BACKGROUND: In the context of expanding digital health tools, the health system is ready for Learning Health System (LHS) models. These models, with proper governance and stakeholder engagement, enable the integration of digital infrastructure to provide feedback to all relevant parties including clinicians and consumers on performance against best practice standards, as well as fostering innovation and aligning healthcare with patient needs. The LHS literature primarily includes opinion or consensus-based frameworks and lacks validation or evidence of benefit. Our aim was to outline a rigorously codesigned, evidence-based LHS framework and present a national case study of an LHS-aligned national stroke program that has delivered clinical benefit. MAIN TEXT: Current core components of a LHS involve capturing evidence from communities and stakeholders (quadrant 1), integrating evidence from research findings (quadrant 2), leveraging evidence from data and practice (quadrant 3), and generating evidence from implementation (quadrant 4) for iterative system-level improvement. The Australian Stroke program was selected as the case study as it provides an exemplar of how an iterative LHS works in practice at a national level encompassing and integrating evidence from all four LHS quadrants. Using this case study, we demonstrate how to apply evidence-based processes to healthcare improvement and embed real-world research for optimising healthcare improvement. We emphasize the transition from research as an endpoint, to research as an enabler and a solution for impact in healthcare improvement. CONCLUSIONS: The Australian Stroke program has nationally improved stroke care since 2007, showcasing the value of integrated LHS-aligned approaches for tangible impact on outcomes. This LHS case study is a practical example for other health conditions and settings to follow suit.
Learning Health System , Stroke , Humans , Stroke/therapy , Australia , Evidence-Based Medicine , Evidence-Based Practice/methods
Having the right information at the right time and at the fingertips of the right individuals is not just a necessity for a well-functioning healthcare system but it is also the difference between life and death for Canadians. It is particularly critical to enable improved access to and quality of care for equity-deserving individuals because these data eliminate blind spots for clinicians, policy makers and system planners. The COVID-19 pandemic put a spotlight on the health data challenges that exist across Canada and the tangible impact those have on the healthcare system's ability to meet the needs of underserved populations. It sparked unified urgency at the federal and provincial/territorial levels to build a learning health system powered by connected health data for clinical care, patient access, care organization operations, health system use and population/public health. Person-centric data content standards will lie at the foundation of Canada's learning health system, enabling the creation and exchange of data.
Learning Health System , North American People , Pandemics , Humans , Canada , Delivery of Health Care
[ABSTRACT]. This article points out deficiencies in present-day definitions of public health surveillance, which include data collection, analysis, interpretation and dissemination, but not public health action. Controlling a public health problem of concern requires a public health response that goes beyond information dissemination. It is unde- sirable to have public health divided into data generation processes (public health surveillance) and data use processes (public health response), managed by two separate groups (surveillance experts and policy-makers). It is time to rethink the need to modernize the definition of public health surveillance, inspired by the authors’ enhanced Data, Information, Knowledge, Intelligence and Wisdom model. Our recommendations include expanding the scope of public health surveillance beyond information dissemination to comprise actionable knowledge (intelligence); mandating surveillance experts to assist policy-makers in making evidence-informed decisions; encouraging surveillance experts to become policy-makers; and incorporating public health literacy training – from data to knowledge to wisdom – into the curricula for all public health professionals. Work on modernizing the scope and definition of public health surveillance will be a good starting point.
[RESUMEN]. En este artículo se señalan las deficiencias de las definiciones actuales de la vigilancia de salud pública, que incluyen la recopilación, el análisis, la interpretación y la difusión de los datos, pero no las medidas de salud pública. El control de un problema de salud pública de interés exige una respuesta de salud pública que vaya más allá de la difusión de información. No es deseable que la salud pública esté dividida por un lado en procesos de generación de datos (vigilancia de salud pública) y por otro en procesos de uso de datos (respuesta de salud pública), gestionados por dos grupos diferentes (expertos en vigilancia y responsables de la formulación de políticas). Ha llegado el momento de replantear la necesidad de modernizar la definición de la vigilancia de salud pública tomando como referencia el modelo mejorado de Datos, Información, Cono- cimiento, Inteligencia y Sabiduría de los autores. Entre las recomendaciones que se proponen se encuentran las de ampliar el alcance de la vigilancia de salud pública más allá de la difusión de información para que incluya también el conocimiento aplicable (inteligencia); instar a los expertos en vigilancia a que presten ayuda a los responsables de la formulación de políticas en la toma de decisiones basadas en la evidencia; alentar a los expertos en vigilancia a que se conviertan en responsables de la formulación de políticas; e incorporar la formación en conocimientos básicos de salud pública (desde los datos hasta los conocimientos y la sabiduría) en los planes de estudio de todos los profesionales de la salud pública. Un buen punto de partida será trabajar en la modernización del alcance y la definición de la vigilancia de salud pública.
