Romana Hasnain-Wynia, Rachel Everhart, Nancy Wittmer, Laura J. Podewils, Thomas D. MacKenzie
Introduction
A learning health system (LHS) strives to improve clinical practice and outcomes through applied research and quality improvement (QI). However, distinguishing between research and QI has been a persistent challenge. While research involving human subjects is highly regulated, QI remains largely unregulated, lacking in comparable oversight. With confusion and uncertainty surrounding this distinction and little practical guidance for QI activities, Denver Health's LHS developed a model of practice for reviewing QI projects.
Methods
In 2018, Denver Health, an integrated academic safety-net delivery system, established the Quality Improvement Review Committee (QuIRC) as a practical approach for distinguishing between QI and human subjects research. We describe the institutional structure and processes, from identifying the problem to establishing the committee and charter, to obtaining institution support, and finally implementation and improvement, ensuring transparency and protections of disseminated QI work.
Results
Over 7 years, the QuIRC has reviewed 379 submissions, with 78% approved as QI (non-human subjects research), 8% referred to the Colorado Multiple Institutional Review Board, and 13% requiring clarification or being withdrawn. A standardized review process, clear charter, broad organizational representation, executive sponsorship, and IRB collaboration enhanced transparency and engagement. The QuIRC has facilitated QI dissemination, supporting the LHS framework and increasing recognition of the impact of QI on clinical care and patient outcomes.
Conclusions
The QuIRC framework has improved clarity and oversight of QI activities at Denver Health. These practical approaches can be adapted by other health care systems, contributing to broader efforts to establish national guidance for QI oversight.
{"title":"From ethical guidance to practice: Oversight of quality improvement activities at Denver Health, a learning health system","authors":"Romana Hasnain-Wynia, Rachel Everhart, Nancy Wittmer, Laura J. Podewils, Thomas D. MacKenzie","doi":"10.1002/lrh2.70030","DOIUrl":"https://doi.org/10.1002/lrh2.70030","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>A learning health system (LHS) strives to improve clinical practice and outcomes through applied research and quality improvement (QI). However, distinguishing between research and QI has been a persistent challenge. While research involving human subjects is highly regulated, QI remains largely unregulated, lacking in comparable oversight. With confusion and uncertainty surrounding this distinction and little practical guidance for QI activities, Denver Health's LHS developed a model of practice for reviewing QI projects.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In 2018, Denver Health, an integrated academic safety-net delivery system, established the Quality Improvement Review Committee (QuIRC) as a practical approach for distinguishing between QI and human subjects research. We describe the institutional structure and processes, from identifying the problem to establishing the committee and charter, to obtaining institution support, and finally implementation and improvement, ensuring transparency and protections of disseminated QI work.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Over 7 years, the QuIRC has reviewed 379 submissions, with 78% approved as QI (non-human subjects research), 8% referred to the Colorado Multiple Institutional Review Board, and 13% requiring clarification or being withdrawn. A standardized review process, clear charter, broad organizational representation, executive sponsorship, and IRB collaboration enhanced transparency and engagement. The QuIRC has facilitated QI dissemination, supporting the LHS framework and increasing recognition of the impact of QI on clinical care and patient outcomes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The QuIRC framework has improved clarity and oversight of QI activities at Denver Health. These practical approaches can be adapted by other health care systems, contributing to broader efforts to establish national guidance for QI oversight.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145375300","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}
Deanna Chaukos, Sandalia Genus, Tim Guimond, Maria Mylopoulos
Background
Health care is fragmented, stigmatizing, and often does not meet the needs of people living with HIV who present to care with significant complexity. Integrated care is an evidence-based solution, but rarely is enacted across hospital and community settings. Education for community workers that builds capacity toward integrated care is an essential missing piece.
Methods
Here we describe a qualitative study of the ECHO HIV Psychiatry, a virtual educational series that supports a community of practice of community workers in the HIV sector in Toronto, Canada. The educational series is 9 sessions long and occurs twice/year, reporting here on 4 cycles of the series, from April 2023 to December 2024. Utilizing participant interviews (n = 29) and ethnographic observation of education sessions, we conducted an abductive analysis, utilizing concepts of adaptive expertise and Knowledge Building Communities (KBCs) to better understand our participant narratives. Adaptive expertise is a theoretical framework in health professions education that describes capabilities that support healthcare workers to navigate complexity in modern healthcare. KBCs in healthcare leverage collaboration and diverse perspectives to support the generation of new solutions.
Results
Participants' main learning from the ECHO was an approach to caring for clients with significant complexity (including mental health concerns), and the learning mechanisms which supported this include: (1) Explicit value placed on diverse domains of knowledge created psychological safety for risk taking; (2) Perspective exchange with people in different roles facilitated confidence for community workers, as well as epistemic humility (humility about what is known or knowable); and (3) Learning in the ECHO led to new knowledge creation through collaboration and improvisation.
