Towards effective clinical decision support systems: A systematic review

Retrieving similar cases for clinical decision support in the context of revascularization of lower limbs

BACKGROUND

Vascular surgeons face complex decisions regarding revascularization of lower limbs of patients, for which there is no decision support solution yet available.

OBJECTIVE

Relying on Case-Based Reasoning approach, this paper addresses the issue of case retrieving in the context of lower limb revascularization. To this end we propose a similarity measure based on relevant clinical attributes selection and weighting. We analyze its performance for clinical decision support.

METHOD

Retrieval process is used to anticipate the success or failure of a revascularization strategy for a new patient based on the surgical procedure outcome of similar past patient cases. Using clinical knowledge in Global vascular Guidelines, cases were structured and formalized. They were described by several attributes reflecting the patient risk estimation, limb pathology staging, localization of atherosclerotic lesions, vascular history and revascularization type. To construct the similarity measure for lower limb revascularization CBR, three distance metrics (Euclidean, Cosine and Heterogeneous), three attributes selection scenarios as well as two weighting strategies were identified and compared.

RESULTS

A case base of 17 patients was built and used to assess the relevance of the retrieval process when predicting the long-term outcome of the revascularization procedure. Similarity measure showed good performance and clinical relevance when using a heterogeneous distance with attributes and weights derived from information in official guidelines, vascular history and type of revascularization.

CONCLUSION

By investigating and validating the similar cases retrieving process, we showed that CBR appears to be a promising approach for clinical decision support in the context of lower extremity arterial disease.

Clinical decision support amidst a global pandemic: Value of near real-time feedback in advancing appropriate post-discharge opioid prescribing for surgical patients

IMPLEMENTATION LESSONS

Non-evidence based factors influence post-surgical opioid prescribing practices. Delivering automated near real-time opioid prescribing feedback may encourage providers to prescribe opioid quantities which are more aligned with patient consumption and institutional guidelines. COVID-19 presented unprecedented challenges to healthcare delivery. We observed a substantial deviation in guideline-concordant opioids prescribing during the initial outbreak. However, our institution's pre-existing opioid prescribing feedback system and decision aid may have helped limit the duration and magnitude of the observed deviations by informing prescribers of atypically large opioid prescriptions and encouraging use of institutional data. Combined with provider education, a non-directive decision aid, in the form of near, real-time email feedback, may be an effective mechanism to advance evidence-based opioid prescribing, as it retains flexibility and provider autonomy while encouraging data-driven decision making.

Interpretable AI-assisted clinical decision making for treatment selection for brain metastases in radiation therapy

BACKGROUND

AI modeling CDM can improve the quality and efficiency of clinical practice or provide secondary opinion consultations for patients with limited medical resources to address healthcare disparities.

PURPOSE

In this study, we developed an interpretable AI model to select radiotherapy treatment options, that is, whole-brain radiation therapy (WBRT) versus stereotactic radiosurgery (SRS), for patients with brain metastases.

MATERIALS/METHODS

A total of 232 patients with brain metastases treated by radiation therapy from 2018 to 2023 were obtained. CT/MR images with contoured target lesions and organs-at-risk (OARs) as well as non-image-based clinical parameters were extracted and digitized as inputs to the model. These parameters included (1) tumor size, shape, location, and proximity of lesions to OARs; (2) age; (3) the number of brain metastases; (4) Eastern Cooperative Oncology Group (ECOG) performance status; (5) presence of neurologic symptoms; (6) if surgery was performed (either pre/post-op RT); (7) newly diagnosed cancer with brain metastases (de-novo) versus re-treatment (either local or distant in the brain); (8) primary cancer histology; (9) presence of extracranial metastases; (10) extent of extracranial disease (progression vs. stable); and (11) receipt of systemic therapy. One vanilla and two interpretable 3D convolutional neural networks (CNN) models were developed. The vanilla one-path model (VM-1) uses only images as input, while the two interpretable models use both images and clinical parameters as inputs with two (IM-2) and 11 (IM-11) independent paths, respectively. This novel design allowed the model to calculate a class activation score for each input to interpret its relative weighting and importance in decision-making. The actual radiotherapy treatment (WBRT or SRS) used for the patients was used as ground truth for model training. The model performance was assessed by Stratified-10-fold cross-validation, with each fold consisting of selected 184 training, 24 validation, and 24 testing subjects.

RESULT

A total of 232 brain metastases patients treated by WBRT or SRS were evaluated, including 80 WBRT and 152 SRS patients. Based on the images alone, the VM-1 model prescribed correctly for 143 (94%) SRS and 67 (84%) WBRT cases. Based on both images and clinical parameters, the IM-2 model prescribed correctly for 149 (98%) SRS and 74 (93%) WBRT cases. IM-11 provided the most interpretability with a relative weighting for each input as follows: CT image (59.5%), ECOG performance status (7.5%), re-treatment (5%), extracranial metastases (1.5%), number of brain metastases (9.5%), neurologic symptoms (3%), pre/post-surgery (2%), primary cancer histology (2%), age (1%), progressive extracranial disease (6%), and receipt of systemic therapy (4.5%), reflecting the importance of all these inputs in clinical decision-making.

CONCLUSION

Interpretable CNN models were successfully developed to use CT/MR images and non-image-based clinical parameters to predict the treatment selection between WBRT and SRS for brain metastases patients. The interpretability makes the model more transparent, carrying profound importance for the prospective integration of these models into routine clinical practice, particularly for informing real-time clinical decision-making.

Interpretation of cardiopulmonary exercise test by GPT - promising tool as a first step to identify normal results

BACKGROUND

Cardiopulmonary exercise testing (CPET) is used in the evaluation of unexplained dyspnea. However, its interpretation requires expertise that is often not available. We aim to evaluate the utility of ChatGPT (GPT) in interpreting CPET results.

RESEARCH DESIGN AND METHODS

This cross-sectional study included 150 patients who underwent CPET. Two expert pulmonologists categorized the results as normal or abnormal (cardiovascular, pulmonary, or other exercise limitations), being the gold standard. GPT versions 3.5 (GPT-3.5) and 4 (GPT-4) analyzed the same data using pre-defined structured inputs.

