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Background: Migrants to the UK face disproportionate risk of infections, non-communicable diseases, and under-immunisation compounded by healthcare access barriers. Current UK migrant screening strategies are unstandardised with poor implementation and low uptake. Health Catch-UP! is a collaboratively produced digital clinical decision support system that applies current guidelines (UKHSA and NICE) to provide primary care professionals with individualised multi-disease screening (7 infectious diseases/blood-borne viruses, 3 chronic parasitic infections, 3 non-communicable disease or risk factors) and catch-up vaccination prompts for migrant patients. Methods: We carried out a mixed-methods process evaluation of Health Catch-UP! in two urban primary healthcare practices to integrate Health Catch-UP! into the electronic health record system of primary care, using the Medical Research Council framework for complex intervention evaluation. We collected quantitative data (demographics, patients screened, disease detection and catch-up vaccination rates) and qualitative participant interviews to explore acceptability and feasibility. Results: Ninety-nine migrants were assessed by Health Catch-UP! across two sites (S1, S2). 96.0% (n = 97) had complete demographics coding with Asia 31.3% (n = 31) and Africa 25.2% (n = 25), the most common continents of birth (S1 n = 92 [48.9% female (n = 44); mean age 60.6 years (SD 14.26)]; and S2 n = 7 [85.7% male (n = 6); mean age 39.4 years (SD16.97)]. 61.6% (n = 61) of participants were eligible for screening for at least one condition and uptake of screening was high 86.9% (n = 53). Twelve new conditions were identified (12.1% of study population) including hepatitis C (n = 1), hypercholesteraemia (n = 6), pre-diabetes (n = 4), and diabetes (n = 1). Health Catch-UP! identified that 100% (n = 99) of patients had no immunisations recorded; however, subsequent catch-up vaccination uptake was poor (2.0%, n = 1). Qualitative data supported acceptability and feasibility of Health Catch-UP! from staff and patient perspectives, and recommended Health Catch-UP! integration into routine care (e.g. NHS health checks) with an implementation package including staff and patient support materials, standardised care pathways (screening and catch-up vaccination, laboratory, and management), and financial incentivisation. Conclusions: Clinical Decision Support Systems like Health Catch-UP! can improve disease detection and implementation of screening guidance for migrant patients but require robust testing, resourcing, and an effective implementation package to support both patients and staff. [ABSTRACT FROM AUTHOR]

Background/Objective: Gastric cancer (GC) is a complex disease representing a significant global health concern. Advanced tools for the early diagnosis and prediction of adverse outcomes are crucial. In this context, artificial intelligence (AI) plays a fundamental role. The aim of this work was to develop a diagnostic and prognostic tool for GC, providing support to clinicians in critical decision-making and enabling personalised strategies. Methods: Different machine learning and deep learning techniques were explored to build diagnostic and prognostic models, ensuring model interpretability and transparency through explainable AI methods. These models were developed and cross-validated using data from 590 Spanish Caucasian patients with primary GC and 633 cancer-free individuals. Up to 261 variables were analysed, including demographic, environmental, clinical, tumoral, and genetic data. Variables such as Helicobacter pylori infection, tobacco use, family history of GC, TNM staging, metastasis, tumour location, treatment received, gender, age, and genetic factors (single nucleotide polymorphisms) were selected as inputs due to their association with the risk and progression of the disease. Results: The XGBoost algorithm (version 1.7.4) achieved the best performance for diagnosis, with an AUC value of 0.68 using 5-fold cross-validation. As for prognosis, the Random Survival Forest algorithm achieved a C-index of 0.77. Of interest, the incorporation of genetic data into the clinical–demographics models significantly increased discriminatory ability in both diagnostic and prognostic models. Conclusions: This article presents GastricAITool, a simple and intuitive decision support tool for the diagnosis and prognosis of GC. [ABSTRACT FROM AUTHOR]

Abstract Background Migrants to the UK face disproportionate risk of infections, non-communicable diseases, and under-immunisation compounded by healthcare access barriers. Current UK migrant screening strategies are unstandardised with poor implementation and low uptake. Health Catch-UP! is a collaboratively produced digital clinical decision support system that applies current guidelines (UKHSA and NICE) to provide primary care professionals with individualised multi-disease screening (7 infectious diseases/blood-borne viruses, 3 chronic parasitic infections, 3 non-communicable disease or risk factors) and catch-up vaccination prompts for migrant patients. Methods We carried out a mixed-methods process evaluation of Health Catch-UP! in two urban primary healthcare practices to integrate Health Catch-UP! into the electronic health record system of primary care, using the Medical Research Council framework for complex intervention evaluation. We collected quantitative data (demographics, patients screened, disease detection and catch-up vaccination rates) and qualitative participant interviews to explore acceptability and feasibility. Results Ninety-nine migrants were assessed by Health Catch-UP! across two sites (S1, S2). 96.0% (n = 97) had complete demographics coding with Asia 31.3% (n = 31) and Africa 25.2% (n = 25), the most common continents of birth (S1 n = 92 [48.9% female (n = 44); mean age 60.6 years (SD 14.26)]; and S2 n = 7 [85.7% male (n = 6); mean age 39.4 years (SD16.97)]. 61.6% (n = 61) of participants were eligible for screening for at least one condition and uptake of screening was high 86.9% (n = 53). Twelve new conditions were identified (12.1% of study population) including hepatitis C (n = 1), hypercholesteraemia (n = 6), pre-diabetes (n = 4), and diabetes (n = 1). Health Catch-UP! identified that 100% (n = 99) of patients had no immunisations recorded; however, subsequent catch-up vaccination uptake was poor (2.0%, n = 1). Qualitative data supported acceptability and feasibility of Health Catch-UP! from staff and patient perspectives, and recommended Health Catch-UP! integration into routine care (e.g. NHS health checks) with an implementation package including staff and patient support materials, standardised care pathways (screening and catch-up vaccination, laboratory, and management), and financial incentivisation. Conclusions Clinical Decision Support Systems like Health Catch-UP! can improve disease detection and implementation of screening guidance for migrant patients but require robust testing, resourcing, and an effective implementation package to support both patients and staff.

Abstract Background Antibiotics are globally overprescribed for the treatment of upper respiratory tract infections (URTI), especially in persons living with HIV. However, most URTIs are caused by viruses, and antibiotics are not indicated. De-implementation is perceived as an important area of research that can lead to reductions in unnecessary, wasteful, or harmful practices, such as excessive or inappropriate antibiotic use for URTI, through the employment of evidence-based interventions to reduce these practices. Research into strategies that lead to successful de-implementation of unnecessary antibiotic prescriptions within the primary health care setting is limited in Mozambique. In this study, we propose a protocol designed to evaluate the use of a clinical decision support algorithm (CDSA) for promoting the de-implementation of unnecessary antibiotic prescriptions for URTI among ambulatory HIV-infected adult patients in primary healthcare settings. Methods This study is a multicenter, two-arm, cluster randomized controlled trial, involving six primary health care facilities in Maputo and Matola municipalities in Mozambique, guided by an innovative implementation science framework, the Dynamic Adaption Process. In total, 380 HIV-infected patients with URTI symptoms will be enrolled, with 190 patients assigned to both the intervention and control arms. For intervention sites, the CDSAs will be posted on either the exam room wall or on the clinician´s exam room desk for ease of reference during clinical visits. Our sample size is powered to detect a reduction in antibiotic use by 15%. We will evaluate the effectiveness and implementation outcomes and examine the effect of multi-level (sites and patients) factors in promoting the de-implementation of unnecessary antibiotic prescriptions. The effectiveness and implementation of our antibiotic de-implementation strategy are the primary outcomes, whereas the clinical endpoints are the secondary outcomes. Discussion This research will provide evidence on the effectiveness of the use of the CDSA in promoting the de-implementation of unnecessary antibiotic prescribing in treating acute URTI, among ambulatory HIV-infected patients. Findings will bring evidence for the need to scale up strategies for the de-implementation of unnecessary antibiotic prescription practices in additional healthcare sites within the country. Trial registration ISRCTN, ISRCTN88272350. Registered 16 May 2024, https://www.isrctn.com/ISRCTN88272350

