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Purpose: This scoping review aims to explore the current applications of ChatGPT in the retina field, highlighting its potential, challenges, and limitations. Methods: A comprehensive literature search was conducted across multiple databases, including PubMed, Scopus, MEDLINE, and Embase, to identify relevant articles published from 2022 onwards. The inclusion criteria focused on studies evaluating the use of ChatGPT in retinal healthcare. Data were extracted and synthesized to map the scope of ChatGPT's applications in retinal care, categorizing articles into various practical application areas such as academic research, charting, coding, diagnosis, disease management, and patient counseling. Results: A total of 68 articles were included in the review, distributed across several categories: 8 related to academics and research, 5 to charting, 1 to coding and billing, 44 to diagnosis, 49 to disease management, 2 to literature consulting, 23 to medical education, and 33 to patient counseling. Many articles were classified into multiple categories due to overlapping topics. The findings indicate that while ChatGPT shows significant promise in areas such as medical education and diagnostic support, concerns regarding accuracy, reliability, and the potential for misinformation remain prevalent. Conclusion: ChatGPT offers substantial potential in advancing retinal healthcare by supporting clinical decision-making, enhancing patient education, and automating administrative tasks. However, its current limitations, particularly in clinical accuracy and the risk of generating misinformation, necessitate cautious integration into practice, with continuous oversight from healthcare professionals. Future developments should focus on improving accuracy, incorporating up-to-date medical guidelines, and minimizing the risks associated with AI-driven healthcare tools. [ABSTRACT FROM AUTHOR]

Abstract Purpose This scoping review aims to explore the current applications of ChatGPT in the retina field, highlighting its potential, challenges, and limitations. Methods A comprehensive literature search was conducted across multiple databases, including PubMed, Scopus, MEDLINE, and Embase, to identify relevant articles published from 2022 onwards. The inclusion criteria focused on studies evaluating the use of ChatGPT in retinal healthcare. Data were extracted and synthesized to map the scope of ChatGPT’s applications in retinal care, categorizing articles into various practical application areas such as academic research, charting, coding, diagnosis, disease management, and patient counseling. Results A total of 68 articles were included in the review, distributed across several categories: 8 related to academics and research, 5 to charting, 1 to coding and billing, 44 to diagnosis, 49 to disease management, 2 to literature consulting, 23 to medical education, and 33 to patient counseling. Many articles were classified into multiple categories due to overlapping topics. The findings indicate that while ChatGPT shows significant promise in areas such as medical education and diagnostic support, concerns regarding accuracy, reliability, and the potential for misinformation remain prevalent. Conclusion ChatGPT offers substantial potential in advancing retinal healthcare by supporting clinical decision-making, enhancing patient education, and automating administrative tasks. However, its current limitations, particularly in clinical accuracy and the risk of generating misinformation, necessitate cautious integration into practice, with continuous oversight from healthcare professionals. Future developments should focus on improving accuracy, incorporating up-to-date medical guidelines, and minimizing the risks associated with AI-driven healthcare tools.

Abstract Background Medication errors and associated adverse drug events (ADE) are a major cause of morbidity and mortality worldwide. In recent years, the prevention of medication errors has become a high priority in healthcare systems. In order to improve medication safety, computerized Clinical Decision Support Systems (CDSS) are increasingly being integrated into the medication process. Accordingly, a growing number of studies have investigated the medication safety-related effectiveness of CDSS. However, the outcome measures used are heterogeneous, leading to unclear evidence. The primary aim of this study is to summarize and categorize the outcomes used in interventional studies evaluating the effects of CDSS on medication safety in primary and long-term care. Methods We systematically searched PubMed, Embase, CINAHL, and Cochrane Library for interventional studies evaluating the effects of CDSS targeting medication safety and patient-related outcomes. We extracted methodological characteristics, outcomes and empirical findings from the included studies. Outcomes were assigned to three main categories: process-related, harm-related, and cost-related. Risk of bias was assessed using the Evidence Project risk of bias tool. Results Thirty-two studies met the inclusion criteria. Almost all studies (n = 31) used process-related outcomes, followed by harm-related outcomes (n = 11). Only three studies used cost-related outcomes. Most studies used outcomes from only one category and no study used outcomes from all three categories. The definition and operationalization of outcomes varied widely between the included studies, even within outcome categories. Overall, evidence on CDSS effectiveness was mixed. A significant intervention effect was demonstrated by nine of fifteen studies with process-related primary outcomes (60%) but only one out of five studies with harm-related primary outcomes (20%). The included studies faced a number of methodological problems that limit the comparability and generalizability of their results. Conclusions Evidence on the effectiveness of CDSS is currently inconclusive due in part to inconsistent outcome definitions and methodological problems in the literature. Additional high-quality studies are therefore needed to provide a comprehensive account of CDSS effectiveness. These studies should follow established methodological guidelines and recommendations and use a comprehensive set of harm-, process- and cost-related outcomes with agreed-upon and consistent definitions. Prospero registration CRD42023464746

Introduction Design Methods Results Conclusion Protocol Registration Accurate and rapid triage can reduce undertriage and overtriage, which may improve emergency department flow. This study aimed to identify the effects of a prospective study applying artificial intelligence‐based triage in the clinical field.Systematic review of prospective studies.CINAHL, Cochrane, Embase, PubMed, ProQuest, KISS, and RISS were searched from March 9 to April 18, 2023. All the data were screened independently by three researchers. The review included prospective studies that measured outcomes related to AI‐based triage. Three researchers extracted data and independently assessed the study's quality using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) protocol.Of 1633 studies, seven met the inclusion criteria for this review. Most studies applied machine learning to triage, and only one was based on fuzzy logic. All studies, except one, utilized a five‐level triage classification system. Regarding model performance, the feed‐forward neural network achieved a precision of 33% in the level 1 classification, whereas the fuzzy clip model achieved a specificity and sensitivity of 99%. The accuracy of the model's triage prediction ranged from 80.5% to 99.1%. Other outcomes included time reduction, overtriage and undertriage checks, mistriage factors, and patient care and prognosis outcomes.Triage nurses in the emergency department can use artificial intelligence as a supportive means for triage. Ultimately, we hope to be a resource that can reduce undertriage and positively affect patient health.We have registered our review in PROSPERO (registration number: CRD 42023415232). [ABSTRACT FROM AUTHOR]

Background: Medication errors and associated adverse drug events (ADE) are a major cause of morbidity and mortality worldwide. In recent years, the prevention of medication errors has become a high priority in healthcare systems. In order to improve medication safety, computerized Clinical Decision Support Systems (CDSS) are increasingly being integrated into the medication process. Accordingly, a growing number of studies have investigated the medication safety-related effectiveness of CDSS. However, the outcome measures used are heterogeneous, leading to unclear evidence. The primary aim of this study is to summarize and categorize the outcomes used in interventional studies evaluating the effects of CDSS on medication safety in primary and long-term care. Methods: We systematically searched PubMed, Embase, CINAHL, and Cochrane Library for interventional studies evaluating the effects of CDSS targeting medication safety and patient-related outcomes. We extracted methodological characteristics, outcomes and empirical findings from the included studies. Outcomes were assigned to three main categories: process-related, harm-related, and cost-related. Risk of bias was assessed using the Evidence Project risk of bias tool. Results: Thirty-two studies met the inclusion criteria. Almost all studies (n = 31) used process-related outcomes, followed by harm-related outcomes (n = 11). Only three studies used cost-related outcomes. Most studies used outcomes from only one category and no study used outcomes from all three categories. The definition and operationalization of outcomes varied widely between the included studies, even within outcome categories. Overall, evidence on CDSS effectiveness was mixed. A significant intervention effect was demonstrated by nine of fifteen studies with process-related primary outcomes (60%) but only one out of five studies with harm-related primary outcomes (20%). The included studies faced a number of methodological problems that limit the comparability and generalizability of their results. Conclusions: Evidence on the effectiveness of CDSS is currently inconclusive due in part to inconsistent outcome definitions and methodological problems in the literature. Additional high-quality studies are therefore needed to provide a comprehensive account of CDSS effectiveness. These studies should follow established methodological guidelines and recommendations and use a comprehensive set of harm-, process- and cost-related outcomes with agreed-upon and consistent definitions. Prospero registration: CRD42023464746 [ABSTRACT FROM AUTHOR]

With the wide application of artificial intelligence (AI) in the medical field, more and more AI-based clinical decision support systems have been applied in the clinical diagnosis, screening, and other fields. Early-stage clinical evaluation is important for evaluating the clinical performance, safety, and human factors of AI-based clinical decision support systems, and laying the foundation for large-scale trials. However, the transparency and integrity of the clinical reports need to be improved. The Developmental and Exploratory Clinical Investigations of DEcision Support Systems Driven by Artificial Intelligence (DECIDE-AI) was officially published online in May 2022. Based on this guideline and relative literature, this paper explores the transparent reporting of early-stage clinical evaluation of AI-based clinical decision support systems, in order to help developers and researchers better understand and apply the relevant guidelines, and improve the reporting transparency of early-stage clinical evaluation of AI-based clinical decision support systems.

