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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]

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]

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

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]

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.

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]

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 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.

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 ).

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]

Purpose: This study aims to evaluate healthcare practitioners' perceptions of electronic health record (EHR) systems and their effectiveness in supporting clinical decision-making in Tanzanian Primary Healthcare (PHC) facilities., Design/methodology/approach: A mixed-methods approach was employed, combining quantitative data from structured questionnaires and qualitative insights from open-ended responses. The study was conducted in the Dodoma region of Tanzania, focusing on a diverse representation of PHC facilities, including district hospitals, health centres and dispensaries. Data were analysed using multiple linear regression for quantitative data, and thematic analysis was applied to qualitative responses., Findings: The results revealed that while EHR systems are widely used in Tanzanian PHC facilities, their impact on clinical decision-making remains limited. Only a moderate portion of practitioners perceived EHR systems as effective in decision support, and frequent system use was negatively correlated with user satisfaction. Challenges such as inadequate training and support, system crashes, slow performance and poor usability and integration into clinical workflows were significant barriers to effectively utilising EHR systems., Originality/value: This study contributes to the limited literature on EHR system implementation in low-resource settings, specifically Tanzania, by focusing on decision-support features within EHR systems. The findings offer valuable insights for healthcare policymakers, system designers and practitioners to optimise EHR implementation and improve healthcare outcomes in resource-constrained environments., (© Emerald Publishing Limited.)

We show the value of real-time data generated by a computerized decision support system in primary care in strengthening pneumonia surveillance. The system showed a 66% (95% CI 64%-67%) increase in community-acquired pneumonia from 2018 to 2023 for the population of France, 1 month before a national alert was issued.

Objectives: To reflect on the achievements of the Agency for Healthcare Research and Quality's (AHRQ) Digital Healthcare Research Program over the past 20 years, evaluate its impact on US healthcare quality and safety, and outline current and future priorities for digital healthcare research and innovation., Process: The article reviews key milestones in AHRQ's digital healthcare initiatives, including its founding and its advances in telehealthcare and clinical decision support. It highlights AHRQ's contributions to advancing technology integration in healthcare, promoting patient safety, and addressing equity gaps. The article also examines the evolving role of artificial intelligence (AI) in healthcare delivery., Conclusions: AHRQ's Digital Healthcare Research Program has significantly contributed to improving healthcare quality. As digital technologies evolve, particularly with AI, the program remains focused on enhancing safety, equity, and efficiency in healthcare. Continued research and investment will be essential to maintaining progress and addressing new challenges., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Heparin-induced thrombocytopenia (HIT) is a rare complication of heparin exposure with potential for significant morbidity and mortality. Early identification and treatment can prevent catastrophic thrombosis. Herein, we report the performance of a platelet count-based clinical decision support system (CDSS) where providers received a notification when a patient had a platelet count decline of ≥50 %. In the 90-day study period, the CDSS sent 302 notifications on 270 patients. Notifications were frequently inappropriate; 25 % had an expected platelet count decline (organ donation, stem cell transplant), an inaccurate count, or no heparin exposure. Patient testing for HIT prompted by the CDSS was not in accordance with best practice guidelines in most circumstances. For example, 36 % had a low probability 4Ts score, while 42 % with an intermediate or high probability 4Ts score were not tested. Due to concern for lack of efficacy, the CDSS was discontinued. Analysis of an 8-month period before and after discontinuation showed a significant decrease in the number of enzyme immunoassays ordered (547 vs. 386) without a change in the number of patients with HIT identified (13 vs. 13) or the rate of thrombosis in those with confirmed HIT (62 % vs. 62 %). In conclusion, a CDSS based on platelet count decline contributed to "alert fatigue" via inappropriate notification and did not improve evidence-based HIT testing. In addition, its removal did not decrease or delay HIT identification. Additional efforts are needed to better define how CDSS can support the rapid diagnosis and appropriate treatment of patients with HIT., 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 Ltd. All rights reserved.)

