Achieving Optimal Population Cardiovascular Health Requires an Interdisciplinary Team and a Learning Healthcare System: A Scientific Statement From the American Heart Association.

Population cardiovascular health, or improving cardiovascular health among patients and the population at large, requires a redoubling of primordial and primary prevention efforts as declines in cardiovascular disease mortality have decelerated over the past decade. Great potential exists for healthcare systems-based approaches to aid in reversing these trends. A learning healthcare system, in which population cardiovascular health metrics are measured, evaluated, intervened on, and re-evaluated, can serve as a model for developing the evidence base for developing, deploying, and disseminating interventions. This scientific statement on optimizing population cardiovascular health summarizes the current evidence for such an approach; reviews contemporary sources for relevant performance and clinical metrics; highlights the role of implementation science strategies; and advocates for an interdisciplinary team approach to enhance the impact of this work.

Computers, confounding, clusters, consent, cost, COVID and consultation: how the Health and Disability Code impedes the learning health system.

The Health and Disability Code precludes any research involving a competent patient without the informed consent of the participant. A learning health system requires rigorous evaluation of both new and established clinical practice, including low-risk components of usual care pathways. When comparing two accepted practices, the only way to control for unknown confounders is by randomisation. In some limited circumstances, particularly when comparing groups or clusters of patients, this comparison can only practicably be undertaken without consent. The current Code impedes a learning health system and is detrimental to the health of New Zealanders. It urgently needs updating.

Using and improving distributed data networks to generate actionable evidence: the case of real-world outcomes in the Food and Drug Administration's Sentinel system.

The US Food and Drug Administration (FDA) Sentinel System uses a distributed data network, a common data model, curated real-world data, and distributed analytic tools to generate evidence for FDA decision-making. Sentinel system needs include analytic flexibility, transparency, and reproducibility while protecting patient privacy. Based on over a decade of experience, a critical system limitation is the inability to identify enough medical conditions of interest in observational data to a satisfactory level of accuracy. Improving the system's ability to use computable phenotypes will require an "all of the above" approach that improves use of electronic health data while incorporating the growing array of complementary electronic health record data sources. FDA recently funded a Sentinel System Innovation Center and a Community Building and Outreach Center that will provide a platform for collaboration across disciplines to promote better use of real-world data for decision-making.

LAGOS: learning health systems and how they can integrate with patient care.

PROBLEM: Learning health systems (LHS) are an underexplored concept. How LHS will operate in clinical practice is not well understood. This paper investigates the relationships between LHS, clinical care process specifications (CCPS) and the established levels of medical practice to enable LHS integration into daily healthcare practice. METHODS: Concept analysis and thematic analysis were used to develop an LHS characterisation. Pathway theory was used to create a framework by relating LHS, CCPS, health information systems and the levels of medical practice. A case study approach evaluates the framework in an established health informatics project. RESULTS: Five concepts were identified and used to define the LHS learning cycle. A framework was developed with five pathways, each having three levels of practice specificity spanning population to precision medicine. The framework was evaluated through application to case studies not previously understood to be LHS. DISCUSSION: Clinicians show limited understanding of LHS, increasing resistance and limiting adoption and integration into care routine. Evaluation of the presented framework demonstrates that its use enables: (1) correct analysis and characterisation of LHS; (2) alignment and integration into the healthcare conceptual setting; (3) identification of the degree and level of patient application; and (4) impact on the overall healthcare system. CONCLUSION: This paper contributes a theoretical framework for analysis, characterisation and use of LHS. The framework allows clinicians and informaticians to correctly identify, characterise and integrate LHS within their daily routine. The overall contribution improves understanding, practice and evaluation of the LHS application in healthcare.

AHRQ Series on Improving Translation of Evidence: Linking Evidence Reports and Performance Measures to Help Learning Health Systems Use New Information for Improvement.

