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Diagnostic prediction model for screening of elevated low-density and non-high-density lipoproteins in young Thai adults between 20 and 40 years of age.
IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2025-01-30 DOI: 10.1136/bmjhci-2024-101180
Wuttipat Kiratipaisarl, Vithawat Surawattanasakul, Wachiranun Sirikul, Phichayut Phinyo

Background: Low-density lipoprotein cholesterol (LDL-C) and non-high-density lipoprotein cholesterol (non-HDL-C) levels are paramount in atherosclerotic cardiovascular disease risk management. However, 94.4% of Thai young adult are unaware of their condition. A diagnostic prediction model may assist in screening and alleviating underdiagnosis.

Objectives: Development and internal validation of diagnostic prediction models on elevated LDL-C (≥160 mg/dL) and non-HDL-C (≥160 mg/dL).

Methods: Retrospective, single-centre, tertiary-care hospital annual health examination data from 29 March 2018 to 30 August 2023 was analysed. Two models with 11 predictors from anthropometry and bioimpedance are fitted with multivariable binary logistic regression predicting elevated LDL-C and non-HDL-C. Predictor selection used the backward stepwise elimination. Four performance metrics were quantified: discrimination using area under the receiver-operating characteristic curve (AuROC); calibration by calibration plot; utility by decision curve analysis and instability by performance instability plots. Internal validation was carried out using 500 repetitions of bootstrap-resampling.

Results: Dataset included 2222 LDL-C and 5149 non-HDL-C investigations, 303 were classed as elevated LDL-C (13.6%) and 1013 as elevated non-HDL-C cases (19.7%). Two predictors, gender and metabolic age, were identified in the LDL-C model with AuROC 0.639 (95% CI 0.617 to 0.661), poor calibration, and utility in the 7%-25% probability range. Three predictors-gender, diastolic blood pressure and metabolic age-were identified in the non-HDL-C model with AuROC 0.722 (95% CI 0.705 to 0.738), good calibration and utility in 9%-55% probability range.

Discussion and conclusion: Overall results demonstrated acceptable discrimination for non-HDL-C model but inadequate performance of LDL-C model for clinical practice. An external validation study should be planned for non-HDL-C model.

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引用次数: 0
Barcode medication administration system use and safety implications: a data-driven longitudinal study supported by clinical observation. 条形码给药系统的使用和安全性影响:一项由临床观察支持的数据驱动的纵向研究。
IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2025-01-19 DOI: 10.1136/bmjhci-2024-101214
Rachel Williams, Kumud Kantilal, Kenneth K C Man, Ann Blandford, Yogini Jani

Objectives: Barcode medication administration (BCMA) systems may improve patient safety with successful integration and use. This study aimed to explore the barriers and enablers for the successful use of a BCMA system by examining the patterns of medication and patient scanning over time and potential safety implications.

Methods: Retrospective longitudinal study informed by prospective clinical observations using data extracted from five hospital wards over the first 16 months after implementation to determine trends in medication and patient scanning rates, reasons for non-compliance and scanning mismatch alerts. Regression models were applied to explore factors influencing medication scanning rates across wards of different specialties.

Results: Electronic data on 613 868 medication administrations showed overall medication scanning rates per ward ranged from 5.6% to 67% and patient scanning rates from 4.6% to 89%. Reported reasons for not scanning medications were 'barcode not readable' and 'unavailability of scanners'. Scanning rates declined over time and the pattern of reason codes for not scanning also changed. Factors associated with higher scanning rates included a locally led quality improvement (QI) initiative, the medication administration time and the medication formulation, for example, tablets and liquids. Overall, 37% of scanning alerts resulted in a change in user action. Staff tried to comply with the BCMA system workflow, but workarounds were observed.

Discussion: Compliance with BCMA systems varied across wards and changed over time. QI initiatives hold promise to ensure sustained use of BCMA systems.

Conclusions: BCMA systems may help to improve medication safety, but further research is needed to confirm sustained safety benefits.

