首页 > 最新文献

Learning Health Systems最新文献

英文 中文
ROAD2H: Development and evaluation of an open-source explainable artificial intelligence approach for managing co-morbidity and clinical guidelines ROAD2H:开发和评估用于管理共病和临床指南的开源可解释人工智能方法
IF 3.1 Q2 HEALTH POLICY & SERVICES Pub Date : 2023-09-12 DOI: 10.1002/lrh2.10391
Jesús Domínguez, Denys Prociuk, Branko Marović, Kristijonas Čyras, Oana Cocarascu, Francis Ruiz, Ella Mi, Emma Mi, Christian Ramtale, Antonio Rago, Ara Darzi, Francesca Toni, Vasa Curcin, Brendan Delaney

Introduction

Clinical decision support (CDS) systems (CDSSs) that integrate clinical guidelines need to reflect real-world co-morbidity. In patient-specific clinical contexts, transparent recommendations that allow for contraindications and other conflicts arising from co-morbidity are a requirement. In this work, we develop and evaluate a non-proprietary, standards-based approach to the deployment of computable guidelines with explainable argumentation, integrated with a commercial electronic health record (EHR) system in Serbia, a middle-income country in West Balkans.

Methods

We used an ontological framework, the Transition-based Medical Recommendation (TMR) model, to represent, and reason about, guideline concepts, and chose the 2017 International global initiative for chronic obstructive lung disease (GOLD) guideline and a Serbian hospital as the deployment and evaluation site, respectively. To mitigate potential guideline conflicts, we used a TMR-based implementation of the Assumptions-Based Argumentation framework extended with preferences and Goals (ABA+G). Remote EHR integration of computable guidelines was via a microservice architecture based on HL7 FHIR and CDS Hooks. A prototype integration was developed to manage chronic obstructive pulmonary disease (COPD) with comorbid cardiovascular or chronic kidney diseases, and a mixed-methods evaluation was conducted with 20 simulated cases and five pulmonologists.

Results

Pulmonologists agreed 97% of the time with the GOLD-based COPD symptom severity assessment assigned to each patient by the CDSS, and 98% of the time with one of the proposed COPD care plans. Comments were favourable on the principles of explainable argumentation; inclusion of additional co-morbidities was suggested in the future along with customisation of the level of explanation with expertise.

Conclusion

An ontological model provided a flexible means of providing argumentation and explainable artificial intelligence for a long-term condition. Extension to other guidelines and multiple co-morbidities is needed to test the approach further.

引言 整合临床指南的临床决策支持系统(CDS)需要反映现实世界中的共病情况。在针对特定患者的临床环境中,必须提供透明的建议,以考虑到禁忌症和其他因并发症而产生的冲突。在这项工作中,我们开发并评估了一种非专有的、基于标准的方法,用于部署具有可解释论证的可计算指南,并与塞尔维亚(西巴尔干半岛的一个中等收入国家)的商用电子健康记录(EHR)系统集成。 方法 我们使用本体论框架--基于转换的医疗建议(TMR)模型--来表示指南概念并对其进行推理,并选择 2017 年国际慢性阻塞性肺病全球倡议(GOLD)指南和一家塞尔维亚医院分别作为部署和评估地点。为了缓解潜在的指南冲突,我们使用了基于假设的论证框架(ABA+G)的 TMR 实现。可计算指南的远程 EHR 集成是通过基于 HL7 FHIR 和 CDS Hooks 的微服务架构实现的。开发了一个集成原型,用于管理合并心血管疾病或慢性肾脏疾病的慢性阻塞性肺病(COPD),并对 20 个模拟病例和 5 位肺病专家进行了混合方法评估。 结果 97% 的肺科医生同意 CDSS 为每位患者指定的基于 GOLD 的慢性阻塞性肺病症状严重程度评估,98% 的肺科医生同意建议的慢性阻塞性肺病护理计划之一。对可解释论证原则的评论是积极的;建议今后纳入更多并发症,并根据专业知识定制解释程度。 结论 本体论模型为长期病症提供了灵活的论证方法和可解释的人工智能。需要将其推广到其他指南和多种并发症中,以进一步测试这种方法。
{"title":"ROAD2H: Development and evaluation of an open-source explainable artificial intelligence approach for managing co-morbidity and clinical guidelines","authors":"Jesús Domínguez,&nbsp;Denys Prociuk,&nbsp;Branko Marović,&nbsp;Kristijonas Čyras,&nbsp;Oana Cocarascu,&nbsp;Francis Ruiz,&nbsp;Ella Mi,&nbsp;Emma Mi,&nbsp;Christian Ramtale,&nbsp;Antonio Rago,&nbsp;Ara Darzi,&nbsp;Francesca Toni,&nbsp;Vasa Curcin,&nbsp;Brendan Delaney","doi":"10.1002/lrh2.10391","DOIUrl":"10.1002/lrh2.10391","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Clinical decision support (CDS) systems (CDSSs) that integrate clinical guidelines need to reflect real-world co-morbidity. In patient-specific clinical contexts, transparent recommendations that allow for contraindications and other conflicts arising from co-morbidity are a requirement. In this work, we develop and evaluate a non-proprietary, standards-based approach to the deployment of computable guidelines with explainable argumentation, integrated with a commercial electronic health record (EHR) system in Serbia, a middle-income country in West Balkans.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We used an ontological framework, the Transition-based Medical Recommendation (TMR) model, to represent, and reason about, guideline concepts, and chose the 2017 International global initiative for chronic obstructive lung disease (GOLD) guideline and a Serbian hospital as the deployment and evaluation site, respectively. To mitigate potential guideline conflicts, we used a TMR-based implementation of the Assumptions-Based Argumentation framework extended with preferences and Goals (ABA+G). Remote EHR integration of computable guidelines was via a microservice architecture based on HL7 FHIR and CDS Hooks. A prototype integration was developed to manage chronic obstructive pulmonary disease (COPD) with comorbid cardiovascular or chronic kidney diseases, and a mixed-methods evaluation was conducted with 20 simulated cases and five pulmonologists.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Pulmonologists agreed 97% of the time with the GOLD-based COPD symptom severity assessment assigned to each patient by the CDSS, and 98% of the time with one of the proposed COPD care plans. Comments were favourable on the principles of explainable argumentation; inclusion of additional co-morbidities was suggested in the future along with customisation of the level of explanation with expertise.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>An ontological model provided a flexible means of providing argumentation and explainable artificial intelligence for a long-term condition. Extension to other guidelines and multiple co-morbidities is needed to test the approach further.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10391","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135831130","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
A computable biomedical knowledge object for calculating in-hospital mortality for patients admitted with acute myocardial infarction 一个可计算的生物医学知识对象,用于计算急性心肌梗死患者的住院死亡率。
IF 3.1 Q2 HEALTH POLICY & SERVICES Pub Date : 2023-09-11 DOI: 10.1002/lrh2.10388
Rosemarie Sadsad, Gema Ruber, Johnson Zhou, Steven Nicklin, Guy Tsafnat