Although transarterial chemoembolization has improved as an interventional method for hepatocellular carcinoma, subsequent postembolization syndrome is a threat to the patients' quality of life. This study aimed to evaluate the effectiveness of a clinical decision support system in postembolization syndrome management across nurses and patient outcomes. This study is a randomized controlled trial. We included 40 RNs and 51 hospitalized patients in the study. For nurses in the experimental group, a clinical decision support system and a handbook were provided for 6 weeks, and for nurses in the control group, only a handbook was provided. Notably, the experimental group exhibited statistically significant improvements in patient-centered caring attitude, pain management barrier identification, and comfort care competence after clinical decision support system implementation. Moreover, patients' symptom interference during the experimental period significantly decreased compared with before the intervention. This study offers insights into the potential of clinical decision support system in refining nursing practices and nurturing patient well-being, presenting prospects for advancing patient-centered care and nursing competence. The clinical decision support system contents, encompassing postembolization syndrome risk prediction and care recommendations, should underscore its role in fostering a patient-centered care attitude and bolster nurses' comfort care competence., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Integrating artificial intelligence (AI) in healthcare settings has the potential to benefit clinical decision-making. Addressing challenges such as ensuring trustworthiness, mitigating bias, and maintaining safety is paramount. The lack of established methodologies for pre- and post-deployment evaluation of AI tools regarding crucial attributes such as transparency, performance monitoring, and adverse event reporting makes this situation challenging., Objectives: This paper aims to make practical suggestions for creating methods, rules, and guidelines to ensure that the development, testing, supervision, and use of AI in clinical decision support (CDS) systems are done well and safely for patients., Materials and Methods: In May 2023, the Division of Clinical Informatics at Beth Israel Deaconess Medical Center and the American Medical Informatics Association co-sponsored a working group on AI in healthcare. In August 2023, there were 4 webinars on AI topics and a 2-day workshop in September 2023 for consensus-building. The event included over 200 industry stakeholders, including clinicians, software developers, academics, ethicists, attorneys, government policy experts, scientists, and patients. The goal was to identify challenges associated with the trusted use of AI-enabled CDS in medical practice. Key issues were identified, and solutions were proposed through qualitative analysis and a 4-month iterative consensus process., Results: Our work culminated in several key recommendations: (1) building safe and trustworthy systems; (2) developing validation, verification, and certification processes for AI-CDS systems; (3) providing a means of safety monitoring and reporting at the national level; and (4) ensuring that appropriate documentation and end-user training are provided., Discussion: AI-enabled Clinical Decision Support (AI-CDS) systems promise to revolutionize healthcare decision-making, necessitating a comprehensive framework for their development, implementation, and regulation that emphasizes trustworthiness, transparency, and safety. This framework encompasses various aspects including model training, explainability, validation, certification, monitoring, and continuous evaluation, while also addressing challenges such as data privacy, fairness, and the need for regulatory oversight to ensure responsible integration of AI into clinical workflow., Conclusions: Achieving responsible AI-CDS systems requires a collective effort from many healthcare stakeholders. This involves implementing robust safety, monitoring, and transparency measures while fostering innovation. Future steps include testing and piloting proposed trust mechanisms, such as safety reporting protocols, and establishing best practice guidelines., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objectives: To explore home healthcare (HHC) clinicians' needs for Clinical Decision Support Systems (CDSS) information delivery for early risk warning within HHC workflows., Methods: Guided by the CDS "Five-Rights" framework, we conducted semi-structured interviews with multidisciplinary HHC clinicians from April 2023 to August 2023. We used deductive and inductive content analysis to investigate informants' responses regarding CDSS information delivery., Results: Interviews with thirteen HHC clinicians yielded 16 codes mapping to the CDS "Five-Rights" framework (right information, right person, right format, right channel, right time) and 11 codes for unintended consequences and training needs. Clinicians favored risk levels displayed in color-coded horizontal bars, concrete risk indicators in bullet points, and actionable instructions in the existing EHR system. They preferred non-intrusive risk alerts requiring mandatory confirmation. Clinicians anticipated risk information updates aligned with patient's condition severity and their visit pace. Additionally, they requested training to understand the CDSS's underlying logic, and raised concerns about information accuracy and data privacy., Discussion: While recognizing CDSS's value in enhancing early risk warning, clinicians highlighted concerns about increased workload, alert fatigue, and CDSS misuse. The top risk factors identified by machine learning algorithms, especially text features, can be ambiguous due to a lack of context. Future research should ensure that CDSS outputs align with clinical evidence and are explainable., Conclusion: This study identified HHC clinicians' expectations, preferences, adaptations, and unintended uses of CDSS for early risk warning. Our findings endorse operationalizing the CDS "Five-Rights" framework to optimize CDSS information delivery and integration into HHC workflows., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Introduction: The urgency and complexity of emergency room (ER) settings require precise and swift decision-making processes for patient care. Ensuring the timely execution of critical examinations and interventions is vital for reducing diagnostic errors, but the literature highlights a need for innovative approaches to optimize diagnostic accuracy and patient outcomes. In response, our study endeavors to create predictive models for timely examinations and interventions by leveraging the patient's symptoms and vital signs recorded during triage, and in so doing, augment traditional diagnostic methodologies., Methods: Focusing on four key areas-medication dispensing, vital interventions, laboratory testing, and emergency radiology exams, the study employed Natural Language Processing (NLP) and seven advanced machine learning techniques. The research was centered around the innovative use of BioClinicalBERT, a state-of-the-art NLP framework., Results: BioClinicalBERT emerged as the superior model, outperforming others in predictive accuracy. The integration of physiological data with patient narrative symptoms demonstrated greater effectiveness compared to models based solely on textual data. The robustness of our approach was confirmed by an Area Under the Receiver Operating Characteristic curve (AUROC) score of 0.9., Conclusion: The findings of our study underscore the feasibility of establishing a decision support system for emergency patients, targeting timely interventions and examinations based on a nuanced analysis of symptoms. By using an advanced natural language processing technique, our approach shows promise for enhancing diagnostic accuracy. However, the current model is not yet fully mature for direct implementation into daily clinical practice. Recognizing the imperative nature of precision in the ER environment, future research endeavors must focus on refining and expanding predictive models to include detailed timely examinations and interventions. Although the progress achieved in this study represents an encouraging step towards a more innovative and technology-driven paradigm in emergency care, full clinical integration warrants further exploration and 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 Elsevier B.V. All rights reserved.)

Background: Caring for people with dementia is perceived as one of the most challenging caring roles, so effective and practical support is essential. One such innovative approach to internet-based tailored health intervention is the use of recommender system., Objective: This study develops a dementia care intelligent recommender system (DCIRS) that can provide personalized and timely care recommendations for caregivers when they encounter difficult various care problems in people with dementia., Methods: The development process was divided into 3 stages. In stage 1, we complete the construction of the domain knowledge graph of dementia care. In stage 2, the established domain knowledge graph of dementia care was introduced into the recommendation model by the way of graph embedding to form a recommendation model composed of graph embedding module and recommendation module. In stage 3, on the basis of the application of knowledge graph and recommendation mode, DCIRS was developed, for practical use. In addition, DCIRS has been validated for accuracy for assessing the correctness of the profiling and recommendation, by enrolling 56 caregivers., Results: The proposed DCIRS has functions of knowledge graph management and dementia care decision support. Experiments on 56 caregivers in single class recommendation task; the value of accuracy is equals to 98.92% and indicates the high capability of DCIRS., Conclusions: This study was a pioneering research to develop a more comprehensive DCIRS for caregivers of people with dementia. According to the evaluation results, our DCIRS showing high specificity and accuracy. This system can provide a novel perspective for patient-centered and needs-based support of caregivers of people with dementia., 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 B.V.)