目的:条形码给药(BCMA)系统的成功整合和使用可以提高患者的安全性。本研究旨在通过检查药物和患者扫描模式以及潜在的安全影响,探索BCMA系统成功使用的障碍和促进因素。方法:回顾性纵向研究,采用前瞻性临床观察,使用实施后前16个月从5个医院病房提取的数据,以确定药物和患者扫描率的趋势,不遵守的原因和扫描不匹配警报。应用回归模型探讨不同专科病房药物扫描率的影响因素。结果:613868次给药的电子数据显示,每个病房的总体药物扫描率为5.6% ~ 67%,患者扫描率为4.6% ~ 89%。据报道,不扫描药物的原因是“条形码不可读”和“扫描仪不可用”。扫描率随着时间的推移而下降,不扫描的原因码模式也发生了变化。与高扫描率相关的因素包括当地主导的质量改进(QI)倡议,给药时间和药物配方,例如片剂和液体。总的来说,37%的扫描警报导致了用户行为的改变。员工试图遵守BCMA系统工作流程,但发现了变通办法。讨论:对BCMA系统的依从性因病房而异,并随时间而变化。QI倡议承诺确保BCMA系统的持续使用。结论:BCMA系统可能有助于提高用药安全性,但需要进一步的研究来确认持续的安全效益。
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引用次数: 0
Analysing expression of loneliness and insomnia through social intelligence analysis. 通过社会智力分析分析孤独和失眠的表现。
IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2025-01-19 DOI: 10.1136/bmjhci-2024-101116
Hurmat Ali Shah, Mowafa Househ

Background: Loneliness and insomnia are mutually occurring conditions. This paper investigates whether keywords depicting loneliness and insomnia are expressed together on social media. Understanding loneliness through data fills the gaps or validates the literature on loneliness from sociological and psychological perspectives. Loneliness is associated with various physical and mental health conditions but there are opportunities to understand it from the perspectives and lens of health informatics through social media data. Because loneliness is a subjective phenomenon, therefore, the self-reporting nature of social media data can provide an intimate glimpse into the feelings associated with loneliness.

Methods: This study uses sentiment analysis of collected tweets on loneliness and insomnia to filter out the tweets that have negative connotations. Those tweets are then further analysed to find out categories and themes associated with loneliness and insomnia.

Results: Through the frequency of word occurrence analysis, it was seen that the association between loneliness and insomnia can be found. The association, in the tweets analysed, is mediated by words denoting depressive symptoms. Moreover, the themes and categories which are associated with the expression of both loneliness and insomnia are those of personal feelings and time.

背景:孤独和失眠是相互发生的情况。本文调查描述孤独和失眠的关键词是否在社交媒体上一起表达。通过数据来理解孤独填补了空白,或者从社会学和心理学的角度验证了关于孤独的文献。孤独与各种身心健康状况有关,但有机会通过社交媒体数据从健康信息学的角度和视角来理解它。因为孤独是一种主观现象,因此,社交媒体数据的自我报告性质可以提供与孤独相关的感受的亲密一瞥。方法:本研究对收集到的关于孤独和失眠的推文进行情绪分析,过滤出具有负面内涵的推文。然后对这些推文进行进一步分析,找出与孤独和失眠相关的类别和主题。结果:通过单词出现频率分析,发现孤独感与失眠之间存在关联。在分析的推文中,这种关联是由表示抑郁症状的词语介导的。此外,与孤独和失眠表达相关的主题和类别是个人情感和时间的主题和类别。
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引用次数: 0
The feasibility and effectiveness of telecare consultations in a nurse-led post-acute stroke clinic. 护士主导的急性脑卒中后门诊远程会诊的可行性和有效性。
IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2025-01-19 DOI: 10.1136/bmjhci-2024-101082
Arkers Kwan Ching Wong, Robbie Mian Wang, Frances Kam Yuet Wong, Bernard Man Kam Yuen, Ching Sing Fong, Shun Tim Chan, Vivian Wai Yan Kwok

Background: Telecare may provide an alternative to maintaining post-acute stroke care services in making benefit to both the providers and the stroke survivors, although study is needed to investigate its feasibility and effectiveness in integrating this innovative delivery mode into a routine.