Introduction

Quality indicators play an essential role in a learning health system. They help healthcare providers to monitor the quality and safety of care delivered and to identify areas for improvement. Clinical quality indicators, therefore, need to be based on real world data. Generating reliable and actionable data routinely is challenging. Healthcare data are often stored in different formats and use different terminologies and coding systems, making it difficult to generate and compare indicator reports from different sources.

Methods

The Observational Health Sciences and Informatics community maintains the Observational Medical Outcomes Partnership Common Data Model (OMOP). This is an open data standard providing a computable and interoperable format for real world data. We implemented a Computable Biomedical Knowledge Object (CBK) in the Piano Platform based on OMOP. The CBK calculates an inpatient quality indicator and was illustrated using synthetic electronic health record (EHR) data in the open OMOP standard.

Results

The CBK reported the in-hospital mortality of patients admitted for acute myocardial infarction (AMI) for the synthetic EHR dataset and includes interactive visualizations and the results of calculations. Value sets composed of OMOP concept codes for AMI and comorbidities used in the indicator calculation were also created.

Conclusion

Computable biomedical knowledge (CBK) objects that operate on OMOP data can be reused across datasets that conform to OMOP. With OMOP being a widely used interoperability standard, quality indicators embedded in CBKs can accelerate the generation of evidence for targeted quality and safety management, improving care to benefit larger populations.

引言:质量指标在学习健康系统中发挥着至关重要的作用。他们帮助医疗保健提供者监测所提供的护理的质量和安全性,并确定需要改进的领域。因此,临床质量指标需要以真实世界的数据为基础。常规生成可靠且可操作的数据具有挑战性。医疗保健数据通常以不同的格式存储,使用不同的术语和编码系统,因此很难生成和比较来自不同来源的指标报告。方法:观察性健康科学和信息学社区维护观察性医疗结果伙伴关系通用数据模型(OMOP)。这是一个开放的数据标准,为真实世界的数据提供了可计算和可互操作的格式。我们在基于OMOP的钢琴平台上实现了一个可计算的生物医学知识对象(CBK)。CBK计算住院患者质量指标,并使用开放式OMOP标准中的合成电子健康记录(EHR)数据进行说明。结果:CBK为合成EHR数据集报告了因急性心肌梗死(AMI)入院的患者的住院死亡率,并包括交互式可视化和计算结果。还创建了由AMI的OMOP概念代码和指标计算中使用的合并症组成的值集。结论:对OMOP数据进行操作的可计算生物医学知识(CBK)对象可以在符合OMOP的数据集之间重复使用。由于OMOP是一种广泛使用的互操作性标准,嵌入CBK中的质量指标可以加速生成有针对性的质量和安全管理的证据,从而改善护理,造福更多人群。
{"title":"A computable biomedical knowledge object for calculating in-hospital mortality for patients admitted with acute myocardial infarction","authors":"Rosemarie Sadsad,&nbsp;Gema Ruber,&nbsp;Johnson Zhou,&nbsp;Steven Nicklin,&nbsp;Guy Tsafnat","doi":"10.1002/lrh2.10388","DOIUrl":"10.1002/lrh2.10388","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Quality indicators play an essential role in a learning health system. They help healthcare providers to monitor the quality and safety of care delivered and to identify areas for improvement. Clinical quality indicators, therefore, need to be based on real world data. Generating reliable and actionable data routinely is challenging. Healthcare data are often stored in different formats and use different terminologies and coding systems, making it difficult to generate and compare indicator reports from different sources.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The Observational Health Sciences and Informatics community maintains the Observational Medical Outcomes Partnership Common Data Model (OMOP). This is an open data standard providing a computable and interoperable format for real world data. We implemented a Computable Biomedical Knowledge Object (CBK) in the Piano Platform based on OMOP. The CBK calculates an inpatient quality indicator and was illustrated using synthetic electronic health record (EHR) data in the open OMOP standard.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The CBK reported the in-hospital mortality of patients admitted for acute myocardial infarction (AMI) for the synthetic EHR dataset and includes interactive visualizations and the results of calculations. Value sets composed of OMOP concept codes for AMI and comorbidities used in the indicator calculation were also created.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Computable biomedical knowledge (CBK) objects that operate on OMOP data can be reused across datasets that conform to OMOP. With OMOP being a widely used interoperability standard, quality indicators embedded in CBKs can accelerate the generation of evidence for targeted quality and safety management, improving care to benefit larger populations.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"7 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10388","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49683341","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
A novel method for continuous measurements of clinical practice guideline adherence 一种连续测量临床实践指南依从性的新方法
IF 3.1 Q2 HEALTH POLICY & SERVICES Pub Date : 2023-09-07 DOI: 10.1002/lrh2.10384
Kees C.W.J. Ebben, Cornelis D. de Kroon, Channa E. Schmeink, Olga L. van der Hel, Thijs van Vegchel, Arturo Moncada-Torres, Ignace H.J.T. de Hingh, Jurrian van der Werf

Introduction

Clinical practice guidelines (hereafter ‘guidelines’) are crucial in providing evidence-based recommendations for physicians and multidisciplinary teams to make informed decisions regarding diagnostics and treatment in various diseases, including cancer. While guideline implementation has been shown to reduce (unwanted) variability and improve outcome of care, monitoring of adherence to guidelines remains challenging. Real-world data collected from cancer registries can provide a continuous source for monitoring adherence levels. In this work, we describe a novel structured approach to guideline evaluation using real-world data that enables continuous monitoring. This method was applied to endometrial cancer patients in the Netherlands and implemented through a prototype web-based dashboard that enables interactive usage and supports various analyses.

Method

The guideline under study was parsed into clinical decision trees (CDTs) and an information standard was drawn up. A dataset from the Netherlands Cancer Registry (NCR) was used and data items from both instruments were mapped. By comparing guideline recommendations with real-world data an adherence classification was determined. The developed prototype can be used to identify and prioritize potential topics for guideline updates.

