Learning Health Systems最新文献

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"title":"Frameworks, guidelines, and tools to develop a learning health system for Indigenous health: An environmental scan for Canada","authors":"Emma Rice,&nbsp;Angela Mashford-Pringle,&nbsp;Jinfan Qiang,&nbsp;Lynn Henderson,&nbsp;Tammy MacLean,&nbsp;Justin Rhoden,&nbsp;Abigail Simms,&nbsp;Sterling Stutz","doi":"10.1002/lrh2.10376","DOIUrl":"10.1002/lrh2.10376","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>First Nations, Inuit, and Métis (FNIM) peoples experience systemic health disparities within Ontario's healthcare system. Learning health systems (LHS) is a rapidly growing interdisciplinary area with the potential to address these inequitable health outcomes through a comprehensive health system that draws on science, informatics, incentives, and culture for ongoing innovation and improvement. However, global literature is in its infancy with grounding theories and principles still emerging. In addition, there is inadequate information on LHS within Ontario's health care context.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We conducted an environmental scan between January and April 2021 and again in June 2022 to identify existing frameworks, guidelines, and tools for designing, developing, implementing, and evaluating an LHS.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We found 37 relevant sources. This paper maps the literature and identifies gaps in knowledge based on five key pillars: (a) data and evidence-driven, (b) patient-centeredness, (c) system-supported, (d) cultural competencies enabled, and (e) the learning health system.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>We provide recommendations for implementation accordingly. The literature on LHS provides a starting point to address the health disparities of FNIM peoples within the healthcare system but Indigenous community partnerships in LHS development and operation will be key to success.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10376","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44196561","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}