BMJ Health & Care Informatics最新文献_第7页

Building a house without foundations? A 24-country qualitative interview study on artificial intelligence in intensive care medicine 建造没有地基的房屋？关于重症监护医学人工智能的 24 国定性访谈研究

IF 4.1 Q2 Computer Science

BMJ Health & Care Informatics

Pub Date : 2024-04-01 DOI: 10.1136/bmjhci-2024-101052

Stuart McLennan, Amelia Fiske, Leo Anthony Celi

Objectives To explore the views of intensive care professionals in high-income countries (HICs) and lower-to-middle-income countries (LMICs) regarding the use and implementation of artificial intelligence (AI) technologies in intensive care units (ICUs). Methods Individual semi-structured qualitative interviews were conducted between December 2021 and August 2022 with 59 intensive care professionals from 24 countries. Transcripts were analysed using conventional content analysis. Results Participants had generally positive views about the potential use of AI in ICUs but also reported some well-known concerns about the use of AI in clinical practice and important technical and non-technical barriers to the implementation of AI. Important differences existed between ICUs regarding their current readiness to implement AI. However, these differences were not primarily between HICs and LMICs, but between a small number of ICUs in large tertiary hospitals in HICs, which were reported to have the necessary digital infrastructure for AI, and nearly all other ICUs in both HICs and LMICs, which were reported to neither have the technical capability to capture the necessary data or use AI, nor the staff with the right knowledge and skills to use the technology. Conclusion Pouring massive amounts of resources into developing AI without first building the necessary digital infrastructure foundation needed for AI is unethical. Real-world implementation and routine use of AI in the vast majority of ICUs in both HICs and LMICs included in our study is unlikely to occur any time soon. ICUs should not be using AI until certain preconditions are met. Data are available upon reasonable request. Our data include pseudonymised transcripts of interviews, which cannot be made publicly available in their entirety because of (1) the terms of our ethics approval; and (2) because participants could be identifiable if placed in the context of the entire transcript. This is in line with current ethical expectations for qualitative interview research. We provide anonymised quotes within the paper to illustrate our findings (corresponding to transcript excerpts), and the complete interview guide used in the study has been included as a Supplementary Information.

目的探讨高收入国家（HICs）和中低收入国家（LMICs）重症监护专业人员对重症监护病房（ICUs）使用和实施人工智能（AI）技术的看法。方法在 2021 年 12 月至 2022 年 8 月期间，对来自 24 个国家的 59 名重症监护专业人员进行了个人半结构化定性访谈。访谈记录采用传统内容分析法进行分析。结果参与者对人工智能在重症监护病房的潜在应用普遍持积极态度，但也报告了一些众所周知的在临床实践中使用人工智能的顾虑，以及实施人工智能的重要技术和非技术障碍。各重症监护室在目前实施人工智能的准备程度上存在着重大差异。然而，这些差异主要不是发生在高收入国家和低收入国家之间，而是发生在高收入国家大型三甲医院的少数重症监护室和几乎所有其他高收入国家和低收入国家的重症监护室之间，前者据说拥有人工智能所需的数字基础设施，而后者据说既不具备获取必要数据或使用人工智能的技术能力，也没有具备使用该技术的适当知识和技能的员工。结论在没有建立人工智能所需的必要数字基础设施基础的情况下，投入大量资源开发人工智能是不道德的。在我们的研究中，高收入国家和低收入国家的绝大多数重症监护病房都不可能在短期内真正实施和常规使用人工智能。在满足某些前提条件之前，重症监护室不应使用人工智能。如有合理要求，可提供数据。我们的数据包括化名后的访谈记录，这些记录不能全部公开，原因是：（1）我们的伦理批准条款；（2）如果将整个记录放在一起，参与者的身份可能会被识别。这符合当前定性访谈研究的伦理要求。我们在论文中提供了匿名引文，以说明我们的研究结果（与记录誊本节选相对应），研究中使用的完整访谈指南已作为补充信息包含在内。