Results

CDTs revealed 68 data items for recording in an information standard. Thirty-two data items from the NCR were mapped onto information standard data items. Four CDTs could sufficiently be populated with NCR data.

Conclusion

The developed methodology can evaluate a guideline to identify potential improvements in recommendations and the success of the implementation strategy. In addition, it is able to identify patient and disease characteristics that influence decision-making in clinical practice. The method supports a cyclical process of developing, implementing and evaluating guidelines and can be scaled to other diseases and settings. It contributes to a learning healthcare cycle that integrates real-world data with external knowledge.

临床实践指南(以下简称“指南”)对于为医生和多学科团队提供基于证据的建议至关重要,以就包括癌症在内的各种疾病的诊断和治疗做出明智的决定。尽管指南的实施已被证明可以减少(不必要的)变异性并改善护理结果,但监测指南的遵守情况仍然具有挑战性。从癌症登记处收集的真实世界数据可以为监测依从性水平提供持续的来源。在这项工作中,我们描述了一种使用真实世界数据进行指南评估的新的结构化方法,该方法能够实现持续监测。该方法应用于荷兰的子宫内膜癌症患者,并通过基于网络的原型仪表板实现,该仪表板能够实现交互式使用并支持各种分析。将正在研究的指南解析为临床决策树(CDT),并制定了信息标准。使用荷兰癌症注册中心(NCR)的数据集,绘制两种仪器的数据项。通过将指南建议与真实世界的数据进行比较,确定了依从性分类。开发的原型可用于识别潜在主题并确定其优先级,以便更新指南。CDT揭示了在信息标准中记录的68个数据项。NCR中的32个数据项被映射到信息标准数据项上。NCR数据可以充分填充四个CDT。所制定的方法可以评估指导方针,以确定建议中的潜在改进和执行战略的成功。此外,它能够识别影响临床实践决策的患者和疾病特征。该方法支持制定、实施和评估指南的周期性过程,并可扩展到其他疾病和环境。它有助于学习医疗保健周期,将真实世界的数据与外部知识相结合。
{"title":"A novel method for continuous measurements of clinical practice guideline adherence","authors":"Kees C.W.J. Ebben,&nbsp;Cornelis D. de Kroon,&nbsp;Channa E. Schmeink,&nbsp;Olga L. van der Hel,&nbsp;Thijs van Vegchel,&nbsp;Arturo Moncada-Torres,&nbsp;Ignace H.J.T. de Hingh,&nbsp;Jurrian van der Werf","doi":"10.1002/lrh2.10384","DOIUrl":"10.1002/lrh2.10384","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Clinical practice guidelines (hereafter ‘guidelines’) are crucial in providing evidence-based recommendations for physicians and multidisciplinary teams to make informed decisions regarding diagnostics and treatment in various diseases, including cancer. While guideline implementation has been shown to reduce (unwanted) variability and improve outcome of care, monitoring of adherence to guidelines remains challenging. Real-world data collected from cancer registries can provide a continuous source for monitoring adherence levels. In this work, we describe a novel structured approach to guideline evaluation using real-world data that enables continuous monitoring. This method was applied to endometrial cancer patients in the Netherlands and implemented through a prototype web-based dashboard that enables interactive usage and supports various analyses.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>The guideline under study was parsed into clinical decision trees (CDTs) and an information standard was drawn up. A dataset from the Netherlands Cancer Registry (NCR) was used and data items from both instruments were mapped. By comparing guideline recommendations with real-world data an adherence classification was determined. The developed prototype can be used to identify and prioritize potential topics for guideline updates.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>CDTs revealed 68 data items for recording in an information standard. Thirty-two data items from the NCR were mapped onto information standard data items. Four CDTs could sufficiently be populated with NCR data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The developed methodology can evaluate a guideline to identify potential improvements in recommendations and the success of the implementation strategy. In addition, it is able to identify patient and disease characteristics that influence decision-making in clinical practice. The method supports a cyclical process of developing, implementing and evaluating guidelines and can be scaled to other diseases and settings. It contributes to a learning healthcare cycle that integrates real-world data with external knowledge.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"7 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10384","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45479740","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
Factors influencing maternal death surveillance and review implementation in Dodoma City, Tanzania. A qualitative case study 影响坦桑尼亚多多马市产妇死亡监测和审查执行情况的因素。定性案例研究
IF 3.1 Q2 HEALTH POLICY & SERVICES Pub Date : 2023-09-04 DOI: 10.1002/lrh2.10390
Nelson M. Rumbeli, Furaha August, Valeria Silvestri, Nathanael Sirili

Background

With 295 000 maternal deaths in 2017, 94% in low- and middle-income countries, maternal death is a matter of global public health concern. To address it, Maternal Death Surveillance and Response (MDSR) strategy was introduced in 2013 by the World Health Organization. With a reported maternal mortality ratio of 556:100000 per live births, Tanzania adopted the strategy in 2015. Studies are needed to understand factors influencing the implementation of MDSR in this specific setting.

Aims and Objectives

The study aimed to assess the processes influencing MDSR implementation in Dodoma city council.

Methods

A qualitative case study was conceptualized according to the Consolidated Framework for Implementation Research, focusing on implementation process domain. Members of MDSR committees were enrolled by purposeful sampling in the five health centres in Dodoma where the strategy was fully implemented and functional. In-depth interviews were conducted with key informants concerning the implementation processes influencing MDSR. Saturation was reached with the 15th respondent. Qualitative inductive content analysis was used to analyse data.

Results

The inclusiveness in participatory planning process, stakeholders’ readiness and accountability and collective learning were acknowledged as factors positively influencing the implementation of MDSR strategy by respondents. The interaction and alignment of influential factors were essential for successful implementation.

Conclusions

MDSR implementation is positively influenced by factors that interact and converge in the building of a learning health system, to increase knowledge through practice and improve practice through knowledge. Further studies are needed to analyse the influence of additional factors at different levels of implementation to fully understand and empower the MDSR implementation network, and to better target the goal of closing the knowledge to practice loop.

