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Early warning scores (EWS) are routinely used in hospitals to assess a patient's risk of deterioration. EWS are traditionally recorded on paper observation charts but are increasingly recorded digitally. In either case, evidence for the clinical effectiveness of such scores is mixed, and previous studies have not considered whether EWS leads to changes in how deteriorating patients are managed. This study aims to examine whether the introduction of a digital EWS system was associated with more frequent observation of patients with abnormal vital signs, a precursor to earlier clinical intervention. We conducted a 2-armed stepped-wedge study from February 2015 to December 2016, over 4 hospitals in 1 UK hospital trust. In the control arm, vital signs were recorded using paper observation charts. In the intervention arm, a digital EWS system was used. The primary outcome measure was time to next observation (TTNO), defined as the time between a patient's first elevated EWS (EWS ≥3) and subsequent observations set. Secondary outcomes were time to death in the hospital, length of stay, and time to unplanned intensive care unit admission. Differences between the 2 arms were analyzed using a mixed-effects Cox model. The usability of the system was assessed using the system usability score survey. We included 12,802 admissions, 1084 in the paper (control) arm and 11,718 in the digital EWS (intervention) arm. The system usability score was 77.6, indicating good usability. The median TTNO in the control and intervention arms were 128 (IQR 73-218) minutes and 131 (IQR 73-223) minutes, respectively. The corresponding hazard ratio for TTNO was 0.99 (95% CI 0.91-1.07; P=.73). We demonstrated strong clinical engagement with the system. We found no difference in any of the predefined patient outcomes, suggesting that the introduction of a highly usable electronic system can be achieved without impacting clinical care. Our findings contrast with previous claims that digital EWS systems are associated with improvement in clinical outcomes. Future research should investigate how digital EWS systems can be integrated with new clinical pathways adjusting staff behaviors to improve patient outcomes.

Governmental and non-governmental organisations in low-income countries have transitioned from paper data collection to electronic data collection at a slower pace than those in high-income countries due to limitations of funding, internet stability and trained personnel, among other reasons. This paper chronicles the process employed by one small non-governmental organisation in Malawi from the selection of a programme through implementation, describing challenges and successes along the way in order to facilitate the adoption process for NGOs in sub-Saharan Africa or other low-income countries. Upgrading the data collection system presented a daunting challenge. Despite the significant learning curve for the entire team, the implementation phase proceeded smoothly. Although staff remained apprehensive, they also understood the need for change and fully invested in the process. The benefits of electronic charting outweigh the struggles involved in learning a new system. Key elements of the process that supported success were active engagement of users at all points during the transition, selection of a programme that was well-suited for the size and needs of the NGO, and appropriate support from ancillary staff and outside experts.

Transitions in care are vulnerable periods in health care that can expose patients to preventable errors due to incomplete or delayed communication between health care providers. Transitioning critically ill patients from intensive care units (ICUs) to other patient care units (PCUs) is particularly risky, due to the high acuity of the patients and the diversity of health care providers involved in their care. Instituting structured documentation to standardize written communication between health care providers during transitions has been identified as a promising means to reduce communication breakdowns. We developed an evidence-informed, computer-enabled, ICU-specific structured tool-an electronic transfer (e-transfer) tool-to facilitate and standardize the composition of written transfer summaries in the ICUs of one Canadian city. The tool consisted of 10 primary sections with a user interface combination of structured, automated, and free-text fields. Our overarching goal is to evaluate whether implementation of our e-transfer tool will improve the completeness and timeliness of transfer summaries and streamline communications between health care providers during high-risk transitions. This study is a cluster-specific pre-post trial, with randomized and staggered implementation of the e-transfer tool in four hospitals in Calgary, Alberta. Hospitals (ie, clusters) were allocated randomly to cross over every 2 months from control (ie, dictation only) to intervention (ie, e-transfer tool). Implementation at each site was facilitated with user education, point-of-care support, and audit and feedback. We will compare transfer summaries randomly sampled over 6 months postimplementation to summaries randomly sampled over 6 months preimplementation. The primary outcome will be a binary composite measure of the timeliness and completeness of transfer summaries. Secondary measures will include overall completeness, timeliness, and provider ratings of transfer summaries; hospital and ICU lengths of stay; and post-ICU patient outcomes, including ICU readmission, adverse events, cardiac arrest, rapid response team activation, and mortality. We will use descriptive statistics (ie, medians and means) to describe demographic characteristics. The primary outcome will be compared within each hospital pre- and postimplementation using separate logistic regression models for each hospital, with adjustment for patient characteristics. Participating hospitals were cluster randomized to the intervention between July 2018 and January 2019. Preliminary extraction of ICU patient admission lists was completed in September 2019. We anticipate that evaluation data collection will be completed by early 2021, with first results ready for publication in spring or summer 2021. This study will report the impact of implementing an evidence-informed, computer-enabled, ICU-specific structured transfer tool on communication and preventable medical errors among patients transferred from the ICU to other hospital care units. ClinicalTrials.gov NCT03590002; https://www.clinicaltrials.gov/ct2/show/NCT03590002. DERR1-10.2196/18675.

