BMJ Health & Care Informatics最新文献

Objectives: This study developed clinical evidence-based pathways (CEBPWs) to standardise treatment protocols, align diagnosis-reimbursement criteria, detect upcoding and enable early overtreatment warnings.

Methods: The CEBPWs were developed based on hospitalised patient-level data from 1 January 2022 to 31 June 2024 in 166 public hospitals in 16 administrative districts of Shanghai. It includes a total of 5 319 336 cases, involving 3 688 108 groups of 'diagnosis+therapy'. 2.61 billion records of hospitalisation charges and 876.45 million records of outpatient charges were collected. GROWTH algorithm was used to find the combination of frequently charged items for examination, treatment, drugs and devices in 'diagnosis+therapy' group.

Results: CEBPWs comprise five key elements: objective evidence identification, accurate classification, value weighting, frequency weighting and temporal sequencing of evidence. We applied CEBPWs to 224 diseases, detecting issues including upcoding, overtreatment and fragmented care episodes to enhance healthcare quality. CEBPWs achieve 100% coverage in diagnostics, therapy and consumables, with 81.81% drug coverage. The pilot evaluation showed that there were violations in 433 cases, with a frequency deviation of 8.64% and cost deviation of 10.82%, with 8.95% for diagnosis, 9.44% for therapy, 14.81% for drugs and 8.98% for consumables.

Discussion: We were developed CEBPWs, breaking the limitations of the clinical pathways is that the experience of clinical experts rather than objective criterion based on the characteristics of big data and lack of diagnostic and therapy standards integrated with payment standards.

Conclusion: The results indicate that CEBPW is critical tool for hospital management and regulation, address many drawbacks of clinical pathways.

Objectives: Due to the big disease burden of undiagnosed chronic obstructive pulmonary disease (COPD), we aimed to investigate the differences in the characteristics and risk factors of patients with undiagnosed COPD in China.

Methods: We used data from the 'Happy Breathing' Programme through April 2023. Current study is a cohort design. Participants were divided into high risk, undiagnosed and diagnosed COPD. Univariate logistic regression, lasso regression, decision tree, random forest and gradient boosting machine were used to screen the variables. Comparisons were conducted between undiagnosed and patients with diagnosed COPD.

Results: A total of 1603 high-risk, 4688 undiagnosed and 1634 patients with diagnosed COPD were identified. Patients with undiagnosed COPD had the lowest level of education, the poorest COPD-related knowledge and most biofuel users compared with high-risk populations and diagnosed patients (p<0.001). After multivariable logistic regression analysis, COPD-related knowledge score (OR=0.96, 95% CI 0.95 to 0.97), COPD Assessment Test Score (OR=1.01, 95% CI 1.00 to 1.02) and modified Medical Research Council Dyspnea Scale (OR=1.26, 95% CI 1.14 to 1.39) remained significant. Analysis of follow-up data showed that patients with undiagnosed COPD had lighter symptoms and experienced less acute exacerbations than diagnosed patients (p<0.001).

Discussion: Most patients with COPD remain undiagnosed until they feel dyspnoea or hospitalisation due to acute exacerbation. Undiagnosed COPD contributes significantly to the disease burden.

Conclusion: In China, patients with undiagnosed COPD were poorly educated, consumed more biofuels, smoked more and had limited COPD-related knowledge. Patients with undiagnosed COPD are also at risk of acute exacerbation.

Trial registration number: NCT04318912.

Objectives: This study aimed to evaluate the feasibility of using a large language model (LLM) to generate patient information leaflets (PILs) with improved readability based on PILs in the field of interventional radiology.

Methods: PILs were acquired from the Cardiovascular and Interventional Radiology Society of Europe website, reformatted, and uploaded to the GPT-4 user interface with a prompt aimed to simplify the language. Automated readability metrics were used to evaluate the readability of original and LLM-modified PILs. Factual accuracy was assessed by human evaluation from three consultant interventional radiologists using an agreed marking scheme.

