BMJ Health & Care Informatics最新文献

Introduction: Emergency departments are facing increasing strain due to overcrowding and resource shortages, leading to the suspension of some services. Stratifying the clinical risk-defined as the severity and likelihood of harm-is crucial for anticipating care needs and supporting decision-making. Implementing predictive models for clinical risk management offers a technological solution to this challenge. This systematic review will evaluate the performance and usefulness of a predictive model for managing the clinical risk of people who visit the emergency department.

Methods and analysis: Eight electronic databases will be searched (CINAHL Plus, Health Technology Assessment Database, MedicLatina, MEDLINE, PubMed, Scopus, Cochrane Plus Collection, Web of Science). Risk of bias will be assessed using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and Prediction Model Risk of Bias Assessment Tool.

Ethics and dissemination: Ethical approval is not required. Results will be disseminated through peer-reviewed publications.

Prospero registration number: CRD42024556926.

Background: Monitoring early childhood growth is vital, as growth faltering could indicate nutritional or health issues requiring prompt intervention. Our study's aim was to assess the performance of a length-weight artificial intelligence (LWAI) tool for predicting children's length and weight from smartphone images.

Methods: This observational, single-centre study recruited children aged 0-18 months. Investigators measured length and weight in clinic using WHO standard recommendations and captured six images per child in a supine position, while parents took six similar images at home. Within each image, LWAI identifies specific body landmarks and a reference object, then extracts and uses image features to predict the child's length and weight. The LWAI's performance was assessed by comparing length/weight prediction versus actual measurements. User experience was collected through questionnaires.

Results: A total of 215 participants (mean age 6.1 months) were included, and length/weight predictions were generated for 98% (2184/2224) of the images. The mean absolute error (MAE) and mean absolute percentage error (MAPE) for length were 2.47 cm (4.04%) for individual images and 1.89 cm (3.18%) for grouped images (participants with ≥9 images). The corresponding MAE/MAPE for weight were 0.69 kg (11.68%) and 0.56 kg (9.02%), respectively. Regarding usability, 97% of parents who reported not routinely measuring their child's growth indicated that they would start doing so regularly if a digital tool was available to them.

Conclusions: The LWAI tool can predict length and weight in children ≤18 months, offering a practical, convenient, artificial intelligence-powered alternative for growth monitoring in home and clinical settings.

Trial registration number: NCT05079776.

Background: Advance directives (AD) are crucial for aligning healthcare with end-of-life preferences, yet documentation rates remain low, often only 5-15%. Leveraging electronic health records (EHRs) for automated outreach may offer a promising strategy to enhance AD completion without placing additional burdens on already busy clinicians.

Methods: We evaluated the feasibility and effectiveness of EHR-based AD outreach within the Yale New Haven Health System (YNHHS). In April 2024, a targeted message was sent via Epic's MyChart over seven business days, coinciding with National Healthcare Decisions Day. Patients aged ≥65 years with an active MyChart, no existing AD documentation and at least one primary care visit within 2 years were eligible; those hospitalised or in hospice were excluded. The message provided education about advance care planning, encouraged completion of a Healthcare Representative Form and/or Living Will Form and offered instructions for uploading documents directly to the EHR or returning them to a primary care provider's office. A reminder was sent 90 days later.

Results: Outreach reached 25 571 patients, with 61% viewing the MyChart message. Six months after intervention, AD completion across YNHHS rose from 39.9% (28 324/70 911) to 42.8% (30 230/70 583), translating to a 7.5% conversion rate in the targeted cohort. There was no observed increase in patient messaging or clinical staff workload.

Conclusion: These findings suggest that EHR-integrated campaigns can effectively increase AD documentation among older adults without straining providers. By prompting patients to complete forms at their convenience, this scalable and sustainable intervention may be adapted for wider populations and other preventive or chronic care needs.

Objectives: Critical incident reporting systems (CIRS) collect narrative reports on medical errors, but emotional signals within these reports, potential indicators of perceived risk and systemic weakness, are rarely examined. This cross-sectional study applied large language model-based sentiment analysis to explore how emotional expression in CIRS data may support artificial intelligence-enhanced patient safety monitoring.

