BMJ Health & Care Informatics最新文献

Objectives: The Public Health Inspector (PHI) Monthly Report is a critical document that provides insights into environmental, occupational health and food safety aspects within each Medical Officer of Health area in Sri Lanka. Currently, PHIs use a paper format to track these key health indicators, resulting in incomplete and inaccurate national data. This study evaluates the usability of a DHIS2 (District Health Information Software 2) based digital solution to improve PHI reporting.

Methods: The DHIS2 system was customised to address the gaps in the current reporting process, and its usability was evaluated using the System Usability Scale (SUS) with 50 stakeholders who tested the system.

Results: The DHIS2 platform was flexible enough to be customised to meet the requirements of the new electronic Environmental, Occupational Health and Food Safety Information Management System (eEOHFSIMS). The system achieved an average SUS score of 72.25, exceeding the accepted benchmark of 68, with a high SD of 13.37. However, a 92% knowledge gap remained.

Discussion: Digitising the PHI monthly report using DHIS2 addresses the challenges of traditional paper-based reporting, enabling timely monitoring of public health indicators. The favourable SUS score confirms the system's high usability, yet the knowledge gap underscores the need for ongoing user training to ensure data quality.

Conclusions: The eEOHFSIMS demonstrated its capacity to deliver accurate, complete and timely data, greatly benefiting Sri Lanka's primary healthcare services. This system enhancement supports better-informed decision-making, aligns with national health policies and enables continuous monitoring and evaluation of public health services.

Objectives: Most patients presenting with chest pain in the emergency medical services (EMS) setting are suspected of non-ST-elevation acute coronary syndrome (NSTE-ACS). Distinguishing true NSTE-ACS from non-cardiac chest pain based solely on the ECG is challenging. The aim of this study is to develop and validate a convolutional neural network (CNN)-based model for risk stratification of suspected NSTE-ACS patients and to compare its performance with currently available prehospital diagnostic tools.

Methods: For this study, an internal training cohort and an external validation cohort were used, both consisting of suspected NSTE-ACS patients. A CNN (ECG interpretation by CNN (ECG-AI)) was trained and validated to detect NSTE-ACS. The diagnostic value of ECG-AI in detecting NSTE-ACS was compared with on-site ECG analyses by an EMS paramedic (ECG-EMS), point-of-care troponin assessment and a validated prehospital clinical risk score (prehospital History, ECG, Age, Risk factors and POC-troponin (preHEART)).

Results: A total of 5645 patients suspected of NSTE-ACS were included. In the external validation cohort (n=754), 27% were diagnosed with NSTE-ACS. ECG-AI had a better diagnostic performance than ECG-EMS (area under the curve (AUROC) 0.70 (0.66 to 0.74) vs AUROC 0.65 (0.61 to 0.70), p=0.045) for diagnosing NSTE-ACS. The overall diagnostic accuracy of preHEART was AUROC 0.78 (0.74 to 0.82) and superior compared with ECG-AI (p=0.001). Incorporating ECG-AI into preHEART led to a significant improvement in diagnostic performance (AUROC 0.83 (0.79 to 0.86), p<0.001).

Discussion: Correctly identifying patients who are at low risk for having NSTE-ACS is crucial for optimal triage in the prehospital setting. Recent studies have shown that these low-risk patients could potentially be left at home or transferred to a general practitioner, leading to less emergency department overcrowding and lower healthcare costs. Other studies demonstrated better overall diagnostic performance compared with our artificial intelligence (AI) model. However, these studies were aimed at a study population with a high prevalence of occlusive myocardial infarction, which could explain the differing levels of diagnostic performance.

Conclusion: Integrating AI in prehospital ECG interpretation improves the identification of patients at low risk for having NSTE-ACS. Nonetheless, clinical risk scores currently yield the best diagnostic performance and their accuracy could be further enhanced through AI. Our results pave the way for new studies focused on exploring the role of AI in prehospital risk-stratification efforts.