[RESUMO]. Este artigo aponta deficiências nas definições atuais de vigilância em saúde pública, que incluem coleta, análise, interpretação e disseminação de dados, mas não ações de saúde pública. O controle de um prob- lema preocupante de saúde pública exige uma resposta de saúde pública que vá além da disseminação de informações. A saúde pública não deve ser dividida em processos de geração de dados (vigilância em saúde pública) e processos de uso de dados (resposta de saúde pública) gerenciados por dois grupos distintos (especialistas em vigilância e formuladores de políticas). É hora de repensar a necessidade de modernizar a definição de vigilância em saúde pública, inspirada no modelo aprimorado de Dados, Informações, Con- hecimento, Inteligência e Sabedoria dos autores. Nossas recomendações incluem: expansão do escopo da vigilância em saúde pública para além da disseminação de informações, de modo a abranger conhecimentos acionáveis (inteligência); obrigatoriedade de que os especialistas em vigilância auxiliem os formuladores de políticas na tomada de decisões baseadas em evidências; incentivo para que os especialistas em vigilân- cia se tornem formuladores de políticas; e incorporação de capacitação em letramento em saúde pública (partindo dos dados para o conhecimento e em seguida para a sabedoria) nos currículos de todos os profis- sionais de saúde pública. O trabalho de modernizar o escopo e a definição de vigilância em saúde pública será um bom ponto de partida.
Public Health Surveillance , Data Collection , Health Information Management , Population Health Management , Health Literacy , Learning Health System , Intelligence , Public Health Surveillance , Data Collection , Health Information Management , Population Health Management , Health Literacy , Learning Health System , Intelligence , Public Health Surveillance , Data Collection , Health Information Management , Population Health Management , Health Literacy , Learning Health System , Intelligence
Learning health systems (LHSs) embed social accountability into everyday workflows and can inform how governments build bridges across the digital health divide. They shape partnerships using rapid cycles of data-driven learning to respond to patients' calls to action for equity from digital health. Adopting the LHS approach involves re-distributing power, which is likely to be met with resistance. We use the LHS example of British Columbia's 811 services to highlight how infrastructure was created to provide care and answer questions about access to digital health, outcomes from it and the financial impact passed on to patients. In the concluding section, we offer an accountability framework that facilitates partnerships in making digital health more equitable.
Learning Health System , Humans , Digital Health
AIM: Early interventions are well understood to improve psychosis outcomes, but their successful implementation remains limited. This article introduces a three-step roadmap for advancing the implementation of evidence-based practices to operate as a learning health system, which can be applied to early interventions for psychosis and is intended for an audience that is relatively new to systematic approaches to implementation. METHODS: The roadmap is grounded in implementation science, which specializes in methods to promote routine use of evidence-based innovations. The roadmap draws on learning health system principles that call for commitment of leadership, application of evidence, examination of care experiences, and study of health outcomes. Examples are discussed for each roadmap step, emphasizing both data- and stakeholder-related considerations applicable throughout the roadmap. CONCLUSIONS: Early psychosis care is a promising topic through which to discuss the critical need to move evidence into practice. Despite remarkable advances in early psychosis interventions, population-level impact of those interventions is yet to be realized. By providing an introduction to how implementation science principles can be operationalized in a learning health system and sharing examples from early psychosis care, this article prompts inclusion of a wider audience in essential discourse on the role that implementation science can play for moving evidence into practice for other realms of psychiatric care as well. To this end, the proposed roadmap can serve as a conceptual guiding template and framework through which various psychiatric services can methodically pursue timely implementation of evidence-based interventions for higher quality care and improved outcomes.