BACKGROUND: Evidence-based Practice Center (EPC) reports are lengthy and difficult for health systems to navigate. A quality measure index was created to allow health systems to more efficiently access information relevant to their needs. METHODS: Two tables were embedded in an EPC report. The first identified quality measures covered by the report with descriptive information. The second contained page numbers in the report's executive summary addressing these quality measures, with hyperlinks to navigate to those pages. The researchers received feedback on the tables from four health system representatives and enhanced the tables. An exercise with two health system-targeted scenarios was then created. Three trainees (one medical fellow and two pharmacy students) and two health system representatives from a large community and a small rural health system completed the exercise, with and without the enhanced tables. They timed how long it took to find answers to scenario questions and provided general feedback. RESULTS: It took 63.4% less time to find quality measure information when the hyperlinked quality measure indexing tables were used (11.0 ± 5.0 vs. 4.0 ± 3.5 minutes; p = 0.002). The health system representatives stated that the quality measure indexed tables were very easy to use and that if these tables were used in future reports they were "somewhat" or "very likely" to use quality measure-indexed EPC reports in the future. CONCLUSION: A unique concept that can allow EPC reports to be more user friendly to health systems was identified. The refined quality measure-indexed tables enhanced the efficiency of finding information and the overall likability of the report.

Implementing Evidence-Based Screening and Counseling for Unhealthy Alcohol Use with Epic-Based Electronic Health Record Tools.

BACKGROUND: Multiple national organizations recommend screening and counseling adults for unhealthy alcohol use. METHODS: An evidence-based approach to screening and counseling using Epic electronic health record (EHR) tools was implemented in a general medicine clinic. A dissemination package with actionable steps for clinics and systems wishing to implement similar processes was then produced. To evaluate the initial implementation and quality improvement project, run charts were created to track patients screened, patients counseled, and fidelity to protocols, and members of the original project team were interviewed to assess facilitators and barriers. The draft dissemination package was revised after feedback from health system representatives (key informants). RESULTS: More than 9,000 patients (73.9% of those eligible) were screened in 20 months. Sixty-four percent of patients with positive initial screens had documented screening-related assessment; 39.7% (141/355) were offered counseling when indicated. Initial project team members identified EHR tools, clinic leadership, quality improvement culture, a multidisciplinary team, and training for providers and nurses as facilitators; and competing demands, patient population size, and nursing staff/resident turnover as barriers. Six key informants evaluated the dissemination package. Most rated 10 of the 12 sections as very useful; all rated components specific to implementing alcohol screening and counseling as very useful. Ratings for general guidance on implementing evidence-based services in primary care were more mixed. CONCLUSION: Evidence-based screening and counseling for unhealthy alcohol use can be implemented with EHR tools. A dissemination guide was viewed favorably by key informants and can serve as a guide for other clinics and systems.

Artificial Intelligence in Primary Health Care: Perceptions, Issues, and Challenges.

BACKGROUND: Artificial intelligence (AI) is heralded as an approach that might augment or substitute for the limited processing power of the human brain of primary health care (PHC) professionals. However, there are concerns that AI-mediated decisions may be hard to validate and challenge, or may result in rogue decisions. OBJECTIVE: To form consensus about perceptions, issues, and challenges of AI in primary care. METHOD: A three-round Delphi study was conducted. Round 1 explored experts' viewpoints on AI in PHC (n=20). Round 2 rated the appropriateness of statements arising from round one (n=12). The third round was an online panel discussion of findings (n=8) with the members of both the International Medical Informatics Association and the European Federation of Medical Informatics Primary Health Care Informatics Working Groups. RESULTS: PHC and informatics experts reported AI has potential to improve managerial and clinical decisions and processes, and this would be facilitated by common data standards. The respondents did not agree that AI applications should learn and adapt to clinician preferences or behaviour and they did not agree on the extent of AI potential for harm to patients. It was more difficult to assess the impact of AI-based applications on continuity and coordination of care. CONCLUSION: While the use of AI in medicine should enhance healthcare delivery, we need to ensure meticulous design and evaluation of AI applications. The primary care informatics community needs to be proactive and to guide the ethical and rigorous development of AI applications so that they will be safe and effective.