Background: Antibiotics are globally overprescribed for the treatment of upper respiratory tract infections (URTI), especially in persons living with HIV. However, most URTIs are caused by viruses, and antibiotics are not indicated. De-implementation is perceived as an important area of research that can lead to reductions in unnecessary, wasteful, or harmful practices, such as excessive or inappropriate antibiotic use for URTI, through the employment of evidence-based interventions to reduce these practices. Research into strategies that lead to successful de-implementation of unnecessary antibiotic prescriptions within the primary health care setting is limited in Mozambique. In this study, we propose a protocol designed to evaluate the use of a clinical decision support algorithm (CDSA) for promoting the de-implementation of unnecessary antibiotic prescriptions for URTI among ambulatory HIV-infected adult patients in primary healthcare settings. Methods: This study is a multicenter, two-arm, cluster randomized controlled trial, involving six primary health care facilities in Maputo and Matola municipalities in Mozambique, guided by an innovative implementation science framework, the Dynamic Adaption Process. In total, 380 HIV-infected patients with URTI symptoms will be enrolled, with 190 patients assigned to both the intervention and control arms. For intervention sites, the CDSAs will be posted on either the exam room wall or on the clinician´s exam room desk for ease of reference during clinical visits. Our sample size is powered to detect a reduction in antibiotic use by 15%. We will evaluate the effectiveness and implementation outcomes and examine the effect of multi-level (sites and patients) factors in promoting the de-implementation of unnecessary antibiotic prescriptions. The effectiveness and implementation of our antibiotic de-implementation strategy are the primary outcomes, whereas the clinical endpoints are the secondary outcomes. Discussion: This research will provide evidence on the effectiveness of the use of the CDSA in promoting the de-implementation of unnecessary antibiotic prescribing in treating acute URTI, among ambulatory HIV-infected patients. Findings will bring evidence for the need to scale up strategies for the de-implementation of unnecessary antibiotic prescription practices in additional healthcare sites within the country. Trial registration: ISRCTN, ISRCTN88272350. Registered 16 May 2024, https://www.isrctn.com/ISRCTN88272350 [ABSTRACT FROM AUTHOR]

Background: Breast cancer (BrCa) is a predominant malignancy, with metastasis occurring in one in eight patients, nearly half of which target the bone, leading to serious complications such as pain, fractures, and compromised mobility. Structural rigidity, crucial for bone strength, becomes compromised with osteolytic lesions, highlighting the vulnerability and increased fracture risk in affected areas. Historically, two-dimensional radiographs have been employed to predict these fracture risks; however, their limitations in capturing the three-dimensional structural and material changes in bone have raised concerns. Recent advances in CT-based Structural Rigidity Analysis (CTRA), offer a promising, more accurate non-invasive 3D approach. This study aims to assess the efficacy of CTRA in monitoring osteolytic lesions' progression and response to therapy, suggesting its potential superiority over existing methodologies in guiding treatment strategies. Methods: Twenty-seven female nude rats underwent femoral intra-medullary inoculation with MDA-MB-231 human breast cancer cells or saline control. They were divided into Control, Cancer Control, Ibandronate, and Paclitaxel groups. Osteolytic progression was monitored weekly using biplanar radiography, quantitative computed tomography (QCT), and dual-energy X-ray absorptiometry (DEXA). CTRA was employed to predict fracture risk, normalized using the contralateral femur. Statistical analyses, including Kruskal-Wallis and ANOVA, assessed differences in outcomes among groups and over time. Results: Biplanar radiographs showed treatment benefits over time; however, only certain time-specific differences between the Control and other treatment groups were discernible. Notably, observer subjectivity in X-ray scoring became evident, with significant inter-operator variations. DEXA measurements for metaphyseal Bone Mineral Content (BMC) did not exhibit notable differences between groups. Although diaphyseal BMC highlighted some variance, it did not reveal significant differences between treatments at specific time points, suggesting a limited ability for DEXA to differentiate between treatment effects. In contrast, the CTRA consistently demonstrated variations across different treatments, effectively capturing bone rigidity changes over time, and the axial- (EA), bending- (EI), and torsional rigidity (GJ) outcomes from the CTRA method successfully distinguished differences among treatments at specific time points. Conclusion: Traditional approaches, such as biplanar radiographs and DEXA, have exhibited inherent limitations, notably observer bias and time-specific inefficacies. Our study accentuates the capability of CTRA in capturing real-time, progressive changes in bone structure, with the potential to predict fractures more accurately and provide a more objective analysis. Ultimately, this innovative approach may bridge the existing gap in clinical guidelines, ushering in enhanced Clinical Decision Support Tool (CDST) for both surgical and non-surgical treatments. [ABSTRACT FROM AUTHOR]

A commentary published in Frontiers in Immunology discusses the use of flow cytometry to analyze tumor-infiltrating lymphocytes (TILs) in patients with diffuse large B-cell lymphoma (DLBCL). The study collected data from 102 DLBCL patients and found that TILs have a positive impact on patient survival. The authors suggest that incorporating TIL data into risk prediction models could be beneficial in clinical practice, but more research is needed. The study also highlights the potential of tumor-infiltrating B cells as a favorable prognostic factor in DLBCL and calls for further investigation into their functions and potential therapeutic applications. The study was supported by the Natural Science Foundation of Shandong Province and the authors have no conflicts of interest. The views expressed in the article are solely those of the authors and do not represent their affiliated organizations or the publisher. [Extracted from the article]

Wrong dose calculation medication errors are widespread in pediatric patients mainly due to weight-based dosing. PediPain app is a clinical decision support tool that provides weight- and age- based dosages for various analgesics. We hypothesized that the use of a clinical decision support tool, the PediPain app versus pocket calculators for calculating pain medication dosages in children reduces the incidence of wrong dosage calculations and shortens the time taken for calculations. The study was a randomised controlled trial comparing the PediPain app vs. pocket calculator for performing eight weight-based calculations for opioids and other analgesics. Participants were healthcare providers routinely administering opioids and other analgesics in their practice. The primary outcome was the incidence of wrong dose calculations. Secondary outcomes were the incidence of wrong dose calculations in simple versus complex calculations; time taken to complete calculations; the occurrence of tenfold; hundredfold errors; and wrong-key presses. A total of 140 residents, fellows and nurses were recruited between June 2018 and November 2019; 70 participants were randomized to control group (pocket calculator) and 70 to the intervention group (PediPain App). After randomization two participants assigned to PediPain group completed the simulation in the control group by mistake. Analysis was by intention-to-treat (PediPain app = 68 participants, pocket calculator = 72 participants). The overall incidence of wrong dose calculation was 178/576 (30.9%) for the control and 23/544 (4.23%) for PediPain App, P < 0·001. The risk difference was − 32.8% [-38.7%, -26.9%] for complex and − 20.5% [-26.3%, -14.8%] for simple calculations. Calculations took longer within control group (median of 69 Sects. [50, 96]) compared to PediPain app group, (median 48 Sects. [38, 63]), P < 0.001. There were no differences in other secondary outcomes. A weight-based clinical decision support tool, the PediPain app reduced the incidence of wrong doses calculation. Clinical decision support tools calculating medications may be valuable instruments for reducing medication errors, especially in the pediatric population. Key Points: Question: Can a clinical decision support tool app decrease opioids and pain medications' wrong dose calculations errors in the pediatric patients compared to a calculator and reference book?. Findings: A weight-based clinical decision support tool, the PediPain app, reduced the incidence of wrong doses calculations compared to the use of a calculator and reference handbook. Meaning: Our study demonstrates the potential for favorable clinical outcomes that clinical decision support tools could bring to the medical field, especially for clinical situations with a high cognitive workload. [ABSTRACT FROM AUTHOR]

Background/Objective: Gastric cancer (GC) is a complex disease representing a significant global health concern. Advanced tools for the early diagnosis and prediction of adverse outcomes are crucial. In this context, artificial intelligence (AI) plays a fundamental role. The aim of this work was to develop a diagnostic and prognostic tool for GC, providing support to clinicians in critical decision-making and enabling personalised strategies. Methods: Different machine learning and deep learning techniques were explored to build diagnostic and prognostic models, ensuring model interpretability and transparency through explainable AI methods. These models were developed and cross-validated using data from 590 Spanish Caucasian patients with primary GC and 633 cancer-free individuals. Up to 261 variables were analysed, including demographic, environmental, clinical, tumoral, and genetic data. Variables such as Helicobacter pylori infection, tobacco use, family history of GC, TNM staging, metastasis, tumour location, treatment received, gender, age, and genetic factors (single nucleotide polymorphisms) were selected as inputs due to their association with the risk and progression of the disease. Results: The XGBoost algorithm (version 1.7.4) achieved the best performance for diagnosis, with an AUC value of 0.68 using 5-fold cross-validation. As for prognosis, the Random Survival Forest algorithm achieved a C-index of 0.77. Of interest, the incorporation of genetic data into the clinical–demographics models significantly increased discriminatory ability in both diagnostic and prognostic models. Conclusions: This article presents GastricAITool, a simple and intuitive decision support tool for the diagnosis and prognosis of GC.