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引用次数: 0

Personalised prediction of maintenance dialysis initiation in patients with chronic kidney disease stages 3–5: a multicentre study using the machine learning approach 对慢性肾病 3-5 期患者开始维持性透析的个性化预测：使用机器学习方法的多中心研究

IF 4.1 Q2 Computer Science

BMJ Health & Care Informatics

Pub Date : 2024-04-01 DOI: 10.1136/bmjhci-2023-100893

Anh Trung Hoang, Phung-Anh Nguyen, Thanh Phuc Phan, Gia Tuyen Do, Huu Dung Nguyen, I-Jen Chiu, Chu-Lin Chou, Yu-Chen Ko, Tzu-Hao Chang, Chih-Wei Huang, Usman Iqbal, Yung-Ho Hsu, Mai-Szu Wu, Chia-Te Liao

Background Optimal timing for initiating maintenance dialysis in patients with chronic kidney disease (CKD) stages 3–5 is challenging. This study aimed to develop and validate a machine learning (ML) model for early personalised prediction of maintenance dialysis initiation within 1-year and 3-year timeframes among patients with CKD stages 3–5. Methods Retrospective electronic health record data from the Taipei Medical University clinical research database were used. Newly diagnosed patients with CKD stages 3–5 between 2008 and 2017 were identified. The observation period spanned from the diagnosis of CKD stages 3–5 until the maintenance dialysis initiation or a maximum follow-up of 3 years. Predictive models were developed using patient demographics, comorbidities, laboratory data and medications. The dataset was divided into training and testing sets to ensure robust model performance. Model evaluation metrics, including area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value and F1 score, were employed. Results A total of 6123 and 5279 patients were included for 1 year and 3 years of the model development. The artificial neural network demonstrated better performance in predicting maintenance dialysis initiation within 1 year and 3 years, with AUC values of 0.96 and 0.92, respectively. Important features such as baseline estimated glomerular filtration rate and albuminuria significantly contributed to the predictive model. Conclusion This study demonstrates the efficacy of an ML approach in developing a highly predictive model for estimating the timing of maintenance dialysis initiation in patients with CKD stages 3–5. These findings have important implications for personalised treatment strategies, enabling improved clinical decision-making and potentially enhancing patient outcomes. Data may be obtained from a third party and are not publicly available.

背景慢性肾脏病（CKD）3-5 期患者开始维持性透析的最佳时机具有挑战性。本研究旨在开发并验证一种机器学习（ML）模型，用于早期个性化预测 CKD 3-5 期患者在 1 年和 3 年内开始维持性透析的时间。方法使用台北医学大学临床研究数据库中的回顾性电子健康记录数据。研究对象为 2008 年至 2017 年间新确诊的 CKD 3-5 期患者。观察期从确诊为 CKD 3-5 期开始，直至开始维持性透析或最长随访 3 年。利用患者人口统计学特征、合并症、实验室数据和药物建立了预测模型。数据集分为训练集和测试集，以确保模型性能稳定。模型评估指标包括曲线下面积（AUC）、灵敏度、特异性、阳性预测值、阴性预测值和 F1 分数。结果在模型开发的 1 年和 3 年中，分别纳入了 6123 名和 5279 名患者。人工神经网络在预测 1 年和 3 年内开始维持性透析方面表现更佳，AUC 值分别为 0.96 和 0.92。基线估计肾小球滤过率和白蛋白尿等重要特征对预测模型有显著贡献。结论本研究证明了多变量方法在开发高度预测模型方面的有效性，该模型可用于估计 CKD 3-5 期患者开始维持性透析的时间。这些发现对个性化治疗策略具有重要意义，有助于改善临床决策，并有可能提高患者的治疗效果。数据可能来自第三方，不对外公开。

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引用次数: 0

Development of a scoring system to quantify errors from semantic characteristics in incident reports 开发评分系统，从事故报告中的语义特征量化错误

