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INTRODUCTION: User-centered data visualizations can reduce physician cognitive load and support clinical decision making. To facilitate the selection of appropriate visualizations for single patient health data summaries, this scoping review provides a literature overview of possible visualization techniques and the corresponding reported user-centered design phases. METHODS: The publication databases PubMed, Web of Science, IEEE Xplore and ACM Digital Library were searched for relevant articles from 2017 to 2022. RESULTS: Of the 777 articles screened, 78 articles were included in the final analysis. The most commonly used visualization techniques are table, scatterplot-line timeline, text and event timelines, with 24 other visualization techniques identified. The testing phase of the user centered design process is reported most frequently. CONCLUSION: This scoping review can support developers in the selection of suitable visualizations for single patient health data by revealing the design space of possible visualization techniques.

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INTRODUCTION: Automation bias poses a significant challenge to the effectiveness of Clinical Decision Support Systems (CDSS), potentially compromising diagnostic accuracy. Previous research highlights trust, self-confidence, and task difficulty as key determinants. With the increasing availability of AI-enabled CDSS, automation bias attains new attention. This study therefore aims to identify factors influencing automation bias in a diagnostic task. METHODS: A quantitative intervention study with participants from different backgrounds (n = 210) was conducted, employing regression analysis to analyze potential factors. Automation bias was measured as the agreement rate with wrong AI-enabled recommendations. RESULTS AND DISCUSSION: Diagnostic performance, certified wound care training, physician profession, and female gender significantly reduced false agreement rates. Higher perceived benefit of the system was significantly associated with promoting false agreement. Strategies like comprehensive diagnostic training are pivotal in the prevention of automation bias when implementing CDSS. CONCLUSION: Considering factors influencing automation bias when introducing a CDSS is critical to fully leverage the benefits of such a system. This study highlights that non-specialists, who stand to gain the most from CDSS, are also the most susceptible to automation bias, emphasizing the need for specialized training to mitigate this risk and ensure diagnostic accuracy and patient safety.

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INTRODUCTION: In nursing, professionals are expected to base their practice on evidence-based knowledge, however the successful implementation of this knowledge into nursing practice is not always assured. Clinical Decision Support Systems (CDSS) are considered to bridge this evidence-practice gap. METHODS: This study examines the extent to which evidence-based nursing (EBN) practices influence the use of CDSS and identifies what additional factors from acceptance theories such as UTAUT play a role. RESULTS AND DISCUSSION: Our findings from three regression models revealed that nursing professionals and nursing students who employ evidence-based practices are not more likely to use an evidence-based CDSS. The relationship between an EBN composite score (model 1) or is individual dimensions (model 2) and CDSS use was not significant. However, a more comprehensive model (model 3), incorporating items from the UTAUT such as Social Influences, Facilitating Conditions, Performance Expectancy, and Effort Expectancy, supplemented by Satisfaction demonstrated a significant variance explained (R2 = 0.279). Performance Expectancy and Satisfaction were found to be significantly associated with CDSS utilization. CONCLUSION: This underscores the importance of user-friendliness and practical utility of a CDSS. Despite potential limitations in generalizability and a limited sample size, the results provide insights into that CDSS first and foremost underly the same mechanisms of use as other health IT systems.

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BACKGROUND: Rifampicin resistant tuberculosis remains a global health problem with almost half a million new cases annually. In high-income countries patients empirically start a standardized treatment regimen, followed by an individualized regimen guided by drug susceptibility test (DST) results. In most settings, DST information is not available or is limited to isoniazid and fluoroquinolones. Whole genome sequencing could more accurately guide individualized treatment as the full drug resistance profile is obtained with a single test. Whole genome sequencing has not reached its full potential for patient care, in part due to the complexity of translating a resistance profile into the most effective individualized regimen. METHODS: We developed a treatment recommender clinical decision support system (CDSS) and an accompanying web application for user-friendly recommendation of the optimal individualized treatment regimen to a clinician. RESULTS: Following expert stakeholder meetings and literature review, nine drug features and 14 treatment regimen features were identified and quantified. Using machine learning, a model was developed to predict the optimal treatment regimen based on a training set of 3895 treatment regimen-expert feedback pairs. The acceptability of the treatment recommender CDSS was assessed as part of a clinical trial and in a routine care setting. Within the clinical trial setting, all patients received the CDSS recommended treatment. In 8 of 20 cases, the initial recommendation was recomputed because of stock out, clinical contra-indication or toxicity. In routine care setting, physicians rejected the treatment recommendation in 7 out of 15 cases because it deviated from the national TB treatment guidelines. A survey indicated that the treatment recommender CDSS is easy to use and useful in clinical practice but requires digital infrastructure support and training. CONCLUSIONS: Our findings suggest that global implementation of the novel treatment recommender CDSS holds the potential to improve treatment outcomes of patients with RR-TB, especially those with 'difficult-to-treat' forms of RR-TB.

