Clinical decision support systems (CDSS) are used to improve clinical and service outcomes, yet evidence from low- and middle-income countries (LMICs) is dispersed. This protocol outlines methods to quantify the impact of CDSS on patient and healthcare delivery outcomes in LMICs. We will include comparative quantitative designs (randomized trials, controlled before-after, interrupted time series, comparative cohorts) evaluating CDSS in World Bank-defined LMICs. Standalone qualitative studies are excluded; mixed-methods studies are eligible only if they report comparative quantitative outcomes, for which we will extract the quantitative component. Searches (from inception to 30 September 2024) will cover MEDLINE, Embase, CINAHL, CENTRAL, Web of Science, Global Health, Scopus, IEEE Xplore, LILACS, African Index Medicus, and IndMED, plus grey sources. Screening and extraction will be performed in duplicate. Risk of bias will be assessed with RoB 2 (randomized trials) and ROBINS-I (non-randomized). Random-effects meta-analysis will be performed where outcomes are conceptually or statistically comparable; otherwise, a structured narrative synthesis will be presented. Heterogeneity will be explored using relative and absolute metrics and a priori subgroups or meta-regression (condition area, care level, CDSS type, readiness proxies, study design).

Over the past few years, the field of artificial intelligence (AI) has shown great promise in various domains, including medicine. A potential use case for AI in medicine is its application in managing advanced-stage cancer treatment, where limited evidence often makes treatment choices reliant on the personal expertise of the physicians. The complex nature of oncological disease processes and the multitude of factors that need to be considered when making treatment decisions make it difficult to rely solely on evidence-based trial data, which is often limited and may exclude certain patient populations. This results in physicians making decisions on a case-by-case basis, drawing on their experience of previous cases, which is not always objective and may be limited by the small number of cases they have observed. In this context, the use of clinical decision support systems (CDSS) using similaritybased AI approaches can potentially contribute to better oncology treatment by supporting physicians in the selection of treatment methods [1, 2]. One approach is Case-Based Reasoning (CBR), a subfield of AI that deals with experience-based problem solving.

Explainable AI (XAI) has become a crucial component of Clinical Decision Support Systems (CDSS) to enhance transparency, trust, and clinical adoption. However, while many XAI methods have been proposed, their effectiveness in real-world medical settings remains underexplored. This paper provides a survey of human-centered evaluations of Explainable AI methods in Clinical Decision Support Systems. By categorizing existing works based on XAI methodologies, evaluation frameworks, and clinical adoption challenges, we offer a structured understanding of the landscape. Our findings reveal key challenges in the integration of XAI into healthcare workflows and propose a structured framework to align the evaluation methods of XAI with the clinical needs of stakeholders.

Medication errors significantly threaten patient safety, leading to adverse drug events and substantial economic burdens on healthcare systems. Clinical Decision Support Systems (CDSSs) aimed at mitigating these errors often face limitations, including reliance on static databases and rule-based algorithms, which can result in high false alert rates and alert fatigue among clinicians. This paper introduces HELIOT, an innovative CDSS for drug allergy management, integrating Large Language Models (LLMs) with a comprehensive pharmaceutical data repository. HELIOT leverages advanced natural language processing capabilities to interpret complex medical texts and synthesize unstructured data, overcoming the limitations of traditional CDSSs. An empirical evaluation using a synthetic patient dataset and expert-verified ground truth demonstrates HELIOT's high accuracy, precision, recall, and F1 score, uniformly reaching 100\% across multiple experimental runs. The results underscore HELIOT's potential to enhance decision support in clinical settings, offering a scalable, efficient, and reliable solution for managing drug allergies.

Emergency department (ED) overcrowding and the complexity of rapid decision-making in critical care settings pose significant challenges to healthcare systems worldwide. While clinical decision support systems (CDSS) have shown promise, the integration of large language models (LLMs) offers new possibilities for enhancing triage accuracy and clinical decision-making. This study presents an LLM-driven CDSS designed to assist ED physicians and nurses in patient triage, treatment planning, and overall emergency care management. We developed a multi-agent CDSS utilizing Llama-3-70b as the base LLM, orchestrated by CrewAI and Langchain. The system comprises four AI agents emulating key ED roles: Triage Nurse, Emergency Physician, Pharmacist, and ED Coordinator. It incorporates the Korean Triage and Acuity Scale (KTAS) for triage assessment and integrates with the RxNorm API for medication management. The model was evaluated using the Asclepius dataset, with performance assessed by a clinical emergency medicine specialist. The CDSS demonstrated high accuracy in triage decision-making compared to the baseline of a single-agent system. Furthermore, the system exhibited strong performance in critical areas, including primary diagnosis, critical findings identification, disposition decision-making, treatment planning, and resource allocation. Our multi-agent CDSS demonstrates significant potential for supporting comprehensive emergency care management. By leveraging state-of-the-art AI technologies, this system offers a scalable and adaptable tool that could enhance emergency medical care delivery, potentially alleviating ED overcrowding and improving patient outcomes. This work contributes to the growing field of AI applications in emergency medicine and offers a promising direction for future research and clinical implementation.