Objectives: The objectives of this study are to assess the feasibility and effectiveness of telecare consultations in a nurse-led post-acute stroke clinic.

Methods: A pre- and post-test one group quasi-experimental design was adopted. Subjects were recruited in the clinic and received three secondary stroke care consultations in 3 months via telecare from stroke nurses. Data were collected at pre- and post-intervention. A Wilcoxon signed-rank test was used to compare the two time-points for differences in effectiveness.

Results: Ninety-two stroke survivors participated. The drop-out rate was 27%. The majority perceived the programme as time-friendly and cost-saving and as alleviating their health-related worries. At the 3-month follow-up, notable improvements were observed in the activities of daily living and the strength domain of stroke-specific quality of life.

Conclusions: Integrating telecare consultations within nurse-led stroke clinics is a feasible and acceptable strategy for monitoring the health and fostering the self-care abilities of individuals following their discharge from hospital after an acute stroke episode.

背景:远程医疗可能是维持急性卒中后护理服务的另一种选择,使提供者和卒中幸存者都受益,尽管需要研究将这种创新的交付模式整合到常规中的可行性和有效性。目的:本研究的目的是评估远程会诊在护士主导的急性脑卒中后诊所的可行性和有效性。方法:采用前测和后测一组准实验设计。受试者在诊所招募,并在3个月内通过远程护理接受3次卒中护理咨询。在干预前和干预后收集数据。采用Wilcoxon符号秩检验比较两个时间点的有效性差异。结果:92名中风幸存者参与。辍学率为27%。大多数人认为该方案省时、节省费用,减轻了他们对健康方面的担忧。在3个月的随访中,观察到日常生活活动和中风特定生活质量的力量领域的显着改善。结论:在护士主导的中风诊所中整合远程会诊是一种可行且可接受的策略,可以监测急性中风发作后个人出院后的健康状况并培养他们的自我护理能力。
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引用次数: 0
Large language models for data extraction from unstructured and semi-structured electronic health records: a multiple model performance evaluation. 用于从非结构化和半结构化电子健康记录中提取数据的大型语言模型:多模型性能评估。
IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2025-01-19 DOI: 10.1136/bmjhci-2024-101139
Vasileios Ntinopoulos, Hector Rodriguez Cetina Biefer, Igor Tudorache, Nestoras Papadopoulos, Dragan Odavic, Petar Risteski, Achim Haeussler, Omer Dzemali

Objectives: We aimed to evaluate the performance of multiple large language models (LLMs) in data extraction from unstructured and semi-structured electronic health records.

Methods: 50 synthetic medical notes in English, containing a structured and an unstructured part, were drafted and evaluated by domain experts, and subsequently used for LLM-prompting. 18 LLMs were evaluated against a baseline transformer-based model. Performance assessment comprised four entity extraction and five binary classification tasks with a total of 450 predictions for each LLM. LLM-response consistency assessment was performed over three same-prompt iterations.

Results: Claude 3.0 Opus, Claude 3.0 Sonnet, Claude 2.0, GPT 4, Claude 2.1, Gemini Advanced, PaLM 2 chat-bison and Llama 3-70b exhibited an excellent overall accuracy >0.98 (0.995, 0.988, 0.988, 0.988, 0.986, 0.982, 0.982, and 0.982, respectively), significantly higher than the baseline RoBERTa model (0.742). Claude 2.0, Claude 2.1, Claude 3.0 Opus, PaLM 2 chat-bison, GPT 4, Claude 3.0 Sonnet and Llama 3-70b showed a marginally higher and Gemini Advanced a marginally lower multiple-run consistency than the baseline model RoBERTa (Krippendorff's alpha value 1, 0.998, 0.996, 0.996, 0.992, 0.991, 0.989, 0.988, and 0.985, respectively).

Discussion: Claude 3.0 Opus, Claude 3.0 Sonnet, Claude 2.0, GPT 4, Claude 2.1, Gemini Advanced, PaLM 2 chat bison and Llama 3-70b performed the best, exhibiting outstanding performance in both entity extraction and binary classification, with highly consistent responses over multiple same-prompt iterations. Their use could leverage data for research and unburden healthcare professionals. Real-data analyses are warranted to confirm their performance in a real-world setting.