Aims: We aim to examine and understand the work processes of antimicrobial stewardship (AMS) teams across 2 hospitals that use the same digital intervention, and to identify the barriers and enablers to effective AMS in each setting. Methods: Employing a contextual inquiry approach informed by the Systems Engineering Initiative for Patient Safety (SEIPS) model, observations and semistructured interviews were conducted with AMS team members (n = 15) in 2 Australian hospitals. Qualitative data analysis was conducted, mapping themes to the SEIPS framework. Results: Both hospitals utilized similar systems, however, they displayed variations in AMS processes, particularly in postprescription review, interdepartmental AMS meetings and the utilization of digital tools. An antimicrobial dashboard was available at both hospitals but was utilized more at the hospital where the AMS team members were involved in the dashboard's development, and there were user champions. At the hospital where the dashboard was utilized less, participants were unaware of key features, and interoperability issues were observed. Establishing strong relationships between the AMS team and prescribers emerged as key to effective AMS at both hospitals. However, organizational and cultural differences were found, with 1 hospital reporting insufficient support from executive leadership, increased prescriber autonomy and resource constraints. Conclusion: Organizational and cultural elements, such as executive support, resource allocation and interdepartmental relationships, played a crucial role in achieving AMS goals. System interoperability and user champions further promoted the adoption of digital tools, potentially improving AMS outcomes through increased user engagement and acceptance. [ABSTRACT FROM AUTHOR]

Introduction: Considering the increase in the proportion of the older population worldwide, the demand for health system resources also arises. These tools optimize clinical decision-making, thus avoiding iatrogenesis and thus contributing to a better quality of life for the older population. In response, we created an online web application, the APIMedOlder, that provides access to healthcare professionals to allow healthcare professionals to access potentially inappropriate medication identification criteria through a useful tool with a simplified profile, allowing its applicability in clinical practice. This study aims to assess the usability of the APIMedOlder online web application by healthcare professionals. Methods: A questionnaire, based on the System Usability Scale, was distributed among 15 healthcare professionals (five pharmacists, four physicians, three pharmacy technicians, and three nurses), to fully explore the website. Results: Overall, healthcare professionals’ evaluation of the usability of the APIMedOlder online web application was rated as “Best imaginable” (mean score of 87.17 points), with individual scores ranging from 75 to 100 points. Internal consistency of α = 0.881 (CI 95%: 0.766 - 0.953) was achieved. Specific questionnaire items contributing to this high score included ease of use, learning efficiency, and integration of functions. Conclusion: The overall evaluation of the developed tool was positive, with this online application being recognized as being easy to use and having well-integrated functions.

Objective: To assess the agreement among three different online drug-drug interaction (DDI) checkers for the detection of psychotropic drug interactions among dental patients in the state of Minas Gerais, Brazil. Material and Methods: Between January and December 2017, a cross-sectional study was conducted in Minas Gerais with data on pharmaceutical claims of psychotropic drugs prescribed by dental practitioners. Data from the Pharmaceutical Management System provided the drug dispensing history of the patients, allowing the identification of those on concomitant medication use. The occurrence of DDI was determined by entering the name of the drugs taken by each patient into Merative Micromedex®, Medscape®, and DrugBank. The degree of agreement among the three DDI online checkers was analyzed using the Fleiss' kappa test. Results: Overall, 797 dental patients were found to be taking some psychotropic medication with other drugs simultaneously. The number of patients with DDI varied according to Micromedex® (n= 366), Medscape® (n= 473), and DrugBank (n= 736). The agreement between the DDI checkers was poor (Fleiss' kappa: 0.165; p

Abstract Introduction Multidisciplinary team meetings (MDMs), also known as tumor conferences, are a cornerstone of cancer treatments. However, barriers such as incomplete patient information or logistical challenges can postpone tumor board decisions and delay patient treatment, potentially affecting clinical outcomes. Therapeutic Assistance and Decision algorithms for hepatobiliary tumor Boards (ADBoard) aims to reduce this delay by providing automated data extraction and high-quality, evidence-based treatment recommendations. Methods and analysis With the help of natural language processing, relevant patient information will be automatically extracted from electronic medical records and used to complete a classic tumor conference protocol. A machine learning model is trained on retrospective MDM data and clinical guidelines to recommend treatment options for patients in our inclusion criteria. Study participants will be randomized to either MDM with ADBoard (Arm A: MDM-AB) or conventional MDM (Arm B: MDM-C). The concordance of recommendations of both groups will be compared using interrater reliability. We hypothesize that the therapy recommendations of ADBoard would be in high agreement with those of the MDM-C, with a Cohen’s kappa value of ≥ 0.75. Furthermore, our secondary hypotheses state that the completeness of patient information presented in MDM is higher when using ADBoard than without, and the explainability of tumor board protocols in MDM-AB is higher compared to MDM-C as measured by the System Causability Scale. Discussion The implementation of ADBoard aims to improve the quality and completeness of the data required for MDM decision-making and to propose therapeutic recommendations that consider current medical evidence and guidelines in a transparent and reproducible manner. Ethics and dissemination The project was approved by the Ethics Committee of the Charité – Universitätsmedizin Berlin. Registration details The study was registered on ClinicalTrials.gov (trial identifying number: NCT05681949; https://clinicaltrials.gov/study/NCT05681949 ) on 12 January 2023.

Abstract Background Healthcare providers have to make ethically complex clinical decisions which may be a source of stress. Researchers have recently introduced Artificial Intelligence (AI)-based applications to assist in clinical ethical decision-making. However, the use of such tools is controversial. This review aims to provide a comprehensive overview of the reasons given in the academic literature for and against their use. Methods PubMed, Web of Science, Philpapers.org and Google Scholar were searched for all relevant publications. The resulting set of publications was title and abstract screened according to defined inclusion and exclusion criteria, resulting in 44 papers whose full texts were analysed using the Kuckartz method of qualitative text analysis. Results Artificial Intelligence might increase patient autonomy by improving the accuracy of predictions and allowing patients to receive their preferred treatment. It is thought to increase beneficence by providing reliable information, thereby, supporting surrogate decision-making. Some authors fear that reducing ethical decision-making to statistical correlations may limit autonomy. Others argue that AI may not be able to replicate the process of ethical deliberation because it lacks human characteristics. Concerns have been raised about issues of justice, as AI may replicate existing biases in the decision-making process. Conclusions The prospective benefits of using AI in clinical ethical decision-making are manifold, but its development and use should be undertaken carefully to avoid ethical pitfalls. Several issues that are central to the discussion of Clinical Decision Support Systems, such as justice, explicability or human–machine interaction, have been neglected in the debate on AI for clinical ethics so far. Trial registration This review is registered at Open Science Framework ( https://osf.io/wvcs9 ).

ABSTRACT Objective: To develop and validate predictive models to estimate the number of COVID-19 patients hospitalized in the intensive care units and general wards of a private not-for-profit hospital in São Paulo, Brazil. Methods: Two main models were developed. The first model calculated hospital occupation as the difference between predicted COVID-19 patient admissions, transfers between departments, and discharges, estimating admissions based on their weekly moving averages, segmented by general wards and intensive care units. Patient discharge predictions were based on a length of stay predictive model, assessing the clinical characteristics of patients hospitalized with COVID-19, including age group and usage of mechanical ventilation devices. The second model estimated hospital occupation based on the correlation with the number of telemedicine visits by patients diagnosed with COVID-19, utilizing correlational analysis to define the lag that maximized the correlation between the studied series. Both models were monitored for 365 days, from May 20th, 2021, to May 20th, 2022. Results: The first model predicted the number of hospitalized patients by department within an interval of up to 14 days. The second model estimated the total number of hospitalized patients for the following 8 days, considering calls attended by Hospital Israelita Albert Einstein’s telemedicine department. Considering the average daily predicted values for the intensive care unit and general ward across a forecast horizon of 8 days, as limited by the second model, the first and second models obtained R² values of 0.900 and 0.996, respectively and mean absolute errors of 8.885 and 2.524 beds, respectively. The performances of both models were monitored using the mean error, mean absolute error, and root mean squared error as a function of the forecast horizon in days. Conclusion: The model based on telemedicine use was the most accurate in the current analysis and was used to estimate COVID-19 hospital occupancy 8 days in advance, validating predictions of this nature in similar clinical contexts. The results encourage the expansion of this method to other pathologies, aiming to guarantee the standards of hospital care and conscious consumption of resources.