Conclusions
Results of this study demonstrate how education can support community workers with an approach to complexity, and that this kind of learning may empower community workers to expand the scope of their role, collaborate across hospital and community, and create new solutions to difficult-to-solve problems in health care. These are features of a Knowledge Building Community.
{"title":"From “Community of Practice” to “Knowledge Building Community”—A qualitative study of project ECHO as facilitator of adaptive expertise in frontline community workers","authors":"Deanna Chaukos, Sandalia Genus, Tim Guimond, Maria Mylopoulos","doi":"10.1002/lrh2.70028","DOIUrl":"https://doi.org/10.1002/lrh2.70028","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Health care is fragmented, stigmatizing, and often does not meet the needs of people living with HIV who present to care with significant complexity. Integrated care is an evidence-based solution, but rarely is enacted across hospital and community settings. Education for community workers that builds capacity toward integrated care is an essential missing piece.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Here we describe a qualitative study of the ECHO HIV Psychiatry, a virtual educational series that supports a community of practice of community workers in the HIV sector in Toronto, Canada. The educational series is 9 sessions long and occurs twice/year, reporting here on 4 cycles of the series, from April 2023 to December 2024. Utilizing participant interviews (<i>n</i> = 29) and ethnographic observation of education sessions, we conducted an abductive analysis, utilizing concepts of adaptive expertise and Knowledge Building Communities (KBCs) to better understand our participant narratives. Adaptive expertise is a theoretical framework in health professions education that describes capabilities that support healthcare workers to navigate complexity in modern healthcare. KBCs in healthcare leverage collaboration and diverse perspectives to support the generation of new solutions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Participants' main learning from the ECHO was an <i>approach</i> to caring for clients with significant complexity (including mental health concerns), and the learning mechanisms which supported this include: (1) Explicit value placed on diverse domains of knowledge created psychological safety for risk taking; (2) Perspective exchange with people in different roles facilitated confidence for community workers, as well as epistemic humility (humility about what is known or knowable); and (3) Learning in the ECHO led to new knowledge creation through collaboration and improvisation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Results of this study demonstrate how education can support community workers with an approach to complexity, and that this kind of learning may empower community workers to expand the scope of their role, collaborate across hospital and community, and create new solutions to difficult-to-solve problems in health care. These are features of a Knowledge Building Community.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145375355","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}
Teri A. Manolio, Renee Rider, Carol J. Bult, Rex L. Chisholm, Patricia A. Deverka, Geoffrey S. Ginsburg, Eric D. Green, Gail P. Jarvik, George A. Mensah, Jahnavi Narula, Erin M. Ramos, Mary V. Relling, Dan M. Roden, Robb Rowley, Noura S. Abul-Husn, Adam H. Buchanan, Christopher G. Chute, Guilherme Del Fiol, Gai Elhanan, Susanne B. Haga, Rizwan Hamid, Carol R. Horowitz, Peter J. Hulick, Cynthia A. James, Janina M. Jeff, Bruce Korf, Latrice Landry, Deven McGraw, Howard L. McLeod, Nancy J. Mendelsohn, Travis Osterman, Casey Overby Taylor, Daryl Pritchard, Heidi L. Rehm, Krystal S. Tsosie, Jason L. Vassy, Karriem Watson, Ken Wiley Jr, Marc S. Williams
Introduction
Identifying key characteristics of exemplar genomic learning healthcare systems (gLHS) and knowledge gaps that can be explored by collaboration among them is likely to accelerate the sharing of best practices and generation of evidence that informs the use of genomics in clinical care.
Methods
Deliberations of an expert group convened by the National Human Genome Research Institute (NHGRI) supplemented by relevant literature.
Results
Recent advances in genomic data standardization, automated clinical decision support, increased interoperability, and improved genomic technologies have enabled the development of several robust gLHS. They remain concentrated in major academic centers, however, and operate largely independently. Sharing their methods and tools would increase access to these innovations and advance the field. Several gLHS have expressed willingness to collaborate in a coalition designed to gather, evaluate, and disseminate best practices and development needs. Such a coalition has recently been formed under the leadership of NHGRI.
Conclusion
Increased collaboration, interoperability, and sharing of genomic information and strategies across gLHS can help define, refine, and disseminate best practices. Such cooperation can improve genomic variant curation and interpretation, diagnostic accuracy, evidence generation, and ultimately patient care through seamless integration of research as an integral component of good clinical care.