Conclusion: Claude 3.0 Opus, Claude 3.0 Sonnet, Claude 2.0, GPT 4, Claude 2.1, Gemini Advanced, PaLM 2 chat-bison and Llama 3-70b seem to be able to reliably extract data from unstructured and semi-structured electronic health records. Further analyses using real data are warranted to confirm their performance in a real-world setting.

目的:我们旨在评估多个大语言模型(llm)在从非结构化和半结构化电子健康记录中提取数据方面的性能。方法:由领域专家起草并评估50份英文合成医学笔记,其中包含结构化和非结构化部分,随后用于llm提示。根据基于变压器的基线模型对18个llm进行了评估。性能评估包括四个实体提取和五个二元分类任务,每个LLM总共有450个预测。在三个相同提示的迭代中执行llm响应一致性评估。结果:Claude 3.0 Opus、Claude 3.0 Sonnet、Claude 2.0、GPT 4、Claude 2.1、Gemini Advanced、PaLM 2 chat-bison和Llama 3-70b的总体准确率为0.98(分别为0.995、0.988、0.988、0.988、0.986、0.982、0.982和0.982),显著高于基线RoBERTa模型(0.742)。与基线模型RoBERTa相比,Claude 2.0、Claude 2.1、Claude 3.0 Opus、PaLM 2 chat-bison、GPT 4、Claude 3.0 Sonnet和Llama 3-70b的多次运行一致性略高,而Gemini Advanced的多次运行一致性略低(Krippendorff α值分别为0.998、0.996、0.996、0.992、0.991、0.989、0.988和0.985)。讨论:Claude 3.0 Opus, Claude 3.0 Sonnet, Claude 2.0, GPT 4, Claude 2.1, Gemini Advanced, PaLM 2 chat bison和Llama 3-70b表现最好,在实体提取和二元分类方面都表现出色,多次相同提示迭代的响应高度一致。它们的使用可以利用数据进行研究,减轻医疗保健专业人员的负担。需要进行实时数据分析,以确认其在实际环境中的性能。结论:Claude 3.0 Opus、Claude 3.0 Sonnet、Claude 2.0、GPT 4、Claude 2.1、Gemini Advanced、PaLM 2 chat-bison和Llama 3-70b似乎能够可靠地从非结构化和半结构化的电子健康记录中提取数据。有必要使用实际数据进行进一步分析,以确认它们在实际环境中的性能。
{"title":"Large language models for data extraction from unstructured and semi-structured electronic health records: a multiple model performance evaluation.","authors":"Vasileios Ntinopoulos, Hector Rodriguez Cetina Biefer, Igor Tudorache, Nestoras Papadopoulos, Dragan Odavic, Petar Risteski, Achim Haeussler, Omer Dzemali","doi":"10.1136/bmjhci-2024-101139","DOIUrl":"10.1136/bmjhci-2024-101139","url":null,"abstract":"<p><strong>Objectives: </strong>We aimed to evaluate the performance of multiple large language models (LLMs) in data extraction from unstructured and semi-structured electronic health records.</p><p><strong>Methods: </strong>50 synthetic medical notes in English, containing a structured and an unstructured part, were drafted and evaluated by domain experts, and subsequently used for LLM-prompting. 18 LLMs were evaluated against a baseline transformer-based model. Performance assessment comprised four entity extraction and five binary classification tasks with a total of 450 predictions for each LLM. LLM-response consistency assessment was performed over three same-prompt iterations.</p><p><strong>Results: </strong>Claude 3.0 Opus, Claude 3.0 Sonnet, Claude 2.0, GPT 4, Claude 2.1, Gemini Advanced, PaLM 2 chat-bison and Llama 3-70b exhibited an excellent overall accuracy >0.98 (0.995, 0.988, 0.988, 0.988, 0.986, 0.982, 0.982, and 0.982, respectively), significantly higher than the baseline RoBERTa model (0.742). Claude 2.0, Claude 2.1, Claude 3.0 Opus, PaLM 2 chat-bison, GPT 4, Claude 3.0 Sonnet and Llama 3-70b showed a marginally higher and Gemini Advanced a marginally lower multiple-run consistency than the baseline model RoBERTa (Krippendorff's alpha value 1, 0.998, 0.996, 0.996, 0.992, 0.991, 0.989, 0.988, and 0.985, respectively).</p><p><strong>Discussion: </strong>Claude 3.0 Opus, Claude 3.0 Sonnet, Claude 2.0, GPT 4, Claude 2.1, Gemini Advanced, PaLM 2 chat bison and Llama 3-70b performed the best, exhibiting outstanding performance in both entity extraction and binary classification, with highly consistent responses over multiple same-prompt iterations. Their use could leverage data for research and unburden healthcare professionals. Real-data analyses are warranted to confirm their performance in a real-world setting.</p><p><strong>Conclusion: </strong>Claude 3.0 Opus, Claude 3.0 Sonnet, Claude 2.0, GPT 4, Claude 2.1, Gemini Advanced, PaLM 2 chat-bison and Llama 3-70b seem to be able to reliably extract data from unstructured and semi-structured electronic health records. Further analyses using real data are warranted to confirm their performance in a real-world setting.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11751965/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142999865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Patient perspective on predictive models in healthcare: translation into practice, ethical implications and limitations? 患者对医疗保健预测模型的看法:转化为实践、伦理影响和局限性?
IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2025-01-16 DOI: 10.1136/bmjhci-2024-101153
Sarah Markham