ObjectiveTo develop a Dissociative Seizures Likelihood Score (DSLS), which is a comprehensive, evidence-based tool using information available during the first outpatient visit to identify patients with "probable" dissociative seizures (DS) to allow early triage to more extensive diagnostic assessment.MethodsBased on data from 1616 patients with video-electroencephalography (vEEG) confirmed diagnoses, we compared the clinical history from a single neurology interview of patients in five mutually exclusive groups: epileptic seizures (ES), DS, physiologic nonepileptic seizure-like events (PSLE), mixed DS plus ES, and inconclusive monitoring. We used data-driven methods to determine the diagnostic utility of 76 features from retrospective chart review and applied this model to prospective interviews.ResultsThe DSLS using recursive feature elimination (RFE) correctly identified 77% (95% confidence interval (CI), 74-80%) of prospective patients with either ES or DS, with a sensitivity of 74% and specificity of 84%. This accuracy was not significantly inferior than neurologists' impression (84%, 95% CI: 80-88%) and the kappa between neurologists' and the DSLS was 21% (95% CI: 1-41%). Only 3% of patients with DS were missed by both the fellows and our score (95% CI 0-11%).SignificanceThe evidence-based DSLS establishes one method to reliably identify some patients with probable DS using clinical history. The DSLS supports and does not replace clinical decision making. While not all patients with DS can be identified by clinical history alone, these methods combined with clinical judgement could be used to identify patients who warrant further diagnostic assessment at a comprehensive epilepsy center.

Abstract Background Clinical decision aids may support shared decision-making for screening mammography. To inform shared decision-making between patients and their providers, this study examines how patterns of using an EHR-integrated decision aid and accompanying verbal patient-provider communication predict decision-making satisfaction. Methods For 51 patient visits during which a mammography decision aid was used, linguistic characteristics of patient-provider verbal communication were extracted from transcribed audio recordings and system logs automatically captured uses of the decision aid. Surveys assessed patients’ post-visit decisional satisfaction and its subcomponents. Linear mixed effects models assessed how patients’ satisfaction with decision making was related to patterns of verbal communication and navigation of the decision aid. Results The results indicate that providers’ use of quantitative language during the encounter was positively associated with patients’ overall satisfaction, feeling informed, and values clarity. Patients’ question-asking was negatively associated with overall satisfaction, values clarity, and certainty perception. Where system use data indicated the dyad had cycled through the decision-making process more than once (“looping” back through pages of the decision aid), patients reported improved satisfaction with shared decision making and all subcomponents. Overall satisfaction, perceived support, certainty, and perceived effectiveness of decision-making were lowest when a high number of navigating clicks occurred absent “looping.” Conclusions Linguistic features of patient-provider communication and system use data of a decision aid predict patients’ satisfaction with shared decision making. Our findings have implications for the design of decision aid tools and clinician training to support more effective shared decision-making for screening mammography.

We formulate the estimation of monotone response surface of multiple factors as the inverse of an iteration of partially ordered classifier ensembles. Each ensemble (called product-of-independent-probability-escalation (PIPE)-classifiers) is a projection of Bayes classifiers on the constrained space. We prove that the inverse of PIPE-classifiers (iPIPE) exists, and propose algorithms to efficiently compute iPIPE by reducing the space over which optimisation is conducted. The methods are applied in analysis and simulation settings where the surface dimension is higher than what the isotonic regression literature typically considers. Simulation shows that iPIPE-based credible intervals achieve nominal coverage probability and are more precise compared to unconstrained estimation. [ABSTRACT FROM AUTHOR]

Actigraphy may provide new insights into clinical outcomes and symptom management of patients through passive, continuous data collection. We used the GENEActiv smartwatch to passively collect actigraphy, wrist temperature, and ambient light data from 27 participants after stroke or probable brain transient ischemic attack (TIA) over 42 periods of device wear. We computed 323 features using established algorithms and proposed 25 novel features to characterize sleep and temperature. We investigated statistical associations between the extracted features and clinical outcomes evaluated using clinically validated questionnaires to gain insight into post-stroke recovery. We subsequently fitted logistic regression models to replicate clinical diagnosis (stroke or TIA) and disability due to stroke. The model generalization performance was assessed using a leave-one-subject-out cross validation method with the selected feature subsets, reporting the area under the curve (AUC). We found that several novel features were strongly correlated ( | r | > 0.3 ) with stroke symptoms and mental health measures. Using selected novel features, we obtained an AUC of 0.766 to estimate diagnosis and an AUC of 0.749 to estimate whether disability due to stroke was present. Collectively, these findings suggest that features extracted from the temperature smartwatch sensor may reveal additional clinically useful information over and above existing actigraphy-based features. [ABSTRACT FROM AUTHOR]

Pneumonia is a respiratory illness that is highly infectious and affecting one or both lungs. It can lead to fatalities if left undiagnosed and untreated in time. While a radiologist can diagnose pneumonia just by looking at a chest X-ray, there are certain factors that may increase the chances of a misdiagnosis such as fatigue, multi-presenting symptoms, or inadequate overall experience in assessment of patients with low-prevalence presentations. A publicly available chest X-ray dataset was used in this study to create a portable executable clinical decision support tool to help determine the existence of pneumonia on a given chest X-ray image. ResNet50, DenseNet201, Xception, MobileNetV2, and ResNet101 were retrained to confirm pneumonia in chest X-rays both on the original and augmented dataset. An ensemble network was also constructed to improve the results by combining the strengths of the included pre-trained models using average probability. In this study, a total of 37 experiments were performed. Since there are instances when no single model excels on all metrics, the model with the highest Matthews Correlation Coefficient (MCC) was selected as the best model. MCC outputs an optimal score only if the classifier was able to get a high percentage for both positive and negative samples. To increase further the performance of the ensemble model, a novel method was introduced that determines which pre-trained model will give the greatest increase in the ensemble model performance when removed from the ensemble. The result is the identification of the best ensemble model variant consisting of only ResNet50, DenseNet201, Xception, and ResNet101 with Accuracy=93%, Precision=91%, Recall=98%, F1-Score=94%, Specificity=83%, MCC=85%. A stand-alone no-install application was created for this purpose to enable those with limited access or no access to internet to run the tool on their Window-based computer. Our tool can be used as portable stand-alone clinical decision support tool by radiologists when evaluating possible pneumonia case. It can also be used as a teaching tool for general practitioners and medical students. [ABSTRACT FROM AUTHOR]

Background: Clinical decision aids may support shared decision-making for screening mammography. To inform shared decision-making between patients and their providers, this study examines how patterns of using an EHR-integrated decision aid and accompanying verbal patient-provider communication predict decision-making satisfaction. Methods: For 51 patient visits during which a mammography decision aid was used, linguistic characteristics of patient-provider verbal communication were extracted from transcribed audio recordings and system logs automatically captured uses of the decision aid. Surveys assessed patients' post-visit decisional satisfaction and its subcomponents. Linear mixed effects models assessed how patients' satisfaction with decision making was related to patterns of verbal communication and navigation of the decision aid. Results: The results indicate that providers' use of quantitative language during the encounter was positively associated with patients' overall satisfaction, feeling informed, and values clarity. Patients' question-asking was negatively associated with overall satisfaction, values clarity, and certainty perception. Where system use data indicated the dyad had cycled through the decision-making process more than once ("looping" back through pages of the decision aid), patients reported improved satisfaction with shared decision making and all subcomponents. Overall satisfaction, perceived support, certainty, and perceived effectiveness of decision-making were lowest when a high number of navigating clicks occurred absent "looping." Conclusions: Linguistic features of patient-provider communication and system use data of a decision aid predict patients' satisfaction with shared decision making. Our findings have implications for the design of decision aid tools and clinician training to support more effective shared decision-making for screening mammography. [ABSTRACT FROM AUTHOR]

In the treatment of ischemic stroke, timely and efficient recanalization of occluded brain arteries can successfully salvage the ischemic brain. Thrombolysis is the first-line treatment for ischemic stroke. Machine learning models have the potential to select patients who could benefit the most from thrombolysis. In this study, we identified 29 related previous machine learning models, reviewed the models on the accuracy and feasibility, and proposed corresponding improvements. Regarding accuracy, lack of long-term outcome, treatment option consideration, and advanced radiological features were found in many previous studies in terms of model conceptualization. Regarding interpretability, most of the previous models chose restrictive models for high interpretability and did not mention processing time consideration. In the future, model conceptualization could be improved based on comprehensive neurological domain knowledge and feasibility needs to be achieved by elaborate computer science algorithms to increase the interpretability of flexible algorithms and shorten the processing time of the pipeline interpreting medical images. [ABSTRACT FROM AUTHOR]