{"title":"Advancing the science of genomic learning healthcare systems","authors":"Teri A. Manolio, Renee Rider, Carol J. Bult, Rex L. Chisholm, Patricia A. Deverka, Geoffrey S. Ginsburg, Eric D. Green, Gail P. Jarvik, George A. Mensah, Jahnavi Narula, Erin M. Ramos, Mary V. Relling, Dan M. Roden, Robb Rowley, Noura S. Abul-Husn, Adam H. Buchanan, Christopher G. Chute, Guilherme Del Fiol, Gai Elhanan, Susanne B. Haga, Rizwan Hamid, Carol R. Horowitz, Peter J. Hulick, Cynthia A. James, Janina M. Jeff, Bruce Korf, Latrice Landry, Deven McGraw, Howard L. McLeod, Nancy J. Mendelsohn, Travis Osterman, Casey Overby Taylor, Daryl Pritchard, Heidi L. Rehm, Krystal S. Tsosie, Jason L. Vassy, Karriem Watson, Ken Wiley Jr, Marc S. Williams","doi":"10.1002/lrh2.70027","DOIUrl":"https://doi.org/10.1002/lrh2.70027","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Identifying key characteristics of exemplar genomic learning healthcare systems (gLHS) and knowledge gaps that can be explored by collaboration among them is likely to accelerate the sharing of best practices and generation of evidence that informs the use of genomics in clinical care.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Deliberations of an expert group convened by the National Human Genome Research Institute (NHGRI) supplemented by relevant literature.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Recent advances in genomic data standardization, automated clinical decision support, increased interoperability, and improved genomic technologies have enabled the development of several robust gLHS. They remain concentrated in major academic centers, however, and operate largely independently. Sharing their methods and tools would increase access to these innovations and advance the field. Several gLHS have expressed willingness to collaborate in a coalition designed to gather, evaluate, and disseminate best practices and development needs. Such a coalition has recently been formed under the leadership of NHGRI.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Increased collaboration, interoperability, and sharing of genomic information and strategies across gLHS can help define, refine, and disseminate best practices. Such cooperation can improve genomic variant curation and interpretation, diagnostic accuracy, evidence generation, and ultimately patient care through seamless integration of research as an integral component of good clinical care.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145375032","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}
Miranda Field, Christine Mulligan, Nicole D'souza, Raegan Mazurka
Background
This commentary explores the critical role of embedded researchers in advancing Learning Health Systems (LHS) within the context of Canada's mental health systems.
Context
The Canadian Mental Health Association has highlighted worsening mental health conditions, gaps in care, and disparities in access and outcomes.
Approach
LHS offers a promising approach to address system challenges by transforming data into practical knowledge to drive continuous and rapid improvement. However, translating this vision into practice remains a challenge.
Commentary
As four researchers currently embedded within the mental health system, working within public, nonprofit, and community settings, we argue that embedded researchers are an essential but often overlooked component of the workforce needed to implement LHS and improve mental health care. Embedded researchers, situated directly within the mental health sector, leverage their proximity to decision-makers, knowledge users, and communities to bridge the gap between research, practice, and policy.
Conclusion
This paper discusses the unique contributions of embedded researchers in driving systemic change, particularly within the three phases of the LHS cycle: data-to-knowledge, knowledge-to-practice, and practice-to-data.
{"title":"Transforming mental health systems: The role of embedded researchers in advancing learning health systems","authors":"Miranda Field, Christine Mulligan, Nicole D'souza, Raegan Mazurka","doi":"10.1002/lrh2.70021","DOIUrl":"https://doi.org/10.1002/lrh2.70021","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>This commentary explores the critical role of embedded researchers in advancing Learning Health Systems (LHS) within the context of Canada's mental health systems.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Context</h3>\u0000 \u0000 <p>The Canadian Mental Health Association has highlighted worsening mental health conditions, gaps in care, and disparities in access and outcomes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Approach</h3>\u0000 \u0000 <p>LHS offers a promising approach to address system challenges by transforming data into practical knowledge to drive continuous and rapid improvement. However, translating this vision into practice remains a challenge.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Commentary</h3>\u0000 \u0000 <p>As four researchers currently embedded within the mental health system, working within public, nonprofit, and community settings, we argue that embedded researchers are an essential but often overlooked component of the workforce needed to implement LHS and improve mental health care. Embedded researchers, situated directly within the mental health sector, leverage their proximity to decision-makers, knowledge users, and communities to bridge the gap between research, practice, and policy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This paper discusses the unique contributions of embedded researchers in driving systemic change, particularly within the three phases of the LHS cycle: data-to-knowledge, knowledge-to-practice, and practice-to-data.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145375126","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}
Deborah A. Marshall, Simron Sidhu, Elizabeth Oddone Paolucci, Elena Lopatina, Natasha Gallant, Kiran Pohar Manhas, Kim McGrail, Tracy Wasylak, Sandra Zelinsky, Stirling Bryan, Tom Noseworthy
This overview outlines the development and implementation of the Health System Impact Fellowship (HSIF) National Cohort Training Program (NCTP)—a national training program for embedded health services and policy research (HSPR) in Canada. The program aims to improve HSPR capacity and make a recognizable impact within health systems. The HSIF NCTP aimed to achieve three specific goals related to advancing the community of practice in health services research: (1) providing tools and learning opportunities in HSPR competency areas, enabling the CoP to advance learning health systems nationally; (2) creating deliberate, ongoing networking opportunities that encourage diverse HSIF members to engage meaningfully, thereby strengthening community of practice collaboration; and (3) laying the groundwork for the evolution and sustainability of the community of practice within Canada's integrated HSRP ecosystem. Analysis of the program's evolution reveals critical elements to its development and implementation, including but not limited to adaptive learning environments that respond to emerging needs, cross-sectoral collaboration fostered through mentorship, and balanced instructional formats that combine theoretical depth with practical application. The curriculum, co-developed by fellows and faculty, emphasizes critical analysis of complex health system challenges. Insights from implementing and refining the program offer valuable lessons for developing embedded research training initiatives in healthcare settings.
