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Objectives: The aims of this study are to develop and validate a clinical decision support system based on demographics, prostate-specific antigen (PSA), microRNA (miRNA), and MRI for the detection of prostate cancer (PCa) and clinical significant (cs) PCa, and to assess if this system performs better compared to MRI alone. Methods: This retrospective, multicenter, observational study included 222 patients (mean age 66, range 46-75 years) who underwent prostate MRI, miRNA (let-7a-5p and miR-103a-3p) assessment, and biopsy. Monoparametric and multiparametric models including age, PSA, miRNA, and MRI outcome were trained on 65% of the data and then validated on the remaining 35% to predict both PCa (any Gleason grade [GG]) and csPCa (GG ≥ 2 vs GG = 1/negative). Accuracy, sensitivity, specificity, positive and negative predictive value (NPV), and area under the receiver operating characteristic curve were calculated. Results: MRI outcome was the best predictor in the monoparametric model for both detection of PCa, with sensitivity of 90% (95%CI 73–98%) and NPV of 93% (95%CI 82–98%), and for csPCa identification, with sensitivity of 91% (95%CI 72–99%) and NPV of 95% (95%CI 84–99%). Sensitivity and NPV of PSA + miRNA for the detection of csPCa were not statistically different from the other models including MRI alone. Conclusion: MRI stand-alone yielded the best prediction models for both PCa and csPCa detection in biopsy-naïve patients. The use of miRNAs let-7a-5p and miR-103a-3p did not improve classification performances compared to MRI stand-alone results. Clinical relevance statement: The use of miRNA (let-7a-5p and miR-103a-3p), PSA, and MRI in a clinical decision support system (CDSS) does not improve MRI stand-alone performance in the detection of PCa and csPCa. Key Points: • Clinical decision support systems including MRI improve the detection of both prostate cancer and clinically significant prostate cancer with respect to PSA test and/or microRNA. • The use of miRNAs let-7a-5p and miR-103a-3p did not significantly improve MRI stand-alone performance. • Results of this study were in line with previous works on MRI and microRNA. [ABSTRACT FROM AUTHOR]

Purpose: The purposes of this study were to develop a prediction model for pressure injury using a machine learning algorithm and to integrate it into clinical practice. Methods: This was a retrospective study of tertiary hospitals in Seoul, Korea. It analyzed patients in 12 departments where many pressure injuries occurred, including 8 general wards and 4 intensive care units from January 2018 to May 2022. In total, 182 variables were included in the model development. A pressure injury prediction model was developed using the gradient boosting algorithm, logistic regression, and decision tree methods, and it was compared to the Braden scale. Results: Among the 1,389,660 general ward cases, there were 451 cases of pressure injuries, and among 139,897 intensive care unit cases, there were 297 cases of pressure injuries. Among the tested prediction models, the gradient boosting algorithm showed the highest predictive performance. The area under the receiver operating characteristic curve of the gradient boosting algorithm's pressure injury prediction model in the general ward and intensive care unit was 0.86 (95% confidence interval, 0.83~0.89) and 0.83 (95% confidence interval, 0.79~0.87), respectively. This model was integrated into the electronic health record system to show each patient's probability for pressure injury occurrence, and the risk factors calculated every hour. Conclusion: The prediction model developed using the gradient boosting algorithm exhibited higher performance than the Braden scale. A clinical decision support system that automatically assesses pressure injury risk allows nurses to focus on patients at high risk for pressure injuries without increasing their workload. [ABSTRACT FROM AUTHOR]

With increasing electronic medical data and the development of artificial intelligence, clinical decision support systems (CDSSs) assist clinicians in diagnosis and prescription. Traditional knowledge-based CDSSs follow an accumulated medical knowledgebase and a predefined rule system, which clarifies the decision-making process; however, maintenance cost issues exist in the medical data quality control and standardization processes. Non-knowledge-based CDSSs utilize vast amounts of data and algorithms to effectively make decisions; however, the deep learning black-box problem causes unreliable results. EXplainable Artificial Intelligence (XAI)-based CDSSs provide valid rationales and explainable results. These systems ensure trustworthiness and transparency by showing the recommendation and prediction result process using explainable techniques. However, existing systems have limitations, such as the scope of data utilization and the lack of explanatory power of AI models. This study proposes a new XAI-based CDSS framework to address these issues; introduces resources, datasets, and models that can be utilized; and provides a foundation model to support decision-making in various disease domains. Finally, we propose future directions for CDSS technology and highlight societal issues that need to be addressed to emphasize the potential of CDSSs in the future. [ABSTRACT FROM AUTHOR]

Opioid administration is particularly challenging in the perioperative period. Computerized-based Clinical Decision Support Systems (CDSS) are a promising innovation that might improve perioperative pain control. We report the development and feasibility validation of a knowledge-based CDSS aiming at optimizing the management of perioperative pain, postoperative nausea and vomiting (PONV), and laxative medications. This novel CDSS uses patient adaptive testing through a smartphone display, literature-based rules, and individual medical prescriptions to produce direct medical advice for the patient user. Our objective was to test the feasibility of the clinical use of our CDSS in the perioperative setting. This was a prospective single arm, single center, cohort study conducted in Strasbourg University Hospital. The primary outcome was the agreement between the recommendation provided by the experimental device and the recommendation provided by study personnel who interpreted the same care algorithm (control). Thirty-seven patients were included in the study of which 30 (81%) used the experimental device. Agreement between these two care recommendations (computer driven vs. clinician driven) was observed in 51 out 54 uses of the device (94.2% [95% CI 85.9–98.4%]). The agreement level had a probability of 86.6% to exceed the 90% clinically relevant agreement threshold. The knowledge-based, patient CDSS we developed was feasible at providing recommendations for the treatment of pain, PONV and constipation in a perioperative clinical setting. Trial registration number & date The study protocol was registered in ClinicalTrial.gov before enrollment began (NCT05707247 on January 26th, 2023). [ABSTRACT FROM AUTHOR]

Bias in clinical reasoning has been identified as one of the main sources of diagnostic errors. Clinical Decision Support Systems that suggest possible diagnoses and provide information to mitigate cognitive bias could support physicians in finding a less biased diagnosis. We examine the influence of confidence and experience on the probability to adjust the decision after receiving decision aid and whether forming a first opinion beforehand or immediately receiving decision support makes a difference. 103 physicians and medical students participated in an online experiment built on decision tasks formulated to trigger availability and representativeness bias. The analysis showed that the presentation of prevalence data to mitigate availability bias changed the final probability estimate of the diagnosis significantly. Prototypical data to counteract representativeness bias showed no significant change. Medical experience, confidence in the decision, and timing of support had no significant influence on the probability to change the estimate. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to design and evaluate a clinical decision support system (CDSS) identifying inappropriate prescription patterns in the elderly to be used at community pharmacies. Methods: The study was carried out in 20 community pharmacies during a 6-month period on patients ≥65 years. A CDSS was developed and integrated into the pharmacy automation systems to automatically check the medications of the patients for the presence of any potentially inappropriate medications (PIMs). Depending on the preference of the pharmacist the recommendations were communicated with the prescriber or not. The number and characteristics of the PIMs, prescribers' acceptance status of the recommendations, and usability of the CDSS were recorded. Results: During the 6-month period 1250 prescriptions each from an individual patient were evaluated. The median (interquartile range) age of the patients was 73 (63-81) years. The total number of PIMs was 1359 and 59% of the patients had at least one PIM. The most frequently identified PIMs involved proton pump inhibitors (16%) and selective beta-blockers (11.9%). The pharmacists communicated with the prescribers regarding 24.4% of the PIM-involving prescriptions and 85.8% of the prescribers accepted the recommendations. The usability of the CDSS was found to be good. Conclusion: It is anticipated that the widespread use of this product would prevent drug-related adverse events, hospitalizations, morbidities, and mortalities; thus, would improve patients' health and quality of life, as well as lead to better clinical, humanistic, and economic outcomes. [ABSTRACT FROM AUTHOR]

