BMJ Health & Care Informatics最新文献

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.

Background: Research commentaries have the potential for evidence appraisal in emphasising, correcting, shaping and disseminating scientific knowledge.

Objectives: To identify the appropriate bibliographic source for capturing commentary information, this study compares comment data in PubMed and Web of Science (WoS) to assess their applicability in evidence appraisal.

Methods: Using COVID-19 as a case study, with over 27 k COVID-19 papers in PubMed as a baseline, we designed a comparative analysis for commented-commenting relations in two databases from the same dataset pool, making a fair and reliable comparison. We constructed comment networks for each database for network structural analysis and compared the characteristics of commentary materials and commented papers from various facets.

Results: For network comparison, PubMed surpasses WoS with more closed feedback loops, reaching a deeper six-level network compared with WoS' four levels, making PubMed well-suited for evidence appraisal through argument mining. PubMed excels in identifying specialised comments, displaying significantly lower author count (mean, 3.59) and page count (mean, 1.86) than WoS (authors, 4.31, 95% CI of difference of two means = [0.66, 0.79], p<0.001; pages, 2.80, 95% CI of difference of two means = [0.87, 1.01], p<0.001), attributed to PubMed's CICO comment identification algorithm. Commented papers in PubMed also demonstrate higher citations and stronger sentiments, especially significantly elevated disputed rates (PubMed, 24.54%; WoS, 18.8%; baseline, 8.3%; all p<0.0001). Additionally, commented papers in both sources exhibit superior network centrality metrics compared with WoS-only counterparts.

Conclusion: Considering the impact and controversy of commented works, the accuracy of comments and the depth of network interactions, PubMed potentially serves as a valuable resource in evidence appraisal and detection of controversial issues compared with WoS.

Aim: Interactions between patients and healthcare professionals (HCP) during hospital admissions are complex and difficult to interrogate using traditional analysis of electronic patient record (EPR) data. The objective of this study was to determine the feasibility of applying temporal network analytics to EPR data, focusing on HCP-patient interactions over time.

Method: Network (graph) analysis was applied to routinely collected structured data from an EPR for HCP interactions with individual patients during admissions for patients undergoing renal transplantation between May 2019 and June 2023. Networks were constructed per day of admission within a session, defined by whether the patient was in the intensive care unit (ICU) or standard hospital ward. Connections between HCP were defined using a 60 min period. Reports were generated visualising daily interaction network structures, across individual admissions.

Results: 2300 individual networks were constructed from 127 hospital admissions for renal transplantation. The number of nodes or HCP per network varied from 2 to 45, and network metrics provided detail regarding variation in the density and transitivity, changes in structure with different diameters and radii, and variations in centralisation. Each network analysis metric has a contribution to play in describing the dynamics of a daily HCP network and the composite findings provide insights that cannot be determined with standard approaches.

Conclusions: Network analysis provides a novel approach to investigate and visualise patterns of HCP-patient interactions which allow for a deeper understanding of the complex nature of hospital patient care and could have numerous practical operational applications.

Digital footprint data are inspiring a new era in population health and well-being research. Linking these novel data with other datasets is critical for future research wishing to use these data for the public good. In order to succeed, successful collaboration among industry, academics and policy-makers is vital. Therefore, we discuss the benefits and obstacles for these stakeholder groups in using digital footprint data for research in the UK. We advocate for policy-makers' inclusion in research efforts, stress the exceptional potential of digital footprint research to impact policy-making and explore the role of industry as data providers, with a focus on shared value, commercial sensitivity, resource requirements and streamlined processes. We underscore the importance of multidisciplinary approaches, consumer trust and ethical considerations in navigating methodological challenges and further call for increased public engagement to enhance societal acceptability. Finally, we discuss how to overcome methodological challenges, such as reproducibility and sharing of learnings, in future collaborations. By adopting a multiperspective approach to outlining the challenges of working with digital footprint data, our contribution helps to ensure that future research can navigate these challenges effectively while remaining reproducible, ethical and impactful.

Objectives: Multidisciplinary meetings (MDMs) are part of standard of care for patients with cancer. Streamlining is essential for high-quality care and efficiency. This study evaluated the feasibility of implementing a protocol to remove patients with benign breast disease from discussion at the MDM.

Methods: A prospective review of 218 MDMs evaluated patients with benign breast disease over 22 months. This was followed by a protocol implementation phase over 54 MDMs (6.5 months). Patients meeting specific criteria were excluded from discussion.

Results: On average, each MDM consisted of 37 patients, 34.2% of whose conditions were benign and potentially could have been removed from discussion. The implementation phase showed 708/2248 patients (32.5%) were benign of which 631 cases (89%) met the eligibility criteria and were removed from the MDM list allowing more time for discussion of complex cases.

Conclusion: Implementing a protocol can safely exclude patients with benign disease from MDM discussion.

Background: Perceived safety has received attention in the digital transformation of healthcare. However, the impact of perceived safety on the future of digital transformation has not been fully elucidated.

Aim: To investigate perceived safety in the context of the digital transformation of healthcare while considering relevant needs, influencing factors and impacts, including crisis events, to provide recommendations for action based on a participatory, multiperspective, strategic 5-year foresight viewpoint.

Methods: A strategic foresight study is conducted via a participatory mixed-methods design to understand the present related factors that are likely to be relevant to future developments in the digital transformation of healthcare.

Results: We observed that feeling safe plays a complex role in the digital transformation of healthcare. How perceived safety is considered has and will continue to impact the individual, organisational and system levels. Regarding a potential crisis event, controversial consequences have been observed. At its core, digital (health) literacy related to equity of access and human support is one of the crucial aspects in the context of perceived safety related to the successful implementation of digital technologies in healthcare.

Conclusions: The scenarios showed that a continuation of the current situation over the next 5 years may result in partly desirable and partly undesirable outcomes. Concrete key factors should be used in practice to support both education and healthcare quality development and research. The essence of the scenarios should serve as a starting point for research agenda setting and political decision-making in the future. However, additional research is needed to quantify the correlations among the relevant factors.