In this perspective article, we consider the use of predictive models in healthcare and associated challenges. We will argue that patients can play a valuable role in supporting the safe and practicable embedding of such tools and provide some examples.

在这篇透视图文章中,我们将考虑在医疗保健中使用预测模型以及相关挑战。我们将论证患者可以在支持这些工具的安全和实用的嵌入中发挥重要作用,并提供一些例子。
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引用次数: 0
Finding a constrained number of predictor phenotypes for multiple outcome prediction. 为多结果预测寻找有限数量的预测因子表型。
IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2025-01-16 DOI: 10.1136/bmjhci-2024-101227
Jenna M Reps, Jenna Wong, Egill A Fridgeirsson, Chungsoo Kim, Luis H John, Ross D Williams, Renae R Fisher, Patrick B Ryan

Background: Prognostic models help aid medical decision-making. Various prognostic models are available via websites such as MDCalc, but these models typically predict one outcome, for example, stroke risk. Each model requires individual predictors, for example, age, lab results and comorbidities. There is no clinical tool available to predict multiple outcomes from a list of common medical predictors.

Objective: Identify a constrained set of outcome-agnostic predictors.

Methods: We proposed a novel technique aggregating the standardised mean difference across hundreds of outcomes to learn a constrained set of predictors that appear to be predictive of many outcomes. Model performance was evaluated using the constrained set of predictors across eight prediction tasks. We compared against existing models, models using only age/sex predictors and models without any predictor constraints.

Results: We identified 67 predictors in our constrained set, plus age/sex. Our predictors included illnesses in the following categories: cardiovascular, kidney/liver, mental health, gastrointestinal, infectious and oncologic. Models developed using the constrained set of predictors achieved comparable discrimination compared with models using hundreds or thousands of predictors for five of the eight prediction tasks and slightly lower discrimination for three of the eight tasks. The constrained predictor models performed as good or better than all existing clinical models.

Conclusions: It is possible to develop models for hundreds or thousands of outcomes that use the same small set of predictors. This makes it feasible to implement many prediction models via a single website form. Our set of predictors can also be used for future models and prognostic model research.