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INTRODUCTION: Deprescribing fall-risk increasing drugs (FRIDs) is promising for reducing the risk of falling in older adults. Applying appropriate deprescribing in practice can be difficult due to the outcome uncertainties associated with stopping FRIDs. The ADFICE_IT intervention addresses this complexity with a clinical decision support system (CDSS) that facilitates optimum deprescribing of FRIDs by using a fall-risk prediction model, aggregation of deprescribing guidelines, and joint medication management. METHODS: The development process of the CDSS is described in this paper. Development followed a user-centered design approach in which users and experts were involved throughout each phase. In phase I, a prototype of the CDSS was developed which involved a literature and systematic review, European survey (n = 581), and semi-structured interviews with clinicians (n = 19), as well as the aggregation and testing of deprescribing guidelines and the development of the fall-risk prediction model. In phase II, the feasibility of the CDSS was tested by means of two usability testing rounds with users (n = 11). RESULTS: The final CDSS consists of five web pages. A connection between the Electronic Health Record allows for the retrieval of patient data into the CDSS. Key design requirements for the CDSS include easy-to-use features for fast-paced clinical environments, actionable deprescribing recommendations, information transparency, and visualization of the patient's fall-risk estimation. Key elements for the software include a modular architecture, open source, and good security. CONCLUSION: The ADFICE_IT CDSS supports physicians in deprescribing FRIDs optimally to prevent falls in older patients. Due to continuous user and expert involvement, each new feedback round led to an improved version of the system. Currently, a cluster-randomized controlled trial with process evaluation at hospitals in the Netherlands is being conducted to test the effect of the CDSS on falls. The trial is registered with ClinicalTrials.gov (date; 7-7-2022, identifier: NCT05449470).

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BACKGROUND: Despite the emerging application of clinical decision support systems (CDSS) in pregnancy care and the proliferation of artificial intelligence (AI) over the last decade, it remains understudied regarding the role of AI in CDSS specialized for pregnancy care. OBJECTIVE: To identify and synthesize AI-augmented CDSS in pregnancy care, CDSS functionality, AI methodologies, and clinical implementation, we reported a systematic review based on empirical studies that examined AI-augmented CDSS in pregnancy care. METHODS: We retrieved studies that examined AI-augmented CDSS in pregnancy care using database queries involved with titles, abstracts, keywords, and MeSH (Medical Subject Headings) terms. Bibliographic records from their inception to 2022 were retrieved from PubMed/MEDLINE (n=206), Embase (n=101), and ACM Digital Library (n=377), followed by eligibility screening and literature review. The eligibility criteria include empirical studies that (1) developed or tested AI methods, (2) developed or tested CDSS or CDSS components, and (3) focused on pregnancy care. Data of studies used for review and appraisal include title, abstract, keywords, MeSH terms, full text, and supplements. Publications with ancillary information or overlapping outcomes were synthesized as one single study. Reviewers independently reviewed and assessed the quality of selected studies. RESULTS: We identified 30 distinct studies of 684 studies from their inception to 2022. Topics of clinical applications covered AI-augmented CDSS from prenatal, early pregnancy, obstetric care, and postpartum care. Topics of CDSS functions include diagnostic support, clinical prediction, therapeutics recommendation, and knowledge base. CONCLUSIONS: Our review acknowledged recent advances in CDSS studies including early diagnosis of prenatal abnormalities, cost-effective surveillance, prenatal ultrasound support, and ontology development. To recommend future directions, we also noted key gaps from existing studies, including (1) decision support in current childbirth deliveries without using observational data from consequential fetal or maternal outcomes in future pregnancies; (2) scarcity of studies in identifying several high-profile biases from CDSS, including social determinants of health highlighted by the American College of Obstetricians and Gynecologists; and (3) chasm between internally validated CDSS models, external validity, and clinical implementation.