Methods: We analysed 11 056 anonymised German incident reports submitted between 2005 and 2024 using GPT-4 (Generative Pre-trained Transformer 4, gpt-4-turbo-2024-04-09, zero shot) to assign sentiment labels and quantify five emotions (fear, frustration, anger, sadness, guilt; scale 0-1). Emotional profiles were clustered (k-means) and thematic patterns extracted via Latent Dirichlet allocation. Associations were examined using non-parametric tests.

Results: Negative sentiment dominated (95.6%, 95% CI 94.9% to 96.2%). Fear (mean=0.63, SD=0.21) and frustration (mean=0.59, SD=0.19) were most prevalent. Emergency care settings showed higher fear (p<0.05) and guilt (p<0.001). Reports with strong emotional expression, especially fear, guilt and sadness, were less likely to receive formal feedback (43.1% (95% CI 41.7% to 44.5%) vs 48.1% (95% CI 46.5% to 49.7%); absolute difference=5.0 percentage points (95% CI 2.7 to 7.3); p=0.001).

Discussion: Emotion intensity did not consistently correlate with harm severity but was linked to care context and systemic complexity. Emotion clusters reflected distinct clinical and organisational patterns, from acute emergencies to procedural failures.

Conclusion: Emotion-based analysis of incident reports provides insight into perceived burden and care context. Sentiment profiling may improve system interpretability and support emotion-sensitive safety culture and feedback. Leveraging large language models can reduce reviewer workload and enable more targeted triage of emotionally complex reports.

IntroductionRelapse remains a major challenge in the treatment of substance use disorders (SUDs), particularly during follow-up. Digital tools are emerging as supportive resources, but few deliver real-time interventions. This study will examine the effectiveness of a digital relapse prevention plan (DRPP) integrated into a certified mobile application to detect early risk signs and provide immediate, personalised responses.Methods and analysisA multicentre randomised controlled trial will recruit adults with SUD. Participants will be randomised to standard treatment plus a restricted app version (control) or the same treatment with the full app, including automated alerts and DRPP access (experimental). The plan can be activated manually or automatically through smartphone sensors detecting risk patterns. The primary outcome will be time to first clinical relapse, while secondary outcomes will include patient satisfaction with the DRPP, adherence and perceived emotional self-regulation. Findings are expected to provide robust evidence on the feasibility, acceptability and clinical utility of digital relapse prevention strategies.Ethics and disseminationThis study obtained ethical approval (code 25/327) from Committee of Hospital Universitario 12 de Octubre.Trial registration number:NCT07052175.

Objectives: Postoperative complications (PCs) require substantial resources to manage and are cumbersome to monitor. Artificial intelligence (AI), particularly natural language processing (NLP), offers a potential solution by automating and streamlining these processes, but perceived PC rates may differ depending on model optimisation strategies. This study aimed to develop a mock-up AI-driven automated registry for PCs. We hypothesised that using NLP to obtain longitudinal overviews of key quality metrics is feasible, but that optimisation strategies impacted the observed rate of PCs.

Methods: We analysed 100 505 surgical cases from 12 Danish hospitals between 2017 and 2021. Previously validated NLP models were applied to detect seven types of PCs, using two different threshold settings: a set of thresholds optimised for positive predictive value (precision), referred to as F-score of 0.5, and a set of thresholds optimised for sensitivity, referred to as F-score of 2. Trends in PC rates over time were assessed, and hospital-level variations were examined using logistic regression models.

Results: The NLP models detected 8512 or 15 892 PCs, depending on threshold selection, corresponding to total PC rates of 9.14% and 17.1%, respectively. Most PCs showed stable or increasing trends over time, regardless of threshold setting. Regression analyses demonstrated that threshold selection significantly influenced findings, impacting hospital comparisons.

Conclusion: We demonstrate that NLP can be used for automated PC detection. However, threshold selection and additional performance metrics must be carefully considered.

Objectives: Inappropriately treated pain can have deleterious outcomes in infants. Current tools rely on intermittent, subjective observation requiring specialised paediatric skills. This study aimed to diagnose infant pain through continuous monitoring with wireless sensors using Neonatal Pain and Agitation Sedation Scale (NPASS)-derived Clinical Sensor Pain Scale (CSPS) and Automated SPS (ASPS).