Early Medical Intervention , Implementation Science , Learning Health System , Psychotic Disorders , Humans , Psychotic Disorders/therapy , Evidence-Based Practice
BACKGROUND: Pandemics and climate change each challenge health systems through increasing numbers and new types of patients. To adapt to these challenges, leading health systems have embraced a Learning Health System (LHS) approach, aiming to increase the efficiency with which data is translated into actionable knowledge. This rapid review sought to determine how these health systems have used LHS frameworks to both address the challenges posed by the COVID-19 pandemic and climate change, and to prepare for future disturbances, and thus transition towards the LHS2.0. METHODS: Three databases (Embase, Scopus, and PubMed) were searched for peer-reviewed literature published in English in the five years to March 2023. Publications were included if they described a real-world LHS's response to one or more of the following: the COVID-19 pandemic, future pandemics, current climate events, future climate change events. Data were extracted and thematically analyzed using the five dimensions of the Institute of Medicine/Zurynski-Braithwaite's LHS framework: Science and Informatics, Patient-Clinician Partnerships, Continuous Learning Culture, Incentives, and Structure and Governance. RESULTS: The search yielded 182 unique publications, four of which reported on LHSs and climate change. Backward citation tracking yielded 13 additional pandemic-related publications. None of the climate change-related papers met the inclusion criteria. Thirty-two publications were included after full-text review. Most were case studies (n = 12, 38%), narrative descriptions (n = 9, 28%) or empirical studies (n = 9, 28%). Science and Informatics (n = 31, 97%), Continuous Learning Culture (n = 26, 81%), Structure and Governance (n = 23, 72%) were the most frequently discussed LHS dimensions. Incentives (n = 21, 66%) and Patient-Clinician Partnerships (n = 18, 56%) received less attention. Twenty-nine papers (91%) discussed benefits or opportunities created by pandemics to furthering the development of an LHS, compared to 22 papers (69%) that discussed challenges. CONCLUSIONS: An LHS 2.0 approach appears well-suited to responding to the rapidly changing and uncertain conditions of a pandemic, and, by extension, to preparing health systems for the effects of climate change. LHSs that embrace a continuous learning culture can inform patient care, public policy, and public messaging, and those that wisely use IT systems for decision-making can more readily enact surveillance systems for future pandemics and climate change-related events. TRIAL REGISTRATION: PROSPERO pre-registration: CRD42023408896.
COVID-19 , Learning Health System , United States , Humans , Pandemics , Climate Change , COVID-19/epidemiology , Patient Care
PURPOSE: A growing number of health systems are establishing learning health system (LHS) programs, where research focuses on rapidly improving the health system's internal operations and performance. The authors examine funding challenges facing such initiatives and identify strategies for managing tensions between reliance on external research funding and directly contributing to improvement and learning within the researchers' own system. METHOD: Qualitative case studies of LHS research programs in 5 health systems were performed via 38 semistructured interviews (October 2019-April 2021) with 35 diverse respondents. Inductive and deductive rapid qualitative analysis supported interview, system-level, and cross-system summaries and analysis. RESULTS: External funding awards to LHS researchers facilitated some internal improvement and learning, scientific advancements, and the reputation of researchers and their systems, but reliance on external funding also challenged researchers' responsiveness to concerns of system leaders, managers, practitioners, and system needs. Gaps between external funding requirements and internally focused projects arose in objectives, practical applicability, audiences, timetables, routines, skill sets, and researchers' careers. To contribute more directly to system improvement, LHS researchers needed to collaborate with clinicians and other nonresearchers and pivot between long research studies and shorter, dynamic improvement, evaluation, and data analysis projects. With support from system executives, LHS program leaders employed several strategies to enhance researchers' internal contributions. They aligned funded-research topics with long-term system needs, obtained internal funding for implementing and sustaining practice change, and diversified funding sources. CONCLUSIONS: To foster LHS research contributions to internal system learning and improvement, LHS program leaders need to manage tensions between concentrating on externally funded research and fulfilling their mission of providing research-based services to their own system. Health system executives can support LHS programs by setting clear goals for them; appropriately staffing, budgeting, and incentivizing LHS researchers; and developing supportive, system-wide teamwork, skill development programs, and data infrastructures.
Learning Health System , Qualitative Research , Humans , Learning Health System/organization & administration , Health Services Research , Research Support as Topic , Interviews as Topic , Research Personnel
Electronic health records (EHRs) and other real-world data (RWD) are critical to accelerating and scaling care improvement and transformation. To efficiently leverage it for secondary uses, EHR/RWD should be optimally managed and mapped to industry standard concepts (ISCs). Inherent challenges in concept encoding usually result in inefficient and costly workflows and resultant metadata representation structures outside the EHR. Using three related projects to map data to ISCs, we describe the development of standard, repeatable processes for precisely and unambiguously representing EHR data using appropriate ISCs within the EHR platform lifecycle and mappings specific to SNOMED-CT for Demographics, Specialty and Services. Mappings in these 3 areas resulted in ISC mappings of 779 data elements requiring 90 new concept requests to SNOMED-CT and 738 new ISCs mapped into the workflow within an accessible, enterprise-wide EHR resource with supporting processes.
