Learning Health System Research Articles

Enhancing Data Quality through Electronic Health Records in Prison Settings: Towards a Learning Health System

Abstract Utilizing electronic health records (EHRs) in prison settings offers a unique opportunity to establish a learning health system (LHS) aimed at enhancing healthcare quality for incarcerated individuals. Despite the challenges inherent in prison healthcare, leveraging EHRs can facilitate data-driven decision-making and foster continuous improvement initiatives. This study describes the results of an intervention aimed at improving EHR data quality in prisons and explores the potential to enhance the development of a robust LHS. We assessed EHR data quality in 2015 (38 prisons) and 2022 (35 prisons) in the Netherlands over six months. Data were extracted, de-identified, and analyzed across demographic, medication use, (history of) diagnosis, and healthcare usage categories. We measured indicator scores and evaluated variation between prisons in data quality. This project was commissioned by the Custodial Institutions Agency. In 2015, age and gender were recorded for 93% of prisoners, increasing to 100% in 2022. Diagnostic coding use rose from an average of 82% in 2015 to 98% in 2022, with reduced variation between prisons. Consultation frequency per inmate ranged from 3 to 13, reflecting challenges in constructing a uniform denominator due to institutional differences. Mental health problems accounted for 17.7% of recorded disease episodes in 2022. In addition, consultations were most often related to oral health (4.4%), drug abuse (3.2%), sleep (1.8%), fear of diseases (1.6%), and respiratory infections (1.5%). Our findings indicate improvements in EHR data quality in Dutch prisons between 2015 to 2022. Integrating EHR data reuse into prison healthcare systems is pivotal for realizing the vision of an LHS in correctional facilities. This study underscores the potential of advancing EHR recording habits and the reuse of EHR data to enhance healthcare quality in prison settings.

Healthcare in police custody: a conceptual framework for measuring and improving quality of care

Abstract Background Individuals in police custody are often in poor (mental) health and can be in need of medical attention. The delivery of primary healthcare in police custody differs from other custodial and non-custodial settings, due to its short-term character and the reliance on custody officers and healthcare professionals to access care. Therefore, medical care for detainees in police custody requires its own standards and indicators to measure the quality of care. We aimed to develop a conceptual framework for (actionable) indicators to measure the quality of care for people detained in short-term police custody settings in the Netherlands. Methods We conducted a scoping review and consulted with experts. The scoping review involved Embase, Medline, PsycINFO, CINAHL, Criminal Justice Abstracts, PiCarta and Cochrane, and was supplemented with grey literature searches. We consulted twenty-seven stakeholders with a wide variety of expertise in the field of healthcare in police custody to discuss and validate the conceptual framework. Results The conceptual framework consists of 14 domains of structures (e.g. staff and facilities), processes (e.g. triage and coordination of care) and outcomes (e.g. effectiveness and satisfaction) that need to be considered when developing quality indicators for the healthcare provision in police custody. The scoping review yielded 68 publications providing information on common health issues and recommendations related to quality assurance or improvement. Conclusions The conceptual framework will guide the development of (actionable) quality indicators, which will be evaluated on relevance and measurability. These are crucial for monitoring care quality and enhancing a learning health systems approach. This study fills a gap in the existing scientific literature by shedding light on the factors contributing to the quality of primary healthcare provision for people detained in short-term custody settings. Key messages • The conceptual framework guides the development of (actionable) quality indicators for healthcare in police custody. • This study contributes to the creation of a learning health system for healthcare in short-term police custody settings.

From fragmentation to functionality: Enhancing coherence of digital health integration in health systems.

