Learning Health Systems最新文献

{"title":"The prototype of a frailty learning health system: The HARMONY Model","authors":"Kirsten J. Parker,&nbsp;Louise D. Hickman,&nbsp;Julee McDonagh,&nbsp;Richard I. Lindley,&nbsp;Caleb Ferguson","doi":"10.1002/lrh2.10401","DOIUrl":"10.1002/lrh2.10401","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Rapid translation of research findings into clinical practice through innovation is critical to improve health systems and patient outcomes. Access to efficient systems of learning underpinned with real-time data are the future of healthcare. This type of health system will decrease unwarranted clinical variation, accelerate rapid evidence translation, and improve overall healthcare quality.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This paper aims to describe The HARMONY model (acHieving dAta-dRiven quality iMprovement to enhance frailty Outcomes using a learNing health sYstem), a new frailty learning health system model of implementation science and practice improvement. The HARMONY model provides a prototype for clinical quality registry infrastructure and partnership within health care.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The HARMONY model was applied to the Western Sydney Clinical Frailty Registry as the prototype exemplar. The model networks longitudinal frailty data into an accessible and useable format for learning. Creating local capability that networks current data infrastructures to translate and improve quality of care in real-time.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This prototype provides a model of registry data feedback and quality improvement processes in an inpatient aged care and rehabilitation hospital setting to help reduce clinical variation, enhance research translation capacity, and improve care quality.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10401","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139242712","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":"Enrichment of core competencies to maximize health system impact: An analysis of an embedded research training program","authors":"Bahar Kasaai,&nbsp;Erin Thompson,&nbsp;Richard H Glazier,&nbsp;Meghan McMahon","doi":"10.1002/lrh2.10399","DOIUrl":"10.1002/lrh2.10399","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>The Health System Impact (HSI) Fellowship is an embedded research training program that aims to prepare doctoral trainees and postdoctoral fellows for stronger career readiness and greater impact as emerging leaders within and beyond the academy, including in learning health systems (LHS). The program supports fellows to develop 10 leadership and research competencies that comprise the <i>Enriched Core Competency Framework in Health Services and Policy Research</i> through a combination of experiential learning, mentorship, and professional development training. This study tracks competency development of HSI fellows over time and examines fellows' perspectives on which program design elements support their competency development.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A competency assessment tool developed for the program was independently completed by 95 postdoctoral and 36 doctoral fellows (self-assessments) and their respective 203 dyad (academic and health system) supervisors in the 2017 to 2019 program cohorts, who independently rated the strength of fellows' 10 competencies at baseline and several points thereafter. Competency strength ratings were analyzed to understand change over time and differences in ratings across groups (between fellows' sex, supervisor type, and supervisor vs. fellow). Program design element ratings were examined to understand perspectives on their contribution toward fellows' competency development.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Fellows' competency strength significantly improved in all 10 domains over time, based on independent assessments by the fellows and their dyad supervisors. Supervisors tended to rate the fellows' competency strength higher than the fellows did. Differences in competency ratings between male and female fellows (self-assessments) and between academic and health system supervisors were either negligble or not significant. Fellows identified all nine program design elements as enriching their competency development.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The HSI Fellowship provides an opportunity for fellows to develop the full suite of <i>enriched core competencies</i> and to prepare a cadre of emerging leaders with the skills and experience to contribute to the advancement of LHS.