BMJ Health & Care Informatics最新文献

Introduction: Digital healthcare innovation has yielded many prototype clinical decision support (CDS) systems, however, few are fully adopted into practice, despite successful research outcomes. We aimed to explore the characteristics of implementations in clinical practice to inform future innovation.

Methods: Web of Science, Trip Database, PubMed, NHS Digital and the BMA website were searched for examples of CDS systems in May 2022 and updated in June 2023. Papers were included if they reported on a CDS giving pathway advice to a clinician, adopted into regular clinical practice and had sufficient published information for analysis. Examples were excluded if they were only used in a research setting or intended for patients. Articles found in citation searches were assessed alongside a detailed hand search of the grey literature to gather all available information, including commercial information. Examples were excluded if there was insufficient information for analysis. The normalisation process theory (NPT) framework informed analysis.

Results: 22 implemented CDS projects were included, with 53 related publications or sources of information (40 peer-reviewed publications and 13 alternative sources). NPT framework analysis indicated organisational support was paramount to successful adoption of CDS. Ensuring that workflows were optimised for patient care alongside iterative, mixed-methods implementation was key to engaging clinicians.

Conclusion: Extensive searches revealed few examples of CDS available for analysis, highlighting the implementation gap between research and healthcare innovation. Lessons from included projects include the need for organisational support, an underpinning mixed-methods implementation strategy and an iterative approach to address clinician feedback.

Objectives: This project aims to determine the feasibility of predicting future critical care bed availability using data-driven computational forecast modelling and routinely collected hospital bed management data.

Methods: In this proof-of-concept, single-centre data informatics feasibility study, regression-based and classification data science techniques were applied retrospectively to prospectively collect routine hospital-wide bed management data to forecast critical care bed capacity. The availability of at least one critical care bed was forecasted using a forecast horizon of 1, 7 and 14 days in advance.

Results: We demonstrated for the first time the feasibility of forecasting critical care bed capacity without requiring detailed patient-level data using only routinely collected hospital bed management data and interpretable models. Predictive performance for bed availability 1 day in the future was better than 14 days (mean absolute error 1.33 vs 1.61 and area under the curve 0.78 vs 0.73, respectively). By analysing feature importance, we demonstrated that the models relied mainly on critical care and temporal data rather than data from other wards in the hospital.

Discussion: Our data-driven forecasting tool only required hospital bed management data to forecast critical care bed availability. This novel approach means no patient-sensitive data are required in the modelling and warrants further work to refine this approach in future bed availability forecast in other hospital wards.

Conclusions: Data-driven critical care bed availability prediction was possible. Further investigations into its utility in multicentre critical care settings or in other clinical settings are warranted.

Background: The working population encounters unique work-related stressors. Despite these challenges, accessibility to mental healthcare remains limited. Digital technology-enabled mental wellness tools can offer much-needed access to mental healthcare. However, existing literature has given limited attention to their relevance and user engagement, particularly for the working population.

Aim: This study aims to assess user perceptions and feature utilisation of mindline at work, a nationally developed AI-enabled digital platform designed to improve mental wellness in the working population.

Methods: This study adopted a mixed-methods design comprising a survey (n=399) and semistructured interviews (n=40) with office-based working adults. Participants were asked to use mindline at work for 4 weeks. We collected data about utilisation of the platform features, intention for sustained use and perceptions of specific features.

Results: Participants under 5 years of work experience reported lower utilisation of multimedia resources but higher utilisation of emotion self-assessment tools and the AI chatbot compared with their counterparts (p<0.001). The platform received a moderate level of satisfaction (57%) and positive intention for sustained use (58%). Participants regarded mindline at work as an 'essential' safeguard against workplace stress, valuing its secure and non-judgmental space and user anonymity. However, they wanted greater institutional support for office workers' mental wellness to enhance the uptake. The AI chatbot was perceived as useful for self-reflection and problem-solving, despite limited maturity.

