BMJ Health & Care Informatics最新文献

Background: Older patients with diabetic kidney disease (DKD) often do not receive optimal pharmacological treatment. Current clinical practice guidelines (CPGs) do not incorporate the concept of personalised care. Clinical decision support (CDS) algorithms that consider both evidence and personalised care to improve patient outcomes can improve the care of older adults. The aim of this research is to design and validate a CDS algorithm for prescribing renin-angiotensin-aldosterone system inhibitors (RAASi) for older patients with diabetes.

Methods: The design of the CDS tool included the following phases: (1) gathering evidence from systematic reviews and meta-analyses of randomised clinical trials to determine the number needed to treat (NNT) and time-to-benefit (TTB) values applicable to our target population for use in the algorithm. (2) Building a list of potential cases that addressed different prescribing scenarios (starting, adding or switching to RAASi). (3) Reviewing relevant guidelines and extracting all recommendations related to prescribing RAASi for DKD. (4) Matching NNT and TTB with specific clinical cases. (5) Validating the CDS algorithm using Delphi technique.

Results: We created a CDS algorithm that covered 15 possible scenarios and we generated 36 personalised and nine general recommendations based on the calculated and matched NNT and TTB values and considering the patient's life expectancy and functional capacity. The algorithm was validated by experts in three rounds of Delphi study.

Conclusion: We designed an evidence-informed CDS algorithm that integrates considerations often overlooked in CPGs. The next steps include testing the CDS algorithm in a clinical trial.

Burnout and workforce attrition present pressing global challenges in healthcare, severely impacting the quality of patient care and the sustainability of health systems worldwide. Artificial intelligence (AI) has immense potential to reduce the administrative and cognitive burdens that contribute to burnout through innovative solutions such as digital scribes, automated billing and advanced data management systems. However, these innovations also carry significant risks, including potential job displacement, increased complexity of medical information and cases, and the danger of diminishing clinical skills. To fully leverage AI's potential in healthcare, it is essential to prioritise AI technologies that align with stakeholder values and emphasise efforts to re-humanise medical practice. By doing so, AI can contribute to restoring a sense of purpose, fulfilment and efficacy among healthcare workers, reinforcing their essential role as caregivers, rather than distancing them from these core professional attributes.

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.