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Background: Ex-ante identification of the last year in life facilitates a proactive palliative approach. Machine learning models trained on electronic health records (EHR) demonstrate promising performance in cancer prognostication. However, gaps in literature include incomplete reporting of model performance, inadequate alignment of model formulation with implementation use-case, and insufficient explainability hindering trust and adoption in clinical settings. Hence, we aim to develop an explainable machine learning EHR-based model that prompts palliative care processes by predicting for 365-day mortality risk among patients with advanced cancer within an outpatient setting. Methods: Our cohort consisted of 5,926 adults diagnosed with Stage 3 or 4 solid organ cancer between July 1, 2017, and June 30, 2020 and receiving ambulatory cancer care within a tertiary center. The classification problem was modelled using Extreme Gradient Boosting (XGBoost) and aligned to our envisioned use-case: "Given a prediction point that corresponds to an outpatient cancer encounter, predict for mortality within 365-days from prediction point, using EHR data up to 365-days prior." The model was trained with 75% of the dataset (n = 39,416 outpatient encounters) and validated on a 25% hold-out dataset (n = 13,122 outpatient encounters). To explain model outputs, we used Shapley Additive Explanations (SHAP) values. Clinical characteristics, laboratory tests and treatment data were used to train the model. Performance was evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC), while model calibration was assessed using the Brier score. Results: In total, 17,149 of the 52,538 prediction points (32.6%) had a mortality event within the 365-day prediction window. The model demonstrated an AUROC of 0.861 (95% CI 0.856–0.867) and AUPRC of 0.771. The Brier score was 0.147, indicating slight overestimations of mortality risk. Explanatory diagrams utilizing SHAP values allowed visualization of feature impacts on predictions at both the global and individual levels. Conclusion: Our machine learning model demonstrated good discrimination and precision-recall in predicting 365-day mortality risk among individuals with advanced cancer. It has the potential to provide personalized mortality predictions and facilitate earlier integration of palliative care. [ABSTRACT FROM AUTHOR]

AbstractWithin the ethical debate on Machine Learning-driven decision support systems (ML_CDSS), notions such as “human in the loop” or “meaningful human control” are often cited as being necessary for ethical legitimacy. In addition, ethical principles usually serve as the major point of reference in ethical guidance documents, stating that conflicts between principles need to be weighed and balanced against each other. Starting from a neo-Kantian viewpoint inspired by Onora O'Neill, this article makes a concrete suggestion of how to interpret the role of the “human in the loop” and to overcome the perspective of rivaling ethical principles in the evaluation of AI in health care. We argue that patients should be perceived as “fellow workers” and epistemic partners in the interpretation of ML_CDSS outputs. We further highlight that a meaningful process of integrating (rather than weighing and balancing) ethical principles is most appropriate in the evaluation of medical AI. [ABSTRACT FROM AUTHOR]

Background: Severe acute lung failure (ALF) often necessitates veno-venous extracorporeal membrane oxygenation (VV-ECMO), where identifying predictors of weaning success and mortality remains crucial yet challenging. The study aims to identify predictors of weaning success and mortality in adults undergoing VV-ECMO for severe ALF, a gap in current clinical knowledge. Methods and analysis: PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials will be searched for cohort studies examining the predictive factors of successful weaning and mortality in adult patients on VV-ECMO due to severe ALF. Risk of bias assessment will be conducted using the Newcastle-Ottawa scale for each included study. The primary outcomes will be successful weaning from VV-ECMO and all-cause mortality. Between-study heterogeneity will be evaluated using the I2 statistic. Sensitivity, subgroup, and meta-regression analyses will be performed to ascertain potential sources of heterogeneity and assess the robustness of our results. We will use the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) tool to recommend the level of evidence. Discussion: This study seeks to provide clinically significant insights into predictors for weaning and mortality during VV-ECMO treatment for ALF, aiming to support clinical decisions and potentially influence health policy, thereby improving patient outcomes. Ethics and dissemination: Given the absence of direct engagement with human subjects or access to personal medical records, ethical approval for this study is deemed unnecessary. The study findings will be shared at a scientific conference either at the global or national level. Alternatively, the results will be presented for publication in a rigorously peer-reviewed journal regarding critical care medicine. [ABSTRACT FROM AUTHOR]

Background: Integration of a patient's non-invasive imaging data in a digital twin (DT) of the heart can provide valuable insight into the myocardial disease substrates underlying left ventricular (LV) mechanical discoordination. However, when generating a DT, model parameters should be identifiable to obtain robust parameter estimations. In this study, we used the CircAdapt model of the human heart and circulation to find a subset of parameters which were identifiable from LV cavity volume and regional strain measurements of patients with different substrates of left bundle branch block (LBBB) and myocardial infarction (MI). To this end, we included seven patients with heart failure with reduced ejection fraction (HFrEF) and LBBB (study ID: 2018-0863, registration date: 2019–10–07), of which four were non-ischemic (LBBB-only) and three had previous MI (LBBB-MI), and six narrow QRS patients with MI (MI-only) (study ID: NL45241.041.13, registration date: 2013–11–12). Morris screening method (MSM) was applied first to find parameters which were important for LV volume, regional strain, and strain rate indices. Second, this parameter subset was iteratively reduced based on parameter identifiability and reproducibility. Parameter identifiability was based on the diaphony calculated from quasi-Monte Carlo simulations and reproducibility was based on the intraclass correlation coefficient ( ICC ) obtained from repeated parameter estimation using dynamic multi-swarm particle swarm optimization. Goodness-of-fit was defined as the mean squared error ( χ 2 ) of LV myocardial strain, strain rate, and cavity volume. Results: A subset of 270 parameters remained after MSM which produced high-quality DTs of all patients ( χ 2 < 1.6), but minimum parameter reproducibility was poor ( ICC min = 0.01). Iterative reduction yielded a reproducible ( ICC min = 0.83) subset of 75 parameters, including cardiac output, global LV activation duration, regional mechanical activation delay, and regional LV myocardial constitutive properties. This reduced subset produced patient-resembling DTs ( χ 2 < 2.2), while septal-to-lateral wall workload imbalance was higher for the LBBB-only DTs than for the MI-only DTs (p < 0.05). Conclusions: By applying sensitivity and identifiability analysis, we successfully determined a parameter subset of the CircAdapt model which can be used to generate imaging-based DTs of patients with LV mechanical discoordination. Parameters were reproducibly estimated using particle swarm optimization, and derived LV myocardial work distribution was representative for the patient's underlying disease substrate. This DT technology enables patient-specific substrate characterization and can potentially be used to support clinical decision making. [ABSTRACT FROM AUTHOR]

This review aims to explore advancements in perioperative ischemic stroke risk estimation for asymptomatic patients with significant carotid artery stenosis, focusing on Circle of Willis (CoW) morphology based on the CTA or MR diagnostic imaging in the current preoperative diagnostic algorithm. Functional transcranial Doppler (fTCD), near-infrared spectroscopy (NIRS), and optical coherence tomography angiography (OCTA) are discussed in the context of evaluating cerebrovascular reserve capacity and collateral vascular systems, particularly the CoW. These non-invasive diagnostic tools provide additional valuable insights into the cerebral perfusion status. They support biomedical modeling as the gold standard for the prediction of the potential impact of carotid artery stenosis on the hemodynamic changes of cerebral perfusion. Intraoperative risk assessment strategies, including selective shunting, are explored with a focus on CoW variations and their implications for perioperative ischemic stroke and cognitive function decline. By synthesizing these insights, this review underscores the potential of non-invasive diagnostic methods to support clinical decision making and improve asymptomatic patient outcomes by reducing the risk of perioperative ischemic neurological events and preventing further cognitive decline. [ABSTRACT FROM AUTHOR]