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10399","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135406086","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":"Learning from data in dentistry: Summary of the third annual OpenWide conference","authors":"Elsbeth Kalenderian,&nbsp;Kawtar Zouaidi,&nbsp;Jan Yeager,&nbsp;Janelle Urata,&nbsp;Alfa Yansane,&nbsp;Bunmi Tokede,&nbsp;D. Brad Rindal,&nbsp;Heiko Spallek,&nbsp;Joel White,&nbsp;Muhammad Walji","doi":"10.1002/lrh2.10398","DOIUrl":"10.1002/lrh2.10398","url":null,"abstract":"<p>The overarching goal of the third scientific oral health symposium was to introduce the concept of a learning health system to the dental community and to identify and discuss cutting-edge research and strategies using data for improving the quality of dental care and patient safety. Conference participants included clinically active dentists, dental researchers, quality improvement experts, informaticians, insurers, EHR vendors/developers, and members of dental professional organizations and dental service organizations. This report summarizes the main outputs of the third annual OpenWide conference held in Houston, Texas, on October 12, 2022, as an affiliated meeting of the American Dental Association (ADA) 2022 annual conference.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10398","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135780423","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":"Human resource management at the intensive care unit: A pragmatic review and future research agenda for building a learning health system","authors":"Wim J. R. Rietdijk,&nbsp;P. Hugo M. van der Kuy,&nbsp;Corstiaan A. den Uil","doi":"10.1002/lrh2.10395","DOIUrl":"10.1002/lrh2.10395","url":null,"abstract":"<p>Recently, the importance of efficient and effective health care has been recognized, especially during the acute phase of the Coronavirus Disease-2019 (COVID-19) pandemic. Intensive care units (ICUs) have faced an immense workload, with massive numbers of patients being treated in a very short period of time. In general, ICUs are required to deliver high-quality care at all times during the year. At the same time, high-quality organizational goals may not be aligned with the interests, motivation, and development of individual staff members (eg, nurses, and doctors). For management of the ICU, it is important to balance the organizational goals and development of the staff members (“their human capital”), usually referred to as human resource management. Although many studies have considered this area, no holistic view of the topic has been presented. Such a holistic view may help leadership and/or other stakeholders at the ICU to design a better learning health system. This pragmatic review aims to provide a conceptual model for the management of ICUs. Future research may also use this conceptual model for studying important factors for designing and understanding human resources in an ICU.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10395","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135888995","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":"Thanks to our peer reviewers","authors":"","doi":"10.1002/lrh2.10397","DOIUrl":"10.1002/lrh2.10397","url":null,"abstract":"&lt;p&gt;The publication of Issue 4 marks the completion of Volume 7 of &lt;i&gt;Learning Health Systems&lt;/i&gt;. An international, trans-disciplinary, open access publication, the journal has advanced research and scholarship on learning health systems in partnership with our reviewers. With indexing in multiple major sources and the recent news that we have received our first official Impact Factor, we have achieved a publication milestone that signals a sustainable, positive trajectory. Articles from the journal were downloaded over 109,532 times in 2022.&lt;/p&gt;&lt;p&gt;The journal has now published seven &lt;i&gt;Special Issues&lt;/i&gt;: “Patient Empowerment and the Learning Health System” (v.1); “Ethical, Legal, and Social Implications of Learning Health Systems” (v.2); “Learning Health Systems: Connecting Research to Practice Worldwide” (v.3); “Human Phenomics and the Learning Health System” (v.4); “Collaborative Learning Health Systems: Science and Practice” (v.5); and “Education To Meet the Multidisciplinary Workforce Needs of Learning Health Systems” (v.6). “Transforming Health Through Computable Biomedical Knowledge (CBK)” (v.7). Our talented guest editors have been instrumental in helping these &lt;i&gt;Special Issues&lt;/i&gt; come to fruition.&lt;/p&gt;&lt;p&gt;We are keenly aware that these achievements would not have happened without the dedicated efforts and insightful comments of all those individuals who accepted invitations to review submitted articles. With busy schedules and full commitments, these individuals found the time and energy to contribute their expertise to our authors to help ensure that their papers met (and often exceeded) the journal's high standards for publication.&lt;/p&gt;&lt;p&gt;Please accept our sincere gratitude for your outstanding efforts.