BMJ Health & Care Informatics最新文献

Objectives: Atrial fibrillation (AF) requires comprehensive management due to its complex nature. The Atrial Fibrillation Better Care (ABC) pathway, introduced in the 2020 European Society of Cardiology Guidelines, has demonstrated clinical benefits, yet adherence remains suboptimal. This study evaluates the impact of establishing an Atrial Fibrillation Centre (AFC) on ABC pathway adherence in a Chinese teaching hospital.

Methods: This study employed an interrupted time series analysis to assess monthly ABC pathway adherence rates before and after AFC construction. The analysis focused on anticoagulation (A), better symptom control (B) and comorbidity management (C).

Results: Following AFC establishment, the hospital-wide ABC adherence rate increased by 11.82%, with a sustained monthly increase of 0.27%. Improvements were primarily observed in cardiology and internal medicine departments, whereas surgical departments showed minimal change. Anticoagulation and symptom control adherence improved significantly, while comorbidity management remained unchanged.

Discussion: The AFC improved ABC pathway adherence through standardised, multidisciplinary AF management. Significant gains in anticoagulation and symptom control were observed, but rhythm control and comorbidity management remained suboptimal. Barriers include limited ablation access and fragmented care. Future efforts should enhance interdisciplinary collaboration, expand procedural accessibility and integrate long-term cardiovascular risk management to optimise AF care.

Conclusion: Establishing an AFC significantly improved ABC pathway adherence, which proved effective in both stroke prevention and symptom management, particularly in cardiology and internal medicine departments. Future efforts should focus on enhancing rhythm control strategies and optimising comorbidity management to further improve integrated AF care.

Trial registration number: MR-50-24-014759.

Objectives: Using the routine health data in decision-making improves the health service delivery and health system performance. This study was aimed at identifying the level of information utilisation and associated factors in the Routine Health Information Systems (RHIS).

Methods: A concurrent triangulation design of a mixed-methods approach was applied from 1 to 30 April 2023. A sample of 304 health workers was randomly selected, and 18 informants were purposefully interviewed. Standardised Performance of Routine Information System Management tools were used. Multilevel linear mixed model regression and thematic analysis were conducted.

Results: The level of good information utilisation in RHIS was 52.0% (95% CI: 46.2%, 57.7%, p = 0.491). Data visualisation (β=0.053, 95% CI: 0.006, 0.101, p = 0.027), data quality assessment (β=0.054, 95% CI: 0.018, 0.090, p = 0.003), supervision (β=0.135, 95% CI: 0.072, 0.198, p < 0.001), management support (β=0.065, 95% CI: 0.001, 0.129, p = 0.045) and data management skills (β=0.070, 95% CI: 0.023, 0.118, p = 0.004) were significant positive predictors of information utilisation. Conversely, information utilisation decreased in health posts (β=-0.082, 95% CI: -0.160, -0.005, p = 0.037). This finding was further supported by the qualitative data.

Discussion: The level of information utilisation was consistent with other studies in Ethiopia, although previous studies excluded health posts. Data visualisation, institutional management support, type of health institution, conducting data quality assessment, supervision quality and data management skills were significant predictors of information utilisation in the RHIS. Differences in health worker skills and stronger district-level monitoring systems likely explained variation in information utilisation across different types of health institutions.

Conclusion: The utilisation of routine health information was lower. Providing quality supervision, improving the data management skills of health workers and conducting data quality assessments are essential and suggested interventions for enhancing information utilisation.

Objectives: To evaluate whether adherence to oral bisphosphonate in patients with osteoporosis may be improved by teleconsultation (TC) with or without combined use of email to contact the bone specialist on-demand (enhanced TC).

Methods: 103 naïve patients with osteoporosis were prescribed branded alendronate (70 mg weekly) and randomised to three service modalities (presence, TC and enhanced TC), and evaluated for medication adherence after 12 months of follow-up. Patients allocated to the enhanced TC were provided with the opportunity to contact the bone specialists by email without any restriction. Patient-reported outcome(PROMs) and experience measures (PREMs) were evaluated with respect to the service modality.

Results: Of 89 patients who were persistent to therapy, 66% displayed optimal medication adherence, with odds being 4.5 higher in patients receiving enhanced TC versus those receiving the other services. TC service modality was considered in general to be worse in quality than in presence visits, whereas the combination with email use as in enhanced TC was sufficient to compensate for the perceived decrease in quality of care. Enhanced TC did not have any impact on the perception of quality of life as assessed by PROMs.