IF 4.1 Q2 Computer Science

BMJ Health & Care Informatics

Pub Date : 2024-04-01 DOI: 10.1136/bmjhci-2023-100935

Haruhiro Uematsu, Masakazu Uemura, Masaru Kurihara, Hiroo Yamamoto, Tomomi Umemura, Fumimasa Kitano, Mariko Hiramatsu, Yoshimasa Nagao

Objectives Incident reporting systems are widely used to identify risks and enable organisational learning. Free-text descriptions contain important information about factors associated with incidents. This study aimed to develop error scores by extracting information about the presence of error factors in incidents using an original decision-making model that partly relies on natural language processing techniques. Methods We retrospectively analysed free-text data from reports of incidents between January 2012 and December 2022 from Nagoya University Hospital, Japan. The sample data were randomly allocated to equal-sized training and validation datasets. We conducted morphological analysis on free text to segment terms from sentences in the training dataset. We calculated error scores for terms, individual reports and reports from staff groups according to report volume size and compared these with conventional classifications by patient safety experts. We also calculated accuracy, recall, precision and F-score values from the proposed ‘report error score’. Results Overall, 114 013 reports were included. We calculated 36 131 ‘term error scores’ from the 57 006 reports in the training dataset. There was a significant difference in error scores between reports of incidents categorised by experts as arising from errors (p<0.001, d =0.73 (large)) and other incidents. The accuracy, recall, precision and F-score values were 0.8, 0.82, 0.85 and 0.84, respectively. Group error scores were positively associated with expert ratings (correlation coefficient, 0.66; 95% CI 0.54 to 0.75, p<0.001) for all departments. Conclusion Our error scoring system could provide insights to improve patient safety using aggregated incident report data. Data are available upon reasonable request. The data that support the findings of this study are available from the corresponding author upon reasonable request.

目标事故报告系统被广泛用于识别风险和促进组织学习。自由文本描述包含与事件相关因素的重要信息。本研究旨在利用部分依赖于自然语言处理技术的原创决策模型，通过提取事件中存在的错误因素的信息来开发错误评分。方法我们回顾性分析了日本名古屋大学医院 2012 年 1 月至 2022 年 12 月期间事故报告中的自由文本数据。样本数据被随机分配到大小相等的训练数据集和验证数据集。我们对自由文本进行形态分析，从训练数据集中的句子中分割术语。我们根据报告数量的大小计算术语、单个报告和员工小组报告的误差分值，并将其与患者安全专家的传统分类进行比较。我们还根据建议的 "报告错误分数 "计算了准确度、召回率、精确度和 F 分数。结果共纳入 114 013 份报告。我们从训练数据集中的 57 006 份报告中计算出了 36 131 个 "术语错误分数"。被专家归类为由错误引起的事件报告（p<0.001，d =0.73（大））与其他事件报告之间的错误得分存在明显差异。准确度、召回率、精确度和 F 值分别为 0.8、0.82、0.85 和 0.84。所有部门的小组错误评分与专家评分呈正相关（相关系数为 0.66；95% CI 为 0.54 至 0.75，p<0.001）。结论我们的错误评分系统可以利用事故报告汇总数据为改善患者安全提供见解。如有合理要求，可提供相关数据。支持本研究结果的数据可向通讯作者索取。

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引用次数: 0

Generative artificial intelligence and non-pharmacological bias: an experimental study on cancer patient sexual health communications 生成式人工智能与非药物偏差：癌症患者性健康沟通实验研究

