Learning Health Systems最新文献_第4页

Wonderings to research questions: Engaging patients in long COVID research prioritization within a learning health system 研究问题的奇思妙想：在学习型医疗系统中让患者参与长期 COVID 研究的优先排序

IF 3.1 Q2 HEALTH POLICY & SERVICES

Learning Health Systems

Pub Date : 2024-02-28 DOI: 10.1002/lrh2.10410

Ann Blair Kennedy, Ariana Mitcham, Katherine Parris, Faith Albertson, Luis Sanchez Ferrer, Conor O'Boyle, Maushmi K. Patel, Tracey Gartner, Amy M. Broomer, Evan Katzman, Jeanette Coffin, Jennifer T. Grier, Nabil Natafgi

Background

An integral component of research within a learning health system is patient engagement at all stages of the research process. While there are well-defined best practices for engaging with patients on predetermined research questions, there is little specific methodology for engaging patients at the stage of research question formation and prioritization. Further, with an emerging disease such as Long COVID, population-specific strategies for meaningful engagement have not been characterized.

Methods

The COVID-19 Focused Virtual Patient Engagement Studio (CoVIP studio) was a virtual panel created to facilitate patient-centered studies surrounding the effects of long-term COVID (“Long COVID”) also known as post-acute SARS-CoV-2 syndrome (PASC). A diverse group of panelists was recruited and trained in several different areas of knowledge, competencies, and abilities regarding research and Long COVID. A three-step approach was developed that consisted of recording panelists' broad wonderings to generate patient-specific research questions.

Results

The “wonderings” discussed in panelists' training sessions were analyzed to identify specific populations, interventions, comparators, outcomes, and timeframes (PICOT) elements, which were then used to create a survey to identify the elements of greatest importance to the panel. Based on the findings, 10 research questions were formulated using the PICOT format. The panelists then ranked the questions on perceived order of importance and distributed one million fictional grant dollars between the five chosen questions in the second survey. Through this stepwise prioritization process, the project team successfully translated panelists' research wonderings into investigable research questions.

Conclusion

This methodology has implications for the advancement of patient-engaged prioritization both within the scope of Long COVID research and in research on other rare or emerging diseases.

背景在学习型医疗系统中，研究工作不可或缺的一个组成部分就是让患者参与研究过程的各个阶段。虽然在预先确定的研究问题上有明确的患者参与最佳实践，但在研究问题的形成和优先排序阶段，几乎没有让患者参与的具体方法。此外，对于长COVID这样的新兴疾病，还没有针对特定人群的有意义参与策略。方法 COVID-19 聚焦虚拟患者参与工作室（CoVIP studio）是一个虚拟小组，旨在促进围绕长期 COVID（"Long COVID"）（又称急性 SARS-CoV-2 后综合征（PASC））的影响开展以患者为中心的研究。我们招募了一组不同的专家组成员，并对他们进行了有关研究和长期 COVID 的多个不同领域的知识、能力和才干的培训。研究人员制定了一个三步骤方法，包括记录小组成员的广泛疑问，以提出针对患者的研究问题。结果对专家组成员培训课程中讨论的 "疑惑 "进行了分析，以确定特定人群、干预措施、比较者、结果和时间范围（PICOT）要素，然后利用这些要素创建了一项调查，以确定对专家组成员最重要的要素。根据调查结果，使用 PICOT 格式制定了 10 个研究问题。然后，专家小组成员根据认为的重要程度对这些问题进行排序，并在第二次调查中将 100 万虚构赠款分配给所选的五个问题。通过这种按部就班的优先排序过程，项目小组成功地将小组成员的研究疑问转化为可调查的研究问题。结论这种方法对于在 Long COVID 研究范围内以及在其他罕见病或新发疾病的研究中推动患者参与的优先排序工作具有重要意义。

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引用次数: 0

Care partners and consumer health information technology: A framework to guide systems-level initiatives in support of digital health equity 护理合作伙伴和消费者健康信息技术：指导系统级倡议以支持数字健康公平的框架

IF 3.1 Q2 HEALTH POLICY & SERVICES

Learning Health Systems

Pub Date : 2024-02-27 DOI: 10.1002/lrh2.10408

Jennifer L. Wolff, Aleksandra Wec, Danielle Peereboom, Kelly T. Gleason, Halima Amjad, Julia G. Burgdorf, Jessica Cassidy, Catherine M. DesRoches, Chanee D. Fabius, Ariel R. Green, C. T. Lin, Stephanie K. Nothelle, Danielle S. Powell, Catherine A. Riffin, Jamie Smith, Hillary D. Lum

Introduction

Consumer-oriented health information technologies (CHIT) such as the patient portal have a growing role in care delivery redesign initiatives such as the Learning Health System. Care partners commonly navigate CHIT demands alongside persons with complex health and social needs, but their role is not well specified.