{"title":"A national curriculum and community of practice for health services and policy research training: Insights from the Health System Impact Fellowship National Cohort Training Program (HSIF NCTP)","authors":"Deborah A. Marshall, Simron Sidhu, Elizabeth Oddone Paolucci, Elena Lopatina, Natasha Gallant, Kiran Pohar Manhas, Kim McGrail, Tracy Wasylak, Sandra Zelinsky, Stirling Bryan, Tom Noseworthy","doi":"10.1002/lrh2.70025","DOIUrl":"https://doi.org/10.1002/lrh2.70025","url":null,"abstract":"<p>This overview outlines the development and implementation of the Health System Impact Fellowship (HSIF) National Cohort Training Program (NCTP)—a national training program for embedded health services and policy research (HSPR) in Canada. The program aims to improve HSPR capacity and make a recognizable impact within health systems. The HSIF NCTP aimed to achieve three specific goals related to advancing the community of practice in health services research: (1) providing tools and learning opportunities in HSPR competency areas, enabling the CoP to advance learning health systems nationally; (2) creating deliberate, ongoing networking opportunities that encourage diverse HSIF members to engage meaningfully, thereby strengthening community of practice collaboration; and (3) laying the groundwork for the evolution and sustainability of the community of practice within Canada's integrated HSRP ecosystem. Analysis of the program's evolution reveals critical elements to its development and implementation, including but not limited to adaptive learning environments that respond to emerging needs, cross-sectoral collaboration fostered through mentorship, and balanced instructional formats that combine theoretical depth with practical application. The curriculum, co-developed by fellows and faculty, emphasizes critical analysis of complex health system challenges. Insights from implementing and refining the program offer valuable lessons for developing embedded research training initiatives in healthcare settings.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145375119","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}
Han Chang Lim, Howard Wong, Reji Philip, Anton Van Der Vegt, Kim-Kwang Raymond Choo, Jason D. Pole, Clair Sullivan
Objective
Extracting and curating data from large clinical information systems is challenging, and the optimal methodology is often unclear. This review was to systematically investigate and appraise the research literature to assess existing methods used by healthcare organizations to extract data from the electronic medical record (EMR). The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) is used as a comparator for the various methods of data extraction. Our specific research question was: what lessons can be learned from healthcare organizations' experiences with data extraction from EMRs using OMOP CDM as a standardized use case?
Methods
We searched PubMed, Web of Science, Embase, the snowballing citation, and potentially relevant gray literature via Google Scholar for EMR data extraction and validation with OMOP CDM as the standardized use case for studies published between June 2017 and December 2022. A total of 316 candidate articles were examined, but only nine met the inclusion criteria. Two authors screened and assessed articles based on predetermined criteria to examine prevalent techniques and challenges through thematic synthesis and data analysis.
Results
Among all the included articles, the most frequently discussed challenges in EMR data extraction and validation are the lack of a standardized process, data structure, and skilled personnel. Five of nine studies scored above 70% in the article quality assessment process. Three studies used Observational Health Data Sciences and Informatics's suite, and two utilized Staged Optimization of Curation, Regularization, and Annotation of clinical text alongside the semantic transformation framework.
Discussion
The study revealed the importance of standardizing a uniform approach, consistent processes, and tools for EMR data extraction and validation. The identified methods and techniques could streamline the EMR data extraction processes. Our future work will empirically evaluate these methods in collaboration with real-world healthcare organizations.