RESULTS

GPT-3.5 correctly interpreted 67% of the cases. It achieved a sensitivity of 75% and specificity of 98% in identifying normal CPET results. GPT-3.5 had varying results for abnormal CPET tests, depending on the limiting etiology. In contrast, GPT-4 demonstrated improvements in interpreting abnormal tests, with sensitivities of 83% and 92% for respiratory and cardiovascular limitations, respectively. Combining the normal CPET interpretations by both AI models resulted in 91% sensitivity and 98% specificity. Low work rate and peak oxygen consumption were independent predictors for inaccurate interpretations.

CONCLUSIONS

Both GPT-3.5 and GPT-4 succeeded in ruling out abnormal CPET results. This tool could be utilized to differentiate between normal and abnormal results.

AI-driven biomarker discovery: enhancing precision in cancer diagnosis and prognosis

Cancer remains a significant health issue, resulting in around 10 million deaths per year, particularly in developing nations. Demographic changes, socio-economic variables, and lifestyle choices are responsible for the rise in cancer cases. Despite the potential to mitigate the adverse effects of cancer by early detection and the implementation of cancer prevention methods, several nations have limited screening facilities. In oncology, the use of artificial intelligence (AI) represents a transformative advancement in cancer diagnosis, prognosis, and treatment. The use of AI in biomarker discovery improves precision medicine by uncovering biomarker signatures that are essential for early detection and treatment of diseases within vast and diverse datasets. Deep learning and machine learning diagnostics are two examples of AI technologies that are changing the way biomarkers are made by finding patterns in large datasets and making new technologies that make it possible to deliver accurate and effective therapies. Existing gaps include data quality, algorithmic transparency, and ethical concerns around privacy, among others. The advancement of biomarker discovery methodologies with AI seeks to transform cancer by improving patient survival rates through enhanced early diagnosis and targeted therapy. This commentary aims to clarify how AI is improving the identification of novel biomarkers for optimal early diagnosis, focused treatment, and improved clinical outcomes, while also addressing certain obstacles and ethical issues related to the application of artificial intelligence in oncology. Data from reputable scientific databases such as PubMed, Scopus, and ScienceDirect were utilized.

AI-Driven decision-making for personalized elderly care: a fuzzy MCDM-based framework for enhancing treatment recommendations

BACKGROUND

Global healthcare systems face enormous challenges due to the ageing population, demanding novel measures to assure long-term efficacy and viability. The expanding senior population, which requires specialised and efficient healthcare solutions, emphasises the importance of improving healthcare sustainability. Recognising the importance of personalised healthcare recommendations in improving patient outcomes as well as facility sustainability, this study tackles the crucial need for targeted treatments to help the elderly navigate the complicated healthcare landscape.

OBJECTIVES

Through the integration of automation with the Fuzzy VIKOR approach as well as Electronic Health Record (EHR) data, this work seeks to create an automated decision-making mechanism that improves personalised healthcare suggestions for the elderly. By using automated data-driven observations, Fuzzy VIKOR to handle decision-making uncertainty as well as the clinical depth of EHR data, the primary objective is to increase the efficacy and accuracy of treatment choices. In order to guarantee that treatment recommendations are not only medically beneficial but also in line with each patient's needs and preferences, this research aims to close the gap between automated intelligence as well as patient-centered care.

METHOD

The Fuzzy VIKOR approach is used with Electronic Health Record (EHR) data to establish a strong framework for personalised healthcare recommendations. AI techniques are employed to enhance data processing, while Fuzzy VIKOR is used to control uncertainty in decision-making, whereas EHR data gives comprehensive clinical insights. The combination of these aspects enables the creation of a system that compensates for uncertainties in medical knowledge and patient preferences, culminating in a ranked array of treatment alternatives customised to the difficulties of healthcare decision-making for the aged.

RESULTS

The study shows how the proposed methodology improves therapy selection for senior populations. By combining AI-powered analysis, Fuzzy VIKOR, and EHR data, the study provides a refined and personalised approach to healthcare recommendations, providing ranked treatment alternatives based on individual characteristics and preferences. The findings demonstrate the potential of this strategy to handle healthcare complexity and contribute to the developing era of precision medicine.

CONCLUSION

Finally this study makes an important contribution to the continuing discussion about the sustainability of healthcare for the elderly. The combination of AI-driven methodologies, the Fuzzy VIKOR technique and EHR data offers a promising approach to improving therapy selection in the setting of precision medicine. By accepting personalised healthcare recommendations, this study anticipates a future in which elderly people's unique characteristics and preferences are central to decision-making processes, maintaining not only better patient outcomes but also the long-term viability and sustainability of healthcare services for the elderly.

Current perspectives on the use of artificial intelligence in critical patient safety

Intensive Care Units (ICUs) have undergone enhancements in patient safety, and artificial intelligence (AI) emerges as a disruptive technology offering novel opportunities. While the published evidence is limited and presents methodological issues, certain areas show promise, such as decision support systems, detection of adverse events, and prescription error identification. The application of AI in safety may pursue predictive or diagnostic objectives. Implementing AI-based systems necessitates procedures to ensure secure assistance, addressing challenges including trust in such systems, biases, data quality, scalability, and ethical and confidentiality considerations. The development and application of AI demand thorough testing, encompassing retrospective data assessments, real-time validation with prospective cohorts, and efficacy demonstration in clinical trials. Algorithmic transparency and explainability are essential, with active involvement of clinical professionals being crucial in the implementation process.

Impact on clinical guideline adherence of Orient-COVID, a clinical decision support system based on dynamic decision trees for COVID19 management: A randomized simulation trial with medical trainees

BACKGROUND

The adherence of clinicians to clinical practice guidelines is known to be low, including for the management of COVID-19, due to their difficult use at the point of care and their complexity. Clinical decision support systems have been proposed to implement guidelines and improve adherence. One approach is to permit the navigation inside the recommendations, presented as a decision tree, but the size of the tree often limits this approach and may cause erroneous navigation, especially when it does not fit in a single screen.