Methods

We assemble evidence and concepts from the literature describing interpersonal communication, relational coordination theory, and systems-thinking to develop an integrative framework describing the care partner's role in applied CHIT innovations. Our framework describes pathways through which systematic engagement of the care partner affects longitudinal work processes and multi-level outcomes relevant to Learning Health Systems.

Results

Our framework is grounded in relational coordination, an emerging theory for understanding the dynamics of coordinating work that emphasizes role-based relationships and communication, and the Systems Engineering Initiative for Patient Safety (SEIPS) model. Cross-cutting work systems geared toward explicit and purposeful support of the care partner role through CHIT may advance work processes by promoting frequent, timely, accurate, problem-solving communication, reinforced by shared goals, shared knowledge, and mutual respect between patients, care partners, and care team. We further contend that systematic engagement of the care partner in longitudinal work processes exerts beneficial effects on care delivery experiences and efficiencies at both individual and organizational levels. We discuss the utility of our framework through the lens of an illustrative case study involving patient portal-mediated pre-visit agenda setting.

Conclusions

Our framework can be used to guide applied embedded CHIT interventions that support the care partner role and bring value to Learning Health Systems through advancing digital health equity, improving user experiences, and driving efficiencies through improved coordination within complex work systems.

以消费者为导向的医疗信息技术（CHIT）（如患者门户网站）在医疗服务重新设计计划（如学习型医疗系统）中发挥着越来越重要的作用。我们从描述人际沟通、关系协调理论和系统思维的文献中收集了证据和概念，以建立一个综合框架，描述护理合作伙伴在应用 CHIT 创新中的作用。我们的框架描述了护理合作伙伴的系统参与影响纵向工作流程和与学习型医疗系统相关的多层次成果的途径。我们的框架以关系协调为基础，关系协调是理解协调工作动态的新兴理论，它强调基于角色的关系和沟通，以及患者安全系统工程倡议（SEIPS）模型。跨领域工作系统通过 CHIT 为护理合作伙伴角色提供明确而有目的的支持，可促进频繁、及时、准确和解决问题的沟通，并通过患者、护理合作伙伴和护理团队之间的共同目标、共同知识和相互尊重得到加强，从而推进工作进程。我们进一步认为，护理合作伙伴系统性地参与纵向工作流程，对个人和组织层面的护理服务体验和效率都会产生有益的影响。我们的框架可用于指导应用嵌入式 CHIT 干预措施，以支持护理合作伙伴的角色，并通过促进数字健康公平、改善用户体验以及通过改善复杂工作系统内的协调来提高效率，从而为学习型医疗系统带来价值。

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引用次数: 0

Identifying virtual care modality in electronic health record data 从电子健康记录数据中识别虚拟护理模式

IF 3.1 Q2 HEALTH POLICY & SERVICES

Learning Health Systems

Pub Date : 2024-02-26 DOI: 10.1002/lrh2.10411

Annie E. Larson, Kurt C. Stange, John Heintzman, Yui Nishiike, Brenda M. McGrath, Melinda M. Davis, S. Marie Harvey

Background

Virtual care increased dramatically during the COVID-19 pandemic. The specific modality of virtual care (video, audio, eVisits, eConsults, and remote patient monitoring) has important implications for the accessibility and quality of care, but rates of use are relatively unknown. Methods for identifying virtual care modalities, especially in electronic health records (EHR) are inconsistent. This study (a) developed a method to identify virtual care modalities using EHR data and (b) described the distribution of these modalities over a 3-year study period.

Methods

EHR data from 316 primary care safety net clinics throughout the study period (4/1/2020-3/31/2023) were included. Visit type (in-person vs virtual) by adults >18 years old were classified. Expert consultation informed the development of two algorithms to classify virtual care visit modalities; these algorithms prioritized different EHR data elements. We conducted descriptive analyses comparing algorithms and the frequency of virtual care modalities.

Results

Agreement between the algorithms was 96.5% for all visits and 89.3% for virtual care visits. The majority of disagreement between the algorithms was among encounters scheduled as audio-only but billed as a video visit. Restricting to visits where the algorithms agreed on visit modality, there were 2-fold more audio-only than video visits.