Invasive mechanical ventilation is a key supportive therapy for patients on intensive care. There is increasing emphasis on personalised ventilation strategies. Clinical decision support systems (CDSS) have been developed to support this. We conducted a narrative review to assess evidence that could inform device implementation. A search was conducted in MEDLINE (Ovid) and EMBASE. Twenty-nine studies met the inclusion criteria. Role allocation is well described, with interprofessional collaboration dependent on culture, nurse:patient ratio, the use of protocols, and perception of responsibility. There were no descriptions of process measures, quality metrics, or clinical workflow. Nurse-led weaning is well-described, with factors grouped by patient, nurse, and system. Physician-led weaning is heterogenous, guided by subjective and objective information, and 'gestalt'. No studies explored decision-making with CDSS. Several explored facilitators and barriers to implementation, grouped by clinician (facilitators: confidence using CDSS, retaining decision-making ownership; barriers: undermining clinician's role, ambiguity moving off protocol), intervention (facilitators: user-friendly interface, ease of workflow integration, minimal training requirement; barriers: increased documentation time), and organisation (facilitators: system-level mandate; barriers: poor communication, inconsistent training, lack of technical support). One study described factors that support CDSS implementation. There are gaps in our understanding of ventilation practice. A coordinated approach grounded in implementation science is required to support CDSS implementation. Future research should describe factors that guide clinical decision-making throughout mechanical ventilation, with and without CDSS, map clinical workflow, and devise implementation toolkits. Novel research design analogous to a learning organisation, that considers the commercial aspects of device design, is required. [ABSTRACT FROM AUTHOR]

Junhai Zhen,1 Chuan Liu,2 Jixiang Zhang,2 Fei Liao,2 Huabing Xie,1 Cheng Tan,2 Ping An,2 Zhongchun Liu,3 Changqing Jiang,4 Jie Shi,5 Kaichun Wu,6 Weiguo Dong2 1Department of General Practice, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, 430060, People’s Republic of China; 2Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, 430060, People’s Republic of China; 3Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, 430060, People’s Republic of China; 4Department of Clinical Psychology, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, People’s Republic of China; 5Department of Medical Psychology, Chinese People’s Liberation Army Rocket Army Characteristic Medical Center, Beijing, 100032, People’s Republic of China; 6Department of Gastroenterology, Xijing Hospital, Air Force Medical University, Xi’an, 710032, People’s Republic of ChinaCorrespondence: Kaichun Wu, Department of Gastroenterology, Xijing Hospital, Air Force Medical University, Xi’an, 710032, People’s Republic of China, Tel/Fax +8629-84771600, Email kaicwu@fmmu.edu.cn Weiguo Dong, Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei Province, 430060, People’s Republic of China, Tel/Fax +8627-88041911, Email dongweiguo@whu.edu.cnPurpose: Impaired quality of life (QOL) is common in patients with inflammatory bowel disease (IBD). A tool to more quickly identify IBD patients at high risk of impaired QOL improves opportunities for earlier intervention and improves long-term prognosis. The purpose of this study was to use a machine learning (ML) approach to develop risk stratification models for evaluating IBD-related QOL impairments.Patients and Methods: An online questionnaire was used to collect clinical data on 2478 IBD patients from 42 hospitals distributed across 22 provinces in China from September 2021 to May 2022. Eight ML models used to predict the risk of IBD-related QOL impairments were developed and validated. Model performance was evaluated using a set of indexes and the best ML model was explained using a Local Interpretable Model-Agnostic Explanations (LIME) algorithm.Results: The support vector machine (SVM) classifier algorithm-based model outperformed other ML models with an area under the receiver operating characteristic curve (AUC) and an accuracy of 0.80 and 0.71, respectively. The feature importance calculated by the SVM classifier algorithm revealed that glucocorticoid use, anxiety, abdominal pain, sleep disorders, and more severe disease contributed to a higher risk of impaired QOL, while longer disease course and the use of biological agents and immunosuppressants were associated with a lower risk.Conclusion: An ML approach for assessing IBD-related QOL impairments is feasible and effective. This mechanism is a promising tool for gastroenterologists to identify IBD patients at high risk of impaired QOL.Keywords: clinical research, artificial intelligence, model development, clinical decision support system

Objective: This study aimed to design and evaluate a clinical decision support system (CDSS) identifying inappropriate prescription patterns in the elderly to be used at community pharmacies. Methods: The study was carried out in 20 community pharmacies during a 6-month period on patients ≥65 years. A CDSS was developed and integrated into the pharmacy automation systems to automatically check the medications of the patients for the presence of any potentially inappropriate medications (PIMs). Depending on the preference of the pharmacist the recommendations were communicated with the prescriber or not. The number and characteristics of the PIMs, prescribers’ acceptance status of the recommendations, and usability of the CDSS were recorded. Results: During the 6-month period 1250 prescriptions each from an individual patient were evaluated. The median (interquartile range) age of the patients was 73 (63-81) years. The total number of PIMs was 1359 and 59% of the patients had at least one PIM. The most frequently identified PIMs involved proton pump inhibitors (16%) and selective beta-blockers (11.9%). The pharmacists communicated with the prescribers regarding 24.4% of the PIM-involving prescriptions and 85.8% of the prescribers accepted the recommendations. The usability of the CDSS was found to be good. Conclusion: It is anticipated that the widespread use of this product would prevent drug-related adverse events, hospitalizations, morbidities, and mortalities; thus, would improve patients’ health and quality of life, as well as lead to better clinical, humanistic, and economic outcomes.

BACKGROUND: Previously, we presented the process of developing a clinical decision support system (CDSS) for adjusting insulin pump (IP) settings in children with type 1 diabetes mellitus (T1D) and assessing the agreement of the recommendations it generates with the expert opinion. The CDSS demonstrated satisfactory forecasting of glucose profile and agreement rates between recommendations CDSS and experts.AIM: To evaluate the effectiveness and safety of using CDSS in children with T1D, testing the hypothesis of non-inferiority (with a limit of -5%) of relative increase of glucose time in range (TIR) over 6 months.MATERIALS AND METHODS: The trial included 80 children with T1D, divided into two comparable groups of 40 children using the minimization method. Patients in the main group received recommendations for adjusting the IP settings from a physician who uses the CDSS; patients in the control group received recommendations from a physician (control group). Patients were observed for 6 months with remote consultations once a month (7 consultations in total) and monitoring of glycated hemoglobin (HbA1c) at 1, 4 and 7 consultations. The primary outcome is the difference in group mean relative changes in TIR (%), secondary outcomes are TIR (%), HbA1c concentration. RESULTS: The trial was completed by 63 patients 32 in the main group, 31 in the control group. The difference in the mean relative increase in TIR at the 7th consultation in the groups was 3.02%, one-sided 95% CI (-4.55%; inf ). Thus, the lower bound of this CI is greater than the noninferiority limit of -5%, and the noninferiority hypothesis can be accepted. There were no statistically significant differences between groups for all outcomes. The dynamics of the indicators were positive in the main group and had a statistical tendency towards positive changes in the control group.CONCLUSION: The use of CDSS was no less effective in terms of the TIR than the management of the patient by a physician. The use of CDSS in clinical practice can help in regular and frequent monitoring of children with T1D, and standardize at a high level the approach to correction of IP parameters, supplemented with CGM.