IF 4.1 Q2 Computer Science

BMJ Health & Care Informatics

Pub Date : 2024-04-01 DOI: 10.1136/bmjhci-2023-100924

Akiko Hanai, Tetsuo Ishikawa, Shoichiro Kawauchi, Yuta Iida, Eiryo Kawakami

Objectives The objective of this study was to explore the feature of generative artificial intelligence (AI) in asking sexual health among cancer survivors, which are often challenging for patients to discuss. Methods We employed the Generative Pre-trained Transformer-3.5 (GPT) as the generative AI platform and used DocsBot for citation retrieval (June 2023). A structured prompt was devised to generate 100 questions from the AI, based on epidemiological survey data regarding sexual difficulties among cancer survivors. These questions were submitted to Bot1 (standard GPT) and Bot2 (sourced from two clinical guidelines). Results No censorship of sexual expressions or medical terms occurred. Despite the lack of reflection on guideline recommendations, ‘consultation’ was significantly more prevalent in both bots’ responses compared with pharmacological interventions, with ORs of 47.3 (p<0.001) in Bot1 and 97.2 (p<0.001) in Bot2. Discussion Generative AI can serve to provide health information on sensitive topics such as sexual health, despite the potential for policy-restricted content. Responses were biased towards non-pharmacological interventions, which is probably due to a GPT model designed with the ’s prohibition policy on replying to medical topics. This shift warrants attention as it could potentially trigger patients’ expectations for non-pharmacological interventions.

目的本研究旨在探索生成式人工智能（AI）在询问癌症幸存者性健康方面的功能，这对于患者来说往往是具有挑战性的话题。方法我们使用生成式预训练转换器-3.5（GPT）作为生成式人工智能平台，并使用 DocsBot 进行引文检索（2023 年 6 月）。根据有关癌症幸存者性困难的流行病学调查数据，我们设计了一个结构化提示，由人工智能生成 100 个问题。这些问题分别提交给了 Bot1（标准 GPT）和 Bot2（来自两份临床指南）。结果没有对性表达或医学术语进行审查。尽管没有对指南建议进行反思，但与药物干预相比，"咨询 "在两个机器人的回答中都明显更为普遍，Bot1 的 ORs 为 47.3（p<0.001），Bot2 为 97.2（p<0.001）。讨论尽管有可能出现政策限制的内容，但生成式人工智能可以提供有关性健康等敏感话题的健康信息。回复偏向于非药物干预，这可能是由于 GPT 模型在设计时采用了 "禁止回复医疗话题 "的政策。这种转变值得关注，因为它有可能引发患者对非药物干预的期望。

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引用次数: 0

Association between daily step counts and healthy life years: a national cross-sectional study in Japan 每日步数与健康寿命之间的关系：日本一项全国性横断面研究

IF 4.1 Q2 Computer Science

BMJ Health & Care Informatics

Pub Date : 2024-04-01 DOI: 10.1136/bmjhci-2024-101051

Masahiro Nishi, Reo Nagamitsu, Satoaki Matoba

Background Despite accumulating evidence concerning the association between daily step counts and mortality or disease risks, it is unclear whether daily step counts are associated with healthy life years. Methods We used the combined dataset of the Comprehensive Survey of Living Conditions and the National Health and Nutrition Survey conducted for a randomly sampled general population in Japan, 2019. Daily step counts were measured for 4957 adult participants. The associations of daily step counts with activity limitations in daily living and self-assessed health were evaluated using a multivariable logistic regression model. The bootstrap method was employed to mitigate uncertainties in estimating the threshold of daily step counts. Results The median age was 60 (44–71) years, and 2592 (52.3%) were female. The median daily step counts were 5650 (3332–8452). The adjusted OR of activity limitations in daily living for the adjacent daily step counts was 0.27 (95% CI 0.26 to 0.27) for all ages and 0.25 (95% CI 0.25 to 0.26) for older adults at the lowest, with the thresholds of significant association at 9000 step counts. The OR of self-assessed unhealthy status was 0.45 (95% CI 0.44 to 0.46) for all ages and 0.42 (95% CI 0.41 to 0.43) for older adults at the lowest, with the thresholds at 11 000 step counts. Conclusion Daily step counts were significantly associated with activity limitations in daily living and self-assessed health as determinants of healthy life years, up to 9000 and 11 000 step counts, respectively. These results suggest a target of daily step counts to prolong healthy life years within health initiatives. Data may be obtained from a third party and are not publicly available. We are prohibited from publicly opening the data. Data can be accessed through the Household Statistics Office of the Japanese Ministry of Health, Labour and Welfare ().