This article explores the use of ChatGPT, an artificial intelligence language model, in perioperative medicine. While ChatGPT has shown promise in diagnosing medical conditions in simulated scenarios, there are concerns about its real-world applicability, including the possibility of providing incorrect or fabricated responses and relying on user-provided information. The article presents a case study where ChatGPT was used by an orthopedic surgery team to assist in decision-making regarding anticoagulation for a patient with a complex medical condition. The article raises concerns about data quality, hallucinations, patient autonomy, and privacy that arise when using ChatGPT as a clinical decision support tool. The authors caution against relying too heavily on AI systems in medical decision-making and emphasize the need for further research and regulation to ensure their safe and effective use in clinical practice. [Extracted from the article]

Background: The rapid evolution, complexity, and specialization of oncology treatment makes it challenging for physicians to provide care based on the latest and best evidence. We hypothesized that physicians would use evidence-based trusted care pathways if they were easy to use and integrated into clinical workflow at the point of care. Methods: Within a large integrated care delivery system, we assembled clinical experts to define and update drug treatment pathways, encoded them as flowcharts in an online library integrated with the electronic medical record, communicated expectations that clinicians would use these pathways for every eligible patient, and combined data from multiple sources to understand usage over time. Results: We were able to achieve > 75% utilization of eligible protocols ordered through these pathways within two years, with > 90% of individual oncologists having consulted the pathway at least once, despite no requirements or external incentives associated with pathway usage. Feedback from users contributed to improvements and updates to the guidance. Conclusions: By making our clinical decision support easily accessible and actionable, we find that we have made considerable progress toward our goal of having physicians consult the latest evidence in their treatment decisions. [ABSTRACT FROM AUTHOR]

Background: Currently, only about half of U.S. adults achieve current physical activity guidelines. Routine physical activity is not regularly assessed, nor are patients routinely counseled by their healthcare provider on achieving recommended levels. The three-question physical activity vital sign (PAVS) was developed to assess physical activity duration and intensity and identify adults not meeting physical activity guidelines. Clinical decision support provided via a best practice advisory in an electronic health record (EHR) system can be triggered as a prompt, reminding healthcare providers to implement the best practice intervention when appropriate. Remote patient monitoring of physical activity can provide objective data in the EHR. Objectives: This study aimed to evaluate the feasibility and clinical utility of embedding the PAVS and a triggered best practice advisor into the EHR in an ambulatory preventive cardiology practice setting to alert providers to patients reporting low physical activity and prompt healthcare providers to counsel these patients as needed. Methods: Three components based in the EHR were integrated for the purpose of this study: Patients completed the PAVS through their electronic patient portal prior to an office visit, a best practice advisory was created to prompt providers to counsel patients who reported low levels of physical activity, and remote patient monitoring via Fitbit synced to the EHR provided objective physical activity data. The intervention was pilot-tested in the Epic EHR for 1 year (July 1, 2021 to June 30, 2022). Qualitative feedback on the intervention from both providers and patients was obtained at the completion of the study. Results: Monthly assessments of the use of the PAVS and best practice advisory and remote patient monitoring were completed. Patients' completion of the PAVS varied from 35% to 48% per month. The best practice advisory was signed by providers between 2% and 65% and was acknowledged by 2%–22% per month. The majority (58%) of patients were able to sync a Fitbit device to their EHR for remote monitoring. Discussion: Although uptake of each component needs improvement, this pilot demonstrated the feasibility of incorporating a physical activity promotion intervention into the EHR. Qualitative feedback provided guidance for future implementation. [ABSTRACT FROM AUTHOR]

The Canadian Association of Radiologists (CAR) Obstetrics and Gynecology Expert Panel consists of radiologists specializing in obstetrics and gynecology, obstetrics and gynecology physicians, a patient advisor, and an epidemiologist/guideline methodologist. After developing a list of 12 clinical/diagnostic scenarios, a systematic rapid scoping review was undertaken to identify systematically produced referral guidelines that provide recommendations for one or more of these clinical/diagnostic scenarios. Recommendations from 46 guidelines and contextualization criteria in the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) for guidelines framework were used to develop 68 recommendation statements across the 12 scenarios related to the evaluation of obstetrics and gynecology clinical and diagnostic scenarios. This guideline presents the methods of development and the imaging recommendations for a variety of obstetrical and gynecological conditions including pregnancy assessment, recurrent first trimester pregnancy loss, post-partum indications, disorders of menstruation, localization of intra-uterine contraceptive device, infertility assessment, assessment of adnexal mass, pelvic pain of presumed gynecological origin, and pelvic floor evaluation. [ABSTRACT FROM AUTHOR]

Decision Support Systems (DSS) are tools designed to help operators make effective choices in workplace environments where discernment and critical thinking are required for effective performance. Path planning in military operations and general logistics both require individuals to make complex and time-sensitive decisions. However, these decisions can be complex and involve the synthesis of numerous tradeoffs for various paths with dynamically changing conditions. Intelligence collection can vary in difficulty, specifically in terms of the disparity between locations of interest and timing restrictions for when and how information can be collected. Furthermore, plans may need to be changed adaptively mid-operation, as new collection requirements appear, increasing task difficulty. We tested participants in a path planning decision-making exercise with scenarios of varying difficulty in a series of two experiments. In the first experiment, each map displayed two paths simultaneously, relating to two possible routes for the two available trucks. Participants selected the optimal path plan, representing the best solution across multiple routes. In the second experiment, each map displayed a single path, and participants selected the best two paths sequentially. In the first experiment, utilizing the DSS was predictive of adoption of more heuristic decision strategies, and that strategic approach yielded more optimal route selection. In the second experiment, there was a direct effect of the DSS on increased decision performance and a decrease in perceived task workload. [ABSTRACT FROM AUTHOR]

Dental sealants are effective for the prevention of caries in children at elevated risk levels, and increasing the proportion of children and adolescents who have dental sealants on 1 or more molars is a Healthy People 2030 objective. Electronic health record (EHR)-based clinical decision support systems (CDSSs) have the ability to improve patient care. A dental quality measure related to dental sealant placement for children at elevated risk of caries was targeted for improvement using a CDSS. A validated dental quality measure was adapted to assess a patient's need for dental sealant placement. A CDSS was implemented to advise care team members whether a child was at elevated risk of developing caries and had sealant-eligible first or second molars. Data on dental sealant placement at examination visits during a 5-year period were analyzed, including 32 months before CDSS implementation and 28 months after CDSS implementation. From January 1, 2018, through December 31, 2022, the authors assessed 59,047 examination visits for children at elevated risk of developing caries and with sealant-eligible teeth. With the implementation of a CDSS and training to support the clinical care team members in September 2020, the appropriate placement of dental sealants at examination visits increased from 27% through 60% (P <.00001). Integration of a CDSS into the EHR as part of a quality improvement program was effective in increasing the delivery of sealants in eligible first and second molars of children aged 5 through 15 years and considered at high risk of developing caries. An EHR–based CDSS can be implemented to improve standardization and provide timely and appropriate patient care in dental practices. [ABSTRACT FROM AUTHOR]

Purpose: This study examines immigrant Latinas' (ILs') help-seeking behaviors, types of support systems, and access to intimate partner violence (IPV) services during a global health crisis (COVID-19) at a community-based agency in a Northeastern state. Method: Nineteen immigrant Latinas who had prior IPV-related services such as legal aid, advocacy, and support within 1–3 years were recruited for the study. Spanish-speaking telephone interviews averaging between 30 and 45 min were conducted with each participant. Content analysis was the method employed to review the data and generate themes of the participants' experiences. Results: Participants' qualitative responses included an increase of intimate partner violence during the pandemic. Types of support systems included reaching out to police departments, hospitals and health-care settings, and community-based agencies. Findings indicated a 47% positive response rate when working with police officers (e.g., bilingual Spanish-English speaking police officers), and the participants reported being supported by the agency staff where they received services. Conclusion: Recommendations are provided to the community-based agency and other service providers regarding ongoing delivery of services and best practices for ILs throughout the pandemic transitions and beyond. [ABSTRACT FROM AUTHOR]