The Center for Disease Control estimates that over 37 million US adults suffer from chronic kidney disease (CKD), yet 9 out of 10 of these individuals are unaware of their condition due to the absence of symptoms in the early stages. It has a significant impact on patients' quality of life, particularly when it progresses to the need for dialysis. Early prediction of dialysis is crucial as it can significantly improve patient outcomes and assist healthcare providers in making timely and informed decisions. However, developing an effective machine learning (ML)-based Clinical Decision Support System (CDSS) for early dialysis prediction poses a key challenge due to the imbalanced nature of data. To address this challenge, this study evaluates various data augmentation techniques to understand their effectiveness on real-world datasets. We propose a new approach named Binary Gaussian Copula Synthesis (BGCS). BGCS is tailored for binary medical datasets and excels in generating synthetic minority data that mirrors the distribution of the original data. BGCS enhances early dialysis prediction by outperforming traditional methods in detecting dialysis patients. For the best ML model, Random Forest, BCGS achieved a 72% improvement, surpassing the state-of-the-art augmentation approaches. Also, we present a ML-based CDSS, designed to aid clinicians in making informed decisions. CDSS, which utilizes decision tree models, is developed to improve patient outcomes, identify critical variables, and thereby enable clinicians to make proactive decisions, and strategize treatment plans effectively for CKD patients who are more likely to require dialysis in the near future. Through comprehensive feature analysis and meticulous data preparation, we ensure that the CDSS's dialysis predictions are not only accurate but also actionable, providing a valuable tool in the management and treatment of CKD.

Importance: We introduce a novel Retrieval Augmented Generation (RAG)-Large Language Model (LLM) as a Clinical Decision Support System (CDSS) for safe medication prescription. This model addresses the limitations of traditional rule-based CDSS by providing relevant prescribing error alerts tailored to patient context and institutional guidelines. Objective: The study evaluates the efficacy of an LLM-based CDSS in identifying medication errors across various medical and surgical case vignettes, compared to a human expert panel. It also examines clinician preferences among different CDSS integration modalities: junior pharmacist, LLM-based CDSS alone, and a combination of both. Design, Setting, and Participants: Utilizing a RAG model with GPT-4.0, the study involved 61 prescribing error scenarios within 23 clinical vignettes across 12 specialties. An expert panel assessed these cases using the PCNE classification and NCC MERP index. Three junior pharmacists independently reviewed each vignette under simulated conditions. Main Outcomes and Measures: The study assesses the LLM-based CDSS's accuracy, precision, recall, and F1 scores in identifying Drug-Related Problems (DRPs), compared to junior pharmacists alone or in an assistive mode with the CDSS. Results: The co-pilot mode of RAG-LLM significantly improved DRP identification accuracy by 22% over solo pharmacists. It showed higher recall and F1 scores, indicating better detection of severe DRPs, despite a slight decrease in precision. Accuracy varied across categories when pharmacists had access to RAG-LLM responses. Conclusions: The RAG-LLM based CDSS enhances medication error identification accuracy when used with junior pharmacists, especially in detecting severe DRPs.

Clinical Decision Support Systems (CDSS) utilize evidence-based knowledge and patient data to offer real-time recommendations, with Large Language Models (LLMs) emerging as a promising tool to generate plain-text explanations for medical decisions. This study explores the effectiveness and reliability of LLMs in generating explanations for diagnoses based on patient complaints. Three experienced doctors evaluated LLM-generated explanations of the connection between patient complaints and doctor and model-assigned diagnoses across several stages. Experimental results demonstrated that LLM explanations significantly increased doctors' agreement rates with given diagnoses and highlighted potential errors in LLM outputs, ranging from 5% to 30%. The study underscores the potential and challenges of LLMs in healthcare and emphasizes the need for careful integration and evaluation to ensure patient safety and optimal clinical utility.

Clinical decision support systems are software tools that help clinicians to make medical decisions. However, their acceptance by clinicians is usually rather low. A known problem is that they often require clinicians to manually enter lots of patient data, which is long and tedious. Existing solutions, such as the automatic data extraction from electronic health record, are not fully satisfying, because of low data quality and availability. In practice, many systems still include long questionnaire for data entry. In this paper, we propose an original solution to simplify patient data entry, using an adaptive questionnaire, i.e. a questionnaire that evolves during user interaction, showing or hiding questions dynamically. Considering a rule-based decision support systems, we designed methods for translating the system's clinical rules into display rules that determine the items to show in the questionnaire, and methods for determining the optimal order of priority among the items in the questionnaire. We applied this approach to a decision support system implementing STOPP/START v2, a guideline for managing polypharmacy. We show that it permits reducing by about two thirds the number of clinical conditions displayed in the questionnaire. Presented to clinicians during focus group sessions, the adaptive questionnaire was found "pretty easy to use". In the future, this approach could be applied to other guidelines, and adapted for data entry by patients.

Clinical decision support systems (CDSS) augmented with artificial intelligence (AI) models are emerging as potentially valuable tools in healthcare. Despite their promise, the development and implementation of these systems typically encounter several barriers, hindering the potential for widespread adoption. Here we present a case study of a recently developed AI-CDSS, Aifred Health, aimed at supporting the selection and management of treatment in major depressive disorder. We consider both the principles espoused during development and testing of this AI-CDSS, as well as the practical solutions developed to facilitate implementation. We also propose recommendations to consider throughout the building, validation, training, and implementation process of an AI-CDSS. These recommendations include: identifying the key problem, selecting the type of machine learning approach based on this problem, determining the type of data required, determining the format required for a CDSS to provide clinical utility, gathering physician and patient feedback, and validating the tool across multiple settings. Finally, we explore the potential benefits of widespread adoption of these systems, while balancing these against implementation challenges such as ensuring systems do not disrupt the clinical workflow, and designing systems in a manner that engenders trust on the part of end users.