Implementation and evaluation of a system for assessment of the quality of long-term management of patients at a geriatric hospital.

BACKGROUND: The increasing aging population presents a significant challenge, accompanied by a shortage of professional caregivers, adding to the therapeutic burden. Clinical decision support systems, utilizing computerized clinical guidelines, can improve healthcare quality, reduce expenses, save time, and boost caregiver efficiency. OBJECTIVES: 1) Develop and evaluate an automated quality assessment (QA) system for retrospective longitudinal care quality analysis, focusing on clinical staff adherence to evidence-based guidelines (GLs). 2) Assess the system's technical feasibility and functional capability for senior nurse use in geriatric pressure-ulcer management. METHODS: A computational QA system using our Quality Assessment Temporal Patterns (QATP) methodology was designed and implemented. Our methodology transforms the GL's procedural-knowledge into declarative-knowledge temporal-abstraction patterns representing the expected execution trace in the patient's data for correct therapy application. Fuzzy temporal logic allows for partial compliance, reflecting individual and grouped action performance considering their values and temporal aspects. The system was tested using a pressure ulcer treatment GL and data from 100 geriatric patients' Electronic Medical Records (EMR). After technical evaluation for accuracy and feasibility, an extensive functional evaluation was conducted by an experienced nurse, comparing QA scores with and without system support, and versus automated system scores. Time efficiency was also measured. RESULTS: QA scores from the geriatric nurse, with and without system's support, did not significantly differ from those provided by the automated system (p < 0.05), demonstrating the effectiveness and reliability of both manual and automated methods. The system-supported manual QA process reduced scoring time by approximately two-thirds, from an average of 17.3 min per patient manually to about 5.9 min with the system's assistance, highlighting the system's efficiency potential in clinical practice. CONCLUSION: The QA system based on QATP, produces scores consistent with an experienced nurse's assessment for complex care over extended periods. It enables quick and accurate quality care evaluation for multiple patients after brief training. Such automated QA systems may empower nursing staff, enabling them to manage more patients, accurately and consistently, while reducing costs due to saved time and effort, and enhanced compliance with evidence-based guidelines.

Biodegradation behaviour of pharmaceutical compounds and selected metabolites in activated sludge. A forecasting decision system approach.

Society's support upon chemicals over the last few decades has led to their increased production, application and discharge into the environment. Wastewater treatment plants (WWTPs) contain a multitude of these chemicals such us; pharmaceutical compounds (PCs). Often, their biodegradability by activated sludge microorganisms is significant for their elimination during wastewater treatment. In this paper the focus is laid on two PCs carbamazepine (CBZ) and diclofenac (DCF) and their main transformation products (TPs). Laboratory degradation tests with these two pharmaceuticals using activated sludge as inoculum under aerobic conditions were performed and microbial metabolites were analyzed by liquid chromatography-mass spectrometry (LC/MS-MS). In two different Mixed liquid Suspended Solids (MLSS) concentrations the biodegradability by activated sludge of CBZ and DCF were evaluated. Also, this article proposes a decision support system to optimize the prediction process of this type of pharmacological compounds. A study and analysis of the techniques of Support Vector Machine, Random Forest, Decision Trees and Multilayer Perceptron Network is carried out to select the most reliable and accurate predictor for the decision system. There are not significant differences in the removal of DCF with 30 mg MLSS/L and 60 mg MLSS/L. DCF was better removed than CBZ in all experiments studied. The TP detected in the samples were mainly 4-OH-DCF for DCF and 10, 11 EPOXICBZ for CBZ. The results show that the best models are obtained with Random Forest and Multilayer Perceptron Network techniques, with a model fit of more than 95% for both carbamazepine and diclofenac metabolites. Obtaining a root means square errors of 0.80 µg/L for the metabolite 4-OH-DCF for DCF with the technique Random Forest and a root means square errors of 1.13 µg/L for the metabolite 10, 11 EPOXICBZ for CBZ with the Multilayer Perceptron Network technique. Supplementary Information: The online version contains supplementary material available at 10.1007/s40201-023-00890-x.

Event Analysis for Automated Estimation of Absent and Persistent Medication Alerts: Novel Methodology.

