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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]

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

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

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

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

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

ABSTRACT 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]

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 < 0.01) due to their need to access clinical updates. Respondents highly recommended institutional access for either UpToDate® or Lexicomp® be made available. Pre-registration pharmacists should be guided on judicious MMA usage, as they downloaded significantly more apps and were more likely to indicate not knowing which DI recommendation to follow (both p < 0.01).Conclusion: MMA has become an indispensable tool for hospital pharmacists, however there was a tendency to download multiple apps for DI needs. Institutional access can be considered for credible apps identified to ensure accuracy and uniformity of DI recommendations, with purchase decision made after surveying the needs and preferences of end users. [ABSTRACT FROM AUTHOR]

Digital health, an emerging scientific domain, attracts increasing attention as artificial intelligence and relevant software proliferate. Pharmacogenomics (PGx) is a core component of precision/personalized medicine driven by the overarching motto "the right drug, for the right patient, at the right dose, and the right time." PGx takes into consideration patients' genomic variations influencing drug efficacy and side effects. Despite its potentials for individually tailored therapeutics and improved clinical outcomes, adoption of PGx in clinical practice remains slow. We suggest that e-health tools such as clinical decision support systems (CDSSs) can help accelerate the PGx, precision/personalized medicine, and digital health emergence in everyday clinical practice worldwide. Herein, we present a systematic review that examines and maps the PGx-CDSSs used in clinical practice, including their salient features in both technical and clinical dimensions. Using Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines and research of the literature, 29 relevant journal articles were included in total, and 19 PGx-CDSSs were identified. In addition, we observed 10 technical components developed mostly as part of research initiatives, 7 of which could potentially facilitate future PGx-CDSSs implementation worldwide. Most of these initiatives are deployed in the United States, indicating a noticeable lack of, and the veritable need for, similar efforts globally, including Europe.

Background: Type-2 Diabetes Mellitus (T2D) is a growing concern worldwide, and family doctors are called to help diabetic patients manage this chronic disease, also with Medical Nutrition Therapy (MNT). However, MNT for Diabetes is usually standardized, while it would be much more effective if tailored to the patient. There is a gap in patient-tailored MNT which, if addressed, could support family doctors in delivering effective recommendations. In this context, decision support systems (DSSs) are valuable tools for physicians to support MNT for T2D patients - as long as DSSs are transparent to humans in their decision-making process. Indeed, the lack of transparency in data-driven DSS might hinder their adoption in clinical practice, thus leaving family physicians to adopt general nutrition guidelines provided by the national healthcare systems., Method: This work presents a prototypical ontology-based clinical Decision Support System (OnT2D- DSS) aimed at assisting general practice doctors in managing T2D patients, specifically in creating a tailored dietary plan, leveraging clinical expert knowledge. OnT2D-DSS exploits clinical expert knowledge formalized as a domain ontology to identify a patient's phenotype and potential comorbidities, providing personalized MNT recommendations for macro- and micro-nutrient intake. The system can be accessed via a prototypical interface., Results: Two preliminary experiments are conducted to assess both the quality and correctness of the inferences provided by the system and the usability and acceptance of the OnT2D-DSS (conducted with nutrition experts and family doctors, respectively)., Conclusions: Overall, the system is deemed accurate by the nutrition experts and valuable by the family doctors, with minor suggestions for future improvements collected during the experiments., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Cutaneous fungal infections are one of the most common skin conditions, hence, the burden of determining fungal elements upon microscopic examination with periodic acid-Schiff (PAS) and Gomori methenamine silver (GMS) stains, is very time consuming. Despite some morphological variability posing challenges to training artificial intelligence (AI)-based solutions, these structures are favored potential targets, enabling the recruitment of promising AI-based technologies. Herein, we present a novel AI solution for identifying skin fungal infections, potentially providing a decision support system for pathologists. Skin biopsies of patients diagnosed with a cutaneous fungal infection at the Sheba Medical Center, Israel between 2014 and 2023, were used. Samples were stained with PAS and GMS and digitized by the Philips IntelliSite scanner. DeePathology® STUDIO fungal elements were annotated and deemed as ground truth data after an overall revision by two specialist pathologists. Subsequently, they were used to create an AI-based solution, which has been further validated in other regions of interests. The study participants were divided into two cohorts. In the first cohort, the overall sensitivity of the algorithm was 0.8, specificity 0.97, F1 score 0.78; in the second, the overall sensitivity of the algorithm was 0.93, specificity 0.99, F1 score 0.95. The results obtained are encouraging as proof of concept for an AI-based fungi detection algorithm. DeePathology® STUDIO can be employed as a decision support system for pathologists when diagnosing a cutaneous fungal infection using PAS and GMS stains, thereby, saving time and money., Competing Interests: Declaration of Competing Interest None., (Copyright © 2024 Elsevier GmbH. All rights reserved.)