Introduction: Multidisciplinary team meetings (MDMs), also known as tumor conferences, are a cornerstone of cancer treatments. However, barriers such as incomplete patient information or logistical challenges can postpone tumor board decisions and delay patient treatment, potentially affecting clinical outcomes. Therapeutic Assistance and Decision algorithms for hepatobiliary tumor Boards (ADBoard) aims to reduce this delay by providing automated data extraction and high-quality, evidence-based treatment recommendations. Methods and analysis : With the help of natural language processing, relevant patient information will be automatically extracted from electronic medical records and used to complete a classic tumor conference protocol. A machine learning model is trained on retrospective MDM data and clinical guidelines to recommend treatment options for patients in our inclusion criteria. Study participants will be randomized to either MDM with ADBoard (Arm A: MDM-AB) or conventional MDM (Arm B: MDM-C). The concordance of recommendations of both groups will be compared using interrater reliability. We hypothesize that the therapy recommendations of ADBoard would be in high agreement with those of the MDM-C, with a Cohen's kappa value of ≥ 0.75. Furthermore, our secondary hypotheses state that the completeness of patient information presented in MDM is higher when using ADBoard than without, and the explainability of tumor board protocols in MDM-AB is higher compared to MDM-C as measured by the System Causability Scale. Discussion: The implementation of ADBoard aims to improve the quality and completeness of the data required for MDM decision-making and to propose therapeutic recommendations that consider current medical evidence and guidelines in a transparent and reproducible manner. Ethics and dissemination: The project was approved by the Ethics Committee of the Charité – Universitätsmedizin Berlin. Registration details: The study was registered on ClinicalTrials.gov (trial identifying number: NCT05681949; https://clinicaltrials.gov/study/NCT05681949) on 12 January 2023. [ABSTRACT FROM AUTHOR]

Summary: Appropriate evaluation of heparin‐induced thrombocytopenia (HIT) is imperative because of the potentially life‐threatening complications. However, overtesting and overdiagnosis of HIT are common. Our goal was to evaluate the impact of clinical decision support (CDS) based on the HIT computerized‐risk (HIT‐CR) score, designed to reduce unnecessary diagnostic testing. This retrospective observational study evaluated CDS that presented a platelet count versus time graph and 4Ts score calculator to clinicians who initiated a HIT immunoassay order in patients with predicted low risk (HIT‐CR score 0–2). The primary outcome was the proportion of immunoassay orders initiated but cancelled after firing of the CDS advisory. Chart reviews were conducted to assess anticoagulation usage, 4Ts scores and the proportion of patients who had HIT. In a 20‐week period, 319 CDS advisories were presented to users who initiated potentially unnecessary HIT diagnostic testing. The diagnostic test order was discontinued in 80 (25%) patients. Heparin products were continued in 139 (44%) patients, and alternative anticoagulation was not given to 264 (83%). The negative predictive value of the advisory was 98.8% (95% CI: 97.2–99.5). HIT‐CR score‐based CDS can reduce unnecessary diagnostic testing for HIT in patients with a low pretest probability of HIT. [ABSTRACT FROM AUTHOR]

Background: Healthcare providers have to make ethically complex clinical decisions which may be a source of stress. Researchers have recently introduced Artificial Intelligence (AI)-based applications to assist in clinical ethical decision-making. However, the use of such tools is controversial. This review aims to provide a comprehensive overview of the reasons given in the academic literature for and against their use. Methods: PubMed, Web of Science, Philpapers.org and Google Scholar were searched for all relevant publications. The resulting set of publications was title and abstract screened according to defined inclusion and exclusion criteria, resulting in 44 papers whose full texts were analysed using the Kuckartz method of qualitative text analysis. Results: Artificial Intelligence might increase patient autonomy by improving the accuracy of predictions and allowing patients to receive their preferred treatment. It is thought to increase beneficence by providing reliable information, thereby, supporting surrogate decision-making. Some authors fear that reducing ethical decision-making to statistical correlations may limit autonomy. Others argue that AI may not be able to replicate the process of ethical deliberation because it lacks human characteristics. Concerns have been raised about issues of justice, as AI may replicate existing biases in the decision-making process. Conclusions: The prospective benefits of using AI in clinical ethical decision-making are manifold, but its development and use should be undertaken carefully to avoid ethical pitfalls. Several issues that are central to the discussion of Clinical Decision Support Systems, such as justice, explicability or human–machine interaction, have been neglected in the debate on AI for clinical ethics so far. Trial registration: This review is registered at Open Science Framework (https://osf.io/wvcs9). [ABSTRACT FROM AUTHOR]

Abstract Background Traumatic musculoskeletal injuries are a common presentation to emergency care, the first-line investigation often being plain radiography. The interpretation of this imaging frequently falls to less experienced clinicians despite well-established challenges in reporting. This study presents novel data of clinicians’ confidence in interpreting trauma radiographs, their perception of AI in healthcare, and their support for the development of systems applied to skeletal radiography. Methods A novel questionnaire was distributed through a network of collaborators to clinicians across the Southeast of England. Over a three-month period, responses were compiled into a database before undergoing statistical review. Results The responses of 297 participants were included. The mean self-assessed knowledge of AI in healthcare was 3.68 out of ten, with significantly higher knowledge reported by the most senior doctors (Specialty Trainee/Specialty Registrar or above = 4.88). 13.8% of participants reported an awareness of AI in their clinical practice. Overall, participants indicated substantial favourability towards AI in healthcare (7.87) and in AI applied to skeletal radiography (7.75). There was a preference for a hypothetical system indicating positive findings rather than ruling as negative (7.26 vs 6.20). Conclusions This study identifies clear support, amongst a cross section of student and qualified doctors, for both the general use of AI technology in healthcare and in its application to skeletal radiography for trauma. The development of systems to address this demand appear well founded and popular. The engagement of a small but reticent minority should be sought, along with improving the wider education of doctors on AI.

Background: Traumatic musculoskeletal injuries are a common presentation to emergency care, the first-line investigation often being plain radiography. The interpretation of this imaging frequently falls to less experienced clinicians despite well-established challenges in reporting. This study presents novel data of clinicians' confidence in interpreting trauma radiographs, their perception of AI in healthcare, and their support for the development of systems applied to skeletal radiography. Methods: A novel questionnaire was distributed through a network of collaborators to clinicians across the Southeast of England. Over a three-month period, responses were compiled into a database before undergoing statistical review. Results: The responses of 297 participants were included. The mean self-assessed knowledge of AI in healthcare was 3.68 out of ten, with significantly higher knowledge reported by the most senior doctors (Specialty Trainee/Specialty Registrar or above = 4.88). 13.8% of participants reported an awareness of AI in their clinical practice. Overall, participants indicated substantial favourability towards AI in healthcare (7.87) and in AI applied to skeletal radiography (7.75). There was a preference for a hypothetical system indicating positive findings rather than ruling as negative (7.26 vs 6.20). Conclusions: This study identifies clear support, amongst a cross section of student and qualified doctors, for both the general use of AI technology in healthcare and in its application to skeletal radiography for trauma. The development of systems to address this demand appear well founded and popular. The engagement of a small but reticent minority should be sought, along with improving the wider education of doctors on AI. [ABSTRACT FROM AUTHOR]

Abstract Introduction Pharmacists are frequent users of mobile medical apps (MMA) for drug information (DI) and clinical decision-making purposes. However, the wide range of available MMA may be of variable credibility and results in heterogeneous recommendations. The need for subscription may also influence choice of apps. Objective The objective of this study was to determine the usage pattern of MMA among hospital pharmacists, including their perceptions and factors affecting their choice of apps. Methods This cross-sectional study required respondents to fill in an online questionnaire. The questionnaire included sections on respondents' demographic data, MMA usage pattern, perceived usefulness and opinion on subscription fees. Items were adapted from available literature and validated locally. It was made accessible for 6 weeks starting November 2019 for all pharmacists working in the 23 public hospitals in Sarawak to response (universal sampling). Collected data were analysed using descriptive and inferential statistics. Results A response rate of 37.2% was achieved (n = 162). Respondents were heavily reliant on MMA, with 78.4% accessing them multiple times daily. The majority also agreed that MMA contain correct and up-to-date information. A median of 5 apps were downloaded, suggesting an ultimate app catering for all DI needs was lacking. The Malaysian Drug Formulary was the most downloaded app (88.3%), whereas Lexicomp® was the most “well-rounded” in terms of functionality. Clinical pharmacists were significantly more likely to purchase MMA, in particular UpToDate® (p

Low-value testing and treatment contribute to billions of dollars in waste to the United States health care system annually. High frequency, low-cost testing, including prostate-specific antigen (PSA) testing, is a major contributor to this inefficient health care delivery. Despite decreasing mortality of prostate cancer over the last few decades, the reputation of prostate specific antigen (PSA) for prostate cancer screening has fluctuated over the last decade due to lack of clarity of the benefits of screening and high risk for overtreatment. The value of PSA could be improved by efficient implementation of smarter testing strategies that reduce the harms and increase the benefits. [ABSTRACT FROM AUTHOR]

Effective and sustainable strategies are needed to address the burden of preventable deaths among children under-five in resource-constrained settings. The Tools for Integrated Management of Childhood Illness (TIMCI) project aims to support healthcare providers to identify and manage severe illness, whilst promoting resource stewardship, by introducing pulse oximetry and clinical decision support algorithms (CDSAs) to primary care facilities in India, Kenya, Senegal and Tanzania. Health impact is assessed through: a pragmatic parallel group, superiority cluster randomised controlled trial (RCT), with primary care facilities randomly allocated (1:1) in India to pulse oximetry or control, and (1:1:1) in Tanzania to pulse oximetry plus CDSA, pulse oximetry, or control; and through a quasi-experimental pre-post study in Kenya and Senegal. Devices are implemented with guidance and training, mentorship, and community engagement. Sociodemographic and clinical data are collected from caregivers and records of enrolled sick children aged 0-59 months at study facilities, with phone follow-up on Day 7 (and Day 28 in the RCT). The primary outcomes assessed for the RCT are severe complications (mortality and secondary hospitalisations) by Day 7 and primary hospitalisations (within 24 hours and with referral); and, for the pre-post study, referrals and antibiotic. Secondary outcomes on other aspects of health status, hypoxaemia, referral, follow-up and antimicrobial prescription are also evaluated. In all countries, embedded mixed-method studies further evaluate the effects of the intervention on care and care processes, implementation, cost and cost-effectiveness. Pilot and baseline studies started mid-2021, RCT and post-intervention mid-2022, with anticipated completion mid-2023 and first results late-2023. Study approval has been granted by all relevant institutional review boards, national and WHO ethical review committees. Findings will be shared with communities, healthcare providers, Ministries of Health and other local, national and international stakeholders to facilitate evidence-based decision-making on scale-up. Study registration : NCT04910750 and NCT05065320.