Background: Event analysis is a promising approach to estimate the acceptance of medication alerts issued by computerized physician order entry (CPOE) systems with an integrated clinical decision support system (CDSS), particularly when alerts cannot be interactively confirmed in the CPOE-CDSS due to its system architecture. Medication documentation is then reviewed for documented evidence of alert acceptance, which can be a time-consuming process, especially when performed manually. Objective: We present a new automated event analysis approach, which was applied to a large data set generated in a CPOE-CDSS with passive, noninterruptive alerts. Methods: Medication and alert data generated over 3.5 months within the CPOE-CDSS at Heidelberg University Hospital were divided into 24-hour time intervals in which the alert display was correlated with associated prescription changes. Alerts were considered "persistent" if they were displayed in every consecutive 24-hour time interval due to a respective active prescription until patient discharge and were considered "absent" if they were no longer displayed during continuous prescriptions in the subsequent interval. Results: Overall, 1670 patient cases with 11,428 alerts were analyzed. Alerts were displayed for a median of 3 (IQR 1-7) consecutive 24-hour time intervals, with the shortest alerts displayed for drug-allergy interactions and the longest alerts displayed for potentially inappropriate medication for the elderly (PIM). Among the total 11,428 alerts, 56.1% (n=6413) became absent, most commonly among alerts for drug-drug interactions (1915/2366, 80.9%) and least commonly among PIM alerts (199/499, 39.9%). Conclusions: This new approach to estimate alert acceptance based on event analysis can be flexibly adapted to the automated evaluation of passive, noninterruptive alerts. This enables large data sets of longitudinal patient cases to be processed, allows for the derivation of the ratios of persistent and absent alerts, and facilitates the comparison and prospective monitoring of these alerts.

Automated hearing loss type classification based on pure tone audiometry data.

Hearing problems are commonly diagnosed with the use of tonal audiometry, which measures a patient's hearing threshold in both air and bone conduction at various frequencies. Results of audiometry tests, usually represented graphically in the form of an audiogram, need to be interpreted by a professional audiologist in order to determine the exact type of hearing loss and administer proper treatment. However, the small number of professionals in the field can severely delay proper diagnosis. The presented work proposes a neural network solution for classification of tonal audiometry data. The solution, based on the Bidirectional Long Short-Term Memory architecture, has been devised and evaluated for classifying audiometry results into four classes, representing normal hearing, conductive hearing loss, mixed hearing loss, and sensorineural hearing loss. The network was trained using 15,046 test results analysed and categorised by professional audiologists. The proposed model achieves 99.33% classification accuracy on datasets outside of training. In clinical application, the model allows general practitioners to independently classify tonal audiometry results for patient referral. In addition, the proposed solution provides audiologists and otolaryngologists with access to an AI decision support system that has the potential to reduce their burden, improve diagnostic accuracy, and minimise human error.

Outcome of patients with different stages of acute-on-chronic liver failure treated with artificial liver support system.

Background: Elevated international normalized ratio of prothrombin time (PT-INR) is one of the key characteristics of acute-on-chronic liver failure (ACLF). Whether the staging of PT-INR has the ability to screen out subgroups of ACLF patients who would be more eligible for artificial liver support system (ALSS) treatment has not been studied in detail. Methods: A previous study enrolled patients receiving ALSS treatment with regional citrate anticoagulation from January 2018 to December 2019. Patients with different PT-INR intervals were retrospectively enrolled: 1.3 ≤ PT-INR < 1.5 (Pre-stage), 1.5 ≤ PT-INR < 2.0 (Early-stage), 2.0 ≤ PT-INR < 2.5 (Mid-stage), and PT-INR ≥ 2.5 (End-stage). The Cox proportional hazards models were used to estimate the association between stages of ACLF or sessions of ALSS treatment and 90 day mortality. Results: A total of 301 ACLF patients were enrolled. The 90 day mortality risk of Early-stage ACLF patients (adjusted hazard ratio (aHR) (95% confidence interval (CI)), 3.20 (1.15-8.89), p = 0.026), Mid-stage ACLF patients (3.68 (1.34-10.12), p = 0.011), and End-stage ACLF patients (12.74 (4.52-35.91), p < 0.001) were higher than that of Pre-stage ACLF patients, respectively. The 90 day mortality risk of Mid-stage ACLF patients was similar to that of Early-stage ACLF patients (1.15 (0.69-1.94), p = 0.591). The sessions of ALSS treatment was an independent protective factor (aHR (95% CI), 0.81 (0.73-0.90), p < 0.001). The 90 day mortality risk in ACLF patients received 3-5 sessions of ALSS treatment was lower than that of patients received 1-2 sessions (aHR (95% CI), 0.34 (0.20-0.60), p < 0.001), whereas the risk in patients received ≥6 sessions of ALSS treatment was similar to that of patients received 3-5 sessions (0.69 (0.43-1.11), p = 0.128). Conclusion: ACLF patients in Pre-, Early-, and Mid-stages might be more eligible for ALSS treatment. Application of 3-5 sessions of ALSS treatment might be reasonable.