Conclusion

Visit modality classification varies depending upon which data in the EHR are prioritized. Regardless of which algorithm is utilized, safety net clinics rely on audio-only and video visits to provide care in virtual visits. Elimination of reimbursement for audio visits may exacerbate existing inequities in care for low-income patients.

在 COVID-19 大流行期间，虚拟医疗急剧增加。虚拟医疗的具体模式（视频、音频、电子就诊、电子会诊和远程患者监护）对医疗服务的可及性和质量有着重要影响，但使用率却相对未知。识别虚拟医疗模式，尤其是电子健康记录（EHR）中的虚拟医疗模式的方法并不一致。本研究(a)开发了一种使用电子健康记录数据识别虚拟医疗模式的方法，(b)描述了这些模式在3年研究期内的分布情况。对 18 岁以上成年人的就诊类型（亲自就诊与虚拟就诊）进行了分类。通过专家咨询，我们制定了两种算法来对虚拟医疗就诊方式进行分类；这些算法优先考虑不同的 EHR 数据元素。我们对算法和虚拟医疗模式的频率进行了描述性分析。算法之间的一致性在所有就诊中为 96.5%，在虚拟医疗就诊中为 89.3%。在所有就诊中，算法之间的一致率为 96.5%，在虚拟护理就诊中，算法之间的一致率为 89.3%。算法之间的大部分分歧出现在仅安排了音频就诊但作为视频就诊计费的就诊中。仅限于算法就就诊方式达成一致的就诊，纯音频就诊是视频就诊的 2 倍。无论采用哪种算法，安全网诊所都依靠纯音频和视频就诊来提供虚拟就诊护理。取消对音频就诊的报销可能会加剧低收入患者就诊中现有的不公平现象。

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引用次数: 0

Breast cancer learning health system: Patient information from a data and analytics platform characterizes care provided 乳腺癌学习保健系统：来自数据和分析平台的患者信息描述了所提供护理的特点

IF 2.6 Q2 HEALTH POLICY & SERVICES

Learning Health Systems

Pub Date : 2024-02-13 DOI: 10.1002/lrh2.10409

Mark N. Levine, Joel Kemppainen, Morgan Rosenberg, Christopher Pettengell, Jessica Bogach, Tim Whelan, Ashirbani Saha, Jonathan Ranisau, Jeremy Petch

Purpose

In a learning health system (LHS), data gathered from clinical practice informs care and scientific investigation. To demonstrate how a novel data and analytics platform can enable an LHS at a regional cancer center by characterizing the care provided to breast cancer patients.

Methods

Socioeconomic information, tumor characteristics, treatments and outcomes were extracted from the platform and combined to characterize the patient population and their clinical course. Oncologists were asked to identify examples where clinical practice guidelines (CPGs) or policy changes had varying impacts on practice. These constructs were evaluated by extracting the corresponding data.

Results

Breast cancer patients (5768) seen at the Juravinski Cancer Centre between January 2014 and June 2022 were included. The average age was 62.5 years. The commonest histology was invasive ductal carcinoma (74.6%); 77% were estrogen receptor-positive and 15.5% were HER2 Neu positive. Breast-conserving surgery (BCS) occurred in 56%. For the 4294 patients who received systemic therapy, the initial indications were adjuvant (3096), neoadjuvant (828) and palliative (370). Metastases occurred in 531 patients and 495 patients died. Lowest-income patients had a higher mortality rate. For the adoption of CPGs, the uptake for adjuvant bisphosphonate was very low, 8% as predicted, compared to 64% for pertuzumab, a HER2 targeted agent and 40.2% for CD4/6 inhibitors in metastases. During COVID-19, the provincial cancer agency issued a policy to shorten the duration of radiation after BCS. There was a significant reduction in the average number of fractions to the breast by five fractions.

Conclusion

Our platform characterized care and the clinical course of breast cancer patients. Practice changes in response to regulatory developments and policy changes were measured. Establishing a data platform is important for an LHS. The next step is for the data to feedback and change practice, that is, close the loop.