Introduction: Preparing appropriate red blood cells (RBCs) before surgery is crucial for improving both the efficacy of perioperative workflow and patient safety. In particular, thoracic surgery (TS) is a procedure that requires massive transfusion with high variability for each patient. Hence, the precise prediction of RBC requirements for individual patients is becoming increasingly important. This study aimed to 1) develop and validate a machine learning algorithm for personalized RBC predictions for TS patients and 2) assess the usability of a clinical decision support system (CDSS) integrating this artificial intelligence model., Methods: Adult patients who underwent TS between January 2016 and October 2021 were included in this study. Multiple models were developed by employing both traditional statistical- and machine-learning approaches. The primary outcome evaluated the model's performance in predicting RBC requirements through root mean square error and adjusted R 2 . Surgeons and informaticians determined the precision MSBOS-Thoracic Surgery (pMSBOS-TS) algorithm through a consensus process. The usability of the pMSBOS-TS was assessed using the System Usability Scale (SUS) survey with 60 clinicians., Results: We identified 7,843 cases (6,200 for training and 1,643 for test sets) of TSs. Among the models with variable performance indices, the extreme gradient boosting model was selected as the pMSBOS-TS algorithm. The pMSBOS-TS model showed statistically significant lower root mean square error (mean: 3.203 and 95% confidence interval [CI]: 3.186-3.220) compared to the calculated Maximum Surgical Blood Ordering Schedule (MSBOS) and a higher adjusted R 2 (mean: 0.399 and 95% CI: 0.395-0.403) compared to the calculated MSBOS, while requiring approximately 200 fewer packs for RBC preparation compared to the calculated MSBOS. The SUS score of the pMSBOS-TS CDSS was 72.5 points, indicating good acceptability., Conclusions: We successfully developed the pMSBOS-TS capable of predicting personalized RBC transfusion requirements for perioperative patients undergoing TS., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [Financial support for this study was provided entirely by a grant from the Ministry of Health & Welfare, Republic of Korea. The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report.]., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Purpose: This study aimed to develop and assess the performance of an artificial intelligence (AI)-driven decision support system, XRAInet, in accurately identifying pediatric patients with pleural effusion or pneumothorax and determining whether tube thoracostomy intervention is warranted., Methods: In this diagnostic accuracy study, we retrospectively analyzed a data set containing 510 X-ray images from 170 pediatric patients admitted between 2005 and 2022. Patients were categorized into two groups: Tube (requiring tube thoracostomy) and Conservative (managed conservatively). XRAInet, a deep learning-based algorithm, was trained using this data set. We evaluated its performance using various metrics, including mean Average Precision (mAP), recall, precision, and F1 score., Results: XRAInet, achieved a mAP score of 0.918. This result underscores its ability to accurately identify and localize regions necessitating tube thoracostomy for pediatric patients with pneumothorax and pleural effusion. In an independent testing data set, the model exhibited a sensitivity of 64.00% and specificity of 96.15%., Conclusion: In conclusion, XRAInet presents a promising solution for improving the detection and decision-making process for cases of pneumothorax and pleural effusion in pediatric patients using X-ray images. These findings contribute to the expanding field of AI-driven medical imaging, with potential applications for enhancing patient outcomes. Future research endeavors should explore hybrid models, enhance interpretability, address data quality issues, and align with regulatory requirements to ensure the safe and effective deployment of XRAInet in healthcare settings., (© 2024 Wiley Periodicals LLC.)

Introduction: Thiamine is the only therapy for prevention and treatment of Wernicke Encephalopathy among patients with Alcohol Use Disorder (AUD). Despite this fact, up to 75 % of inpatients with AUD are not prescribed thiamine during hospitalization. Even fewer patients are prescribed high-dose thiamine which many experts recommend should be standard of care. Previous attempts to improve thiamine prescribing for inpatients have had limited success., Methods: We conducted an evaluation of thiamine prescribing in the year before and year after an intervention to increase high-dose thiamine prescribing. Pre-post study analysis occurred on two distinct study cohorts: those with alcohol-related diagnoses and those with elevated alcohol levels. The intervention was new electronic health record-based decision support which encouraged high-dose thiamine when any thiamine order was sought. No educational support was provided. The primary outcome was prescription of high-dose thiamine before versus after intervention. Of those with alcohol-related diagnoses, the monthly percentage of thiamine treatment courses including high-dose thiamine were graphed on a control chart., Results: We examined 5307 admissions with alcohol-related diagnoses (2285 pre- and 3022 post-intervention) and 698 admissions with elevated alcohol levels (319 pre- and 379 post-intervention). Among admissions with alcohol-related diagnoses, the intervention was associated with a higher proportion of admissions receiving high-dose thiamine prescriptions in the first 24 h (4.7 % vs. 1.1 %, adjusted odds ratio 4.50, CI 2.93 to 6.89, p < 0.001). A similar difference in high-dose thiamine was seen post-intervention among admissions with elevated alcohol levels (14.3 % vs. 2.5 %, adjusted odds ratio 6.43, CI 3.05 to 13.53, p < 0.001). The control chart among those with an alcohol-related diagnosis demonstrated special cause variation: the median percentage of thiamine treatment courses including high-dose thiamine improved from 8.2 % to 13.0 %., Conclusions: Electronic decision support without educational interventions increased the use of high-dose thiamine among patients with alcohol-related diagnoses and with elevated alcohol levels during hospitalization. This increase occurred immediately in the month after the intervention and was sustained in the year-long study period after., Competing Interests: Declaration of competing interest The authors of the present manuscript have no conflicts of interest to declare., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: The Dutch guideline algorithm for the analysis of anaemia in patients of general practitioners (GPs) was programmed in a Clinical Decision Support system (CDS-anaemia) to support the process of diagnosing the cause of anaemia in the laboratory. This research aims to assess the supplementary benefit provided by the automated algorithm in various demographic categories, including different sexes, age groups and severities of anaemia, in comparison to the manual diagnostic approach employed by GPs., Methods: This was a retrospective cohort study of 5399 primary care patients where the cause of anaemia was diagnosed by GPs with or without the aid of CDS-anaemia within the age groups 18-44, 45-64, 65-79 and 80 and older. Anaemia was defined according to the Dutch College of General Practitioners (DCGP) guideline. Causes of anaemia were based on the DCGP guidelines with the corresponding blood tests. By calculation of rate ratios and percentage differences of the determined cause of anaemia we evaluated the effect of the diagnostic algorithm., Results and Conclusion: The percentage patients in which an underlying cause of anaemia was found increased 34 and 46 percentage points in females and males, respectively, when GPs were supported by CDS-anaemia compared to GPs who were not supported by CDS-anaemia. The highest increase in percentage points when CDS-anaemia was used, was found in younger- and middle-aged males and mild or moderate anaemia., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Inpatient and extended postdischarge thromboprophylaxis of COVID-19 patients remains suboptimal despite antithrombotic guidelines., Objectives: To determine whether a novel electronic health record-agnostic clinical decision support (CDS) tool incorporating the International Medical Prevention Registry on Venous Thromboembolism plus D-dimer (IMPROVE-DD) venous thromboembolism (VTE) scores increases appropriate inpatient and extended postdischarge thromboprophylaxis and improves outcomes in COVID-19 inpatients., Methods: This post hoc analysis of the IMPROVE-DD cluster randomized trial evaluated thromboprophylaxis CDS among COVID-19 inpatients at 4 New York hospitals between December 21, 2020, and January 21, 2022. Hospitals were randomized 1:1 to CDS (intervention, n = 2) vs no CDS (usual care, n = 2). The primary outcome was rate of appropriate thromboprophylaxis. Secondary outcomes included rates of major thromboembolism, all-cause and VTE-related readmissions and death, major bleeding (MB), and all-cause mortality 30 days after discharge., Results: Two thousand four hundred fifty-two COVID-19 inpatients were analyzed (CDS, 1355; no CDS, 1097). Mean age was 73.7 ± 9.37 years; 50.1% of participants were male. CDS adoption was 96.8% (intervention group). CDS was associated with increased appropriate at-discharge extended thromboprophylaxis (42.6% vs 28.8%; odds ratio [OR], 1.83; 95% CI, 1.39-2.41; P < .001). CDS was associated with reduced VTE (OR, 0.54; 95% CI, 0.39-0.75; P < .001), arterial thromboembolism (OR, 0.10; 95% CI, 0.01-0.81; P = .01), total thromboembolism (OR, 0.50; 95% CI, 0.36-0.69; P < .001), and 30-day all-cause readmission/death (OR, 0.78; 95% CI, 0.62-0.99; P = .04). There were no differences in MB, VTE-related readmissions/death, or all-cause mortality., Conclusion: Electronic health record-agnostic CDS incorporating IMPROVE-DD VTE scores had high adoption, was associated with increased appropriate at-discharge extended thromboprophylaxis, and reduced thromboembolism and all-cause readmission/death without increasing MB in COVID-19 inpatients., Competing Interests: Declaration of competing interests A.C.S. reports honoraria from Janssen, Bayer, Bristol Myers Squibb, Pfizer, and Sanofi and research grants from Janssen and Boehringer Ingelheim. He is also a member of the Antithrombotic Trials Leadership and Steering (ATLAS) group, an academic research organization. M.G. reports grant support from Janssen and AstraZeneca. The other authors report no conflicts., (Copyright © 2024 International Society on Thrombosis and Haemostasis. Published by Elsevier Inc. All rights reserved.)