Objectives: An image-based ECG dataset incorporating visual imperfections common to paper-based ECGs, which are typically scanned or photographed into electronic health records, could facilitate clinically useful artificial intelligence (AI)-ECG algorithm development. This study aimed to create a high-fidelity, synthetic image-based ECG dataset.

Methods: ECG images were recreated from the PTB-XL database, a signal-based dataset and image manipulation techniques were applied to mimic imperfections associated with ECGs in real-world settings. Clinical Turing tests were conducted to evaluate the fidelity of the synthetic images, and the performance of current AI-ECG algorithms was assessed using synthetic images containing visual imperfections.

Results: GenECG, an image-based dataset containing 21 799 ECGs with visual imperfections encountered in routine clinical care paired with imperfection-free images, was created. Turing tests confirmed the realism of the images: expert observer accuracy of discrimination between real-world and synthetic ECGs fell from 63.9% (95% CI 58.0% to 69.8%) to 53.3% (95% CI 48.6% to 58.1%) over three rounds of testing, indicating that observers could not distinguish between synthetic and real ECGs. The performance of pre-existing algorithms on synthetic (area under the curve (AUC) 0.592, 95% CI 0.421 to 0.763) and real-world (AUC 0.647, 95% CI 0.520 to 0.774) ECG images containing imperfections was limited. Algorithm fine-tuning with GenECG data improved real-world ECG classification accuracy (AUC 0.821, 95% CI 0.730 to 0.913) demonstrating its potential to augment image-based algorithm development.

Discussion/conclusion: GenECG is the first synthetic image-based ECG dataset to pass a clinical Turing test. The dataset will enable image-based AI-ECG algorithm development, ensuring utility in low resource areas, prehospital settings and hospital environments where signal data are unavailable.

Objectives: While mobile health (mHealth) interventions are widespread, few studies assess impacts at the population level in low-income and middle-income countries. South Africa's tuberculosis (TB) burden is high, and a substantial share of cases remain undiagnosed. We evaluate the impacts of community activations of TBCheck-a WhatsApp/USSD-based chatbot that allows individuals to evaluate themselves for TB risk.

Methods: We use a quasi-experimental approach comparing treated and control subdistricts nationally before and after community activations using dashboard data from the TBCheck platform and weekly or quarterly subdistrict TB test data from the National Health Laboratory Service. Dependent variables are the number of self-screening tests on the platform, total tests and number of positive tests per subdistrict. We employ dynamic difference-in-difference models accounting for subdistrict unobservables and time trends using weekly data, and synthetic control methods matching on preintervention trends in outcomes using quarterly data.

Results: Impact estimates suggest an increase in the number of self-screening tests on the platform (487.53, p-value<0.01) as well as TB tests (107.90, p-value=0.05) in treated relative to control subdistricts due to intervention activities in the week of the intervention. After 2 weeks, impacts on the number of self-screening tests are insignificant (-6.18, p=0.23), and after 1 week, impacts on TB tests are insignificant (36.44, p-value=0.32).

Discussion and conclusion: Activation activities associated with TBCheck led to short-lived and variable impacts on uptake and tests in target subdistricts. Alternative strategies are required for sustained uptake of such mHealth tools.

Objective: This research aimed to identify the needs and priorities concerning structured and unstructured medical data of medical device companies engaged in developing medical software or hardware.

Method: The study was conducted between 23 March and 26 March 2023 and involved a survey of 76 managers, each of whom represented a single Korean medical device company.

Result: Companies focused on novel medical devices and software expressed higher demand for unstructured data than for structured data. However, the accessibility of unstructured data was hampered by high acquisition costs, and both structured and unstructured data suffered from limited openness and high pricing, hindering the progress of digital healthcare technology. To promote the development of innovative medical devices and software with increased value, these challenges must be addressed promptly.