Digital health programs continue to be implemented within Canadian health systems at a steady pace. The effectiveness of digital health initiatives has been rigorously analyzed, with both benefits and drawbacks extensively commented on. While the discussion about digital health continues, both positive and negative perspectives of it are approaching saturation in their themes. Accepting that digital health is here to stay post-pandemic, the focus should shift to strategies and supports needed to avoid the fragmentation of care through digital health implementation. This short article poses three questions which policy-makers and decision-makers should explore as part of a level-setting exercise with involved stakeholders at the outset of a digital health program's consideration. An implementation team should design the digital health program to have equity as its foundational focus, conduct value-based evaluations, and position the program in a learning health system framework to guard against the fragmentation of care.

Using Stay Interviews as a Quality Improvement Tool for Healthcare Workforce Retention

Background and Objectives: The novel coronavirus of 2019 (Covid-19) pandemic exacerbated already persistent health care workforce issues—eg, overwhelming workloads, chronic understaffing, and burnout—associated with turnover intent and actual turnover. While turnover may be inevitable, strategies that help organizations anticipate and mitigate its impact require knowledge about how employees are experiencing their work (not just performing) on an ongoing basis. Unlike performance appraisals, “stay interviewsing” is a proactive management approach to assess the organizational and personal factors that influence current employees’ retention in their roles (ie, why they stay). We describe our process of developing and using a stay interview guide with current Women’s Health Patient-Aligned Care Teams (WH-PACT) employees and comment on the feasibility of conducting these interviews, their acceptability by participating employees, and their potential application to inform retention strategies in learning health systems (LHS) like the Veterans Health Administration (VA). Methods: We conducted 24 stay interviews with current WH-PACT employees, including women’s health primary care providers (WH-PCPs, n = 7), registered nurses (RNs, n = 6), licensed practical nurses (LPNs, n = 6), and medical support assistants (MSAs, n = 5) with established tenure (greater than 3 years for MSAs and LPNs, and greater than 5 years for RNs and WH-PCPs) at VA medical centers and community-based outpatient clinics. Using rapid qualitative analysis, we summarized interview transcripts for preliminary insights that describe the utility of stay interviews to collect feedback on clinician and non-clinician employees’ experiences of work in VA women’s health primary care. Results: The responses and feedback from our participants indicate that stay interviews are potentially feasible for LHS to use as a retention tool and are acceptable to employees for sharing their perspectives. Conclusion: We discuss who should conduct stay interviews and the potential ethical implications organizations must consider before conducting them.

Digital health technology to support patient-centered shared decision making at point of care for juvenile idiopathic arthritis

Despite availability of multiple FDA approved therapies, many children with juvenile idiopathic arthritis (JIA) suffer pain and disability due to uncontrolled disease. The term JIA includes a heterogeneous set of conditions unified by chronic inflammatory arthritis, collectively affecting 1:1,000 children. When reviewing treatment options with families the rheumatologist currently refers to the experience of the average patient in relatively small controlled clinical trials, to consensus-based treatment plans, or increasingly the choice is dictated by the formulary restrictions of insurance payers. The current paradigm for treatment selection does not incorporate real-world evidence of treatment effectiveness centered to the individual patients with whom decisions are to be made. Treatment decisions based on the evidence of the average patient are not optimized to reflect the unique clinical characteristics of an individual with JIA and their disease course, nor does it account for heterogeneous treatment effects. To guide treatment choices centered around each patient, we describe a novel concept of utilizing digital health technology to bring patient-centered information into shared decision-making discussions based on comparative effectiveness analysis of electronic health record or observational clinical registry data of patients with similar characteristics. The envisioned digital tool will organize and present data relevant to the individual patient and enable evidence-based individualized treatment decision making when used in a collaborative manner with the patient family and rheumatologist. Capabilities in digital health technology, data capturing, and analytical methodologies are ripe for this endeavor. This brings the concept of a learning health system directly to the point of care.

Embracing innovation and advancing care: integrating learning health system principles into Inova Schar Heart and Vascular

Embracing innovation and advancing care: integrating learning health system principles into Inova Schar Heart and Vascular

Improving implementation and team communication by integrating a cystic fibrosis transition readiness (CF R.I.S.E.) program into electronic health records.