This study describes the efforts to implement electronic charting in a large public psychiatric outpatient clinic with the objective to improve clinical documentation. Data made available through the quality review process are utilized to evaluate the effectiveness of the electronic intervention. The study is a comparative analysis of the three years before and three years after the point of implementation of electronic charting. Statistical analyses indicate significant findings ( <.0001) in the comparison of the periods before and after implementation in terms of note completion and documentation of medication management, supporting the study's hypothesis that electronic intervention will improve the quality of clinical documentation. This study contributes new knowledge to improve our understanding of the barriers and benefits of implementing and maintaining electronic charting in mental health settings.

Dysphagia in adults affects their quality of life and can lead to life-threatening conditions. The authors draw on both 30 years of experience as clinicians and also on expert testimony in adult, dysphagia-malpractice cases to make five recommendations with the aim of preventing dysphagia-related deaths. They discuss the importance of informed consent documents and suggest the following nursing actions to reduce these often unnecessary tragedies: consider the importance of diet status; understand and follow speech-language-pathologists’ recommendations; be familiar with the dysphagia assessment; be responsive to the need for an instrumental assessment; and ensure dysphagia communication is accurate and disseminated among healthcare professionals. They conclude that most negative dysphagia-management outcomes can be prevented and that nurses play a pivotal role in this prevention.

Emotion charting using multimodal signals has gained great demand for stroke-affected patients, for psychiatrists while examining patients, and for neuromarketing applications. Multimodal signals for emotion charting include electrocardiogram (ECG) signals, electroencephalogram (EEG) signals, and galvanic skin response (GSR) signals. EEG, ECG, and GSR are also known as physiological signals, which can be used for identification of human emotions. Due to the unbiased nature of physiological signals, this field has become a great motivation in recent research as physiological signals are generated autonomously from human central nervous system. Researchers have developed multiple methods for the classification of these signals for emotion detection. However, due to the non-linear nature of these signals and the inclusion of noise, while recording, accurate classification of physiological signals is a challenge for emotion charting. Valence and arousal are two important states for emotion detection; therefore, this paper presents a novel ensemble learning method based on deep learning for the classification of four different emotional states including high valence and high arousal (HVHA), low valence and low arousal (LVLA), high valence and low arousal (HVLA) and low valence high arousal (LVHA). In the proposed method, multimodal signals (EEG, ECG, and GSR) are preprocessed using bandpass filtering and independent components analysis (ICA) for noise removal in EEG signals followed by discrete wavelet transform for time domain to frequency domain conversion. Discrete wavelet transform results in spectrograms of the physiological signal and then features are extracted using stacked autoencoders from those spectrograms. A feature vector is obtained from the bottleneck layer of the autoencoder and is fed to three classifiers SVM (support vector machine), RF (random forest), and LSTM (long short-term memory) followed by majority voting as ensemble classification. The proposed system is trained and tested on the AMIGOS dataset with k-fold cross-validation. The proposed system obtained the highest accuracy of 94.5% and shows improved results of the proposed method compared with other state-of-the-art methods.

Decision aids empower patients to understand how treatment options match their preferences. Choice experiments, a method to clarify values used within decision aids, present patients with hypothetical scenarios to reveal their preferences for treatment characteristics. Given the rise in research embedding choice experiments in decision tools and the emergence of novel developments in embedding methodology, a scoping review is warranted. This scoping review examines how choice experiments are embedded into decision tools and how these tools are evaluated, to identify best practices. This scoping review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Searches were conducted on MEDLINE, PsycInfo, and Web of Science. The methodology, development and evaluation details of decision aids were extracted and summarized using narrative synthesis. Overall, 33 papers reporting 22 tools were included in the scoping review. These tools were developed for various health conditions, including musculoskeletal (7/22, 32%), oncological (8/22, 36%), and chronic conditions (7/22, 32%). Most decision tools (17/22, 77%) were developed in the United States, with the remaining tools originating in the Netherlands, United Kingdom, Canada, and Australia. The number of publications increased, with 73% (16/22) published since 2015, peaking at 4 publications in 2019. The primary purpose of these tools (20/22, 91%) was to help patients compare or choose treatments. Adaptive conjoint analysis was the most frequently used design type (10/22, 45%), followed by conjoint analysis and discrete choice experiments (DCEs; both 4/22, 18%), modified adaptive conjoint analysis (3/22, 14%), and adaptive best-worst conjoint analysis (1/22, 5%). The number of tasks varied depending on the design (6-12 for DCEs and adaptive conjoint vs 16-20 for conjoint analysis designs). Sawtooth software was commonly used (14/22, 64%) to embed choice tasks. Four proof-of-concept embedding methods were identified: scenario analysis, known preference phenotypes, Bayesian collaborative filtering, and penalized multinomial logit model. After completing the choice tasks patients received tailored information, 73% (16/22) of tools provided attribute importance scores, and 23% (5/22) presented a \"best match\" treatment ranking. To convey probabilistic attributes, most tools (13/22, 59%) used a combination of approaches, including percentages, natural frequencies, icon arrays, narratives, and videos. The tools were evaluated across diverse study designs (randomized controlled trials, mixed methods, and cohort studies), with sample sizes ranging from 23 to 743 participants. Over 40 different outcomes were included in the evaluations, with the decisional conflict scale being the most frequently used in 6 tools. This scoping review provides an overview of how choice experiments are embedded into decision tools. It highlights the lack of established best practices for embedding methods, with only 4 proof-of-concept methods identified. Furthermore, the review reveals a lack of consensus on outcome measures, emphasizing the need for standardized outcome selection for future evaluations.