在学习型医疗系统（LHS）中，从临床实践中收集的数据可为护理和科学研究提供依据。从平台中提取社会经济信息、肿瘤特征、治疗方法和结果，并将其结合起来，以描述患者群体及其临床过程。肿瘤学家被要求找出临床实践指南（CPG）或政策变化对实践产生不同影响的实例。2014年1月至2022年6月期间在汝拉温斯基癌症中心就诊的乳腺癌患者（5768人）被纳入其中。平均年龄为 62.5 岁。最常见的组织学类型为浸润性导管癌（74.6%）；77%为雌激素受体阳性，15.5%为HER2 Neu阳性。56%的患者接受了保乳手术（BCS）。在接受系统治疗的 4294 例患者中，最初的适应症为辅助治疗（3096 例）、新辅助治疗（828 例）和姑息治疗（370 例）。531名患者出现转移，495名患者死亡。低收入患者的死亡率较高。在采用 CPGs 方面，辅助治疗双膦酸盐的采用率非常低，仅为预测的 8%，而 HER2 靶向药物百妥珠单抗的采用率为 64%，CD4/6 抑制剂治疗转移瘤的采用率为 40.2%。在 COVID-19 期间，省癌症机构发布了一项政策，缩短了 BCS 后的放疗时间。我们的平台描述了乳腺癌患者的护理和临床过程。我们的平台描述了乳腺癌患者的护理和临床过程，并测量了根据监管发展和政策变化而发生的实践变化。建立数据平台对长期保健服务非常重要。下一步是让数据反馈并改变实践，即实现闭环。

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引用次数: 0

A regional learning health system of congregate care facilities for COVID-19 response 为应对 COVID-19 建立由集中护理设施组成的区域学习保健系统

IF 2.6 Q2 HEALTH POLICY & SERVICES

Learning Health Systems

Pub Date : 2024-01-24 DOI: 10.1002/lrh2.10407

Muhammad A. Zafar, Andrew F. Beck, Chris Chirumbolo, Ken D. Wilson, Kate Haralson, Katherine Rich, Peter Margolis, David Hartley

Introduction

The COVID-19 pandemic disproportionately affected congregate care (CC) facilities due to communal living, presence of vulnerable populations, inadequate preventive resources, and limited ability to respond to the pandemic's rapidly evolving phases. Most facilities function independently and are not organized for collaborative learning and operations.

Methods

We formed a learning health system of CC facilities in our 14-county metropolitan region, coordinated with public health and health care sectors, to address challenges driven by COVID-19. A CC steering committee (SC) was formed that represented diverse institutions and viewpoints, including skilled nursing facilities, transitional care facilities, residential facilities, prisons, and shelters. The SC met regularly and was guided by situational awareness and systems thinking. A regional CC COVID-19 dashboard was developed based on publicly available data and weekly data submitted by participating facilities. Those experiencing outbreaks or supply shortages were quickly identified. As the pandemic progressed, the role of the SC shifted to address new and forecasted needs.

Results

Over 60 facilities participated in data sharing. The SC shared new guidelines, regulations, educational material, and best practices with the participating facilities. Information about testing sites, supplies, vaccination rollout, and facilities that had the capacity to accept COVID-19 patients was regularly disseminated. The SC was able to direct resources to those facilities experiencing outbreaks or supply shortages.

Conclusions

A novel learning health system of regional CC facilities enabled preparedness, situational awareness, collaboration, and rapid dissemination of best practices across pandemic phases. Such collaborative efforts can play an important role in addressing other public and preventive health challenges.

COVID-19 大流行对集中护理（CC）设施的影响尤为严重，因为这些设施都是集体生活，存在易感人群，预防资源不足，应对大流行快速发展阶段的能力有限。我们与公共卫生和医疗保健部门协调，在 14 个县的大都会地区建立了一个由 CC 设施组成的学习型卫生系统，以应对 COVID-19 带来的挑战。我们成立了一个 CC 指导委员会 (SC)，该委员会代表了不同的机构和观点，包括专业护理机构、过渡性护理机构、住宅机构、监狱和避难所。指导委员会定期召开会议，并以态势感知和系统思维为指导。根据公开数据和参与机构每周提交的数据，开发了地区 CC COVID-19 面板。出现疫情或供应短缺的设施被迅速识别出来。随着大流行病的发展，自然科学部门的角色发生了转变，以满足新的和预测的需求。SC 与参与机构分享了新的指导方针、法规、教育材料和最佳实践。有关检测点、供应品、疫苗接种推广以及有能力接收 COVID-19 患者的机构的信息定期发布。由地区 CC 设施组成的新型学习型卫生系统能够在大流行病的各个阶段做好准备、了解情况、开展合作并快速传播最佳实践。这种协作努力可在应对其他公共和预防性健康挑战方面发挥重要作用。