The cystic fibrosis (CF) Responsibility. Independence. Self-care. Education. (R.I.S.E.) program was developed to provide assessment and education, supporting transition readiness for people with cystic fibrosis (pwCF). Lack of integration within electronic health records (EHR) was a barrier to implementation of CF R.I.S.E. University of Michigan was able to integrate CF R.I.S.E. into EHR. To improve implementation and EHR documentation of CF R.I.S.E. module completion by pwCF across two (CF) programs from baseline (10.5%) to 75% per month in 6 months (January through June 2023). Two CF programs utilized quality improvement (QI) methods and tools and ad hoc support by a CF Learning Network (QI) specialist. Eligibility included pwCF ≥16 years old seen in CF clinics who accepted CF R.I.S.E. Beginning January 2, 2023, programs met in biweekly, virtual meetings to discuss implementation. Deidentified data were collected monthly tracking modules completed by pwCF and number of team members engaging with CF R.I.S.E. and documenting in EHR. Data timelines were baseline (November-December 2022), project period (January-June 2023), and post-project (July-December 2023). Completion rates increased from baseline (10.5%) to 48% (range 33% to 81%) through December 2023. During the project, an average 7.7 team members completed an average 19.2 modules per month. Post-project, an average 8 team members completed an average 16.5 modules per month. This collaboration demonstrated how utilization of EHR allowed for successful CF R.I.S.E. improvement at both programs. Shared software utilization and QI initiatives may be a way to facilitate timely dissemination of best practices through learning health systems.

Bridging the gap: a qualitative process evaluation from the perspectives of healthcare professionals of an audit-and-feedback-based intervention to improve transition to adult care for young people living with type 1 diabetes

BackgroundThe transition from pediatric to adult care is a vulnerable time for young people living with type 1 diabetes (T1D). Bridging the Gap (BTG) is an audit-and-feedback (AF) intervention aimed at improving both transitions-in-care processes and diabetes management in the year following transition. As part of BTG, we conducted a qualitative process evaluation to understand: (a) what was implemented and how; and (b) the contextual factors (micro-, meso- and macro-) that affected implementation, outcomes and study processes.MethodsUsing qualitative descriptive methodology, interviews were conducted with 13 healthcare professionals (HCPs) delivering diabetes care to transitioning youth. Participants were asked about their experiences of BTG study processes and feedback tools, the quality improvement (QI) initiatives implemented at their site, and potential spread and scale. Interviews also explored the impacts of COVID-19 on transition care and study processes and results.ResultsFive key themes were identified. Participants’ reflections on the BTG study design indicated they appreciated its flexible, site-specific approach to QI, which they saw as crucial to the success of their initiatives. Engagement with feedback reports and other study resources provided comparative, site-specific data. Participants described the challenges posed by the COVID-19 pandemic and its impacts on patients, care provision and study implementation. Their site-specific QI initiatives resulted in changes to their transition practices. Finally, participants commented on how BTG and its processes fostered a community of practice (CoP) between sites, resulting in new opportunities to collaborate and share experiences.ConclusionsBTG resulted in a CoP among practitioners delivering transition care to youth with T1D, which could be scaled up to promote a learning health system in pediatric diabetes care. Qualitative process evaluation is a useful tool for understanding how contextual factors affect the implementation and outcomes of complex QI interventions.