Background: Atrial fibrillation (AF) is a major risk factor for ischemic stroke, and early AF diagnosis may reduce associated morbidity and mortality. A 10-variable predictive model (UNAFIED) was previously developed to estimate patients' 2-year AF risk. This study evaluated a clinical workflow incorporating UNAFIED for screening, education, and follow-up evaluation of patients visiting a cardiology clinic who may be at an elevated risk of developing AF within 2 years., Methods: Patients were included if they were aged ≥ 40 years with a scheduled in-person visit at the Eskenazi Health Cardiology Clinic between October 25, 2021, and August 10, 2022. Clinical decision support identified patients with an elevated AF risk. Initial screening with 1-lead electrocardiogram devices was offered, and routine clinical practice for diagnosis and management was followed. Physicians were surveyed on their use of the workflow, attitudes toward implementation, and perceived impact on patient care., Results: A total of 2827 patients had a clinic visit during the study period, of whom 1395 were eligible to be screened because they were classified as "elevated risk" by the UNAFIED predictive model. AF or atrial flutter diagnosis was newly documented for 29 patients during the study period. Of the newly diagnosed patients, 13 began anticoagulant therapy to mitigate stroke risk. Physicians (n = 13) who used the workflow most clinic days were more likely to indicate that it was easy to use, was not time-consuming, and improved patient care compared with physicians who only used the workflow occasionally., Conclusions: To our knowledge, this study is the first of its kind to demonstrate clinical application of an electronic health record-based AF predictive model. The newly documented diagnoses, however, did not solely result from implementation of UNAFIED. This non-invasive, inexpensive approach could be adopted by other sites wishing to proactively screen patients at elevated risk for AF. Other sites should verify the model's performance in their own settings and ensure compliance with evolving regulatory requirements where applicable., Competing Interests: Declarations. Ethics approval and consent to participate: The final protocol and any amendments were reviewed and approved by the Indiana University institutional review board (IRB) and Eskenazi Health. The IRBs waived the requirement of signed informed consent, but patients were provided an information sheet about the study and were allowed to decline enrollment. Consent for publication: Not applicable. Competing interests: R.W. Grout, R.J. Kovacs, C. King, and A. Torabi are employees of Indiana University, which received financial support from Pfizer Inc. in connection with the execution of this study. J. Rajkumar was an employee of Eskenazi Health and B. DiRenzo and T.A. Walroth are employees of Eskenazi Health, which received financial support from Pfizer Inc. in connection with the execution of this study. M. Ateya, S. Hart, and S. Sporrer are employees of Pfizer Inc., (© 2024. The Author(s).)

Background: Geriatric comanagement has been shown to improve outcomes of older surgical inpatients. Furthermore, the choice of discharge location, that is, continuity of care, can have a fundamental impact on convalescence. These challenges and demands have led to the SURGE-Ahead project that aims to develop a clinical decision support system (CDSS) for geriatric comanagement in surgical clinics including a decision support for the best continuity of care option, supported by artificial intelligence (AI) algorithms., Objective: This qualitative study aims to explore the current challenges and demands in surgical geriatric patient care. Based on these challenges, the study explores the attitude of interviewees toward the introduction of an AI-supported CDSS (AI-CDSS) in geriatric patient care in surgery, focusing on technical and general wishes about an AI-CDSS, as well as ethical considerations., Methods: In this study, 15 personal interviews with physicians, nurses, physiotherapists, and social workers, employed in surgical departments at a university hospital in Southern Germany, were conducted in April 2022. Interviews were conducted in person, transcribed, and coded by 2 researchers (AU, LB) using content and thematic analysis. During the analysis, quotes were sorted into the main categories of geriatric patient care, use of an AI-CDSS, and ethical considerations by 2 authors (AU, LB). The main themes of the interviews were subsequently described in a narrative synthesis, citing key quotes., Results: In total, 399 quotes were extracted and categorized from the interviews. Most quotes could be assigned to the primary code challenges in geriatric patient care (111 quotes), with the most frequent subcode being medical challenges (45 quotes). More quotes were assigned to the primary code chances of an AI-CDSS (37 quotes), with its most frequent subcode being holistic patient overview (16 quotes), then to the primary code limits of an AI-CDSS (26 quotes). Regarding the primary code technical wishes (37 quotes), most quotes could be assigned to the subcode intuitive usability (15 quotes), followed by mobile availability and easy access (11 quotes). Regarding the main category ethical aspects of an AI-CDSS, most quotes could be assigned to the subcode critical position toward trust in an AI-CDSS (9 quotes), followed by the subcodes respecting the patient's will and individual situation (8 quotes) and responsibility remaining in the hands of humans (7 quotes)., Conclusions: Support regarding medical geriatric challenges and responsible handling of AI-based recommendations, as well as necessity for a holistic approach focused on usability, were the most important topics of health care professionals in surgery regarding development of an AI-CDSS for geriatric care. These findings, together with the wish to preserve the patient-caregiver relationship, will help set the focus for the ongoing development of AI-supported CDSS., (© Adriane Uihlein, Lisa Beissel, Anna Hanane Ajlani, Marcin Orzechowski, Christoph Leinert, Thomas Derya Kocar, Carlos Pankratz, Konrad Schuetze, Florian Gebhard, Florian Steger, Marina Liselotte Fotteler, Michael Denkinger. Originally published in JMIR Aging (https://aging.jmir.org).)