背景尽管有越来越多的证据表明每天的步数与死亡率或疾病风险有关，但每天的步数是否与健康寿命有关尚不清楚。方法我们使用了 2019 年在日本对随机抽样的普通人群进行的 "生活状况综合调查 "和 "国民健康与营养调查 "的合并数据集。对 4957 名成年参与者的每日步数进行了测量。使用多变量逻辑回归模型评估了每日步数与日常生活活动限制和自我评估健康状况之间的关系。在估算每日步数阈值时，采用了引导法来减少不确定性。结果年龄中位数为 60（44-71）岁，2592 人（52.3%）为女性。每日步数中位数为 5650 步（3332-8452 步）。在所有年龄组中，相邻日步数的日常生活活动受限调整 OR 值为 0.27（95% CI 0.26 至 0.27），在最低年龄组中，老年人的调整 OR 值为 0.25（95% CI 0.25 至 0.26），在 9000 步时达到显著关联的临界值。所有年龄段的自我评估不健康状态的OR值为0.45（95% CI 0.44至0.46），最低的老年人为0.42（95% CI 0.41至0.43），临界值为11000步。结论每日步数与日常生活中的活动限制和作为健康寿命年数决定因素的自我健康评估有显著相关性，分别达到 9000 步和 11000 步。这些结果表明，每日步数是健康计划中延长健康寿命的目标。数据可能来自第三方，不对外公开。我们不得公开这些数据。数据可通过日本厚生劳动省家庭统计办公室（）获取。

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引用次数: 0

Prediction of high-risk emergency department revisits from a machine-learning algorithm: a proof-of-concept study 通过机器学习算法预测高风险急诊科复诊：概念验证研究

IF 4.1 Q2 Computer Science

BMJ Health & Care Informatics

Pub Date : 2024-04-01 DOI: 10.1136/bmjhci-2023-100859

Chih-Wei Sung, Joshua Ho, Cheng-Yi Fan, Ching-Yu Chen, Chi-Hsin Chen, Shao-Yung Lin, Jia-How Chang, Jiun-Wei Chen, Edward Pei-Chuan Huang

Background High-risk emergency department (ED) revisit is considered an important quality indicator that may reflect an increase in complications and medical burden. However, because of its multidimensional and highly complex nature, this factor has not been comprehensively investigated. This study aimed to predict high-risk ED revisit with a machine-learning (ML) approach. Methods This 3-year retrospective cohort study assessed adult patients between January 2019 and December 2021 from National Taiwan University Hospital Hsin-Chu Branch with high-risk ED revisit, defined as hospital or intensive care unit admission after ED return within 72 hours. A total of 150 features were preliminarily screened, and 79 were used in the prediction model. Deep learning, random forest, extreme gradient boosting (XGBoost) and stacked ensemble algorithm were used. The stacked ensemble model combined multiple ML models and performed model stacking as a meta-level algorithm. Confusion matrix, accuracy, sensitivity, specificity and area under the receiver operating characteristic curve (AUROC) were used to evaluate performance. Results Analysis was performed for 6282 eligible adult patients: 5025 (80.0%) in the training set and 1257 (20.0%) in the testing set. High-risk ED revisit occurred for 971 (19.3%) of training set patients vs 252 (20.1%) in the testing set. Leading predictors of high-risk ED revisit were age, systolic blood pressure and heart rate. The stacked ensemble model showed more favourable prediction performance (AUROC 0.82) than the other models: deep learning (0.69), random forest (0.78) and XGBoost (0.79). Also, the stacked ensemble model achieved favourable accuracy and specificity. Conclusion The stacked ensemble algorithm exhibited better prediction performance in which the predictions were generated from different ML algorithms to optimally maximise the final set of results. Patients with older age and abnormal systolic blood pressure and heart rate at the index ED visit were vulnerable to high-risk ED revisit. Further studies should be conducted to externally validate the model. Data are available on reasonable request.