The Michigan Collaborative for Type 2 Diabetes (MCT2D): Development and implementation of a statewide collaborative quality initiative

BackgroundType 2 diabetes (T2D) is one of the most prevalent chronic diseases worldwide and a leading cause of cardiorenal disease and mortality. Only one-third of individuals with T2D receive care as recommended by the American Diabetes Association’s clinical practice guidelines. Effective strategies are needed to accelerate the implementation of guideline concordant T2D care.MethodsThe Michigan Collaborative for Type 2 Diabetes (MCT2D) is a statewide population health collaborative quality initiative (CQI) developed to improve the care of all people with T2D in Michigan. MCT2D has developed a learning health system with physician organizations and their constituent practices to support quality improvement initiatives focused on (1) improving use of guideline-directed pharmacotherapy to improve cardiorenal outcomes, (2) increasing evidence-based use of continuous glucose monitoring, and (3) supporting use of lower carbohydrate eating patterns.ResultsBetween 2021 and 2022, MCT2D recruited 28 of the 40 Michigan-based physician organizations participating in Blue Cross’ Physician Group Incentive Program with 336 constituent practices and 1357 physicians in primary care (304), endocrinology (21) and nephrology (11). In January 2022, baseline data included a sample of 96,140 unique individuals with T2D. The baseline HbA1c was ≤ 7.0% for 66.3% of patients (n = 32,787), while 14.9% of patients had a most recent HbA1c ≥ 8.0% (n = 7,393). The most recent body mass index (BMI) was ≥ 30.0 for 64.8% of patients (n = 38,516).DiscussionMCT2D has organized a statewide collaborative to recruit and engage a diverse and large set of physician organizations and their constituent practices. This is a promising opportunity to accelerate adoption of guideline-concordant care for people with T2D and may be a model for other state or regional collaboratives. Future directions include specific evidence-based interventions targeted at reducing diabetes-linked comorbidities and associated healthcare costs as well as strategies focused on T2D prevention among at-risk populations.

Learning health systems to implement chronic disease prevention programs: A novel framework and perspectives from an Australian health service.

Chronic diseases are a considerable burden to health systems, communities, and patients. Much of this burden, however, could be prevented if interventions effective in reducing chronic disease risks were routinely implemented. The aim of this paper is to discuss the role of public health agencies in preventing chronic disease through the application of learning health system (LHS) approaches to improve the implementation of evidence-based interventions. We draw on the literature and our experience operating a local LHS in Australia that has achieved rapid improvements in the implementation of chronic disease prevention interventions. The proposed LHS framework has been adapted to be both implementation and chronic disease prevention focused. The framework describes both broad improvement processes, and the infrastructure and other support (pillars) recommended to support its core functions. The framework serves as a basis for further exploration of the potentially transformative role LHS's may have in addressing the chronic disease health crisis.

Developing a codebook to characterize barriers, enablers, and strategies for implementing learning health systems from a multilevel perspective

AbstractIntroductionLearning health systems (LHSs) play a crucial role in improving healthcare delivery and outcomes through continuous learning and data‐driven decision‐making. Implementation of LHSs spans individual, organization, and systemic levels of healthcare. This paper outlines a systematic approach for developing a comprehensive codebook to identify barriers, enablers, and strategies associated with the establishment and operation of LHS from a multilevel perspective.MethodsThe codebook development process was divided into two phases and employed a coding team. Phase 1 involved the synthesis of previous literature, which drove the development of initial codes. Phase 2 included the testing of the codebook with a pilot dataset to derive new codes or iterative refinement, ensuring robustness, and validity.ResultsThe literature search revealed 12 papers that detailed the barriers, enablers, and strategies for LHS implementation. Micro‐level codes were derived from a mixture of existing literature and our pilot dataset. Most meso‐level codes barriers and enablers were derived from the literature, with some subcodes derived from participant interviews. All strategies for implementation at the meso‐level were identified in the literature. At the macro‐level, all codes and subcodes were from the literature.ConclusionsThe codebook contributes to the advancement of implementation science in LHS. The codebook facilitates effective analysis and understanding of the key factors influencing the success of LHS implementation, offering practical insights for policymakers, healthcare practitioners and researchers engaged in the ongoing evolution of LHS.

Creating a learning health system to include environmental determinants of health: The GroundsWell experience.