Learning Health System , Medicine , Electronic Health Records , Industry , Metadata
Genome-guided precision medicine applies consensus recommendations to the care of patients with particular genetic variants. As electronic health records begin to include patients' genomic data, recommendations will be formulated at an increasing rate. This study examined recommendations related to the current list of 73 actionable genes compiled by the American College of Medical Genetics and Genomics and found that conditions fall generally into five classes (cardiovascular, medication interactions, metabolic, neoplastic, and structural), with recommendations falling into seven categories (actions or circumstances to avoid, evaluation of relatives at risk, pregnancy management, prevention of primary manifestations, prevention of secondary complications, surveillance, and treatment of manifestations). This study represents a first step in facilitating automated, scalable clinical decision support and provides direction on formal representation of the contexts and actions for clinical recommendations derived from genome-informed learning health systems.
Learning Health System , Precision Medicine , Female , Pregnancy , Humans , Consensus , Electronic Health Records , Genomics
Despite learning health systems' focus on including the patients in improving healthcare services, research shows they are still considered participants, not partners. This article aims to provide practical guidance for recognizing and including the Voice of the Patient (VoP) as data in a continuous LHS by describing how the VoP can present itself, how it can be incorporated into the LHS and the barriers and enablers for doing so. Five key domains were identified to consider when including the patient perspective. The use of technology could be a facilitator for patients to provide their perspectives. However, there is a risk of increased health inequity by reducing the VoP of patients with low health or digital literacy.
Learning Health System , Voice , Humans , Literacy , Patients , Technology
In Victoria, Australia, jurisdictional vaccine safety service is conducted by SAEFVIC (Surveillance of Adverse Events Following Vaccination in the Community). SAEFVIC developed a public Vaccine Safety Report (saefvic.online/vaccinesafety) to present key surveillance information. This study applies an interdisciplinary learning health system approach to evaluate the report, taking into consideration public expressions of concern on social media.
Learning Health System , Vaccines , Humans , Vaccines/adverse effects , Vaccination/adverse effects , Interdisciplinary Studies , Victoria
The Learning Health Systems (LHS) framework demonstrates the potential for iterative interrogation of health data in real time and implementation of insights into practice. Yet, the lack of appropriately skilled workforce results in an inability to leverage existing data to design innovative solutions. We developed a tailored professional development program to foster a skilled workforce. The short course is wholly online, for interdisciplinary professionals working in the digital health arena. To transform healthcare systems, the workforce needs an understanding of LHS principles, data driven approaches, and the need for diversly skilled learning communities that can tackle these complex problems together.
Learning Health System , Digital Health , Interdisciplinary Studies , Learning , Workforce
Despite forming the cornerstone of modern clinical practice for decades, implementation of evidence-based medicine at scale remains a crucial challenge for health systems. As a result, there has been a growing need for conceptual models to better contextualise and pragmatize the use of evidence-based medicine, particularly in tandem with patient-centred care. In this commentary, we highlight the emergence of the learning health system as one such model and analyse its potential role in pragmatizing both evidence-based medicine and patient-centred care. We apply the learning health system lens to contextualise the key activity of evidence-based guideline development and implementation, and highlight how current inefficiencies and bottlenecks in the evidence synthesis phase of evidence-based guideline development threaten downstream adherence. Lastly, we introduce the evidence ecosystem as a complementary model to learning health systems, and propose how innovative developments from the evidence ecosystem may be integrated with learning health systems to better enable health impact at speed and scale.
Evidence-Based Medicine , Learning Health System , Humans , Ecosystem
Precision medicine initiatives across the globe have led to a revolution of repositories linking large-scale genomic data with electronic health records, enabling genomic analyses across the entire phenome. Many of these initiatives focus solely on research insights, leading to limited direct benefit to patients. We describe the biobank at the Colorado Center for Personalized Medicine (CCPM Biobank) that was jointly developed by the University of Colorado Anschutz Medical Campus and UCHealth to serve as a unique, dual-purpose research and clinical resource accelerating personalized medicine. This living resource currently has more than 200,000 participants with ongoing recruitment. We highlight the clinical, laboratory, regulatory, and HIPAA-compliant informatics infrastructure along with our stakeholder engagement, consent, recontact, and participant engagement strategies. We characterize aspects of genetic and geographic diversity unique to the Rocky Mountain region, the primary catchment area for CCPM Biobank participants. We leverage linked health and demographic information of the CCPM Biobank participant population to demonstrate the utility of the CCPM Biobank to replicate complex trait associations in the first 33,674 genotyped individuals across multiple disease domains. Finally, we describe our current efforts toward return of clinical genetic test results, including high-impact pathogenic variants and pharmacogenetic information, and our broader goals as the CCPM Biobank continues to grow. Bringing clinical and research interests together fosters unique clinical and translational questions that can be addressed from the large EHR-linked CCPM Biobank resource within a HIPAA- and CLIA-certified environment.
