JMIR人为因素

临床医生对基于人工智能的血液利用计算器的看法:定性探索性研究

2022 - 10 - 31 - t09:15:24内

背景:根据美国食品和药物管理局生物制剂评估和研究中心的数据，卫生保健系统一直在经历输血过度使用的问题。为了最大限度地减少血液制品输血的过度使用，一种基于人工智能(AI)的专有血液利用计算器(BUC)被开发出来，并集成到美国一家医院的电子健康记录中。尽管BUC具有良好的性能，但该技术在临床应用中仍未得到充分应用。本研究旨在探讨临床医生如何看待这种基于人工智能的决策支持系统，从而了解阻碍BUC使用的因素。我们采访了10名临床医生(BUC用户)，直到达到数据饱和点。这些访谈是在一个基于网络的平台上进行的，并被记录下来。这些录音被匿名逐字记录下来。我们使用归纳-演绎主题分析来分析抄本，这涉及到将预先确定的主题应用到数据(演绎)和连续识别数据中出现的新主题(归纳)。我们确定了以下两个主题:(1)工作量和可用性(2)临床决策。临床医生承认BUC对普通住院患者的易用性和实用性。 The clinicians also found the BUC to be useful in making decisions related to blood transfusion. However, some clinicians found the technology to be confusing due to inconsistent automation across different blood work processes. Conclusions: This study highlights that analytical efficacy alone does not ensure technology use or acceptance. The overall system’s design, user perception, and users’ knowledge of the technology are equally important and necessary (limitations, functionality, purpose, and scope). Therefore, the effective integration of AI-based decision support systems, such as the BUC, mandates multidisciplinary engagement, ensuring the adequate initial and recurrent training of AI users while maintaining high analytical efficacy and validity. As a final takeaway, the design of AI systems that are made to perform specific tasks must be self-explanatory, so that the users can easily understand how and when to use the technology. Using any technology on a population for whom it was not initially designed will hinder user perception and the technology’s use.

促进房颤健康公平的虚拟包容性数字健康干预设计(设计)框架:共同设计和发展研究

2022 - 10 - 31 - t09:15:03内

背景:在生活在美国的不同种族和民族背景的个人中，智能手机的拥有量和移动应用程序的使用正在稳步增加。越来越多的技术采用为数字卫生干预创造了绝佳的机会，以增加获得卫生保健的机会。为了成功实施数字卫生干预措施并吸引用户，干预措施的开发应以用户输入为指导，而这最好通过联合设计过程来实现。在用户积极参与的情况下共同设计的数字卫生干预措施有可能增加对准则建议的采纳，从而降低发病率和死亡率，促进卫生公平。我们的目标是为房颤(最常见的心律失常)患者联合设计一种数字健康干预措施，获得患者、护理人员和临床医生的反馈，并描述我们以人为本的设计方法来构建数字健康干预措施。我们与房颤患者(n=8)、他们的护理人员和临床医生(n=8)进行了虚拟会议。我们在共同设计过程中使用了以下7个步骤:第一步，一个虚拟会议，专注于定义挑战，并同情心房纤颤患者和临床医生在日常生活中所面临的问题;第二步，虚拟会议的重点是构思和头脑风暴在第一次会议中确定的首要挑战;第三步，使用现有最低限度可行的房颤应用程序的患者的个体化上机;第4步，在构思过程中生成的前3个想法的虚拟原型; step 5, further ranking by the study investigators and engineers of the ideas that were generated during ideation but were not chosen as top-3 solutions to be prototyped in step 4; step 6, ongoing engineering work to incorporate top-priority features in the app; and step 7, obtaining further feedback from patients and testing the atrial fibrillation digital health intervention in a pilot clinical study. Results: The top challenges identified by patients and caregivers included addressing risk factor modification, medication adherence, and guidance during atrial fibrillation episodes. Challenges identified by clinicians were complementary and included patient education, addressing modifiable atrial fibrillation risk factors, and remote atrial fibrillation episode management. Patients brainstormed more than 30 ideas to address the top challenges, and the clinicians generated more than 20 ideas. Ranking of the ideas informed several novel or modified features aligned with the Theory of Health Behavior Change, features that were geared toward risk factor modification; patient education; rhythm, symptom, and trigger correlation for remote atrial fibrillation management; and social support. Conclusions: We co-designed an atrial fibrillation digital health intervention in partnership with patients, caregivers, and clinicians by virtually engaging in collaborative creation through the design process. We summarize our experience and describe a flexible approach to human-centered design for digital health intervention development that can guide innovative clinical investigators.