We are beginning a new era of Smart Diagnostics—integrated biosensors powered by recent innovations in embedded electronics, cloud computing, and artificial intelligence (AI). Universal and AI-based in vitro diagnostics (IVDs) have the potential to exponentially improve healthcare decision making in the coming years. This perspective covers current trends and challenges in translating Smart Diagnostics. We identify essential elements of Smart Diagnostics platforms through the lens of a clinically validated platform for digitizing biology and its ability to learn disease signatures. This platform for biochemical analyses uses a compact instrument to perform multiclass and multiplex measurements using fully integrated microfluidic cartridges compatible with the point of care. Image analysis digitizes biology by transforming fluorescence signals into inputs for learning disease/health signatures. The result is an intuitive Score reported to the patients and/or providers. This AI-linked universal diagnostic system has been validated through a series of large clinical studies and used to identify signatures for early disease detection and disease severity in several applications, including cardiovascular diseases, COVID-19, and oral cancer. The utility of this Smart Diagnostics platform may extend to multiple cell-based oncology tests via cross-reactive biomarkers spanning oral, colorectal, lung, bladder, esophageal, and cervical cancers, and is well-positioned to improve patient care, management, and outcomes through deployment of this resilient and scalable technology. Lastly, we provide a future perspective on the direction and trajectory of Smart Diagnostics and the transformative effects they will have on health care. [ABSTRACT FROM AUTHOR]

Background: Up to half of the children with new-onset type 1 diabetes present to the hospital with diabetic ketoacidosis, a life-threatening condition that can develop because of diagnostic delay. Three-quarters of Australian children visit their general practitioner (GP) the week before presenting to the hospital with diabetic ketoacidosis. Our prototype, DIRECT-T1DM (Decision-Support for Integrated, Real-Time Evaluation and Clinical Treatment of Type 1 Diabetes Mellitus), is an electronic clinical decision support tool that promotes immediate point-of-care testing in general practice to confirm the suspicion of diabetes. This avoids laboratory testing, which has been documented internationally as a cause of diagnostic delay., Objective: In this investigation, we aimed to pilot and assess the feasibility and acceptability of our prototype to GP end users. We also explored the challenges of diagnosing type 1 diabetes in the Australian general practice context., Methods: In total, 4 GPs, a pediatric endocrinologist, and a PhD candidate were involved in conceptualizing the DIRECT-T1DM prototype, which was developed at the Department of General Practice and Primary Care at the University of Melbourne. Furthermore, 6 GPs were recruited via convenience sampling to evaluate the tool. The study involved 3 phases: a presimulation interview, simulated clinical scenarios, and a postsimulation interview. The interview guide was developed using the Consolidated Framework for Implementation Research (CFIR) as a guide. All phases of the study were video, audio, and screen recorded. Audio recordings were transcribed by the investigating team. Analysis was carried out using CFIR as the underlying framework., Results: Major themes were identified among three domains and 7 constructs of the CFIR: (1) outer setting-time pressure, difficulty in diagnosing pediatric type 1 diabetes, and secondary care considerations influenced GPs' needs regarding DIRECT-T1DM; (2) inner setting-DIRECT-T1DM fits within existing workflows, it has a high relative priority due to its importance in patient safety, and GPs exhibited high tension for change; and (3) innovation-design recommendations included altering coloring to reflect urgency, font style and bolding, specific language, information and guidelines, and inclusion of patient information sheets., Conclusions: End-user acceptability of DIRECT-T1DM was high. This was largely due to its implications for patient safety and its "real-time" nature. DIRECT-T1DM may assist in appropriate management of children with new-onset diabetes, which is an uncommon event in general practice, through safety netting., (©Chiara Beccia, Barbara Hunter, Jo-Anne Manski-Nankervis, Mary White. Originally published in JMIR Formative Research (https://formative.jmir.org), 23.09.2024.)

Aim: To assess the effectiveness of Clinical Decision Support Tools (CDSTs) in enhancing the quality of care outcomes in primary cardiovascular disease (CVD) prevention., Methods: A systematic review was undertaken in accordance with PRISMA guidelines, and included searches in Ovid Medline, Ovid Embase, CINAHL, and Scopus. Eligible studies were randomized controlled trials of CDSTs comprising digital notifications in electronic health systems (EHS/EHR) in various primary healthcare settings, published post-2013, in patients with CVD risks and without established CVD. Two reviewers independently assessed risk of bias using the Cochrane RoB-2 tool. Attainment of clinical targets was analysed using a Restricted Maximum Likelihood random effects meta-analysis. Other relevant outcomes were narratively synthesised due to heterogeneity of studies and outcome metrics., Results: Meta-analysis revealed CDSTs showed improvement in systolic (Mean Standardised Difference (MSD)=0.39, 95 %CI=-0.31, -1.10) and diastolic blood pressure target achievement (MSD=0.34, 95 %CI=-0.24, -0.92), but had no significant impact on lipid (MSD=0.01; 95 %CI=-0.10, 0.11) or glucose target attainment (MSD=-0.19, 95 %CI=-0.66, 0.28). The CDSTs with active prompts increased statin initiation and improved patients' adherence to clinical appointments but had minimal effect on other medications and on enhancing adherence to medication., Conclusion: CDSTs were found to be effective in improving blood pressure clinical target attainments. However, the presence of multi-layered barriers affecting the uptake, longer-term use and active engagement from both clinicians and patients may hinder the full potential for achieving other quality of care outcomes., Lay Summary: The study aimed to evaluate how Clinical Decision Support Tools (CDSTs) impact the quality of care for primary cardiovascular disease (CVD) management. CDSTs are tools designed to support healthcare professionals in delivering the best possible care to patients by providing timely and relevant information at the point of care (ie. digital notifications in electronic health systems). Although CDST are designed to improve the quality of healthcare outcomes, the current evidence of their effectiveness is inconsistent. Therefore, we conducted a systematic review with meta-analysis, to quantify the effectiveness of CDSTs. The eligibility criteria targeted patients with CVD risk factors, but without diagnosed CVD. The meta-analysis found that CDSTs showed improvement in systolic and diastolic blood pressure target achievement but did not significantly impact lipid or glucose target attainment. Specifically, CDSTs showed effectiveness in increasing statin prescribing but not antihypertensives or antidiabetics prescribing. Interventions with CDSTs aimed at increasing screening programmes were effective for patients with kidney diseases and high-risk patients, but not for patients with diabetes or teenage patients with hypertension. Alerts were effective in improving patients' adherence to clinical appointments but not in medication adherence. This study suggests CDSTs are effective in enhancing a limited number of quality of care outcomes in primary CVD prevention, but there is need for future research to explore the mechanisms and context of multiple barriers that may hinder the full potential for cardiovascular health outcomes to be achieved., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier B.V.)

This article describes the rationale for the development of the MSProDiscuss™ clinical decision support (CDS) tool, its development, and insights into how it can help neurologists improve care for patients with multiple sclerosis (MS). MS is a progressive disease characterized by heterogeneous symptoms and variable disease course. There is growing consensus that MS exists on a continuum, with overlap between relapsing–remitting and secondary progressive phenotypes. Evidence demonstrates that neuroaxonal loss occurs from the outset, that progression can occur independent of relapse activity, and that continuous underlying pathological processes may not be reflected by inflammatory activity indicative of the patient's immune response. Early intervention can benefit patients, and there is a need for a tool that assists physicians in rapidly identifying subtle signs of MS progression. MSProDiscuss, developed with physicians and patients, facilitates a structured approach to patient consultations. It analyzes multidimensional data via an algorithm to estimate the likelihood of progression (the MSProDiscuss score), the contribution of various symptoms, and the impact of symptoms on daily living, enabling a more personalized approach to treatment and disease management. Data from CDS tools such as MSProDiscuss offer new insights into disease course and facilitate informed decision-making and a holistic approach to MS patient care. [ABSTRACT FROM AUTHOR]

As of 8 August 2022, SARS-CoV-2, the causative agent of COVID-19, has infected over 585 million people and resulted in more than 6.42 million deaths worldwide. While approved SARS-CoV-2 spike (S) protein-based vaccines induce robust seroconversion in most individuals, dramatically reducing disease severity and the risk of hospitalization, poorer responses are observed in aged, immunocompromised individuals and patients with certain pre-existing health conditions. Further, it is difficult to predict the protection conferred through vaccination or previous infection against new viral variants of concern (VoC) as they emerge. In this context, a rapid quantitative point-of-care (POC) serological assay able to quantify circulating anti-SARS-CoV-2 antibodies would allow clinicians to make informed decisions on the timing of booster shots, permit researchers to measure the level of cross-reactive antibody against new VoC in a previously immunized and/or infected individual, and help assess appropriate convalescent plasma donors, among other applications. Utilizing a lab-on-a-chip ecosystem, we present proof of concept, optimization, and validation of a POC strategy to quantitate COVID-19 humoral protection. This platform covers the entire diagnostic timeline of the disease, seroconversion, and vaccination response spanning multiple doses of immunization in a single POC test. Our results demonstrate that this platform is rapid (~15 min) and quantitative for SARS-CoV-2-specific IgG detection. [ABSTRACT FROM AUTHOR]