Policies aiming to prevent ill health and reduce health inequalities need to consider the full complexity of health systems, including environmental determinants. A learning health system that incorporates environmental factors needs healthcare, social care and non-health data linkage at individual and small-area levels. Our objective was to establish privacy-preserving household record linkage for England to ensure person-level data remain secure and private when linked with data from households or the wider environment. A stakeholder workshop with participants from our regional health board, together with the regional data processor, and the national data provider. The workshop discussed the risks and benefits of household linkages. This group then co-designed actionable dataflows between national and local data controllers and processors. A process was defined whereby the Personal Demographics Service, which includes the addresses of all patients of the National Health Service (NHS) in England, was used to match patients to a home identifier, for the time they are recorded as living at that address. Discussions with NHS England resulted in secure and quality-assured data linkages and a plan to flow these pseudonymised data onwards into regional health boards. Methods were established, including the generation of matching algorithms, transfer processes and information governance approvals. Our collaboration accelerated the development of a new data governance application, facilitating future public health intervention evaluations. These activities have established a secure method for protecting the privacy of NHS patients in England, while allowing linkage of wider environmental data. This enables local health systems to learn from their data and improve health by optimizing non-health factors. Proportionate governance of health and linked non-health data is practical in England for incorporating key environmental factors into a learning health system.

Creating a Learning Health System in a Cancer Center: Generalizability of an Electronic Health Record Phenotype for Advanced Solid Cancer.

To test the generalizability of an electronic health record (EHR) phenotype for patients with advanced solid cancer, which was previously developed in a single cancer center. We compared an algorithm to identify patients with advanced solid cancer from a random sample of patients with active cancer in the Veterans Health Administration (VA) and an academic cancer center with a human-coded reference standard between January 1, 2016, and December 31, 2019. Compared with the human-coded reference standard, the algorithm had high specificity (93%; 95% CI, 87 to 99 and 97%; 95% CI, 93 to 100) and reasonable sensitivity (85%; 95% CI, 76 to 94 and 87%; 95% CI, 77 to 97) in the VA and academic cancer center populations, respectively. Patients with advanced cancer (compared with those with active nonadvanced cancer) had higher mortality at the VA and academic cancer center (29.2% and 17.0% 6-month mortality v 6.8% and 3.5%), respectively. This EHR phenotype can be used to measure and improve the quality of palliative care for patients with advanced cancer within and across health care settings.

Applying Implementation Science to Advance Electronic Health Record-Driven Learning Health Systems: Case Studies, Challenges, and Recommendations.

With the widespread implementation of electronic health records (EHRs), there has been significant progress in developing learning health systems (LHSs) aimed at improving health and health care delivery through rapid and continuous knowledge generation and translation. To support LHSs in achieving these goals, implementation science (IS) and its frameworks are increasingly being leveraged to ensure that LHSs are feasible, rapid, iterative, reliable, reproducible, equitable, and sustainable. However, 6 key challenges limit the application of IS to EHR-driven LHSs: barriers to team science, limited IS experience, data and technology limitations, time and resource constraints, the appropriateness of certain IS approaches, and equity considerations. Using 3 case studies from diverse health settings and 1 IS framework, we illustrate these challenges faced by LHSs and offer solutions to overcome the bottlenecks in applying IS and utilizing EHRs, which often stymie LHS progress. We discuss the lessons learned and provide recommendations for future research and practice, including the need for more guidance on the practical application of IS methods and a renewed emphasis on generating and accessing inclusive data.

Special Issue on Informatics Education A Longitudinal Graduate Medical Education Curriculum in Clinical Informatics: Function, Structure, and Evaluation.