METHODS

We proposed an innovative visual interface to allow clinicians easily navigating inside decision trees for the management of COVID-19 patients. It associates a multi-path tree model with the use of the fisheye visual technique, allowing the visualization of large decision trees in a single screen. To evaluate the impact of this tool on guideline adherence, we conducted a randomized controlled trial in a near-real simulation setting, comparing the decisions taken by medical trainees using Orient-COVID with those taken with paper guidelines or without guidance, when performing on six realistic clinical cases.

RESULTS

The results show that paper guidelines had no impact (p=0.97), while Orient-COVID significantly improved the guideline adherence compared to both other groups (p<0.0003). A significant impact of Orient-COVID was identified on several key points during the management of COVID-19: ordering troponin lab tests, prescribing anticoagulant and oxygen therapy. A multifactor analysis showed no difference between male and female participants.

CONCLUSIONS

The use of an interactive decision tree for the management of COVID-19 significantly improved the clinician adherence to guidelines. Future works will focus on the integration of the system to electronic health records and on the adaptation of the system to other clinical conditions.

Comparative effectiveness of delirium recognition with and without a clinical decision assessment system on outcomes of hospitalized older adults: Cluster randomized controlled trial

BACKGROUND

Early recognition of delirium is essential for effective management, but it often goes unrecognized, resulting in adverse outcomes. Clinical decision support systems can enhance adherence to guidelines and improve patient outcomes. We developed a mobile-based clinical decision assessment tool (3D-DST) based on the 3-minute diagnostic interview for confusion assessment method-defined delirium (3D-CAM). Implementing the 3D-DST may enhance delirium recognition and adherence to interventions among healthcare professionals, potentially improving outcomes in older adults.

OBJECTIVE

To test whether improved recognition of delirium could lead to better clinical outcomes in older adults.

DESIGN

A cluster randomized controlled trial with pair-matching.

SETTING

A tertiary geriatric hospital.

PARTICIPANTS

Patients aged ≥65 years.

METHODS

Four general wards were paired and randomly assigned to the intervention group (two wards) or the control group (two wards). The intervention included routine delirium assessments by nurses using either the 3D-DST or the 3D-CAM, along with delirium prevention and intervention measures carried out by a multidisciplinary team. Outcomes measured included delirium incidence, duration, severity, length of stay, and adherence to the delirium assessment, prevention, and treatment protocol. A trained nursing researcher collected data on demographics, clinical characteristics, and primary and secondary outcomes.

RESULTS

211 eligible patients participated (106 in the intervention group and 105 in the control group), with 21 identified as delirium-positive using the 3D-DST. The median Charlson comorbidity index score among older adults in the intervention group was 1 (1-2), compared to 2 (1-3) in the control group (P = 0.032). Nurses' adherence to delirium assessment was significantly higher in the intervention group than in the control group (73 % vs. 31 %). The recognition rate of delirium among nurses was 89 % in the intervention group and 42 % in the control group. There were no statistically significant differences in delirium duration (6 [3-9] vs. 7 [2-14], P = 0.967), incidence (8.5 % vs. 11.4 %, P = 0.500), severity (2 [1-3] vs. 2 [1-4], P = 0.891) or length of hospital stay (15 [14-18] vs. 18 [13-22], P = 0.568) between the intervention and control groups.

CONCLUSIONS

The 3D-DST enhanced adherence to routine delirium recognition by nurses. However, effective strategies are urgently needed to strengthen multidisciplinary collaboration and enhance adherence to delirium management among healthcare professionals.

REGISTRATION

Chinese Clinical Trial Registry, Identifier: ChiCTR1900028402.

Feasibility of a computerized clinical decision support system delivered via a socially assistive robot during grand rounds: A pilot study

Aims and Objective

The aim of this study was to explore the feasibility, usability and acceptance of integrating Clinical Decision Support Systems with Socially Assistive Robots into hospital grand rounds.

Background

Adopting Clinical Decision Support Systems in healthcare faces challenges such as complexity, poor integration with workflows, and concerns about data privacy and quality. Issues such as too many alerts, confusing errors, and difficulty using the technology in front of patients make adoption challenging and prevent it from fitting into daily workflows. Making Clinical Decision Support System simple, intuitive and user-friendly is essential to enable its use in daily practice to improve patient care and decision-making.

Methods

This six-month pilot study had two participant groups, with total of 40 participants: a longitudinal intervention group (n = 8) and a single-session evaluation group (n = 32). Participants were medical doctors at the University Clinical Center Maribor. The intervention involved implementing a Clinical Decision Support System delivered via a Socially Assistive Robot during hospital grand rounds. We developed a system that employed the HL7 FHIR standard for integrating data from hospital monitors, electronic health records, and patient-reported outcomes into a single dashboard. A Pepper-based SAR provided patient specific recommendations through a voice and SAR tablet enabled interface. Key evaluation metrics were assessed using the System Usability Scale (SUS) and the Unified Theory of Acceptance, Use of Technology (UTAUT2) questionnaire, including Effort Expectancy, Performance Expectancy and open ended questions. The longitudinal group used the system for 6 months and completed the assessments twice, after one week and at the end of the study. The single-session group completed the assessment once, immediately after the experiment. Qualitative data were gathered through open-ended questions. Data analysis included descriptive statistics, paired t-tests, and thematic analysis.

Results

System usability was rated highly across both groups, with the longitudinal group reporting consistently excellent scores (M = 82.08 at final evaluation) compared to the acceptable scores of the single-session group (M = 68.96). Extended exposure improved user engagement, reflected in significant increases in Effort Expectancy and Habit over time. Participants found the system enjoyable to use, and while no significant changes were seen in Performance Expectancy, feedback emphasized its efficiency in saving time and improving access to clinical data, supporting its feasibility and acceptability.