Background: The most common route to a diagnosis of cancer is through primary care. Delays in diagnosing cancer occur when an opportunity to make a timely diagnosis is missed and is evidenced by patients visiting the general practitioner (GP) on multiple occasions before referral to a specialist. Tools that minimize prolonged diagnostic intervals and reduce missed opportunities to investigate patients for cancer are therefore a priority. Objective: This study aims to explore the usefulness and feasibility of a novel quality improvement (QI) tool in which algorithms flag abnormal test results that may be indicative of undiagnosed cancer. This study allows for the optimization of the cancer recommendations before testing the efficacy in a randomized controlled trial. Methods: GPs, practice nurses, practice managers, and consumers were recruited to participate in individual interviews or focus groups. Participants were purposively sampled as part of a pilot and feasibility study, in which primary care practices were receiving recommendations relating to the follow-up of abnormal test results for prostate-specific antigen, thrombocytosis, and iron-deficiency anemia. The Clinical Performance Feedback Intervention Theory (CP-FIT) was applied to the analysis using a thematic approach. Results: A total of 17 interviews and 3 focus groups (n=18) were completed. Participant themes were mapped to CP-FIT across the constructs of context, recipient, and feedback variables. The key facilitators to use were alignment with workflow, recognized need, the perceived importance of the clinical topic, and the GPs' perception that the recommendations were within their control. Barriers to use included competing priorities, usability and complexity of the recommendations, and knowledge of the clinical topic. There was consistency between consumer and practitioner perspectives, reporting language concerns associated with the word cancer, the need for more patient-facing resources, and time constraints of the consultation to address patients'worries. Conclusions: There was a recognized need for the QI tool to support the diagnosis of cancer in primary care, but barriers were identified that hindered the usability and actionability of the recommendations in practice. In response, the tool has been refined and is currently being evaluated as part of a randomized controlled trial. Successful and effective implementation of this QI tool could support the detection of patients at risk of undiagnosed cancer in primary care and assist in preventing unnecessary delays. [ABSTRACT FROM AUTHOR]

Under-diagnosis of pediatric hypertension remains pervasive due to difficulty recognizing elevated systolic blood pressures (SBPs). We performed a retrospective review comparing recognition of and response to elevated SBPs ≥95th percentile before and after development of a clinical decision support tool (CDST) in an academic pediatric system. Of 44,351 encounters, 477 had elevated SBPs with documented recognition of an elevated SBP in 17.9% of encounters pre-CDST that increased to 33.7% post-CDST (P =.001). Post-CDST, 75.5% of elevated SBPs had repeat measurement, with 90.8% of initially elevated SBPs normalizing to <95th percentile. If repeat measurement was obtained and SBP remained elevated, documented recognition increased from 14.0 to 83.3% (P <.0001). These data support using the CDST is associated with increased identification of elevated SBPs in children with greatest improvements associated with repeat SBP measurement. This suggests targeted training and support systems at medical intake would be high yield for increasing recognition of elevated SBP. [ABSTRACT FROM AUTHOR]

Abstract Background Clinical risk prediction models (CRPMs) use patient characteristics to estimate the probability of having or developing a particular disease and/or outcome. While CRPMs are gaining in popularity, they have yet to be widely adopted in clinical practice. The lack of explainability and interpretability has limited their utility. Explainability is the extent of which a model’s prediction process can be described. Interpretability is the degree to which a user can understand the predictions made by a model. Methods The study aimed to demonstrate utility of patient similarity analytics in developing an explainable and interpretable CRPM. Data was extracted from the electronic medical records of patients with type-2 diabetes mellitus, hypertension and dyslipidaemia in a Singapore public primary care clinic. We used modified K-nearest neighbour which incorporated expert input, to develop a patient similarity model on this real-world training dataset (n = 7,041) and validated it on a testing dataset (n = 3,018). The results were compared using logistic regression, random forest (RF) and support vector machine (SVM) models from the same dataset. The patient similarity model was then implemented in a prototype system to demonstrate the identification, explainability and interpretability of similar patients and the prediction process. Results The patient similarity model (AUROC = 0.718) was comparable to the logistic regression (AUROC = 0.695), RF (AUROC = 0.764) and SVM models (AUROC = 0.766). We packaged the patient similarity model in a prototype web application. A proof of concept demonstrated how the application provided both quantitative and qualitative information, in the form of patient narratives. This information was used to better inform and influence clinical decision-making, such as getting a patient to agree to start insulin therapy. Conclusions Patient similarity analytics is a feasible approach to develop an explainable and interpretable CRPM. While the approach is generalizable, it can be used to develop locally relevant information, based on the database it searches. Ultimately, such an approach can generate a more informative CRPMs which can be deployed as part of clinical decision support tools to better facilitate shared decision-making in clinical practice.

Background: Drug-related problems (DRPs) which arise from potentially inappropriate medications (PIMs) are a common problem in older people with multi-morbidity and polypharmacy. Aim: To develop an integrated PIM clinical decision support tool for identification of DRPs in geriatric multi-morbid polypharmacy patients, using the EU(7)-PIM and EURO-FORTA lists, with a focus on high-risk medications. Methods: The integrated PIM tool used the information on PIMs in both databases—the EU(7)-PIM and EURO-FORTA. PIMs were classified into four color groups based on risk profile: high-risk PIMs (should be avoided in older patients) as red, moderate-risk PIMs (require dose and/or treatment duration adjustment) as yellow, low-risk PIMs (low DRP risk) as green, and questionable PIMs (incomplete/missing information) as grey. Results: The summarized list of the high-risk (red and some grey) PIMs contained 81 active substances and medication classes. According to the ATC classification, most of the high-risk PIMs (n = 60, 74.1%) belong to the A, C, and N medication groups and 50.6% (n = 41) of the high-risk PIMs have currently marketing authorization in Estonia. The preliminary list of the moderate- and low-risk (yellow, green, and other grey) PIMs contained 240 active substances and medication classes, but sub-classification of this category into one or another group depends mainly on an individual patient´s clinical characteristics in a concrete analyzed study sample and needs further research. Conclusion: The integrated clinical decision support tool based on the EU(7)-PIM and EURO-FORTA criteria addresses the need for more efficient identification of DRPs. It can be applied to identify PIMs and geriatric prescribing problems in different health care settings, and also in a context of little clinical information available. [ABSTRACT FROM AUTHOR]

Objective: The study provides considerations for generating a phenotype of child abuse and neglect in Emergency Departments (ED) using secondary data from electronic health records (EHR). Implications will be provided for racial bias reduction and the development of further decision support tools to assist in identifying child abuse and neglect.Materials and Methods: We conducted a qualitative study using in-depth interviews with 20 pediatric clinicians working in a single pediatric ED to gain insights about generating an EHR-based phenotype to identify children at risk for abuse and neglect.Results: Three central themes emerged from the interviews: (1) Challenges in diagnosing child abuse and neglect, (2) Health Discipline Differences in Documentation Styles in EHR, and (3) Identification of potential racial bias through documentation.Discussion: Our findings highlight important considerations for generating a phenotype for child abuse and neglect using EHR data. First, information-related challenges include lack of proper previous visit history due to limited information exchanges and scattered documentation within EHRs. Second, there are differences in documentation styles by health disciplines, and clinicians tend to document abuse in different document types within EHRs. Finally, documentation can help identify potential racial bias in suspicion of child abuse and neglect by revealing potential discrepancies in quality of care, and in the language used to document abuse and neglect.Conclusions: Our findings highlight challenges in building an EHR-based risk phenotype for child abuse and neglect. Further research is needed to validate these findings and integrate them into creation of an EHR-based risk phenotype. [ABSTRACT FROM AUTHOR]

Actigraphy may provide new insights into clinical outcomes and symptom management of patients through passive, continuous data collection. We used the GENEActiv smartwatch to passively collect actigraphy, wrist temperature, and ambient light data from 27 participants after stroke or probable brain transient ischemic attack (TIA) over 42 periods of device wear. We computed 323 features using established algorithms and proposed 25 novel features to characterize sleep and temperature. We investigated statistical associations between the extracted features and clinical outcomes evaluated using clinically validated questionnaires to gain insight into post-stroke recovery. We subsequently fitted logistic regression models to replicate clinical diagnosis (stroke or TIA) and disability due to stroke. The model generalization performance was assessed using a leave-one-subject-out cross validation method with the selected feature subsets, reporting the area under the curve (AUC). We found that several novel features were strongly correlated (|r|>0.3) with stroke symptoms and mental health measures. Using selected novel features, we obtained an AUC of 0.766 to estimate diagnosis and an AUC of 0.749 to estimate whether disability due to stroke was present. Collectively, these findings suggest that features extracted from the temperature smartwatch sensor may reveal additional clinically useful information over and above existing actigraphy-based features.