背景:预后模型有助于医疗决策。通过MDCalc等网站可以获得各种预后模型,但这些模型通常预测一种结果,例如中风风险。每个模型都需要单独的预测因素,例如年龄、实验室结果和合并症。目前还没有临床工具可以从常见的医学预测因子列表中预测多种结果。目的:确定一组约束的结果不可知预测因子。方法:我们提出了一种新技术,汇总数百个结果的标准化平均差异,以学习一组约束的预测因子,这些预测因子似乎可以预测许多结果。在八个预测任务中使用约束的预测器集评估模型性能。我们比较了现有的模型,仅使用年龄/性别预测因子的模型和没有任何预测因子约束的模型。结果:我们在约束集中确定了67个预测因子,加上年龄/性别。我们的预测指标包括以下类别的疾病:心血管疾病、肾脏/肝脏疾病、心理健康疾病、胃肠道疾病、传染病和肿瘤疾病。与使用数百或数千个预测因子的模型相比,使用约束预测因子集开发的模型在8个预测任务中的5个任务中实现了相当的歧视,在8个任务中的3个任务中略有降低的歧视。约束预测模型的表现与所有现有的临床模型一样好或更好。结论:有可能为使用相同的一小组预测因子的数百或数千种结果开发模型。这使得通过一个单一的网站表单实现许多预测模型成为可能。我们的预测因子集也可用于未来模型和预测模型研究。
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引用次数: 0
Using routine primary care data in research: (in)efficient case studies and perspectives from the Asthma UK Centre for Applied Research. 在研究中使用常规初级保健数据:(1)来自英国哮喘应用研究中心的有效案例研究和观点。
IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2025-01-09 DOI: 10.1136/bmjhci-2024-101134
Holly Tibble, Rami A Alyami, Andrew Bush, Steve Cunningham, Steven Julious, David Price, Jennifer K Quint, Stephen Turner, Kay Wang, Andrew Wilson, Gwyneth A Davies, Mome Mukherjee, Amy Hai Yan Chan, Deepa Varghese, Tracy Jackson, Noelle Morgan, Luke Daines, Hilary Pinnock

Aim: We aimed to identify enablers and barriers of using primary care routine data for healthcare research, to formulate recommendations for improving efficiency in knowledge discovery.

Background: Data recorded routinely in primary care can be used for estimating the impact of interventions provided within routine care for all people who are clinically eligible. Despite official promotion of 'efficient trial designs', anecdotally researchers in the Asthma UK Centre for Applied Research (AUKCAR) have encountered multiple barriers to accessing and using routine data.

Methods: Using studies within the AUKCAR portfolio as exemplars, we captured limitations, barriers, successes, and strengths through correspondence and discussions with the principal investigators and project managers of the case studies.

Results: We identified 14 studies (8 trials, 2 developmental studies and 4 observational studies). Investigators agreed that using routine primary care data potentially offered a convenient collection of data for effectiveness outcomes, health economic assessment and process evaluation in one data extraction. However, this advantage was overshadowed by time-consuming processes that were major barriers to conducting efficient research. Common themes were multiple layers of information governance approvals in addition to the ethics and local governance approvals required by all health service research; lack of standardisation so that local approvals required diverse paperwork and reached conflicting conclusions as to whether a study should be approved. Practical consequences included a trial that over-recruited by 20% in order to randomise 144 practices with all required permissions, and a 5-year delay in reporting a trial while retrospectively applied regulations were satisfied to allow data linkage.

Conclusions: Overcoming the substantial barriers of using routine primary care data will require a streamlined governance process, standardised understanding/application of regulations and adequate National Health Service IT (Information Technology) capability. Without policy-driven prioritisation of these changes, the potential of this valuable resource will not be leveraged.