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BACKGROUND: The population diagnosed with renal cell carcinoma, especially in Asia, represents 36.6% of global cases, with the incidence rate of renal cell carcinoma in Korea steadily increasing annually. However, treatment options for renal cell carcinoma are diverse, depending on clinical stage and histologic characteristics. Hence, this study aims to develop a machine learning based clinical decision-support system that recommends personalized treatment tailored to the individual health condition of each patient. RESULTS: We reviewed the real-world medical data of 1,867 participants diagnosed with renal cell carcinoma between November 2008 and June 2021 at the Pusan National University Yangsan Hospital in South Korea. Data were manually divided into a follow-up group where the patients did not undergo surgery or chemotherapy (Surveillance), a group where the patients underwent surgery (Surgery), and a group where the patients received chemotherapy before or after surgery (Chemotherapy). Feature selection was conducted to identify the significant clinical factors influencing renal cell carcinoma treatment decisions from 2,058 features. These features included subsets of 20, 50, 75, 100, and 150, as well as the complete set and an additional 50 expert-selected features. We applied representative machine learning algorithms, namely Decision Tree, Random Forest, and Gradient Boosting Machine (GBM). We analyzed the performance of three applied machine learning algorithms, among which the GBM algorithm achieved an accuracy score of 95% (95% CI, 92-98%) for the 100 and 150 feature sets. The GBM algorithm using 100 and 150 features achieved better performance than the algorithm using features selected by clinical experts (93%, 95% CI 89-97%). CONCLUSIONS: We developed a preliminary personalized treatment decision-support system (TDSS) called "RCC-Supporter" by applying machine learning (ML) algorithms to determine personalized treatment for the various clinical situations of RCC patients. Our results demonstrate the feasibility of using machine learning-based clinical decision support systems for treatment decisions in real clinical settings.

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BACKGROUND: In the last few years, there has been an increasing interest in the development of artificial intelligence (AI)-based clinical decision support systems (CDSS). However, there are barriers to the successful implementation of such systems in practice, including the lack of acceptance of these systems. Participatory approaches aim to involve future users in designing applications such as CDSS to be more acceptable, feasible, and fundamentally more relevant for practice. The development of technologies based on AI, however, challenges the process of user involvement and related methods. OBJECTIVE: The aim of this review is to summarize and present the main approaches, methods, practices, and specific challenges for participatory research and development of AI-based decision support systems involving clinicians. METHODS: This scoping review will follow the Joanna Briggs Institute approach to scoping reviews. The search for eligible studies was conducted in the databases MEDLINE via PubMed; ACM Digital Library; Cumulative Index to Nursing and Allied Health; and PsycInfo. The following search filters, adapted to each database, were used: Period January 01, 2012, to October 31, 2023, English and German studies only, abstract available. The scoping review will include studies that involve the development, piloting, implementation, and evaluation of AI-based CDSS (hybrid and data-driven AI approaches). Clinical staff must be involved in a participatory manner. Data retrieval will be accompanied by a manual gray literature search. Potential publications will then be exported into reference management software, and duplicates will be removed. Afterward, the obtained set of papers will be transferred into a systematic review management tool. All publications will be screened, extracted, and analyzed: title and abstract screening will be carried out by 2 independent reviewers. Disagreements will be resolved by involving a third reviewer. Data will be extracted using a data extraction tool prepared for the study. RESULTS: This scoping review protocol was registered on March 11, 2023, at the Open Science Framework. The full-text screening had already started at that time. Of the 3,118 studies screened by title and abstract, 31 were included in the full-text screening. Data collection and analysis as well as manuscript preparation are planned for the second and third quarter of 2024. The manuscript should be submitted towards the end of 2024. CONCLUSIONS: This review will describe the current state of knowledge on participatory development of AI-based decision support systems. The aim is to identify knowledge gaps and provide research impetus. It also aims to provide relevant information for policy makers and practitioners. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58185.

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While very much in its infancy in terms of becoming an established tool, the use of digital technology in community nursing is steadily growing, despite the persistent barriers to, and challenges encountered in its uptake and implementation. The mobile nature and high workload of a community nurse's daily practice should facilitate the rapid uptake of time-saving technology. However, there are indications that technology may not be the panacea it was originally proclaimed to be. Francesca Ramadan elaborates on the past and present applications of digital technology in community nursing and delves into the principles that should shape the future potential of tools such as artificial intelligence, automation technologies and clinical decision support systems.