BACKGROUND: Widely available diabetes devices (continuous glucose monitoring, insulin pump etc.) generate large amount of data and development of an advanced clinical decision support system (CDSS), able to automatically evaluate and optimize insulin therapy, is relevant.AIM: Development of a mathematical model and an CDSS based on it to optimize insulin therapy in children with type 1 diabetes (T1D) and assessment of the agreement between the recommendations of the CDSS and the physician on insulin pump (IP) parameters: basal profile (BP), carbohydrate ratio (CR), correction factor (СF).MATERIALS AND METHODS: Data from 504 children with T1DM were analyzed over the period of 7875 days. The data included glucose, insulin, food, sex, age, height, weight, diabetes duration and HbA1c. We constructed recurrent neural network (RNN) to predict glucose concentration for 30-120 minutes, an algorithm for optimizing IP settings using prediction results. Next, a software product was developed — a CDSS. To assess the agreement of the recommendations of the CDSS and physicians, retrospective data from 40 remote telemedicine consultations of 40 patients with T1D (median age 11.6 years [7; 15]) were used and 960 points of possible adjustments were analyzed. Three degrees of agreement have been introduced: complete agreement, partial agreement, and complete disagreement. The magnitude of the adjustments was also analyzed.RESULTS: The accuracy of glycemic predictions was better or comparable with other similar models. The assessment of agreement for BP, CR and CF, according to the Kappa index, showed slight and weak agreement. The frequency of complete agreement between recommendations for adjusting the ongoing IP therapy between the CDSS and physicians is 37.5–53.8%, and complete inconsistency is 4.5–17.4%. From a clinical point of view, consistency in the frequency of occurrence of the indicator is more important. There were no differences in median IP settings between the CDSS and physicians.CONCLUSION: The CDSS has an acceptable accuracy of glycemic predictions. The CDSS and physicians provide comparable recommendations regarding CSII parameters.

Accidents at work may force workers to face abrupt changes in their daily life: one of the most impactful accident cases consists of the worker remaining in a wheelchair. Return To Work (RTW) of wheelchair users in their working age is still challenging, encompassing the expertise of clinical and rehabilitation personnel and social workers to match the workers’ residual capabilities with job requirements. This work describes a novel and prototypical knowledge-based Decision Support System (DSS) that matches workers’ residual capabilities with job requirements, thus helping vocational therapists and clinical personnel in the RTW decision-making process for WUs. The DSS leverages expert knowledge in the form of ontologies to represent the International Classification of Functioning, Disability, and Health (ICF) and the Occupational Information Network (O*NET). These taxonomies enable both workers’ health conditions and job requirements formalization, which are processed to assess the suitability of a job depending on a worker’s condition. Consequently, the DSS suggests a list of jobs a wheelchair user can still perform, exploiting his/her residual abilities at their best. The manuscript describes the theoretical approach and technological foundations of such DSS, illustrating its development, its output metric, and application. The developed solution was tested with real wheelchair users’ health conditions provided by the Italian National Institute for Insurance against Accidents at Work. The feasibility of an approach based on objective data was thus demonstrated, providing a novel point of view in the critical process of decision-making during RTW.

Abstract Background Arterial hypertension (aHT) is a major cause for premature morbidity and mortality. Control rates remain poor, especially in low- and middle-income countries. Task-shifting to lay village health workers (VHWs) and the use of digital clinical decision support systems may help to overcome the current aHT care cascade gaps. However, evidence on the effectiveness of comprehensive VHW-led aHT care models, in which VHWs provide antihypertensive drug treatment and manage cardiovascular risk factors is scarce. Methods Using the trials within the cohort (TwiCs) design, we are assessing the effectiveness of VHW-led aHT and cardiovascular risk management in two 1:1 cluster-randomized trials nested within the Community-Based chronic disease Care Lesotho (ComBaCaL) cohort study (NCT05596773). The ComBaCaL cohort study is maintained by trained VHWs and includes the consenting inhabitants of 103 randomly selected villages in rural Lesotho. After community-based aHT screening, adult, non-pregnant ComBaCaL cohort participants with uncontrolled aHT (blood pressure (BP) ≥ 140/90 mmHg) are enrolled in the aHT TwiC 1 and those with controlled aHT (BP

Abstract Objectives This study aims to develop an innovative, deep model for thymoma risk stratification using preoperative CT images. Current algorithms predominantly focus on radiomic features or 2D deep features and require manual tumor segmentation by radiologists, limiting their practical applicability. Methods The deep model was trained and tested on a dataset comprising CT images from 147 patients (82 female; mean age, 54 years ± 10) who underwent surgical resection and received subsequent pathological confirmation. The eligible participants were divided into a training cohort (117 patients) and a testing cohort (30 patients) based on the CT scan time. The model consists of two stages: 3D tumor segmentation and risk stratification. The radiomic model and deep model (2D) were constructed for comparative analysis. Model performance was evaluated through dice coefficient, area under the curve (AUC), and accuracy. Results In both the training and testing cohorts, the deep model demonstrated better performance in differentiating thymoma risk, boasting AUCs of 0.998 and 0.893 respectively. This was compared to the radiomic model (AUCs of 0.773 and 0.769) and deep model (2D) (AUCs of 0.981 and 0.760). Notably, the deep model was capable of simultaneously identifying lesions, segmenting the region of interest (ROI), and differentiating the risk of thymoma on arterial phase CT images. Its diagnostic prowess outperformed that of the baseline model. Conclusions The deep model has the potential to serve as an innovative decision-making tool, assisting on clinical prognosis evaluation and the discernment of suitable treatments for different thymoma pathological subtypes. Key Points • This study incorporated both tumor segmentation and risk stratification. • The deep model, using clinical and 3D deep features, effectively predicted thymoma risk. • The deep model improved AUCs by 16.1pt and 17.5pt compared to radiomic model and deep model (2D) respectively.