背景高风险急诊科（ED）再次就诊被认为是一项重要的质量指标，可能反映出并发症和医疗负担的增加。然而，由于其多维性和高度复杂性，这一因素尚未得到全面研究。本研究旨在通过机器学习（ML）方法预测高风险急诊室复诊率。方法这项为期 3 年的回顾性队列研究评估了 2019 年 1 月至 2021 年 12 月期间台大医院新竹分院的高风险 ED 再就诊成人患者。共初步筛选出 150 个特征，其中 79 个用于预测模型。使用了深度学习、随机森林、极梯度提升（XGBoost）和堆叠集合算法。堆叠集合模型结合了多个 ML 模型，作为元级算法进行模型堆叠。混淆矩阵、准确率、灵敏度、特异性和接收者工作特征曲线下面积（AUROC）用于评估性能。结果对 6282 名符合条件的成年患者进行了分析：其中 5025 人（80.0%）在训练集中，1257 人（20.0%）在测试集中。训练集患者中有 971 人（19.3%）再次到急诊室就诊，而测试集患者中有 252 人（20.1%）再次到急诊室就诊。高风险急诊室复诊的主要预测因素是年龄、收缩压和心率。与深度学习（0.69）、随机森林（0.78）和 XGBoost（0.79）等其他模型相比，堆叠集合模型的预测性能更佳（AUROC 0.82）。此外，堆叠集合模型的准确性和特异性也很高。结论叠加集合算法显示出更好的预测性能，其中的预测由不同的多重学习算法生成，以优化最大化最终结果集。在急诊室就诊时年龄较大、收缩压和心率异常的患者很容易在急诊室再次就诊。应开展进一步研究，从外部验证该模型。如有合理要求，可提供相关数据。

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引用次数: 0

‘If you build it, they will come…to the wrong door: evaluating patient and caregiver-initiated ethics consultations via a patient portal’ 如果你建造了它，他们就会来......走错门：通过患者门户网站评估患者和护理人员发起的伦理咨询

IF 4.1 Q2 Computer Science

BMJ Health & Care Informatics

Pub Date : 2024-04-01 DOI: 10.1136/bmjhci-2023-100988

Liz Blackler, Amy E Scharf, Konstantina Matsoukas, Michelle Colletti, Louis P Voigt

Objectives Memorial Sloan Kettering Cancer Center (MSK) sought to empower patients and caregivers to be more proactive in requesting ethics consultations. Methods Functionality was developed on MSK’s electronic patient portal that allowed patients and/or caregivers to request ethics consultations. The Ethics Consultation Service (ECS) responded to all requests, which were documented and analysed. Results Of the 74 requests made through the portal, only one fell under the purview of the ECS. The others were primarily requests for assistance with coordinating clinical care, hospital resources or frustrations with the hospital or clinical team. Discussion To better empower patients and caregivers to engage Ethics, healthcare organisations and ECSs must first provide them with accessible, understandable and iterative educational resources. Conclusion After 19.5 months, the ‘Request Ethics Consultation’ functionality on the patient portal was suspended. Developing resources on the role of Ethics for our patients and caregivers remains a priority. All data relevant to the study are included in the article or uploaded as supplementary information.