Purpose: Treatment for pulmonary hypertension includes medications with risk evaluation and mitigation strategy (REMS) programs. Health-system inpatient pharmacies dispensing these agents must comply with inpatient REMS dispensing criteria. Implementing a health-system policy with computerized provider order entry (CPOE) decision support may improve REMS compliance., Methods: This was a retrospective, quasi-experimental study comparing REMS compliance before and after development of a policy with CPOE decision support that was implemented in August 2019. Patients 18 years of age or older with a diagnosis of pulmonary hypertension were included if they received at least one dose of an endothelin receptor antagonist or riociguat while hospitalized. Patients were included in the preintervention group if they were hospitalized between August 1, 2017, and August 31, 2019, and in the postintervention group if they were hospitalized between September 1, 2019, and August 31, 2021. The primary outcome was the REMS compliance rate. Secondary endpoints included the time to REMS compliance and independent factors associated with failed or delayed REMS compliance., Results: A total of 150 patients were included, with 75 patients in both the pre- and postintervention groups. Compliance increased significantly from the preintervention (50%) to postintervention (92%) group (P < 0.001). Time to compliance was also significantly reduced from 770 minutes in the preintervention group to 140 minutes in the postintervention group (P = 0.031). Factors independently associated with REMS compliance were being in the postintervention group (odds ratio, 16.9; 95% confidence interval, 5.8-49.2) and being admitted to a pulmonary hypertension center for comprehensive care. (odds ratio, 7.8; 95% confidence interval, 2.9-21.2)., Conclusion: A health-system policy with CPOE decision support improved both the rate of and time to compliance with inpatient REMS dispensing procedures., (© American Society of Health-System Pharmacists 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Introduction: Clinical decision support system (CDSS) is an application used to aid decision-making and provide knowledge regarding treatment, diagnosis, and laboratory testing. Despite the associated benefits, the underutilization of CDSS is causing a global challenge. In Palestine, CDSS remains unimplemented, prompting a study on knowledge levels and factors influencing CDSS acceptance among physicians., Objective: The study aimed to assess physicians' knowledge of CDSS and understand factors impacting CDSS adoption, including performance expectancy (PE), effort expectancy (EE), computer self-efficacy, involvement in decision-making, perceived threat to autonomy, and social influence., Methods: A validated questionnaire-based survey consisting of seven constructs was used in this study. The constructs were derived from the unified theory of acceptance and use of technology (UTAUT). The target population was 363 resident physicians and general practitioners (GPs) from three governmental hospitals. In total, 124 participants were enrolled, and data was collected between June 1 and June 30, 2022. Generalized linear regression was used for data analysis., Results: Most participants were males (75%), with an average of 2.9 years of experience. Despite limited CDSS knowledge, participants strongly intended to adopt it (mean = 3.91, SD = 0.65). Our results showed a significant effect of PE (p = 0.000, r = 0.469), EE (p = 0.001, r = 0.294), computer self-efficacy level (p = 0.015, r = 0.218), perceived threat to professional autonomy (p = 0.001, r = -0.302), and social influence (p = 0.047, r = 0.179) on the intention to adopt CDSS. However, involvement in decision-making about CDSS had no significant impact (p = 0.123, r = 0.140)., Conclusion: This study provides the basis for implementing CDSS in Palestinian settings. Despite the readiness of Palestinian healthcare providers, introducing CDSS as an aiding, effort-free tool is a prerequisite for successful CDSS adoption in Palestine., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Sultan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The array of complex and evolving patient data has limited clinical decision making in the emergency department (ED). This study introduces an advanced deep learning algorithm designed to enhance real-time prediction accuracy for integration into a novel Clinical Decision Support System (CDSS). A retrospective study was conducted using data from a level 1 tertiary hospital. The algorithm's predictive performance was evaluated based on in-hospital cardiac arrest, inotropic circulatory support, advanced airway, and intensive care unit admission. We developed an artificial intelligence (AI) algorithm for CDSS that integrates multiple data modalities, including vitals, laboratory, and imaging results from electronic health records. The AI model was trained and tested on a dataset of 237,059 ED visits. The algorithm's predictions, based solely on triage information, significantly outperformed traditional logistic regression models, with notable improvements in the area under the precision-recall curve (AUPRC). Additionally, predictive accuracy improved with the inclusion of continuous data input at shorter intervals. This study suggests the feasibility of using AI algorithms in diverse clinical scenarios, particularly for earlier detection of clinical deterioration. Future work should focus on expanding the dataset and enhancing real-time data integration across multiple centers to further optimize its application within the novel CDSS., Competing Interests: Declarations. Competing interests: All authors except Kyung Hyun Lee and Kwang Joon Kim have filed a joint patent related to the technology involved in this research, listing their respective institutions (Yonsei University College of Medicine, AITRICS) as the applicants. Ji Hoon Kim, the corresponding author, also serves as a medical advisor at AITRICS. While Kwang Joon Kim serves as the chief executive officer at AITRICS, he does not have any conflicts of interest that might be perceived to influence the results and/or discussion reported in this paper. Kyung Hyun Lee has no conflict of interest to declare. Ethical approval: This study adhered to the principles outlined in the Declaration of Helsinki and was approved by the Institutional Review Board of Severance Hospital Human Research Protection Center (approval nos. 4-2022-0544 and 4-2022-0123). Due to the retrospective nature of the study, the need of informed consent was waived by the Institutional Review Board of Severance Hospital Human Research Protection Center., (© 2024. The Author(s).)

Background: This study explored the feasibility of integrating a clinical decision support tool into general practice clinical management software in Australia to prompt for alcohol screening among patients who are pregnant or planning a pregnancy. The study aimed to increase understanding of what is an appropriate and acceptable clinical decision support tool, the circumstances when a prompt to use such a screening tool should occur, and the barriers and enablers of successful implementation., Methods: This feasibility study employed a mixed methods approach and purposive sampling to identify key stakeholders to interview. Participants included vendors of clinical software used in Australian general practice, clinicians in general practice, and relevant others. Data from a literature review and 23 interviews were analysed leading to recommendations which were 'sense-tested' by an additional 22 stakeholders., Results: Although there are at least 18 clinical software packages used in Australian general practice, it is feasible to integrate an alcohol screening tool for pregnancy into software for the majority of general practices in Australia. The AUDIT-C alcohol screening tool for pregnancy was widely accepted as suitable for such a purpose. Clinicians suggested the greatest barriers to screening were lack of time within antenatal consultations and insufficient remuneration for longer consultations. Many clinicians saw opportunity for introducing a multifunctional antenatal tool that could incorporate screening and clinical decision support for alcohol, tobacco and other substance use, mental health, domestic and family violence and potentially other areas informing healthy pregnancy. It could also be used opportunistically for preconception screening and counselling. Deployment of the tool could be supported by an education campaign from professional associations., Conclusion: The integration of a tool for screening for alcohol use among women who are pregnant or planning pregnancy into general practice clinical software is feasible; however, a multifunctional antenatal screening tool, incorporating other psychosocial elements, was considered more useful than a stand-alone alcohol screening tool. Codesign is needed with vendors and end-users to develop an acceptable tool that can be widely implemented. Issues with GP renumeration need also to be addressed to encourage alcohol screening pre-pregnancy and in the early months of pregnancy., Competing Interests: Declarations. Ethics approval and consent to participate: The research was performed in accordance with the relevant guidelines and regulations, e.g. the Australian National Statement on Ethical Conduct in Human Research which is in accordance with the National Health and Medical Research Council Act 1992 and the Declaration of Helsinki. Ethics approval for the study was obtained from The University of Melbourne Human Research Ethics Committee: ID No. 2022–25585-35607–3. The GP interviews were conducted as part of a linked study also approved by The University of Melbourne Human Research Ethics Committee: ID No. 2022–24433-34787–6. All interviewed participants were given plain language information statement and provided written informed consent. The online feedback questionnaire contained a summary of consent and statement that submission of the questionnaire implied consent to participate. Consent for publication: No individual person’s identifiable information is used in this manuscript. The consent form, however, contained the following statement: “I understand that given the small number of participants involved in the study, it may not be possible to guarantee my anonymity.” Competing interests: Authors D.B., R.C. and C.H. are part of the HaBIC Research Information Technology Unit (HaBIC R2) unit in The University of Melbourne Department of General Practice and Primary Care; HaBIC R2 works with general practice clinical software vendors around data acquisition and clinical decision support tools., (© 2024. The Author(s).)

Background: The complexities of diagnosing cancer in general practice has driven the development of quality improvement (QI) interventions, including clinical decision support (CDS) and auditing tools. Future Health Today (FHT) is a novel QI tool, consisting of CDS at the point-of-care, practice population-level auditing, recall, and the monitoring of QI activities., Objectives: Explore the acceptability and usability of the FHT cancer module, which flags patients with abnormal test results that may be indicative of undiagnosed cancer., Methods: Interviews were conducted with general practitioners (GPs) and general practice nurses (GPNs), from practices participating in a randomized trial evaluating the appropriate follow-up of patients. Clinical Performance Feedback Intervention Theory (CP-FIT) was used to analyse and interpret the data., Results: The majority of practices reported not using the auditing and QI components of the tool, only the CDS which was delivered at the point-of-care. The tool was used primarily by GPs; GPNs did not perceive the clinical recommendations to be within their role. For the CDS, facilitators for use included a good workflow fit, ease of use, low time cost, importance, and perceived knowledge gain. Barriers for use of the CDS included accuracy, competing priorities, and the patient population., Conclusions: The CDS aligned with the clinical workflow of GPs, was considered non-disruptive to the consultation and easy to implement into usual care. By applying the CP-FIT theory, we were able to demonstrate the key drivers for GPs using the tool, and what limited the use by GPNs., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background Lowering fresh gas flow (FGF) can help decrease the carbon footprint of the operating room as FGF levels act as an indirect measure of anesthetic gas waste. Objective The aim of this quality improvement project was to reduce clinician FGF during general anesthesia with clinical decision support (CDS) tools within the electronic health record (EHR) at a single institution. Methods A non-interruptive alert to reduce FGF was coded into the anesthesia intraoperative EHR workspace to alert whenever the 10-minute average FGF exceeded 1 L/min. It was targeted at anesthesia residents, attendings, and certified registered nurse anesthetists at a single US large academic level 1 trauma center. The number of general anesthesia cases with a target FGF of ≤2 L/min and the amount of sevoflurane (L/hr) was tracked on an individual and institutional basis. Results Following CDS implementation from July 2023 through July 2024, 2677 of 4573 (58.5%) had a mean FGF ≤2 L/min, demonstrating a 116.7% increase from our institution's baseline of 27.0% (1200 of 4446 cases) from July 2022 to June 2023, corresponding to a sevoflurane usage reduction of 36.7%. Conclusions Implementing a non-interruptive alert in the EHR altered institution-level behaviors to reduce environmentally harmful anesthetic gas emissions.

Background: Clinical decision-making is a crucial skill for undergraduate nursing students to develop. This study assessed the effects of a mobile clinical decision-support (mCDS) app on clinical reasoning and decision-making in undergraduate nursing students and their perceived acceptance of the app., Method: Following the technology acceptance model, a randomized controlled trial with a pretest-posttest design was conducted. Students in the experimental group used an mCDS app (Medscape) for sepsis and cardiac tamponade scenarios, and students in the control group used traditional tools. Outcomes included perceived usefulness, ease of use, attitudes, clinical reasoning, clinical decision-making skills, satisfaction, and behavioral intent to use the app., Results: Students in the experimental group had significantly higher posttest clinical reasoning scores ( p = .04) and cardiac scenario clinical decision-making scores ( p = .01) compared with students in the control group., Conclusion: Integrating mCDS apps into clinical nursing education enhances students' clinical reasoning and decision-making. [ J Nurs Educ . 2024;63(12):835-843.] .