Background: Social needs and social determinants of health (SDOH) significantly outrank medical care when considering the impact on a person's length and quality of life, resulting in poor health outcomes and worsening life expectancy. Integrating social needs and SDOH data along with clinical risk information within operational clinical decision support (CDS) systems built into electronic health records (EHRs) is an effective approach to addressing health-related social needs. To achieve this goal, applied research is needed to develop EHR-integrated CDS tools and closed-loop referral systems and implement and test them in the digital and clinical workflows at health care systems and collaborating community-based organizations (CBOs)., Objective: This study aims to describe the protocol for a mixed methods study including a randomized controlled trial and a qualitative phase assessing the feasibility, acceptability, and effectiveness of an EHR-integrated digital platform to identify patients with social needs and provide navigation services and closed-loop referrals to CBOs to address their social needs., Methods: The randomized controlled trial will enroll and randomize adult patients living in socioeconomically challenged neighborhoods in Baltimore City receiving care at a single academic health care institution in the 3-month intervention (using the digital platform) or the 3-month control (standard-of-care assessment and addressing of social needs) arms (n=295 per arm). To evaluate the feasibility and acceptability of the digital platform and its impact on the clinical and digital workflow and patient care, we will conduct focus groups with the care teams in the health care system (eg, clinical providers, social workers, and care managers) and collaborating CBOs. The outcomes will be the acceptability, feasibility, and effectiveness of the CDS tool and closed-loop referral system., Results: This clinical trial opened to enrollment in June 2023 and will be completed in March 2025. Initial results are expected to be published in spring 2025. We will report feasibility outcome measures as weekly use rates of the digital platform. The acceptability outcome measure will be the provider's and patient's responses to the truthfulness of a statement indicating a willingness to use the platform in the future. Effectiveness will be measured by tracking a 3-month change in identified social needs and provided navigation services as well as clinical outcomes such as hospitalization and emergency department visits., Conclusions: The results of this investigation are expected to contribute to our understanding of the use of digital interventions and the implementation of such interventions in digital and clinical workflows to enhance the health care system and CBO ability related to social needs assessment and intervention. These results may inform the construction of a future multi-institutional trial designed to test the effectiveness of this intervention across different health care systems and care settings., Trial Registration: ClinicalTrials.gov NCT05574699; https://clinicaltrials.gov/study/NCT05574699., International Registered Report Identifier (irrid): DERR1-10.2196/57316., (©Elham Hatef, Thomas Richards, Kristin Topel, Sofia Hail, Christopher Kitchen, Katherine Shaw, Talan Zhang, Elyse C Lasser, Jonathan P Weiner. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 23.07.2024.)

Background: Clinical risk prediction models (CRPMs) use patient characteristics to estimate the probability of having or developing a particular disease and/or outcome. While CRPMs are gaining in popularity, they have yet to be widely adopted in clinical practice. The lack of explainability and interpretability has limited their utility. Explainability is the extent of which a model's prediction process can be described. Interpretability is the degree to which a user can understand the predictions made by a model.Methods: The study aimed to demonstrate utility of patient similarity analytics in developing an explainable and interpretable CRPM. Data was extracted from the electronic medical records of patients with type-2 diabetes mellitus, hypertension and dyslipidaemia in a Singapore public primary care clinic. We used modified K-nearest neighbour which incorporated expert input, to develop a patient similarity model on this real-world training dataset (n = 7,041) and validated it on a testing dataset (n = 3,018). The results were compared using logistic regression, random forest (RF) and support vector machine (SVM) models from the same dataset. The patient similarity model was then implemented in a prototype system to demonstrate the identification, explainability and interpretability of similar patients and the prediction process.Results: The patient similarity model (AUROC = 0.718) was comparable to the logistic regression (AUROC = 0.695), RF (AUROC = 0.764) and SVM models (AUROC = 0.766). We packaged the patient similarity model in a prototype web application. A proof of concept demonstrated how the application provided both quantitative and qualitative information, in the form of patient narratives. This information was used to better inform and influence clinical decision-making, such as getting a patient to agree to start insulin therapy.Conclusions: Patient similarity analytics is a feasible approach to develop an explainable and interpretable CRPM. While the approach is generalizable, it can be used to develop locally relevant information, based on the database it searches. Ultimately, such an approach can generate a more informative CRPMs which can be deployed as part of clinical decision support tools to better facilitate shared decision-making in clinical practice. [ABSTRACT FROM AUTHOR]

Abstract Background Low back pain is one of the most common and disabling health problems in Canada and internationally. In most cases, low back pain is a benign, self-limiting condition that can be managed with little diagnostic investigation or treatment. Yet contrary to clinical practice guideline recommendations, diagnostic imaging (here meaning X-ray, MRI, CT) is commonly used in the assessment of low back pain. Diagnostic imaging is of limited value in most cases, exposing patients to unnecessary radiation and leading to increased health services use and worse patient health outcomes. The Choosing Wisely campaign has highlighted the need to reduce diagnostic imaging for low back pain; however, no clinical decision rules are available. Methods This project will develop a clinical decision support tool for appropriate use of diagnostic imaging for patients with low back pain in the emergency department. We will conduct a prospective cohort study at five Canadian emergency departments. The study will follow recommendations for prediction model development and testing. The study population will be 4000 patients presenting to the emergency department with low back pain. We will assess potential clinical indications of emergent-cause (i.e., “red flag” items), including clinical characteristics and past history. Our outcome, emergent-cause for low back pain such as fracture, cancer, infection, or cauda equina syndrome, will be assessed at discharge and at 1-, 3-, and 12-month follow-up periods using information from self-report and health administrative data. We will construct and assess the performance of a multivariable prediction model that has strong measurement properties, presented as a clinical decision support tool acceptable to knowledge users. Discussion Practice guidelines describe “red flags” for which diagnostic imaging is likely appropriate. However, recommendations across guidelines are discordant, and few studies have evaluated these criteria to determine which characteristics best predict emergent etiology that warrant diagnostic imaging. A clinical decision support tool, that recommends diagnostic imaging where appropriate, has the potential to improve clinical care and patient outcomes and reduce costs associated with managing low back pain patients.

Background: A common barrier to implementation of precision medicine is the lack of use of published clinical practice guidelines. Consequently, a user-friendly mechanism to easily adopt these guidelines is imperative. Purpose: The purpose of this study was to evaluate the perceptions of a prototype of a clinical decision support tool through a mobile application for pharmacogenetics. Methodological orientation: A case study on a patient requiring pharmacogenetic testing was provided to the participants. The participants were given up to 30 minutes to identify the correct dosing in the clinical decision support tool based on clinical evidence-based guidelines. Immediately after the utilization of the mobile app, focus group interviews were conducted to identify the perceptions of the tool, obstacles associated with the tool, and suggestions for improvement of the tool. Sample: Focus group interviews with 23 nurse practitioners and nurse practitioner students were conducted. Field notes and audio recordings were taken. Conclusions: Specific feedback for improvement in the font and size of text, color contrast, use of drug calculator, automatic input, and desire for further development of education portal were found within the data. The findings revealed useful feedback to adjust the prototype to improve the ease of use among nurse practitioners. Implications for practice: The revision of this mobile app will improve user friendliness to increase applicability within health care. The mobile app can be used for future research to identify improvements in patient outcomes after implementing this tool. [ABSTRACT FROM AUTHOR]

Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Background: Primary care plays a key role in the management of type 2 diabetes. Sodium-glucose cotransporter 2 (SGLT2) inhibitors have been demonstrated to reduce hospitalization and cardiac and renal complications. Tools that optimize management, including appropriate prescribing, are a priority for treating chronic diseases. Future Health Today (FHT) is software that facilitates clinical decision support and quality improvement. FHT applies algorithms to data stored in electronic medical records in general practice to identify patients who are at risk of a chronic disease or who have a chronic disease that may benefit from intensification of management. The platform continues to evolve because of rigorous evaluation, continuous improvement, and expansion of the conditions hosted on the platform. FHT currently displays recommendations for the identification and management of chronic kidney disease, cardiovascular disease, type 2 diabetes, and cancer risk. A new module will be introduced to FHT focusing on SGLT2 inhibitors in patients with type 2 diabetes who have chronic kidney diseases, cardiovascular diseases, or risk factors for cardiovascular disease., Objective: The study aims to explore the barriers and enablers to the implementation of an SGLT2 inhibitor module within the Future Health Today software., Methods: Clinic staff were recruited to participate in interviews on their experience in their use of a tool to improve prescribing behavior for SGLT2 inhibitors. Thematic analysis was guided by Clinical Performance Feedback Intervention Theory., Results: In total, 16 interviews were completed. Identified enablers of use included workflow alignment, clinical appropriateness, and active delivery of the module. Key barriers to use were competing priorities, staff engagement, and knowledge of the clinical topic., Conclusions: There is a recognized benefit to the use of a clinical decision support tool to support type 2 diabetes management, but barriers were identified that impeded the usability and actionability of the module. Successful and effective implementation of this tool could support the optimization of patient management of type 2 diabetes in primary care., (©Matthew Suen, Jo-Anne Manski-Nankervis, Caroline McBride, Natalie Lumsden, Barbara Hunter. Originally published in JMIR Formative Research (https://formative.jmir.org), 13.03.2024.)

The Preemptive Pharmacogenetic-guided Metoprolol Management for Atrial Fibrillation in Cardiac Surgery (PREEMPTIVE) pilot trial aims to use existing institutional resources to develop a process for integrating CYP2D6 pharmacogenetic test results into the patient electronic health record, to develop an evidence-based clinical decision support tool to facilitate CYP2D6 genotype-guided metoprolol administration in the cardiac surgery setting, and to determine the impact of implementing this CYP2D6 genotype-guided integrated approach on the incidence of postoperative atrial fibrillation (AF), provider, and cost outcomes. One-arm Bayesian adaptive design clinical trial. Single center, university hospital. The authors will screen (including CYP2D6 genotype) up to 600 (264 ± 144 expected under the adaptive design) cardiac surgery patients, and enroll up to 200 (88 ± 48 expected) poor, intermediate, and ultrarapid CYP2D6 metabolizers over a period of 2 years at a tertiary academic center. All consented and enrolled patients will receive the intervention of CYP2D6 genotype-guided metoprolol management based on CYP2D6 phenotype classified as a poor, intermediate, extensive (normal), or ultrarapid metabolizer. The primary outcome will be the incidence of postoperative AF. Secondary outcomes relating to rates of CYP2D6 genotype-guided prescription changes, costs, lengths of stay, and implementation metrics also will be investigated. The PREEMPTIVE pilot study is the first perioperative pilot trial to provide essential information for the design of a future, large-scale trial comparing CYP2D6 genotype-guided metoprolol management with a nontailored strategy in terms of managing AF. In addition, secondary outcomes regarding implementation, clinical benefit, safety, and cost-effectiveness in patients undergoing cardiac surgery will be examined. [ABSTRACT FROM AUTHOR]

Background: The diagnosis of cancer in primary care is complex and challenging. Electronic clinical decision support tools (eCDSTs) have been proposed as an approach to improve GP decision making, but no systematic review has examined their role in cancer diagnosis. Aim: To investigate whether eCDSTs improve diagnostic decision making for cancer in primary care and to determine which elements influence successful implementation. Design and setting: A systematic review of relevant studies conducted worldwide and published in English between 1 January 1998 and 31 December 2018. Method: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials were searched, and a consultation of reference lists and citation tracking was carried out. Exclusion criteria included the absence of eCDSTs used in asymptomatic populations, and studies that did not involve support delivered to the GP. The most relevant Joanna Briggs Institute Critical Appraisal Checklists were applied according to study design of the included paper. Results: Of the nine studies included, three showed improvements in decision making for cancer diagnosis, three demonstrated positive effects on secondary clinical or health service outcomes such as prescribing, quality of referrals, or cost-effectiveness, and one study found a reduction in time to cancer diagnosis. Barriers to implementation included trust, the compatibility of eCDST recommendations with the GP's role as a gatekeeper, and impact on workflow. Conclusion: eCDSTs have the capacity to improve decision making for a cancer diagnosis, but the optimal mode of delivery remains unclear. Although such tools could assist GPs in the future, further well-designed trials of all eCDSTs are needed to determine their cost-effectiveness and the most appropriate implementation methods. [ABSTRACT FROM AUTHOR]

Background: Odontogenic cellulitis are frequent infections of the head and neck fascial spaces that can sometimes spread and be life-threatening, requiring urgent hospitalization. Early diagnosis of facial cellulitis with diffuse inflammatory process is crucial in patient management but not always obvious in the field. Medical infrared thermography (MIT) is a noninvasive tool increasingly used to evaluate skin temperature maps and delineate inflammatory lesions. Objective: The aim of this work was to evaluate the use of MIT to improve the clinical examination of patients with facial cellulitis. Methods: Image processing work was carried out to highlight the thermal gradient resulting from inflammation linked to infection, in 2 patients with facial cellulitis. Results: In real-time, MIT allowed to precisely locate the inflammatory focus linked to cellulitis with no propagation to danger areas such as infraorbital space or around pharyngeal axis. Conclusion: Here, we show the first cases using MIT as a powerful complementary tool in the clinical evaluation of patients with facial cellulitis. Significance: This technology could help optimize the hospitalization decision through a facilitated assessment of infection spread in head and neck tissues and helping to incision for drainage.

Numerous studies have reported on the high accuracy of using voice tasks for the remote detection and monitoring of Parkinson’s Disease (PD). Most of these studies, however, report findings on a small number of voice recordings, often collected under acoustically controlled conditions, and therefore cannot scale at large without specialized equipment. In this study, we aimed to evaluate the potential of using voice as a population-based PD screening tool in resource-constrained settings. Using the standard telephone network, we processed 11,942 sustained vowel /a/ phonations from a US-English cohort comprising 1078 PD and 5453 control participants. We characterized each phonation using 304 dysphonia measures to quantify a range of vocal impairments. Given that this is a highly unbalanced problem, we used the following strategy: we selected a balanced subset (n = 3000 samples) for training and testing using 10-fold cross-validation (CV), and the remaining (unbalanced held-out dataset, n = 8942) samples for further model validation. Using robust feature selection methods we selected 27 dysphonia measures to present into a radial-basis-function support vector machine and demonstrated differentiation of PD participants from controls with 67.43% sensitivity and 67.25% specificity. These findings could help pave the way forward toward the development of an inexpensive, remote, and reliable diagnostic support tool for PD using voice as a digital biomarker.

Background: Multiple antiretroviral (ARV) regimens are effective at achieving HIV viral suppression, but differ in pill burden, side effects, barriers to resistance, and impact on comorbidities. Current guidelines advocate for an individualized approach to ARV regimen selection, but synthesizing these modifying factors is complex and time-consuming. Methods: We describe the development of HIV-ASSIST (https://www.hivassist.com), a free, online decision support tool for ARV selection and HIV education. HIV-ASSIST ranks potential ARV options for any given patient scenario using a composite objective of achieving viral suppression while maximizing tolerability and adherence. We used a multiple-criteria decision analysis framework to construct mathematical algorithms and synthesize various patient-specific (eg, comorbidities and treatment history) and virus-specific (eg, HIV mutations) attributes. We then conducted a validation study to evaluate HIV-ASSIST with prescribing practices of experienced HIV providers at 4 large academic centers. We report on concordance of provider ARV selections with the 5 top-ranked HIV-ASSIST regimens for 10 diverse hypothetical patient-case scenarios. Results: In the validation cohort of 17 experienced HIV providers, we found 99% concordance between HIV-ASSIST recommendations and provider ARV selections for 4 case-scenarios of ARV-naive patients. Among 6 cases of ARV-experienced patients (3 with and 3 without viremia), there was 84% and 88% concordance, respectively. Among 3 cases of ARV-experienced patients with viremia, providers reported 20 different ARV selections, suggesting substantial heterogeneity in ARV preferences in clinical practice. Conclusions: HIV-ASSIST is a novel patient-centric educational decision support tool that provides ARV recommendations concordant with experienced HIV providers for a diverse set of patient scenarios. [ABSTRACT FROM AUTHOR]