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引用次数: 0

Socio-technical infrastructure for a learning health system 学习型卫生系统的社会技术基础设施

IF 3.1 Q2 HEALTH POLICY & SERVICES

Learning Health Systems

Pub Date : 2024-01-16 DOI: 10.1002/lrh2.10405

Charles P. Friedman, Edwin A. Lomotan, Joshua E. Richardson, Jennifer L. Ridgeway

This commentary is in many ways a follow-on to, and elaboration of, the commentary published in the July issue of this journal.¹ The previous commentary introduced three characteristics that contribute to the uniqueness of learning health systems (LHSs) as an approach to health improvement. The three characteristics introduced there were: “(1) a multi-stakeholder learning community that is focused on the (targeted) problem and collaboratively executes the entire cycle; (2) embracing, at the outset, the uncertainty of how to improve against the problem by undertaking a rigorous discovery process before any implementation takes place; and (3) supporting multiple co-occurring cycles with a socio-technical infrastructure to create a learning system.”

This commentary focuses on the very important third characteristic, infrastructure. It examines the role of infrastructure in the overall architecture of an LHS and describes LHS infrastructure in terms of 10 interconnected socio-technical services accompanied by a brief description of each. Like the previous commentary, this one seeks to bring an increased level of focus to discussions of LHSs and move an emerging field, what is coming to be called “Learning Health System Science”,² toward a sharper conception of its core principles.

Critically, LHS infrastructure must extend beyond digital technology in order to support improvement of individual and population health. The infrastructure must be socio-technical in the sense that it incorporates the roles that a wide range of people must play at different levels of social organization: as individuals, as teams, as members of organizations, and as citizens of civil society.⁵ Technology, alone, only establishes a potential for health improvement through an LHS.

Viewing its infrastructure in terms of socio-technical services could be beneficial in several ways beyond working toward a consensus view of LHS structure and function. Most notably, such a modular approach could lead to sharing of interoperable infrastructure components and the possibility that sharing of such components might promote the more rapid adoption of LHS methods. Moreover, compatibility of LHS architectures could enable smaller scale LHSs to compose into a single system that functions at larger scale. Logical next steps to mature LHS infrastructure would include building consensus around the constituent services and developing specifications for each one.

The authors have no conflicts of interest to declare.

1 上一篇评论介绍了学习型卫生系统（LHSs）作为一种卫生改进方法的三个独特性。这三个特点是"（1）多方利益相关者组成的学习社区，专注于（目标）问题并合作执行整个周期；（2）在开始时，通过在任何实施之前进行严格的探索过程，接受如何改善问题的不确定性；以及（3）通过社会技术基础设施支持多个共同发生的周期，以创建一个学习系统。它探讨了基础设施在学习系统整体架构中的作用，并从 10 项相互关联的社会技术服务的角度描述了学习系统的基础设施，同时对每项服务进行了简要说明。与前一篇评论一样，这篇评论旨在提高对学习型健康系统讨论的关注度，并推动这一新兴领域（即将被称为 "学习型健康系统科学"）2 对其核心原则形成更清晰的概念。这种基础设施必须是社会技术性的，因为它包含了各种人在社会组织的不同层面上必须扮演的角色：作为个人、团队、组织成员以及公民社会的公民。最值得注意的是，这种模块化的方法可以共享可互操作的基础设施组件，而这些组件的共享有可能促进更快地采用长效健康系统方法。此外，LHS 架构的兼容性可使较小规模的 LHS 组成一个单一系统，在更大规模上发挥作用。成熟的 LHS 基础设施的下一步工作包括就组成服务达成共识，并为每项服务制定规范。

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引用次数: 0

Privacy-preserving record linkage across disparate institutions and datasets to enable a learning health system: The national COVID cohort collaborative (N3C) experience 在不同机构和数据集之间建立保护隐私的记录链接，以实现学习型医疗系统：国家 COVID 队列协作（N3C）的经验

IF 3.1 Q2 HEALTH POLICY & SERVICES

Learning Health Systems

Pub Date : 2024-01-11 DOI: 10.1002/lrh2.10404

Umberto Tachinardi, Shaun J. Grannis, Sam G. Michael, Leonie Misquitta, Jayme Dahlin, Usman Sheikh, Abel Kho, Jasmin Phua, Sara S. Rogovin, Benjamin Amor, Maya Choudhury, Philip Sparks, Amin Mannaa, Saad Ljazouli, Joel Saltz, Fred Prior, Ahmen Baghal, Kenneth Gersing, Peter J. Embi

Introduction

Research driven by real-world clinical data is increasingly vital to enabling learning health systems, but integrating such data from across disparate health systems is challenging. As part of the NCATS National COVID Cohort Collaborative (N3C), the N3C Data Enclave was established as a centralized repository of deidentified and harmonized COVID-19 patient data from institutions across the US. However, making this data most useful for research requires linking it with information such as mortality data, images, and viral variants. The objective of this project was to establish privacy-preserving record linkage (PPRL) methods to ensure that patient-level EHR data remains secure and private when governance-approved linkages with other datasets occur.