Evaluating ChatGPT-4's Accuracy in Identifying Final Diagnoses Within Differential Diagnoses Compared With Those of Physicians: Experimental Study for Diagnostic Cases.

BACKGROUND: The potential of artificial intelligence (AI) chatbots, particularly ChatGPT with GPT-4 (OpenAI), in assisting with medical diagnosis is an emerging research area. However, it is not yet clear how well AI chatbots can evaluate whether the final diagnosis is included in differential diagnosis lists. OBJECTIVE: This study aims to assess the capability of GPT-4 in identifying the final diagnosis from differential-diagnosis lists and to compare its performance with that of physicians for case report series. METHODS: We used a database of differential-diagnosis lists from case reports in the American Journal of Case Reports, corresponding to final diagnoses. These lists were generated by 3 AI systems: GPT-4, Google Bard (currently Google Gemini), and Large Language Models by Meta AI 2 (LLaMA2). The primary outcome was focused on whether GPT-4's evaluations identified the final diagnosis within these lists. None of these AIs received additional medical training or reinforcement. For comparison, 2 independent physicians also evaluated the lists, with any inconsistencies resolved by another physician. RESULTS: The 3 AIs generated a total of 1176 differential diagnosis lists from 392 case descriptions. GPT-4's evaluations concurred with those of the physicians in 966 out of 1176 lists (82.1%). The Cohen κ coefficient was 0.63 (95% CI 0.56-0.69), indicating a fair to good agreement between GPT-4 and the physicians' evaluations. CONCLUSIONS: GPT-4 demonstrated a fair to good agreement in identifying the final diagnosis from differential-diagnosis lists, comparable to physicians for case report series. Its ability to compare differential diagnosis lists with final diagnoses suggests its potential to aid clinical decision-making support through diagnostic feedback. While GPT-4 showed a fair to good agreement for evaluation, its application in real-world scenarios and further validation in diverse clinical environments are essential to fully understand its utility in the diagnostic process.

Integration of Full-Size Graywater Membrane-Aerated Biological Reactor with Reverse Osmosis System for Space-Based Wastewater Treatment.

To date, life support systems on the International Space Center (ISS) or those planned for upcoming moon/Mars missions have not included biological reactors for wastewater treatment, despite their ubiquitous use for the treatment of terrestrial wastewaters. However, the new focus on partial gravity habitats reduces the required complexity of treatment systems compared with those operating in micro-gravity, and the likely addition of large-volume wastewaters with surfactant loads (e.g., laundry and shower) makes the current ISS wastewater treatment system inappropriate due to the foaming potential from surfactants, increased consumable requirements due to the use of non-regenerative systems (e.g., mixed adsorbent beds), the complexity of the system, and sensitivity to failures from precipitation and/or biological fouling. Hybrid systems that combine simple biological reactors with desalination (e.g., Reverse Osmosis (RO)) could reduce system and consumable mass and complexity. Our objective was to evaluate a system composed of a membrane-aerated bioreactor (MABR) coupled to a low-pressure commercial RO system to process partial gravity habitat wastewater. The MABR was able to serve as the only wastewater collection tank (variable volume), receiving all wastewaters as they were produced. The MABR treated more than 20,750 L of graywater and was able to remove more than 90% of dissolved organic carbon (DOC), producing an effluent with DOC < 14 mg/L and BOD < 12 mg/L and oxidizing >90% of the ammoniacal nitrogen into NOx-. A single RO membrane (260 g) was able to process >3000 L of MABR effluent and produced a RO permeate with DOC < 5 mg/L, TN < 2 mg/L, and TDS < 10 mg/L, which would essentially meet ISS potable water standards after disinfection. The system has an un-optimized mass and volume of 128.5 kg. Consumables include oxygen (~4 g/crew-day), RO membranes, and a prefilter (1.7 g/crew-day). For a one-year mission with four crew, the total system + consumable mass are ~141 kg, which would produce ~15,150 kg of treated water, resulting in a pay-back period of 13.4 days (3.35 days for a crew of four). Given that the MABR in this study operated for 500 days, while in previous studies, similar systems operated for more than 3 years, the total system costs would be exceedingly low. These results highlight the potential application of hybrid treatment systems for space habitats, which may also have a direct application to terrestrial applications where source-separated systems are employed.