Objective: Our objective is to develop and validate TrajVis, an interactive tool that assists clinicians in using artificial intelligence (AI) models to leverage patients' longitudinal electronic medical records (EMRs) for personalized precision management of chronic disease progression., Materials and Methods: We first perform requirement analysis with clinicians and data scientists to determine the visual analytics tasks of the TrajVis system as well as its design and functionalities. A graph AI model for chronic kidney disease (CKD) trajectory inference named DisEase PrOgression Trajectory (DEPOT) is used for system development and demonstration. TrajVis is implemented as a full-stack web application with synthetic EMR data derived from the Atrium Health Wake Forest Baptist Translational Data Warehouse and the Indiana Network for Patient Care research database. A case study with a nephrologist and a user experience survey of clinicians and data scientists are conducted to evaluate the TrajVis system., Results: The TrajVis clinical information system is composed of 4 panels: the Patient View for demographic and clinical information, the Trajectory View to visualize the DEPOT-derived CKD trajectories in latent space, the Clinical Indicator View to elucidate longitudinal patterns of clinical features and interpret DEPOT predictions, and the Analysis View to demonstrate personal CKD progression trajectories. System evaluations suggest that TrajVis supports clinicians in summarizing clinical data, identifying individualized risk predictors, and visualizing patient disease progression trajectories, overcoming the barriers of AI implementation in healthcare., Discussion: The TrajVis system provides a novel visualization solution which is complimentary to other risk estimators such as the Kidney Failure Risk Equations., Conclusion: TrajVis bridges the gap between the fast-growing AI/ML modeling and the clinical use of such models for personalized and precision management of chronic diseases., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Background: There has been a substantial increase in the development of artificial intelligence (AI) tools for clinical decision support. Historically, these were mostly knowledge-based systems, but recent advances include non-knowledge-based systems using some form of machine learning. The ability of health care professionals to trust technology and understand how it benefits patients or improves care delivery is known to be important for their adoption of that technology. For non-knowledge-based AI tools for clinical decision support, these issues are poorly understood., Objective: The aim of this study is to qualitatively synthesize evidence on the experiences of health care professionals in routinely using non-knowledge-based AI tools to support their clinical decision-making., Methods: In June 2023, we searched 4 electronic databases, MEDLINE, Embase, CINAHL, and Web of Science, with no language or date limit. We also contacted relevant experts and searched reference lists of the included studies. We included studies of any design that reported the experiences of health care professionals using non-knowledge-based systems for clinical decision support in their work settings. We completed double independent quality assessment for all included studies using the Mixed Methods Appraisal Tool. We used a theoretically informed thematic approach to synthesize the findings., Results: After screening 7552 titles and 182 full-text articles, we included 25 studies conducted in 9 different countries. Most of the included studies were qualitative (n=13), and the remaining were quantitative (n=9) and mixed methods (n=3). Overall, we identified 7 themes: health care professionals' understanding of AI applications, level of trust and confidence in AI tools, judging the value added by AI, data availability and limitations of AI, time and competing priorities, concern about governance, and collaboration to facilitate the implementation and use of AI. The most frequently occurring are the first 3 themes. For example, many studies reported that health care professionals were concerned about not understanding the AI outputs or the rationale behind them. There were issues with confidence in the accuracy of the AI applications and their recommendations. Some health care professionals believed that AI provided added value and improved decision-making, and some reported that it only served as a confirmation of their clinical judgment, while others did not find it useful at all., Conclusions: Our review identified several important issues documented in various studies on health care professionals' use of AI tools in real-world health care settings. Opinions of health care professionals regarding the added value of AI tools for supporting clinical decision-making varied widely, and many professionals had concerns about their understanding of and trust in this technology. The findings of this review emphasize the need for concerted efforts to optimize the integration of AI tools in real-world health care settings., Trial Registration: PROSPERO CRD42022336359; https://tinyurl.com/2yunvkmb., (©Abimbola Ayorinde, Daniel Opoku Mensah, Julia Walsh, Iman Ghosh, Siti Aishah Ibrahim, Jeffry Hogg, Niels Peek, Frances Griffiths. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.10.2024.)