目的纪念斯隆-凯特琳癌症中心（MSK）希望增强患者和护理人员的能力，使他们能够更加积极主动地请求伦理咨询。方法在 MSK 的电子患者门户网站上开发了允许患者和/或护理人员请求伦理会诊的功能。伦理会诊服务（ECS）对所有请求做出了回应，并对这些请求进行了记录和分析。结果在通过门户网站提出的 74 项请求中，只有一项属于伦理咨询服务的范围。其他请求主要是要求协助协调临床护理、医院资源或对医院或临床团队的不满。讨论为了更好地增强患者和护理人员参与伦理的能力，医疗机构和 ECS 必须首先为他们提供方便、易懂和可重复的教育资源。结论在 19.5 个月后，患者门户网站上的 "请求伦理咨询 "功能被暂停。为患者和护理人员开发有关伦理作用的资源仍是当务之急。所有与研究相关的数据均已包含在文章中或作为补充信息上传。

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引用次数: 0

Codesign of health technology interventions to support best-practice perioperative care and surgical waitlist management. 对医疗技术干预措施进行代码设计，以支持最佳实践围手术期护理和手术候诊名单管理。

IF 4.1 Q2 Computer Science

BMJ Health & Care Informatics

Pub Date : 2024-03-12 DOI: 10.1136/bmjhci-2023-100928

Sarah Joy Aitken, Sophie James, Amy Lawrence, Anthony Glover, Henry Pleass, Janani Thillianadesan, Sue Monaro, Kerry Hitos, Vasi Naganathan

Objectives: This project aimed to determine where health technology can support best-practice perioperative care for patients waiting for surgery.

Methods: An exploratory codesign process used personas and journey mapping in three interprofessional workshops to identify key challenges in perioperative care across four health districts in Sydney, Australia. Through participatory methodology, the research inquiry directly involved perioperative clinicians. In three facilitated workshops, clinician and patient participants codesigned potential digital interventions to support perioperative pathways. Workshop output was coded and thematically analysed, using design principles.

Results: Codesign workshops, involving 51 participants, were conducted October to November 2022. Participants designed seven patient personas, with consumer representatives confirming acceptability and diversity. Interprofessional team members and consumers mapped key clinical moments, feelings and barriers for each persona during a hypothetical perioperative journey. Six key themes were identified: 'preventative care', 'personalised care', 'integrated communication', 'shared decision-making', 'care transitions' and 'partnership'. Twenty potential solutions were proposed, with top priorities a digital dashboard and virtual care coordination.

Discussion: Our findings emphasise the importance of interprofessional collaboration, patient and family engagement and supporting health technology infrastructure. Through user-based codesign, participants identified potential opportunities where health technology could improve system efficiencies and enhance care quality for patients waiting for surgical procedures. The codesign approach embedded users in the development of locally-driven, contextually oriented policies to address current perioperative service challenges, such as prolonged waiting times and care fragmentation.

Conclusion: Health technology innovation provides opportunities to improve perioperative care and integrate clinical information. Future research will prototype priority solutions for further implementation and evaluation.

目标该项目旨在确定医疗技术在哪些方面可以为等待手术的病人提供最佳的围手术期护理：方法：在三个跨专业研讨会上，采用角色和旅程映射进行了探索性的代码设计过程，以确定澳大利亚悉尼四个医疗区围手术期护理所面临的主要挑战。通过参与式方法，围手术期临床医生直接参与了研究调查。在三场研讨会上，临床医生和患者参与者对支持围手术期路径的潜在数字干预措施进行了编码。利用设计原则对研讨会成果进行编码和主题分析：编码设计研讨会于 2022 年 10 月至 11 月举行，共有 51 人参加。与会者设计了七个病人角色，消费者代表确认了角色的可接受性和多样性。跨专业团队成员和消费者绘制了每个角色在假设围手术期过程中的关键临床时刻、感受和障碍。确定了六个关键主题：预防性护理"、"个性化护理"、"综合沟通"、"共同决策"、"护理过渡 "和 "伙伴关系"。提出了 20 个潜在解决方案，其中最优先的是数字仪表板和虚拟护理协调：讨论：我们的研究结果强调了跨专业合作、患者和家庭参与以及支持医疗技术基础设施的重要性。通过以用户为基础的代码设计，参与者发现了医疗技术可以提高系统效率、改善等待手术的患者护理质量的潜在机会。代码设计方法让用户参与制定以当地情况为导向的政策，以应对当前围手术期服务面临的挑战，如等待时间过长和护理分散等：结论：医疗技术创新为改善围手术期护理和整合临床信息提供了机遇。未来的研究将为进一步实施和评估优先解决方案提供原型。