Override rate of drug-drug interaction alerts in clinical decision support systems: A brief systematic review and meta-analysis.

Primary studies have demonstrated that despite being useful, most of the drug-drug interaction (DDI) alerts generated by clinical decision support systems are overridden by prescribers. To provide more information about this issue, we conducted a systematic review and meta-analysis on the prevalence of DDI alerts generated by CDSS and alert overrides by physicians. The search strategy was implemented by applying the terms and MeSH headings and conducted in the MEDLINE/PubMed, EMBASE, Web of Science, Scopus, LILACS, and Google Scholar databases. Blinded reviewers screened 1873 records and 86 full studies, and 16 articles were included for analysis. The overall prevalence of alert generated by CDSS was 13% (CI95% 5-24%, p-value <0.0001, I^2 = 100%), and the overall prevalence of alert override by physicians was 90% (CI95% 85-95%, p-value <0.0001, I^2 = 100%). This systematic review and meta-analysis presents a high rate of alert overrides, even after CDSS adjustments that significantly reduced the number of alerts. After analyzing the articles included in this review, it was clear that the CDSS alerts physicians about potential DDI should be developed with a focus on the user experience, thus increasing their confidence and satisfaction, which may increase patient clinical safety.

Evaluation of a Computer-Aided Clinical Decision Support System for Point-of-Care Use in Low-Resource Primary Care Settings: Acceptability Evaluation Study.

BACKGROUND: A clinical decision support system (CDSS) based on the logic and philosophy of clinical pathways is critical for managing the quality of health care and for standardizing care processes. Using such a system at a point-of-care setting is becoming more frequent these days. However, in a low-resource setting (LRS), such systems are frequently overlooked. OBJECTIVE: The purpose of the study was to evaluate the user acceptance of a CDSS in LRSs. METHODS: The CDSS evaluation was carried out at the Jimma Health Center and the Jimma Higher Two Health Center, Jimma, Ethiopia. The evaluation was based on 22 parameters organized into 6 categories: ease of use, system quality, information quality, decision changes, process changes, and user acceptance. A Mann-Whitney U test was used to investigate whether the difference between the 2 health centers was significant (2-tailed, 95% CI; α=.05). Pearson correlation and partial least squares structural equation modeling (PLS-SEM) was used to identify the relationship and factors influencing the overall acceptance of the CDSS in an LRS. RESULTS: On the basis of 116 antenatal care, pregnant patient care, and postnatal care cases, 73 CDSS evaluation responses were recorded. We found that the 2 health centers did not differ significantly on 16 evaluation parameters. We did, however, detect a statistically significant difference in 6 parameters (P<.05). PLS-SEM results showed that the coefficient of determination, R2, of perceived user acceptance was 0.703. More precisely, the perceived ease of use (ß=.015, P=.91) and information quality (ß=.149, P=.25) had no positive effect on CDSS acceptance but, rather, on the system quality and perceived benefits of the CDSS, with P<.05 and ß=.321 and ß=.486, respectively. Furthermore, the perceived ease of use was influenced by information quality and system quality, with an R2 value of 0.479, indicating that the influence of information quality on the ease of use is significant but the influence of system quality on the ease of use is not, with ß=.678 (P<.05) and ß=.021(P=.89), respectively. Moreover, the influence of decision changes (ß=.374, P<.05) and process changes (ß=.749, P<.05) both was significant on perceived benefits (R2=0.983). CONCLUSIONS: This study concludes that users are more likely to accept and use a CDSS at the point of care when it is easy to grasp the perceived benefits and system quality in terms of health care professionals' needs. We believe that the CDSS acceptance model developed in this study reveals specific factors and variables that constitute a step toward the effective adoption and deployment of a CDSS in LRSs.

Optimal use of ß-lactams in neonates: machine learning-based clinical decision support system.

BACKGROUND: Accurate prediction of the optimal dose for ß-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 ß-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 ß-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.

Evaluating the Prevalence of Burnout Among Health Care Professionals Related to Electronic Health Record Use: Systematic Review and Meta-Analysis.