Although the Emergency Department (ED) offers a unique setting to provide early palliative care, staffing limitations curtail hospitals from establishing ED-palliative partnerships. Feasibility of a two-step ED-palliative screening protocol was defined by two criteria: a ≥ 50% increase in palliative consults originating from the ED and a ≥ 50% consultation completion rate for patients who screened positive for unmet palliative needs. A clinical decision support tool identified patients with treatment/code status limitations and prompted a care coordination referral. Care coordinators screened patients for unmet palliative needs using a content-validated screening tool and consulted palliative care for positive screens. Palliative care consultations originating from the ED increased by 110% from 32 to 67 consultations, and 57% (40/70) of patients who screened positive for unmet palliative needs received a consultation. Our project demonstrated feasibility of a two-step ED-palliative protocol by increasing palliative care consultation without necessitating additional staff. [ABSTRACT FROM AUTHOR]

Purpose: Despite the robust expansion of research surrounding artificial intelligence (AI) and machine learning (ML) and their applications to medicine, these methodologies often remain opaque and inaccessible to many otolaryngologists. Especially, with the increasing ubiquity of large-language models (LLMs), such as ChatGPT and their potential implementation in clinical practice, clinicians may benefit from a baseline understanding of some aspects of AI. In this narrative review, we seek to clarify underlying concepts, illustrate applications to otolaryngology, and highlight future directions and limitations of these tools. Methods: Recent literature regarding AI principles and otolaryngologic applications of ML and LLMs was reviewed via search in PubMed and Google Scholar. Results: Significant recent strides have been made in otolaryngology research utilizing AI and ML, across all subspecialties, including neurotology, head and neck oncology, laryngology, rhinology, and sleep surgery. Potential applications suggested by recent publications include screening and diagnosis, predictive tools, clinical decision support, and clinical workflow improvement via LLMs. Ongoing concerns regarding AI in medicine include ethical concerns around bias and data sharing, as well as the "black box" problem and limitations in explainability. Conclusions: Potential implementations of AI in otolaryngology are rapidly expanding. While implementation in clinical practice remains theoretical for most of these tools, their potential power to influence the practice of otolaryngology is substantial. Level of evidence: 4. [ABSTRACT FROM AUTHOR]

Biometric technology has various applications in smart healthcare systems, including patient authentication, health monitoring, telemedicine, clinical decision support, and personalized care. In addition, medical records contain sensitive and personal information, making them vulnerable to unauthorized access and theft. Because biometric data is distinct and unchangeable, unlike passwords or PINs, using biometric technologies in smart healthcare systems creates privacy problems. This creates privacy concerns as this information is highly sensitive and can be used to identify an individual, making it a valuable target for malicious actors. Subsequently, the storage and use of biometric data in smart healthcare systems must be handled with care to ensure that individuals' privacy rights are protected. Privacy by design is a concept that emphasizes the importance of incorporating privacy considerations into the design and development of products, services, and systems. In this paper, we presented different forms of biometric factors and technologies and their applications in the smart healthcare system to enhance security and privacy in relation to principles of privacy by design. In addition, the study analyzed a variety of attacks and techniques that can be utilized to compromise biometric technology in a smart healthcare system and presented some open research questions. [ABSTRACT FROM AUTHOR]

Background: Primary care professionals encounter difficulties coordinating the continuum of care between primary care providers and second-line specialists and adhere to practice guidelines pertaining to diabetic foot ulcers management. Family medicine groups are providing primary care services aimed to improve access, interdisciplinary care, coordination and quality of health services, and reduce emergency department visits. Most professionals working in family medicine groups are primary care physicians and registered nurses. The aim of this study was to develop and validate an interprofessional decision support tool to guide the management of diabetic foot ulcers for primary care professionals working within the family medicine group model. Methods: A one-page decision tool developed by the research team was validated by an expert panel using a three-round Delphi protocol held between December 2019 and August 2021. The tool includes 43 individual actions and a care pathway from initial presentation to secondary prevention. Data collection was realized with both paper and electronic questionnaires, and answers were compiled in an electronic spreadsheet. Data was analyzed with use of descriptive statistics, and consensus for each item was defined as ≥ 80% agreement. Results: Experts from 12 pre-identified professions of the diabetic foot ulcer interdisciplinary care team were included, 39 participants out of the 59 invited to first round (66.1%), 34 out of 39 for second (87.2%) and 22 out of 34 for third (64.7%) rounds. All items included in the final version of the decision support tool reached consensus and were deemed clear, relevant and feasible. One or more professionals were identified to be responsible for every action to be taken. Conclusions: This study provided a comprehensive decision support tool to guide primary care professionals in the management of diabetic foot ulcers. Implementation and evaluation in the clinical setting will need to be undertaken in the future. [ABSTRACT FROM AUTHOR]

Nowadays, machine learning (ML) has attained a high level of achievement in many contexts. Considering the significance of ML in medical and bioinformatics owing to its accuracy, many investigators discussed multiple solutions for developing the function of medical and bioinformatics challenges using deep learning (DL) techniques. The importance of DL in Internet of Things (IoT)-based bio- and medical informatics lies in its ability to analyze and interpret large amounts of complex and diverse data in real time, providing insights that can improve healthcare outcomes and increase efficiency in the healthcare industry. Several applications of DL in IoT-based bio- and medical informatics include diagnosis, treatment recommendation, clinical decision support, image analysis, wearable monitoring, and drug discovery. The review aims to comprehensively evaluate and synthesize the existing body of the literature on applying deep learning in the intersection of the IoT with bio- and medical informatics. In this paper, we categorized the most cutting-edge DL solutions for medical and bioinformatics issues into five categories based on the DL technique utilized: convolutional neural network, recurrent neural network, generative adversarial network, multilayer perception, and hybrid methods. A systematic literature review was applied to study each one in terms of effective properties, like the main idea, benefits, drawbacks, methods, simulation environment, and datasets. After that, cutting-edge research on DL approaches and applications for bioinformatics concerns was emphasized. In addition, several challenges that contributed to DL implementation for medical and bioinformatics have been addressed, which are predicted to motivate more studies to develop medical and bioinformatics research progressively. According to the findings, most articles are evaluated using features like accuracy, sensitivity, specificity, F-score, latency, adaptability, and scalability. [ABSTRACT FROM AUTHOR]

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: Decision-making in healthcare is increasingly complex; notably in hospital environments where the information density is high, e.g., emergency departments, oncology departments, and psychiatry departments. This study aims to discover decisions from logged data to improve the decision-making process. Methods: The Design Science Research Methodology (DSRM) was chosen to design an artifact (algorithm) for the discovery and visualization of decisions. The DSRM's different activities are explained, from the definition of the problem to the evaluation of the artifact. During the design and development activities, the algorithm itself is created. During the demonstration and evaluation activities, the algorithm was tested with an authentic synthetic dataset. Results: The results show the design and simulation of an algorithm for the discovery and visualization of decisions. A fuzzy classifier algorithm was adapted for (1) discovering decisions from a decision log and (2) visualizing the decisions using the Decision Model and Notation standard. Conclusions: In this paper, we show that decisions can be discovered from a decision log and visualized for the improvement of the decision-making process of healthcare professionals or to support the periodic evaluation of protocols and guidelines. [ABSTRACT FROM AUTHOR]

Longitudinal data, while often limited, contain valuable insights into features impacting clinical outcomes. To predict the progression of chronic kidney disease (CKD) in patients with metabolic syndrome, particularly those transitioning from stage 3a to 3b, where data are scarce, utilizing feature ensemble techniques can be advantageous. It can effectively identify crucial risk factors, influencing CKD progression, thereby enhancing model performance. Machine learning (ML) methods have gained popularity due to their ability to perform feature selection and handle complex feature interactions more effectively than traditional approaches. However, different ML methods yield varying feature importance information. This study proposes a multiphase hybrid risk factor evaluation scheme to consider the diverse feature information generated by ML methods. The scheme incorporates variable ensemble rules (VERs) to combine feature importance information, thereby aiding in the identification of important features influencing CKD progression and supporting clinical decision making. In the proposed scheme, we employ six ML models—Lasso, RF, MARS, LightGBM, XGBoost, and CatBoost—each renowned for its distinct feature selection mechanisms and widespread usage in clinical studies. By implementing our proposed scheme, thirteen features affecting CKD progression are identified, and a promising AUC score of 0.883 can be achieved when constructing a model with them. [ABSTRACT FROM AUTHOR]