Conclusions

This research supports the potential of robotic technologies to transform CDSS into more interactive, efficient, and user-friendly tools for healthcare professionals. The paper also suggests further research directions and technical improvements to maximize the impact of innovative technologies in healthcare.

Adherence to Lung Protective Ventilation in ARDS: A Mixed Methods Study Using Real-Time Continuously Monitored Ventilation Data

Background: Lung-protective ventilation is a standard intervention for mitigating ventilator-induced lung injury in patients with ARDS. Despite its efficacy, adherence to contemporary evidence-based guidelines remains suboptimal. We aimed to identify factors that affect the adherence of staff to applying lung-protective ventilation guidelines by analyzing real-time, continuously monitored ventilation data over a 5-year longitudinal period. Methods: We conducted retrospective cohort and qualitative studies. Subjects with billing code J80 who survived at least 48 h of continuous mandatory ventilation with volume control in critical care settings between January 1, 2018, and December 31, 2022, were eligible. Tidal volume was measured dynamically (1-min resolution) and averaged hourly. The lung-protective ventilation setting studied was ≤6 mL/kg predicted body weight. A subgroup analysis was conducted by considering COVID-19 status. Focus groups of critical-care providers were convened to investigate the possible reasons for the non-utilization of lung-protective ventilation. Results: Among 1,055 subjects, 42.4% were on lung-protective ventilation settings at 48 h. Male sex was correlated with lung-protective ventilation (odds ratio [OR] 1.63, 95% CI 1.08-2.47), whereas age ≥60 y was associated with no lung-protective ventilation use (OR 0.61, 95% CI 0.39-0.94] in the subjects with non-COVID-19 etiologies. Improved staff adherence was observed in the subjects with COVID-19 early in the pandemic when COVID-19 (OR 1.48, 95% CI 1.07-2.04), male sex (OR 2.42, 95% CI 1.79-3.29), and neuromuscular blocking agent use within 48 h (OR 1.69, 95% CI 1.25-2.29) were correlated with staff placing subjects on lung-protective ventilation. However, lung-protective ventilation use occurred less frequently by staff managing subjects with cancer (OR 0.59, 95% CI 0.35-0.99) and hypertension (OR 0.62, 95% CI 0.45-0.85). Focus groups supported these findings and highlighted the need for an accurate height measurement on unit admission to determine the appropriate target tidal volume. Conclusions: Staff are not yet universally adherent to lung-protective ventilation best practices. Strategies, for example, continuous monitoring, with frequent feedback to clinical teams may help.

Integrating expert knowledge with machine learning for AI-based stroke identifications and treatment systems

Stroke is a leading cause of mortality and disability worldwide, requiring early detection and timely intervention to improve patient outcomes. However, in resource-limited locations, the lack of specialists often leads to delayed and inaccurate diagnoses. To address this, we propose an AI-driven stroke identification and treatment system that integrates expert knowledge with machine learning, enabling healthcare providers to make informed decisions without direct specialist input. The data for this study were obtained from Debre Berhan Referral Hospital through expert interviews, prescriptions, and from a public dataset in the Kaggle platform. Feature selection was performed using decision trees, Chi-Square tests, Elastic Net coefficients, and correlation analysis. Additionally, we applied to Shapley Additive Explanations to demonstrate the feasibility of feature selection in AI model development. Machine learning models, including Decision Tree, Random Forest, and Support Vector Machine, were evaluated, and Random Forest classifier achieved the highest accuracy of 99.4% using k-fold cross-validation technique. Expert knowledge was encoded in Prolog, while machine learning models were implemented in Python to develop a hybrid expert system. Medical professionals evaluated the system, confirming its effectiveness as a decision-support tool for stroke diagnosis and treatment. This approach demonstrates the potential of AI-driven expert systems to enhance stroke management, particularly in regions with limited access to specialized care.

Psychological Factors That Contribute to the Use of Video Consultations in Health Care: Systematic Review

BACKGROUND

There are numerous benefits to delivering care via video consultations (VCs). Yet, the willingness of health care professionals (HCPs) to use video as a modality of care is one of the greatest barriers to its adoption. Decisions regarding whether to use video may be based on assumptions and concerns that are not necessarily borne of evidence. To effectively address psychological barriers to VC, it is essential to gain a better understanding of specific factors (eg, attitudes, beliefs, and emotions) that influence HCPs' VC use.

OBJECTIVE

This study's aim was to conduct a systematic literature review of psychological factors in HCPs that impair or promote VC use.

METHODS

Databases were searched in February 2023 for peer-reviewed primary research papers on VCs that discussed psychological factors of health professionals affecting the use of video to deliver health services. A psychological factor was defined as an intraindividual influence related to, or in reaction to, VC use-in this case, the individual being an HCP. Search terms included variations on "telehealth," "clinician," and psychological factors (eg, attitude and beliefs) in combination. Peer-reviewed papers of all methodological approaches were included if they were in an Australian setting and the full text was available in English. Studies where the main intervention was another digital health modality (eg, remote monitoring and telephone) were excluded. Studies were also excluded if they only reported on extrinsic factors (eg, environmental or economic). Information extracted included author, year, medical specialty, psychological component mentioned, explanation as to why the psychological factor was related to VC use, and exemplar quotes from the paper that correspond to a psychological component. Each extracted psychological factor was classified as a positive, negative, ambivalent, or neutral perspective on VC, and a thematic analysis then generated the factors and themes. Theories of behavior were considered and discussed to help frame the interaction between themes.

RESULTS

From 4592 studies, data were extracted from 90 peer-reviewed papers. Cognitive and emotional motivators and inhibitors, such as emotional responses, self-efficacy, attitudes, and perceived impact on the clinician as a professional, all interact to influence HCP engagement in VCs. These factors were complex and impacted upon one another. A cyclical relationship between these factors and intention to engage in VCs and actual use of VCs was found. These findings were used to form the psychological attributes of VC engagement (PAVE) model. Evidence suggests that HCPs fall within 4 key user categories based on the amount of cognitive and practical effort needed to deliver VCs.