2017年孕产妇死亡29.5万例,其中94%发生在低收入和中等收入国家,孕产妇死亡是一个全球公共卫生关注的问题。为了解决这一问题,世界卫生组织于2013年推出了孕产妇死亡监测和应对战略。据报告,坦桑尼亚每活产产妇死亡率为556:100000,坦桑尼亚于2015年通过了该战略。需要进行研究以了解在这一特定环境中影响MDSR实施的因素。该研究旨在评估影响Dodoma市议会执行MDSR的进程。根据实施研究的统一框架,提出了定性案例研究的概念,重点关注实施过程域。通过有目的的抽样,在充分执行和发挥作用的Dodoma的五个保健中心招募了MDSR委员会的成员。对影响MDSR实施过程的关键信息提供者进行了深入访谈。第15个应答者达到饱和。采用定性归纳含量分析法对数据进行分析。答复者承认参与性规划进程的包容性、利益攸关方的准备和问责以及集体学习是积极影响千年发展目标战略实施的因素。影响因素的相互作用和协调一致对于成功实施至关重要。在学习型卫生系统建设中,通过实践增加知识,通过知识改善实践,这些相互作用和融合的因素对MDSR的实施产生积极影响。需要进一步的研究来分析其他因素在不同实施层面的影响,以充分了解和增强MDSR实施网络的能力,并更好地实现关闭知识到实践循环的目标。
{"title":"Factors influencing maternal death surveillance and review implementation in Dodoma City, Tanzania. A qualitative case study","authors":"Nelson M. Rumbeli,&nbsp;Furaha August,&nbsp;Valeria Silvestri,&nbsp;Nathanael Sirili","doi":"10.1002/lrh2.10390","DOIUrl":"10.1002/lrh2.10390","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>With 295 000 maternal deaths in 2017, 94% in low- and middle-income countries, maternal death is a matter of global public health concern. To address it, Maternal Death Surveillance and Response (MDSR) strategy was introduced in 2013 by the World Health Organization. With a reported maternal mortality ratio of 556:100000 per live births, Tanzania adopted the strategy in 2015. Studies are needed to understand factors influencing the implementation of MDSR in this specific setting.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Aims and Objectives</h3>\u0000 \u0000 <p>The study aimed to assess the processes influencing MDSR implementation in Dodoma city council.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A qualitative case study was conceptualized according to the Consolidated Framework for Implementation Research, focusing on implementation process domain. Members of MDSR committees were enrolled by purposeful sampling in the five health centres in Dodoma where the strategy was fully implemented and functional. In-depth interviews were conducted with key informants concerning the implementation processes influencing MDSR. Saturation was reached with the 15th respondent. Qualitative inductive content analysis was used to analyse data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The inclusiveness in participatory planning process, stakeholders’ readiness and accountability and collective learning were acknowledged as factors positively influencing the implementation of MDSR strategy by respondents. The interaction and alignment of influential factors were essential for successful implementation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>MDSR implementation is positively influenced by factors that interact and converge in the building of a learning health system, to increase knowledge through practice and improve practice through knowledge. Further studies are needed to analyse the influence of additional factors at different levels of implementation to fully understand and empower the MDSR implementation network, and to better target the goal of closing the knowledge to practice loop.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10390","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46881213","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
Sync for Genes Phase 5: Computable artifacts for sharing dynamically annotated FHIR-formatted genomic variants 同步基因阶段5:可计算的工件共享动态注释FHIR格式的基因组变异
IF 3.1 Q2 HEALTH POLICY & SERVICES Pub Date : 2023-08-30 DOI: 10.1002/lrh2.10385
Robert Dolin, Bret S. E. Heale, Rohan Gupta, Carla Alvarez, Justin Aronson, Aziz Boxwala, Shaileshbhai R. Gothi, Ammar Husami, James Shalaby, Lawrence Babb, Alex Wagner, Srikar Chamala

Introduction

Variant annotation is a critical component in next-generation sequencing, enabling a sequencing lab to comb through a sea of variants in order to hone in on those likely to be most significant, and providing clinicians with necessary context for decision-making. But with the rapid evolution of genomics knowledge, reported annotations can quickly become out-of-date. Under the ONC Sync for Genes program, our team sought to standardize the sharing of dynamically annotated variants (e.g., variants annotated on demand, based on current knowledge). The computable biomedical knowledge artifacts that were developed enable a clinical decision support (CDS) application to surface up-to-date annotations to clinicians.

Methods

The work reported in this article relies on the Health Level 7 Fast Healthcare Interoperability Resources (FHIR) Genomics and Global Alliance for Genomics and Health (GA4GH) Variant Annotation (VA) standards. We developed a CDS pipeline that dynamically annotates patient's variants through an intersection with current knowledge and serves up the FHIR-encoded variants and annotations (diagnostic and therapeutic implications, molecular consequences, population allele frequencies) via FHIR Genomics Operations. ClinVar, CIViC, and PharmGKB were used as knowledge sources, encoded as per the GA4GH VA specification.

Results

Primary public artifacts from this project include a GitHub repository with all source code, a Swagger interface that allows anyone to visualize and interact with the code using only a web browser, and a backend database where all (synthetic and anonymized) patient data and knowledge are housed.

Conclusions

We found that variant annotation varies in complexity based on the variant type, and that various bioinformatics strategies can greatly improve automated annotation fidelity. More importantly, we demonstrated the feasibility of an ecosystem where genomic knowledge bases have standardized knowledge (e.g., based on the GA4GH VA spec), and CDS applications can dynamically leverage that knowledge to provide real-time decision support, based on current knowledge, to clinicians at the point of care.