Discussion: In patients with osteoporosis, TC did not provide any advantage over traditional in presence visits in terms of improvement of adherence to therapy. However, when TC was combined with email to contact the bone specialist on demand, there was a significant improvement in adherence to the prescribed drug.

Conclusions: Patients with osteoporosis need to be supported after drug prescription to guarantee optimal medication therapy.

Objectives: Pulmonary function testing (PFT) data, such as forced expiratory volume (FEV₁) has become increasingly siloed from the electronic health record (EHR). We hypothesised that FEV₁ %pred is independently associated with mortality risk, even after adjusting for the Care Assessment Needs (CAN) score, a validated method developed by the Veterans Health Administration (VA) to predict mortality. Additionally, we hypothesised that the integration of PFT data into the EHR has declined in recent years.

Methods: We conducted a retrospective cohort study using national VA data on PFTs from 2013 to 2018. Using logistic regression adjusted for CAN scores, we assessed the associations between FEV1 percent predicted (%pred) and all-cause mortality at 1 year and 5 years.

Results: While the number of PFTs performed has generally increased since 2000, the integration of PFT data into the EHR has declined since 2006. The CAN-adjusted odds of 1-year mortality were 2.94 (95% CI: 2.66 to 3.24) for those with FEV₁ %pred <35%, compared with those with FEV₁ %pred ≥70%, while 5-year mortality odds were 3.83 (95% CI: 3.58 to 4.09).

Discussion: Our study shows that FEV₁ %pred is statistically significantly associated with increased risk of mortality, above and beyond the CAN score. However, the declining integration of PFT data into the VA EHR highlights a concerning trend of isolating critical test results from clinical care.

Conclusion: Among people with FEV₁ recorded in the EHR, FEV₁ %pred is statistically significantly associated with increased risk of both 1-year and 5-year mortality, above and beyond the CAN score.

Background: Precision and genomic medicine have significant potential to improve population health. However, despite rapid technological development and increasing data complexity, practical applications of precision medicine remain limited. There is also a lack of evaluation of unintended consequences and a failure to use theory-based implementation frameworks to manage risks and ensure sustainability.

Methods: This work provides a conceptual overview of evaluation challenges related to precision medicine platforms, based on existing literature. It proposes a theory-informed proactive process evaluation framework to guide the development and assessment of these platforms.

Results: The proposed framework considers infrastructural, socio-organisational and system-level factors. It raises key questions, such as: How will platforms integrate with existing infrastructures? How will they transform care pathways and the delivery of care across settings?

Conclusions: Rapid technological advances challenge markets and regulatory environments. Agile evaluation approaches are crucial for building a sustainable innovation ecosystem for precision medicine platforms.

Objectives: Clinical trial enrolment is critical for the development and approval of novel cancer therapeutics, but patient identification and recruitment to clinical trials remains low and multiple trials accrue slowly or fail to meet accrual goals. Informatics solutions may facilitate clinical trial screening, ideally improving patient engagement and enrolment. Our objective is to develop and implement a system to efficiently screen queried patients for available clinical trials.

Methods: At Stanford, we designed and implemented a personalised clinical trial matching system, integrating our electronic medical record, clinical trials management system and a third-party software-based solution to directly connect providers with clinical research coordinators and appropriate trials.

Results: Over 3 years of a staged rollout, significant increases in clinical trial screening requests and subsequent enrolment have been observed. The total number of screening referrals increased from 20 in the first year to 236 in the third year. Enrolment related to screening referrals, the 'conversion rate', ranged from 16% to 26% of referred patients.

Conclusion: Clinical trial matching systems can increase awareness of available trials and provide a mechanism to increase clinical trial accrual, especially when implemented at the point of care for easy access at treatment decision points. Here, we describe the process of creating and implementing a bespoke clinical trial matching software integrated into the electronic medical record. Having validated the utility of the platform, we will focus on further efforts to drive utilisation through software features.

