BMJ Health & Care Informatics最新文献

Background: The prevalence of chronic coronary heart diseases (CHDs) increases with age in the elderly, which represents one of the top-ranked causes of death and disease burden.

Methods: This study aimed to investigate the management efficiency of telemedicine based on the remote multiparameter monitoring in elderly patients with CHD. A total of 1248 elderly patients diagnosed with CHD were enrolled. The subjects were randomly divided into two groups, wearable equipment-based telemedical management (WTM) group and traditional follow-up management (TFM) group. Face-to-face clinical interview at least once every 2 months was required in TFM group to collect the medical records. Patients in WTM group were provided with wearable equipment to complete remote monitoring, real-time alerts and health intervention via virtual consultations and remote medication recommendations.

Results: The mean age of patients in WTM group and TFM group was 71.1 (68.0-82.0) years and 71.0 (68.0-81.0) years, respectively‏. After a 12-month management, patients in WTM group presented a lower occurrence of hospitalisation (HR 0.59, 95% CI=0.47 to 0.73, p<0.0001) and major adverse cardiac events (HR 0.60, 95% CI=0.44 to 0.82, p=0.0012) compared with patients in TFM group.

Conclusion: The multiparameter telemedical management could help with the out-of-hospital management and reduce the incidence of rehospitalisation in elderly patients with CHD.

Objective: To co-design artificial intelligence (AI)-based clinical informatics workflows to routinely analyse patient-reported experience measures (PREMs) in hospitals.

Methods: The context was public hospitals (n=114) and health services (n=16) in a large state in Australia serving a population of ~5 million. We conducted a participatory action research study with multidisciplinary healthcare professionals, managers, data analysts, consumer representatives and industry professionals (n=16) across three phases: (1) defining the problem, (2) current workflow and co-designing a future workflow and (3) developing proof-of-concept AI-based workflows. Co-designed workflows were deductively mapped to a validated feasibility framework to inform future clinical piloting. Qualitative data underwent inductive thematic analysis.

Results: Between 2020 and 2022 (n=16 health services), 175 282 PREMs inpatient surveys received 23 982 open-ended responses (mean response rate, 13.7%). Existing PREMs workflows were problematic due to overwhelming data volume, analytical limitations, poor integration with health service workflows and inequitable resource distribution. Three potential semiautomated, AI-based (unsupervised machine learning) workflows were developed to address the identified problems: (1) no code (simple reports, no analytics), (2) low code (PowerBI dashboard, descriptive analytics) and (3) high code (Power BI dashboard, descriptive analytics, clinical unit-level interactive reporting).

Discussion: The manual analysis of free-text PREMs data is laborious and difficult at scale. Automating analysis with AI could sharpen the focus on consumer input and accelerate quality improvement cycles in hospitals. Future research should investigate how AI-based workflows impact healthcare quality and safety.

Conclusion: AI-based clinical informatics workflows to routinely analyse free-text PREMs data were co-designed with multidisciplinary end-users and are ready for clinical piloting.

Introduction: In Asian countries, stigma against psychiatric disorders and shortage of manpower are the two major challenges that hinder people from receiving treatments. Chatbots can surely help people surpass the stigmatising and manpower shortage challenges. Since a comprehensive review in the Asian context is lacking, this systematic review will evaluate the effects of chatbot interventions on the mental well-being of the general population in Asia.

Methods and analysis: Four electronic databases (PubMed, CINAHL, PsycINFO and MEDLINE) will be searched until December 2024. Randomised controlled trials with English/Chinese full text available will be included. Random-effect models will be used for meta-analyses. The risk of bias (RoB) and certainty of evidence across studies will be assessed using the Cochrane RoB2 and Grading of Recommendation Assessment, Development and Evaluation tools, respectively.

Ethics and dissemination: This study will not require ethical approval. The findings will be disseminated through peer-reviewed publications.

Funding: School Research Grant of the Tung Wah College (2023-04-52-SRG230401) PROSPERO REGISTRATION NUMBER: CRD42024546316.

Objectives: We aim to evaluate the accuracy of radiologists and radiology residents in the detection of paediatric appendicular fractures with and without the help of a commercially available fracture detection artificial intelligence (AI) solution in the hopes of showing potential clinical benefits in a general hospital setting.

Methods: This was a retrospective study involving three associate consultants (AC) and three senior residents (SR) in radiology, who acted as readers. One reader from each human group interpreted the radiographs with the aid of AI. Cases were categorised into concordant and discordant cases between each interpreting group. Discordant cases were further evaluated by three independent subspecialty radiology consultants to determine the final diagnosis. A total of 500 anonymised paediatric patient cases (aged 2-15 years) who presented to a tertiary general hospital with a Children's emergency were retrospectively collected. Main outcome measures include the presence of fracture, accuracy of readers with and without AI, and total time taken to interpret the radiographs.

Results: The AI solution alone showed the highest accuracy (area under the receiver operating characteristic curve 0.97; AC: 95% CI -0.055 to 0.320, p=0; SR: 95% CI 0.244 to 0.598, p=0). The two readers aided with AI had higher area under curves compared with readers without AI support (AC: 95% CI -0.303 to 0.465, p=0; SR: 95% CI -0.154 to 0.331, p=0). These differences were statistically significant.