变体注释是下一代测序的一个关键组成部分,它使测序实验室能够梳理大量的变体,以磨练那些可能最重要的变体,并为临床医生提供必要的决策背景。但是随着基因组学知识的快速发展,报告的注释可能很快就会过时。在ONC基因同步计划下,我们的团队试图标准化动态注释变体的共享(例如,根据当前知识按需注释的变体)。开发的可计算生物医学知识工件使临床决策支持(CDS)应用程序能够向临床医生提供最新的注释。本文中报告的工作依赖于Health Level 7快速医疗互操作性资源(FHIR)基因组学和全球基因组学与健康联盟(GA4GH)变体注释(VA)标准。我们开发了一个CDS管道,通过与当前知识的交叉动态注释患者的变异,并通过FHIR基因组学操作提供FHIR编码的变异和注释(诊断和治疗意义,分子后果,种群等位基因频率)。ClinVar、CIViC和PharmGKB被用作知识来源,按照GA4GH VA规范进行编码。该项目的主要公共工件包括一个包含所有源代码的GitHub存储库,一个允许任何人仅使用web浏览器就可以可视化并与代码交互的Swagger接口,以及一个存储所有(合成和匿名)患者数据和知识的后端数据库。研究发现,变异注释的复杂性随变异类型的不同而不同,各种生物信息学策略可以大大提高自动注释的保真度。更重要的是,我们展示了一个生态系统的可行性,其中基因组知识库具有标准化知识(例如,基于GA4GH VA规范),CDS应用程序可以动态地利用这些知识,根据当前知识为临床医生提供实时决策支持。
{"title":"Sync for Genes Phase 5: Computable artifacts for sharing dynamically annotated FHIR-formatted genomic variants","authors":"Robert Dolin,&nbsp;Bret S. E. Heale,&nbsp;Rohan Gupta,&nbsp;Carla Alvarez,&nbsp;Justin Aronson,&nbsp;Aziz Boxwala,&nbsp;Shaileshbhai R. Gothi,&nbsp;Ammar Husami,&nbsp;James Shalaby,&nbsp;Lawrence Babb,&nbsp;Alex Wagner,&nbsp;Srikar Chamala","doi":"10.1002/lrh2.10385","DOIUrl":"10.1002/lrh2.10385","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Variant annotation is a critical component in next-generation sequencing, enabling a sequencing lab to comb through a sea of variants in order to hone in on those likely to be most significant, and providing clinicians with necessary context for decision-making. But with the rapid evolution of genomics knowledge, reported annotations can quickly become out-of-date. Under the ONC Sync for Genes program, our team sought to standardize the sharing of dynamically annotated variants (e.g., variants annotated on demand, based on current knowledge). The computable biomedical knowledge artifacts that were developed enable a clinical decision support (CDS) application to surface up-to-date annotations to clinicians.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The work reported in this article relies on the Health Level 7 Fast Healthcare Interoperability Resources (FHIR) Genomics and Global Alliance for Genomics and Health (GA4GH) Variant Annotation (VA) standards. We developed a CDS pipeline that dynamically annotates patient's variants through an intersection with current knowledge and serves up the FHIR-encoded variants and annotations (diagnostic and therapeutic implications, molecular consequences, population allele frequencies) via FHIR Genomics Operations. ClinVar, CIViC, and PharmGKB were used as knowledge sources, encoded as per the GA4GH VA specification.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Primary public artifacts from this project include a GitHub repository with all source code, a Swagger interface that allows anyone to visualize and interact with the code using only a web browser, and a backend database where all (synthetic and anonymized) patient data and knowledge are housed.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>We found that variant annotation varies in complexity based on the variant type, and that various bioinformatics strategies can greatly improve automated annotation fidelity. More importantly, we demonstrated the feasibility of an ecosystem where genomic knowledge bases have standardized knowledge (e.g., based on the GA4GH VA spec), and CDS applications can dynamically leverage that knowledge to provide real-time decision support, based on current knowledge, to clinicians at the point of care.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"7 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10385","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41266288","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
Which computable biomedical knowledge objects will be regulated? Results of a UK workshop discussing the regulation of knowledge libraries and software as a medical device 哪些可计算的生物医学知识对象将受到监管?英国讨论知识库和软件作为医疗设备的监管的研讨会结果
IF 3.1 Q2 HEALTH POLICY & SERVICES Pub Date : 2023-08-25 DOI: 10.1002/lrh2.10386
Jeremy C. Wyatt, Philip Scott, Johan Ordish, Matthew South, Mark Thomas, Caroline Jones, Sue Lacey-Bryant, workshop participants

Introduction

To understand when knowledge objects in a computable biomedical knowledge library are likely to be subject to regulation as a medical device in the United Kingdom.

Methods

A briefing paper was circulated to a multi-disciplinary group of 25 including regulators, lawyers and others with insights into device regulation. A 1-day workshop was convened to discuss questions relating to our aim. A discussion paper was drafted by lead authors and circulated to other authors for their comments and contributions.

Results

This article reports on those deliberations and describes how UK device regulators are likely to treat the different kinds of knowledge objects that may be stored in computable biomedical knowledge libraries. While our focus is the likely approach of UK regulators, our analogies and analysis will also be relevant to the approaches taken by regulators elsewhere. We include a table examining the implications for each of the four knowledge levels described by Boxwala in 2011 and propose an additional level.

Conclusions

If a knowledge object is described as directly executable for a medical purpose to provide decision support, it will generally be in scope of UK regulation as “software as a medical device.” However, if the knowledge object consists of an algorithm, a ruleset, pseudocode or some other representation that is not directly executable and whose developers make no claim that it can be used for a medical purpose, it is not likely to be subject to regulation. We expect similar reasoning to be applied by regulators in other countries.

了解可计算生物医学知识库中的知识对象何时可能作为英国的医疗器械受到监管。一份简报分发给了一个由25人组成的多学科小组,其中包括监管机构、律师和其他对器械监管有深入了解的人。召开了为期一天的研讨会,讨论与我们的目标有关的问题。主要作者起草了一份讨论文件,并分发给其他作者征求他们的意见和贡献。本文报告了这些审议情况,并描述了英国设备监管机构可能如何处理可能存储在可计算生物医学知识库中的不同类型的知识对象。虽然我们的重点是英国监管机构可能采取的方法,但我们的类比和分析也将与其他地方监管机构采取的方法相关。我们包括一个表,检查Boxwala在2011年描述的四个知识水平中的每一个的含义,并提出了一个额外的水平。如果知识对象被描述为可直接执行用于医疗目的以提供决策支持,那么它通常在英国法规的范围内被称为“作为医疗设备的软件”。然而,如果知识对象由算法、规则集、,伪代码或其他不能直接执行的表示,并且其开发人员没有声称它可以用于医疗目的,它不太可能受到监管。我们预计其他国家的监管机构也会采用类似的理由。
{"title":"Which computable biomedical knowledge objects will be regulated? Results of a UK workshop discussing the regulation of knowledge libraries and software as a medical device","authors":"Jeremy C. Wyatt,&nbsp;Philip Scott,&nbsp;Johan Ordish,&nbsp;Matthew South,&nbsp;Mark Thomas,&nbsp;Caroline Jones,&nbsp;Sue Lacey-Bryant,&nbsp;workshop participants","doi":"10.1002/lrh2.10386","DOIUrl":"10.1002/lrh2.10386","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>To understand when knowledge objects in a computable biomedical knowledge library are likely to be subject to regulation as a medical device in the United Kingdom.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A briefing paper was circulated to a multi-disciplinary group of 25 including regulators, lawyers and others with insights into device regulation. A 1-day workshop was convened to discuss questions relating to our aim. A discussion paper was drafted by lead authors and circulated to other authors for their comments and contributions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>This article reports on those deliberations and describes how UK device regulators are likely to treat the different kinds of knowledge objects that may be stored in computable biomedical knowledge libraries. While our focus is the likely approach of UK regulators, our analogies and analysis will also be relevant to the approaches taken by regulators elsewhere. We include a table examining the implications for each of the four knowledge levels described by Boxwala in 2011 and propose an additional level.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>If a knowledge object is described as directly executable for a medical purpose to provide decision support, it will generally be in scope of UK regulation as “software as a medical device.” However, if the knowledge object consists of an algorithm, a ruleset, pseudocode or some other representation that is not directly executable and whose developers make no claim that it can be used for a medical purpose, it is not likely to be subject to regulation. We expect similar reasoning to be applied by regulators in other countries.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"7 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10386","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45912963","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: 1
Evidence Hub: A place to exchange medical knowledge and form communities 证据中心:一个交流医学知识和建立社区的地方。
IF 3.1 Q2 HEALTH POLICY & SERVICES Pub Date : 2023-08-23 DOI: 10.1002/lrh2.10387
Kenny Hong, Druvinka Bandaranayake, Guy Tsafnat