CONCLUSIONS

Although further research is needed to validate the current findings, this study provides opportunity for more targeted interventions that address psychological factors impeding effective use of VCs.

Outcomes of clinical decision support systems in real-world perioperative care: a systematic review and meta-analysis

BACKGROUND

Although clinical decision support systems (CDSS) have been developed to enhance the quality and efficiency of surgeries, little is known regarding the practical effects in real-world perioperative care.

OBJECTIVE

To systematically review and meta-analyze the current impact of CDSS on various aspects of perioperative care, providing evidence support for future research on CDSS development and clinical implementation.

METHODS

This systematic review and meta-analysis followed the Cochrane Handbook and PRISMA statement guidelines, searching databases up to 2 February 2024, including MEDLINE, PubMed, Embase, Cochrane, and Web of Science. It included studies on the effectiveness of CDSS in assisting perioperative decision-making, involving anesthesiologists, doctors, or surgical patients, and reporting at least one outcome such as complications, mortality, length of stay, compliance, or cost.

RESULTS

Forty studies met inclusion criteria, analyzing outcomes from 408 357 participants, predominantly in developed countries. Most perioperative CDSS use was associated with improved guideline adherence, decreased medication errors, and some improvements in patient safety measures such as reduced postoperative nausea and vomiting and myocardial injury. However, reported results varied widely, and no significant improvement in postoperative mortality was observed.

CONCLUSION

The preliminary findings of this review offer an overview of the potential use of CDSS in real-world perioperative situations to enhance patient and anesthesiologist outcomes, but further researches with broader outcome dimensions, involving more stakeholders, and with longer follow-up periods are warranted for the critical evaluation of CDSS and then in better facilitate clinical adoption.

Adaptive questionnaires for facilitating patient data entry in clinical decision support systems: methods and application to STOPP/START v2

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 a lot 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 determining the relationships between rules and 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, both on clinical cases and real patient data. 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.

Managing Postembolization Syndrome Through a Machine Learning-Based Clinical Decision Support System: A Randomized Controlled Trial

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

Interpretable Clinical Decision-Making Application for Etiological Diagnosis of Ventricular Tachycardia Based on Machine Learning

Background: Ventricular tachycardia (VT) can broadly be categorised into ischemic heart disease, non-ischemic structural heart disease, and idiopathic VT. There are few studies related to the application of machine learning for the etiological diagnosis of VT, and the interpretable methods are still in the exploratory stage for clinical decision-making applications. Objectives: The aim is to propose a machine learning model for the etiological diagnosis of VT. Interpretable results based on models are compared with expert knowledge, and interpretable evaluation protocols for clinical decision-making applications are developed. Methods: A total of 1305 VT patient data from 1 January 2013 to 1 September 2023 at the Arrhythmia Centre of Fuwai Hospital were included in the study. Clinical data collected during hospitalisation included demographics, medical history, vital signs, echocardiographic results, and laboratory test outcomes. Results: The XGBoost model demonstrated the best performance in VT etiological diagnosis (precision, recall, and F1 were 88.4%, 88.5%, and 88.4%, respectively). A total of four interpretable machine learning methods applicable to clinical decision-making were evaluated in terms of visualisation, clinical usability, clinical applicability, and efficiency with expert knowledge interpretation. Conclusions: The XGBoost model demonstrated superior performance in the etiological diagnosis of VT, and SHAP and decision tree interpretable methods are more favoured by clinicians for decision-making.

AI Algorithms for Modeling the Risk, Progression, and Treatment of Sepsis, Including Early-Onset Sepsis-A Systematic Review

Sepsis remains a significant contributor to neonatal mortality worldwide. However, the nonspecific nature of sepsis symptoms in neonates often leads to the necessity of empirical treatment, placing a burden of ineffective treatment on patients. Furthermore, the global challenge of antimicrobial resistance is exacerbating the situation. Artificial intelligence (AI) is transforming medical practice and in hospital settings. AI shows great potential for assessing sepsis risk and devising optimal treatment strategies. Background/Objectives: This review aims to investigate the application of AI in the detection and management of neonatal sepsis. Methods: A systematic literature review (SLR) evaluating AI methods in modeling and classifying sepsis between 1 January 2014, and 1 January 2024, was conducted. PubMed, Scopus, Cochrane, and Web of Science were systematically searched for English-language studies focusing on neonatal sepsis. Results: The analyzed studies predominantly utilized retrospective electronic medical record (EMR) data to develop, validate, and test AI models to predict sepsis occurrence and relevant parameters. Key predictors included low gestational age, low birth weight, high results of C-reactive protein and white blood cell counts, and tachycardia and respiratory failure. Machine learning models such as logistic regression, random forest, K-nearest neighbor (KNN), support vector machine (SVM), and XGBoost demonstrated effectiveness in this context. Conclusions: The summarized results of this review highlight the great promise of AI as a clinical decision support system for diagnostics, risk assessment, and personalized therapy selection in managing neonatal sepsis.

A clinical decision support tool for metabolic dysfunction-associated steatohepatitis in real-world clinical settings: a mixed-method implementation research study protocol

Aim: A clinical decision support (CDS) tool for metabolic dysfunction-associated steatohepatitis (MASH) was developed to align health systems with clinical guidelines detailed in the MASH Clinical Care Pathway and improve patients' proactive self-management of their disease. The tool includes a provider-facing web-based application and a mobile application (app) for patients. This protocol outlines a pilot study that will systematically evaluate the implementation of the tool in real-world clinical practice settings. Materials & methods: This implementation research study will use a simultaneous mixed-methods design and is guided by the Consolidated Framework for Implementation Research. The CDS tool for MASH will be piloted for ≥3 months at multiple US-based sites with eligible gastroenterologists and hepatologists (n = 5-10 per site) and their patients (n = 50-100 per site) with MASH or suspected MASH. Each pilot site may choose one or all focus areas within the tool (i.e., risk stratification, screening and referral, or patient care management), based on on-site capabilities. Prior to and at the end of the pilot period, providers and patients will complete quantitative surveys and partake in semi-structured interviews. Outcomes will include understanding the feasibility of implementing the tool in real-world clinical settings, its effectiveness in increasing patient screenings and risk stratification for MASH, its ability to improve provider and patient knowledge of MASH, barriers to adoption of the tool and the tool's capacity to enhance patient engagement and satisfaction with their care. Conclusion: Findings will inform the scalable implementation of the tool to ensure patients at risk for MASH are identified early, referred to specialty care when necessary and managed appropriately. Successful integration of the patient app can contribute to better health outcomes for patients by facilitating their active participation in the management of their condition.