Learning Health System , Precision Medicine , Humans , Biological Specimen Banks , Colorado , Genomics
Leadership is vital to a well-functioning and effective health system. This importance was underscored during the COVID-19 pandemic. As disparities in infection and mortality rates became pronounced, greater calls for equity-informed healthcare emerged. These calls led some leaders to use the Learning Health System (LHS) approach to quickly transform research into healthcare practice to mitigate inequities causing these rates. The LHS is a relatively new framework informed by many within and outside health systems, supported by decision-makers and financial arrangements and encouraged by a culture that fosters quick learning and improvements. Although studies indicate the LHS can enhance patients' health outcomes, scarce literature exists on health leaders' use and incorporation of equity into the LHS. This article begins addressing this gap by examining how equity can be incorporated into LHS activities and discussing ways leaders can ensure equity is considered and achieved in rapid learning cycles.
COVID-19 , Leadership , Learning Health System , Humans , COVID-19/epidemiology , SARS-CoV-2 , Health Equity , Pandemics
PURPOSE: Over the past 2 decades, many academic health centers (AHCs) have implemented learning health systems (LHSs). However, the LHS has been defined with limited input from AHC leaders. This has implications because these individuals play a critical role in LHS implementation and sustainability. This study aims to demonstrate how an international group of AHC leaders defines the LHS, and to identify key considerations they would pose to their leadership teams to implement and sustain the LHS. METHOD: A semistructured survey was developed and administered in 2022 to members of the Association of Academic Health Centers President's Council on the Learning Health System to explore how AHC leaders define the LHS in relation to their leadership roles. The authors then conducted a focus group, informed by the survey, with these leaders. The focus group was structured using the nominal group technique to facilitate consensus on an LHS definition and key considerations. The authors mapped the findings to an existing LHS framework, which includes 7 components: organizational, performance, ethics and security, scientific, information technology, data, and patient outcomes. RESULTS: Thirteen AHC leaders (100%) completed the survey and 10 participated in the focus group. The AHC leaders developed the following LHS definition: "A learning health system is a health care system in which clinical and care-related data are systematically integrated to catalyze discovery and implementation of new knowledge that benefits patients, the community, and the organization through improved outcomes." The key considerations mapped to all LHS framework components, but participants also described as important the ability to communicate the LHS concept and be able to rapidly adjust to unforeseen circumstances. CONCLUSIONS: The LHS definition and considerations developed in this study provide a shared foundation and road map for future discussions among leaders of AHCs interested in implementing and sustaining an LHS.
Learning Health System , Humans , Leadership , Global Health , Delivery of Health Care , Government Programs
Health systems are interested in increasing colorectal cancer (CRC) screening rates as CRC is a leading cause of preventable cancer death. Learning health systems are ones that use data to continually improve care. Data can and should include qualitative local perspectives to improve patient and provider education and care. This study sought to understand local perspectives on CRC screening to inform future strategies to increase screening rates across our integrated health system. Health insurance plan members who were eligible for CRC screening were invited to participate in semi-structured phone interviews. Qualitative content analysis was conducted using an inductive approach. Forty member interviews were completed and analyzed. Identified barriers included ambivalence about screening options (e.g., "If it had the same performance, I'd rather do home fecal sample test. But I'm just too skeptical [so I do the colonoscopy]."), negative prior CRC screening experiences, and competing priorities. Identified facilitators included a positive general attitude towards health (e.g., "I'm a rule follower. There are certain things I'll bend rules. But certain medical things, you just got to do."), social support, a perceived risk of developing CRC, and positive prior CRC screening experiences. Study findings were used by the health system leaders to inform the selection of CRC screening outreach and education strategies to be tested in a future simulation model. For example, the identified barrier related to ambivalence about screening options led to a proposed revision of outreach materials that describe screening types more clearly.
Colorectal Neoplasms , Learning Health System , Humans , Early Detection of Cancer , Colorectal Neoplasms/diagnosis , Colorectal Neoplasms/prevention & control , Colonoscopy , Occult Blood , Mass Screening