Conclusion: Our study demonstrates excellent results in the detection of paediatric appendicular fractures using a commercially available AI solution. There is potential for the AI solution to function autonomously.

With the fast development of artificial intelligence (AI) and its applications in medicine, it is often said that the time for intelligent medicine is arriving, if not already have arrived. While there is no doubt that AI-centred intelligent medicine will transform current healthcare, it is necessary to test and re-test medical AI (MAI) products before they are implemented in the real world. From the perspective of ensuring safety, accuracy and efficiency, it is imperative that MAIs undergo stress tests in a systematic and comprehensive manner where stress tests subject MAIs to workloads and environments beyond tests carried out by their developers. In such stress tests, potential bottlenecks or failures of MAIs may be identified and fed back to developers to optimise the products. To avoid bias and ensure fairness, stress tests should be prepared and administered by an independent body.

Objectives: Increasing operational pressures on emergency departments (ED) make it imperative to quickly and accurately identify patients requiring urgent clinical intervention. The widespread adoption of electronic health records (EHR) makes rich feature patient data sets more readily available. These large data stores lend themselves to use in modern machine learning (ML) models. This paper investigates the use of transformer-based models to identify critical deterioration in unplanned ED admissions, using free-text fields, such as triage notes, and tabular data, including early warning scores (EWS).

Design: A retrospective ML study.

Setting: A large ED in a UK university teaching hospital.

Methods: We extracted rich feature sets of routine clinical data from the EHR and systematically measured the performance of tree- and transformer-based models for predicting patient mortality or admission to critical care within 24 hours of presentation to ED. We compared our proposed models to the National EWS (NEWS).

Results: Models were trained on 174 393 admission records. We found that models including free-text triage notes outperform structured tabular data models, achieving an average precision of 0.92, compared with 0.75 for tree-based models and 0.12 for NEWS.

Conclusions: Our findings suggests that machine learning models using free-text data have the potential to improve clinical decision-making in the ED; our techniques significantly reduce alert rate while detecting most high-risk patients missed by NEWS.

Objectives: Technology-related prescribing errors curtail the positive impacts of computerised provider order entry (CPOE) on medication safety. Understanding how technology-related errors (TREs) occur can inform CPOE optimisation. Previously, we developed a classification of the underlying mechanisms of TREs using prescribing error data from two adult hospitals. Our objective was to update the classification using paediatric prescribing error data and to assess the reliability with which reviewers could independently apply the classification.

Materials and methods: Using data on 1696 prescribing errors identified by chart review in 2016 and 2017 at a tertiary paediatric hospital, we identified errors that were technology-related. These errors were investigated to classify their underlying mechanisms using our previously developed classification, and new categories were added based on the data. A two-step process was used to identify and classify TREs involving a review of the error in the CPOE and simulating the error in the CPOE testing environment.

Results: The technology-related error mechanism (TREM) classification comprises six mechanism categories, one contributing factor and 19 subcategories. The categories are as follows: (1) incorrect system configuration or system malfunction, (2) opening or using the wrong patient record, (3) selection errors, (4) construction errors, (5) editing errors, (6) errors that occur when using workflows that differ from a paper-based system (7) contributing factor: use of hybrid systems.

Conclusion: TREs remain a critical issue for CPOE. The updated TREM classification provides a systematic means of assessing and monitoring TREs to inform and prioritise system improvements and has now been updated for the paediatric setting.

Background: The early detection of hypertension using simple visual images in a way that does not require physical interaction or additional devices may improve quality of care in the era of telemedicine. Pharyngeal images include vascular morphological information and may therefore be useful for identifying hypertension.

Objectives: This study sought to develop a deep learning-based artificial intelligence algorithm for identifying hypertension from pharyngeal images.

Methods: We conducted a secondary analysis of data from a clinical trial, in which demographic information, vital signs and pharyngeal images were obtained from patients with influenza-like symptoms in multiple primary care clinics in Japan. A deep learning-based algorithm that included a multi-instance convolutional neural network was trained to detect hypertension from pharyngeal images and demographic information. The classification performance was measured by area under the receiver operating characteristic curve. Importance heatmaps of the convolutional neural network were also examined to interpret the algorithm.

Results: This study included 7710 patients from 64 clinics. The training dataset comprised 6171 patients from 51 clinics (460 positive cases), and the test dataset comprised 1539 patients from 13 clinics (130 positive cases). Our algorithm achieved an area under the receiver operating characteristic curve of 0.922 (95% CI, 0.904 to 0.940), significantly improving over the baseline prediction model incorporating only demographic information, which scored 0.887 (95% CI, 0.862 to 0.911). Our algorithm had consistent classification performance across all age and sex subgroups. Importance heatmaps revealed that the algorithm focused on the posterior pharyngeal wall area, where blood vessels are mainly located.

Conclusions: The results indicate that a deep learning-based algorithm can detect hypertension with high accuracy using pharyngeal images.