Objectives: To determine whether the addition of a primary aldosteronism (PA) predictive model to a secondary hypertension decision support tool increases screening for PA in a primary care setting., Materials and Methods: One hundred fifty-three primary care clinics were randomized to receive a secondary hypertension decision support tool with or without an integrated predictive model between August 2023 and April 2024., Results: For patients with risk scores in the top 1 percentile, 63/2896 (2.2%) patients where the alert was displayed in model clinics had the order set launched, while 12/1210 (1.0%) in no-model clinics had the order set launched ( P = .014 ). Nineteen of 2896 (0.66%) of these highest risk patients in model clinics had an aldosterone-to-renin ratio (ARR) ordered compared to 0/1210 (0.0%) patients in no-model clinics ( P = .010 ). For patients with scores not in the top 1 percentile, 438/20 493 (2.1%) patients in model clinics had the order set launched compared to 273/17 820 (1.5%) in no-model clinics ( P  < .001). One hundred twenty-four of 20 493 (0.61%) in model clinics had an ARR ordered compared to 34/17 820 (0.19%) in the no-model clinics ( P  < .001)., Discussion: The addition of a PA predictive model to secondary hypertension alert displays and triggering criteria along with order set displays and order preselection criteria results in a statistically and clinically significant increase in screening for PA, a condition that clinicians insufficiently screen for currently., Conclusion: Addition of a predictive model for an under-screened condition to traditional clinical decision support may increase screening for these conditions., Competing Interests: None declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

The care of people with multiple long-term conditions (MLTCs) is complex and time-consuming, often denying them the agency to self-manage their conditions-or for the clinician they visit to provide streamlined, person-centred care. We reconfigured The Practical Approach to Care Kit, our established, evidence-based, policy-aligned clinical decision support tool for low-resource primary care settings, to provide consolidated clinical guidance for a patient journey through a primary care facility. This places the patient at the centre of that journey and shifts the screening, monitoring and health education activities of multimorbidity care more equitably among the members of the primary care team. This work forms part of a study called ENHANCE, exploring how best to streamline MLTC care in South Africa with its high burden of communicable, non-communicable and mental health conditions. This practice paper describes the four steps of codeveloping this clinical decision support tool for eleven common long-term conditions with local stakeholders (deciding the approach, constructing the content, clinical editing, and design and formatting) along with the features of the tool designed to facilitate its usability at point of care. The process highlighted tensions around prioritising one condition over another, curative over preventive treatment and pharmacological therapies over advice-giving, along with the challenges of balancing the large volume of content with a person-centred approach. If successful, the tool could augment the response to MLTC care in South Africa and other low-resource settings. In addition, our development process may contribute to scant literature around methodologies for clinical decision support development., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Background: Clinical decision support systems (CDSS) based on machine-learning (ML) models are emerging within psychiatry. If patients do not trust this technology, its implementation may disrupt the patient-clinician relationship. Therefore, the aim was to examine whether receiving basic information about ML-based CDSS increased trust in them., Methods: We conducted an online randomized survey experiment in the Psychiatric Services of the Central Denmark Region. The participating patients were randomized into one of three arms: Intervention = information on clinical decision-making supported by an ML model; Active control = information on a standard clinical decision process, and Blank control = no information. The participants were unaware of the experiment. Subsequently, participants were asked about different aspects of trust and distrust regarding ML-based CDSS. The effect of the intervention was assessed by comparing scores of trust and distrust between the allocation arms., Results: Out of 5800 invitees, 992 completed the survey experiment. The intervention increased trust in ML-based CDSS when compared to the active control (mean increase in trust: 5% [95% CI: 1%; 9%], p  = 0.0096) and the blank control arm (mean increase in trust: 4% [1%; 8%], p  = 0.015). Similarly, the intervention reduced distrust in ML-based CDSS when compared to the active control (mean decrease in distrust: -3%[-1%; -5%], p  = 0.021) and the blank control arm (mean decrease in distrust: -4% [-1%; -8%], p  = 0.022). No statistically significant differences were observed between the active and the blank control arms., Conclusions: Receiving basic information on ML-based CDSS in hospital psychiatry may increase patient trust in such systems.

Background: Integrating decision support systems into telemedicine may optimize consultation efficiency and adherence to clinical guidelines; however, the extent of such effects remains underexplored., Objective: This study aims to evaluate the use of ICD (International Classification of Disease)-coded prescription decision support systems (PDSSs) and the effects of these systems on consultation duration and guideline adherence during telemedicine encounters., Methods: In this retrospective, single-center, observational study conducted from October 2021 to March 2022, adult patients who sought urgent digital care via direct-to-consumer video consultations were included. Physicians had access to current guidelines and could use an ICD-triggered PDSS (which was introduced in January 2022 after a preliminary test in the preceding month) for 26 guideline-based conditions. This study analyzed the impact of implementing automated prescription systems and compared these systems to manual prescription processes in terms of consultation duration and guideline adherence., Results: This study included 10,485 telemedicine encounters involving 9644 patients, with 12,346 prescriptions issued by 290 physicians. Automated prescriptions were used in 5022 (40.67%) of the consultations following system integration. Before introducing decision support, 4497 (36.42%) prescriptions were issued, which increased to 7849 (63.57%) postimplementation. The physician's average consultation time decreased significantly to 9.5 (SD 5.5) minutes from 11.2 (SD 5.9) minutes after PDSS implementation (P<.001). Of the 12,346 prescriptions, 8683 (70.34%) were aligned with disease-specific international guidelines tailored for telemedicine encounters. Primary medication adherence in accordance with existing guidelines was significantly greater in the decision support group than in the manual group (n=4697, 93.53% vs n=1389, 49.14%; P<.001)., Conclusions: Most of the physicians adopted the PDSS, and the results demonstrated the use of the ICD-code system in reducing consultation times and increasing guideline adherence. These systems appear to be valuable for enhancing the efficiency and quality of telemedicine consultations by supporting evidence-based clinical decision-making., (©Tarso Augusto Duenhas Accorsi, Anderson Aires Eduardo, Carlos Guilherme Baptista, Flavio Tocci Moreira, Renata Albaladejo Morbeck, Karen Francine Köhler, Karine de Amicis Lima, Carlos Henrique Sartorato Pedrotti. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 25.10.2024.)