BACKGROUND: Burnout among health care professionals is a significant concern, with detrimental effects on health care service quality and patient outcomes. The use of the electronic health record (EHR) system has been identified as a significant contributor to burnout among health care professionals. OBJECTIVE: This systematic review and meta-analysis aims to assess the prevalence of burnout among health care professionals associated with the use of the EHR system, thereby providing evidence to improve health information systems and develop strategies to measure and mitigate burnout. METHODS: We conducted a comprehensive search of the PubMed, Embase, and Web of Science databases for English-language peer-reviewed articles published between January 1, 2009, and December 31, 2022. Two independent reviewers applied inclusion and exclusion criteria, and study quality was assessed using the Joanna Briggs Institute checklist and the Newcastle-Ottawa Scale. Meta-analyses were performed using R (version 4.1.3; R Foundation for Statistical Computing), with EndNote X7 (Clarivate) for reference management. RESULTS: The review included 32 cross-sectional studies and 5 case-control studies with a total of 66,556 participants, mainly physicians and registered nurses. The pooled prevalence of burnout among health care professionals in cross-sectional studies was 40.4% (95% CI 37.5%-43.2%). Case-control studies indicated a higher likelihood of burnout among health care professionals who spent more time on EHR-related tasks outside work (odds ratio 2.43, 95% CI 2.31-2.57). CONCLUSIONS: The findings highlight the association between the increased use of the EHR system and burnout among health care professionals. Potential solutions include optimizing EHR systems, implementing automated dictation or note-taking, employing scribes to reduce documentation burden, and leveraging artificial intelligence to enhance EHR system efficiency and reduce the risk of burnout. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42021281173; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021281173.

Assessing the Utility, Impact, and Adoption Challenges of an Artificial Intelligence-Enabled Prescription Advisory Tool for Type 2 Diabetes Management: Qualitative Study.

BACKGROUND: The clinical management of type 2 diabetes mellitus (T2DM) presents a significant challenge due to the constantly evolving clinical practice guidelines and growing array of drug classes available. Evidence suggests that artificial intelligence (AI)-enabled clinical decision support systems (CDSSs) have proven to be effective in assisting clinicians with informed decision-making. Despite the merits of AI-driven CDSSs, a significant research gap exists concerning the early-stage implementation and adoption of AI-enabled CDSSs in T2DM management. OBJECTIVE: This study aimed to explore the perspectives of clinicians on the use and impact of the AI-enabled Prescription Advisory (APA) tool, developed using a multi-institution diabetes registry and implemented in specialist endocrinology clinics, and the challenges to its adoption and application. METHODS: We conducted focus group discussions using a semistructured interview guide with purposively selected endocrinologists from a tertiary hospital. The focus group discussions were audio-recorded and transcribed verbatim. Data were thematically analyzed. RESULTS: A total of 13 clinicians participated in 4 focus group discussions. Our findings suggest that the APA tool offered several useful features to assist clinicians in effectively managing T2DM. Specifically, clinicians viewed the AI-generated medication alterations as a good knowledge resource in supporting the clinician's decision-making on drug modifications at the point of care, particularly for patients with comorbidities. The complication risk prediction was seen as positively impacting patient care by facilitating early doctor-patient communication and initiating prompt clinical responses. However, the interpretability of the risk scores, concerns about overreliance and automation bias, and issues surrounding accountability and liability hindered the adoption of the APA tool in clinical practice. CONCLUSIONS: Although the APA tool holds great potential as a valuable resource for improving patient care, further efforts are required to address clinicians' concerns and improve the tool's acceptance and applicability in relevant contexts.

The need for acute assessments in home healthcare - Swedish registered nurses' experiences.

PURPOSE: The study aims to describe Swedish RNs' experiences of acute assessments at home. More patients with complex nursing needs are cared for at home due to an ageing population. Registered nurses (RNs) who work with home healthcare need a broad medical competence and clinical experience alongside adapted decision support systems for maintaining patient safety in acute assessments within home healthcare. METHODS: A content analysis of qualitative survey data from RNs (n = 19) working within home healthcare in Sweden. RESULTS: There were challenges in the acute assessments at home due to a lack of competence since several of the RNs did not have much experience working as an RN in home healthcare. Important information was missing about the patients, such as access to medical records due to organizational challenges and limited access to equipment and materials. The RNs needed support in the form of cooperation with a physician, support from colleagues, and a decision support system. CONCLUSION: To increase the possibility of patient-safe assessments at home, skills development, collegial support, and an adapted decision support system are needed. Collaboration with primary healthcare, on-call physicians, and nursing staff, and having the opportunity to consult with someone also provide security in acute assessments.