Methods: Clinically stable neonatal intensive care unit infants undergoing phlebotomy were recorded with wireless sensors and video, capturing vital signs, extremity movement and vocalisations. Clinicians and non-clinicians scored the sensor data with CSPS and videos with NPASS; ASPS was applied to the sensor data. Median scores were compared, inter-rater reliability assessed with intraclass correlation coefficients (ICC) and cross-scale comparisons performed using Wilcoxon signed-rank and Kruskal-Wallis tests.

Results: CSPS and ASPS closely aligned with NPASS scores, supporting their validity for continuous infant pain assessment. In 32 infants, the median CSPS score was 3 (IQR 2, 5), with excellent reliability (ICC, 95% CI 92 to 97), high internal consistency (Cronbach's α=0.99) and 95% absolute agreement, comparable to NPASS (p=0.95). Clinician and non-clinician scores were more consistent using CSPS than NPASS. ASPS also performed well, with a median score of 3 (IQR 1, 5), yielding results similar to CSPS (p=0.94) and NPASS (p=0.56).

Conclusions: Wireless biosensors enabled objective monitoring of infant pain. CSPS and ASPS showed validity and reliability for diagnosing acute procedural pain, and feasibility for clinical use. Findings support the development of automated, real-time tools to reduce subjectivity and improve infant pain management, with the potential to advance treatment models and outcomes.

Objectives: COVID-19 has challenged health systems in low-income and middle-income countries, particularly in rural areas where communities face barriers to information, prevention and timely care. Digital health interventions delivered through community health workers (CHWs) offer a promising approach to closing these gaps. This study evaluated whether a CHW-led digital intervention improved knowledge, attitudes and practices (KAP), vaccination uptake, COVID-19 outcomes and access to hospital care in rural Indonesia.

Methods: A quasi-experimental study was conducted from June 2022 to June 2023 with 10 023 individuals across four intervention and four control villages in Malang Regency. In intervention villages, CHWs used a mobile application (AREEMA Skrining Mandiri) to conduct contact tracing, symptom screening, vaccination outreach and referrals, while control villages received standard care. Outcomes included KAP, vaccine uptake, COVID-19 diagnoses and related hospitalisations.

Results: Intervention participants demonstrated greater improvements in attitudes (mean change=3.5, SD=2.1) and practices (0.02, SD=2.2) compared with controls (attitudes: -2.2, SD=4.6; practices: -2.0, SD=2.1). Vaccine uptake was higher in intervention villages (50.6% vs 40.9%), while COVID-19 diagnoses were lower (1.5% vs 2.4%). Among diagnosed cases, hospitalisation was more frequent in intervention villages (21.3% vs 14.5%).

Discussion: The intervention enhanced CHWs' effectiveness in promoting protective behaviours, facilitating early detection and improving referrals. These findings highlight the potential scalability of CHW-led digital health strategies in low-resource settings.

Conclusion: Integrating digital tools into CHW-led care can strengthen COVID-19 prevention, vaccination and access to hospital care in rural populations.

Objectives: The prevalence of non-communicable diseases (NCDs) is rising in low- and middle-income countries, including Malawi, yet spatial inequalities in NCD healthcare coverage remain poorly understood. In this research, we aim to: (1) develop a novel hierarchical geospatial framework to assess population coverage and accessibility of NCD services in Malawi and (2) identify underserved areas and provide evidence for targeted resource allocation.

Methods: Using 2019 Malawi Harmonized Health Facility Assessment Survey, hierarchical catchment areas were defined by facility type-primary healthcare (PHCs), district-level and central hospitals, with distance thresholds of 5 km walking, 25 km driving and 100 km driving, respectively. Incorporating facility readiness, we computed population coverage at the third administrative level. When estimating spatial accessibility, we used enhanced two-step floating catchment area, applying Gaussian distance decay for chronic conditions and inverse power for acute conditions.

Results: Secondary and tertiary facilities (STFs) covered over 60% of population, providing broader NCD service than PHCs, where coverage was lower than 20%, particularly for acute conditions. Population coverage was higher in central and southeastern Malawi, notably around Mzuzu, Lilongwe and Blantyre. However, at least 24% of the population were not covered for any NCD conditions. Additionally, only 11.9% of the population lived in regions of high or very high accessibility to PHCs.

Discussion: We found substantial geographic inequalities in NCD service coverage and access, highlighting underserved regions and the demand to strengthen PHC readiness.

Conclusion: This hierarchical geospatial approach offers insights for resource allocation and improving healthcare equity in other low-resource settings.