A roadmap for empowering cardiovascular disease patients: a 5P-Medicine approach and technological integration

This article explores the multifaceted concept of cardiovascular disease (CVD) patients' empowerment, emphasizing a shift from compliance-oriented models to active patient participation. In recognizing that cardiovascular disease is a paramount global health challenge, this study illuminates the pressing need for empowering patients, underscoring their role as active participants in their healthcare journey. Grounded in 5P-Medicine principles-Predictive, Preventive, Participatory, Personalized, and Precision Medicine-the importance of empowering CVD patients through analytics, prevention, participatory decision making, and personalized treatments is highlighted. Incorporating a comprehensive overview of patient empowerment strategies, including self-management, health literacy, patient involvement, and shared decision making, the article advocates for tailored approaches aligned with individual needs, cultural contexts, and healthcare systems. Technological integration is examined to enhance patient engagement and personalized healthcare experiences. The critical role of patient-centered design in integrating digital tools for CVD management is emphasized, ensuring successful adoption and meaningful impact on healthcare outcomes. The conclusion proposes vital research questions addressing challenges and opportunities in CVD patient empowerment. These questions stress the importance of medical community research, understanding user expectations, evaluating existing technologies, defining ideal empowerment scenarios, and conducting a literature review for informed advancements. This article lays the foundation for future research, contributing to ongoing patient-centered healthcare evolution, especially in empowering individuals with a 5P-Medicine approach to cardiovascular diseases.

Perspectives of Decision Support System TeleRehab in the Management of Post-Stroke Telerehabilitation

Stroke is the main cause of disability among adults. Decision-making in stroke rehabilitation is increasingly complex; therefore, the use of decision support systems by healthcare providers is becoming a necessity. However, there is a significant lack of software for the management of post-stroke telerehabilitation (TR). This paper presents the results of the developed software "TeleRehab" to support the decision-making of clinicians and healthcare providers in post-stroke TR. We designed a Python-based software with a graphical user interface to manage post-stroke TR. We searched Scopus, ScienceDirect, and PubMed databases to obtain research papers with results of clinical trials for post-stroke TR and to form the knowledge base of the software. The findings show that TeleRehab suggests recommendations for TR to provide practitioners with optimal and real-time support. We observed feasible outcomes of the software based on synthetic data of patients with balance problems, spatial neglect, and upper and lower extremities dysfunctions. Also, the software demonstrated excellent usability and acceptability scores among healthcare professionals.

Advancements in risk stratification and management strategies in primary cardiovascular prevention

Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of morbidity and mortality worldwide, highlighting the urgent need for advancements in risk assessment and management strategies. Although significant progress has been made recently, identifying and managing apparently healthy individuals at a higher risk of developing atherosclerosis and those with subclinical atherosclerosis still poses significant challenges. Traditional risk assessment tools have limitations in accurately predicting future events and fail to encompass the complexity of the atherosclerosis trajectory. In this review, we describe novel approaches in biomarkers, genetics, advanced imaging techniques, and artificial intelligence that have emerged to address this gap. Moreover, polygenic risk scores and imaging modalities such as coronary artery calcium scoring, and coronary computed tomography angiography offer promising avenues for enhancing primary cardiovascular risk stratification and personalised intervention strategies. On the other hand, interventions aiming against atherosclerosis development or promoting plaque regression have gained attention in primary ASCVD prevention. Therefore, the potential role of drugs like statins, ezetimibe, proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, omega-3 fatty acids, antihypertensive agents, as well as glucose-lowering and anti-inflammatory drugs are also discussed. Since findings regarding the efficacy of these interventions vary, further research is still required to elucidate their mechanisms of action, optimize treatment regimens, and determine their long-term effects on ASCVD outcomes. In conclusion, advancements in strategies addressing atherosclerosis prevention and plaque regression present promising avenues for enhancing primary ASCVD prevention through personalised approaches tailored to individual risk profiles. Nevertheless, ongoing research efforts are imperative to refine these strategies further and maximise their effectiveness in safeguarding cardiovascular health.

The Impact of Artificial Intelligence on Allergy Diagnosis and Treatment

PURPOSE OF REVIEW

Artificial intelligence (AI), be it neuronal networks, machine learning or deep learning, has numerous beneficial effects on healthcare systems; however, its potential applications and diagnostic capabilities for immunologic diseases have yet to be explored. Understanding AI systems can help healthcare workers better assimilate artificial intelligence into their practice and unravel its potential in diagnostics, clinical research, and disease management.

RECENT FINDINGS

We reviewed recent advancements in AI systems and their integration in healthcare systems, along with their potential benefits in the diagnosis and management of diseases. We explored machine learning as employed in allergy diagnosis and its learning patterns from patient datasets, as well as the possible advantages of using AI in the field of research related to allergic reactions and even remote monitoring. Considering the ethical challenges and privacy concerns raised by clinicians and patients with regard to integrating AI in healthcare, we explored the new guidelines adapted by regulatory bodies. Despite these challenges, AI appears to have been successfully incorporated into various healthcare systems and is providing patient-centered solutions while simultaneously assisting healthcare workers. Artificial intelligence offers new hope in the field of immunologic disease diagnosis, monitoring, and management and thus has the potential to revolutionize healthcare systems.