An ethical assessment of professional opinions on concerns, chances, and limitations of the implementation of an artificial intelligence-based technology into the geriatric patient treatment and continuity of care.

With the introduction of an artificial intelligence-based dashboard into the clinic, the project SURGE-Ahead responds to the importance of improving perioperative geriatric patient treatment and continuity of care. The use of artificial intelligence to process and analyze data automatically, aims at an evidence-based evaluation of the patient's health condition and recommending treatment options. However, its development and introduction raise ethical questions. To ascertain professional perspectives on the clinical use of the dashboard, we have conducted 19 semi-structured qualitative interviews with head physicians, computer scientists, jurists, and ethicists. The application of a qualitative content analysis and thematic analysis enabled the detection of main ethical concerns, chances, and limitations. These ethical considerations were categorized: changes of the patient-physician relationship and the current social reality are expected, causing de-skilling and an active participation of the artificial intelligence. The interviewees anticipated a redistribution of human resources, time, knowledge, and experiences as well as expenses and financing. Concerns of privacy, accuracy, transparency, and explainability were stated, and an insufficient data basis, an intensifying of existing inequalities and systematic discrimination considering a fair access emphasized. Concluding, the patient-physician relationship, social reality, redistribution of resources, fair access, as well as data-related aspects of the artificial intelligence-based system could conflict with the ethical principles of autonomy, non-maleficence, beneficence, and social justice. To respond to these ethical concerns, a responsible use of the dashboard and a critical verification of therapy suggestions is mandatory, and the application limited by questions at the end of life and taking life-changing decisions.

Community-based management of arterial hypertension and cardiovascular risk factors by lay village health workers for people with controlled and uncontrolled blood pressure in rural Lesotho: joint protocol for two cluster-randomized trials within the ComBaCaL cohort study (ComBaCaL aHT Twic 1 and ComBaCaL aHT TwiC 2).

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 < 140/90 mmHg) in the aHT TwiC 2. In intervention villages, VHWs offer lifestyle counseling, basic guideline-directed antihypertensive, lipid-lowering, and antiplatelet treatment supported by a tablet-based decision support application to eligible participants. In control villages, participants are referred to a health facility for therapeutic management. The primary endpoint for both TwiCs is the proportion of participants with controlled BP levels (< 140/90 mmHg) 12 months after enrolment. We hypothesize that the intervention is superior regarding BP control rates in participants with uncontrolled BP (aHT TwiC 1) and non-inferior in participants with controlled BP at baseline (aHT TwiC 2). DISCUSSION: The TwiCs were launched on September 08, 2023. On May 20, 2024, 697 and 750 participants were enrolled in TwiC 1 and TwiC 2. To our knowledge, these TwiCs are the first trials to assess task-shifting of aHT care to VHWs at the community level, including the prescription of basic antihypertensive, lipid-lowering, and antiplatelet medication in Africa. The ComBaCaL cohort and nested TwiCs are operating within the routine VHW program and countries with similar community health worker programs may benefit from the findings. TRIAL REGISTRATION: ClinicalTrials.gov NCT05684055. Registered on January 04, 2023.

Use of Artificial Intelligence in Triage in Hospital Emergency Departments: A Scoping Review.