&lt;/p&gt;&lt;p&gt;&lt;i&gt;Charles P. Friedman&lt;/i&gt;, Editor in Chief.&lt;/p&gt;&lt;p&gt;&lt;b&gt;&lt;i&gt;Learning Health Systems&lt;/i&gt; Peer Reviewers&lt;/b&gt;&lt;/p&gt;&lt;p&gt;&lt;i&gt;Note&lt;/i&gt;: These are the Reviewers for articles &lt;i&gt;published&lt;/i&gt; in all four issues of Volume 7 and for all articles currently &lt;i&gt;published&lt;/i&gt; and &lt;i&gt;posted online&lt;/i&gt; in Early View.&lt;/p&gt;&lt;p&gt;Julia Adler-Milstein (United States)&lt;/p&gt;&lt;p&gt;Tesfa Michael Alaro (Ethiopia)&lt;/p&gt;&lt;p&gt;Eta Berner (United States)&lt;/p&gt;&lt;p&gt;Sarah Birken (United States)&lt;/p&gt;&lt;p&gt;Juli Bollinger (United States)&lt;/p&gt;&lt;p&gt;Nicholas Bowersox (United States)&lt;/p&gt;&lt;p&gt;Erica Breuer (Australia)&lt;/p&gt;&lt;p&gt;Melinda Buntin (United States)&lt;/p&gt;&lt;p&gt;Michael Cantor (United States)&lt;/p&gt;&lt;p&gt;Jonathan Casey (United States)&lt;/p&gt;&lt;p&gt;Harold Collard (United States)&lt;/p&gt;&lt;p&gt;Marisa Conte (United States)&lt;/p&gt;&lt;p&gt;Derek Corrigan (Ireland)&lt;/p&gt;&lt;p&gt;Catherine Diederich (United States)&lt;/p&gt;&lt;p&gt;Margo Edmunds (United States)&lt;/p&gt;&lt;p&gt;Ayca Erdogan (United States)&lt;/p&gt;&lt;p&gt;Amanuel Ergado (Ethiopia)&lt;/p&gt;&lt;p&gt;Stephan Fihn (United States)&lt;/p&gt;&lt;p&gt;Allen Flynn (United States)&lt;/p&gt;&lt;p&gt;Thomas Foley (United Kingdom of Great Britain and Northern Ireland)&lt;/p&gt;&lt;p&gt;Tina Foster (United States)&lt;/p&gt;&lt;p&gt;Brandy Fureman (United States)&lt;/p&gt;&lt;p&gt;Sarah Greene (United States)&lt;/p&gt;&lt;p&gt;Robert Greenes (United States)&lt;/p&gt;&lt;p&gt;Gary Groot (Canada)&lt;/p&gt;&lt;p&gt;W. Ed Hammond (United","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"7 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10397","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49683345","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":"Transforming health and well-being through publishing computable biomedical knowledge (CBK)","authors":"Güneş Koru","doi":"10.1002/lrh2.10396","DOIUrl":"10.1002/lrh2.10396","url":null,"abstract":"<p>Computable biomedical knowledge artifacts (CBKs) are software programs that transform input data into practical output. CBKs are expected to play a critical role in the future of learning health systems. While there has been rapid growth in the development of CBKs, broad adoption is hampered by limited verification, documentation, and dissemination channels. To address these issues, the Learning Health Systems journal created a track dedicated to publishing CBKs through a peer-review process. Peer review of CBKs should improve reproducibility, reuse, trust, and recognition in biomedical fields, contributing to learning health systems. This special issue introduces the CBK track with four manuscripts reporting a functioning CBK, and another four manuscripts tackling methodological, policy, deployment, and platform issues related to fostering a healthy ecosystem for CBKs. It is our hope that the potential of CBKs exemplified and highlighted by these quality publications will encourage scientists within learning health systems and related biomedical fields to engage with this new form of scientific discourse.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"7 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10396","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49683346","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":"Collaborative implementation of an evidence-based package of integrated primary mental healthcare using quality improvement within a learning health systems approach: Lessons from the Mental health INTegration programme in South Africa","authors":"Sithabisile Gugulethu Gigaba,&nbsp;Zamasomi Luvuno,&nbsp;Arvin Bhana,&nbsp;Andre Janse van Rensburg,&nbsp;Londiwe Mthethwa,&nbsp;Deepa Rao,&nbsp;Nikiwe Hongo,&nbsp;Inge Petersen","doi":"10.1002/lrh2.10389","DOIUrl":"10.1002/lrh2.10389","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Introduction&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The treatment gap for mental health disorders persists in low- and middle-income countries despite overwhelming evidence of the efficacy of task-sharing mental health interventions. Key barriers in the uptake of these innovations include the absence of policy to support implementation and diverting of staff from usual routines in health systems that are already overstretched. South Africa enjoys a conducive policy environment; however, strategies for operationalizing the policy ideals are lacking. This paper describes the Mental health INTegration Programme (MhINT), which adopted a health system strengthening approach to embed an evidence-based task-sharing care package for depression to integrate mental health care into chronic care at primary health care (PHC).