Background and purpose Methods Results Conclusions Multiple sclerosis (MS) is a complex autoimmune disease of the central nervous system, with numerous therapeutic options, but a lack of biomarkers to support a mechanistic approach to precision medicine. A computational approach to precision medicine could proceed from clinical decision support systems (CDSSs). They are digital tools aiming to empower physicians through the clinical applications of information technology and massive data. However, the process of their clinical development is still maturing; we aimed to review it in the field of MS.For this scoping review, we screened systematically the PubMed database. We identified 24 articles reporting 14 CDSS projects and compared their technical and software development aspects.The projects position themselves in various contexts of usage with various algorithmic approaches: expert systems, CDSSs based on similar patients' data visualization, and model‐based CDSSs implementing mathematical predictive models. So far, no project has completed its clinical development up to certification for clinical use with global release. Some CDSSs have been replaced at subsequent project iterations. The most advanced projects did not necessarily report every step of clinical development in a dedicated article (proof of concept, offline validation, refined prototype, live clinical evaluation, comparative prospective evaluation). They seek different software distribution options to integrate into health care: internal usage, “peer‐to‐peer,” and marketing distribution.This review illustrates the potential of clinical applications of information technology and massive data to support MS management and helps clarify the roadmap for future projects as a multidisciplinary and multistep process. [ABSTRACT FROM AUTHOR]

Clinical Decision Support Systems (CDSS) have been implemented in almost all healthcare settings. Laboratory medicine (LM), is one of the most important structured health data stores, but efforts are still needed to clarify the use and scope of these tools, especially in the laboratory setting. The aim is to clarify CDSS concept in LM, in the last decade. There is no consensus on the definition of CDSS in LM. A theoretical definition of CDSS in LM should capture the aim of driving significant improvements in LM mission, prevention, diagnosis, monitoring, and disease treatment. We identified the types, workflow and data sources of CDSS. The main applications of CDSS in LM were diagnostic support and clinical management, patient safety, workflow improvements, and cost containment. Laboratory professionals, with their expertise in quality improvement and quality assurance, have a chance to be leaders in CDSS. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Pharmaceutical decision support systems (PDSSs) use reasoning software to match patient data to modelled situations likely to cause drug-related problems (DRPs) or adverse drug events. To aid decision-making, modelled situations must be linked to well-defined systemic clinical risks. Aim: To obtain expert consensus on the level of clinical risk for patients associated with each modelled situation that could be addressed using a PDSS. Method: A two-round e-Delphi survey was conducted from February to April 2022, involving 20 experts from four French-speaking countries. Participants had to rate modelled situations on two five-point Likert scales, assessing the likelihood of clinical consequences and their severity. The degree of consensus was determined as the proportion of participants providing risk scores in line with the median. The combined median scores for likelihood and severity provided the level of risk according to the Clinical Risk Situation for Patients (CRiSP) scale, formalized via validated tools. Results: The expert panel achieved consensus (≥ 75% agreement) on 48 out of 52 modelled clinical situations. Among these, 45 were categorized as high or extreme risk. The most common DRP identified was overdosing, accounting for 22% of cases. Furthermore, DRPs involving cardiovascular, psychiatric, and endocrinological drug classes were prevalent, constituting 45, 13, and 9% of cases, respectively. Conclusion: Through consensus, our study identified 45 modelled clinical situations associated with high or extreme risks. This study highlights the interest of using PDSSs to prevent harm in patients and, on a large scale, document the impact of the pharmacist in preventing, intercepting and managing iatrogenic drug risk. [ABSTRACT FROM AUTHOR]

The hypothesis of this randomized controlled trial was that a clinical decision support system (CDSS) would increase adherence to the Mediterranean diet (MD) among adolescent females with polycystic ovary syndrome (PCOS). The objective was to assess the impact of personalized MD plans delivered via a CDSS on nutritional status and psychological well-being. Forty adolescent females (15–17 years) with PCOS were randomly assigned to the MD group (n = 20) or the Control group (n = 20). The MD group received personalized MD plans every 15 days via a CDSS, while the Control group received general nutritional advice. Assessments were conducted at baseline and after 3 months. Results showed significantly increased MD adherence in the MD group compared to the Control group (p < 0.001). The MD group exhibited lower intakes of energy, total fat, saturated fat, and cholesterol, and higher intakes of monounsaturated fat and fiber (p < 0.05). Serum calcium and vitamin D status (p < 0.05), as well as anxiety (p < 0.05) were improved. In conclusion, tailored dietary interventions based on MD principles, delivered via a CDSS, effectively manage PCOS in adolescent females. These findings highlight the potential benefits of using technology to promote dietary adherence and improve health outcomes in this population. ClinicalTrials.gov registry: NCT06380010. [ABSTRACT FROM AUTHOR]

Objectives: This study aims to develop an innovative, deep model for thymoma risk stratification using preoperative CT images. Current algorithms predominantly focus on radiomic features or 2D deep features and require manual tumor segmentation by radiologists, limiting their practical applicability. Methods: The deep model was trained and tested on a dataset comprising CT images from 147 patients (82 female; mean age, 54 years ± 10) who underwent surgical resection and received subsequent pathological confirmation. The eligible participants were divided into a training cohort (117 patients) and a testing cohort (30 patients) based on the CT scan time. The model consists of two stages: 3D tumor segmentation and risk stratification. The radiomic model and deep model (2D) were constructed for comparative analysis. Model performance was evaluated through dice coefficient, area under the curve (AUC), and accuracy. Results: In both the training and testing cohorts, the deep model demonstrated better performance in differentiating thymoma risk, boasting AUCs of 0.998 and 0.893 respectively. This was compared to the radiomic model (AUCs of 0.773 and 0.769) and deep model (2D) (AUCs of 0.981 and 0.760). Notably, the deep model was capable of simultaneously identifying lesions, segmenting the region of interest (ROI), and differentiating the risk of thymoma on arterial phase CT images. Its diagnostic prowess outperformed that of the baseline model. Conclusions: The deep model has the potential to serve as an innovative decision-making tool, assisting on clinical prognosis evaluation and the discernment of suitable treatments for different thymoma pathological subtypes. Key Points: • This study incorporated both tumor segmentation and risk stratification. • The deep model, using clinical and 3D deep features, effectively predicted thymoma risk. • The deep model improved AUCs by 16.1pt and 17.5pt compared to radiomic model and deep model (2D) respectively. [ABSTRACT FROM AUTHOR]

Continuous unfractionated heparin is widely used in intensive care, yet its complex pharmacokinetic properties complicate the determination of appropriate doses. To address this challenge, we developed machine learning models to predict over- and under-dosing, based on anti-Xa results, using a monocentric retrospective dataset. The random forest model achieved a mean AUROC of 0.80 [0.77-0.83], while the XGB model reached a mean AUROC of 0.80 [0.76-0.83]. Feature importance was employed to enhance the interpretability of the model, a critical factor for clinician acceptance. After prospective validation, machine learning models such as those developed in this study could be implemented within a computerized physician order entry (CPOE) as a clinical decision support system (CDSS). [ABSTRACT FROM AUTHOR]

The application of machine learning algorithms in clinical decision support systems (CDSS) holds great promise for advancing patient care, yet practical implementation faces significant evaluation challenges. Through a scoping review, we investigate the common definitions of ground truth to collect clinically relevant reference values, as well as the typical metrics and combinations employed for assessing trueness. Our analysis reveals that ground truth definition is mostly not in accordance with the standard ISO expectation and that used combination of metrics does not usually cover all aspects of CDSS trueness, particularly neglecting the negative class perspective. [ABSTRACT FROM AUTHOR]

This paper reports lessons learned during the early phases of the user-centered design process for an explanation user interface for an AI-based clinical decision support system for the intensive care unit. This paper focuses on identifying and verifying physicians' explanation needs in a multi-center, multi-country project. The explanation needs identified through context analysis and user requirements prioritization in an initial center differed from those identified through questionnaire responses from N= 9 physicians after a multi-center project workshop. These results highlight the caution that should be taken when eliciting explanation needs during the user-centered design process. [ABSTRACT FROM AUTHOR]

