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.
Learning 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.
The 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.
The 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.
The 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.
The reliability of data-driven predictions in real-world scenarios remains uncertain. This study aimed to develop and validate a machine-learning-based model for predicting clinical outcomes using real-world data from an electronic clinical pathway (ePath) system.
All available data were collected from patients with lung cancer who underwent video-assisted thoracoscopic surgery at two independent hospitals utilizing the ePath system. The primary clinical outcome of interest was prolonged air leak (PAL), defined as drainage removal more than 2 days post-surgery. Data-driven prediction models were developed in a cohort of 314 patients from a university hospital applying sparse linear regression models (least absolute shrinkage and selection operator, ridge, and elastic net) and decision tree ensemble models (random forest and extreme gradient boosting). Model performance was then validated in a cohort of 154 patients from a tertiary hospital using the area under the receiver operating characteristic curve (AUROC) and calibration plots.
To mitigate bias, variables with missing data related to PAL or those with high rates of missing data were excluded from the dataset. Fivefold cross-validation indicated improved AUROCs when utilizing key variables, even post-imputation of missing data. Dichotomizing continuous variables enhanced performance, particularly when fewer variables were employed in the decision tree ensemble models. Consequently, regression models incorporating seven key variables in complete case analysis demonstrated superior discriminatory ability for both internal (AUROCs: 0.77–0.84) and external cohorts (AUROCs: 0.75–0.84). These models exhibited satisfactory calibration in both cohorts.
The data-driven prediction model implementing the ePath system exhibited adequate performance in predicting PAL post-video-assisted thoracoscopic surgery, optimizing variables and considering population characteristics in a real-world setting.
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.
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.
Ongoing experience and emerging evidence suggest that Collaborative Learning Health Systems (CLHSs) are a promising approach to transforming population outcomes and organizational care processes. As implied in their name, this type of Learning Health System both promotes and relies upon collaboration to achieve its aims. However, to realize the promise of the Collaborative Learning Health System, a better understanding of engagement as a catalyst for collaboration is necessary. In this commentary, we elaborate the phenomenon of engagement. We define engagement in the context of CLHSs, consider dimensions of engagement, and further explore the role of engagement as a catalyst of network functioning. We conclude by offering an agenda for research and practice intended to develop an understanding of engagement so as to further advance the theory and practice of CLHS efforts and ultimately promote the uptake of the CLHS model.
Public health systems worldwide face increasing challenges in addressing complex health issues and improving population health outcomes. This experience report introduces the concept of a Learning Public Health System (LPHS) as a potential solution to transform public health practice. Building upon the framework of a Learning Health System (LHS) in healthcare, the LPHS aims to create a dynamic, data-driven ecosystem that continuously improves public health interventions and policies. This report explores the definition, benefits, challenges, and implementation strategies of an LPHS, highlighting its potential to revolutionize public health practice.
This report employs a comparative analysis approach, examining the similarities and differences between an LPHS and an LHS. It also identifies and elaborates on the potential benefits, challenges, and barriers to implementing an LPHS. Additionally, the study investigates promising national initiatives that exemplify elements of an LPHS in action.
An LPHS integrates data from diverse sources to inform knowledge generation, policy development, and operational improvements. Key benefits of implementing an LPHS include improved disease prevention, evidence-informed policy-making, and enhanced health outcomes. However, several challenges were identified, such as interoperability issues, governance concerns, funding limitations, and cultural factors that may impede the widespread adoption of an LPHS.
Implementation of an LPHS has the potential to significantly transform public health practice. To realize this potential, a call to action is issued for stakeholders across the public health ecosystem. Recommendations include investing in informatics infrastructure, prioritizing workforce development, establishing robust data governance frameworks, and creating incentives to support the development and implementation of a LPHS. By addressing these key areas, public health systems can evolve to become more responsive, efficient, and effective in improving population health outcomes.
The COVID-19 pandemic challenged primary care to rapidly innovate. In response, the Western Victorian Primary Health Network (WVPHN) developed a COVID-19 online Community of Practice comprising general practitioners (GPs), practice nurses, pharmacists, aged care and disability workers, health administrators, public health experts, medical specialists, and consumers. This Experience Report describes our progress toward a durable organizational learning health system (LHS) model through the COVID-19 pandemic crisis and beyond.
In March 2020, we commenced weekly Community of Practice sessions, adopting the Project ECHO (Extension of Community Health Outcomes) model for a virtual information-sharing network that aims to bring clinicians together to develop collective knowledge. Our work was underpinned by the LHS framework proposed by Menear et al. and aligned with Kotter's eight-step change model.
There were four key phases in the development of our LHS: build a Community of Practice; facilitate iterative change; develop supportive organizational infrastructure; and establish a sustainable, ongoing LHS. In total, the Community of Practice supported 83 unique COVID-19 ECHO sessions involving 3192 h of clinician participation and over 10 000 h of organizational commitment. Six larger sessions were run between March 2020 and September 2022 with 3192 attendances. New models of care and care pathways were codeveloped in sessions and network leaders contributed to the development of guidelines and policy advice. These innovations enabled WVPHN to lead the Australian state of Victoria on rates of COVID vaccine uptake and GP antiviral prescribing.
The COVID-19 pandemic created a sense of urgency that helped stimulate a regional primary care-based Community of Practice and LHS. A robust theoretical framework and established change management theory supported the purposeful implementation of our LHS. Reflection on challenges and successes may provide insights to support the implementation of LHS models in other primary care settings.
Publishing medical metadata stored in case report forms (CRFs) is a prerequisite for the development of a learning health system (LHS) by fostering reuse of metadata and standardization in health research. The aim of our study was to investigate medical researchers' (MRs) willingness to share CRFs, to identify reasons for and against CRF sharing, and to determine if and under which conditions MRs might consider sharing CRF metadata via a public registry.
We examined CRF data sharing commitments for 1842 interventional trials registered on the German Clinical Trials Registry (DRKS) from January 1, 2020, to December 31, 2021. We invited 1360 individuals registered as contacts on DRKS to participate in a web-based survey between May 10, 2022, and June 30, 2022.
Only 0.3% (5/1842) of data sharing commitments in DRKS included a plan to share blank CRFs. Survey results showed high support for CRF sharing. More than 70% of respondents (223/301) were willing to share their CRFs, and 83.7% (252/301) were interested in CRF reuse. The most frequently reported reason for CRF sharing was improvement of comparability and interpretability of patient data (244/301; 81.0%). The most frequently reported reason against CRF sharing was missing approval by the sponsor (160/301; 53.2%). Researchers conducting commercial trials were significantly less likely to share CRFs than those conducting noncommercial trials (63.3% vs. 76.2%, OR 0.54, 95% CI 0.32–0.92) and they were less likely to reuse CRFs (78.5% vs. 84.6%, OR 0.66, 95% CI 0.35–1.24). The most frequently mentioned prerequisite for publication of CRFs in a public registry was its trustworthiness (244/301, 81.1%).
Data sharing commitments in DRKS revealed a low awareness of CRF sharing. Survey results showed generally strong support for CRF sharing, including the willingness to publish CRFs in a public registry, although legal and practical barriers were identified.
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