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引用次数: 0

Invitation to join the Healthcare AI Language Group: HeALgroup.AI Initiative. 邀请加入医疗保健人工智能语言组：HeALgroup.AI 计划。

IF 4.1 Q2 Computer Science

BMJ Health & Care Informatics

Pub Date : 2024-03-12 DOI: 10.1136/bmjhci-2023-100884

Sebastian Manuel Staubli, Basel Jobeir, Michael Spiro, Dimitri Aristotle Raptis

引用次数: 0

Bibliometric analysis of the 3-year trends (2018-2021) in literature on artificial intelligence in ophthalmology and vision sciences. 眼科学和视觉科学领域人工智能文献的 3 年趋势（2018-2021 年）文献计量分析。

IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES

BMJ Health & Care Informatics

Pub Date : 2024-02-28 DOI: 10.1136/bmjhci-2023-100780

Hayley Monson, Jeffrey Demaine, Adrianna Perryman, Tina Felfeli

Objectives: The objective of this analysis is to present a current view of the field of ophthalmology and vision research and artificial intelligence (AI) from topical and geographical perspectives. This will clarify the direction of the field in the future and aid clinicians in adapting to new technological developments.

Methods: A comprehensive search of four different databases was conducted. Statistical and bibliometric analysis were done to characterise the literature. Softwares used included the R Studio bibliometrix package, and VOSviewer.

Results: A total of 3939 articles were included in the final bibliometric analysis. Diabetic retinopathy (391, 6% of the top 100 keywords) was the most frequently occurring indexed keyword by a large margin. The highest impact literature was produced by the least populated countries and in those countries who collaborate internationally. This was confirmed via a hypothesis test where no correlation was found between gross number of published articles and average number of citations (p value=0.866, r=0.038), while graphing ratio of international collaboration against average citations produced a positive correlation (r=0.283). Majority of publications were found to be concentrated in journals specialising in vision and computer science, with this category of journals having the highest number of publications per journal (18.00 publications/journal), though they represented a small proportion of the total journals (<1%).

Conclusion: This study provides a unique characterisation of the literature at the intersection of AI and ophthalmology and presents correlations between article impact and geography, in addition to summarising popular research topics.

目标：本分析报告旨在从专题和地理角度介绍眼科和视觉研究以及人工智能（AI）领域的现状。这将明确该领域未来的发展方向，并帮助临床医生适应新的技术发展：方法：对四个不同的数据库进行了全面检索。方法：对四个不同的数据库进行了全面搜索，并进行了统计和文献计量分析，以确定文献的特点。使用的软件包括 R Studio bibliometrix 软件包和 VOSviewer：最终的文献计量分析共纳入了 3939 篇文章。糖尿病视网膜病变（391篇，占前100个关键词的6%）是出现频率最高的索引关键词。人口最少的国家和开展国际合作的国家发表的文献影响最大。通过假设检验证实了这一点，即发表文章总数与平均引用次数之间没有相关性（P 值=0.866，r=0.038），而国际合作比率与平均引用次数之间的曲线图则产生了正相关性（r=0.283）。大部分论文集中在视觉和计算机科学专业期刊上，这类期刊的论文数量最多（18.00 篇/期刊），但在期刊总数中所占比例较小（结论：这类期刊的论文数量最多，但在期刊总数中所占比例较小）：本研究对人工智能与眼科学交叉领域的文献进行了独特的描述，除了总结热门研究课题外，还介绍了文章影响力与地域之间的相关性。

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