Conclusion: Identifying the unique benefits of specific features for different segments of working adults can foster a personalised user experience and promote mental well-being. Increasing workplace awareness is essential for platform adoption.

Objective: Collaborate, Analyse, Research and Audit (CARA) project set out to provide an infrastructure to enable Irish general practitioners (GPs) to use their routinely collected patient management software (PMS) data to better understand their patient population, disease management and prescribing through data dashboards. This paper explains the design and development of the CARA infrastructure.

Methods: The first exemplar dashboard was developed with GPs and focused on antibiotic prescribing to develop and showcase the proposed infrastructure. The data integration process involved extracting, loading and transforming de-identified patient data into data models which connect to the interactive dashboards for GPs to visualise, compare and audit their data.

Results: The architecture of the CARA infrastructure includes two main sections: extract, load and transform process (ELT, de-identified patient data into data models) and a Representational State Transfer Application Programming Interface (REST API) (which provides the security barrier between the data models and their visualisation on the CARA dashboard). CARAconnect was created to facilitate the extraction and de-identification of patient data from the practice database.

Discussion: The CARA infrastructure allows seamless connectivity with and compatibility with the main PMS in Irish general practice and provides a reproducible template to access and visualise patient data. CARA includes two dashboards, a practice overview and a topic-specific dashboard (example focused on antibiotic prescribing), which includes an audit tool, filters (within practice) and between-practice comparisons.

Conclusion: CARA supports evidence-based decision-making by providing GPs with valuable insights through interactive data dashboards to optimise patient care, identify potential areas for improvement and benchmark their performance against other practices.Supplementary file 1. Graphical abstract.

Objectives: This study explored attitudes, subjective norms, and perceived behavioural control of participants across urban, rural and remote settings and examined intention-to-use telemedicine (defined in this study as remote patient-clinician consultations) during the COVID-19 pandemic.

Methods: This is a cross-sectional study. 12 focus group discussions were conducted with 60 diverse telemedicine user and non-user participants across 3 study settings. Analysis of responses was done to understand the attitudes, norms and perceived behavioural control of participants. This explored the relationship between the aforementioned factors and intention to use.

Results: Both users and non-users of telemedicine relayed that the benefits of telemedicine include protection from COVID-19 exposure, decreased out-of-pocket expenses and better work-life balance. Both groups also relayed perceived barriers to telemedicine. Users from the urban site relayed that the lack of preferred physicians discouraged use. Users from the rural and remote sites were concerned about spending on resources (ie, compatible smartphones) to access telemedicine. Non-users from all three sites mentioned that they would not try telemedicine if they felt overwhelmed prior to access.

Discussion: First-hand experiences, peer promotions, and maximising resource support instil hope that telemedicine can help people gain more access to healthcare. However, utilisation will remain low if patients feel overwhelmed by the behavioural modifications and material resources needed to access telemedicine. Boosting infrastructure must come with improving confidence and trust among people.

Conclusion: Sustainable access beyond the pandemic requires an understanding of factors that prevent usage. Sufficient investment in infrastructure and other related resources is needed if telemedicine will be used to address inequities in healthcare access, especially in rural and remote areas.

Background: Despite the increasing availability of electronic healthcare record (EHR) data and wide availability of plug-and-play machine learning (ML) Application Programming Interfaces, the adoption of data-driven decision-making within routine hospital workflows thus far, has remained limited. Through the lens of deriving clusters of diagnoses by age, this study investigated the type of ML analysis that can be performed using EHR data and how results could be communicated to lay stakeholders.

Methods: Observational EHR data from a tertiary paediatric hospital, containing 61 522 unique patients and 3315 unique ICD-10 diagnosis codes was used, after preprocessing. K-means clustering was applied to identify age distributions of patient diagnoses. The final model was selected using quantitative metrics and expert assessment of the clinical validity of the clusters. Additionally, uncertainty over preprocessing decisions was analysed.