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BACKGROUND: Breast cancer is a leading global health concern, necessitating advancements in recurrence prediction and management. The development of an artificial intelligence (AI)-based clinical decision support system (AI-CDSS) using ChatGPT addresses this need with the aim of enhancing both prediction accuracy and user accessibility. OBJECTIVE: This study aims to develop and validate an advanced machine learning model for a web-based AI-CDSS application, leveraging the question-and-answer guidance capabilities of ChatGPT to enhance data preprocessing and model development, thereby improving the prediction of breast cancer recurrence. METHODS: This study focused on developing an advanced machine learning model by leveraging data from the Tri-Service General Hospital breast cancer registry of 3577 patients (2004-2016). As a tertiary medical center, it accepts referrals from four branches-3 branches in the northern region and 1 branch on an offshore island in our country-that manage chronic diseases but refer complex surgical cases, including breast cancer, to the main center, enriching our study population's diversity. Model training used patient data from 2004 to 2012, with subsequent validation using data from 2013 to 2016, ensuring comprehensive assessment and robustness of our predictive models. ChatGPT is integral to preprocessing and model development, aiding in hormone receptor categorization, age binning, and one-hot encoding. Techniques such as the synthetic minority oversampling technique address the imbalance of data sets. Various algorithms, including light gradient-boosting machine, gradient boosting, and extreme gradient boosting, were used, and their performance was evaluated using metrics such as the area under the curve, accuracy, sensitivity, and F1-score. RESULTS: The light gradient-boosting machine model demonstrated superior performance, with an area under the curve of 0.80, followed closely by the gradient boosting and extreme gradient boosting models. The web interface of the AI-CDSS tool was effectively tested in clinical decision-making scenarios, proving its use in personalized treatment planning and patient involvement. CONCLUSIONS: The AI-CDSS tool, enhanced by ChatGPT, marks a significant advancement in breast cancer recurrence prediction, offering a more individualized and accessible approach for clinicians and patients. Although promising, further validation in diverse clinical settings is recommended to confirm its efficacy and expand its use.

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BACKGROUND: Overcrowded emergency departments (EDs) are associated with higher morbidity and mortality and suboptimal quality-of-care. Most ED flow management strategies focus on early identification and redirection of low-acuity patients to primary care settings. To assess the impact of redirecting low-acuity ED patients to medical clinics using an electronic clinical decision support system on four ED performance indicators. METHODS: We performed a retrospective observational study in the ED of a Canadian tertiary trauma center where a redirection process for low-acuity patients was implemented. The process was based on a clinical decision support system relying on an algorithm based on chief complaint, performed by nurses at triage and not involving physician assessment. All patients visiting the ED from 2013 to 2017 were included. We compared ED performance indicators before and after implementation of the redirection process (June 2015): length-of-triage, time-to-initial-physician-assessment, length-of-stay and rate of patients leaving without being seen. We performed an interrupted time series analysis adjusted for age, gender, time of visit, triage category and overcrowding. RESULTS: Of 242,972 ED attendees over the study period, 9546 (8% of 121,116 post-intervention patients) were redirected to a nearby primary medical clinic. After the redirection process was implemented, length-of-triage increased by 1 min [1;2], time-to-initial assessment decreased by 13 min [-16;-11], length-of-stay for non-redirected patients increased by 29 min [13;44] (p < 0.001), minus 20 min [-42;1] (p = 0.066) for patients assigned to triage 5 category. The rate of patients leaving without being seen decreased by 2% [-3;-2] (p < 0.001). CONCLUSION: Implementing a redirection process for low-acuity ED patients based on a clinical support system was associated with improvements in two of four ED performance indicators.

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

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BACKGROUND: While Human Factors (HF) methods have been applied to the design of decision support systems (DSS) to aid clinical decision-making, the role of HF to improve decision-support for population health outcomes is less understood. We sought to comprehensively understand how HF methods have been used in designing digital population health DSS. MATERIALS AND METHODS: We searched English documents published in health sciences and engineering databases (Medline, Embase, PsychINFO, Scopus, Comendex, Inspec, IEEE Xplore) between January 1990 and September 2023 describing the development, validation or application of HF principles to decision support tools in population health. RESULTS: We identified 21,581 unique records and included 153 studies for data extraction and synthesis. We included research articles that had a target end-user in population health and that used HF. HF methods were applied throughout the design lifecycle. Users were engaged early in the design lifecycle in the needs assessment and requirements gathering phase and design and prototyping phase with qualitative methods such as interviews. In later stages in the lifecycle, during user testing and evaluation, and post deployment evaluation, quantitative methods were more frequently used. However, only three studies used an experimental framework or conducted A/B testing. CONCLUSIONS: While HF have been applied in a variety of contexts in the design of data-driven DSSs for population health, few have used Human Factors to its full potential. We offer recommendations for how HF can be leveraged throughout the design lifecycle. Most crucially, system designers should engage with users early on and throughout the design process. Our findings can support stakeholders to further empower public health systems.