Clinical decision support systems (CDSS) based on artificial intelligence (AI) are complex socio-technical innovations and are increasingly being used in medicine and nursing to improve the overall quality and efficiency of care, while also addressing limited financial and human resources. However, in addition to such intended clinical and organisational effects, far-reaching ethical, social and legal implications of AI-based CDSS on patient care and nursing are to be expected. To date, these normative-social implications have not been sufficiently investigated. The BMBF-funded project DESIREE (DEcision Support In Routine and Emergency HEalth Care: Ethical and Social Implications) has developed recommendations for the responsible design and use of clinical decision support systems. This article focuses primarily on ethical and social aspects of AI-based CDSS that could have a negative impact on patient health. Our recommendations are intended as additions to existing recommendations and are divided into the following action fields with relevance across all stakeholder groups: development, clinical use, information and consent, education and training, and (accompanying) research., (© 2024. The Author(s).)

Background: Guideline adherence in the medical field leaves room for improvement. Digitalised decision support helps improve compliance. However, the complex nature of the guidelines makes implementation in clinical practice difficult., Methods: This single-centre prospective study included 204 adult ASA physical status 3-4 patients undergoing elective noncardiac surgery at a German university hospital. Agreement of clearance for surgery between a guideline expert and a digital guideline support tool was investigated. The decision made by the on-duty anaesthetists (standard approach) was assessed for agreement with the expert in a cross-over design. The main outcome was the level of agreement between digital guideline support and the expert., Results: The digital guideline support approach cleared 18.1% of the patients for surgery, the standard approach cleared 74.0%, and the expert approach cleared 47.5%. Agreement of the expert decision with digital guideline support (66.7%) and the standard approach (67.6%) was fair (Cohen's kappa 0.37 [interquartile range 0.26-0.48] vs 0.31 [0.21-0.42], P=0.6). Taking the expert decision as a benchmark, correct clearance using digital guideline support was 50.5%, and correct clearance using the standard approach was 44.6%. Digital guideline support incorrectly asked for additional examinations in 31.4% of the patients, whereas the standard approach did not consider conditions that would have justified additional examinations before surgery in 29.4%., Conclusions: Strict guideline adherence for clearance for surgery through digitalised decision support inadequately considered patients, clinical context. Vague formulations, weak recommendations, and low-quality evidence complicate guideline translation into explicit rules., Clinical Trial Registration: NCT04058769., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: This study evaluated the effect of an artificial intelligence (AI)-based clinical decision support system on the performance and diagnostic confidence of endoscopists in their assessment of Barrett's esophagus (BE)., Methods: 96 standardized endoscopy videos were assessed by 22 endoscopists with varying degrees of BE experience from 12 centers. Assessment was randomized into two video sets: group A (review first without AI and second with AI) and group B (review first with AI and second without AI). Endoscopists were required to evaluate each video for the presence of Barrett's esophagus-related neoplasia (BERN) and then decide on a spot for a targeted biopsy. After the second assessment, they were allowed to change their clinical decision and confidence level., Results: AI had a stand-alone sensitivity, specificity, and accuracy of 92.2%, 68.9%, and 81.3%, respectively. Without AI, BE experts had an overall sensitivity, specificity, and accuracy of 83.3%, 58.1%, and 71.5%, respectively. With AI, BE nonexperts showed a significant improvement in sensitivity and specificity when videos were assessed a second time with AI (sensitivity 69.8% [95%CI 65.2%-74.2%] to 78.0% [95%CI 74.0%-82.0%]; specificity 67.3% [95%CI 62.5%-72.2%] to 72.7% [95%CI 68.2%-77.3%]). In addition, the diagnostic confidence of BE nonexperts improved significantly with AI., Conclusion: BE nonexperts benefitted significantly from additional AI. BE experts and nonexperts remained significantly below the stand-alone performance of AI, suggesting that there may be other factors influencing endoscopists' decisions to follow or discard AI advice., Competing Interests: N. Parsa is VP of medical affairs at Satisfai Health. M. Byrne is CEO and Founder of Satisfai Health. H. Messmann has received lecture fees from Olympus, Ambu, IPSEN, Medtronic, and Falk, and research grants from Olympus and Satisfai; he is also a consultant for Satisafi. A. Ebigbo has held lectures for Olympus, Fuji, Pentax, Medtronic, Falk, and Ambu. The remaining authors declare that they have no conflict of interest., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