Background: Abstract review is a time and labor-consuming step in the systematic and scoping literature review in medicine. Text mining methods, typically natural language processing (NLP), may efficiently replace manual abstract screening. This study applies NLP to a deliberately selected literature review problem, the trend of using NLP in medical research, to demonstrate the performance of this automated abstract review model. Methods: Scanning PubMed, Embase, PsycINFO, and CINAHL databases, we identified 22,294 with a final selection of 12,817 English abstracts published between 2000 and 2021. We invented a manual classification of medical fields, three variables, i.e., the context of use (COU), text source (TS), and primary research field (PRF). A training dataset was developed after reviewing 485 abstracts. We used a language model called Bidirectional Encoder Representations from Transformers to classify the abstracts. To evaluate the performance of the trained models, we report a micro f1-score and accuracy. Results: The trained models' micro f1-score for classifying abstracts, into three variables were 77.35% for COU, 76.24% for TS, and 85.64% for PRF. The average annual growth rate (AAGR) of the publications was 20.99% between 2000 and 2020 (72.01 articles (95% CI: 56.80–78.30) yearly increase), with 81.76% of the abstracts published between 2010 and 2020. Studies on neoplasms constituted 27.66% of the entire corpus with an AAGR of 42.41%, followed by studies on mental conditions (AAGR = 39.28%). While electronic health or medical records comprised the highest proportion of text sources (57.12%), omics databases had the highest growth among all text sources with an AAGR of 65.08%. The most common NLP application was clinical decision support (25.45%). Conclusions: BioBERT showed an acceptable performance in the abstract review. If future research shows the high performance of this language model, it can reliably replace manual abstract reviews. [ABSTRACT FROM AUTHOR]

Background Malaria is an important cause of mortality in African children. Identification of biomarkers to identify children at risk of mortality has the potential to improve outcomes. Methods We evaluated 11 biomarkers of host response in 592 children with severe malaria. The primary outcome was biomarker performance for predicting mortality. Biomarkers were evaluated using receiver operating characteristic (ROC) curve analysis comparing the area under the ROC curve (AUROC). Results Mortality was 7.3% among children in the study with 72% of deaths occurring within 24 hours of admission. Among the candidate biomarkers, soluble triggering receptor expressed on myeloid cells 1 (sTREM-1) had the highest AUROC (0.78 [95% confidence interval,.70–.86]), outperforming several other biomarkers including C-reactive protein and procalcitonin. sTREM-1 was the top-performing biomarker across prespecified subgroups (malaria definition, site, sex, nutritional status, age). Using established cutoffs, we evaluated mortality across sTREM-1 risk zones. Among children with acute kidney injury, 39.9% of children with a critical-risk sTREM-1 result had an indication for dialysis. When evaluated relative to a disease severity score, sTREM-1 improved mortality prediction (difference in AUROC, P =.016). Conclusions sTREM-1 is a promising biomarker to guide rational allocation of clinical resources and should be integrated into clinical decision support algorithms, particularly when acute kidney injury is suspected. [ABSTRACT FROM AUTHOR]

Background The development of chatbot artificial intelligence (AI) has raised major questions about their use in healthcare. We assessed the quality and safety of the management suggested by Chat Generative Pre-training Transformer 4 (ChatGPT-4) in real-life practice for patients with positive blood cultures. Methods Over a 4-week period in a tertiary care hospital, data from consecutive infectious diseases (ID) consultations for a first positive blood culture were prospectively provided to ChatGPT-4. Data were requested to propose a comprehensive management plan (suspected/confirmed diagnosis, workup, antibiotic therapy, source control, follow-up). We compared the management plan suggested by ChatGPT-4 with the plan suggested by ID consultants based on literature and guidelines. Comparisons were performed by 2 ID physicians not involved in patient management. Results Forty-four cases with a first episode of positive blood culture were included. ChatGPT-4 provided detailed and well-written responses in all cases. AI's diagnoses were identical to those of the consultant in 26 (59%) cases. Suggested diagnostic workups were satisfactory (ie, no missing important diagnostic tests) in 35 (80%) cases; empirical antimicrobial therapies were adequate in 28 (64%) cases and harmful in 1 (2%). Source control plans were inadequate in 4 (9%) cases. Definitive antibiotic therapies were optimal in 16 (36%) patients and harmful in 2 (5%). Overall, management plans were considered optimal in only 1 patient, as satisfactory in 17 (39%), and as harmful in 7 (16%). Conclusions The use of ChatGPT-4 without consultant input remains hazardous when seeking expert medical advice in 2023, especially for severe IDs. [ABSTRACT FROM AUTHOR]

The article discusses the importance of integrating molecular data into electronic health records (EHRs) for oncology treatment. It highlights the challenges of the current system, where molecular profiling results are often buried in PDF reports and require manual workflows. However, advancements in EHRs and data interoperability standards have allowed for the transmission of discrete molecular data elements into EHRs. This integration enables clinicians to access precision medicine features, such as AI tools, user interfaces for finding molecular results, clinical decision support, and clinical trial enrollment. The article also mentions specific examples of EHR systems that have successfully integrated molecular data. The authors emphasize the need to move from simply delivering molecular information to delivering actionable insights and call for collaboration between molecular technology companies, EHR systems, clinicians, and other stakeholders to enable the use of clinical decision support tools within the oncology workflow. [Extracted from the article]

Background: Both memory clinic professionals and patients see value in digital tools, yet these hardly find their way to clinical practice. We explored the usability of a digital tool to support the diagnostic work-up in daily memory clinic practice. We evaluated four modules that integrate multi-modal patient data (1.cognitive test; cCOG, and 2. MRI quantification; cMRI) into useful diagnostic information for clinicians (3. cDSI) and understandable and personalized information for patients (4. patient report). Methods: We conducted a mixed-methods study in five Dutch memory clinics. Fourteen clinicians (11 geriatric specialists/residents, two neurologists, one nurse practitioner) were invited to integrate the tool into routine care with 43 new memory clinic patients. We evaluated usability and user experiences through quantitative data from questionnaires (patients, care partners, clinicians), enriched with thematically analyzed qualitative data from interviews (clinicians). Results: We observed wide variation in tool use among clinicians. Our core findings were that clinicians: 1) were mainly positive about the patient report, since it contributes to patient-centered and personalized communication. This was endorsed by patients and care partners, who indicated that the patient report was useful and understandable and helped them to better understand their diagnosis, 2) considered the tool acceptable in addition to their own clinical competence, 3) indicated that the usefulness of the tool depended on the patient population and purpose of the diagnostic process, 4) addressed facilitators (ease of use, practice makes perfect) and barriers (high workload, lack of experience, data unavailability). Conclusion: This multicenter usability study revealed a willingness to adopt a digital tool to support the diagnostic process in memory clinics. Clinicians, patients, and care partners appreciated the personalized diagnostic report. More attention to education and training of clinicians is needed to utilize the full functionality of the tool and foster implementation in actual daily practice. These findings provide an important step towards a lasting adoption of digital tools in memory clinic practice. [ABSTRACT FROM AUTHOR]