Findings: Four age clusters of diseases were identified, broadly aligning to ages between: 0 and 1; 1 and 5; 5 and 13; 13 and 18. Diagnoses, within the clusters, aligned to existing knowledge regarding the propensity of presentation at different ages, and sequential clusters presented known disease progressions. The results validated similar methodologies within the literature. The impact of uncertainty induced by preprocessing decisions was large at the individual diagnoses but not at a population level. Strategies for mitigating, or communicating, this uncertainty were successfully demonstrated.

Conclusion: Unsupervised ML applied to EHR data identifies clinically relevant age distributions of diagnoses which can augment existing decision making. However, biases within healthcare datasets dramatically impact results if not appropriately mitigated or communicated.

In the following narrative review, we discuss the potential role of large language models (LLMs) in medical device innovation, specifically examples using generative pretrained transformer-4. Throughout the biodesign process, LLMs can offer prompt-driven insights, aiding problem identification, knowledge assimilation and decision-making. Intellectual property analysis, regulatory assessment and market analysis emerge as key LLM applications. Through case examples, we underscore LLMs' transformative ability to democratise information access and expertise, facilitating inclusive innovation in medical devices as well as its effectiveness with providing real-time, individualised feedback for innovators of all experience levels. By mitigating entry barriers, LLMs accelerate transformative advancements, fostering collaboration among established and emerging stakeholders.

Background and objectives: Patient-clinician communication and shared decision-making face challenges in the perioperative period. Chatbots have emerged as valuable support tools in perioperative care. A simultaneous and complete comparison of overall benefits and harm of chatbot application is conducted.

Materials: MEDLINE, EMBASE and the Cochrane Library were systematically searched for studies published before May 2023 on the benefits and harm of chatbots used in the perioperative period. The major outcomes assessed were patient satisfaction and knowledge acquisition. Untransformed proportion (PR) with a 95% CI was used for the analysis of continuous data. Risk of bias was assessed using the Cochrane Risk of Bias assessment tool version 2 and the Methodological Index for Non-Randomised Studies.

Results: Eight trials comprising 1073 adults from four countries were included. Most interventions (n = 5, 62.5%) targeted perioperative care in orthopaedics. Most interventions use rule-based chatbots (n = 7, 87.5%). This meta-analysis found that the majority of the participants were satisfied with the use of chatbots (mean proportion=0.73; 95% CI: 0.62 to 0.85), and agreed that they gained knowledge in their perioperative period (mean proportion=0.80; 95% CI: 0.74 to 0.87).

Conclusion: This review demonstrates that perioperative chatbots are well received by the majority of patients with no reports of harm to-date. Chatbots may be considered as an aid in perioperative communication between patients and clinicians and shared decision-making. These findings may be used to guide the healthcare providers, policymakers and researchers for enhancing perioperative care.

Objectives: We assessed the feasibility of ChatGPT for patients with type 2 diabetes seeking information about exercise.

Methods: In this pilot study, two physicians with expertise in diabetes care and rehabilitative treatment in Republic of Korea discussed and determined the 14 most asked questions on exercise for managing type 2 diabetes by patients in clinical practice. Each question was inputted into ChatGPT (V.4.0), and the answers from ChatGPT were assessed. The Likert scale was calculated for each category of validity (1-4), safety (1-4) and utility (1-4) based on position statements of the American Diabetes Association and American College of Sports Medicine.

Results: Regarding validity, 4 of 14 ChatGPT (28.6%) responses were scored as 3, indicating accurate but incomplete information. The other 10 responses (71.4%) were scored as 4, indicating complete accuracy with complete information. Safety and utility scored 4 (no danger and completely useful) for all 14 ChatGPT responses.

Conclusion: ChatGPT can be used as supplementary educational material for diabetic exercise. However, users should be aware that ChatGPT may provide incomplete answers to some questions on exercise for type 2 diabetes.