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Introduction: In 2021, a computerized physician order entry (CPOE) system with an integrated clinical decision support system (CDSS) was implemented at a tertiary care center for the treatment of mental health conditions in Lübeck, Germany. To date, no study has been reported on the types and prevalence of drug-related problems (DRPs) before and after CPOE implementation in a psychiatric inpatient setting. The aim of this retrospective before-and-after cohort study was to investigate whether the implementation of a CPOE system with CDSS accompanied by the introduction of regular medication plausibility checks by a pharmacist led to a decrease of DRPs during hospitalization and unsolved DRPs at discharge in psychiatric inpatients. Methods: Medication charts and electronic patient records of 54 patients before (cohort I) and 65 patients after (cohort II) CPOE implementation were reviewed retrospectively by a clinical pharmacist. All identified DRPs were collected and classified based on 'The PCNE Classification V9.1', the German database DokuPIK, and the 'NCC MERP Taxonomy of Medication Errors'. Results: 325 DRPs were identified in 54 patients with a mean of 6 DRPs per patient and 151.9 DRPs per 1000 patient days in cohort I. In cohort II, 214 DRPs were identified in 65 patients with a mean of 3.3 DRPs per patient and 81.3 DRPs per 1000 patient days. The odds of having a DRP were significantly lower in cohort II (OR=0.545, 95% CI 0.412-0.721, p<0.001). The most frequent DRP in cohort I was an erroneous prescription (n=113, 34.8%), which was significantly reduced in cohort II (n=12, 5.6%, p<0.001). During the retrospective in-depth review, more DRPs were identified than during the daily plausibility analyses. At hospital discharge, patients had significantly less unsolved DRPs in cohort II than in cohort I. Discussion: The implementation of a CPOE system with an integrated CDSS reduced the overall prevalence of DRPs, especially of prescription errors, and led to a smaller rate of unsolved DRPs in psychiatric inpatients at hospital discharge. Not all DRPs were found by plausibility analyses based on the medication charts. A more interactive and interdisciplinary patient-oriented approach might result in the resolution of more DRPs. [ABSTRACT FROM AUTHOR]

Depression can significantly impact people's mental health, and recent research shows that social media can provide decision-making support for health-care professionals and serve as supplementary information for understanding patients' health status. Deep learning models are also able to assess an individual's likelihood of experiencing depression. However, data availability on social media is often limited due to privacy concerns, even though deep learning models benefit from having more data to analyze. To address this issue, this study proposes a methodological framework system for clinical decision support that uses federated deep learning (FDL) to identify individuals experiencing depression and provide intervention decisions for clinicians. The proposed framework involves evaluation of datasets from three social media platforms, and the experimental results demonstrate that our method achieves state-of-the-art results. The study aims to provide a clinical decision support system with evolvable features that can deliver precise solutions and assist health-care professionals in medical diagnosis. The proposed framework that incorporates social media data and deep learning models can provide valuable insights into patients' health status, support personalized treatment decisions, and adapt to changing health-care needs. [ABSTRACT FROM AUTHOR]

Background: The 'PenCS Flu Topbar' app was deployed in Central Queensland (CQ), Australia, medical practices through a pilot programme in March 2021. Methods: We evaluated the app's user experience and examined whether the introduction of 'PenCS Flu Topbar' in medical practices could improve the coverage of NIP‐funded influenza vaccinations. We conducted a mixed‐method study including a qualitative analysis of in‐depth interviews with key end‐users and a quantitative analysis of influenza vaccine administrative data. Results: 'PenCS Flu Topbar' app users reported positive experiences identifying patients eligible for NIP‐funded seasonal influenza vaccination. A total of 3606 NIP‐funded influenza vaccinations was administered in the eight intervention practices, 14% higher than the eight control practices. NIP‐funded vaccination coverage within practices was significantly higher in the intervention practices (31.2%) than in the control practices (27.3%) (absolute difference: 3.9%; 95% CI: 2.9%–5.0%; p < 0.001). The coverage was substantially higher in Aboriginal and Torres Strait Islander people aged more than 6 months, pregnant women and children aged 6 months to less than 5 years for the practices where the app was introduced when compared to control practices: incidence rate ratio (IRR) 2.4 (95% CI: 1.8–3.2), IRR 2.7 (95% CI: 1.8–4.2) and IRR 2.3 (1.8–2.9) times higher, respectively. Conclusions: Our evaluation indicated that the 'PenCS Flu Topbar' app is useful for identifying the patients eligible for NIP‐funded influenza vaccination and is likely to increase NIP‐funded influenza vaccine coverage in the eligible populations. Future impact evaluation including a greater number of practices and a wider geographical area is essential. [ABSTRACT FROM AUTHOR]

To break through the current bottleneck in home-based older care globally, we developed an intelligent and integrated older care model (SMART model) to facilitate integrated care for home-dwelling older people. As a knowledge-based clinical decision support system, the SMART model relies on rules and algorithms to ensure its transparent and well-supported decision-making process with clear rationales. Therefore, we conducted a mixed study combining qualitative research, literature review of the latest literature and guidelines, and expert consultation. Following the intervention mapping framework and nursing process, we determined 138 care problems along with their diagnostic criteria and care goals. Building upon this, we curated 450 evidence-informed methods, each accompanied by at least one implementation approach. Two sets of IF-THEN rules and algorithms including diagnostic rules and method trigger rules were employed to trigger appropriate care problems and customized methods and implementation approaches. [ABSTRACT FROM AUTHOR]

Copyright of Anaesthesiologie & Intensivmedizin is the property of DGAI e.V. - Deutsche Gesellschaft fur Anasthesiologie und Intensivmedizin e.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Long COVID is a condition that affects a significant proportion of patients who have had COVID-19. It is characterised by the persistence of associated symptoms after the acute phase of the illness has subsided. Although several studies have investigated the risk factors associated with long COVID, identifying which patients will experience long-term symptoms remains a complex task. Among the various symptoms, dyspnea is one of the most prominent due to its close association with the respiratory nature of COVID-19 and its disabling consequences. This work proposes a new intelligent clinical decision support system to predict dyspnea 12 months after a severe episode of COVID-19 based on the SeguiCovid database from the Álvaro Cunqueiro Hospital in Vigo (Galicia, Spain). The database is initially processed using a CART-type decision tree to identify the variables with the highest predictive power. Based on these variables, a cascade of expert systems has been defined with Mamdani-type fuzzy-inference engines. The rules for each system were generated using the Wang-Mendel automatic rule generation algorithm. At the output of the cascade, a risk indicator is obtained, which allows for the categorisation of patients into two groups: those with dyspnea and those without dyspnea at 12 months. This simplifies follow-up and the performance of studies aimed at those patients at risk. The system has produced satisfactory results in initial tests, supported by an AUC of 0.75, demonstrating the potential and usefulness of this tool in clinical practice. [ABSTRACT FROM AUTHOR]

Primary tinnitus is a disabling disease with an unknown pathogenesis and a high incidence rate in China. Its diagnosis and treatment are complex and difficult to control. Although many treatments are available for primary tinnitus, their efficacy is often unsatisfactory. This paper proposes a new diagnosis and treatment method using knowledge graphs, and an intelligent assistant decision system is developed. To support diagnosis, a knowledge graph is created as a decision support tool using traditional Chinese medicine (TCM). Based on the knowledge graph, a model for the syndrome differentiation of tinnitus in TCM is built. At tinnitus treatment, an intelligent recommandation model for pentatonic music using knowledge graph based heterogeneous label propagation is then used to provide patients with personalized treatment plans. According to evaluation results, the proposed method achieves an accuracy of 87.1 % in tinnitus diagnosis. Compared with the control group, the recommended pentatonic music had a more obvious effect, and the efficacy of the five types of tinnitus was increased by 33.34 %, 33.33 %, 20 %, 26.67 %, 33.34 %, respectively. The system developed in this paper will help clinicians improve the diagnosis and treatment of tinnitus while reducing unnecessary medical expenses and offering significant social and economic benefits.