目的从大型临床信息系统中提取和整理数据具有挑战性，最佳方法往往不明确。本综述旨在系统地调查和评估研究文献，以评估医疗机构用于从电子病历（EMR）中提取数据的现有方法。观察性医疗结果伙伴关系（OMOP）公共数据模型（CDM）被用作各种数据提取方法的比较器。我们的具体研究问题是：可以从医疗保健组织使用OMOP CDM作为标准化用例从emr中提取数据的经验中学到什么教训？方法通过谷歌Scholar检索PubMed、Web of Science、Embase、滚雪球引文和可能相关的灰色文献，提取EMR数据，并以OMOP CDM作为2017年6月至2022年12月发表的研究的标准化用例进行验证。共审查了316篇候选文章，但只有9篇符合纳入标准。两位作者根据预先确定的标准筛选和评估文章，通过专题综合和数据分析审查流行的技术和挑战。结果在所有纳入的文章中，EMR数据提取和验证中最常讨论的挑战是缺乏标准化的过程、数据结构和熟练的人员。9项研究中有5项在文章质量评估过程中得分在70%以上。三项研究使用了观察健康数据科学和信息学套件，两项研究使用了临床文本的阶段性优化、规范化和注释以及语义转换框架。该研究揭示了标准化统一方法、一致流程和EMR数据提取和验证工具的重要性。所确定的方法和技术可以简化EMR数据提取过程。我们未来的工作将与现实世界的医疗保健组织合作，对这些方法进行实证评估。
{"title":"Streamlining electronic medical record data extraction and validation in digital hospitals: A systematic review to identify optimal approaches and methods","authors":"Han Chang Lim, Howard Wong, Reji Philip, Anton Van Der Vegt, Kim-Kwang Raymond Choo, Jason D. Pole, Clair Sullivan","doi":"10.1002/lrh2.70024","DOIUrl":"https://doi.org/10.1002/lrh2.70024","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>Extracting and curating data from large clinical information systems is challenging, and the optimal methodology is often unclear. This review was to systematically investigate and appraise the research literature to assess existing methods used by healthcare organizations to extract data from the electronic medical record (EMR). The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) is used as a comparator for the various methods of data extraction. Our specific research question was: what lessons can be learned from healthcare organizations' experiences with data extraction from EMRs using OMOP CDM as a standardized use case?</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We searched PubMed, Web of Science, Embase, the snowballing citation, and potentially relevant gray literature via Google Scholar for EMR data extraction and validation with OMOP CDM as the standardized use case for studies published between June 2017 and December 2022. A total of 316 candidate articles were examined, but only nine met the inclusion criteria. Two authors screened and assessed articles based on predetermined criteria to examine prevalent techniques and challenges through thematic synthesis and data analysis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Among all the included articles, the most frequently discussed challenges in EMR data extraction and validation are the lack of a standardized process, data structure, and skilled personnel. Five of nine studies scored above 70% in the article quality assessment process. Three studies used Observational Health Data Sciences and Informatics's suite, and two utilized Staged Optimization of Curation, Regularization, and Annotation of clinical text alongside the semantic transformation framework.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>The study revealed the importance of standardizing a uniform approach, consistent processes, and tools for EMR data extraction and validation. The identified methods and techniques could streamline the EMR data extraction processes. Our future work will empirically evaluate these methods in collaboration with real-world healthcare organizations.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145375127","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}
Alison M. Cogan, Shonali G. Gaudino, James E. Green II, Lewis E. Kazis, Mary D. Slavin, Jeffrey C. Schneider, Joseph T. Giacino
Introduction
We report on the process of using a learning health systems (LHS) approach to design a data visualization dashboard to monitor the rehabilitation progress of patients with disorders of consciousness (DoC) after severe brain injury.
Methods
Our team conducted a qualitative study using informational interviews with key informants to understand informational needs and priorities for the dashboard from the perspectives of rehabilitation therapists, family members of patients with DoC, and third-party payors. We used a thematic survey approach to organize the findings with the following categories: (a) how the dashboard will be used; (b) content to be displayed; (c) organization and design of content; and (d) technical requirements. We used an iterative process to develop the dashboard, with multiple opportunities for stakeholder feedback.
Results
Seven people participated in informational interviews (n = 2 rehabilitation therapists; n = 2 family members; n = 3 third-party payor representatives). The primary intended use of the dashboard is communication and facilitation of shared understanding across clinical teams, rehabilitation teams, and patients' families, and between payors and facilities. Desired content includes core metrics applied by the DoC program for diagnosis and monitoring. There is a high priority for making the display easily understandable and interpretable. Technical requirements include the ability to pull data for display from existing items in the electronic health record to minimize additional burden on therapists. User feedback on the design resulted in a streamlined main screen, with additional detail accessible by clicking into each assessment.
Conclusions
In the unique case of patients with DoC, who cannot speak for themselves, effective communication among rehabilitation clinicians, family members or care partners, and third-party payors is highly important for optimal care. The key benefit of using an LHS approach is positioning the team to proactively design the dashboard to account for the needs and preferences of different end users.