The integration of artificial intelligence (AI) and machine learning (ML) in healthcare has become a major point of interest and raises the question of its impact on the emergency department (ED) triaging process. AI's capacity to emulate human cognitive processes coupled with advancements in computing has shown positive outcomes in various aspects of healthcare but little is known about the use of AI in triaging patients in ED. AI algorithms may allow for earlier diagnosis and intervention; however, overconfident answers may present dangers to patients. The purpose of this review was to explore comprehensively recently published literature regarding the effect of AI and ML in ED triage and identify research gaps. A systemized search was conducted in September 2023 using the electronic databases EMBASE, Ovid MEDLINE, and Web of Science. To meet inclusion criteria, articles had to be peer-reviewed, written in English, and based on primary data research studies published in US journals 2013-2023. Other criteria included 1) studies with patients needing to be admitted to hospital EDs, 2) AI must have been used when triaging a patient, and 3) patient outcomes must be represented. The search was conducted using controlled descriptors from the Medical Subject Headings (MeSH) that included the terms "artificial intelligence" OR "machine learning" AND "emergency ward" OR "emergency care" OR "emergency department" OR "emergency room" AND "patient triage" OR "triage" OR "triaging." The search initially identified 1,142 citations. After a rigorous, systemized screening process and critical appraisal of the evidence, 29 studies were selected for the final review. The findings indicated that 1) ML models consistently demonstrated superior discrimination abilities compared to conventional triage systems, 2) the integration of AI into the triage process yielded significant enhancements in predictive accuracy, disease identification, and risk assessment, 3) ML accurately determined the necessity of hospitalization for patients requiring urgent attention, and 4) ML improved resource allocation and quality of patient care, including predicting length of stay. The suggested superiority of ML models in prioritizing patients in the ED holds the potential to redefine triage precision.

From unstructured accident reports to a hybrid decision support system for occupational risk management: The consensus converging approach.

INTRODUCTION: Workplace accidents in the petroleum industry can cause catastrophic damage to people, property, and the environment. Earlier studies in this domain indicate that the majority of the accident report information is available in unstructured text format. Conventional techniques for the analysis of accident data are time-consuming and heavily dependent on experts' subject knowledge, experience, and judgment. There is a need to develop a machine learning-based decision support system to analyze the vast amounts of unstructured text data that are frequently overlooked due to a lack of appropriate methodology. METHOD: To address this gap in the literature, we propose a hybrid methodology that uses improved text-mining techniques combined with an un-bias group decision-making framework to combine the output of objective weights (based on text mining) and subjective weights (based on expert opinion) of risk factors to prioritize them. Based on the contextual word embedding models and term frequencies, we extracted five important clusters of risk factors comprising more than 32 risk sub-factors. A heterogeneous group of experts and employees in the petroleum industry were contacted to obtain their opinions on the extracted risk factors, and the best-worst method was used to convert their opinions to weights. CONCLUSIONS AND PRACTICAL APPLICATIONS: The applicability of our proposed framework was tested on the data compiled from the accident data released by the petroleum industries in India. Our framework can be extended to accident data from any industry, to reduce analysis time and improve the accuracy in classifying and prioritizing risk factors.

Hospital Use of a Web-Based Clinical Knowledge Support System and In-Training Examination Performance Among Postgraduate Resident Physicians in Japan: Nationwide Observational Study.

Background: The relationship between educational outcomes and the use of web-based clinical knowledge support systems in teaching hospitals remains unknown in Japan. A previous study on this topic could have been affected by recall bias because of the use of a self-reported questionnaire. Objective: We aimed to explore the relationship between the use of the Wolters Kluwer UpToDate clinical knowledge support system in teaching hospitals and residents' General Medicine In-Training Examination (GM-ITE) scores. In this study, we objectively evaluated the relationship between the total number of UpToDate hospital use logs and the GM-ITE scores. Methods: This nationwide cross-sectional study included postgraduate year-1 and -2 residents who had taken the examination in the 2020 academic year. Hospital-level information was obtained from published web pages, and UpToDate hospital use logs were provided by Wolters Kluwer. We evaluated the relationship between the total number of UpToDate hospital use logs and residents' GM-ITE scores. We analyzed 215 teaching hospitals with at least 5 GM-ITE examinees and hospital use logs from 2017 to 2019. Results: The study population consisted of 3013 residents from 215 teaching hospitals with at least 5 GM-ITE examinees and web-based resource use log data from 2017 to 2019. High-use hospital residents had significantly higher GM-ITE scores than low-use hospital residents (mean 26.9, SD 2.0 vs mean 26.2, SD 2.3; P=.009; Cohen d=0.35, 95% CI 0.08-0.62). The GM-ITE scores were significantly correlated with the total number of hospital use logs (Pearson r=0.28; P<.001). The multilevel analysis revealed a positive association between the total number of logs divided by the number of hospital physicians and the GM-ITE scores (estimated coefficient=0.36, 95% CI 0.14-0.59; P=.001). Conclusions: The findings suggest that the development of residents' clinical reasoning abilities through UpToDate is associated with high GM-ITE scores. Thus, higher use of UpToDate may lead physicians and residents in high-use hospitals to increase the implementation of evidence-based medicine, leading to high educational outcomes.