Rationale: Cardiac monitoring has often been identified as an area of overutilization and remains a limited resource in many hospitals. With the aim of reducing telemetry overuse, we added clinical decision support to our health system's telemetry order with guidance on appropriate indications for monitoring. The new order requires selection of an appropriate clinical indication., Aims and Objectives: In this study, we aimed to understand provider engagement with this tool by assessing concordance between selected indications within the order and the clinical presence of those conditions as documented within the patient chart., Methods: We randomly selected 100 telemetry orders from July to October 2022 across four different hospitals at NYU Langone Health. Two independent, blinded reviewers used a structured protocol to identify documentation of actual indications for telemetry in each selected chart. We calculated the rate of concordance between indications selected in the order and indications that were determined to be clinically present on chart review., Results: There were 30,839 telemetry orders placed during the study timeframe. Overall concordance between the selection within the order and the actual indication was 48% (95% confidence interval [CI], 38.21%-57.79%). We observed especially low concordance rates for vague indications, such as 'Other', and for 'Confirmed Stroke', which was the only indication allowing for indefinite telemetry., Conclusion: The overall low concordance suggests a disconnect between the support tool and clinical practice. Providers are more likely to select an indication that reduces downstream work regardless of a patient's true clinical indication., (© 2024 John Wiley & Sons Ltd.)

Objective: MRI utilization in the United States is relatively higher than in other parts of the world and inpatient MRI utilization is particularly difficult to manage given the lack of direct reimbursement. Body MRI studies present an opportunity to reduce inpatient MRI utilization since they are generally the least emergent. Our objective was to use a targeted questionnaire to probe the necessity of inpatient body MRI orders and present an opportunity to either cancel them or transition them to the outpatient realm METHODS: A 9-item questionnaire was devised asking questions about the urgency of the inpatient MRI order including the urgent management question, an inpatient procedure or whether it was recommended by a consultant. Peer-to-peer discussion walking through each of the questions was conducted by radiology housestaff with the ordering clinicians and responses recorded., Results: 845 recorded responses reported a lack of specific clinical question in 23.9% of orders, 68.9% were recommended by a non-radiology consulting service and 16.1% were recommended by radiology studies. 17.0% orders were felt to be outpatient appropriate and 23.3% were considered possibly appropriate for the outpatient setting. 3.9% were canceled and 4.9% were transitioned to outpatient orders., Discussion: Engaging in a focused discussion about the urgency and appropriateness of an inpatient MRI body order following a list of scripted questions has the potential to reduce utilization. This approach also highlights the relatively high rate of indication uncertainty among ordering clinicians and the central role of consultants in prompting orders., 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 Inc. All rights reserved.)