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The MhINT care package consisting of psycho-education talks, nurse-led mental health assessment, and a structured psychosocial counselling intervention provided by lay counsellors was implemented in Amajuba district in KwaZulu-Natal over a 2-year period. A learning health systems approach was adopted, using continuous quality improvement (CQI) strategies to facilitate embedding of the intervention.&lt;/p&gt;\u0000 \u0000 &lt;p&gt;MhINT was implemented along five phases: the project phase wherein teams to drive implementation were formed; the diagnostic phase where routinely collected data were used to identify system barriers to integrated mental health care; the intervention phase consisting of capacity building and using Plan-Do-Study-Act cycles to address implementation barriers and the impact and sustaining improvement phases entailed assessing the impact of the program and initiation of system-level interventions to sustain and institutionalize successful change ideas.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Integrated planning and monitoring were enabled by including key mental health service indicators in weekly meetings designed to track the performance of noncommunicable diseases and human immunovirus clinical programmes. Lack of standardization in mental health screening prompted the validation of a mental health screening tool and testing feasibility of its use in centralized screening stations. A culture of collaborative problem-solving was promoted through CQI data-driven learning sessions. The province-level screening rate increased by 10%, whilst the district screening rate increased by 7% and new patients initiated to mental health treatment increased by 16%.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10389","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135898448","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":"Modelling clinical narrative as computable knowledge: The NICE computable implementation guidance project","authors":"Philip Scott,&nbsp;Michaela Heigl,&nbsp;Charles McCay,&nbsp;Polly Shepperdson,&nbsp;Elia Lima-Walton,&nbsp;Elisavet Andrikopoulou,&nbsp;Klara Brunnhuber,&nbsp;Gary Cornelius,&nbsp;Susan Faulding,&nbsp;Ben McAlister,&nbsp;Shaun Rowark,&nbsp;Matthew South,&nbsp;Mark R. Thomas,&nbsp;Justin Whatling,&nbsp;John Williams,&nbsp;Jeremy C. Wyatt,&nbsp;Felix Greaves","doi":"10.1002/lrh2.10394","DOIUrl":"10.1002/lrh2.10394","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Translating narrative clinical guidelines to computable knowledge is a long-standing challenge that has seen a diverse range of approaches. The UK National Institute for Health and Care Excellence (NICE) Content Advisory Board (CAB) aims ultimately to (1) guide clinical decision support and other software developers to increase traceability, fidelity and consistency in supporting clinical use of NICE recommendations, (2) guide local practice audit and intervention to reduce unwarranted variation, (3) provide feedback to NICE on how future recommendations should be developed.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>The first phase of work was to explore a range of technical approaches to transition NICE toward the production of natively digital content.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Following an initial ‘collaborathon’ in November 2022, the NICE Computable Implementation Guidance project (NCIG) was established. We held a series of workstream calls approximately fortnightly, focusing on (1) user stories and trigger events, (2) information model and definitions, (3) horizon-scanning and output format. A second collaborathon was held in March 2023 to consolidate progress across the workstreams and agree residual actions to complete.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>While we initially focussed on technical implementation standards, we decided that an intermediate logical model was a more achievable first step in the journey from narrative to fully computable representation. NCIG adopted the WHO Digital Adaptation Kit (DAK) as a technology-agnostic method to model user scenarios, personae, processes and workflow, core data elements and decision-support logic. Further work will address indicators, such as prescribing compliance, and implementation in document templates for primary care patient record systems.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The project has shown that the WHO DAK, with some modification, is a promising approach to build technology-neutral logical specifications of NICE recommendations. Implementation of concurrent computable modelling by multidisciplinary teams during guideline development poses methodological and cultural questions that are complex but tractable given suitable will and leadership.