Introduction: Clinical decision support (CDS) systems (CDSSs) that integrate clinical guidelines need to reflect real-world co-morbidity. In patient-specific clinical contexts, transparent recommendations that allow for contraindications and other conflicts arising from co-morbidity are a requirement. In this work, we develop and evaluate a non-proprietary, standards-based approach to the deployment of computable guidelines with explainable argumentation, integrated with a commercial electronic health record (EHR) system in Serbia, a middle-income country in West Balkans. Methods: We used an ontological framework, the Transition-based Medical Recommendation (TMR) model, to represent, and reason about, guideline concepts, and chose the 2017 International global initiative for chronic obstructive lung disease (GOLD) guideline and a Serbian hospital as the deployment and evaluation site, respectively. To mitigate potential guideline conflicts, we used a TMR-based implementation of the Assumptions-Based Argumentation framework extended with preferences and Goals (ABA+G). Remote EHR integration of computable guidelines was via a microservice architecture based on HL7 FHIR and CDS Hooks. A prototype integration was developed to manage chronic obstructive pulmonary disease (COPD) with comorbid cardiovascular or chronic kidney diseases, and a mixed-methods evaluation was conducted with 20 simulated cases and five pulmonologists. Results: Pulmonologists agreed 97% of the time with the GOLD-based COPD symptom severity assessment assigned to each patient by the CDSS, and 98% of the time with one of the proposed COPD care plans. Comments were favourable on the principles of explainable argumentation; inclusion of additional co-morbidities was suggested in the future along with customisation of the level of explanation with expertise. Conclusion: An ontological model provided a flexible means of providing argumentation and explainable artificial intelligence for a long-term condition. Extension to other guidelines and multiple co-morbidities is needed to test the approach further. [ABSTRACT FROM AUTHOR]

Background Pharmacogenomics is an emerging field that explores the relationship between a person's genetic makeup and their response to drugs. Objectives Computer applications have been instrumental in advancing pharmacogenomics research and facilitating personalized medicine. In this abstract, we review some of the key computer applications used in pharmacogenomics research, including bioinformatics tools for analyzing genetic data, machine learning algorithms for predicting drug response, and clinical decision support systems for guiding drug selection and dosing. We also highlight the challenges and opportunities in the field of pharmacogenomics, including the need for large-scale, multi-omics datasets, and the importance of developing robust and interpretable machine learning models. Overall, computer applications have played a critical role in enabling pharmacogenomics research and have the potential to revolutionize the practice of medicine by facilitating the development of more personalized and effective drug therapies. [ABSTRACT FROM AUTHOR]

This study was designed to investigate the state quo of the appropriateness of alerts overrides of the medication-related clinical decision support system (MRCDSS) in China. The medication-related alerts in one hospital from Jan 2022 to Dec 2022 were acquired and sampled. Rates of alert overrides, appropriateness of alert generation and physicians' responses were observed. Total 14,612 medicationrelated alerts (≤ level 3) were recorded, of those, 12,659 (86.6%) alerts were overridden. The top 3 alert types were: drug and diagnosis contraindications (23.8%), drug and test value contraindications (23.3%), and compatibility issues (17.7%). Of all sampled 1,501 alerts, 80.2% of them were appropriately overridden by the physicians. The appropriate rate of alert generation was 57.9% and the inappropriate rate was 42.1%. The inappropriate rate of physicians' responses was 17.8%, and 2.0% physicians' responses were undetermined. A few medications accounted for over 10% of overrides, 88.3% of "overridden reasons" inputted by the physicians were meaningless characters or values, indicating an obvious "alert fatigue" in these physicians. Our results indicated that the overridden rate of MRCDSS in China was still high, and appropriateness of generation of alert was quite low. These data indicated that the MRCDSS currently using in China still needs constantly optimization and timely maintenance. Proper sensitivity to reduce triggering of useless alerts and generation of alert fatigue might play a vital role. We believed that these findings are helpful for better understanding the state quo of MRCDSS in China and providing useful insights for future developing and improving MRCDSS. [ABSTRACT FROM AUTHOR]

The article discusses the importance of integrating molecular data into electronic health records (EHRs) for oncology treatment. It highlights the challenges of the current system, where molecular profiling results are often buried in PDF reports and require manual workflows. However, advancements in EHRs and data interoperability standards have allowed for the transmission of discrete molecular data elements into EHRs. This integration enables clinicians to access precision medicine features, such as AI tools, user interfaces for finding molecular results, clinical decision support, and clinical trial enrollment. The article also mentions specific examples of EHR systems that have successfully integrated molecular data. The authors emphasize the need to move from simply delivering molecular information to delivering actionable insights and call for collaboration between molecular technology companies, EHR systems, clinicians, and other stakeholders to achieve this goal. [Extracted from the article]

The technological capability of artificial intelligence (AI) continues to advance with great strength. Recently, the release of large language models has taken the world by storm with concurrent excitement and concern. As a consequence of their impressive ability and versatility, their provide a potential opportunity for implementation in oncology. Areas of possible application include supporting clinical decision making, education, and contributing to cancer research. Despite the promises that these novel systems can offer, several limitations and barriers challenge their implementation. It is imperative that concerns, such as accountability, data inaccuracy, and data protection, are addressed prior to their integration in oncology. As the progression of artificial intelligence systems continues, new ethical and practical dilemmas will also be approached; thus, the evaluation of these limitations and concerns will be dynamic in nature. This review offers a comprehensive overview of the potential application of large language models in oncology, as well as concerns surrounding their implementation in cancer care. [ABSTRACT FROM AUTHOR]

Background: Introducing peanut products early can prevent peanut allergy (PA). The "Addendum guidelines for the prevention of PA in the United States" (PPA guidelines) recommend early introduction of peanut products to low and moderate risk infants and evaluation prior to starting peanut products for infants at high risk for PA (those with severe eczema and/or egg allergy). Rapid adoption of guidelines could aid in lowering the prevalence of PA. The Intervention to Reduce Early (Peanut) Allergy in Children (iREACH) trial was designed to promote PPA guideline adherence by pediatric clinicians. Methods: A two‐arm, cluster‐randomized, controlled clinical trial was designed to measure the effectiveness of an intervention that included clinician education and accompanying clinical decision support tools integrated in electronic health records (EHR) versus standard care. Randomization was at the practice level (n = 30). Primary aims evaluated over an 18‐month trial period assess adherence to the PPA guidelines using EHR documentation at 4‐ and 6‐month well‐child care visits aided by natural language processing. A secondary aim will evaluate the effectiveness in decreasing the incidence of PA by age 2.5 years using EHR documentation and caregiver surveys. The unit of observation for evaluations are individual children with clustering at the practice level. Conclusion: Application of this intervention has the potential to inform the development of strategies to speed implementation of PPA guidelines. [ABSTRACT FROM AUTHOR]

This article discusses the integration of digital health and artificial intelligence (AI) in precision medicine. It explains how digital health technologies, such as electronic health records and wearable devices, provide comprehensive patient data for personalized treatment strategies. AI, powered by large datasets, can analyze this data to improve diagnosis, prognosis, and treatment outcomes. The article also highlights contributions from a special issue on the topic, covering areas such as prostate cancer prognosis, the impact of COVID-19 on pulmonary tuberculosis, and the use of AI tools in medical literature searches. Overall, the article emphasizes the potential of AI and digital health in improving patient care and decision-making in precision medicine, while also addressing challenges and the need for collaboration and ethical considerations. [Extracted from the article]

Blood cancer is often overlooked but is a significant cause of death in the UK. Lack of awareness and disparities in access to information and support are major issues. Avapritinib, a KIT D816V inhibitor, shows promise as a treatment for advanced systemic mastocytosis. Various studies in hematology and transfusion medicine explore topics such as immunomodulatory role of eltrombopag, JAK2 variant allele burden, cerebral microbleeds, plasma cell neoplasms, ABO matching for platelet transfusions, and electronic transfusion systems. Studies on lymphoma treatment discuss the use of glofitamab, epcoritamab, brentuximab vedotin, and bendamustine. Other studies cover topics like prophylactic platelet transfusion, transfusion-associated circulatory overload risk assessments, nivolumab for relapsed classical Hodgkin lymphoma, and patient journey maps for sickle cell disorder. Additional studies examine critical illness, extracorporeal membrane oxygenation, emicizumab for severe hemophilia A, rivaroxaban for cerebral venous sinus thrombosis, and CAR-T cell therapy outcomes. Studies related to hematological diseases and treatments cover topics like CMV reactivation, BEAM autologous transplantation, obe-cel for B-cell acute lymphoblastic leukemia, and allogeneic hematopoietic cell transplantation for myelofibrosis. Leukemia studies [Extracted from the article]