Background: Hospitalization rates for childhood pneumonia vary widely. Risk-based clinical decision support (CDS) interventions may reduce unwarranted variation., Methods: We conducted a pragmatic randomized trial in two US pediatric emergency departments (EDs) comparing electronic health record (EHR)-integrated prognostic CDS versus usual care for promoting appropriate ED disposition in children (<18 years) with pneumonia. Encounters were randomized 1:1 to usual care versus custom CDS featuring a validated pneumonia severity score predicting risk for severe in-hospital outcomes. Clinicians retained full decision-making authority. The primary outcome was inappropriate ED disposition, defined as early transition to lower- or higher-level care. Safety and implementation outcomes were also evaluated., Results: The study enrolled 536 encounters (269 usual care and 267 CDS). Baseline characteristics were similar across arms. Inappropriate disposition occurred in 3% of usual care encounters and 2% of CDS encounters (adjusted odds ratio: 0.99, 95% confidence interval: [0.32, 2.95]). Length of stay was also similar and adverse safety outcomes were uncommon in both arms. The tool's custom user interface and content were viewed as strengths by surveyed clinicians (>70% satisfied). Implementation barriers include intrinsic (e.g., reaching the right person at the right time) and extrinsic factors (i.e., global pandemic)., Conclusions: EHR-based prognostic CDS did not improve ED disposition decisions for children with pneumonia. Although the intervention's content was favorably received, low subject accrual and workflow integration problems likely limited effectiveness. Clinical Trials Registration: NCT06033079., (© 2024 Society of Hospital Medicine.)

Hydroxyurea treatment for children with sickle cell anemia (SCA) is effective and life-saving. Stepwise escalation to maximum tolerated dose (MTD) provides optimal benefits, but is logistically challenging and time-consuming, especially in low-income countries where most people with SCA live. Model-informed precision dosing (MIPD) of hydroxyurea expedites MTD determination and improves outcomes compared with trial-and-error dose adjustments. HdxSim, a user-friendly, online, clinical decision support tool was developed to facilitate hydroxyurea MIPD and evaluated using real-world pharmacokinetic (PK) data. First-dose hydroxyurea PK profiles were analyzed from two clinical trial datasets (Hydroxyurea Study of Long-Term Effects (HUSTLE), NCT00305175 and Therapeutic Response Evaluation and Adherence Trial (TREAT), NCT02286154). Areas under the concentration-time curve (AUC) estimated by HdxSim were compared with those determined using traditional trapezoidal methodology and PK software (MWPharm-DOS). The doses predicted by HdxSim and MWPharm-DOS were compared with the observed clinical MTD. For HUSTLE participants, HdxSim accurately estimated hydroxyurea AUC compared with the trapezoidal method, with < 20% variance. The average (mean ± SD) AUC for TREAT participants estimated with HdxSim (68.6 ± 18.0 mg*hour/L) was lower than MWPharm-DOS (78.6 ± 20.7 mg*hour/L, P = 0.012), but the average recommended doses were not different (425 vs. 423 mg/day, P = 0.97). Moreover, HdxSim was non-inferior to MWPharm-DOS at predicting clinical MTD (absolute difference 3.9 ± 5.8 vs. 4.9 ± 8.2 mg/kg/day, P = 0.19). HdxSim accurately estimates hydroxyurea exposure and is noninferior to traditional PK approaches at predicting the clinical hydroxyurea MTD. Hydroxyurea dosing based on target exposure leads to improved outcomes in children with SCA, and has the potential to make PK-guided hydroxyurea dosing more accessible to this neglected population globally., (© 2023 The Authors. Clinical Pharmacology & Therapeutics © 2023 American Society for Clinical Pharmacology and Therapeutics.)