Results: LLM-modified PILs had significantly lower mean reading grade (9.5±0.5) compared with original PILs (11.1±0.1) (p<0.01). However, the recommended reading grade of 6 (expected to be understood by 11- to 12-year-old children) was not achieved. Human evaluation revealed that most LLM-modified PILs had minor concerns regarding factual accuracy, but no errors that could result in serious patient harm were detected.

Discussion: LLMs appear to be a powerful tool in improving the readability of PILs within the field of interventional radiology. However, clinical experts are still required in PIL development to ensure the factual accuracy of these augmented documents is not compromised.

Conclusion: LLMs should be considered as a useful tool to assist with the development and revision of PILs in the field of interventional radiology.

Electronic health records (EHRs) provide comprehensive patient data, which could be better used to enhance informed decision-making, resource allocation and coordinated care, thereby optimising healthcare delivery. However, in mental healthcare, critical information, such as on risk factors, precipitants and treatment responses, is often embedded in unstructured text, limiting the ability to automate at scale measures to identify and prioritise local populations and patients, which potentially hinders timely prevention and intervention. We describe the development and proof-of-concept implementation of Visual & Interactive Engagement With Electronic Records, a clinical informatics platform designed to enhance direct patient care and population health management by improving the accessibility and usability of EHR data. We further outline strategies that were employed in this work to foster informatics innovation through interdisciplinary and cross-organisational collaboration to support integrated, personalised care and detail how these advancements were piloted and implemented within a large UK mental health National Health Service Foundation Trust to improve patient outcomes at an individual patient, clinician, clinical team and organisational level.

Objectives: Tailoring medication dosing to an individual's traits is complex, but artificial intelligence (AI) advancements enable greater precision. Our study objectives were to gauge healthcare providers' perspectives on AI-guided precision dosing and to identify barriers and enablers for adopting AI-guided precision dosing into clinical practice.

Methods: We conducted a qualitative study using purposive sampling to select a diverse group of healthcare providers, thereby broadening the viewpoints. We explored their receptiveness to AI-enabled dosing and sought to uncover implementation challenges. During the interviews, we introduced CURATE.AI as an example of an AI dosing tool. We analysed the data using deductive methods, coding the data according to the Unified Theory of Acceptance and Use of Technology framework.

Results: We interviewed 16 participants (9 doctors, 4 nurses and 3 pharmacists). Interviews revealed diverse perspectives, from hopeful anticipation to recognised challenges. While acknowledging AI's potential to enhance decision-making and patient safety, concerns about AI's suitability for complex cases, erosion of critical thinking, liability protection, and trust arose. Moreover, transparency, understandability of AI output and human oversight were seen as essential to mitigate risks and promote acceptance.

Discussion: AI-enabled dosing tools have the potential to optimise dosing and improve patient safety, but adoption barriers remain. Successful implementation will require technically robust tools and careful alignment with clinical workflows and user expectations.

Conclusion: Our study highlights the hopeful anticipation and complex challenges of introducing AI-enabled dosing into clinical practice. As AI inevitably becomes a part of healthcare, ongoing evaluation is essential to demonstrate value and promote adoption.

Objectives: Electronic prescriptions (e-prescriptions) introduce drug product selection mismatches during pharmacy data entry. System Approach to Verifying Electronic Prescriptions (SAV E-Rx) detects and alerts pharmacy staff to clinically significant occurrences. This study evaluates outcomes of the identified mismatches.

Methods: A retrospective analysis was conducted using 1 year of e-prescriptions and dispensing data from 14 community pharmacies across 9 US states. SAV E-Rx screened the data, and flagged mismatches were reviewed by pharmacists using the Common Formats for Event Reporting. Data were analysed using descriptive statistics, the Mann-Whitney U test and χ² tests.

Results: Of 1 250 804 records processed, 699 662 included sufficient data for comparison. Pharmacists classified 587 (88.7%) flagged records as intended mismatches and 75 (11.3%) as unintended. Intended mismatches involved ingredients (26.2%), strengths (53.7%) and dosage forms (47.4%), mainly due to prescriber-approved substitutions (62.4%). Unintended mismatches stemmed from ingredients (42.7%), strengths (36.0%) and dosage forms (54.7%) discrepancies, primarily reported as human error (82.7%) and labelling issues (76.0%). Future alerts were favoured for unintended mismatches (96.0%) compared with intended mismatches (56.7%) (p<0.001).