Clinical diagnostic tools can disadvantage subgroups due to poor model generalisability, which can be caused by unrepresentative training data. Practical deployment solutions to mitigate harm for subgroups from models with differential performance have yet to be established. This paper will build on existing work that considers a selective deployment approach where poorly performing subgroups are excluded from deployments. Alternatively, the proposed 'mitigated deployment' strategy requires safety nets to be built into clinical workflows to safeguard under-represented groups in a universal deployment. This approach relies on human-artificial intelligence collaboration and postmarket evaluation to continually improve model performance across subgroups with real-world data. Using a real-world case study, the benefits and limitations of mitigated deployment are explored. This will add to the tools available to healthcare organisations when considering how to safely deploy models with differential performance across subgroups.

Objectives: In the ever-evolving landscape of healthcare, the integration of digital systems and medical devices is increasingly important for modernising healthcare delivery. However, the acceptance and adoption of emerging technologies by healthcare staff present challenges. The purpose of this research was to apply relevant knowledge to inform and improve a conceptual framework (ARC): early exploration and agile adoption of emerging healthcare technology. We report on an expert-led Delphi study to evaluate consensus regarding the framework.

Method: The ARC conceptual framework, presented as four successive phases: imagine, educate, validate and score, was evaluated by 23 experts over two rounds. Experts first agreed/disagreed with 31 enabling statements relating to the early exploration and evaluation of new technology. The expert panel made recommendations (n=20), which were incorporated into round 2 with a checklist to evaluate the potential of a new technology.

Results: All participating experts completed round 1, and 13 completed round 2. Consensus (defined as >75% agreement) was achieved for 93.4% (n=57) of statements, with consensus without exception achieved for 34.4% (n=21) items and 16 new items added to the improved ARC framework, including on the appropriate use of simulation studies.

Discussion: The main findings highlight the importance of demonstration spaces, time in clinical environments with clinical teams, data-driven benefits and structured debriefs with staff.

Conclusion: A Delphi approach achieved expert consensus regarding the ARC framework for engaging with new technology and preparing the healthcare workforce for its use. Further advocacy is required to negotiate stakeholder involvement and interdisciplinary cooperation.

Objectives: To investigate whether machine learning (ML) algorithms can learn racial or ethnic information from the vital signs alone.

Methods: A retrospective cohort study of critically ill patients between 2014 and 2015 from the multicentre eICU-CRD critical care database involving 335 intensive care units in 208 US hospitals, containing 200 859 admissions. We extracted 10 763 critical care admissions of patients aged 18 and over, alive during the first 24 hours after admission, with recorded race or ethnicity as well as at least two measurements of heart rate, oxygen saturation, respiratory rate and blood pressure. Pairs of subgroups were matched based on age, gender, admission diagnosis and disease severity. XGBoost, Random Forest and Logistic Regression algorithms were used to predict recorded race or ethnicity based on the values of vital signs.

Results: Models derived from only four vital signs can predict patients' recorded race or ethnicity with an area under the curve (AUC) of 0.74 (±0.030) between White and Black patients, AUC of 0.74 (±0.030) between Hispanic and Black patients and AUC of 0.67 (±0.072) between Hispanic and White patients, even when controlling for known factors. There were very small, but statistically significant differences between heart rate, oxygen saturation and blood pressure, but not respiration rate and invasively measured oxygen saturation.

Discussion: ML algorithms can extract racial or ethnicity information from vital signs alone across diverse patient populations, even when controlling for known biases such as pulse oximetry variations and comorbidities. The model correctly classified the race or ethnicity in two out of three patients, indicating that this outcome is not random.

Conclusion: Vital signs embed racial information that can be learnt by ML algorithms, posing a significant risk to equitable clinical decision-making. Mitigating measures might be challenging, considering the fundamental role of vital signs in clinical decision-making.

Introduction: Multimorbidity (MM), defined as two or more chronic diseases in an individual, is linked to adverse outcomes. MM is increasing in sub-Saharan Africa due to rapidly advancing epidemiological and social transitions. The Multimorbidity in Africa: Digital Innovation, Visualisation and Application Research Hub (MADIVA) aims to address MM by developing data science solutions informed by stakeholder engagement.

Methods and analysis: MADIVA uses complex, individual-level datasets from research centres in rural Bushbuckridge, South Africa and urban Nairobi, Kenya. These datasets will be harmonised, linked and curated, and then used to develop MM risk prediction models, novel data science methods and interactive dashboards for research and clinical use. Pilot projects and mentorship programmes will support data science capacity development.

Ethics and dissemination: Ethics approval has been granted. Dissemination will occur through scientific meetings and publications. MADIVA is committed to making data FAIR: findable, accessible, interoperable and reusable.