Background: Clinical pathways are sequences of events followed during the clinical care of a group of patients who meet pre-defined criteria. They have many applications ranging from healthcare evaluation and optimization to clinical decision support. These pathways can be discovered from existing healthcare data, in particular with machine learning which is a family of methods used to learn patterns from data. This review provides a comprehensive overview of the literature concerning the use of machine learning methods for clinical pathway discovery from patient data., Methods: Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method , we conducted a systematic review of the existing literature. We searched 6 databases, i.e., ACM Digital Library, ScienceDirect, Web of Science, PubMed, IEEE Xplore, and Scopus spanning from January 2004 to December 2023 using search terms pertinent to clinical pathways and their development. Subsequently, the retrieved papers were analyzed to assess their relevance to the scope of this study., Results: In total, 131 papers that met the specified inclusion criteria were identified. These papers expressed diverse motivations behind data-driven clinical pathway discovery ranging from knowledge discovery to conformance checking with established clinical guidelines (derived from existing literature and clinical experts). Notably, the predominant methods employed (67.2%, n=88) involved unsupervised machine learning techniques, such as clustering and process mining., Conclusions: Relevant clinical pathways can be discovered from patient data using machine learning methods, with the desirable potential to aid clinical decision-making in healthcare. However, to reach this objective, the methods used to discover pathways should be reproducible, and rigorous performance evaluation by clinical experts needs to be conducted for validation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Improving care for gender-diverse (GD) patients necessitates developing informatics tools and approaches to support optimal laboratory testing. This requires increased functionality and standardization of laboratory information system/electronic health record and data collection processes. Data tailored to accommodate immediate clinical care and clinical decision support (CDS) also have an impact on interoperability and downstream data needs for patients. Informatics tools can shape the clinical care experience for GD patients by careful design of laboratory-patient interactions., Competing Interests: Disclosure The authors have nothing to disclose., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Rheumatology faces a critical shortage of health-care professionals, exacerbated by an ageing patient population and escalating costs, resulting in widening gaps in care. Exponential advances in digital health technologies (DHTs) in the past 5 years offer new opportunities to address these challenges and could contribute to overall improved health care. However, keeping pace with innovations and integrating them into clinical practice can be challenging. This Review explores the transformative potential of DHTs for rheumatology in reshaping the entire patient pathway and redefining the roles of patients and providers, and discusses the potential barriers to DHT integration. Key technologies, such as large language models, clinical decision-support systems, digital therapeutics, electronic patient-reported outcomes, digital biomarkers, robots, self-sampling devices and artificial intelligence-based scribes, can be implemented along the patient pathway. A digital-first hybrid stepped-care patient pathway could combine in-person and remote care, enabling personalized and continuous monitoring through a digital safety net. The potential benefits and risks of transforming the traditional patient-provider relationship into a digital health triad with technology are discussed. Collaborative efforts are needed to navigate the evolving digital health landscape and harness the potential of DHTs to improve rheumatology care., Competing Interests: Competing interests: J.K. declares research support from Abbvie, BMBF, EIT Health, GSK, Sanofi and Vila Health, and honoraria and consulting fees from Abbvie, AstraZeneca, BMS, Boehringer Ingelheim, Chugai, GAIA, Galapagos, GSK, Janssen, Lilly, Medac, Novartis, Pfizer, Sobi, Rheumaakademie, UCB, Vila Health and Werfen. T.H. declares research support from Fresenius Kabi, Eli Lilly and Pfizer, and honoraria and consulting fees from Roche, Novartis, BMS, Eli Lilly, Janssen, UCB, Pfizer and Werfen. T.H. holds patents with and is a board member of Atreon and Vtuls. L.G. declares no competing interests., (© 2024. Springer Nature Limited.)

Background and Objective: Primary care providers (PCPs) use poorly organized patient information in electronic health records (EHR) within a limited time when treating patients with chronic pain. Clinical decision support (CDS) tools assist PCPs by synthesizing patient information and prompting guideline-concordant treatment decisions. A CDS tool- Chronic Pain OneSheet was developed through a user-centered design process to support PCP's decision-making for patients with chronic noncancer pain. OneSheet aggregates relevant patient information in one place in the EHR. OneSheet also guides PCPs in completing guideline-recommended opioid risk management tasks, tracking patient treatments, and documenting pain-related symptoms. Our objective was to identify barriers, facilitators, and recommendations to increase OneSheet use for chronic noncancer pain management in primary care., Methods: We conducted 19 qualitative interviews with PCPs from two academic health systems who had access to OneSheet in their EHR. Interview transcripts were coded to identify common themes using a modified thematic approach., Results: PCPs identified several barriers to using OneSheet, including limited time to address patient needs associated with multiple chronic conditions, resistance to changing established workflows, and complex OneSheet display. PCPs reported several facilitators to using OneSheet, such as OneSheet's ability to serve as a hub for chronic pain data, easy access to features that facilitate completing mandatory tasks and improved planning for certain patient visits. PCPs recommended prioritizing access to commonly used features, adding display customization capabilities, and expanding access to patients and other team members to increase OneSheet use., Conclusion: Our findings highlight the importance of acknowledging the PCP workflow and task load when designing CDS tools. Future CDS tools should balance the extent of information provided with assisting PCPs to fulfill mandatory tasks. Expanding CDS tools to multiple care team members and patients can also lead to higher use by facilitating data entry, leading to more streamlined care delivery., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [Christopher A. Harle discloses past research grant funding for studies of information technology to support pain care to his institution from Security Risk Solutions, Inc. and the Agency for Healthcare Research and Quality. Christopher A. Harle also discloses personal fees from Indiana Health Information Exchange, personal fees from New York eHealth Collaborative, personal fees from RTI International, outside the submitted work. Olena Mazurenko discloses past and current research grant funding for studies of information technology to support pain care to her institution from Security Risk Solutions, Inc. and the Agency for Healthcare Research and Quality. Meredith C.B. Adams and Robert W. Hurley are supported by National Institutes of Health funding through the NIH K08 EB022631, U24 NS115708, and R33 DA046085. Joshua R. Vest is a founder and equity holder in Uppstroms Inc., a health technology company. The other authors have no conflicts to report]., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Pediatric high blood pressure (BP) predicts future cardiovascular disease events. High BP is improperly measured, underrecognized, and undermanaged especially in disadvantaged populations. In a large, diverse, academic pediatric practice, we detail the associations of a comprehensive initiative with high BP provider recognition., Methods: A comprehensive BP initiative was promulgated including (1) retraining providers and staff on BP management; (2) deploying equipment at 55+ sites; and (3) electronic decision support tool alerting staff and clinicians and suggesting management. During the 14-month preintervention and 14-month postintervention periods, data on BP and patient characteristics were collected. The outcome was incident BP recognition defined as any of the following: BP-specific International Classification of Diseases, Tenth Revision , diagnosis; problem list entry; specialty referral; diagnostic testing; repeat visit; or antihypertensives. Dichotomized as under versus at or over 13 years of age, analyses utilized interrupted time series and multivariable-adjusted logistic regression., Results: From preintervention (children, n=105 674; adolescents, n=54 365) to postintervention (children, n=87 917; adolescents, n=56 470), the proportion measured with high BP declined in children and adolescents (30% versus 14% and 30% versus 15%, respectively, each P <0.001). Post-intervention, high BP provider recognition was 58% higher in children (4.6%-7.3%) and 43% higher in adolescents (7.9% versus 11.3%; P <0.001 for both). The improvement was not different in disadvantaged race, ethnicity, or zip codes., Conclusions: A comprehensive BP initiative was associated with more appropriate high BP measurement and recognition including in disadvantaged populations. Future work may address the low overall and nonsustained recognition and consideration of electronic decision support for pediatric BP management and mitigating disparities., Competing Interests: None.

Background: 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., Methods: 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)., Results: 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)., Conclusions: 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Herbal prescription recommendation (HPR) is a hotspot in the research of clinical intelligent decision support. Recently plentiful HPR models based on deep neural networks have been proposed. Owing to insufficient data, e.g., lack of knowledge of molecular, TCM theory, and herbal dosage in HPR modeling, the existing models suffer from challenges, e.g., plain prediction precision, and are far from real-world clinics., Purpose: To address these problems, we proposed a novel herbal prescription recommendation model with the representation fusion of large TCM semantics and molecular knowledge (termed PresRecRF)., Study Design and Methods: PresRecRF comprises three key modules. The representation learning module consists of two key components: a molecular knowledge representation component, integrating molecular knowledge into the herb-symptom-protein knowledge graph to enhance representations for herbs and symptoms; and a TCM knowledge representation component, leveraging BERT and ChatGPT to acquire TCM knowledge-enriched semantic representations. We introduced a representation fusion module to effectively merge molecular and TCM semantic representations. In the herb recommendation module, a multi-task objective loss is implemented to predict both herbs and dosages simultaneously., Results: The experimental results on two clinical datasets show that PresRecRF can achieve the optimal performance. Further analysis of ablation, hyper-parameters, and case studies indicate the effectiveness and reliability of the proposed model, suggesting that it can help precision medicine and treatment recommendations., Conclusion: The entire process of the proposed PresRecRF model closely mirrors the actual diagnosis and treatment procedures carried out by doctors, which are better applied in real clinical scenarios. The source codes of PresRecRF is available at https://github.com/2020MEAI/PresRecRF., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier GmbH.)