Background: Artificial intelligence (AI) has the capability to analyze vast amounts of data and has been applied in various healthcare sectors. However, its effectiveness in aiding pharmacotherapy decision-making remains uncertain due to the intricate, patient-specific, and dynamic nature of this field. Objective: This study sought to investigate the potential of AI in guiding pharmacotherapy decisions using clinical data such as diagnoses, laboratory results, and vital signs obtained from routine patient care. Methods: Data of a previous study on medication therapy optimization was updated and adapted for the purpose of this study. Analysis was conducted using R software along with the tidymodels extension packages. The dataset was split into 74% for training and 26% for testing. Decision trees were selected as the primary model due to their simplicity, transparency, and interpretability. To prevent overfitting, bootstrapping techniques were employed, and hyperparameters were fine-tuned. Performance metrics such as areas under the curve and accuracies were computed. Results: The study cohort comprised 101 elderly patients with multiple diagnoses and complex medication regimens. The AI model demonstrated prediction accuracies ranging from 38% to 100% for various cardiovascular drug classes. Laboratory data and vital signs could not be interpreted, as the effect and dependence were unclear for the model. The study revealed that the issue of AI lag time in responding to sudden changes could be addressed by manually adjusting decision trees, a task not feasible with neural networks. Conclusion: In conclusion, the AI model exhibited promise in recommending appropriate medications for individual patients. While the study identified several obstacles during model development, most were successfully resolved. Future AI studies need to include the drug effect, not only the drug, if laboratory data is part of the decision. This could assist with interpreting their potential relationship. Human oversight and intervention remain essential for an AI-driven pharmacotherapy decision support system to ensure safe and effective patient care.

Summary: Background: Accurate prediction of the optimal dose for β-lactam antibiotics in neonatal sepsis is challenging. We aimed to evaluate whether a reliable clinical decision support system (CDSS) based on machine learning (ML) can assist clinicians in making optimal dose selections. Methods: Five β-lactam antibiotics (amoxicillin, ceftazidime, cefotaxime, meropenem and latamoxef), commonly used to treat neonatal sepsis, were selected. The CDSS was constructed by incorporating the drug, patient, dosage, pharmacodynamic, and microbiological factors. The CatBoost ML algorithm was used to build the CDSS. Real-world studies were used to evaluate the CDSS performance. Virtual trials were used to compare the CDSS-optimized doses with guideline-recommended doses. Findings: For a specific drug, by entering the patient characteristics and pharmacodynamic (PD) target (50%/70%/100% fraction of time that the free drug concentration is above the minimal inhibitory concentration [fT > MIC]), the CDSS can determine whether the planned dosing regimen will achieve the PD target and suggest an optimal dose. The prediction accuracy of all five drugs was >80.0% in the real-world validation. Compared with the PopPK model, the overall accuracy, precision, recall, and F1-Score improved by 10.7%, 22.1%, 64.2%, and 43.1%, respectively. Using the CDSS-optimized doses, the average probability of target concentration attainment increased by 58.2% compared to the guideline-recommended doses. Interpretation: An ML-based CDSS was successfully constructed to assist clinicians in selecting optimal β-lactam antibiotic doses. Funding: This work was supported by the National Natural Science Foundation of China; Distinguished Young and Middle-aged Scholar of Shandong University; National Key Research and Development Program of China.

Abstract Background Self-harm presents a significant public health challenge. Emergency departments (EDs) are crucial healthcare settings in managing self-harm, but clinician uncertainty in risk assessment may contribute to ineffective care. Clinical Decision Support Systems (CDSSs) show promise in enhancing care processes, but their effective implementation in self-harm management remains unexplored. Methods PERMANENS comprises a combination of methodologies and study designs aimed at developing a CDSS prototype that assists clinicians in the personalized assessment and management of ED patients presenting with self-harm. Ensemble prediction models will be constructed by applying machine learning techniques on electronic registry data from four sites, i.e., Catalonia (Spain), Ireland, Norway, and Sweden. These models will predict key adverse outcomes including self-harm repetition, suicide, premature death, and lack of post-discharge care. Available registry data include routinely collected electronic health record data, mortality data, and administrative data, and will be harmonized using the OMOP Common Data Model, ensuring consistency in terminologies, vocabularies and coding schemes. A clinical knowledge base of effective suicide prevention interventions will be developed rooted in a systematic review of clinical practice guidelines, including quality assessment of guidelines using the AGREE II tool. The CDSS software prototype will include a backend that integrates the prediction models and the clinical knowledge base to enable accurate patient risk stratification and subsequent intervention allocation. The CDSS frontend will enable personalized risk assessment and will provide tailored treatment plans, following a tiered evidence-based approach. Implementation research will ensure the CDSS’ practical functionality and feasibility, and will include periodic meetings with user-advisory groups, mixed-methods research to identify currently unmet needs in self-harm risk assessment, and small-scale usability testing of the CDSS prototype software. Discussion Through the development of the proposed CDSS software prototype, PERMANENS aims to standardize care, enhance clinician confidence, improve patient satisfaction, and increase treatment compliance. The routine integration of CDSS for self-harm risk assessment within healthcare systems holds significant potential in effectively reducing suicide mortality rates by facilitating personalized and timely delivery of effective interventions on a large scale for individuals at risk of suicide.

BACKGROUND: MODY (maturity-onset diabetes of the young) is a rare monogenic form of diabetes mellitus, the gold standard of diagnosis is mutations detection in the genes responsible for the development of this form diabetes. Genetic test is expensive and takes a lot of time. The diagnostic criteria for MODY are well known. The development of clinical decision support system (CDSS) which allows physicians based on clinical data to determine who should have molecular genetic testing is relevant.AIM: Provided a retrospective analysis of clinical data of the patients with T1DM and MODY, from 0 to 18 years old, regardless of the duration of the disease to develop the model. Based on clinical data, a feedforward neural network (NN) was implemented - a multilayer perceptron.MATERIALS AND METHODS: Development of the most effective algorithm for predicting MODY in children based on available clinical indicators of 1710 patients with diabetes under the age of 18 years using a multilayer feedforward neural network.RESULTS: The sample consisted of 1710 children under the age of 18 years with T1DM (78%) and MODY (22%) diabetes. For the final configuration of NS the following predictors were selected: gender, age at passport age, age at the diagnosis with DM, HbA1c, BMI SDS, family history of DM, treatment. The performance (quality) assessment of the NN was carried out on a test sample (the area under the ROC (receiver operating characteristics) curve reached 0.97). The positive predictive value of PCPR was achieved at a cut-off value of 0.40 (predicted probability of MODY diabetes 40%). At which the sensitivity was 98%, specificity 93%, PCR with prevalence correction was 78%, and PCR with prevalence correction was 99%, the overall accuracy of the model was 94%.Based on the NN model, a CDSS was developed to determine whether a patient has MODY diabetes, implemented as an application.CONCLUSION: The clinical prediction model MODY developed in this work based on the NN, uses the clinical characteristic available for each patient to determine the probability of the patient having MODY. The use of the developed model in clinical practice will assist in the selection of patients for diagnostic genetic testing for MODY, which will allow for the efficient allocation of healthcare resources, the selection of personalized treatment and patient monitoring.