Objectives Physician documentation plays a central role in the delivery of safe patient care. It describes a physician’s clinical decision-making and supports essential communication between healthcare providers within the patient’s circle of care. Good documentation can potentially also decrease a physician’s medico-legal risk. This study provides examples of documentation issues attributed to physicians practicing emergency medicine as identified by peer experts in civil legal actions, regulatory authority complaints (College) and hospital complaints (collectively, medico-legal cases) in Canada. Methods We conducted a descriptive study and content analysis of medico-legal cases involving emergency department physicians from a national repository at the Canadian Medical Protective Association. Cases with peer expert criticism of an emergency physician’s documentation, which were closed between 2016 and 2020, and occurred in an emergency department were included in our analysis. Results Of the 1628 cases involving emergency medicine, our inclusion criteria identified that absent or insufficiently detailed documentation was present in 24% of cases (391/1,628). A detailed review of 20% of the cases (79/391), selected randomly, found that documentation issues were most often associated with the assessment and investigation stage of care. This pertained to documenting details of the clinical examination, relevant medical history, diagnosis, and differential diagnosis. Conclusions For physicians practicing emergency medicine, criticism of documentation was frequently observed in medico-legal cases. Based on the findings of this study and the expert criticism related to documentation, emergency medicine physicians may consider reflecting upon their documentation of the care provided to determine if their documentation provides a clear and accurate chronicle of the care and the rationale for their clinical decisions.

In the Neonatal Intensive Care Unit (NICU), clinicians must balance the demands of constant patient monitoring with the need for precise documentation and clear communication with colleagues and families. To address the clinical burden of documenting patient care and health status, this paper presents two complementary AI-based systems. First, a GAN-driven NICU Patient Simulator is developed to generate realistic neonatal vital sign data and discrete clinical intervention events, typical of care in the NICU. While useful for a variety of research goals, this simulator provides a safe and controllable data source essential for the development and validation of the second system: the LLM-powered Neonatal Patient Status Summarizer (NPSS). The NPSS fuses the output of multiple machine learning systems, each extracting specific aspects of patient care and health, together with vital sign data from a patient monitor. Leveraging Retrieval-Augmented Generation (RAG) to incorporate neonatal-specific reference data, the NPSS enables several key use cases, including generating parent-friendly updates, summarizing patient status for clinician handovers, and automatically populating patient records for charting. Simulator validation demonstrates the high fidelity of the simulated data relative to available infant data in Physionet. The NPSS is evaluated using an automated LLM-as-judge framework across repeated test scenarios. To mitigate self-preference bias, evaluations were conducted using three distinct LLM judges (OpenAI o3-mini, Llama-3, and Mistral). Across judges, the NPSS achieved consistently high relevance scores (0.95–0.99) and strong groundedness scores (0.80–0.91), indicating that generated summaries remain on-topic and faithful to the underlying simulator data. Once validated, the NPSS will reduce charting workload, improve shift handover efficiency, and streamline parental updates, addressing key clinical bottlenecks in NICU data workflows.

This research boarded on a novel initiative to replace the error-prone and labour-intensive process of converting Paper Nautical Chart (PNC) symbols to Electronic Navigational Chart (ENC) symbols with a more efficient and automated manner using Artificial Intelligence (AI). The proposed method applies the Convolutional Neural Network and YOLOv5 model to recognise and convert symbols from PNC into their corresponding ENC formats. The model's competence was evaluated with performance metrics including Precision, Recall, Average Precision, and mean Average Precision. Among the different variations of the YOLOv5 models tested, the YOLOv5m version revealed the best performance achieving a mean Average Precision of 0 ⋅ 837 for all features. A confusion matrix was used to visualise the model's classification accuracy for various chart symbols, underlining strengths and identifying areas for improvements. While the model has demonstrated high ability in identifying symbols like ‘Obstruction’ and ‘Major/Minor Lights’, it exhibited lesser accuracy with ‘Visible Wreck’ and ‘Background’ categories. Further, the developed graphical user interface (GUI) allowed users to interact with the artificial neural network model without demanding detailed knowledge of the underlying programming or model architecture.