Background: Breast Cancer (BC) poses significant challenges in treatment decision-making. Multiple first treatment lines are currently available, determined by several patient-specific factors that need to be considered in the decision-making process., Purpose: To present CureMate, a Clinical Decision Support System to predict the most effective initial treatment for BC patients. Different artificial intelligence models based on demographic, anatomopathological and magnetic resonance imaging variables are studied. CureMate's web application allows for easy use of the best model., Methods: A database of 232 BCE patients, each described by 29 variables, was established. Out of four machine learning algorithms, specifically Decision Tree Classifier (DTC), Gaussian Naïve Bayes (GNB), k-Nearest Neighbor (K-NN), and Support Vector Machine (SVM), the most suitable model for the task was identified, optimized and independently tested., Results: SVM was identified as the best model for BC treatment planning, resulting in a test accuracy of 0.933. CureMate's web application, including the SVM model, allows for introducing the relevant patient variables and displays the suggested first treatment step, as well as a diagram of the following steps., Conclusion: The results demonstrate CureMate's high accuracy and effectiveness in clinical settings, indicating its potential to aid practitioners in making informed therapeutic decisions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Despite the recognized benefits of integrating patient perspectives into healthcare design and clinical decision support, theoretical approaches and standardized methods are lacking. Various strategies, such as developing pathways, have evolved to address these challenges. Previous research emphasized the need for a framework for care pathways that includes theoretical principles, extensive user involvement, and data from electronic health records to bridge the gap between different fields and disciplines. Standardizing the representation of the patient perspective could facilitate its sharing across healthcare organizations and domains and its integration into journal systems, shifting the balance of power from the provider to the patient., Objectives: This study aims to 1) Identify research approaches taken to develop patient-centred, integrated, care pathways supported by electronic health records 2) Propose a socio-technical framework for designing patient-centred care pathways across multiple healthcare levels that integrates the voice of the patient with the knowledge of the care provider and technological perspectives., Methods: This study conducted a scoping review following the Joanna Briggs Institute guidelines and PRISMA-ScR protocol. The databases PubMed, Scopus, Web of Science, ProQuest, IEEE, and Google Scholar were searched using a key term search strategy including variations of patient-centred, integrated care, pathway, framework and model to identify relevant studies. Eligible articles included peer-reviewed literature documenting methodologies for mapping patient-centred, integrated care pathways in healthcare service design., Results: This review summarizes the application of care pathway modelling practices across various areas of healthcare innovation. The search resulted in 410 studies, with 16 articles included after the full review and grey literature search., Conclusions: Our research illustrated incorporating patient perspectives into modelling care pathways and healthcare service design. Regardless of the medical domain, our methodology proposes an approach for modelling patient-centred, integrated care pathways across the care continuum, including using electronic health records to support the pathways., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Influenza is an important public health issue given its significant burden of disease. In Italy, the unsatisfactory coverage rate in people ≥65 years underlines the need to improve the current vaccination pathway. This study aims to define an integrated pathway across primary and secondary care, facilitated by a digital clinical decision support system (CDSS), to enhance vaccination coverage in people ≥65 years by actively recruiting patients in hospitals and administering vaccination. Moreover, the study seeks to gauge the potential epidemiological and economic impact of this approach. The methodology consisted of two main phases: definition of the integrated pathway and CDSS and estimation of the potential epidemiological and economic impact resulting from the implementation of the pathway in the whole Lazio region. Assuming an increase of influenza vaccination coverage from the current rate of 60% to 65% in ≥65 years old population in the Lazio region thanks to the pathway implementation, an increase of 8% in avoided influenza cases, avoided influenza- or pneumonia-related hospitalizations and avoided influenza-related outpatient visits was estimated with a relative increase in savings for hospitalizations and outpatient visits of up to 11.85%. Setting the vaccination coverage at 70%, the impact is doubled. Alongside offering a predictive estimate of the pathway's potential impact, both epidemiological and economic, this project, with its robust methodology, may serve as a scalable and transferable model for enhancing vaccination coverage at national and international level., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Public Health Association.)

Purpose of Review: This review explores the relevance of eHealth technologies to address unmet needs in pediatric respiratory allergies, particularly allergic rhinitis (AR) and asthma. Given the increasing burden of these conditions, there is a pressing need for effective solutions to enhance disease surveillance, diagnosis, and management., Recent Findings: Recent literature highlights the potential of eHealth tools to transform pediatric respiratory allergy care. The use of digital data for infodemiology, application of machine learning models to improve diagnostic sensitivity, smartphone apps with digital patient reported outcome measure (PROMs) and embedded sensors to monitor disease, healthcare professional dashboards with real-time data monitoring and clinical decision support systems (CDSS) are advances emerging to optimize pediatric respiratory allergy care., Summary: Integrating eHealth technologies into the pediatric respiratory allergy care pathway is a potential solution for current healthcare challenges to better meet the needs of children with AR and asthma. However, while the potential of eHealth is evident, its widespread implementation in real-world practice requires continued research, collaboration, and efforts to overcome existing barriers., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Objectives: To determine whether clinical decision support systems (CDSS) for acute kidney injury (AKI) would enhance patient outcomes in terms of mortality, dialysis, and acute kidney damage progression., Methods: The systematic review and meta-analysis included the relevant randomized controlled trials (RCTs) retrieved from PubMed, EMBASE, Web of Science, Cochrane, and SCOPUS databases until 21st January 2024. The meta-analysis was done using (RevMan 5.4.1). PROSPERO ID: CRD42024517399., Results: Our meta-analysis included ten RCTs with 18,355 patients. There was no significant difference between CDSS and usual care in all-cause mortality (RR: 1.00 with 95% CI [0.93, 1.07], p  = 0.91) and renal replacement therapy (RR: 1.11 with 95% CI [0.99, 1.24], p  = 0.07). However, CDSS was significantly associated with a decreased incidence of hyperkalemia (RR: 0.27 with 95% CI [0.10, 0.73], p  = 0.01) and increased eGFR change (MD: 1.97 with 95% CI [0.47, 3.48], p  = 0.01)., Conclusions: CDSS were not associated with clinical benefit in patients with AKI, with no effect on all-cause mortality or the need for renal replacement therapy. However, CDSS reduced the incidence of hyperkalemia and improved eGFR change in AKI patients.

Aims: Emergency department (ED) providers play an important role in the management of patients with acute heart failure (AHF). We present findings from a pilot study of an electronic decision support that includes personalized risk estimates using the STRIDE-HF risk tool and tailored recommendations for initiating guideline directed medical therapy (GDMT) among appropriate patients., Methods: Among ED patients treated for AHF who were discharged from the ED or the ED-based observation unit in two EDs from 1 January 2023 to 31 July 2023, we assess prescriptions to the four classes of GDMT at two intervals: (1) ED arrival and (2) ED discharge. Specifically, we report active prescriptions for beta-blockers (BBs), renin-angiotensin receptor system inhibitors (RASis), sodium-glucose transport protein 2 inhibitors (SGLT2is) and mineralocorticoid receptor antagonists (MRA) among patients with reduced ejection fraction (HFrEF) and mildly reduced (HFmrEF). Second, we describe rates of 30-day serious adverse events (SAE) (death, cardiopulmonary resuscitation, balloon-pump insertion, intubation, new dialysis, myocardial infarction or coronary revascularization) among patients predicted to be very low risk by STRIDE-HF and discharged home., Results: Among 234 discharged patients, 55% were female and 76% were non-White. We found 51 (21.8%), 21 (9.0%) and 126 (53.8%) had HFrEF, HFmEF and HFpEF, respectively, while 36 (15.4%) were missing EF, and 51 (22%) were very low risk, 82 (35%) were low risk, 60 (26%) were medium risk and 41 (18%) were high risk. Among HFrEF patients, 68.6%, 66.7%, 25.5% and 19.6% were on a RASi, BB, SGLT2i and MRA, respectively, at ED arrival, while 42.9%, 66.7%, 14.3% and 4.8% of HFmrEF patients were on a RASi, BB, SGLT2i and MRA, respectively. Among patients with HFpEF, only 6 (4.8%) were on an SGLT2i at ED arrival. The most prescribed new medication at ED discharge was an SGLT2i, with a nearly 10% increase in the proportion of patients with an active prescription for SGLT2i at ED discharge among HFrEF and HFmEF patients. We observed no 30-day SAE among the 51 patients predicted to be very low risk and discharged home., Conclusions: Ongoing treatment with GDMT at ED arrival was sub-optimal. Initiation among appropriate patients at discharge may be feasible and safe., (© 2024 The Author(s). ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.)

