR did not demonstrate clear trends, the mean ACΔ-RHR for the cohort first reached 0 on POD 12 and stabilized on POD 14 (95 % confidence interval: POD [11,17]). Subgroup analysis showed that ACΔ-RHR stabilized on POD 9 for age of 3–10 years, POD 12 for age of 11–17 years, POD 12 for females and POD 10 for males.</div></div><div><h3>Conclusions</h3><div>The ACΔ-RHR is a promising clinical metric that could enhance post-surgical patient monitoring, such as for children following laparoscopic appendectomy for complicated appendicitis.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"192 ","pages":"Article 105655"},"PeriodicalIF":3.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large language models can support generation of standardized discharge summaries – A retrospective study utilizing ChatGPT-4 and electronic health records","authors":"Arne Schwieger ,&nbsp;Katrin Angst ,&nbsp;Mateo de Bardeci ,&nbsp;Achim Burrer ,&nbsp;Flurin Cathomas ,&nbsp;Stefano Ferrea ,&nbsp;Franziska Grätz ,&nbsp;Marius Knorr ,&nbsp;Golo Kronenberg ,&nbsp;Tobias Spiller ,&nbsp;David Troi ,&nbsp;Erich Seifritz ,&nbsp;Samantha Weber ,&nbsp;Sebastian Olbrich","doi":"10.1016/j.ijmedinf.2024.105654","DOIUrl":"10.1016/j.ijmedinf.2024.105654","url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate whether psychiatric discharge summaries (DS) generated with ChatGPT-4 from electronic health records (EHR) can match the quality of DS written by psychiatric residents.</div></div><div><h3>Methods</h3><div>At a psychiatric primary care hospital, we compared 20 inpatient DS, written by residents, to those written with ChatGPT-4 from pseudonymized residents’ notes of the patients’ EHRs and a standardized prompt. 8 blinded psychiatry specialists rated both versions on a custom Likert scale from 1 to 5 across 15 quality subcategories. The primary outcome was the overall rating difference between the two groups. The secondary outcomes were the rating differences at the level of individual question, case, and rater.</div></div><div><h3>Results</h3><div>Human-written DS were rated significantly higher than AI (mean ratings: human 3.78, AI 3.12, p &lt; 0.05). They surpassed AI significantly in 12/15 questions and 16/20 cases and were favored significantly by 7/8 raters. For “low expected correction effort”, human DS were rated as 67 % favorable, 19 % neutral, and 14 % unfavorable, whereas AI-DS were rated as 22 % favorable, 33 % neutral, and 45 % unfavorable. Hallucinations were present in 40 % of AI-DS, with 37.5 % deemed highly clinically relevant. Minor content mistakes were found in 30 % of AI and 10 % of human DS. Raters correctly identified AI-DS with 81 % sensitivity and 75 % specificity.</div></div><div><h3>Discussion</h3><div>Overall, AI-DS did not match the quality of resident-written DS but performed similarly in 20% of cases and were rated as favorable for “low expected correction effort” in 22% of cases. AI-DS lacked most in content specificity, ability to distill key case information, and coherence but performed adequately in conciseness, adherence to formalities, relevance of included content, and form.</div></div><div><h3>Conclusion</h3><div>LLM-written DS show potential as templates for physicians to finalize, potentially saving time in the future.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"192 ","pages":"Article 105654"},"PeriodicalIF":3.7,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review and proposed framework for sustainable learning healthcare systems","authors":"Olga Golburean ,&nbsp;Espen Solbakken Nordheim ,&nbsp;Arild Faxvaag ,&nbsp;Rune Pedersen ,&nbsp;Ove Lintvedt ,&nbsp;Luis Marco-Ruiz","doi":"10.1016/j.ijmedinf.2024.105652","DOIUrl":"10.1016/j.ijmedinf.2024.105652","url":null,"abstract":"<div><h3>Background</h3><div>The healthcare sector is a complex domain that faces challenges in effectively learning from practices and outcome data. The Learning Health System (LHS) has emerged as a potential framework to improve healthcare by promoting continuous learning. However, its adoption remains limited, often involving only a single clinical department or a part of the LHS cycle. There is a need to gain a better understanding of implementing LHS on a larger scale.</div></div><div><h3>Aim</h3><div>To identify complete implementations of the LHS for providing recommendations into their implementation strategies, success factors, barriers, and outcomes.</div></div><div><h3>Methods</h3><div>A systematic review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines using PubMed and Scopus databases. Data from the included papers were thematically categorized into four primary areas: (1) Scale of LHS Implementation; (2) Implementation strategies and the factors that facilitated the implementation of LHS; (3) LHS outcomes; and (4) Barriers /challenges related to the LHS implementation.</div></div><div><h3>Results</h3><div>We identified 1,279 papers, of which 37 were included in the final analysis. Barriers to implementing LHS included interoperability, data integration, electronic health records (EHRs) challenges, organizational culture, leadership, and regulatory hurdles. Most LHS initiatives lacked discussion on long-term economic sustainability models, and only 16 papers provided objective measurements of performance changes. Drawing from the findings of the included studies, this paper offers recommendations for the effective implementation of the LHS.