Methods

Separate agreements and approval processes govern N3C data contribution and data access. The Linkage Honest Broker (LHB), an independent neutral party (the Regenstrief Institute), ensures data linkages are robust and secure by adding an extra layer of separation between protected health information and clinical data. The LHB's PPRL methods (including algorithms, processes, and governance) match patient records using “deidentified tokens,” which are hashed combinations of identifier fields that define a match across data repositories without using patients' clear-text identifiers.

Results

These methods enable three linkage functions: Deduplication, Linking Multiple Datasets, and Cohort Discovery. To date, two external repositories have been cross-linked. As of March 1, 2023, 43 sites have signed the LHB Agreement; 35 sites have sent tokens generated for 9 528 998 patients. In this initial cohort, the LHB identified 135 037 matches and 68 596 duplicates.

Conclusion

This large-scale linkage study using deidentified datasets of varying characteristics established secure methods for protecting the privacy of N3C patient data when linked for research purposes. This technology has potential for use with registries for other diseases and conditions.

引言由真实世界临床数据驱动的研究对实现学习型医疗系统越来越重要，但整合来自不同医疗系统的此类数据却极具挑战性。作为 NCATS 国家 COVID 队列合作（N3C）的一部分，N3C 数据飞地（N3C Data Enclave）的建立是为了集中存放来自美国各机构的去身份化和统一的 COVID-19 患者数据。然而，要使这些数据在研究中发挥最大作用，需要将其与死亡率数据、图像和病毒变异等信息联系起来。本项目的目的是建立隐私保护记录链接（PPRL）方法，以确保在与其他数据集进行管理批准的链接时，患者级电子病历数据仍能保持安全和隐私。方法对 N3C 数据贡献和数据访问实行单独的协议和审批程序。链接诚信经纪人（LHB）是一个独立的中立方（Regenstrief 研究所），通过在受保护健康信息和临床数据之间增加一层额外的隔离，确保数据链接的稳健性和安全性。LHB 的 PPRL 方法（包括算法、流程和管理）使用 "去标识符 "匹配患者记录，"去标识符 "是标识符字段的散列组合，可在不使用患者明文标识符的情况下定义跨数据存储库的匹配。结果这些方法实现了三种链接功能：重复数据消除、多数据集链接和队列发现。迄今为止，已有两个外部资料库实现了交叉链接。截至 2023 年 3 月 1 日，43 个研究机构签署了 LHB 协议；35 个研究机构发送了为 9 528 998 名患者生成的令牌。在这个初始队列中，LHB 发现了 135 037 个匹配项和 68 596 个重复项。结论这项使用不同特征的去身份化数据集进行的大规模链接研究确立了以研究为目的进行链接时保护 N3C 患者数据隐私的安全方法。这项技术有望用于其他疾病和病症的登记。

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引用次数: 0

Exploring the use of ChatGPT as a virtual health coach for chronic disease management 探索将 ChatGPT 作为虚拟健康指导用于慢性病管理

IF 2.6 Q2 HEALTH POLICY & SERVICES

Learning Health Systems

Pub Date : 2024-01-11 DOI: 10.1002/lrh2.10406

Fahad M. Al-Anezi

Introduction

ChatGPT has been widely researched for its potential in gealthcare applications. However, its efficcy as a virtual health coach is one of the important areas, which can significantly contribute to the sustainablility in healthcare operations, especially in managing critical illnesses. Therefore, this study aims to analyze the use of ChatGPT as a virtual health coach for chronic disease managemet.

Methods

This study used a quasi-experimental design because ChatGPT is a relatively new technology and few people have experience with it. Patients who were receiving care outside of the hospital were included. Semi-structured interviews were conducted after a 2-week period in which participants used ChatGPT to search for health information about chronic disease management. Thirty-nine outpatients were interviewed and thematic analysis was used to analyze the interview data.

Results

The findings suggested both opportunities and challenges of using ChatGPT as a virtual health coach for chronic disease management. The major opportunities identified included life-long learning, improved health literacy, cost-effectiveness, behavioral change support, scalability, and accessibility. The major challenges identified included limited physical examination, lack of human connection, legal and ethical complications, and lack of accuracy and reliability.