Introduction: This review aims to synthesise the literature on the efficacy, evolution, and challenges of implementing Clincian Decision Support Systems (CDSS) in the realm of mental health, addiction, and concurrent disorders., Methods: Following PRISMA guidelines, a systematic review and meta-analysis were performed. Searches conducted in databases such as MEDLINE, Embase, CINAHL, PsycINFO, and Web of Science through 25 May 2023, yielded 27,344 records. After necessary exclusions, 69 records were allocated for detailed synthesis. In the examination of patient outcomes with a focus on metrics such as therapeutic efficacy, patient satisfaction, and treatment acceptance, meta-analytic techniques were employed to synthesise data from randomised controlled trials., Results: A total of 69 studies were included, revealing a shift from knowledge-based models pre-2017 to a rise in data-driven models post-2017. The majority of models were found to be in Stage 2 or 4 of maturity. The meta-analysis showed an effect size of -0.11 for addiction-related outcomes and a stronger effect size of -0.50 for patient satisfaction and acceptance of CDSS., Discussion: The results indicate a shift from knowledge-based to data-driven CDSS approaches, aligned with advances in machine learning and big data. Although the immediate impact on addiction outcomes is modest, higher patient satisfaction suggests promise for wider CDSS use. Identified challenges include alert fatigue and opaque AI models., Conclusion: CDSS shows promise in mental health and addiction treatment but requires a nuanced approach for effective and ethical implementation. The results emphasise the need for continued research to ensure optimised and equitable use in healthcare settings., (© 2024 The Author(s). Journal of Evaluation in Clinical Practice published by John Wiley & Sons Ltd.)

There has been a steady rise in the use of clinical decision support (CDS) tools to guide nephrology as well as general clinical care. Through guidance set by federal agencies and concerns raised by clinical investigators, there has been an equal rise in understanding whether such tools exhibit algorithmic bias leading to unfairness. This has spurred the more fundamental question of whether sensitive variables such as race should be included in CDS tools. In order to properly answer this question, it is necessary to understand how algorithmic bias arises. We break down 3 sources of bias encountered when using electronic health record data to develop CDS tools: (1) use of proxy variables, (2) observability concerns and (3) underlying heterogeneity. We discuss how answering the question of whether to include sensitive variables like race often hinges more on qualitative considerations than on quantitative analysis, dependent on the function that the sensitive variable serves. Based on our experience with our own institution's CDS governance group, we show how health system-based governance committees play a central role in guiding these difficult and important considerations. Ultimately, our goal is to foster a community practice of model development and governance teams that emphasizes consciousness about sensitive variables and prioritizes equity., (Copyright © 2024 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.)

Background: Despite atrial fibrillation guideline recommendations, many patients with heart failure with reduced ejection fraction (EF) continue to receive IV diltiazem for acute rate control., Objective: Our institution recently implemented a clinical decision support system (CDSS)-based tool that recommends against the use of diltiazem in patients with an EF ≤ 40%. The objective of this study was to evaluate outcomes of adherence to the aforementioned CDSS-based tool., Methods: This multi-hospital, retrospective study assessed patients who triggered the CDSS alert and compared those who did and did not discontinue diltiazem. The primary outcome was the occurrence of clinical deterioration. The primary endpoint was compared utilizing a Fisher's Exact Test, and a multivariate logistic regression model was developed to confirm the results of the primary analysis., Results: A total of 246 patients were included in this study with 146 patients in the nonadherent group (received diltiazem) and 100 patients in the adherent group (did not receive diltiazem). There was a higher proportion of patients experiencing clinical deterioration in the alert nonadherence group (33% vs 21%, P = 0.044), including increased utilization of inotropes and vasopressors, and higher rate of transfer to ICU., Conclusion and Relevance: In patients with heart failure with reduced EF, diltiazem use after nonadherence to a CDSS alert resulted in an increased risk of clinical deterioration. This study highlights the need for improved provider adherence to diltiazem clinical decision support systems., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Purpose: The treatment of severely injured patients in the resuscitation room of an emergency department requires numerous critical decisions, often under immense time pressure, which places very high demands on the facility and the interdisciplinary team. Computer-based cognitive aids are a valuable tool, especially in education and training of medical professionals. For the management of polytrauma cases, TraumaFlow, a workflow management-based clinical decision support system, was developed. The system supports the registration and coordination of activities in the resuscitation room and actively recommends diagnosis and treatment actions., Methods: Based on medical guidelines, a resuscitation room algorithm was developed according to the cABCDE scheme. The algorithm was then modeled using the process description language BPMN 2.0 and implemented in a workflow management system. In addition, a web-based user interface that provides assistance functions was developed. An evaluation study was conducted with 11 final-year medical students and three residents to assess the applicability of TraumaFlow in a case-based training scenario., Results: TraumaFlow significantly improved guideline-based decision-making, provided more complete therapy, and reduced treatment errors. The system was shown to be beneficial not only for the education of low- and medium-experienced users but also for the training of highly experienced physicians. 92% of the participants felt more confident with computer-aided decision support and considered TraumaFlow useful for the training of polytrauma treatment. In addition, 62% acknowledged a higher training effect., Conclusion: TraumaFlow enables real-time decision support for the treatment of polytrauma patients. It improves guideline-based decision-making in complex and critical situations and reduces treatment errors. Supporting functions, such as the automatic treatment documentation and the calculation of medical scores, enable the trauma team to focus on the primary task. TraumaFlow was developed to support the training of medical students and experienced professionals. Each training session is documented and can be objectively and qualitatively evaluated., Competing Interests: Declarations. Competing interests: The authors declare that they have no competing interests. Ethics approval and consent to participate: The questionnaire and methodology for this study were approved by the local ethics committee (reference no. 118/22-ek). Consent to participate: Informed consent was obtained from all individual participants included in the study., (© 2024. The Author(s).)

Objective: Currently, CT pulmonary angiogram (CTPA) for evaluating acute pulmonary embolism (PE) in emergency departments (EDs) is overused and with low yields. The goal of this study is to assess the impact of an evidence-based clinical decision support (CDS) tool, aimed at optimizing appropriate use of CTPA for evaluating PE., Methods: The study was performed at EDs in a large health care system and included nine academic and community hospitals. The primary outcome was the percent difference in utilization (number of CTPAs performed per number of ED visits) and secondary outcome was yield (percentage of CTPAs positive for acute PE), comparing 12 months before (June 1, 2021, to May 31, 2022) versus 12 months after (June 1, 2022, to May 31, 2023) a systemwide implementation of the CDS. Univariate and multivariable analyses using logistic regression were performed to assess factors associated with diagnosis of acute PE. Statistical process control charts were used to assess monthly trends in utilization and yield., Results: Among 931,677 visits to EDs, 28,101 CTPAs were performed on 24,675 patients. In all, 14,825 CTPAs were performed among 455,038 visits (3.26%) pre-intervention and 13,276 among 476,639 visits (2.79%) postintervention, a 14.51% relative decrease in CTPA utilization (χ 2 , P < .001). CTPA yield remained unchanged (1,371 of 14,825 = 9.25% pre- versus 1,184 of 13,276 = 8.92% postintervention; χ 2 , P = .34). Patients with coronavirus disease of 2019 diagnosis before CTPA had higher probability of acute PE. Statistical process control charts demonstrated seasonal variation in utilization (Friedman test, P = .047)., Discussion: Implementing a CDS based on validated decision rules was associated with a significant reduction in CTPA utilization. The change was immediate and sustained for 12 months postintervention., (Copyright © 2024 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Background: Factors associated with the appropriateness of antibiotic prescribing in primary care have been poorly explored. In particular, the impact of computerised decision-support systems (CDSS) remains unknown., Objectives: We aim at investigating the uptake of CDSS and its association with physician characteristics and professional activity., Methods: Since May 2022, users of a CDSS for antibiotic prescribing in primary care in France have been invited, when registering, to complete three case vignettes assessing clinical situations frequently encountered in general practice and identified as at risk of antibiotic misuse. Appropriateness of antibiotic prescribing was defined as the rate of answers in line with the current guidelines, computed by individuals and by specific questions. Physician's characteristics associated with individual appropriate antibiotic prescribing (< 50%, 50-75% and > 75% appropriateness) were identified by multivariate ordinal logistic regression., Results: In June 2023, 60,067 physicians had registered on the CDSS. Among the 13,851 physicians who answered all case vignettes, the median individual appropriateness level of antibiotic prescribing was 77.8% [Interquartile range, 66.7%-88.9%], and was < 50% for 1,353 physicians (10%). In the multivariate analysis, physicians' characteristics associated with appropriateness were prior use of the CDSS (OR = 1.71, 95% CI 1.56-1.87), being a general practitioner vs. other specialist (OR = 1.34, 95% CI 1.20-1.49), working in primary care (OR = 1.14, 95% CI 1.02-1.27), mentoring students (OR = 1.12, 95% CI 1.04-1.21) age (OR = 0.69 per 10 years increase, 95% CI 0.67-0.71)., Conclusion: Individual appropriateness for antibiotic prescribing was high among CDSS users, with a higher rate in young general practitioners, previously using the system. CDSS could improve antibiotic prescribing in primary care.