Introduction

Medical knowledge is complex and constantly evolving, making it challenging to disseminate and retrieve effectively. To address these challenges, researchers are exploring the use of formal knowledge representations that can be easily interpreted by computers.

Methods

Evidence Hub is a new, free, online platform that hosts computable clinical knowledge in the form of “Knowledge Objects”. These objects represent various types of computer-interpretable knowledge. The platform includes features that encourage advancing medical knowledge, such as public discussion threads for civil discourse about each Knowledge Object, thus building communities of interest that can form and reach consensus on the correctness, applicability, and proper use of the object. Knowledge Objects are maintained by volunteers and published on Evidence Hub under GPL 2.0. Peer review and quality assurance are provided by volunteers.

Results

Users can explore Evidence Hub and participate in discussions using a web browser. An application programming interface allows applications to register themselves as handlers of specific object types and provide editing and execution capabilities for particular object types.

Conclusions

By providing a platform for computable clinical knowledge and fostering discussion and collaboration, Evidence Hub improves the dissemination and use of medical knowledge.

引言:医学知识是复杂且不断发展的,因此有效传播和检索具有挑战性。为了应对这些挑战,研究人员正在探索使用计算机可以轻松解释的形式知识表示。方法:Evidence Hub是一个新的、免费的在线平台,以“知识对象”的形式托管可计算的临床知识。这些对象表示各种类型的计算机可解释知识。该平台包括鼓励推进医学知识的功能,例如关于每个知识对象的民间话语的公共讨论线程,从而建立能够就对象的正确性、适用性和正确使用形成并达成共识的利益共同体。知识对象由志愿者维护,并在GPL2.0下发布在证据中心。同行评审和质量保证由志愿者提供。结果:用户可以使用web浏览器浏览证据中心并参与讨论。应用程序编程接口允许应用程序将其自身注册为特定对象类型的处理程序,并为特定的对象类型提供编辑和执行功能。结论:通过提供一个可计算临床知识的平台,促进讨论和合作,证据中心改进了医学知识的传播和使用。
{"title":"Evidence Hub: A place to exchange medical knowledge and form communities","authors":"Kenny Hong,&nbsp;Druvinka Bandaranayake,&nbsp;Guy Tsafnat","doi":"10.1002/lrh2.10387","DOIUrl":"10.1002/lrh2.10387","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Medical knowledge is complex and constantly evolving, making it challenging to disseminate and retrieve effectively. To address these challenges, researchers are exploring the use of formal knowledge representations that can be easily interpreted by computers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Evidence Hub is a new, free, online platform that hosts computable clinical knowledge in the form of “Knowledge Objects”. These objects represent various types of computer-interpretable knowledge. The platform includes features that encourage advancing medical knowledge, such as public discussion threads for civil discourse about each Knowledge Object, thus building communities of interest that can form and reach consensus on the correctness, applicability, and proper use of the object. Knowledge Objects are maintained by volunteers and published on Evidence Hub under GPL 2.0. Peer review and quality assurance are provided by volunteers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Users can explore Evidence Hub and participate in discussions using a web browser. An application programming interface allows applications to register themselves as handlers of specific object types and provide editing and execution capabilities for particular object types.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>By providing a platform for computable clinical knowledge and fostering discussion and collaboration, Evidence Hub improves the dissemination and use of medical knowledge.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"7 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10387","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49683343","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: 1
Developing a relational playbook for cardiology teams to cultivate supportive learning environments, enhance clinician well-being, and veteran care 为心脏病学团队制定一个关系剧本,以培养支持性的学习环境,提高临床医生的幸福感和退伍军人的护理
IF 3.1 Q2 HEALTH POLICY & SERVICES Pub Date : 2023-08-10 DOI: 10.1002/lrh2.10383
Heather M. Gilmartin, Brigid Connelly, Edward Hess, Candice Mueller, Mary E. Plomondon, Stephen W. Waldo, Catherine Battaglia

Introduction

Despite the Veterans Health Administration (VA) efforts to become a learning health system (LHS) and high-reliability organization (HRO), interventions to build supportive learning environments within teams are not reliably implemented, contributing to high levels of burnout, turnover, and variation in care. Supportive learning environments build capabilities for teaching and learning, empower teams to safely trial and adapt new things, and adopt highly reliable work practices (eg, debriefs). Innovative approaches to create supportive learning environments are needed to advance LHS and HRO theory and research into practice.

Methods

To guide the identification of evidence-based interventions that cultivate supportive learning environments, the authors used a longitudinal, mixed-methods design and LHS and HRO frameworks. We partnered with the 81 VA cardiac catheterization laboratories and conducted surveys, interviews, and literature reviews that informed a Relational Playbook for Cardiology Teams.

Results

The Relational Playbook resources and 50 evidence-based interventions are organized into five LHS and HRO-guided chapters: Create a positive culture, teamwork, leading teams, joy in work, communication, and high reliability. The interventions are designed for managers to integrate into existing meetings or trainings to cultivate supportive learning environments.