Background: Emergency department triage is a vital and complex decision-making process. There is limited knowledge about nurses' experiences with triage decision support systems in emergency departments., Aim: This study was conducted to examine nurses' experiences with a computer-based triage decision support system in the emergency department., Study Design: This is a qualitative and phenomenological study. Data were collected through interviews from 14 triage nurses who used a triage decision support system in the emergency department of a university hospital. The nurses were recruited for semi-structured interviews. All interviews were recorded on a voice recorder and then transcribed. Data were analysed with the inductive content analysis method. The interviewer asked comprehensive questions about the nurses' experiences with the triage decision support system., Results: Three main and 11 sub-themes were elicited as a result of the analysis of the in-depth interviews: (a) the facilitating the triage decision theme, which included help in case of a dilemma, team collaboration, monitoring/supervision, and error reduction sub-themes; (b) the contribution to professionalism theme, which included ease of learning and teaching triage, professional autonomy, creating a database, and evidence-based practice sub-themes; (c) the areas that need improvement theme, which included reducing screen clicks, the effect of the hospital automation system performance, and clinical descriptors not included in the algorithm sub-themes., Conclusion: Triage nurses stated that the decision support system was beneficial and facilitated decision-making. The decision support system enabled triage nurses to make their own decisions using their clinical knowledge and experience, without a restriction on their professional autonomy, and this was perceived positively. It was emphasized that this system could be a support tool in educating nurses new to triage. However, nurses stated that the pace of the decision support system was affected by the performance of the hospital automation system and that the large number of steps used for electing items caused a waste of time. They also added that these were technical areas that needed improvement., Relevance to Clinical Practice: The study provides important data that will help healthcare organizations and professionals better understand the emergency department nurse triage decision support system and gain a versatile, comprehensive, and general understanding., (© 2024 The Authors. Nursing in Critical Care published by John Wiley & Sons Ltd on behalf of British Association of Critical Care Nurses.)

Background: Regular physical activity (PA) is a component of cardiovascular health and is associated with a lower risk of cardiovascular disease (CVD). However, only about half of US adults achieved the current PA recommendations., Objective: The study purpose was to implement PA counseling using a clinical decision support tool in a preventive cardiology clinic and to assess changes in CVD risk factors in a sample of patients enrolled over 12 weeks of PA monitoring., Methods: This intervention, piloted for 1 year, had 3 components embedded in the electronic health record: assessment of patients' PA, an electronic prompt for providers to counsel patients reporting low PA, and patient monitoring using a Fitbit. Cardiovascular disease risk factors included PA (self-report and Fitbit), body mass index, blood pressure, lipids, and cardiorespiratory fitness assessed with the 6-minute walk test. Depression and quality of life were also assessed. Paired t tests assessed changes in CVD risk., Results: The sample who enrolled in the remote patient monitoring (n = 59) were primarily female (51%), White adults (76%) with a mean age of 61.13 ± 11.6 years. Self-reported PA significantly improved over 12 weeks ( P = .005), but not Fitbit steps ( P = .07). There was a significant improvement in cardiorespiratory fitness (469 ± 108 vs 494 ± 132 m, P = .0034), and 23 participants (42%) improved at least 25 m, signifying a clinically meaningful improvement. Only 4 participants were lost to follow-up over 12 weeks of monitoring., Conclusions: Patients may need more frequent reminders to be active after an initial counseling session, perhaps getting automated messages based on their step counts syncing to their electronic health record., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Objective: Our article demonstrates the effectiveness of using a validated framework to create a ChatGPT prompt that generates valid nursing care plan suggestions for one hypothetical older patient with lung cancer., Method: This study describes the methodology for creating ChatGPT prompts that generate consistent care plan suggestions and its application for a lung cancer case scenario. After entering a nursing assessment of the patient's condition into ChatGPT, we asked it to generate care plan suggestions. Subsequently, we assessed the quality of the care plans produced by ChatGPT., Results: While not all the suggested care plan terms (11 out of 16) utilized standardized nursing terminology, the ChatGPT-generated care plan closely matched the gold standard in scope and nature, correctly prioritizing oxygenation and ventilation needs., Conclusion: Using a validated framework prompt to generate nursing care plan suggestions with ChatGPT demonstrates its potential value as a decision support tool for optimizing cancer care documentation., (© 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.)