Conclusion

ChatGPT-based technologies may serve as a supplementary or intermediate support system. However, such applications for managing chronic diseases must protect privacy and promote both short- and long-term positive outcomes.

人们对 ChatGPT 在地理保健应用方面的潜力进行了广泛研究。然而，它作为虚拟健康指导的功效是其中一个重要领域，可以极大地促进医疗保健业务的可持续发展，尤其是在管理重大疾病方面。本研究采用了准实验设计，因为 ChatGPT 是一项相对较新的技术，很少有人使用过。研究对象包括在医院外接受治疗的患者。在参与者使用 ChatGPT 搜索有关慢性病管理的健康信息两周后，进行了半结构化访谈。研究结果表明，使用 ChatGPT 作为慢性病管理的虚拟健康指导既是机遇也是挑战。发现的主要机遇包括终身学习、提高健康素养、成本效益、行为改变支持、可扩展性和可及性。所发现的主要挑战包括有限的身体检查、缺乏人与人之间的联系、法律和伦理方面的复杂问题以及缺乏准确性和可靠性。然而，这类用于管理慢性疾病的应用程序必须保护隐私，并促进短期和长期的积极成果。

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引用次数: 0

Using the consolidated framework for implementation research (CFIR) to guide implementation of cardio-oncology services 利用实施研究综合框架（CFIR）指导肿瘤心脏病学服务的实施

IF 3.1 Q2 HEALTH POLICY & SERVICES

Learning Health Systems

Pub Date : 2023-12-13 DOI: 10.1002/lrh2.10402

Jessica Miller Clouser, Colleen A. McMullen, Akosua K. Adu, Gretchen Wells, Amit Arbune, Jing Li

Introduction

Cardio-oncology focuses on diagnosing and preventing adverse cardiovascular outcomes in cancer patients. Interdisciplinary cardio-oncology services address the spectrum of prevention, detection, monitoring, and treatment of cancer patients at risk of cardio-toxicity and aim to improve the continuum of cardiac care for oncology patients. The goal of this study was to engage clinician and administrative stakeholders to assess multilevel needs, barriers, and expectations regarding cardio oncology services.

Methods

We interviewed clinicians and administrators at an academic medical center using the Consolidated Framework for Implementation Research (CFIR) to understand multilevel determinants influencing cardio-oncology service implementation. We also conducted a web-based survey to assess the knowledge, attitude, and perceptions of cardio-oncology services held by local and regional clinicians who may refer cardio-oncology patients to the study site.

Results

Multiple facilitators to cardio-oncology service implementation emerged. Interview participants believed cardio-oncology services could benefit patients and the organization by providing a competitive advantage. A majority (74%) of clinicians surveyed thought a cardio-oncology service would significantly improve cancer patients’ prognoses. Implementation barriers discussed included costs and a siloed organizational structure that complicated cross-service collaboration. In the clinician survey, differences in the views toward cardio-oncology services held by cardiology versus oncology providers would need to be negotiated in future cardio-oncology service development. For example, while most providers accepted similar risk of cardio-toxicity when consenting patients for cancer therapy in a curative setting, cardiologists accepted significantly higher levels of risk than oncologists in an incurable setting: 75% of oncologists accepted 1-5% risk; 77% of cardiologists accepted ≥5% risk).

Conclusions

Participants supported implementation and development of cardio-oncology services. Respondents also noted multi-level barriers that could be addressed to maximize the potential for success. Engaging administrators and clinicians from cardiology and oncology disciplines in the future development of such services can help ensure maximal relevance and uptake.