早期败血症识别的临床决策支持系统的期望特征:对医院临床医生的访谈研究

2022 - 10 - 21 - t09:15:02内

败血症是美国卫生保健系统的主要负担，每年有超过75万例病例，总费用约为200亿美元。脓毒症治疗的特点是及早和适当地开始抗生素治疗。尽管败血症临床决策支持(CDS)系统可以为临床医生提供疑似败血症或即将到来的临床衰退的早期预测，但这种系统并没有可靠地证明临床结果或护理过程的改善。越来越多的证据表明，将脓毒症CDS系统集成到临床工作流程中、获得临床医生的信任以及使脓毒症CDS系统与临床床边相关等挑战都是成功部署的障碍。然而，在实现这些实现和部署目标方面存在着重大的知识差距。我们的目的是基于临床医生过去的经验来识别脓毒症CDS系统中预测信息的感知，探索临床医生对假设的脓毒症CDS系统的感知，并确定CDS系统的特征，这将有助于在多学科、基于团队的临床环境中促进疑似脓毒症的及时识别和管理。我们在2020年9月至2021年3月期间对一家大型学术医疗中心的执业床边护士、高级实践提供者和医生进行了半结构化访谈。我们使用了改进的人为因素方法(由于COVID-19大流行，通过视频电话进行上下文采访和认知演练)，并使用编码的溯因方法进行了主题分析，以确定采访记录中的重要模式和概念。结果:我们采访了6名床边护士和9名负责订购抗生素的临床医生(高级实践提供者或医生)，他们在住院患者设置中有4年(IQR 4-6.5年)工作经验。然后，我们将数据专题分析中的关键内容合成为四个领域:临床医生对预测模型和警报的感知; previous experiences of clinician encounters with predictive information and risk scores; desired characteristics of a CDS system build, including predictions, supporting information, and delivery methods for a potential alert; and the clinical relevance and potential utility of a CDS system. These 4 domains were strongly linked to clinicians’ perceptions of the likelihood of adoption and the impact on clinical workflows when diagnosing and managing patients with suspected sepsis. Ultimately, clinicians desired a trusted and actionable CDS system to improve sepsis care. Conclusions: Building a trusted and actionable sepsis CDS alert is paramount to achieving acceptability and use among clinicians. These findings can inform the development, implementation, and deployment strategies for CDS systems that support the early detection and treatment of sepsis. This study also highlights several key opportunities when eliciting clinician input before the development and deployment of prediction models.

外卖司机在COVID-19大流行期间关于COVID-19预防和保护行为的健康知识:在泰国南部的横断面调查

2022 - 10 - 13 - t09:45:02内

2019年，COVID-19在全球传播，引发大流行，给卫生系统和经济带来了前所未有的挑战和压力。食品外卖服务已经成为消费者购买食品的一个重要媒介，以限制人与人之间的接触。因此，快递司机在工作中暴露于COVID-19感染的风险很高。据我们所知，没有研究分析这一人群中关于COVID-19预防的卫生素养(HL)维度。本研究旨在探讨泰国南部送餐司机的HL对COVID-19预防的现状及其相关因素。在2021年7月至8月进行的一项横断面调查之后，招募了泰国南部上南部和下南部地区的泰国外卖司机，参与COVID-19强制封锁期间的工作。一份在线结构化问卷由面试官口头执行并记录。单变量和多变量线性回归用于探索独立相关因素。结果:401名司机中，291名(72.6%)为男性。中位年龄为31岁(19-64岁)。 The median number of months working as a driver was 12 months, and the median number of working hours was 9 hours per day. The median number of daily food orders was 20, while the median daily income was Thai baht (THB) 600 (US $15.90). Social media (Facebook and Line) was a common source of health information. The most common information required was about the COVID-19 vaccine, medications, and treatment. Most drivers (285/401, 71.1%) had excellent HL levels regarding COVID-19 prevention. Only the practical application of information was statistically correlated with behavior (r=0.38, P<.001). Drivers in the lower south of Thailand were more likely to have excellent HL than other drivers (β=7.03, P<.001). Those who frequently accessed information through YouTube (β=–2.17, P=.01) and relatives (β=–4.19, P<.001) were less likely to have excellent HL levels. Conclusions: Understanding HL among food delivery drivers would be useful for planning effective interventions that target this population. Conventional health education through social media alone may not be effective at educating people about COVID-19 prevention. Information literacy skills could determine individuals’ HL and drivers’ behaviors.