Acute bacterial upper respiratory infections are common indications for antibiotics in pediatrics, and many prescriptions may be inappropriate. Novel approaches to outpatient antimicrobial stewardship interventions are needed. This quasi-experimental study of an order set and best practice advisory alert targeting cefdinir prescriptions demonstrated an 8.4% decrease in cefdinir prescribing (P ≤.001). [ABSTRACT FROM AUTHOR]

Amidst the US overdose epidemic, policymakers, law enforcement agencies, and healthcare institutions have contributed to a decrease in opioid prescribing, assuming reduced mortality would result—an assumption we now understand was oversimplified. At this intersection between public health and public safety domains as they relate to opioid prescribing, unregulated and proprietary clinical decision support tools have emerged without rigorous external validation or public data sharing. In the following piece, we discuss challenges facing clinicians practicing medicine amidst unregulated clinical decision support tools, using the case of Bamboo Health's NarxCare—a prescription drug monitoring program–based analytics platform marketed as a clinical decision support tool—that is already positioned to impact over 1 billion patient encounters annually. We argue that sufficient evidence does not yet exist to support NarxCare's wide implementation, and that clinical decision support tools like NarxCare have flourished in recent years due to a lack of federal regulatory oversight and shielding by their proprietary formulas, which have facilitated their unchecked and outsized influence on patient care. Finally, we suggest specific actions by federal regulatory agencies, healthcare institutions, individual clinicians, and researchers, as well as academic journals, to mitigate potential harms associated with unregulated clinical decision support tools. [ABSTRACT FROM AUTHOR]

Background: While many falls are preventable, they remain a leading cause of injury and death in older adults. Primary care clinics largely rely on screening questionnaires to identify people at risk of falls. Limitations of standard fall risk screening questionnaires include suboptimal accuracy, missing data, and non‐standard formats, which hinder early identification of risk and prevention of fall injury. We used machine learning methods to develop and evaluate electronic health record (EHR)‐based tools to identify older adults at risk of fall‐related injuries in a primary care population and compared this approach to standard fall screening questionnaires. Methods: Using patient‐level clinical data from an integrated healthcare system consisting of 16‐member institutions, we conducted a case–control study to develop and evaluate prediction models for fall‐related injuries in older adults. Questionnaire‐derived prediction with three questions from a commonly used fall risk screening tool was evaluated. We then developed four temporal machine learning models using routinely available longitudinal EHR data to predict the future risk of fall injury. We also developed a fall injury‐prevention clinical decision support (CDS) implementation prototype to link preventative interventions to patient‐specific fall injury risk factors. Results: Questionnaire‐based risk screening achieved area under the receiver operating characteristic curve (AUC) up to 0.59 with 23% to 33% similarity for each pair of three fall injury screening questions. EHR‐based machine learning risk screening showed significantly improved performance (best AUROC = 0.76), with similar prediction performance between 6‐month and one‐year prediction models. Conclusions: The current method of questionnaire‐based fall risk screening of older adults is suboptimal with redundant items, inadequate precision, and no linkage to prevention. A machine learning fall injury prediction method can accurately predict risk with superior sensitivity while freeing up clinical time for initiating personalized fall prevention interventions. The developed algorithm and data science pipeline can impact routine primary care fall prevention practice. [ABSTRACT FROM AUTHOR]

The article discusses an analysis of the Enhancing Quality of Prescribing Practices for Older Adults Discharged from the Emergency Department (EQUIPPED) implementation trial to determine trends in gabapentinoid prescribing among U.S. Veterans Affairns (VA) emergency departments (ED) implementing the program. Cited are a comparison of gabapentinoid prescribing during the pre- and post-implementation periods in the dashboard and academic detailing groups, and limitations of the study.

Thromboprophylaxis of hospitalized patients at risk of venous thromboembolism (VTE) presents challenges owing to patient heterogeneity and lack of adoption of evidence-based methods. Intuitive practices for thromboprophylaxis have resulted in many patients being inappropriately prophylaxed. We conducted a narrative review summarizing system-wide thromboprophylaxis interventions in hospitalized patients. Multiple interventions for thromboprophylaxis have been tested, including multifaceted approaches such as national VTE prevention programs with audits, pre-printed order entry, passive alerts (either human or electronic), and more recently, the use of active clinical decision support (CDS) tools incorporated into electronic health records (EHRs). Multifaceted health-system and order entry interventions have shown mixed results in their ability to increase appropriate thromboprophylaxis and reduce VTE unless mandated through a national VTE prevention program, though the latter approach is potentially costly and effort- and time-dependent. Studies utilizing passive human or electronic alerts have also shown mixed results in increasing appropriate thromboprophylaxis and reducing VTE. Recently, a universal cloud-based and EHR-agnostic CDS VTE tool incorporating a validated VTE risk score revealed high adoption and effectiveness in increasing appropriate thromboprophylaxis and reducing major thromboembolism. Active CDS tools hold promise in improving appropriate thromboprophylaxis, especially with further refinement and widespread implementation within various EHRs and clinical workflows. [ABSTRACT FROM AUTHOR]

BACKGROUND: Collaboration for the Diagnosis and Evaluation of Acute Coronary Syndrome (CoDE-ACS) is a validated clinical decision support tool that uses machine learning with or without serial cardiac troponin measurements at a flexible time point to calculate the probability of myocardial infarction (MI). How CoDE-ACS performs at different time points for serial measurement and compares with guideline-recommended diagnostic pathways that rely on fixed thresholds and time points is uncertain. METHODS: Patients with possible MI without ST-segment-elevation were enrolled at 12 sites in 5 countries and underwent serial high-sensitivity cardiac troponin I concentration measurement at 0, 1, and 2 hours. Diagnostic performance of the CoDE-ACS model at each time point was determined for index type 1 MI and the effectiveness of previously validated low- and high-probability scores compared with guideline-recommended European Society of Cardiology (ESC) 0/1-hour, ESC 0/2-hour, and High-STEACS (High-Sensitivity Troponin in the Evaluation of Patients With Suspected Acute Coronary Syndrome) pathways. RESULTS: In total, 4105 patients (mean age, 61 years [interquartile range, 50-74]; 32% women) were included, among whom 575 (14%) had type 1 MI. At presentation, CoDE-ACS identified 56% of patients as low probability, with a negative predictive value and sensitivity of 99.7% (95% CI, 99.5%-99.9%) and 99.0% (98.6%-99.2%), ruling out more patients than the ESC 0-hour and High-STEACS (25% and 35%) pathways. Incorporating a second cardiac troponin measurement, CoDE-ACS identified 65% or 68% of patients as low probability at 1 or 2 hours, for an identical negative predictive value of 99.7% (99.5%-99.9%); 19% or 18% as high probability, with a positive predictive value of 64.9% (63.5%-66.4%) and 68.8% (67.3%-70.1%); and 16% or 14% as intermediate probability. In comparison, after serial measurements, the ESC 0/1-hour, ESC 0/2-hour, and High-STEACS pathways identified 49%, 53%, and 71% of patients as low risk, with a negative predictive value of 100% (99.9%-100%), 100% (99.9%-100%), and 99.7% (99.5%-99.8%); and 20%, 19%, or 29% as high risk, with a positive predictive value of 61.5% (60.0%-63.0%), 65.8% (64.3%-67.2%), and 48.3% (46.8%-49.8%), resulting in 31%, 28%, or 0%, who require further observation in the emergency department, respectively. CONCLUSIONS: CoDE-ACS performs consistently irrespective of the timing of serial cardiac troponin measurement, identifying more patients as low probability with comparable performance to guideline-recommended pathways for MI. Whether care guided by probabilities can improve the early diagnosis of MI requires prospective evaluation. [ABSTRACT FROM AUTHOR]