</div></div><div><h3>Conclusion</h3><div>The establishment of sustainable LHS necessitates several key components. First, there is a need to develop long-term economic sustainability models. Secondly, governance at the national level should promote common Application Programming Interfaces (APIs) across LHS implementations, communication channels to share tacit knowledge, efficient Institutional Review Board, ethical approval processes, and connect various initiatives currently operating independently. Lastly, the success of LHS relies not only on technological infrastructure but also on the active participation of multidisciplinary teams in decision-making and sharing of tacit knowledge.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"192 ","pages":"Article 105652"},"PeriodicalIF":3.7,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hospital antimicrobial stewardship team perceptions and usability of a computerized clinical decision support system","authors":"Alexandre Baudet ,&nbsp;Marie-Jo Brennstuhl ,&nbsp;Alexandre Charmillon ,&nbsp;Florence Meyer ,&nbsp;Céline Pulcini ,&nbsp;Nathalie Thilly ,&nbsp;Béatrice Demoré ,&nbsp;Arnaud Florentin","doi":"10.1016/j.ijmedinf.2024.105653","DOIUrl":"10.1016/j.ijmedinf.2024.105653","url":null,"abstract":"<div><h3>Background</h3><div>Antimicrobial stewardship (AMS) programs aim to optimize antibiotic use through a panel of interventions. The implementation of computerized clinical decision support systems (CDSSs) offers new opportunities for semiautomated antimicrobial review by AMS teams. This study aimed to evaluate the perceived facilitators, barriers and benefits of end-users related to a commercial CDSS recently implemented in a hospital and to assess its usability.</div></div><div><h3>Methods</h3><div>A mixed-method approach was used among AMS team members nine months after the implementation of the CDSS in a university hospital in northeastern France. A qualitative analysis based on individual semistructured interviews was conducted to collect end-users’ perceptions. A quantitative analysis was performed using the System Usability Scale (SUS).</div></div><div><h3>Results</h3><div>Eleven AMS team members agreed to participate. The qualitative analysis revealed technical, organizational and human barriers and facilitators of CDSS implementation. Effective collaboration with information technology teams was crucial for ensuring the installation and configuration of the software. CDSS adoption by the AMS team required time, human resources, training, adaptation and a clinical leader. Moreover, the CDSS had to be well designed, user-friendly and provide benefits to AMS activities. The quantitative analysis indicated that the CDSS was a “good” system in terms of perceived ease of use (median SUS score: 77.5/100).</div></div><div><h3>Conclusions</h3><div>This study shows the value of the studied CDSS to support AMS activities. It reveals barriers, facilitators and benefits to the implementation and adoption of the CDSS. These barriers and facilitators could be considered to facilitate the implementation of the software in other hospitals.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"192 ","pages":"Article 105653"},"PeriodicalIF":3.7,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and expert inspections of the user interface for a primary care decision support system","authors":"Michaela Christina Neff ,&nbsp;Dania Schütze ,&nbsp;Svea Holtz ,&nbsp;Susanne Maria Köhler ,&nbsp;Jessica Vasseur ,&nbsp;Najia Ahmadi ,&nbsp;Holger Storf ,&nbsp;Jannik Schaaf","doi":"10.1016/j.ijmedinf.2024.105651","DOIUrl":"10.1016/j.ijmedinf.2024.105651","url":null,"abstract":"<div><h3>Background</h3><div>General practitioners play a unique key role in diagnosing patients with unclear diseases. Decision support systems in primary care can assist with diagnosis provided that they are efficient and user-friendly.</div></div><div><h3>Objectives</h3><div>The objective of this study is to develop a high-fidelity prototype of the user interface of a clinical decision support system for primary care, particularly for diagnosis support in unclear diseases, using expert inspections at an early stage of development to ensure a high level of usability.</div></div><div><h3>Methods</h3><div>The user interface prototype was iteratively developed based on previous research, design principles, and usability guidelines. During the development phase, three usability inspections were carried out by all experts at four-week intervals as heuristic walkthrough. Each inspection consisted of two parts: 1) Task-based inspection 2) Free exploration and evaluation based on usability heuristics. Five domain experts assessed the current status of development.</div><div>The tasks in the inspections were based on the task model derived in the requirements analysis: perform data entry, review and discuss results, schedule further diagnostics, refer to specialists and close case.</div></div><div><h3>Results</h3><div>As a result of this iterative development, a high-fidelity, clickable user interface prototype was created that is able to fulfil all six tasks of our task model. The usability inspections identified a total of 196 usability issues (for all 3 inspections; Part 1: 90 issues, Part 2: 106 issues), ranging in severity from minor to severe. These served the continuous adjustment and improvement of the prototype. All main tasks were completed successfully despite these problems.</div></div><div><h3>Conclusion</h3><div>Usability inspections through heuristic walkthroughs can support and optimise the development of a user-centred decision support system in order to ensure its suitability for performing relevant tasks.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"192 ","pages":"Article 105651"},"PeriodicalIF":3.7,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}