Abstract Background Digital transformation is key for healthcare to meet future needs and expectations and compete equally with new actors on the market. Medical digital diagnosis tools and clinical decision support systems (CDSS) are improving and offer new opportunities. To introduce new technology in healthcare can however be a challenging mission, too often ending in failure, with excessive costs or the actual transformation work not being carried out at all. It is unclear how to drive the establishment to reach desired results in this environment, and how industrial experiences can be used to support healthcare. Objective The objective of this study was to develop a holistic approach for introducing new information technology (IT), such as a CDSS, into a primary healthcare organization supported by industry best practices for digital transformation. Methods This qualitative study used a combined inductive and deductive method where the perceptions and beliefs of selected primary healthcare stakeholders were used as directions for developing an approach that could utilize existing industry best practices for digital transformation. Results A holistic healthcare-ified approach including 20 requirements was developed, that meet the needs of healthcare. The voice of healthcare was used as prism to healthcare-ify the industry practices and adapt it to match specific healthcare conditions. An example was provided showing how the research could be put into practice. Conclusions This study proposed a holistic approach, based on industry best practices, but adapted to healthcare using the voice of healthcare as a bridge, that may be used to introduce CDSS and other IT into a primary healthcare organization and step up the needed digitalization.

Saskia Herrmann,1,2 Jana Giesel-Gerstmeier,1 Annika Demuth,1 Dominic Fenske1 1Hospital Pharmacy, Helios Kliniken Gmbh, Berlin, Berlin, Germany; 2Department of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, Friedrich Schiller University Jena, Jena, Thuringia, GermanyCorrespondence: Dominic Fenske, Krankenhausapotheke, Helios Klinikum Erfurt, Nordhäuser Straße 74, 99089, Erfurt, Thuringia, Germany, Tel +49 361 781 71100, Fax +49 361 781 71105, Email dominic.fenske@helios-gesundheit.dePurpose: Computerized physician order entry (CPOE) and clinical decision support systems (CDSS) are used internationally since the 1980s. These systems reduce costs, enhance drug therapy safety, and improve quality of care. A few years ago, there was a growing effort to digitize the healthcare sector in Germany. Implementing such systems like CPOE-CDSS requires training for effective adoption and, more important, acceptance by the users. Potential improvements for the software and implementation process can be derived from the users’ perspective. The implementation process is globally relevant and applicable across professions due to the constant advancement of digitalization. The study assessed the implementation of medication software and overall satisfaction.Methods: In an anonymous voluntary online survey, physicians and nursing staff were asked about their satisfaction with the new CPOE-CDSS. The survey comprised single-choice queries on a Likert scale, categorizing into general information, digital medication administration, drug safety, and software introduction. In addition multiple-choice questions are mentioned. Data analysis was performed using Microsoft Office Excel 2016 and GraphPad PRISM 9.5.0.Results: Nurses and physicians’ satisfaction with the new software increased with usage hours. The software’s performance and loading times have clearly had a negative impact, which leads to a low satisfaction of only 20% among physicians and 17% among nurses. 53% of nurses find the program’s training period unsuitable for their daily use, while 57% of physicians approve the training’s scope for their professional group. Both professions agree that drug-related problems are easier to detect using CPOE-CDSS, with 76% of nurses and 75% of physicians agreeing. The study provides unbiased feedback on software implementation.Conclusion: In conclusion, digitizing healthcare requires managing change, effective training, and addressing software functionality concerns to ensure improved medication safety and streamlined processes. Interfaces, performance optimization, and training remain crucial for software acceptance and effectiveness.Keywords: computerized physician order entry, clinical decision support system, change management, feedback, electronic prescribing

Abstract The challenge of making flexible, standard, and early medical diagnoses is significant. However, some limitations are not fully overcome. First, the diagnosis rules established by medical experts or learned from a trained dataset prove static and too general. It leads to decisions that lack adaptive flexibility when finding new circumstances. Secondly, medical terminological interoperability is highly critical. It increases realism and medical progress and avoids isolated systems and the difficulty of data exchange, analysis, and interpretation. Third, criteria for diagnosis are often heterogeneous and changeable. It includes symptoms, patient history, demographic, treatment, genetics, biochemistry, and imaging. Symptoms represent a high-impact indicator for early detection. It is important that we deal with these symptoms differently, which have a great relationship with semantics, vary widely, and have linguistic information. This negatively affects early diagnosis decision-making. Depending on the circumstances, the diagnosis is made solo on imaging and some medical tests. In this case, although the accuracy of the diagnosis is very high, can these decisions be considered an early diagnosis or prove the condition is deteriorating? Our contribution in this paper is to present a real medical diagnostic system based on semantics, fuzzy, and dynamic decision rules. We attempt to integrate ontology semantics reasoning and fuzzy inference. It promotes fuzzy reasoning and handles knowledge representation problems. In complications and symptoms, ontological semantic reasoning improves the process of evaluating rules in terms of interpretability, dynamism, and intelligence. A real-world case study, ADNI, is presented involving the field of Alzheimer’s disease (AD). The proposed system has indicated the possibility of the system to diagnose AD with an accuracy of 97.2%, 95.4%, 94.8%, 93.1%, and 96.3% for AD, LMCI, EMCI, SMC, and CN respectively.

Abstract Background Clinical decision support systems (CDSSs) have the potential to improve quality of care, patient safety, and efficiency because of their ability to perform medical tasks in a more data-driven, evidence-based, and semi-autonomous way. However, CDSSs may also affect the professional identity of health professionals. Some professionals might experience these systems as a threat to their professional identity, as CDSSs could partially substitute clinical competencies, autonomy, or control over the care process. Other professionals may experience an empowerment of the role in the medical system. The purpose of this study is to uncover the role of professional identity in CDSS implementation and to identify core human, technological, and organizational factors that may determine the effect of CDSSs on professional identity. Methods We conducted a systematic literature review and included peer-reviewed empirical studies from two electronic databases (PubMed, Web of Science) that reported on key factors to CDSS implementation and were published between 2010 and 2023. Our explorative, inductive thematic analysis assessed the antecedents of professional identity-related mechanisms from the perspective of different health care professionals (i.e., physicians, residents, nurse practitioners, pharmacists). Results One hundred thirty-one qualitative, quantitative, or mixed-method studies from over 60 journals were included in this review. The thematic analysis found three dimensions of professional identity-related mechanisms that influence CDSS implementation success: perceived threat or enhancement of professional control and autonomy, perceived threat or enhancement of professional skills and expertise, and perceived loss or gain of control over patient relationships. At the technological level, the most common issues were the system’s ability to fit into existing clinical workflows and organizational structures, and its ability to meet user needs. At the organizational level, time pressure and tension, as well as internal communication and involvement of end users were most frequently reported. At the human level, individual attitudes and emotional responses, as well as familiarity with the system, most often influenced the CDSS implementation. Our results show that professional identity-related mechanisms are driven by these factors and influence CDSS implementation success. The perception of the change of professional identity is influenced by the user’s professional status and expertise and is improved over the course of implementation. Conclusion This review highlights the need for health care managers to evaluate perceived professional identity threats to health care professionals across all implementation phases when introducing a CDSS and to consider their varying manifestations among different health care professionals. Moreover, it highlights the importance of innovation and change management approaches, such as involving health professionals in the design and implementation process to mitigate threat perceptions. We provide future areas of research for the evaluation of the professional identity construct within health care.