Personalized clinical decision support systems are increasingly being adopted due to the emergence of data-driven technologies, with this approach now gaining recognition in critical care. The task of incorporating diverse patient conditions and treatment procedures into critical care decision-making can be challenging due to the heterogeneous nature of medical data. Advances in Artificial Intelligence (AI), particularly Reinforcement Learning (RL) techniques, enables the development of personalized treatment strategies for severe illnesses by using a learning agent to recommend optimal policies. In this study, we propose a Deep Reinforcement Learning (DRL) model with a tailored reward function and an LSTM-GRU-derived state representation to formulate optimal treatment policies for vasopressor administration in stabilizing patient physiological states in critical care settings. Using an ICU dataset and the Medical Information Mart for Intensive Care (MIMIC-III) dataset, we focus on patients with Acute Respiratory Distress Syndrome (ARDS) that has led to Sepsis, to derive optimal policies that can prioritize patient recovery over patient survival. Both the DDQN (RepDRL-DDQN) and Dueling DDQN (RepDRL-DDDQN) versions of the DRL model surpass the baseline performance, with the proposed model's learning agent achieving an optimal learning process across our performance measuring schemes. The robust state representation served as the foundation for enhancing the model's performance, ultimately providing an optimal treatment policy focused on rapid patient recovery. [ABSTRACT FROM AUTHOR]

The document titled "Guidelines for the specification, implementation and management of IT systems in hospital transfusion laboratories: A British Society for Haematology Guideline" provides guidelines for hospital blood transfusion laboratories on the specification, implementation, and management of Laboratory Information Management Systems (LIMS). The guidelines address the use of IT systems in transfusion medicine, including electronic blood administration systems, recording administration/final fate information, information management, and system management. The document emphasizes the importance of patient identification, stock management, stock ordering, and stock tracking in transfusion laboratories. It also clarifies the use of terms relating to gender in clinical transfusion practice and provides recommendations for compliance with the guidelines. The document provides guidelines for the management of unused units in the transfusion process, as well as the management of patient records and data integrity. It discusses the use of unique patient identifiers, the merging and linking of patient records, and the generation of transfusion requests. The document highlights the benefits of electronic request systems and the use of Order Comms in improving patient safety and the efficiency of the transfusion process. It also addresses sample collection and the handling of samples and requests in the laboratory. The text discusses the importance of accurate patient identification and record-keeping in a laboratory information management system (LIMS) for blood transfusion processes. It emphasizes the need for unique patient identification, barcode labeling of samples, and matching requests to patient records. The LIMS should also support electronic transfer of information, automated testing processes [Extracted from the article]

Artificial intelligence (AI) and machine learning (ML) are becoming vital in laboratory medicine and the broader context of healthcare. In this review article, we summarized the development of ML models and how they contribute to clinical laboratory workflow and improve patient outcomes. The process of ML model development involves data collection, data cleansing, feature engineering, model development, and optimization. These models, once finalized, are subjected to thorough performance assessments and validations. Recently, due to the complexity inherent in model development, automated ML tools were also introduced to streamline the process, enabling non-experts to create models. Clinical Decision Support Systems (CDSS) use ML techniques on large datasets to aid healthcare professionals in test result interpretation. They are revolutionizing laboratory medicine, enabling labs to work more efficiently with less human supervision across pre-analytical, analytical, and post-analytical phases. Despite contributions of the ML tools at all analytical phases, their integration presents challenges like potential model uncertainties, black-box algorithms, and deskilling of professionals. Additionally, acquiring diverse datasets is hard, and models' complexity can limit clinical use. In conclusion, ML-based CDSS in healthcare can greatly enhance clinical decision-making. However, successful adoption demands collaboration among professionals and stakeholders, utilizing hybrid intelligence, external validation, and performance assessments. [ABSTRACT FROM AUTHOR]

Background: Electronic clinical decision support systems (eCDSS), such as the 'Systematic Tool to Reduce Inappropriate Prescribing' Assistant (STRIPA), have become promising tools for assisting general practitioners (GPs) with conducting medication reviews in older adults. Little is known about how GPs perceive eCDSS-assisted recommendations for pharmacotherapy optimization. The aim of this study was to explore the implementation of a medication review intervention centered around STRIPA in the 'Optimising PharmacoTherapy In the multimorbid elderly in primary CAre' (OPTICA) trial. Methods: We used an explanatory mixed methods design combining quantitative and qualitative data. First, quantitative data about the acceptance and implementation of eCDSS-generated recommendations from GPs (n = 21) and their patients (n = 160) in the OPTICA intervention group were collected. Then, semi-structured qualitative interviews were conducted with GPs from the OPTICA intervention group (n = 8), and interview data were analyzed through thematic analysis. Results: In quantitative findings, GPs reported averages of 13 min spent per patient preparing the eCDSS, 10 min performing medication reviews, and 5 min discussing prescribing recommendations with patients. On average, out of the mean generated 3.7 recommendations (SD=1.8). One recommendation to stop or start a medication was reported to be implemented per patient in the intervention group (SD=1.2). Overall, GPs found the STRIPA useful and acceptable. They particularly appreciated its ability to generate recommendations based on large amounts of patient information. During qualitative interviews, GPs reported the main reasons for limited implementation of STRIPA were related to problems with data sourcing (e.g., incomplete data imports), preparation of the eCDSS (e.g., time expenditure for updating and adapting information), its functionality (e.g., technical problems downloading PDF recommendation reports), and appropriateness of recommendations. Conclusions: Qualitative findings help explain the relatively low implementation of recommendations demonstrated by quantitative findings, but also show GPs' overall acceptance of STRIPA. Our results provide crucial insights for adapting STRIPA to make it more suitable for regular use in future primary care settings (e.g., necessity to improve data imports). Trial registration: Clinicaltrials.gov NCT03724539, date of first registration: 29/10/2018. [ABSTRACT FROM AUTHOR]

Opioids are commonly prescribed for the management of acute orthopedic trauma pain, including nonoperative distal radius fractures. This prospective study aimed to determine if a clinical decision support intervention influenced prescribing decisions for patients with known risk factors. We sought to quantify frequency of opioid prescriptions for acute nonoperative distal radius fractures treated. We performed a prospective study at one large health care system. Utilizing umbrella code S52.5, we identified all distal radius fractures treated nonoperatively, and the encounters were merged with the Prescription Reporting with Immediate Medication Mapping (PRIMUM) database to identify encounters with opioid prescriptions and patients with risk factors for opioid use disorder. We used multivariable logistic regression to determine patient characteristics associated with the prescription of an opioid. Among encounters that triggered the PRIMUM alert, we calculated the percentage of encounters where the PRIMUM alert influenced the prescribing decision. Of 2984 encounters, 1244 (41.7%) included an opioid prescription. Age increment is a significant factor to more likely receive opioid prescriptions (p < 0.0001) after adjusting for other factors. Among encounters where the physician received an alert, those that triggered the alert for early refill were more likely to influence physicians' opioid prescribing when compared with other risk factors (p = 0.0088). Over 90% of patients (106/118) continued to receive an opioid medication despite having a known risk factor for abuse. Additionally, we found older patients were more likely to be prescribed opioids for nonoperatively managed distal radius fractures. [ABSTRACT FROM AUTHOR]