Discussion: A crucial solution involves establishing a secure medical data trade platform that ensures the exchange of reliable and accurate medical information while adhering to legal regulations. By facilitating the circulation of high-quality medical data at appropriate prices through an invigorated medical data trade ecosystem, numerous medical device development companies with innovative ideas will have the opportunity to challenge the industry, consequently lowering the entry barriers to development.

Conclusion: This holds the potential to revolutionise the medical device industry by enabling safe, efficient and equitable access to medical data, fostering groundbreaking advancements in healthcare technology.

The UK's health datasets are among the most comprehensive and inclusive globally, enabling groundbreaking research during the COVID-19 pandemic. However, restrictions on data sharing between secure data environments (SDEs) imposed limitations on the ability to carry out joint analyses across multiple separate datasets. There are currently significant efforts underway to enable such analyses using methods such as federated analytics (FA) and virtual SDEs. FA involves distributed data analysis without sharing raw data but does require sharing summary statistics. Virtual SDEs in principle allow researchers to access data across multiple SDEs, but in practice, data transfers may be restricted by information governance concerns.Secure multiparty computation (SMPC) is a cryptographic approach that allows multiple parties to perform joint analyses over private datasets with zero information sharing. SMPC may eliminate the need for data-sharing agreements and statistical disclosure control, offering a compelling alternative to FA and virtual SDEs. SMPC comes with a higher computational burden than traditional pooled analysis. However, efficient implementations of SMPC can enable a wide range of practical, secure analyses to be carried out.This perspective reviews the strengths and limitations of FA, virtual SDEs and SMPC as approaches to joint analyses across SDEs. We argue that while efforts to implement FA and virtual SDEs are ongoing in the UK, SMPC remains underexplored. Given its unique advantages, we propose that SMPC deserves greater attention as a transformative solution for enabling secure, cross-SDE analyses of private health data.

Objectives: Despite opioids comprising a majority of drug allergy alert (DAA) overrides, few studies have designed recommendations for clinical decision support (CDS) systems. We determined the frequency of opioid analogue DAA overrides and assessed DAAs overridden by patient and provider type, the most common allergic reactions documented in electronic health records (EHR), reaction severity, and associated hypersensitivity.

Methods: We conducted an observational cross-sectional study of DAAs in two geographically remote health systems. Patients were included if they were 18 years or older and had an opioid DAA generated when a medication was ordered. Patient and provider demographics, drug allergies, medication ordered, alert overrides, drug allergy reactions and DAA history were collected. Opioid analogue allergies were analysed by reaction type documented in the EHR, reaction severities (high, medium or low) and hypersensitivity reaction. Based on these factors, alerts were recommended to be interruptive requiring a coded response or changed to be non-interruptive (informational).

Results: There were 700 493 alerts concerning opioid analogues fired for 50 527 patients across both sites, and 71.8% of these alerts were overridden. Nearly three-quarters of overridden reactions had a low to medium severity level at both institutions. Only 29.3% of the overridden alerts were truly immune-mediated.

Discussion: Our recommendations would reduce interruptive alerts in half by converting them to non-interruptive alerts (46.4%). The data suggest opportunities to improve opioid-related CDS systems.

Conclusions: We evaluated overrides of opioids and used this data to suggest ways to redesign DAAs to decrease alert override rates, combat alert fatigue and improve patient safety.

Objectives: This study examined whether incorporating free-text entries into structured general practice records improves the detection of long-term conditions (LTCs) and multimorbidity (MM) in New Zealand (NZ) general practices.

Methods: Data from 374 071 deidentified individuals in general practices were analysed to identify 61 LTCs. Structured data were extracted using Read codes from a national master list, and clinical raters independently identified condition-related free-text, including synonyms, negation terms and common misspellings in randomised samples. Keywords were categorised and refined through ten iterative tests. Programmatic text classification was developed and assessed against gold-standard clinician ratings, using sensitivity, specificity, positive predictive value (PPV) and F₁-score.