Discussion: While routine substitutions are a normal part of quality and timely care, unintended mismatches may pose clinical risks. These errors can arise from human factors and workflow challenges, including high prescription volumes and manual overrides. SAV E-Rx serves as an independent, automated safety net that flags mismatches, catching postdispensing errors that would otherwise go unnoticed.

Conclusions: E-prescription errors remain a safety concern. Routine implementation of SAV E-Rx could enhance error detection and enable timely interventions.

Objectives: Large language model (LLM)-based tools offer potential for clinical practice but raise concerns regarding output accuracy, patient safety and data security. We aimed to assess Swiss clinicians' use, knowledge and perception of LLMs and identify associated factors.

Methods: An anonymous online survey was distributed via 34 medical societies in Switzerland. The primary outcome was frequent use of LLMs (at least weekly use). The secondary outcome was higher knowledge regarding LLMs (score above the median in an 11-item test). Qualitative analysis explored clinicians' perceptions of LLM-related opportunities and risks.

Results: Among 685 participants (response rate 29.0%), 225 (32.8%) reported frequent use of LLMs, 25 (3.6%) reported having used a specific medical LLM and 42 (6%) reported the availability of workplace LLM guidelines. The median knowledge test score was 6 points (IQR 4-8 points). Multivariable analysis showed that younger age, male sex and research activity were significantly associated with frequent use and higher knowledge. Qualitative analysis identified administrative support, analytical assistance and access to information as key opportunities. The main risks identified were declining clinical skills, poor output quality and legal or ethical concerns.

Discussion: The study highlights a notable adoption of LLMs among Swiss clinicians, particularly among younger, male and research-active individuals. However, the limited availability of workplace guidelines raises concerns about safe and effective use.

Conclusion: The gap between widespread LLM use and the scarcity of workplace guidelines underscores the need for accessible educational resources and clinical guidelines to mitigate potential risks and promote informed use.

Objectives: The 'tumour, node, metastasis' (TNM) classification of colorectal cancer (CRC) predicts prognosis and so is vital to consider in analyses of patterns and outcomes of care when using electronic health records. Unfortunately, it is often only available in free-text reports. This study aimed to develop regex-based text-processing algorithms that identify the reports describing CRC and extract the TNM staging at a low computational cost.

Methods: The CRC and TNM extraction algorithms were iteratively developed using 58 634 imaging and pathology reports of patients with CRC from the Oxford University Hospitals (OUH) and Royal Marsden (RMH) NHS Foundation Trusts (FT), with additional input from Imperial College Healthcare and Christie NHS FTs. The algorithms were evaluated on a stratified random sample of 400 OUH development data reports and 400 newer 'unseen' OUH reports. The reports were annotated with the help of two clinicians.

Results: The CRC algorithm achieved at least 93.0% positive predictive value (PPV), 72.1% sensitivity, 64.0% negative predictive value (NPV) and 90.1% specificity for primary CRC on pathology reports. On imaging reports, it demonstrated at least 78.0% PPV, 91.8% sensitivity, 93.0% NPV and 80.9% specificity. For the main T/N/M categories, the TNM algorithm achieved PPVs of at least 93.9% (T), 97.7% (N) and 97.2% (M), and sensitivities of 63.6% (T), 89.6% (N) and 64.8% (M). NPVs were at least 45.0% (T), 91.1% (N), 88.4% (M), and specificities 95.7% (T), 98.1% (N), 99.3% (M). Reductions in performance were mostly due to implicit staging. For extracting explicit TNM stages, current or historical, the algorithm made no errors on 400 pathology reports and six errors on 400 imaging reports.

Conclusion: The TNM algorithm accurately extracts explicit TNM staging, but other methods are needed for retrieving implicit stages. The CRC algorithm is accurate on non-supplementary reports, but outputs need additional review if higher precision is required.