There is a need to integrate informatics education into medical training programs given the rise in demand for health informaticians and the call on the Accreditation Council for Graduate Medical Education (ACGME) and the body of undergraduate medical education (UGME) for implementation of informatics curricula. This report outlines a 2-year longitudinal informatics curriculum now currently in its seventh year of implementation. This report is intended to inform United States (US) Graduate Medical Education (GME) program leaders of the necessary requirements for implementation of a similar program at their institution. The curriculum aligns with the core content for the subspecialty of clinical informatics (CI) and is led by a multidisciplinary team with both informatics and clinical expertise. This educational pathway has a low direct cost and is a practical example of the academic learning health system (aLHS) in action. The pathway is housed within an internal medicine department at a large tertiary academic medical center. The curriculum has yielded 13 graduates from both internal medicine (11, 85%) and pediatrics (2, 15%) whose projects have spanned acute and ambulatory care and multiple specialties. Projects have included Clinical Decision Support (CDS) tools, of which some will be leveraged as substrate in applications seeking extramural funding. Graduates have gone on to CI board certification and fellowship, as well as several other specialties, creating a distributed network of clinicians with specialized experience in applied CI. An informatics curriculum at the GME level may increase matriculation to CI fellowship and more broadly increase development of the CI workforce through building a cadre of physicians with HIT expertise across specialties without formal CI board certification. We offer an example of a longitudinal pathway which is rooted in aLHS principles. The pathway requires a dedicated multidisciplinary team and departmental and IT leadership support.

Mapping the patient journey: utilizing clinical informatics for a conceptual approach to identify aspects of emergency department access block

BackgroundThe study aims to map the patient journey in a regional Emergency Department (ED), identify factors causing extended Length of Stay (LOS) in ED during pre-COVID (2016–2019), COVID (2020–2022) and post-COVID (2023), and analyse the patient journey using clinical informatics. Through systematic analysis and root cause identification, the study seeks to establish a learning health system. This system will enhance patient experiences and improve timely and effective care delivery.MethodsSummarized data on ED presentations data were sourced from January 2016 to August 2023 for a 294-bed regional hospital in New South Wales (NSW). The patient’s journey from ED to admission inpatient wards was analysed using clinical informatics. The time spent in each segment of the ED journey was analysed separately and subdivided by specialty for further analysis.ResultsPatient characteristics remained similar year on year. The average ED LOS increased from 5.59 h during the pre-COVID period (2016–2019) to 8.48 h during the COVID period (2020–2022) and 8.52 h during the post-COVID period (2023). The hospital’s bed occupancy rate remained consistent before and during the COVID-19 pandemic, with a slight improvement afterwards, except for a decrease in 2020. The occupancy rate for ED-accessible beds during the pre-COVID period is higher than during the COVID and post-COVID periods. The admission from ED to Inpatient wards decreased from 35% pre-COVID to 28% during COVID. Of the total LOS, the ‘Bed Request’ to ‘Bed Ready’ time segment increased from 11.41% in 2016 to 41.19% in 2022. The admission of medical specialty consistently made up most admissions (32%) which increased during the COVID. The medical specialty also spent the most time in the ED, peaking at 880 min in 2022.ConclusionClinical Informatics is an effective tool to visualise the bottlenecks in clinical workflows. The ‘Bed Request’ to ‘Bed Ready’ time segment was the largest contributor to ED LOS and may have been attributed to a lack of bed availability. Patients took an average of 50 min to be transferred to Inpatient wards upon admission. Future studies could utilize insights gained to develop specific key performance indicators aimed at improving ED LOS. Moreover, we are currently working on improving real-time access to inpatient beds but are currently hampered by the ‘manual update’ of bed status that is controlled by the individual wards. We aim to build an automated system that can monitor and update bed availability (e.g., patient discharge) with minimal human intervention.

Utility of learning health systems in radiation oncology.