Background: Telemedicine holds immense potential to revolutionise healthcare delivery, particularly in resource-limited settings and for patients with chronic diseases. Despite proven benefits and policy reforms, the use of telemedicine remains low due to several patient, technology, and system-level barriers. Assisted telemedicine employs trained health professionals to connect patients with physicians, which can improve access and scope of telemedicine. The study aims to describe the design, service utilisation and chronic disease outcomes following the implementation of an assisted telemedicine initiative., Methods: This is an observational implementation study. Barriers and potential solutions to the implementation of telemedicine were identified through interviews with key stakeholders. The assisted telemedicine solution using an interoperable platform integrating electronic health records, point-of-care diagnostics, and electronic clinical decision support systems was designed and piloted at three telemedicine clinics in Tamil Nadu, India. Nurses were trained in platform use and facilitation of tele-consultations. Health records of all patients from March 2021 to June 2023 were included in the analysis. Data were analysed to assess the utilisation of clinic services and improvements in health outcomes in patients with diabetes mellitus and hypertension., Results: Over 2.4 years, 11,388 patients with a mean age of 45 (± 20) years and median age of 48 years, predominantly female (59.3%), accessed the clinics. The team completed 15,437 lab investigations and 26,998 consultations. Among 5542 (48.6%) patients that reported chronic conditions, diabetes mellitus (61%) and hypertension (45%) were the most frequent. In patients with diabetes mellitus and hypertension, 43% and 75.3% were newly diagnosed, respectively. Diabetes mellitus and hypertension patients had significant reductions in fasting blood sugar (-33.0 mg/dL (95% CI (-42.4, -23.7, P < 0.001)), and systolic (-9.6 mmHg (95% CI (-12.1, -7.0), P < 0.0001)) and diastolic blood pressure (-5.5 mmHg (95% CI (-7.0, -4.08), P < 0.0001)) at nine months from first visit, respectively., Conclusions: The 'Digisahayam' model demonstrated feasibility in enhancing healthcare accessibility and quality by bridging healthcare gaps, diagnosing chronic conditions, and improving patient outcomes. The model presents a scalable and sustainable approach to revolutionising patient care and achieving digital health equity, with the potential for adaptation in similar settings worldwide., (© 2024. The Author(s).)

Background: The end tuberculosis (TB) strategy requires a novel patient treatment approach contrary to the one-size-fits-all model. It is well known that each patient's physiology is different and leads to various rates of drug elimination. Therapeutic drug monitoring (TDM) offers a way to manage drug dosage adaptation but requires trained pharmacologists, which is scarce in resource-limited settings., Objective: We will develop an automated clinical decision support system (CDSS) to help practitioners with the dosage adaptation of rifampicin, one of the essential medical drugs targeting TB, that is known for large pharmacokinetic variability and frequent suboptimal blood exposure. Such an advanced system will encourage the spread of a dosage-individualization culture, including among practitioners not specialized in pharmacology. Thus, the objectives of this project are to (1) develop the appropriate population pharmacokinetic (popPK) model for rifampicin for Tanzanian patients, (2) optimize the reporting of relevant information to practitioners for drug dosage adjustment, (3) automate the delivery of the report in line with the measurement of drug concentration, and (4) validate and implement the final system in the field., Methods: A total of 3 teams will combine their efforts to deliver the first automated TDM CDSS for TB. A cross-sectional study will be conducted to define the best way to display information to clinicians. In parallel, a rifampicin popPK model will be developed taking advantage of the published literature, complemented with data provided by existing literature data from the Pan-African Consortium for the Evaluation of Antituberculosis Antibiotics (panACEA), and samples collected within this project. A decision tree will be designed and implemented as a CDSS, and an automated report generation will be developed and validated through selected case studies. Expert pharmacologists will validate the CDSS, and finally, field implementation in Tanzania will occur, coupled with a prospective study to assess clinicians' adherence to the CDSS recommendations., Results: The TuberXpert project started in November 2022. In July 2024, the clinical study in Tanzania was completed with the enrollment of 50 patients to gather the required data to build a popPK model for rifampicin, together with a qualitative study defining the report design, as well as the CDSS general architecture definition., Conclusions: At the end of the TuberXpert project, Tanzania will possess a new tool to help the practitioners with the adaptation of drug dosage targeting complicated TB cases (TB or HIV, TB or diabetes mellitus, and TB or malnutrition). This automated system will be validated and used in the field and will be proposed to other countries affected by endemic TB. In addition, this approach will serve as proof of concept regarding the feasibility and suitability of CDSS-assisted TDM for further anti-TB drugs in TB-burdened areas deprived of TDM experts, including second-line treatments considered important to monitor., International Registered Report Identifier (irrid): DERR1-10.2196/58720., (©Yann Thoma, Annie E Cathignol, Yuan J Pétermann, Margaretha L Sariko, Bibie Said, Chantal Csajka, Monia Guidi, Stellah G Mpagama. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 21.10.2024.)

Introduction: Acute febrile illness (AFI), traditionally attributed to malaria, is a common reason for seeking primary healthcare in rural South and Southeast Asia. However, malaria transmission has declined while health workers are often poorly equipped to manage non-malarial AFIs. This results in indiscriminate antibiotic prescribing and care escalation, which promotes antibiotic resistance and may increase healthcare costs. To address this problem, an electronic clinical decision support algorithm (eCDSA) called 'Electronic clinical Decision support for Acute fever Management (EDAM)' has been developed for primary health workers which integrates clinical, epidemiological and vital sign data with simple point-of-care tests to produce a diagnosis and management plan., Methods and Analysis: This is a pragmatic cluster-randomised trial aiming to assess the effect of EDAM and related training on antibiotic prescribing rates in rural Cambodian primary health centres (PHCs) as the primary outcome, along with a range of secondary outcomes including safety. Patients with AFI are eligible for recruitment if they are aged ≥1 year. A cluster is defined as a PHC and PHCs will be randomised to control (standard of care) and intervention (EDAM and associated training) arms, with 15 PHCs per arm. Patients will be followed up after 7 days to ascertain the safety profile of EDAM. Each PHC will recruit 152 patients (total 4560), based on a baseline antibiotic prescription rate of 25% and expected reduction to 17.5% with EDAM., Ethics and Dissemination: Results will be published in international peer-reviewed journals to inform the design of future versions of EDAM and of future trials of similar eCDSAs and other digital health interventions targeted towards rural populations. This study was approved by the Oxford University Tropical Research Ethics Committee (550-23) and the Cambodian National Ethics Committee for Health Research (395-NECHR)., Trial Registration Number: International Standard Randomized Controlled Trial Number Registry (ISRCTN15157105)., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Background: Computerized clinical decision support systems (CDSSs) enhance patient care through real-time, evidence-based guidance for health care professionals. Despite this, the effective implementation of these systems for health services presents multifaceted challenges, leading to inappropriate use and abandonment over the course of time. Using the Non-Adoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) framework, this qualitative study examined CDSS adoption in a metropolitan health service, identifying determinants across implementation stages to optimize CDSS integration into health care practice., Objective: This study aims to identify the theory-informed (NASSS) determinants, which included multiple CDSS interventions across a 2-year period, both at the health-service level and at the individual hospital setting, that either facilitate or hinder the application of CDSSs within a metropolitan health service. In addition, this study aimed to map these determinants onto specific stages of the implementation process, thereby developing a system-level understanding of CDSS application across implementation stages., Methods: Participants involved in various stages of the implementation process were recruited (N=30). Participants took part in interviews and focus groups. We used a hybrid inductive-deductive qualitative content analysis and a framework mapping approach to categorize findings into barriers, enablers, or neutral determinants aligned to NASSS framework domains. These determinants were also mapped to implementation stages using the Active Implementation Framework stages approach., Results: Participants comprised clinical adopters (14/30, 47%), organizational champions (5/30, 16%), and those with roles in organizational clinical informatics (5/30, 16%). Most determinants were mapped to the organization level, technology, and adopter subdomains. However, the study findings also demonstrated a relative lack of long-term implementation planning. Consequently, determinants were not uniformly distributed across the stages of implementation, with 61.1% (77/126) identified in the exploration stage, 30.9% (39/126) in the full implementation stage, and 4.7% (6/126) in the installation stages. Stakeholders engaged in more preimplementation and full-scale implementation activities, with fewer cycles of monitoring and iteration activities identified., Conclusions: These findings addressed a substantial knowledge gap in the literature using systems thinking principles to identify the interdependent dynamics of CDSS implementation. A lack of sustained implementation strategies (ie, training and longer-term, adopter-level championing) weakened the sociotechnical network between developers and adopters, leading to communication barriers. More rigorous implementation planning, encompassing all 4 implementation stages, may, in a way, help in addressing the barriers identified and enhancing enablers., (©Manasha Fernando, Bridget Abell, Steven M McPhail, Zephanie Tyack, Amina Tariq, Sundresan Naicker. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 17.10.2024.)