Optimising a clinical decision support tool to improve chronic kidney disease management in general practice

BACKGROUND

Early identification and treatment of chronic disease is associated with better clinical outcomes, lower costs, and reduced hospitalisation. Primary care is ideally placed to identify patients at risk of, or in the early stages of, chronic disease and to implement prevention and early intervention measures. This paper evaluates the implementation of a technological intervention called Future Health Today that integrates with general practice EMRs to (1) identify patients at-risk of, or with undiagnosed or untreated, chronic kidney disease (CKD), and (2) provide guideline concordant recommendations for patient care. The evaluation aimed to identify the barriers and facilitators to successful implementation.

METHODS

Future Health Today was implemented in 12 general practices in Victoria, Australia. Fifty-two interviews with 30 practice staff were undertaken between July 2020 and April 2021. Practice characteristics were collected directly from practices via survey. Data were analysed using inductive and deductive qualitative analysis strategies, using Clinical Performance - Feedback Intervention Theory (CP-FIT) for theoretical guidance.

RESULTS

Future Health Today was acceptable, user friendly and useful to general practice staff, and supported clinical performance improvement in the identification and management of chronic kidney disease. CP-FIT variables supporting use of FHT included the simplicity of design and delivery of actionable feedback via FHT, good fit within existing workflow, strong engagement with practices and positive attitudes toward FHT. Context variables provided the main barriers to use and were largely situated in the external context of practices (including pressures arising from the COVID-19 pandemic) and technical glitches impacting installation and early use. Participants primarily utilised the point of care prompt rather than the patient management dashboard due to its continued presence, and immediacy and relevance of the recommendations on the prompt, suggesting mechanisms of compatibility, complexity, actionability and credibility influenced use. Most practices continued using FHT after the evaluation phase was complete.

CONCLUSIONS

This study demonstrates that FHT is a useful and acceptable software platform that provides direct support to general practice in identifying and managing patients with CKD. Further research is underway to explore the effectiveness of FHT, and to expand the conditions on the platform.

Expanding access to veterinary clinical decision support in resource-limited settings: a scoping review of clinical decision support tools in medicine and antimicrobial stewardship

Introduction

Digital clinical decision support (CDS) tools are of growing importance in supporting healthcare professionals in understanding complex clinical problems and arriving at decisions that improve patient outcomes. CDS tools are also increasingly used to improve antimicrobial stewardship (AMS) practices in healthcare settings. However, far fewer CDS tools are available in lowerand middle-income countries (LMICs) and in animal health settings, where their use in improving diagnostic and treatment decision-making is likely to have the greatest impact. The aim of this study was to evaluate digital CDS tools designed as a direct aid to support diagnosis and/or treatment decisionmaking, by reviewing their scope, functions, methodologies, and quality. Recommendations for the development of veterinary CDS tools in LMICs are then provided.

Methods

The review considered studies and reports published between January 2017 and October 2023 in the English language in peer-reviewed and gray literature.

Results

A total of 41 studies and reports detailing CDS tools were included in the final review, with 35 CDS tools designed for human healthcare settings and six tools for animal healthcare settings. Of the tools reviewed, the majority were deployed in high-income countries (80.5%). Support for AMS programs was a feature in 12 (29.3%) of the tools, with 10 tools in human healthcare settings. The capabilities of the CDS tools varied when reviewed against the GUIDES checklist.

Discussion

We recommend a methodological approach for the development of veterinary CDS tools in LMICs predicated on securing sufficient and sustainable funding. Employing a multidisciplinary development team is an important first step. Developing standalone CDS tools using Bayesian algorithms based on local expert knowledge will provide users with rapid and reliable access to quality guidance on diagnoses and treatments. Such tools are likely to contribute to improved disease management on farms and reduce inappropriate antimicrobial use, thus supporting AMS practices in areas of high need.

Interprofessional Evaluation of a Medication Clinical Decision Support System Prior to Implementation

BACKGROUND

 Computerized physician order entry (CPOE) and clinical decision support systems (CDSS) are widespread due to increasing digitalization of hospitals. They can be associated with reduced medication errors and improved patient safety, but also with well-known risks (e.g., overalerting, nonadoption).

OBJECTIVES

 Therefore, we aimed to evaluate a commonly used CDSS containing Medication-Safety-Validators (e.g., drug-drug interactions), which can be locally activated or deactivated, to identify limitations and thereby potentially optimize the use of the CDSS in clinical routine.

METHODS

 Within the implementation process of Meona (commercial CPOE/CDSS) at a German University hospital, we conducted an interprofessional evaluation of the CDSS and its included Medication-Safety-Validators following a defined algorithm: (1) general evaluation, (2) systematic technical and content-related validation, (3) decision of activation or deactivation, and possibly (4) choosing the activation mode (interruptive or passive). We completed the in-depth evaluation for exemplarily chosen Medication-Safety-Validators. Moreover, we performed a survey among 12 German University hospitals using Meona to compare their configurations.

RESULTS

 Based on the evaluation, we deactivated 3 of 10 Medication-Safety-Validators due to technical or content-related limitations. For the seven activated Medication-Safety-Validators, we chose the interruptive option ["PUSH-(&PULL)-modus"] four times (4/7), and a new, on-demand option ["only-PULL-modus"] three times (3/7). The site-specific configuration (activation or deactivation) differed across all participating hospitals in the survey and led to varying medication safety alerts for identical patient cases.

CONCLUSION

 An interprofessional evaluation of CPOE and CDSS prior to implementation in clinical routine is crucial to detect limitations. This can contribute to a sustainable utilization and thereby possibly increase medication safety.

The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature

Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes.

Clinical decision support systems (CDSS) in assistance to COVID-19 diagnosis: A scoping review on types and evaluation methods

Background and Aims

Due to the COVID-19 pandemic, a precise and reliable diagnosis of this disease is critical. The use of clinical decision support systems (CDSS) can help facilitate the diagnosis of COVID-19. This scoping review aimed to investigate the role of CDSS in diagnosing COVID-19.

Methods

We searched four databases (Web of Science, PubMed, Scopus, and Embase) using three groups of keywords related to CDSS, COVID-19, and diagnosis. To collect data from studies, we utilized a data extraction form that consisted of eight fields. Three researchers selected relevant articles and extracted data using a data collection form. To resolve any disagreements, we consulted with a fourth researcher.