心脏肿瘤学侧重于诊断和预防癌症患者的不良心血管后果。跨学科肿瘤心脏病学服务涉及有心脏毒性风险的癌症患者的预防、检测、监测和治疗，旨在改善肿瘤患者心脏护理的连续性。本研究的目标是让临床医生和行政管理人员参与进来，评估多层次的需求、障碍以及对肿瘤心脏病学服务的期望。我们采用实施研究综合框架（CFIR）采访了一家学术医疗中心的临床医生和行政管理人员，以了解影响肿瘤心脏病学服务实施的多层次决定因素。我们还进行了一项网络调查，以评估当地和区域临床医生对心外科肿瘤服务的认识、态度和看法，这些临床医生可能会将心外科肿瘤患者转诊到研究地点。访谈参与者认为，心脏肿瘤学服务可为患者和机构带来竞争优势，从而使其受益。大多数（74%）受访临床医生认为，心脏肿瘤学服务将显著改善癌症患者的预后。所讨论的实施障碍包括成本和使跨服务协作复杂化的孤立组织结构。在临床医生的调查中，心脏科与肿瘤科医疗服务提供者对心脏肿瘤学服务的看法存在差异，这需要在未来的心脏肿瘤学服务发展中进行协商。例如，虽然大多数医疗机构在同意患者接受治愈性癌症治疗时都接受类似的心脏毒性风险，但在无法治愈的情况下，心脏科医生接受的风险水平明显高于肿瘤科医生：75% 的肿瘤学家接受 1-5% 的风险；77% 的心脏病学家接受≥5% 的风险）。与会者支持实施和发展肿瘤心脏病学服务。受访者还指出，要最大限度地提高成功的可能性，可以解决多层次的障碍。让心脏病学和肿瘤学学科的管理者和临床医生参与此类服务的未来发展，有助于确保最大程度的相关性和吸收。

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引用次数: 0

Building a regional pediatric asthma learning health system in support of optimal, equitable outcomes 建立地区小儿哮喘学习保健系统，支持取得最佳、公平的成果

IF 3.1 Q2 HEALTH POLICY & SERVICES

Learning Health Systems

Pub Date : 2023-12-11 DOI: 10.1002/lrh2.10403

Andrew F. Beck, Michael Seid, Karen M. McDowell, Mfonobong Udoko, Susan C. Cronin, Dimitrios Makrozahopoulos, Tricia Powers, Sonja Fairbanks, Jonelle Prideaux, Lisa M. Vaughn, Elizabeth Hente, Sophia Thurmond, Ndidi I. Unaka

Introduction

Asthma is characterized by preventable morbidity, cost, and inequity. We sought to build an Asthma Learning Health System (ALHS) to coordinate regional pediatric asthma improvement activities.

Methods

We generated quantitative and qualitative insights pertinent to a better, more equitable care delivery system. We used electronic health record data to calculate asthma hospitalization rates for youth in our region. We completed an “environmental scan” to catalog the breadth of asthma-related efforts occurring in our children's hospital and across the region. We supplemented the scan with group-level assessments and focus groups with parents, clinicians, and community partners. We used insights from this descriptive epidemiology to inform the definition of shared aims, drivers, measures, and prototype interventions.

Results

Greater Cincinnati's youth are hospitalized for asthma at a rate three times greater than the U.S. average. Black youth are hospitalized at a rate five times greater than non-Black youth. Certain neighborhoods bear the disproportionate burden of asthma morbidity. Across Cincinnati, there are many asthma-relevant activities that seek to confront this morbidity; however, efforts are largely disconnected. Qualitative insights highlighted the importance of cross-sector coordination, evidence-based acute and preventive care, healthy homes and neighborhoods, and accountability. These insights also led to a shared, regional aim: to equitably reduce asthma-related hospitalizations. Early interventions have included population-level pattern recognition, multidisciplinary asthma action huddles, and enhanced social needs screening and response.

Conclusion

Learning health system methods are uniquely suited to asthma's complexity. Our nascent ALHS provides a scaffold atop which we can pursue better, more equitable regional asthma outcomes.

哮喘的特点是可预防的发病率、成本和不公平。我们试图建立一个哮喘学习健康系统（ALHS），以协调地区性儿科哮喘改善活动。我们利用电子健康记录数据来计算本地区青少年的哮喘住院率。我们完成了一次 "环境扫描"，对儿童医院和整个地区与哮喘相关的工作进行了分类。我们还与家长、临床医生和社区合作伙伴一起进行了小组评估和焦点小组讨论，对扫描结果进行了补充。大辛辛那提地区青少年因哮喘住院的比例是全美平均水平的三倍。黑人青少年的住院率是非黑人青少年的五倍。某些社区的哮喘发病率过高。在整个辛辛那提，有许多与哮喘相关的活动都在努力应对这一发病率问题；然而，这些努力在很大程度上是脱节的。定性分析强调了跨部门协调、循证急症和预防保健、健康家庭和社区以及问责制的重要性。这些见解还促成了一个共同的地区目标：公平地减少与哮喘有关的住院治疗。早期的干预措施包括人口层面的模式识别、多学科哮喘行动小组，以及加强社会需求筛查和响应。我们新生的 ALHS 提供了一个脚手架，在此基础上，我们可以追求更好、更公平的地区性哮喘成果。

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引用次数: 0