Smart technologies and connected health are providing opportunities for improved healthcare for chronic conditions. Acceptance by healthcare professionals (HCPs) and patients is crucial for successful implementation. Evidence-based standards, technological infrastructure and regulatory processes are needed to integrate digital tools into clinical practice. Personal health records provide continuity and aid decision-making, while machine-learning algorithms may help in optimizing therapies and improving outcomes. Digital healthcare can negate geographical barriers, enabling patients in remote areas to access specialist endocrine expertise. We review available and developing digital tools to manage care for patients requiring growth hormone (GH) therapy for growth failure conditions. GH is most often administered via daily injections over several years; continuous adherence is necessary but may become insufficient. Future development and integration of electronic platforms for GH therapy requires involvement of all stakeholders in design-thinking approaches and human-factor testing. Growzen Connect is an innovative digital ecosystem designed to increase the management and monitoring of GH therapy, comprising the easypod device and connected mobile apps. It provides a real-time overview of a patient's therapy, including adherence and growth response, which aids decision-making by HCPs and empowers patients to engage in their therapy journey. Incorporating prediction models for adherence and growth in the ecosystem helps patients build treatment habits and allows issues to be addressed in a timely fashion. A connected ecosystem for GH therapy can enhance outcomes and empower patients, fostering a collaborative and patient-centered approach that is more proactive, beyond the traditional clinic-based approach., Competing Interests: EK is an employee of Merck Healthcare KGaA, Darmstadt, Germany and holds shares in the company. MB is an employee of Tiba Medical Inc., Beaverton, OR, USA. PDo has a consultancy agreement with Merck Healthcare KGaA, Darmstadt, Germany. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Dimitri, van Dommelen, Banerjee, Bellazzi, Ciaccio, de Arriba Muñoz, Loche, Zaini, Halabi, Bagha and Koledova.)

Background: Sepsis is a common cause of serious illness and death. Sepsis management remains challenging and suboptimal. To support rapid sepsis diagnosis and treatment, screening tools have been embedded into hospital digital systems to appear as digital alerts. The implementation of digital alerts to improve the management of sepsis and deterioration is a complex intervention that has to fit with team workflow and the views and practices of hospital staff. Despite the importance of human decision-making and behavior in optimal implementation, there are limited qualitative studies that explore the views and experiences of health care professionals regarding digital alerts as sepsis or deterioration computerized clinician decision support systems (CCDSSs)., Objective: This study aims to explore the views and experiences of health care professionals on the use of sepsis or deterioration CCDSSs and to identify barriers and facilitators to their implementation and use in National Health Service (NHS) hospitals., Methods: We conducted a qualitative, multisite study with unstructured observations and semistructured interviews with health care professionals from emergency departments, outreach teams, and intensive or acute units in 3 NHS hospital trusts in England. Data from both interviews and observations were analyzed together inductively using thematic analysis., Results: A total of 22 health care professionals were interviewed, and 12 observation sessions were undertaken. A total of four themes regarding digital alerts were identified: (1) support decision-making as nested in electronic health records, but never substitute professionals' knowledge and experience; (2) remind to take action according to the context, such as the hospital unit and the job role; (3) improve the alerts and their introduction, by making them more accessible, easy to use, not intrusive, more accurate, as well as integrated across the whole health care system; and (4) contextual factors affecting views and use of alerts in the NHS trusts. Digital alerts are more optimally used in general hospital units with a lower senior decision maker:patient ratio and by health care professionals with experience of a similar technology. Better use of the alerts was associated with quality improvement initiatives and continuous sepsis training. The trusts' features, such as the presence of a 24/7 emergency outreach team, good technological resources, and staffing and teamwork, favored a more optimal use., Conclusions: Trust implementation of sepsis or deterioration CCDSSs requires support on multiple levels and at all phases of the intervention, starting from a prego-live analysis addressing organizational needs and readiness. Advancements toward minimally disruptive and smart digital alerts as sepsis or deterioration CCDSSs, which are more accurate and specific but at the same time scalable and accessible, require policy changes and investments in multidisciplinary research., (©Runa Lazzarino, Aleksandra J Borek, Kate Honeyford, John Welch, Andrew J Brent, Anne Kinderlerer, Graham Cooke, Shashank Patil, Anthony Gordon, Ben Glampson, Philippa Goodman, Peter Ghazal, Ron Daniels, Céire E Costelloe, Sarah Tonkin-Crine. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 15.10.2024.)