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"7 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10394","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49683344","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":"Lessons learned from the development of a national registry on dementia care and support based on linked national health and administrative data","authors":"Iris van der Heide,&nbsp;Anneke L. Francke,&nbsp;Carola Döpp,&nbsp;Marianne Heins,&nbsp;Hein P. J. van Hout,&nbsp;Robert A. Verheij,&nbsp;Karlijn J. Joling","doi":"10.1002/lrh2.10392","DOIUrl":"10.1002/lrh2.10392","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>This paper provides insight into the development of the Dutch Dementia Care and Support Registry and the lessons that can be learned from it. The aim of this Registry was to contribute to quality improvement in dementia care and support.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This paper describes how the Registry was set up in four stages, reflecting the four FAIR principles: the selection of data sources (Findability); obtaining access to the selected data sources (Accessibility); data linkage (Interoperability); and the reuse of data (Reusability).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The linkage of 16 different data sources, including national routine health and administrative data appeared to be technically and legally feasible. The linked data in the Registry offers rich information about (the use of) care for persons with dementia across various healthcare settings, including but not limited to primary care, secondary care, long-term care and medication use, that cannot be obtained from single data sources.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>A key lesson learned is that in order to reuse the data for quality improvement in practice, it is essential to involve healthcare professionals in setting up the Registry and to guide them in the interpretation of the data.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10392","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135718798","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":"An application of computable biomedical knowledge to transform patient centered scheduling","authors":"Namita Azad,&nbsp;Carolyn Armstrong,&nbsp;Corinne Depue,&nbsp;Timothy J. Crimmins,&nbsp;Jonathan C. Touson","doi":"10.1002/lrh2.10393","DOIUrl":"10.1002/lrh2.10393","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Efficient appointment scheduling in the outpatient setting is challenged by two main factors: variability and uncertainty leading to undesirable wait times for patients or physician overtime, and events such as no-shows, cancellations, or walk-ins can result in physician idle time and under-utilization of resources. Some methods have been developed to optimize scheduling and minimize wait and idle times in the inpatient setting but are limited in the outpatient setting.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>People and Organization Development, an internal group of organizational developers, led the development of a solution that selects the optimal group of appointments for a patient that minimizes the time between associated procedures as well as lead time built using a linear integer program. This program takes appointment requests, availability of resources, order constraints, and time preferences as inputs, and provides a list of the most optimal groupings as an output. Included in the methodology is the technical infrastructure necessary to deploy this within an electronic medical record system.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Implementation and Test Plan</h3>\u0000 \u0000 <p>A pilot has been designed to run this algorithm in a single department. The pilot will include training staff on the new workflow, and conducting informal interviews to gather qualitative data on performance. Key performance indicators such as schedule utilization, resource idle time, patient satisfaction, average appointment lead time, and average waiting time will be closely monitored.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>The model is limited in accounting for variability in appointment length potentially resulting in inaccurate schedules for healthcare providers and patients. Future states would incorporate certain visit types starting through machine learning techniques. Additionally expanding our data pipeline and processing, developing greater communication software, and expanding our research to include other departments and subspecialties, will enhance the accuracy and flexibility of the algorithm and enable healthcare providers to provide better care to their patients.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"7 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10393","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49683342","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}