{"title":"Developing a data visualization tool for adults with disorders of consciousness: Qualitative analysis of user perspectives","authors":"Alison M. Cogan, Shonali G. Gaudino, James E. Green II, Lewis E. Kazis, Mary D. Slavin, Jeffrey C. Schneider, Joseph T. Giacino","doi":"10.1002/lrh2.70023","DOIUrl":"https://doi.org/10.1002/lrh2.70023","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>We report on the process of using a learning health systems (LHS) approach to design a data visualization dashboard to monitor the rehabilitation progress of patients with disorders of consciousness (DoC) after severe brain injury.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Our team conducted a qualitative study using informational interviews with key informants to understand informational needs and priorities for the dashboard from the perspectives of rehabilitation therapists, family members of patients with DoC, and third-party payors. We used a thematic survey approach to organize the findings with the following categories: (a) how the dashboard will be used; (b) content to be displayed; (c) organization and design of content; and (d) technical requirements. We used an iterative process to develop the dashboard, with multiple opportunities for stakeholder feedback.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Seven people participated in informational interviews (<i>n</i> = 2 rehabilitation therapists; <i>n</i> = 2 family members; <i>n</i> = 3 third-party payor representatives). The primary intended use of the dashboard is communication and facilitation of shared understanding across clinical teams, rehabilitation teams, and patients' families, and between payors and facilities. Desired content includes core metrics applied by the DoC program for diagnosis and monitoring. There is a high priority for making the display easily understandable and interpretable. Technical requirements include the ability to pull data for display from existing items in the electronic health record to minimize additional burden on therapists. User feedback on the design resulted in a streamlined main screen, with additional detail accessible by clicking into each assessment.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>In the unique case of patients with DoC, who cannot speak for themselves, effective communication among rehabilitation clinicians, family members or care partners, and third-party payors is highly important for optimal care. The key benefit of using an LHS approach is positioning the team to proactively design the dashboard to account for the needs and preferences of different end users.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145374955","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}
Michael R. Cauley, Anna E. Berry, Carolyn M. Porta, Rachel K. Apple, Sunil Kripalani, Mark Linzer, Emily C. O'Brien, Russell L. Rothman, Christianne L. Roumie
Background
The Learning Health System (LHS) framework is designed to enhance healthcare by systematically integrating internal data and external evidence to promote quality, safety, and efficiency, aligning science, informatics, incentives, and culture for continuous improvement, innovation, and equity.
Methods
The Agency for Healthcare Research and Quality (AHRQ) also outlined key LHS learning goals structured around four interconnected approaches: (1) evidence generation to create new knowledge, (2) evidence adoption to translate findings into practice, (3) evidence dissemination to share best practices across systems, and (4) evidence management to integrate internal and external insights using technology and informatics. We propose this model can also enhance workforce well-being (also termed “flourishing” or eudaimonia by Aristotle) through system-level changes informed by rigorously collected local data.
Findings
Healthcare workers who flourish realize their purpose, improve patient health, and align themselves through daily decisions and actions toward this end. However, excessive workload, documentation burden, and unsupported caregiving responsibilities can detract from this goal. A wide range of LHS methods can be applied to address healthcare worker well-being and result in LHS cycles of learning and improvement. We present four examples demonstrating how LHS-concordant research methods align with AHRQ's learning goals to transition from mitigating burnout to actively promoting flourishing.
Contribution
Together, the application of the AHRQ learning goals forms a continuous feedback loop that facilitates mutual enhancement between healthcare delivery and research, advancing clinician well-being and system-wide improvement. This change in focus offers a new method for the design and evaluation of workforce well-being interventions, can restore excellence in patient care, and contributes to creating sustainable, human-centered healthcare systems.
{"title":"Learning Health System study designs for the evaluation of workforce interventions to cultivate eudaimonia (flourishing)","authors":"Michael R. Cauley, Anna E. Berry, Carolyn M. Porta, Rachel K. Apple, Sunil Kripalani, Mark Linzer, Emily C. O'Brien, Russell L. Rothman, Christianne L. Roumie","doi":"10.1002/lrh2.70022","DOIUrl":"https://doi.org/10.1002/lrh2.70022","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The Learning Health System (LHS) framework is designed to enhance healthcare by systematically integrating internal data and external evidence to promote quality, safety, and efficiency, aligning science, informatics, incentives, and culture for continuous improvement, innovation, and equity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The Agency for Healthcare Research and Quality (AHRQ) also outlined key LHS learning goals structured around four interconnected approaches: (1) evidence generation to create new knowledge, (2) evidence adoption to translate findings into practice, (3) evidence dissemination to share best practices across systems, and (4) evidence management to integrate internal and external insights using technology and informatics. We propose this model can also enhance workforce well-being (also termed “flourishing” or <i>eudaimonia</i> by Aristotle) through system-level changes informed by rigorously collected local data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Findings</h3>\u0000 \u0000 <p>Healthcare workers who <i>flourish</i> realize their purpose, improve patient health, and align themselves through daily decisions and actions toward this end. However, excessive workload, documentation burden, and unsupported caregiving responsibilities can detract from this goal. A wide range of LHS methods can be applied to address healthcare worker well-being and result in LHS cycles of learning and improvement. We present four examples demonstrating how LHS-concordant research methods align with AHRQ's learning goals to transition from mitigating burnout to actively promoting flourishing.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Contribution</h3>\u0000 \u0000 <p>Together, the application of the AHRQ learning goals forms a continuous feedback loop that facilitates mutual enhancement between healthcare delivery and research, advancing clinician well-being and system-wide improvement. This change in focus offers a new method for the design and evaluation of workforce well-being interventions, can restore excellence in patient care, and contributes to creating sustainable, human-centered healthcare systems.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145375033","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}
Sarah B. Floyd, Ashley Mills, Jason Woloff, Christian Lowson, Coleman Hilton, Donna Oeffinger, Steven Hwang
Introduction
There remains a lack of understanding of the etiology and treatment effectiveness for Adolescent idiopathic scoliosis (AIS). The objective of this study was to develop and validate a computable phenotype for patients with AIS to facilitate rapid learning through large-scale observational research using real-world data.