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BACKGROUND: A digital decision support system in healthcare is a digital health intervention that assists healthcare professionals in decision-making by providing treatment recommendations and enhancing diagnostic accuracy and quality of care. This will be the first study in Pakistan to assess the system's usability, acceptability and effectiveness in improving healthcare outcomes while also evaluating the perceived quality of care. This comprehensive assessment will inform policy development in areas such as the scale-up of digital health interventions, data privacy and technology interoperability. Measures of effectiveness will include changes in clinical outcomes through a patient exit feedback survey. This study aims to evaluate the role of digital decision support systems in healthcare decision-making, which may be integrated into Pakistan's tele-primary healthcare system. METHODS: The study will employ a multimethod approach. The data collection tools are adapted from the WHO's digital health intervention monitoring and evaluation framework and include a technology assessment, healthcare provider surveys, patient exit interviews and focus group discussions with healthcare providers. Purposive sampling will be used for qualitative interviews with providers (doctors) and patients. Government stakeholders, private sectors, multilateral, academia and policymakers will be engaged through a consultative meeting. We will also conduct a literature review, as well as a comprehensive analysis of existing studies, documents and data relevant to digital decision support systems and digital health interventions implemented globally, and assess the performance, challenges and opportunities. ETHICS AND DISSEMINATION: The study has been approved by the Ethics Review Committee at The Aga Khan University (2023-8514-26533). The dissemination of study findings through scientific publications and seminars will enable programme managers and policymakers to design tools to improve the quality of care provided through telemedicine platforms. This will contribute to efficient decision-making, access and quality of care for primary healthcare in low-income and middle-income countries. This study will also inform policy regarding the scale-up of decision support systems in primary care settings, data privacy and technology interoperability.

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OBJECTIVES: To assess whether genotype-guided selection of oral antiplatelet drugs using a clinical decision support (CDS) algorithm reduces the rate of major adverse cardiovascular and cerebrovascular events (MACCEs) among Caribbean Hispanic patients, after 6 months. DESIGN: An open-label, multicentre, non-randomised clinical trial. SETTING: Eight secondary and tertiary care hospitals (public and private) in Puerto Rico. PARTICIPANTS: 300 Caribbean Hispanic patients on clopidogrel, both genders, underwent percutaneous coronary intervention (PCI) for acute coronary syndromes, stable ischaemic heart disease and documented extracardiac vascular diseases. INTERVENTIONS: Patients were separated into standard-of-care (SoC) and genotype-guided (pharmacogenetic (PGx)-CDS) groups (150 each) and stratified by risk scores. Risk scores were calculated based on a previously developed CDS risk prediction algorithm designed to make actionable treatment recommendations for each patient. Individual platelet function, genotypes, clinical and demographic data were included. Ticagrelor was recommended for patients with a high-risk score ≥2 in the PGx-CDS group only, the rest were kept or de-escalated to clopidogrel. The intervention took place within 3-5 days after PCI. Adherence medication score was also measured. PRIMARY AND SECONDARY OUTCOMES: The occurrence rate of MACCEs (primary) and bleeding episodes (secondary). Statistical associations between patient time free of events and predictor variables (ie, treatment groups, risk scores) were tested using Kaplan-Meier survival analyses and Cox proportional-hazards regression models. RESULTS: The genotype-guided group had a clinically lower but not significantly different risk of MACCEs compared with the SoC group (8.7% vs 10.7%, p=0.56; HR=0.56). Among high-risk score patients, genotype-driven guidance of antiplatelet therapy showed superiority over SoC in reducing MACCE incidence 6 months postcoronary stenting (adjusted HR=0.104; p< 0.0001). CONCLUSIONS: The potential benefit of implementing our PGx-CDS algorithm to significantly reduce the incidence rate of MACCEs in post-PCI Caribbean Hispanic patients on clopidogrel was observed exclusively among high-risk patients, with apparently no evident effect in other patient groups. TRIAL REGISTRATION NUMBER: NCT03419325.

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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.

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