妊娠期糖尿病自我管理的行为改变应用程序:理想特征的设计和评估

2022 - 10 - 12 - t09:45:03内

背景:妊娠期糖尿病(GDM)对健康有相当大的影响，而且越来越严重，因为它增加了母亲和后代出现短期和长期健康问题的风险。GDM通常可以通过更健康的生活方式来治疗，如适当的饮食调整和充分的体育活动。尽管远程医疗干预每周或更频繁地从卫生保健专业人员那里得到反馈，已显示出改善GDM女性血糖控制的潜力，但没有卫生保健专业人员广泛投入的应用程序是有限的，并没有被证明是有效的。为了提高GDM应用程序的效率，已经提出了个性化和支持方面的不同功能，但对于这些功能应该如何设计的知识还缺乏。本研究的目的是调查GDM应用程序应该如何设计，基于之前的文献考虑所需的功能。我们设计了一个互动式GDM原型应用程序，提供了所需功能的示例实现，如通过应用程序提供自动和个性化的建议和社会支持。GDM女性探索了原型，并在半结构化访谈中提供反馈。我们发现(1)GDM应用程序中的自我跟踪数据应该扩展为书面反馈，(2)习惯和目标应该高度可定制，以便有用，(3)应用程序应该有不同的功能来提供社会支持，(4)如果发生了不寻常的事情，应该通过应用程序通知医疗保健专业人员。此外，我们还发现了另外两个主题。首先，应该提供那些最近被诊断为GDM的女性可以快速学习的基本功能，但也应该提供更深层次的功能，以保持妊娠后期GDM女性的兴趣。其次，GDM女性可能会有罪恶感，应用程序应该对不利行为有宽容和支持的态度。 Conclusions: The feedback on the GDM prototype app supported the need for desirable features and provided new insights into how these features should be incorporated into GDM apps. We expect that following the proposed designs and feedback will increase the efficacy of GDM self-management apps. Trial Registration: ClinicalTrials.gov NCT03941652; https://clinicaltrials.gov/ct2/show/NCT03941652

随时回答医院护理人员的问题:基于人工智能的聊天机器人在法国一家医院的概念验证研究

2022 - 10 - 11 - t10:15:02内

获得卫生保健方面的准确信息是护理人员避免用药错误的关键，特别是在COVID - 19大流行等健康危机期间，工作人员和药物回路的重组。因此，医院药房的作用是回答护理人员的问题。一些可能需要药剂师的专业知识，一些应该由药学技术人员回答，但其他的是简单和冗余的，并可能提供自动响应。我们的目标是开发和实现一个聊天机器人，24小时回答医院护理人员关于药物和药房组织的问题，并对该工具进行评估。ADDIE(分析、设计、开发、实施和评估)模型由3名医院药师、2名创新与转型部门成员和IT服务提供商组成的多专业团队使用。在分析护理人员对药品和药房组织的需求的基础上，设计并开发了一个聊天机器人。然后在将该工具实现到医院内部网之前对其进行评估。它的相关性和与测试人员的对话通过IT提供商的后台办公室进行监控。对来自5个卫生服务机构的5名医院药剂师和33名护理人员的需求分析使我们确定了7个关于药物和药房组织的主题(如开放时间和特定处方)。经过一年的聊天机器人设计和开发，测试版本获得了很好的评价分数:它的速度是8.2分(满分10分)，可用性是8.1分(满分10分)，外观是7.5分(满分10分)。 Testers were generally satisfied (70%) and were hoping for the content to be enhanced. Conclusions: The chatbot seems to be a relevant tool for hospital caregivers, helping them obtain reliable and verified information they need on drugs and pharmacy organization. In the context of significant mobility of nursing staff during the health crisis due to the COVID-19 pandemic, the chatbot could be a suitable tool for transmitting relevant information related to drug circuits or specific procedures. To our knowledge, this is the first time that such a tool has been designed for caregivers. Its development further continued by means of tests conducted with other users such as pharmacy technicians and via the integration of additional data before the implementation on the 2 hospital sites.