Conclusions

LHS and HRO frameworks describe how organizations can continually learn and deliver nearly error-free services. The Playbook resources and interventions translate LHS and HRO frameworks for real-world implementation by healthcare managers. This work will cultivate supportive learning environments, employee well-being, and Veteran safety while providing insights into LHS and HRO theory, research, and practice.

尽管退伍军人健康管理局(VA)努力成为一个学习健康系统(LHS)和高可靠性组织(HRO),但在团队内建立支持性学习环境的干预措施并没有得到可靠实施,导致了高水平的倦怠、离职和护理变化。支持性学习环境建立教学能力,使团队能够安全地尝试和适应新事物,并采用高度可靠的工作实践(如汇报)。需要创新方法来创造支持性学习环境,以推进LHS和HRO理论和研究的实践。为了指导识别培养支持性学习环境的基于证据的干预措施,作者使用了纵向、混合方法设计以及LHS和HRO框架。我们与81个VA心导管插入术实验室合作,进行了调查、访谈和文献综述,为心脏病学团队的关系行动手册提供了信息。关系行动手册资源和50项基于证据的干预措施被组织成五个LHS和HRO指导的章节:创造积极的文化、团队合作、领导团队、工作乐趣、沟通和高可靠性。这些干预措施旨在让管理者融入现有的会议或培训,以培养支持性的学习环境。LHS和HRO框架描述了组织如何不断学习和提供几乎无错误的服务。行动手册资源和干预措施将LHS和HRO框架转化为医疗管理人员在现实世界中实施的框架。这项工作将培养支持性的学习环境、员工福祉和退伍军人安全,同时提供对LHS和HRO理论、研究和实践的见解。
{"title":"Developing a relational playbook for cardiology teams to cultivate supportive learning environments, enhance clinician well-being, and veteran care","authors":"Heather M. Gilmartin,&nbsp;Brigid Connelly,&nbsp;Edward Hess,&nbsp;Candice Mueller,&nbsp;Mary E. Plomondon,&nbsp;Stephen W. Waldo,&nbsp;Catherine Battaglia","doi":"10.1002/lrh2.10383","DOIUrl":"10.1002/lrh2.10383","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Despite the Veterans Health Administration (VA) efforts to become a learning health system (LHS) and high-reliability organization (HRO), interventions to build supportive learning environments within teams are not reliably implemented, contributing to high levels of burnout, turnover, and variation in care. Supportive learning environments build capabilities for teaching and learning, empower teams to safely trial and adapt new things, and adopt highly reliable work practices (eg, debriefs). Innovative approaches to create supportive learning environments are needed to advance LHS and HRO theory and research into practice.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>To guide the identification of evidence-based interventions that cultivate supportive learning environments, the authors used a longitudinal, mixed-methods design and LHS and HRO frameworks. We partnered with the 81 VA cardiac catheterization laboratories and conducted surveys, interviews, and literature reviews that informed a Relational Playbook for Cardiology Teams.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The Relational Playbook resources and 50 evidence-based interventions are organized into five LHS and HRO-guided chapters: Create a positive culture, teamwork, leading teams, joy in work, communication, and high reliability. The interventions are designed for managers to integrate into existing meetings or trainings to cultivate supportive learning environments.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>LHS and HRO frameworks describe how organizations can continually learn and deliver nearly error-free services. The Playbook resources and interventions translate LHS and HRO frameworks for real-world implementation by healthcare managers. This work will cultivate supportive learning environments, employee well-being, and Veteran safety while providing insights into LHS and HRO theory, research, and practice.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10383","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49168261","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
Assessment of learning health system science competency in the equity and justice domain 公平和公正领域学习卫生系统科学能力的评估
IF 3.1 Q2 HEALTH POLICY & SERVICES Pub Date : 2023-07-31 DOI: 10.1002/lrh2.10381
Patricia D. Franklin, Denise Drane

Seven knowledge domains were originally defined for the learning health system (LHS) scientist. To assess proficiency in each of these domains, we developed and published an assessment tool for use by emerging LHS scientists and training programs. (LHS, October 2022). In mid-2022, the AHRQ adopted an eighth LHS knowledge domain, Equity and Justice. The addition of this eighth domain emphasizes the importance and centrality of equity in the LHS and improvement science. To extend our prior LHS competency assessment, we developed a proficiency assessment for the new equity and justice domain. Content experts and trainees iteratively defined, reviewed, and edited the assessment criteria. The items were developed by trainees and experts at one LHS training center with experience conducting research focused on healthcare inequities among marginalized populations. The proficiency assessment criteria for the Equity domain apply the same four levels of mastery: “no exposure,” “foundational awareness,” “emerging,” and “proficient” as were used for original competencies. LHS training programs can use these proficiency criteria to monitor skills among emerging scientists across the eight domains, with particular attention to equity and justice.

最初为学习型健康系统(LHS)科学家定义了七个知识领域。为了评估每个领域的熟练程度,我们开发并发布了一个评估工具,供新兴的学习型医疗系统科学家和培训项目使用。(LHS, 2022 年 10 月)。2022 年年中,AHRQ 通过了第八个 LHS 知识领域--"公平与正义"。这第八个领域的增加强调了公平在 LHS 和改进科学中的重要性和核心地位。为了扩展之前的 LHS 能力评估,我们为新的公平与正义领域开发了一个能力评估。内容专家和受训人员反复确定、审查和编辑了评估标准。这些项目是由一个 LHS 培训中心的学员和专家共同开发的,他们在开展以边缘化人群医疗保健不平等问题为重点的研究方面拥有丰富的经验。公平领域的能力评估标准采用了与原始能力相同的四个掌握程度:"未接触"、"基础意识"、"初露锋芒 "和 "精通"。LHS 培训计划可以使用这些能力标准来监测新兴科学家在八个领域中的技能,并特别关注公平与正义。
{"title":"Assessment of learning health system science competency in the equity and justice domain","authors":"Patricia D. Franklin,&nbsp;Denise Drane","doi":"10.1002/lrh2.10381","DOIUrl":"10.1002/lrh2.10381","url":null,"abstract":"<p>Seven knowledge domains were originally defined for the learning health system (LHS) scientist. To assess proficiency in each of these domains, we developed and published an assessment tool for use by emerging LHS scientists and training programs. (LHS, October 2022). In mid-2022, the AHRQ adopted an eighth LHS knowledge domain, Equity and Justice. The addition of this eighth domain emphasizes the importance and centrality of equity in the LHS and improvement science. To extend our prior LHS competency assessment, we developed a proficiency assessment for the new equity and justice domain. Content experts and trainees iteratively defined, reviewed, and edited the assessment criteria. The items were developed by trainees and experts at one LHS training center with experience conducting research focused on healthcare inequities among marginalized populations. The proficiency assessment criteria for the Equity domain apply the same four levels of mastery: “no exposure,” “foundational awareness,” “emerging,” and “proficient” as were used for original competencies. LHS training programs can use these proficiency criteria to monitor skills among emerging scientists across the eight domains, with particular attention to equity and justice.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10381","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43782656","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
Predictive modeling for infectious diarrheal disease in pediatric populations: A systematic review 儿科人群感染性腹泻疾病的预测模型:一项系统综述
IF 3.1 Q2 HEALTH POLICY & SERVICES Pub Date : 2023-07-29 DOI: 10.1002/lrh2.10382
Billy Ogwel, Vincent Mzazi, Bryan O. Nyawanda, Gabriel Otieno, Richard Omore