Assessment of vulnerability to flood risk in the Padma River Basin using hydro-morphometric modeling and flood susceptibility mapping.

An evaluation of flood vulnerability is needed to identify flood risk locations and determine mitigation methods. This research introduces an integrated method combining hydro-morphometric modeling and flood susceptibility mapping to assess Padma River Basin's flood risk. Flood zoning, flooding classes, and resource flood risk were explicitly analyzed in this river basin study. Flood risk was calculated using GIS-based hydro-morphometric modeling. Using Horton's and Strahler's methods, drainage density, stream density, and stream order of the Padma River Basin were determined. The Padma River Basin has five sub-basins: A, B, C, D, and E, with stream densities of 0.53 km-2, 0.13 km-2, 0.25 km-2, 0.30 km-2, and 0.28 km-2 and drainage densities of 0.63 km-1, 0.16 km-1, 0.29 km-1, 0.35 km-1, and 0.33 km-1, respectively. Sub-basin A is the most prone to floods due to its high stream and drainage density, whereas B and C are the least susceptible. This study used elevation, TWI, slope, precipitation, NDVI, distance from road, drainage density, distance from river, LU/LC, and soil type to create a flood vulnerability map incorporating GIS and AHP with pair-wise comparison matrix (PCM). The study's flood zoning shows that the northeastern part of this basin is more likely to flood than the southwestern part due to its elevation and high-order streams. Moderate River Flooding, the region's most hazardous flood class, covers 48.19% of the flooding area, including 1078.30 km2 of agricultural land, 94.86 km2 of bare soil, 486.39 km2 of settlements, 586.42 km2 of vegetation cover, and 39.34 km2 of water bodies. The developed hydro-morphometric model, the flood susceptibility map, and the analysis of this data may be utilized to offer long-term advance alarm insight into areas potentially to be invaded by a flood catastrophe, boosting hazard mitigation and planning.

An Ontology-Based Decision Support System for Tailored Clinical Nutrition Recommendations for Patients With Chronic Obstructive Pulmonary Disease: Development and Acceptability Study.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a chronic condition among the main causes of morbidity and mortality worldwide, representing a burden on health care systems. Scientific literature highlights that nutrition is pivotal in respiratory inflammatory processes connected to COPD, including exacerbations. Patients with COPD have an increased risk of developing nutrition-related comorbidities, such as diabetes, cardiovascular diseases, and malnutrition. Moreover, these patients often manifest sarcopenia and cachexia. Therefore, an adequate nutritional assessment and therapy are essential to help individuals with COPD in managing the progress of the disease. However, the role of nutrition in pulmonary rehabilitation (PR) programs is often underestimated due to a lack of resources and dedicated services, mostly because pneumologists may lack the specialized training for such a discipline. OBJECTIVE: This work proposes a novel knowledge-based decision support system to support pneumologists in considering nutritional aspects in PR. The system provides clinicians with patient-tailored dietary recommendations leveraging expert knowledge. METHODS: The expert knowledge-acquired from experts and clinical literature-was formalized in domain ontologies and rules, which were developed leveraging the support of Italian clinicians with expertise in the rehabilitation of patients with COPD. Thus, by following an agile ontology engineering methodology, the relevant formal ontologies were developed to act as a backbone for an application targeted at pneumologists. The recommendations provided by the decision support system were validated by a group of nutrition experts, whereas the acceptability of such an application in the context of PR was evaluated by pneumologists. RESULTS: A total of 7 dieticians (mean age 46.60, SD 13.35 years) were interviewed to assess their level of agreement with the decision support system's recommendations by evaluating 5 patients' health conditions. The preliminary results indicate that the system performed more than adequately (with an overall average score of 4.23, SD 0.52 out of 5 points), providing meaningful and safe recommendations in compliance with clinical practice. With regard to the acceptability of the system by lung specialists (mean age 44.71, SD 11.94 years), the usefulness and relevance of the proposed solution were extremely positive-the scores on each of the perceived usefulness subscales of the technology acceptance model 3 were 4.86 (SD 0.38) out of 5 points, whereas the score on the intention to use subscale was 4.14 (SD 0.38) out of 5 points. CONCLUSIONS: Although designed for the Italian clinical context, the proposed system can be adapted for any other national clinical context by modifying the domain ontologies, thus providing a multidisciplinary approach to the management of patients with COPD.