Results: A quarter of general practitioner classifications contained either unrecognised Read codes or consisted of free-text only. Clinician inter-rater reliability was high (kappa ≥0.9). Compared with clinical gold standard, text classification yielded an average sensitivity of 88%, specificity of 99% and PPV of 95%, with an F₁-score range of 82%-95%. Incorporating free text increased LTC prevalence from 42.1% to 46.3%, reducing misclassification of MM diagnoses by identifying 12 626 additional patients with MM and 15 972 additional patients with at least one LTC.

Discussion: In the course of workflow, general practitioners face barriers to accurate LTC coding or may simply annotate with text-based descriptions. Programmatic text classification has demonstrated high performance and identified many more patients receiving LTC care.

Conclusions: Combining structured and unstructured data optimises MM detection in NZ general practices and has the potential to improve case management, follow-up care and allocation of healthcare resources.

Objectives: The aim of this study was to compare the performances of residents and ChatGPT in answering validated questions and assess paediatric surgery residents' acceptance, perceptions and readiness to integrate artificial intelligence (AI) into clinical practice.

Methods: We conducted a cross-sectional study using randomly selected questions and clinical cases on paediatric surgery topics. We examined residents' acceptance of AI before and after comparing their results to ChatGPT's results using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. Data analysis was performed using Jamovi V.2.4.12.0.

Results: 30 residents participated. ChatGPT-4.0's median score was 13.75, while ChatGPT-3.5's was 8.75. The median score among residents was 8.13. Differences appeared statistically significant. ChatGPT outperformed residents specifically in definition questions (ChatGPT-4.0 vs residents, p<0.0001; ChatGPT-3.5 vs residents, p=0.03). In the UTAUT2 Questionnaire, respondents expressed a more positive evaluation of ChatGPT with higher mean values for each construct and lower fear of technology after learning about test scores.

Discussion: ChatGPT performed better than residents in knowledge-based questions and simple clinical cases. The accuracy of ChatGPT declined when confronted with more complex questions. The UTAUT questionnaire results showed that learning about the potential of ChatGPT could lead to a shift in perception, resulting in a more positive attitude towards AI.

Conclusion: Our study reveals residents' positive receptivity towards AI, especially after being confronted with its efficacy. These results highlight the importance of integrating AI-related topics into medical curricula and residency to help future physicians and surgeons better understand the advantages and limitations of AI.

Objectives: Since the COVID-19 pandemic started, an increasing number of individuals use online resources to obtain health-related information, including myopia management. We aimed to obtain the search interest of global public health on myopia and prevention/treatment methods before and after the outbreak of COVID-19.

Methods: Google Trends was used to assess the global search interest in myopia and related terms. Data spanning from 2019 to 2023 were collected and segmented into three periods: prepandemic (2019), pandemic year (2020) and postpandemic (2021-2023). Each average search volume index was used to examine public awareness with the Kruskal-Wallis test.

Results: Among commonly used search keywords, Myopia is preferred over more colloquial terms (Nearsighted and Shortsighted). During the pandemic, searches for Myopia remained stable, while postpandemic, interest in Myopia surged (31.54%, p<0.0001). Myopia prevention/treatment awareness from 2019 to 2023 indicated notable shifts. In 2020, searches for Contact lenses and Spectacles decreased (-14.09% and -21.97%, respectively, both p<0.0001). These trends persisted postpandemic, with declining searches for Orthokeratology (-41.38%, p<0.01). Public interest for Light therapy (43.00%, p<0.0001) and Atropine (27.42%, p<0.0001) increased.

Discussion: The research highlights significant increases in global online awareness of myopia during the COVID-19 pandemic. The extended period of remote work during the pandemic may alter lifestyle habits and affect public perception of treatment options. Those findings could offer valuable perspectives on global interest patterns related to myopia, which supports subsequent myopia studies and is further useful to develop relevant public health policies.