Study Design
Four computable phenotype (CP) algorithms were developed and tested. The algorithms were executed against the Shriners Children's (SC) Research Data Warehouse using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) from January 1, 2016 to December 31, 2022. CPs composed of diagnosis and imaging procedure utilization codes were evaluated iteratively against a prospective registry of scoliosis patients. The highest-performing phenotype was then evaluated through manual chart review for validation. Demographic characteristics of the patients meeting the phenotype definition were assessed.
Results
The four alternative CPs ranged from 24 103 to 15 292 unique patients. The CP that balanced sensitivity (92.7%) and specificity (81.8%) when evaluated against a prospective registry of scoliosis patients was chosen as the final AIS CP. Among 50 patients with phenotype-confirmed AIS, 36 (72%) had chart-validated AIS, and 14 (28%) were identified as false positives. Of the 14 false positives, 6 cases had a diagnosis of spinal asymmetry. Among the patients meeting the phenotype definition, the average age of patients with AIS treated at SC is 13.6 years (SD = 1.64) and patients are primarily female (73.7%) and white (56.2%).
Conclusion
The CP had good performance in identifying pediatric patients with AIS. Future refinements to the algorithm should include the use of x-ray parameters or the application of natural language processing to unstructured EHR data to better distinguish AIS cases from other spinal diagnoses. This CP is a fundamental step to facilitate a learning health system environment that can rapidly develop evidence to improve pediatric patient outcomes.
{"title":"Development and validation of a computable phenotype for adolescent idiopathic scoliosis","authors":"Sarah B. Floyd, Ashley Mills, Jason Woloff, Christian Lowson, Coleman Hilton, Donna Oeffinger, Steven Hwang","doi":"10.1002/lrh2.70018","DOIUrl":"https://doi.org/10.1002/lrh2.70018","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>There remains a lack of understanding of the etiology and treatment effectiveness for Adolescent idiopathic scoliosis (AIS). The objective of this study was to develop and validate a computable phenotype for patients with AIS to facilitate rapid learning through large-scale observational research using real-world data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>Four computable phenotype (CP) algorithms were developed and tested. The algorithms were executed against the Shriners Children's (SC) Research Data Warehouse using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) from January 1, 2016 to December 31, 2022. CPs composed of diagnosis and imaging procedure utilization codes were evaluated iteratively against a prospective registry of scoliosis patients. The highest-performing phenotype was then evaluated through manual chart review for validation. Demographic characteristics of the patients meeting the phenotype definition were assessed.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The four alternative CPs ranged from 24 103 to 15 292 unique patients. The CP that balanced sensitivity (92.7%) and specificity (81.8%) when evaluated against a prospective registry of scoliosis patients was chosen as the final AIS CP. Among 50 patients with phenotype-confirmed AIS, 36 (72%) had chart-validated AIS, and 14 (28%) were identified as false positives. Of the 14 false positives, 6 cases had a diagnosis of spinal asymmetry. Among the patients meeting the phenotype definition, the average age of patients with AIS treated at SC is 13.6 years (SD = 1.64) and patients are primarily female (73.7%) and white (56.2%).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The CP had good performance in identifying pediatric patients with AIS. Future refinements to the algorithm should include the use of x-ray parameters or the application of natural language processing to unstructured EHR data to better distinguish AIS cases from other spinal diagnoses. This CP is a fundamental step to facilitate a learning health system environment that can rapidly develop evidence to improve pediatric patient outcomes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145375132","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}
Tamba Mina Millimouno, Kéfilath Olatoyossi Akankè Bello, Jean-Paul Dossou, Alexandre Delamou, Bruno Meessen
District Health Systems (DHS) are essential for delivering healthcare services, particularly in low- and middle-income countries. However, they often struggle with inefficiencies, inadequate data utilization, and limited learning capacity. Transforming DHS into Learning Health Systems (LHS) offers a strategic pathway to strengthening health systems governance, improving service delivery, and fostering continuous adaptation. This paper draws insights from District.Team, a digital learning platform piloted in Benin and Guinea in 2016–2017, designed to enhance real-time knowledge exchange and decision-making among District Health Management Teams (DHMTs). The District.Team strategy employed structured learning cycles to address key health system challenges, including maternal deaths surveillance and response and epidemiologic preparedness. The platform enabled peer-to-peer