将机器人引入沙特阿拉伯的门诊配药和药物管理过程:一个由制药领导的多学科六西格玛绩效改进项目的回顾性回顾

2022 - 10 - 11 - t09:00:03内

门诊药房管理的目标是提高患者安全，提高服务质量，降低成本。六西格玛方法通过消除可变性来提高质量，其目标是实现几乎无错误的过程。制药任务的自动化可能提供更高的效率和安全性。目标:目标是衡量自动化集成对门诊药房服务、安全和效率、人员重新分配和调整以及工作流的影响。需要解决的六西格玛问题定义如下:目前的门诊配药系统在等待时间和与药学专业人员接触时间方面否定了患者的质量，在药物标签错误和处方配药不完整方面对患者存在风险，在时间和资源方面存在潜在的浪费。我们描述了在一所大学医院的一个大型门诊药房引入自动化的过程。使用六西格玛方法，因为它关注持续改进，也产生了一个路线图，将跟踪和监控集成到其过程中。对门诊部活动的回顾集中在非增值(NVA)药剂师任务，改善患者体验和患者安全。建立了度量变更影响的度量标准，并为服务的每个阶段创建了包含周转时间(tat)的流程图分析。选择离散事件进行纠正、改进或缓解。 From the review, the team selected key outcome metrics, including storage, picking and delivery dispensing rates, patient and prescription load per day, average packs and lines per prescription, and lines held. Our goal was total automation of stock management. We deployed 2 robotic dispensing units to feed 9 dispensing desks. The automated units were integrated with hospital information technology (HIT) that supports appointments, medication records, and prescriptions. Results: Postautomation, the total patient time in the department, including the time interacting with the pharmacist for medication education and counseling, dropped from 17.093 to 11.812 digital minutes, with an appreciable increase in patient-pharmacist time. The percentage of incomplete prescriptions dispensed versus orders decreased from 3.0% to 1.83%. The dispensing error rate dropped from 1.00% to 0.24%. Assessed via a “basket” of medications, wastage cost was reduced by 83.9%. During implementation, it was found that NVA tasks that were replaced by automated processes were responsible for an extensive loss of pharmacist time. The productivity ratio postautomation was 1.26. Conclusions: The Six Sigma methodology allowed for rapid transformation of the medication management process. The risk priority numbers (RPNs) for the “wrong patient-wrong medication error” reduced by a ratio of 5.25:1 and for “patient leaves unit with inadequate counseling” postautomation by 2.5:1. Automation allowed for ring-fencing of patient-pharmacist time. This time needs to be structured for optimal effectiveness.

动机性访谈会话代理:父母作为孩子健康饮食的代理:开发和用户测试

2022年10月- 07 - t09:30:02内

背景:大量采用现成的会话药物(CAs)带来了整合治疗干预的机会。动机性访谈(MI)可以与ca相结合，以实现低成本的访问。对于那些因为时间和资源有限而缺乏与孩子一起健康饮食动力的父母来说，MI尤其有益。我们开发了一种动机性访谈会话代理(MICA)，以改善父母的健康饮食，并将其作为子女健康行为改变的代理。代理关系涉及到一个人作为另一个人行为改变的催化剂。父母作为代理人，可以改变孩子的行为。我们进行了MICA样机的用户测试，了解家长对MICA样机的感知接受度和有用性。共有24位幼儿的父母参加了MICA的2次用户测试，间隔约2周。在每个用户测试环节，家长与MICA原型互动后，我们采用定性访谈的方式了解家长对MICA改进的看法和建议。结果显示，参与者认为mica在帮助他们自我反省和激励他们与孩子一起采用更健康的饮食习惯方面是有用的。 Participants further suggested various ways in which MICA can help them safely manage their children’s eating behaviors and provide customized support for their proxy needs and goals. Conclusions: We have discussed how the user experience of CAs can be improved to uniquely offer support to parents who serve as proxies in changing the behavior of their children. We have concluded with implications for a larger context of designing MI-based CAs for supporting proxy relationships for health behavior change.