Many approaches to automated epileptic seizure detection share a common challenge — the trade-off between recall and precision. This study aims to develop a novel approach for reducing false positive predictions in seizure detection tasks applied to real-world EEG recordings. We propose a multi-stage modeling framework, for which the novelty lies in combination of traditional machine learning outlier detection with state-of-the-art convolutional neural networks. Our dataset includes raw epileptic EEG data directly from the hospital. Continuous wavelet analysis is employed for EEG preprocessing and feature extraction. We evaluated the performance of the proposed two-stage algorithm, and it demonstrated a slight decrease in recall but a significant improvement in precision in comparison to machine-learning-only or neural-network-only algorithms. We hypothesize that this finding aligns well with our previous research and relates to the fundamental properties of epileptic EEG, including the extreme behavior of seizures. Finally, we propose a potential practical application of the developed approach within a clinical decision support system.

Clinical Decision Support Systems (CDSS) have revolutionized healthcare by leveraging modern technologies such as internet of things (IoT), artificial intelligence (AI), predictive analysis, nano-medicine, and virtual & augmented reality. IoT-based CDSS is of interest in particular. In a hospital scenario, a patient’s vital signs like heart rate, blood pressure, respiration rate, ECG, EEG etc. are monitored with the use of embedded sensor devices, also called smart medical devices. These devices collect real-time data which is relayed to a compute device where several algorithms are employed to perform computations on said data to arrive at a prognosis e.g. real-time onset of hypotension can be detected by running predictive algorithms on real-time blood pressure data. The computation in IoT-based CDSS is done predominantly on the cloud, wherein the real-time data collected is relayed to a centralized cloud server. However, latency is a major drawback in a cloud-based monitoring system. Increased latency is of greater concern in healthcare applications as the decision-making process is time-sensitive. Edge computing can potentially overcome this drawback, wherein computation is done on edge-network devices rather than the cloud. While edge computing for IoT-based CDSS has been explored in literature, there are gaps in their implementations. A majority of literature dealing with edge computing for IoT-based healthcare only demonstrates a single application and does not address the varying data acquisition rates for different vital signs. Each prognosis or diagnosis requires different subsets of vital signs, and the underlying algorithm uses different sizes of data e.g. detecting arrhythmia requires processing of ECG data which is a time series data, and detecting cardiovascular disease requires blood pressure, cholesterol and certain habits of the patient which are mostly single points of data. This paper explores the use of edge computing in CDSS, quantifies its performance with respect to number of devices, sense time interval or intertransmission rate, and the size of data, and proposes a unified IoT edge gateway architecture to combine multiple patterns of data and computation algorithms to achieve reduced latency and network utilization. Simulation results show that edge computing reduces the latency of decision by approximately 87 times, and the network utilization by 1.5 times. The results show the efficacy of edge computing for implementing IoT-based CDSS and also demonstrate scalability with regard to the number of devices and the size and intertransmission rate of data.

AbstractObjective: Skin examination to detect cutaneous melanomas is commonly performed in primary care. In recent years, clinical decision support systems (CDSS) based on artificial intelligence (AI) have been introduced within several diagnostic fields.Setting: This study employs a variety of qualitative and quantitative methodologies to investigate the feasibility of an AI-based CDSS to detect cutaneous melanoma in primary care.Subjects and Design: Fifteen primary care physicians (PCPs) underwent near-live simulations using the CDSS on a simulated patient, and subsequent individual semi-structured interviews were explored with a hybrid thematic analysis approach. Additionally, twenty-five PCPs performed a reader study (diagnostic assessment on the basis of image interpretation) of 18 dermoscopic images, both with and without help from AI, investigating the value of adding AI support to a PCPs decision. Perceived instrument usability was rated on the System Usability Scale (SUS).Results: From the interviews, the importance of trust in the CDSS emerged as a central concern. Scientific evidence supporting sufficient diagnostic accuracy of the CDSS was expressed as an important factor that could increase trust. Access to AI decision support when evaluating dermoscopic images proved valuable as it formally increased the physician’s diagnostic accuracy. A mean SUS score of 84.8, corresponding to ‘good’ usability, was measured.Conclusion: AI-based CDSS might play an important future role in cutaneous melanoma diagnostics, provided sufficient evidence of diagnostic accuracy and usability supporting its trustworthiness among the users.

目的 探索脑血管病临床决策支持系统（clinical decision support system，CDSS）对卒中医疗服务质量关键绩效指标改进是否具有积极作用。 方法 回顾性连续纳入首都医科大学附属北京天坛医院血管神经病学2病区应用脑血管病CDSS前（2020年1—11月）住院治疗的缺血性卒中患者作为对照组，应用脑血管病CDSS后（2021年1—11月）收治的缺血性卒中患者作为干预组，比较两组患者的基线特征和缺血性卒中医疗服务质量关键绩效指标，评估脑血管病CDSS对卒中医疗服务质量的影响。 结果 本研究共纳入1331例患者，其中对照组651例，干预组680例。对照组平均年龄为（71.7±11.8）岁，男性490例（75.3%）；干预组平均年龄（72.3±10.2）岁，男性498例（73.2%）。缺血性卒中医疗服务质量关键绩效指标中，干预组入院48 h内不能行走患者进行深静脉血栓预防率（86.3% vs. 65.0%， P＜0.01）、出院时患者抗栓治疗率（98.1% vs. 96.2%，P=0.03）、出院时合并心房颤动的患者抗凝治疗率（70.1% vs. 44.2%，P＜0.01）均高于对照组。 结论 脑血管病CDSS有助于改善缺血性卒中患者的医疗服务质量关键绩效指标。 Abstract: Objective This study aimed to explore whether cerebrovascular disease clinical decision support system (CDSS) could improve the key performance indicators of medical care quality. Methods In our study, ischemic stroke patients hospitalized in Ward 2 of Vascular Neurology, Beijing Tiantan Hospital, Capital Medical University before applying cerebrovascular disease CDSS (January to November 2020) were retrospectively included as the control group. Ischemic stroke patients admitted after the application of CDSS assisted diagnosis and treatment (January to November 2021) were included as the intervention group. The baseline characteristics and key performance indicators of medical care quality for ischemic stroke in these two groups were compared to assess the impact of cerebrovascular disease CDSS on medical care quality of stroke. Results A total of 1331 patients were included in this study, including 651 in the control group and 680 in the intervention group. The mean age of the control group was (71.7±11.8) years, with 490 males (75.3%), and the mean age of the intervention group was (72.3±10.2) years, with 498 males (73.2%). Among the key performance indicators of medical care quality of ischemic stroke, the proportion of patients who were unable to walk within 48 h of admission received deep vein thrombosis prevention (86.3% vs. 65.0%, P

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.