Results

A search of the databases retrieved 2199 articles, of which 68 were included in this review after removing duplicates and irrelevant articles. The studies used nonknowledge-based CDSS (n = 52) and knowledge-based CDSS (n = 16). Convolutional Neural Networks (CNN) (n = 33) and Support Vector Machine (SVM) (n = 8) were employed to design the CDSS in most of the studies. Accuracy (n = 43) and sensitivity (n = 35) were the most common metrics for evaluating CDSS.

Conclusion

CDSS for COVID-19 diagnosis have been developed mainly through machine learning (ML) methods. The greater use of these techniques can be due to their availability of public data sets about chest imaging. Although these studies indicate high accuracy for CDSS based on ML, their novelty and data set biases raise questions about replacing these systems as clinician assistants in decision-making. Further studies are needed to improve and compare the robustness and reliability of nonknowledge-based and knowledge-based CDSS in COVID-19 diagnosis.

The role of artificial intelligence in radiotherapy clinical practice

This review article visits the current state of artificial intelligence (AI) in radiotherapy clinical practice. We will discuss how AI has a place in the modern radiotherapy workflow at the level of automatic segmentation and planning, two applications which have seen real-work implementation. A special emphasis will be placed on the role AI can play in online adaptive radiotherapy, such as performed at MR-linacs, where online plan adaptation is a procedure which could benefit from automation to reduce on-couch time for patients. Pseudo-CT generation and AI for motion tracking will be introduced in the scope of online adaptive radiotherapy as well. We further discuss the use of AI for decision-making and response assessment, for example for personalized prescription and treatment selection, risk stratification for outcomes and toxicities, and AI for quantitative imaging and response assessment. Finally, the challenges of generalizability and ethical aspects will be covered. With this, we provide a comprehensive overview of the current and future applications of AI in radiotherapy.

Accuracy of a clinical decision support system based on the 3-minute diagnostic interview for CAM-defined delirium: A validation study✰

OBJECTIVE

To evaluate the accuracy of the 3D-DST for delirium assessment in older adults by the nurse researcher.

METHODS

The 3D-DST was administered by a trained nurse researcher to assess delirium among eligible older adults (aged ≥70 years). The criteria for identifying delirium was based on the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-V).

RESULTS

A total of 95 older adults were enrolled in the current study, and 23 patients were identified as positive for delirium by the psychiatrist. The sensitivity and specificity of the 3D-DST were 96% and 94%, respectively. High sensitivities of the 3D-DST were also observed among patients with hypoactive delirium (95%) and those with cognitive impairment (93%).

CONCLUSION

The 3D-DST was demonstrated as an appropriate instrument with highly acceptable sensitivities and specificities for delirium detection in hospitalized older patients.

Identifying barriers and facilitators to successful implementation of computerized clinical decision support systems in hospitals: a NASSS framework-informed scoping review

BACKGROUND

Successful implementation and utilization of Computerized Clinical Decision Support Systems (CDSS) in hospitals is complex and challenging. Implementation science, and in particular the Nonadoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) framework, may offer a systematic approach for identifying and addressing these challenges. This review aimed to identify, categorize, and describe barriers and facilitators to CDSS implementation in hospital settings and map them to the NASSS framework. Exploring the applicability of the NASSS framework to CDSS implementation was a secondary aim.

METHODS

Electronic database searches were conducted (21 July 2020; updated 5 April 2022) in Ovid MEDLINE, Embase, Scopus, PyscInfo, and CINAHL. Original research studies reporting on measured or perceived barriers and/or facilitators to implementation and adoption of CDSS in hospital settings, or attitudes of healthcare professionals towards CDSS were included. Articles with a primary focus on CDSS development were excluded. No language or date restrictions were applied. We used qualitative content analysis to identify determinants and organize them into higher-order themes, which were then reflexively mapped to the NASSS framework.

RESULTS

Forty-four publications were included. These comprised a range of study designs, geographic locations, participants, technology types, CDSS functions, and clinical contexts of implementation. A total of 227 individual barriers and 130 individual facilitators were identified across the included studies. The most commonly reported influences on implementation were fit of CDSS with workflows (19 studies), the usefulness of the CDSS output in practice (17 studies), CDSS technical dependencies and design (16 studies), trust of users in the CDSS input data and evidence base (15 studies), and the contextual fit of the CDSS with the user's role or clinical setting (14 studies). Most determinants could be appropriately categorized into domains of the NASSS framework with barriers and facilitators in the "Technology," "Organization," and "Adopters" domains most frequently reported. No determinants were assigned to the "Embedding and Adaptation Over Time" domain.

CONCLUSIONS

This review identified the most common determinants which could be targeted for modification to either remove barriers or facilitate the adoption and use of CDSS within hospitals. Greater adoption of implementation theory should be encouraged to support CDSS implementation.

Supporting patients and clinicians during the breast cancer care path with AI: The Arianna solution

The onset of cancer disease is a traumatic experience for both patients and their families that suddenly change the patient's life and is accompanied by important physical, emotional, and psycho-social problems. The complexity of this scenario has been exacerbated by the COVID-19 pandemic which dramatically affected the continuity of the provision of optimal care to chronic patients. Telemedicine can support the management of oncology care paths by furnishing a suite of effective and efficient tools to monitor the therapies of cancer patients. In particular, this is a suitable setting for therapies that are administered at home. In this paper, we present an AI-based system, called Arianna, designed and implemented to support and monitor patients treated by the professionals belonging to the Breast Cancer Unit Network (BCU-Net) along the entire clinical path of breast cancer treatment. We describe in this work the three modules composing the Arianna system (the tools for patients and clinicians, and the symbolic AI-based module). The system has been validated in a qualitative way and we demonstrated how the Arianna solution reached a high level of acceptability by all types of end-users by making it suitable for a concrete integration into the daily practice of the BCU-Net.