从测试者到合作者——电子健康解决方案开发中成功患者参与的价值和方法:定性专家访谈研究

2022 - 10 - 06 - t09:30:03内

研究表明，患者参与最常在研究开始或测试现成的原型时进行，较少在执行阶段等其他阶段进行。以往的研究报告指出，在卫生服务规划和评估中，患者通常被赋予咨询作用，而不是决策作用。本研究有两个目标:更好地理解将患者纳入电子卫生技术发展的挑战和机遇，以及如何克服已确定的差距的想法，并创建基于研究的端到端实践蓝图，可以指导相关利益攸关方成功地让患者成为所有以人为本的设计阶段的共同创造者，而不仅仅是预先计划好的原型的测试者。方法:对来自欧洲6个国家的20名参与者进行深度半结构化访谈，对关键线人进行访谈。接下来是一个焦点小组来验证最初的发现。与会者包括所有相关利益攸关方群体，包括患者专家、电子健康专家、卫生技术提供者、临床医生、制药公司高管和健康保险专家。结果:本研究表明，让患者参与电子健康发展可以帮助提供不同类型的价值;即，确定未满足的需求，更好的可用性和可取性，更好地融入患者的旅程，更好的采用和粘性，更好的卫生结果，宣传和信任，目标感，以及更好的卫生公平和获取。然而，与会者一致认为，患者通常在开发过程中参与得太晚了，他们大多在测试现成的原型时扮演一个听起来很重要的角色。这些参与差距的理由是由一些突出的障碍驱动的，特别是依从性风险、患者相关因素、权力动态、患者参与只是口头服务、价值感知差、资源缺乏、不信任和灵活性。 On the positive side, the participants also reflected on facilitators for better patient engagement; for instance, engaging through engagement partners, novel approaches such as the rise of professional patient experts, embedding patients in development teams, expectation management, and professional moderation services. Conclusions: Overcoming the current gaps in patient engagement in eHealth development requires consolidated efforts from all stakeholders in a complex health care ecosystem. The shift toward more patient-driven eHealth development requires education and awareness; frameworks to monitor and evaluate the value of patient engagement; regulatory clarity and simplification; platforms to facilitate patient access and identification; patient incentivization, transparency, and trust; and a mindset shift toward value-based health care.

聊天机器人在公共卫生领域的发展和使用:范围审查

2022 - 10 - 05 - t10:00:03内

聊天机器人是一种计算机程序，它提供类似对话的界面，人们可以通过它访问信息和服务。COVID-19大流行推动了聊天机器人的使用大幅增加，以支持和补充传统卫生保健系统。然而，尽管聊天机器人的使用得到了重视，但支持在公共卫生领域开发和部署聊天机器人的证据仍然有限。最近的综述集中在COVID-19大流行期间聊天机器人的使用，以及更普遍的会话代理在医疗保健中的使用。本文对该研究进行了补充，并通过评估在公共卫生领域使用聊天机器人的研究证据的广度和范围，填补了文献中的空白。这一范围审查有3个主要目标:(1)确定在公共卫生领域有最多证据证明聊天机器人的发展和使用的应用领域;(2)确定在这些领域部署的聊天机器人的类型;以及(3)确定在公共卫生应用中评估聊天机器人的方法和方法。本文根据对聊天机器人使用证据的分析，探讨了在公共卫生领域发展和部署聊天机器人的未来研究的意义。根据PRISMA-ScR(范围评审的系统评审和meta分析扩展的首选报告项目)指南，从2021年6月中旬至8月期间，通过在MEDLINE、PubMed、Scopus、Cochrane对照试验中央登记处、IEEE Xplore、ACM数字图书馆和开放灰色数据库中进行搜索，确定了相关研究。 Studies were included if they used or evaluated chatbots for the purpose of prevention or intervention and for which the evidence showed a demonstrable health impact. Results: Of the 1506 studies identified, 32 were included in the review. The results show a substantial increase in the interest of chatbots in the past few years, shortly before the pandemic. Half (16/32, 50%) of the research evaluated chatbots applied to mental health or COVID-19. The studies suggest promise in the application of chatbots, especially to easily automated and repetitive tasks, but overall, the evidence for the efficacy of chatbots for prevention and intervention across all domains is limited at present. Conclusions: More research is needed to fully understand the effectiveness of using chatbots in public health. Concerns with the clinical, legal, and ethical aspects of the use of chatbots for health care are well founded given the speed with which they have been adopted in practice. Future research on their use should address these concerns through the development of expertise and best practices specific to public health, including a greater focus on user experience.