Purpose: This narrative review examines the evolving landscape of artificial intelligence (AI) integration in skull base surgery, exploring its multifaceted applications and impact on various aspects of patient care. Methods: Extensive literature review was conducted to gather insights into the role of AI in skull base surgery. Key aspects such as diagnosis, image analysis, surgical planning, navigation, predictive analytics, clinical decision-making, postoperative care, rehabilitation, and virtual simulations were explored. Studies were sourced from PubMed using keyword search strategy for relevant headings, sub-headings and cross-referencing. Results: AI enhances early diagnosis through diagnostic algorithms that guide investigations based on clinical and radiological data. AI-driven image analysis enables accurate segmentation of intricate structures and extraction of radiomics data, optimizing preoperative planning and predicting treatment response. In surgical planning, AI aids in identifying critical structures, leading to precise interventions. Real-time AI-based navigation offers adaptive guidance, enhancing surgical accuracy and safety. Predictive analytics empower risk assessment, treatment planning, and outcome prediction. AI-driven clinical decision support systems optimize resource allocation and support shared decision-making. Postoperative care benefits from AI's monitoring capabilities and personalized rehabilitation protocols. Virtual simulations powered by AI expedite skill development and decision-making in complex procedures. Conclusion: AI contributes to accurate diagnosis, surgical planning, navigation, predictive analysis, and postoperative care. Ethical considerations and data quality assurance are essential, ensuring responsible AI implementation. While AI serves as a valuable complement to clinical expertise, its potential to enhance decision-making, precision, and efficiency in skull base surgery is evident. [ABSTRACT FROM AUTHOR]

Purpose: Clinical decision support tools (CDSTs) are software that generate patient-specific assessments that can be used to better inform healthcare provider decision making. Machine learning (ML)-based CDSTs have recently been developed for anatomic (aTSA) and reverse (rTSA) total shoulder arthroplasty to facilitate more data-driven, evidence-based decision making. Using this shoulder CDST as an example, this external validation study provides an overview of how ML-based algorithms are developed and discusses the limitations of these tools. Methods: An external validation for a novel CDST was conducted on 243 patients (120F/123M) who received a personalized prediction prior to surgery and had short-term clinical follow-up from 3 months to 2 years after primary aTSA (n = 43) or rTSA (n = 200). The outcome score and active range of motion predictions were compared to each patient's actual result at each timepoint, with the accuracy quantified by the mean absolute error (MAE). Results: The results of this external validation demonstrate the CDST accuracy to be similar (within 10%) or better than the MAEs from the published internal validation. A few predictive models were observed to have substantially lower MAEs than the internal validation, specifically, Constant (31.6% better), active abduction (22.5% better), global shoulder function (20.0% better), active external rotation (19.0% better), and active forward elevation (16.2% better), which is encouraging; however, the sample size was small. Conclusion: A greater understanding of the limitations of ML-based CDSTs will facilitate more responsible use and build trust and confidence, potentially leading to greater adoption. As CDSTs evolve, we anticipate greater shared decision making between the patient and surgeon with the aim of achieving even better outcomes and greater levels of patient satisfaction. [ABSTRACT FROM AUTHOR]

RATIONALE: Maintaining glycemic control of critically ill patients may impact outcomes such as survival, infection, and neuromuscular recovery, but there is equipoise on the target blood levels, monitoring frequency, and methods. OBJECTIVES: The purpose was to update the 2012 Society of Critical Care Medicine and American College of Critical Care Medicine (ACCM) guidelines with a new systematic review of the literature and provide actionable guidance for clinicians. PANEL DESIGN: The total multiprofessional task force of 22, consisting of clinicians and patient/family advocates, and a methodologist applied the processes described in the ACCM guidelines standard operating procedure manual to develop evidence-based recommendations in alignment with the Grading of Recommendations Assessment, Development, and Evaluation Approach (GRADE) methodology. Conflict of interest policies were strictly followed in all phases of the guidelines, including panel selection and voting. METHODS: We conducted a systematic review for each Population, Intervention, Comparator, and Outcomes question related to glycemic management in critically ill children (≥ 42 wk old adjusted gestational age to 18 yr old) and adults, including triggers for initiation of insulin therapy, route of administration, monitoring frequency, role of an explicit decision support tool for protocol maintenance, and methodology for glucose testing. We identified the best available evidence, statistically summarized the evidence, and then assessed the quality of evidence using the GRADE approach. We used the evidence-to-decision framework to formulate recommendations as strong or weak or as a good practice statement. In addition, "In our practice" statements were included when the available evidence was insufficient to support a recommendation, but the panel felt that describing their practice patterns may be appropriate. Additional topics were identified for future research. RESULTS: This guideline is an update of the guidelines for the use of an insulin infusion for the management of hyperglycemia in critically ill patients. It is intended for adult and pediatric practitioners to reassess current practices and direct research into areas with inadequate literature. The panel issued seven statements related to glycemic control in unselected adults (two good practice statements, four conditional recommendations, one research statement) and seven statements for pediatric patients (two good practice statements, one strong recommendation, one conditional recommendation, two "In our practice" statements, and one research statement), with additional detail on specific subset populations where available. CONCLUSIONS: The guidelines panel achieved consensus for adults and children regarding a preference for an insulin infusion for the acute management of hyperglycemia with titration guided by an explicit clinical decision support tool and frequent (≤ 1 hr) monitoring intervals during glycemic instability to minimize hypoglycemia and against targeting intensive glucose levels. These recommendations are intended for consideration within the framework of the patient's existing clinical status. Further research is required to evaluate the role of individualized glycemic targets, continuous glucose monitoring systems, explicit decision support tools, and standardized glycemic control metrics. [ABSTRACT FROM AUTHOR]

Trauma is commonly overlooked or undiagnosed in clinical care settings. Undetected trauma has been associated with elevated substance use highlighting the need to prioritize identifying individuals with undetected trauma through common characteristics. The purpose of this study is to identify classifications of traumatic life experiences and substance use among persons admitted to inpatient psychiatry in Ontario and to identify covariates associated with classification membership. A population-based retrospective cohort study was conducted using interRAI Mental Health (MH) assessment data. Individuals were included who experienced traumatic life events (N = 10,125), in Ontario, Canada between January 1, 2015, to December 31, 2019. Eight latent classes were identified that ranged from low (i.e., Class 1: Interpersonal Issues, Without Substance use) to high (i.e., Class 8: Widespread Trauma, Alcohol & Cannabis Addiction) complexity patterns of traumatic life events and substance use indicators. Classifications with similar trauma profiles were differentiated by patterns of substance use. For example, individuals in Class 2: Safety & Relationship Issues, Without Substance use and Class 3: Safety & Relationship Issues, Alcohol & Cannabis both had many estimates centered around the experience of victimization (e.g., victim of sexual assault, victim of physical assault, victim of emotional abuse). Multinomial logistic regression models highlighted additional factors associated with classifications such as homelessness, where those who were homeless were 2.09–4.02 times more likely to be in Class 6: Widespread Trauma & Substance Addiction. Trauma exposures are complex and varied among persons in inpatient psychiatry and can be further differentiated by substance use patterns. These findings provide a population-based estimate of the trauma experiences of persons in inpatient settings in Ontario, Canada. Findings demonstrate the importance of using comprehensive assessment to support clinical decision making in relation to trauma and substance. [ABSTRACT FROM AUTHOR]

Purpose: Evaluate the effectiveness of the clinical decision support tools (CDSTs), POC Advisor (POCA), and Modified Early Warning System (MEWS) in identifying sepsis risk and influencing time to treatment for inpatients, comparing their respective alert mechanisms. Methods: This study was conducted at two academic university medical center hospitals. Data from adult inpatients in medical-surgical and telemetry units were analyzed from January 1, 2020, to December 31, 2020. Criteria included sepsis-related ICD-10 codes, antibiotic administration, and ordered sepsis labs. Subsequent statistical analyses utilized Fisher's exact test and Wilcoxon Rank Sum test, focusing on mortality differences by age, sex, and race/ethnicity. Results: Among 744 patients, 143 sepsis events were identified, with 83% already receiving treatment upon CDST alert. Group 1 (POCA alert) showed reduced response time compared with MEWS, while Group 3 (MEWS) experienced longer time to treatment. Group 4 included sepsis events missed by both systems. Mortality differences were not significant among the groups. Conclusion: While CDSTs play a role, nursing assessment and clinical judgment are crucial. This study recognized the potential for alarm fatigue due to a high number of CDST-driven alerts, while emphasizing the importance of a collaborative approach for prompt sepsis treatment and potential reduction in sepsis-related mortality. [ABSTRACT FROM AUTHOR]