Patients with wounds pose an important healthcare challenge. Many of these wounds are managed in community care and can take weeks or months to resolve. Delays in wound healing can be perpetuated by clinicians who make poor treatment choices, fail to recognise complications and/or do not seek timely advice. Improving patient outcomes requires a proactive approach to care that includes accurate and timely assessment and re-assessment, treatment of the underlying cause using a multidisciplinary team approach and the use of evidence-based practice and clinical judgement to develop an appropriate treatment plan. A structured approach to care, such as the newly developed T.I.M.E. clinical decision support tool, has the potential to improve wound healing outcomes and reduce the burden of chronic wounds in community nursing services. [ABSTRACT FROM AUTHOR]

Introduction: Invasive fungal infections (IFI) are associated with high morbidity and mortality. A better method of risk stratifying trauma patients for combat-related IFI is needed to improve clinical outcomes while minimizing morbidity related to overtreatment. We sought to develop combat-related IFI clinical decision support (CDS) tools to assist providers to make treatment decisions both near the point of injury and subsequently at definitive treatment centers.Materials and Methods: We utilized a training dataset containing information from 227 combat-injured military personnel to build a Bayesian belief network (BBN) to predict the likelihood of developing IFI using information available at the point of initial resuscitation (THEATER model) and in the tertiary care setting (MEDCEN model). After selecting BBN models, external validation used a separate test dataset of 350 wounded warriors. Furthermore, the performance of the BBN models was compared with a "two-rule model" alone (based on physician experience) and combinations of the BBN models plus the two-rule model. The two-rule model contains plausible IFI criteria, but it has not been formally evaluated, and they are not currently actual clinical guidelines.Results: We found receiver operating characteristic areas under the curve (AUC) of 0.70 (95% CI: [0.62, 0.77]) and 0.68 (95% CI: [0.59, 0.76]) for the THEATER and MEDCEN BBN models, respectively, on cross-validation. External validation with the highest AUC BBN models produced THEATER AUC of 0.68 (95% CI: [0.58, 0.78]) and MEDCEN AUC of 0.67 (95% CI: [0.57, 0.78]). With the incorporation of two-rule model in low IFI-prevalence populations, external validation AUC increased to 0.77 (95% CI: [0.69, 0.84]) for the THEATER model and 0.76 (95% CI:[0.68, 0.85]) for the LRMC model. The two-rule model alone has an AUC of 0.72 (95% CI: [0.63, 0.81]).Conclusions: Overall, the IFI tools produced clinically useful, robust models. However, the clinical utility of these models is highly dependent upon the clinician's individual risk tolerance. The threshold probability for optimal clinical use of this CDS tool is currently being evaluated in an ongoing clinical utilization study. CDS tools, such as these, may facilitate early diagnosis of patients with or at risk for IFI, permitting early or prophylactic treatment with the aim of improving outcomes. [ABSTRACT FROM AUTHOR]

Cisgender women account for 1 of every 5 new US HIV diagnoses, with most cases (85%) attributed to heterosexual contact. HIV preexposure prophylaxis is an effective prevention strategy; however, preexposure prophylaxis awareness and prescriptions among women are low. This study aimed to increase preexposure prophylaxis counseling and uptake among cisgender women attending obstetrics and gynecology clinics. The study included 3 obstetrics and gynecology clinics within a single health system in a high HIV prevalence region. There were 3 phases: baseline (the 3-month period before the clinical trial that included provider education and training of a registered nurse about preexposure prophylaxis), clinical trial (the 3-month period during which eligible patients were randomized to an active control or preexposure prophylaxis registered nurse intervention), and maintenance (the 3-month period after the trial ended). Electronic medical record clinical decision support tools were available to both arms during the clinical trial, which included best practice alerts, order sets, progress note templates, and written and video preexposure prophylaxis educational materials for patients. In the intervention arm, a preexposure prophylaxis nurse contacted and counseled patients and was equipped to prescribe preexposure prophylaxis. Moreover, this study evaluated the phases through the "reach, effectiveness, adoption, implementation, and maintenance" framework. The primary outcome of the study was effectiveness (eg, percentage of eligible patients with documented HIV prevention counseling in the electronic medical record or preexposure prophylaxis prescriptions). The secondary outcomes included reach (eg, percentage of best practice alerts that providers acted on or the percentage of eligible patients who spoke with the preexposure prophylaxis registered nurse), adoption (eg, percentage of eligible patients with a best practice alert that triggered or the percentage of eligible patients the preexposure prophylaxis registered nurse attempted to contact), and maintenance (eg, percentage of patients with documented HIV prevention counseling or preexposure prophylaxis prescriptions during the maintenance phase). There were 904 unique patients in all phases with a mean age of 28.8±7.7 years, and 416 patients (46%) were pregnant; moreover, 436 patients were randomized in the clinical trial phase. Concerning reach and adoption, best practice alerts were triggered for 100% of eligible encounters; however, the providers acted on 52% of them. The preexposure prophylaxis nurse attempted to contact every patient and successfully spoke with 81.2% of them in the preexposure prophylaxis registered nurse arm. Concerning effectiveness, there were significantly more patients counseled about preexposure prophylaxis in the preexposure prophylaxis registered nurse group than in the active control group (66.5% vs 12.3%, respectively; P <.001), although preexposure prophylaxis prescriptions were equivalent (P =1.0). Among the subgroup of patients who were counseled about preexposure prophylaxis, 18.5% of patients in the active control arm and 3.4% in the preexposure prophylaxis registered nurse arm were prescribed preexposure prophylaxis (P =.02). Concerning maintenance, clinical decision support tools alone resulted in preexposure prophylaxis counseling of 1.0% of patients during the maintenance phase vs 0.6% of patients during the baseline phase and 11.2% of patients during the clinical trial phase (P <.001). Preexposure prophylaxis prescriptions were not statistically different among the 3 phases (P =.096). A preexposure prophylaxis nurse effectively increased HIV prevention discussions but did not lead to more preexposure prophylaxis prescriptions than the preexposure prophylaxis–focused clinical decision support tools used by providers. The decrease in preexposure prophylaxis counseling after the trial phase suggests that persistent interventions are needed to maintain effects. [ABSTRACT FROM AUTHOR]

Antipsychotic medications carry an established lifetime risk of metabolic syndrome. This retrospective chart review evaluated feasibility of a metabolic monitoring clinical decision support tool (CDST) for weight, lipid, blood glucose, and blood pressure management of 163 clients in an early psychosis outpatient clinic over 2 years. Each parameter had at least 98 (60.1%) clients with a recorded value, the most being documented for weight with 112 (68.7%) clients. CDST adherence ranged from at least 54.3-100% for non-pharmacologic interventions (e.g. clinic counseling, referral to health program or primary care) and at least 33.3-100% for pharmacologic interventions (e.g. metformin). Though no baseline cardiometabolic abnormalities were identified, dyslipidemia and obesity were later found in 37 (22.7%) and 35 (21.5%) clients, respectively. Only 14 (8.6%) clients were prescribed medications for cardiometabolic abnormalities by psychiatrists in the clinic. Increasing focus on physical health is needed to better this population’s long-term prognosis. [ABSTRACT FROM AUTHOR]

Background The diagnosis of epilepsy is at times elusive for both neurologists and nonneurologists, resulting in delays in diagnosis and therapy. The development of screening methods has been identified as a priority in response to this diagnostic and therapeutic gap. EpiFinder is a novel clinical decision support tool designed to enhance the process of information gathering and integration of patient/proxy respondent data. It is designed specifically to take key terms from a patient's history and incorporate them into a heuristic algorithm that dynamically produces differential diagnoses of epilepsy syndromes. Objective The objective of this study was to test the usability and diagnostic accuracy of the clinical decision support application EpiFinder in an adult population. Methods Fifty-seven patients were prospectively identified upon admission to the Epilepsy Monitoring Unit (EMU) for episode classification from January through June of 2017. Based on semiologic input, the application generates a list of epilepsy syndromes. The EpiFinder-generated diagnosis for each subject was compared to the final diagnosis obtained via continuous video electroencephalogram (cVEEG) monitoring. Results Fifty-three patients had habitual events recorded during their EMU stay. A diagnosis of epilepsy was confirmed (with cVEEG monitoring) in 26 patients while 27 patients were found to have a diagnosis other than epilepsy. The algorithm appropriately predicted differentiation between the presence of an epilepsy syndrome and an alternative diagnosis with 86.8% (46/53 participants) accuracy. EpiFinder correctly identified the presence of epilepsy with a sensitivity of 86.4% (95% confidence interval [CI]: 65.0–97.1) and specificity of 85.1% (95% CI: 70.2–96.4). Conclusion The initial testing of the EpiFinder algorithm suggests possible utility in differentiating between an epilepsy syndrome and an alternative diagnosis in adult patients. [ABSTRACT FROM AUTHOR]