Introduction

Diarrhea is still a significant global public health problem. There are currently no systematic evaluation of the modeling areas and approaches to predict diarrheal illness outcomes. This paper reviews existing research efforts in predictive modeling of infectious diarrheal illness in pediatric populations.

Methods

We conducted a systematic review via a PubMed search for the period 1990–2021. A comprehensive search query was developed through an iterative process and literature on predictive modeling of diarrhea was retrieved. The following filters were applied to the search results: human subjects, English language, and children (birth to 18 years). We carried out a narrative synthesis of the included publications.

Results

Our literature search returned 2671 articles. After manual evaluation, 38 of these articles were included in this review. The most common research topic among the studies were disease forecasts 14 (36.8%), vaccine-related predictions 9 (23.7%), and disease/pathogen detection 5 (13.2%). Majority of these studies were published between 2011 and 2020, 28 (73.7%). The most common technique used in the modeling was machine learning 12 (31.6%) with various algorithms used for the prediction tasks. With change in the landscape of diarrheal etiology after rotavirus vaccine introduction, many open areas (disease forecasts, disease detection, and strain dynamics) remain for pathogen-specific predictive models among etiological agents that have emerged as important. Additionally, the outcomes of diarrheal illness remain under researched. We also observed lack of consistency in the reporting of results of prediction models despite the available guidelines highlighting the need for common data standards and adherence to guidelines on reporting of predictive models for biomedical research.

Conclusions

Our review identified knowledge gaps and opportunities in predictive modeling for diarrheal illness, and limitations in existing attempts whilst advancing some precursory thoughts on how to address them, aiming to invigorate future research efforts in this sphere.

导言 腹泻仍然是一个重大的全球公共卫生问题。目前还没有对预测腹泻疾病结果的建模领域和方法进行系统评估。本文回顾了现有的儿科感染性腹泻疾病预测建模研究工作。 方法 我们通过 PubMed 搜索对 1990-2021 年间的研究进行了系统性回顾。通过迭代过程开发了一个综合搜索查询,并检索了有关腹泻预测模型的文献。搜索结果采用了以下筛选条件:以人为研究对象、英语和儿童(出生至 18 岁)。我们对收录的文献进行了叙述性综述。 结果 我们的文献检索共检索到 2671 篇文章。经过人工评估,其中 38 篇文章被纳入本综述。这些研究中最常见的研究主题是疾病预测 14 篇(36.8%)、疫苗相关预测 9 篇(23.7%)和疾病/病原体检测 5 篇(13.2%)。这些研究大多发表于 2011 年至 2020 年之间,共 28 项(73.7%)。建模中最常用的技术是机器学习,有 12 项(31.6%),预测任务中使用了各种算法。轮状病毒疫苗问世后,腹泻病因学的格局发生了变化,在病原体特异性预测模型方面仍有许多开放领域(疾病预测、疾病检测和菌株动态),这些领域已成为重要的病因。此外,腹泻疾病的结果仍有待研究。我们还注意到,尽管有相关指南,但预测模型结果的报告缺乏一致性,这突出表明需要制定共同的数据标准,并遵守生物医学研究预测模型报告指南。 结论 我们的综述发现了腹泻疾病预测模型方面的知识差距和机遇,以及现有尝试的局限性,同时就如何解决这些问题提出了一些初步想法,旨在为该领域未来的研究工作注入活力。
{"title":"Predictive modeling for infectious diarrheal disease in pediatric populations: A systematic review","authors":"Billy Ogwel,&nbsp;Vincent Mzazi,&nbsp;Bryan O. Nyawanda,&nbsp;Gabriel Otieno,&nbsp;Richard Omore","doi":"10.1002/lrh2.10382","DOIUrl":"10.1002/lrh2.10382","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Diarrhea is still a significant global public health problem. There are currently no systematic evaluation of the modeling areas and approaches to predict diarrheal illness outcomes. This paper reviews existing research efforts in predictive modeling of infectious diarrheal illness in pediatric populations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We conducted a systematic review via a PubMed search for the period 1990–2021. A comprehensive search query was developed through an iterative process and literature on predictive modeling of diarrhea was retrieved. The following filters were applied to the search results: human subjects, English language, and children (birth to 18 years). We carried out a narrative synthesis of the included publications.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Our literature search returned 2671 articles. After manual evaluation, 38 of these articles were included in this review. The most common research topic among the studies were disease forecasts 14 (36.8%), vaccine-related predictions 9 (23.7%), and disease/pathogen detection 5 (13.2%). Majority of these studies were published between 2011 and 2020, 28 (73.7%). The most common technique used in the modeling was machine learning 12 (31.6%) with various algorithms used for the prediction tasks. With change in the landscape of diarrheal etiology after rotavirus vaccine introduction, many open areas (disease forecasts, disease detection, and strain dynamics) remain for pathogen-specific predictive models among etiological agents that have emerged as important. Additionally, the outcomes of diarrheal illness remain under researched. We also observed lack of consistency in the reporting of results of prediction models despite the available guidelines highlighting the need for common data standards and adherence to guidelines on reporting of predictive models for biomedical research.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our review identified knowledge gaps and opportunities in predictive modeling for diarrheal illness, and limitations in existing attempts whilst advancing some precursory thoughts on how to address them, aiming to invigorate future research efforts in this sphere.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10382","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45981999","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
期刊
Learning Health Systems
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1