Background: Antibiotic resistant infections cause over 700,000 deaths worldwide annually. As antimicrobial stewardship (AMS) helps minimise the emergence of antibiotic resistance resulting from inappropriate use of antibiotics in healthcare, we developed ePAMS+ (ePrescribing-based Anti-Microbial Stewardship), an ePrescribing and Medicines Administration (EPMA) system decision-support tool complemented by educational, behavioural and organisational elements., Methods: We conducted a non-randomised before-and-after feasibility trial, implementing ePAMS+ in two English hospitals using the Cerner Millennium EPMA system. Wards of several specialties were included. Patient participants were blinded to whether ePAMS+ was in use; prescribers were not. A mixed-methods evaluation aimed to establish: acceptability and usability of ePAMS+ and trial processes; feasibility of ePAMS+ implementation and quantitative outcome recording; and a Fidelity Index measuring the extent to which ePAMS+ was delivered as intended. Longitudinal semi-structured interviews of doctors, nurses and pharmacists, alongside non-participant observations, gathered qualitative data; we extracted quantitative prescribing data from the EPMA system. Normal linear modelling of the defined daily dose (DDD) of antibiotic per admission quantified its variability, to inform sample size calculations for a future trial of ePAMS+ effectiveness., Results: The research took place during the SARS-CoV-2 pandemic, from April 2021 to November 2022. 60 qualitative interviews were conducted (33 before ePAMS+ implementation, 27 after). 1,958 admissions (1,358 before ePAMS+ implementation; 600 after) included 24,884 antibiotic orders. Qualitative interviews confirmed that some aspects of ePAMS+ , its implementation and training were acceptable, while other features (e.g. enabling combinations of antibiotics to be prescribed) required further development. ePAMS+ uptake was low (28 antibiotic review records from 600 admissions; 0.047 records per admission), preventing full development of a Fidelity Index. Normal linear modelling of antibiotic DDD per admission showed a residual variance of 1.086 (log-transformed scale). Unavailability of indication data prevented measurement of some outcomes (e.g. number of antibiotic courses per indication)., Conclusions: This feasibility trial encountered unforeseen circumstances due to contextual factors and a global pandemic, highlighting the need for careful adaptation of complex intervention implementations to the local setting. We identified key refinements to ePAMS+ to support its wider adoption in clinical practice, requiring further piloting before a confirmatory effectiveness trial., Trial Registration: ISRCTN Registry ISRCTN13429325, 24 March 2022., (© 2024. The Author(s).)

Purpose: Large language models (LLM) show potential for decision support in breast cancer care. Their use in clinical care is currently prohibited by lack of control over sources used for decision-making, explainability of the decision-making process and health data security issues. Recent development of Small Language Models (SLM) is discussed to address these challenges. This preclinical proof-of-concept study tailors an open-source SLM to the German breast cancer guideline (BC-SLM) to evaluate initial clinical accuracy and technical functionality in a preclinical simulation., Methods: A multidisciplinary tumor board (MTB) is used as the gold-standard to assess the initial clinical accuracy in terms of concordance of the BC-SLM with MTB and comparing it to two publicly available LLM, ChatGPT3.5 and 4. The study includes 20 fictional patient profiles and recommendations for 5 treatment modalities, resulting in 100 binary treatment recommendations (recommended or not recommended). Statistical evaluation includes concordance with MTB in % including Cohen's Kappa statistic (κ). Technical functionality is assessed qualitatively in terms of local hosting, adherence to the guideline and information retrieval., Results: The overall concordance amounts to 86% for BC-SLM (κ = 0.721, p < 0.001), 90% for ChatGPT4 (κ = 0.820, p < 0.001) and 83% for ChatGPT3.5 (κ = 0.661, p < 0.001). Specific concordance for each treatment modality ranges from 65 to 100% for BC-SLM, 85-100% for ChatGPT4, and 55-95% for ChatGPT3.5. The BC-SLM is locally functional, adheres to the standards of the German breast cancer guideline and provides referenced sections for its decision-making., Conclusion: The tailored BC-SLM shows initial clinical accuracy and technical functionality, with concordance to the MTB that is comparable to publicly-available LLMs like ChatGPT4 and 3.5. This serves as a proof-of-concept for adapting a SLM to an oncological disease and its guideline to address prevailing issues with LLM by ensuring decision transparency, explainability, source control, and data security, which represents a necessary step towards clinical validation and safe use of language models in clinical oncology., (© 2024. The Author(s).)

Background: Artificial intelligence-driven Clinical Decision Support Systems (AI-CDSS) are being increasingly introduced into various domains of health care for diagnostic, prognostic, therapeutic and other purposes. A significant part of the discourse on ethically appropriate conditions relate to the levels of understanding and explicability needed for ensuring responsible clinical decision-making when using AI-CDSS. Empirical evidence on stakeholders' viewpoints on these issues is scarce so far. The present study complements the empirical-ethical body of research by, on the one hand, investigating the requirements for understanding and explicability in depth with regard to the rationale behind them. On the other hand, it surveys medical students at the end of their studies as stakeholders, of whom little data is available so far, but for whom AI-CDSS will be an important part of their medical practice., Methods: Fifteen semi-structured qualitative interviews (each lasting an average of 56 min) were conducted with German medical students to investigate their perspectives and attitudes on the use of AI-CDSS. The problem-centred interviews draw on two hypothetical case vignettes of AI-CDSS employed in nephrology and surgery. Interviewees' perceptions and convictions of their own clinical role and responsibilities in dealing with AI-CDSS were elicited as well as viewpoints on explicability as well as the necessary level of understanding and competencies needed on the clinicians' side. The qualitative data were analysed according to key principles of qualitative content analysis (Kuckartz)., Results: In response to the central question about the necessary understanding of AI-CDSS tools and the emergence of their outputs as well as the reasons for the requirements placed on them, two types of argumentation could be differentiated inductively from the interviewees' statements: the first type, the clinician as a systemic trustee (or "the one relying"), highlights that there needs to be empirical evidence and adequate approval processes that guarantee minimised harm and a clinical benefit from the employment of an AI-CDSS. Based on proof of these requirements, the use of an AI-CDSS would be appropriate, as according to "the one relying", clinicians should choose those measures that statistically cause the least harm. The second type, the clinician as an individual expert (or "the one controlling"), sets higher prerequisites that go beyond ensuring empirical evidence and adequate approval processes. These higher prerequisites relate to the clinician's necessary level of competence and understanding of how a specific AI-CDSS works and how to use it properly in order to evaluate its outputs and to mitigate potential risks for the individual patient. Both types are unified in their high esteem of evidence-based clinical practice and the need to communicate with the patient on the use of medical AI. However, the interviewees' different conceptions of the clinician's role and responsibilities cause them to have different requirements regarding the clinician's understanding and explicability of an AI-CDSS beyond the proof of benefit., Conclusions: The study results highlight two different types among (future) clinicians regarding their view of the necessary levels of understanding and competence. These findings should inform the debate on appropriate training programmes and professional standards (e.g. clinical practice guidelines) that enable the safe and effective clinical employment of AI-CDSS in various clinical fields. While current approaches search for appropriate minimum requirements of the necessary understanding and competence, the differences between (future) clinicians in terms of their information and understanding needs described here can lead to more differentiated approaches to solutions., (© 2024. The Author(s).)