413 Background: Current health information systems lack a robust framework for longitudinal integration of internal experiences and external evidence into existing workflows. The result is labor intensive, error-prone and fragmented systems plagued with process gaps. Learning health systems (LHS) can be a potential solution to address these gaps. We created and implemented a novel LHS framework in radiation oncology (RO). We hypothesize that this framework can be leveraged to identify and implement opportunities for improvement using a patient-oriented endpoint of time from radiation simulation to treatment start (RSTS). Methods: An RO-LHS was developed through a collaboration between an academic, multi-facility radiation oncology department and an industry partner (Medlever). The end-to-end clinical workflow was embedded into the Medlever platform following an interdisciplinary, iterative analysis with representatives from key teams (clerical, finance and clinical). A novel nomenclature was created to label standardized, customizable templates, which choreograph logic-driven tasks that cover all sub-steps of RSTS. The primary endpoint of RSTS was compared pre- and post-implementation, stratified by key variables, such as facility, staff member, and radiation treatment modality. Results: The iterative workflow analysis identified >700 variables and decision points necessary for >200 cancer and benign conditions routinely managed in RO. >450 treatment protocols were templated.180 logic-driven tasks were created and linked to key parameters, resulting in a choregraphed precision workflow for each patient. Using the first iteration of the build, a total of 512 patients were managed during a 50-day period (03/01/2024-04/20/2024) and were compared to 2875 patients treated prior to implementation. Post-implementation, RSTS was shortened by 36.4% (p<0.001). We identified significant differences in RSTS by radiation modalities (protons vs all others), across facilities, and by providers (p<0.01 for all). Analysis of fractional domain of RSTS, 56% of the time was driven by physician-directed tasks, while 44% was comprised of physics and dosimetry-driven work. Physician-directed tasks had the greatest heterogeneity in time to completion. The biggest variation was noted for the contouring completion task. Significant differences were identified by provider (p<0.001) in nearly each step of the process, including steps not directly tied to physician’s work. Conclusions: Through an inter-disciplinary implementation of the first iteration of a LHS in our RO department, we have significantly shortened RSTS. The implementation and analysis has also enabled us to better understand underlying heterogeneities and identify further opportunities for optimization of RSTS. An LHS framework provides a generalizable solution that could be adapted in other RO clinics.

The translation-to-policy learning cycle to improve public health.

Learning Health Systems (LHSs) have not directly informed evidence-based policymaking. The Translation-to-Policy (T2P) Learning Cycle aligns scientists, end-users, and policymakers to support a repeatable roadmap of innovation and quality improvement to optimize effective policies toward a common public health goal. We describe T2P learning cycle components and provide examples of their application. The T2P Learning Cycle is based on the U.S. Department of Veterans Affairs (VA) Office of Research and Development and Quality Enhancement Research Initiative (QUERI), which supports research and quality improvement addressing national public health priorities to inform policy and ensure programs are evidence-based and work for end-users. Incorporating LHS infrastructure, the T2P Learning Cycle is responsive to the Foundations for Evidence-based Policymaking Act, which requires U.S. government agencies to justify budgets using evidence. The learning community (patients, providers, clinical/policy leaders, and investigators) drives the T2P Learning Cycle, working toward one or more specific, shared priority goals, and supports a repeatable cycle of evidence-building and evaluation. Core T2P Learning Cycle functions observed in the examples from housing/economic security, precision oncology, and aging include governance and standard operating procedures to promote effective priority-setting; complementary research and quality improvement initiatives, which inform ongoing data curation at the learning system level; and sustainment of continuous improvement and evidence-based policymaking. The T2P Learning Cycle integrates research translation with evidence-based policymaking, ensuring that scientific innovations address public health priorities and serve end-users through a repeatable process of research and quality improvement that ensures policies are scientifically based, effective, and sustainable.

S2177 Inadequate Implementation of Guideline-Directed Smoking Cessation Treatment in Common Gastrointestinal Disorders: Insights From a Learning Health System

S2177 Inadequate Implementation of Guideline-Directed Smoking Cessation Treatment in Common Gastrointestinal Disorders: Insights From a Learning Health System

Envisioning public health as a learning health system.

Envisioning public health as a learning health system.