Background: Multiple clinician adjustable parameters impact upon glycemia in people with type 1 diabetes (T1D) using Medtronic Mini Med 780G (MM780G) AHCL. These include glucose targets, carbohydrate ratios (CR), and active insulin time (AIT). Algorithm-based decision support advising upon potential settings adjustments may enhance clinical decision-making., Methods: Single-arm, two-phase exploratory study developing decision support to commence and sustain AHCL. Participants commenced investigational MM780G, then 8 weeks Phase 1-initial optimization tool evaluation, involving algorithm-based decision support with weekly AIT and CR recommendations. Clinicians approved or rejected CR and AIT recommendations based on perceived safety per protocol. Co-design resulted in a refined algorithm evaluated in a further identically configured Phase 2. Phase 2 participants also transitioned to commercial MM780G following "Quick Start" (algorithm-derived tool determining initial AHCL settings using daily insulin dose and weight). We assessed efficacy, safety, and acceptability of decision support using glycemic metrics, and the proportion of accepted CR and AIT settings per phase., Results: Fifty three participants commenced Phase 1 (mean age 24.4; Hba1c 61.5mmol/7.7%). The proportion of CR and AIT accepted by clinicians increased between Phases 1 and 2 respectively: CR 89.2% vs. 98.6%, p < 0.01; AIT 95.2% vs. 99.3%, p < 0.01. Between Phases, mean glucose percentage time < 3.9mmol (< 70mg/dl) reduced (2.1% vs. 1.4%, p = 0.04); change in mean TIR 3.9-10mmol/L (70-180mg/dl) was not statistically significant: 72.9% ± 7.8 and 73.5% ± 8.6. Quick start resulted in stable TIR, and glycemic metrics compared to international guidelines., Conclusion: The co-designed decision support tools were able to deliver safe and effective therapy. They can potentially reduce the burden of diabetes management related decision making for both health care practitioners and patients., Trial Registration: Prospectively registered with Australia/New Zealand Clinical Trials Registry(ANZCTR) on 30th March 2021 as study ACTRN12621000360819., (© 2024. The Author(s).)

Background: Older patients with diabetic kidney disease (DKD) often do not receive optimal pharmacological treatment. Current clinical practice guidelines (CPGs) do not incorporate the concept of personalised care. Clinical decision support (CDS) algorithms that consider both evidence and personalised care to improve patient outcomes can improve the care of older adults. The aim of this research is to design and validate a CDS algorithm for prescribing renin-angiotensin-aldosterone system inhibitors (RAASi) for older patients with diabetes., Methods: The design of the CDS tool included the following phases: (1) gathering evidence from systematic reviews and meta-analyses of randomised clinical trials to determine the number needed to treat (NNT) and time-to-benefit (TTB) values applicable to our target population for use in the algorithm. (2) Building a list of potential cases that addressed different prescribing scenarios (starting, adding or switching to RAASi). (3) Reviewing relevant guidelines and extracting all recommendations related to prescribing RAASi for DKD. (4) Matching NNT and TTB with specific clinical cases. (5) Validating the CDS algorithm using Delphi technique., Results: We created a CDS algorithm that covered 15 possible scenarios and we generated 36 personalised and nine general recommendations based on the calculated and matched NNT and TTB values and considering the patient's life expectancy and functional capacity. The algorithm was validated by experts in three rounds of Delphi study., Conclusion: We designed an evidence-informed CDS algorithm that integrates considerations often overlooked in CPGs. The next steps include testing the CDS algorithm in a clinical trial., Competing Interests: Competing interests: J-PL reported honoraria from Amgen Canada, AstraZeneca Canada and Otsuka Canada for work not related to this study. SH reported honoraria from AstraZeneca Canada, and research support from GSK and Merck unrelated to this study. CS reports funding from Pfizer Canada ULC for research unrelated to this study. The remaining authors have declared no conflict of interests., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Competing Interests: Conflict of interest: N.L.M. is supported by a Research Excellence Award (RE/24/130012) and a Chair Award (CH/F/21/90010) from the British Heart Foundation and reports research grants awarded to the University of Edinburgh from Abbott Diagnostics, Roche Diagnostics, and Siemens Healthineers outside the submitted work and honoraria from Abbott Diagnostics, Siemens Healthineers, Roche Diagnostics, LumiraDx, and Psyros Diagnostics. J.W.P. has undertaken statistical consulting for Abbott Diagnostics, Siemens Healthineers, Radiometer, and QuidelOrtho and is a shareholder of Wayfind Health. M.P.T. has received research support and honoraria from Abbott Diagnostics, Beckman Coulter, QuidelOrtho, Radiometer, Roche Diagnostics, and Siemens Healthineers.