learning, facilitated data visualization, and encouraged collaborative problem-solving. Participation rates of district medical officers (DMOs), the heads of DHMTs, remained high across the five learning cycles in each country. For instance, during Cycle 1 (district health system characteristics), 85% (29/34) of DMOs in Benin and 100% (38/38) in Guinea completed the online questionnaire, with active engagement in online discussions. By the final cycle (maternal deaths surveillance and response), 61% and 74% of DMOs in Guinea participated in questionnaire filling and discussions, while in Benin, 44% contributed to the online discussions. DMOs reported improved decision-making processes, enhanced engagement with health data, and strengthened collaboration. Despite its successes, the District.Team strategy faced challenges such as limited integration into national health programs, weak institutional support, time constraints for DMOs, and infrastructural limitations, including unreliable internet connectivity and electricity shortages. Building on lessons learned, an improved strategy, referred to as District.Team+, is proposed to ensure sustainability and scalability. District.Team+ incorporates a knowledge translation component, aligns with national health information systems (e.g., DHIS2), allows for the conduct of research or integration of existing research, and expands the learning community to include policymakers, healthcare providers, and communities. It aims to strengthen evidence-based practice and decision-making, practice-informed policymaking, and adaptive health management within health systems. District.Team+ highlights the potential of digital learning platforms to support the transformation of DHS into LHS, provided they are embedded in national structures, adequately resourced, and aligned with broader health system priorities.
{"title":"Transforming district health systems into learning health systems: An improved strategy from the District.Team experience in Benin and Guinea","authors":"Tamba Mina Millimouno, Kéfilath Olatoyossi Akankè Bello, Jean-Paul Dossou, Alexandre Delamou, Bruno Meessen","doi":"10.1002/lrh2.70019","DOIUrl":"https://doi.org/10.1002/lrh2.70019","url":null,"abstract":"<p>District Health Systems (DHS) are essential for delivering healthcare services, particularly in low- and middle-income countries. However, they often struggle with inefficiencies, inadequate data utilization, and limited learning capacity. Transforming DHS into Learning Health Systems (LHS) offers a strategic pathway to strengthening health systems governance, improving service delivery, and fostering continuous adaptation. This paper draws insights from <i>District.Team</i>, a digital learning platform piloted in Benin and Guinea in 2016–2017, designed to enhance real-time knowledge exchange and decision-making among District Health Management Teams (DHMTs). The <i>District.Team</i> strategy employed structured learning cycles to address key health system challenges, including maternal deaths surveillance and response and epidemiologic preparedness. The platform enabled peer-to-peer learning, facilitated data visualization, and encouraged collaborative problem-solving. Participation rates of district medical officers (DMOs), the heads of DHMTs, remained high across the five learning cycles in each country. For instance, during Cycle 1 (district health system characteristics), 85% (29/34) of DMOs in Benin and 100% (38/38) in Guinea completed the online questionnaire, with active engagement in online discussions. By the final cycle (maternal deaths surveillance and response), 61% and 74% of DMOs in Guinea participated in questionnaire filling and discussions, while in Benin, 44% contributed to the online discussions. DMOs reported improved decision-making processes, enhanced engagement with health data, and strengthened collaboration. Despite its successes, the District.Team strategy faced challenges such as limited integration into national health programs, weak institutional support, time constraints for DMOs, and infrastructural limitations, including unreliable internet connectivity and electricity shortages. Building on lessons learned, an improved strategy, referred to as <i>District.Team</i><sup><i>+</i></sup>, is proposed to ensure sustainability and scalability. <i>District.Team</i><sup><i>+</i></sup> incorporates a knowledge translation component, aligns with national health information systems (e.g., DHIS2), allows for the conduct of research or integration of existing research, and expands the learning community to include policymakers, healthcare providers, and communities. It aims to strengthen evidence-based practice and decision-making, practice-informed policymaking, and adaptive health management within health systems. <i>District.Team</i><sup><i>+</i></sup> highlights the potential of digital learning platforms to support the transformation of DHS into LHS, provided they are embedded in national structures, adequately resourced, and aligned with broader health system priorities.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"9 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145375358","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}
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