目的:我们的目的是确定在医疗保健研究中使用初级保健常规数据的促进因素和障碍,以制定提高知识发现效率的建议。背景:在初级保健中常规记录的数据可用于评估常规护理中提供的干预措施对所有临床符合条件的人的影响。尽管官方提倡“有效的试验设计”,但英国哮喘应用研究中心(AUKCAR)的研究人员在获取和使用常规数据方面遇到了多重障碍。方法:使用AUKCAR投资组合中的研究作为范例,我们通过与案例研究的主要研究者和项目经理的通信和讨论捕获了限制、障碍、成功和优势。结果:我们纳入了14项研究(8项试验,2项发育研究和4项观察性研究)。研究人员一致认为,使用常规初级保健数据可能为有效性结果、健康经济评估和过程评估提供一个方便的数据收集。然而,这一优势被耗时的过程所掩盖,这些过程是进行有效研究的主要障碍。共同的主题是,除了所有卫生服务研究所需的伦理和地方治理批准外,还要进行多层信息治理批准;缺乏标准化,因此地方审批需要不同的文书工作,并且在是否应该批准一项研究方面得出相互矛盾的结论。实际结果包括,为了随机选择144个实践,在所有必要的许可下,试验过度招募了20%,并且在满足回顾性应用法规以允许数据链接的情况下,延迟5年报告试验。结论:克服使用常规初级保健数据的重大障碍将需要精简的治理过程、对法规的标准化理解/应用以及充分的国家卫生服务IT(信息技术)能力。如果没有政策驱动的这些变化的优先次序,这一宝贵资源的潜力将无法发挥作用。
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引用次数: 0
Sharing data matters: exploring the attitudes of older consumers on an emerging healthy ageing data platform using electronic health records for research. 共享数据很重要:探索老年消费者对利用电子健康记录进行研究的新兴健康老龄化数据平台的态度。
IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2025-01-02 DOI: 10.1136/bmjhci-2024-101126
Kim Naude, David A Snowdon, Emily Parker, Roisin McNaney, Velandai Srikanth, Nadine E Andrew

Background: In Australia, with the recent introduction of electronic health records (EHRs) into hospitals, the use of hospital-based EHRs for research is a relatively new concept. The aim of this study was to explore the attitudes of older healthcare consumers on sharing their health data with an emerging EHR-based Research Data Platform within the National Centre for Healthy Ageing.

Methods: This was a qualitative study. Two workshops were conducted in March 2022 with consumer representatives across Peninsula Health, Victoria, Australia. The workshops comprised three parts: (1) an ice-breaker (2) an introduction to EHR-based research through the presentation of 'use case' scenarios and (3) focus group discussions. Qualitative data were analysed using reflexive thematic analysis.

Results: Consumer participants (n=16) were aged between 62 and 83 years and were of mixed gender. The overarching theme was related to trust in the use of EHR data for research; themes included: (1) benefits of sharing data, (2) uncertainty around data collection processes and (3) data sharing fears. The three themes within the overarching theme all reflect participants' levels of trust.

Conclusion: Our study identified fundamental issues related to trust in the use of EHR data for research, with both healthcare and broader societal factors contributing to consumer attitudes. Processes to support transparent and clear communication with consumers are essential to support the responsible use of EHR data for research.

背景:在澳大利亚,随着最近电子健康记录(EHRs)引入医院,使用基于医院的电子健康记录进行研究是一个相对较新的概念。本研究的目的是探讨老年医疗保健消费者与国家健康老龄化中心内新兴的基于电子病历的研究数据平台分享健康数据的态度。方法:定性研究。2022年3月与澳大利亚维多利亚州半岛健康中心的消费者代表举办了两次讲习班。研讨会由三个部分组成:(1)打破僵局;(2)通过展示“用例”场景介绍基于电子病历的研究;(3)焦点小组讨论。定性数据采用反身性主题分析进行分析。结果:消费者参与者(n=16)年龄在62至83岁之间,性别混合。总体主题与使用电子病历数据进行研究的信任有关;主题包括:(1)共享数据的好处;(2)数据收集过程的不确定性;(3)数据共享的恐惧。总体主题中的三个主题都反映了参与者的信任水平。结论:我们的研究确定了与使用电子病历数据进行研究的信任相关的基本问题,医疗保健和更广泛的社会因素都影响了消费者的态度。支持与消费者进行透明和清晰沟通的流程对于支持负责任地使用电子病历数据进行研究至关重要。
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引用次数: 0
Generative artificial intelligence (AI): a key innovation or just hype in primary care settings? 生成式人工智能(AI):一项关键创新还是初级保健领域的炒作?
IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-12-31 DOI: 10.1136/bmjhci-2024-101367
Annisa Ristya Rahmanti, Usman Iqbal, Sandeep Reddy, Xiaohong W Gao, Huan Xuan Nguyen, Yu-Chuan Jack Li
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引用次数: 0
期刊
BMJ Health & Care Informatics
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