Octavian C. Ioachimescu

Background: The translational journey from biomedical discovery to population health is increasingly complex, requiring coordinated navigation across scientific, clinical, and operational domains. While the Learning Health System (LHS) is a unifying platform developed to accelerate knowledge cycles, evidence generation, testing, and application, its role in governing translational flows across different organization types remains insufficiently specified. Objective: This perspective article proposes a conceptual, structured approach and taxonomy that links translational spectrum ( T ₀ – T ₅ ) to the health system phenotype and identifies the LHS as the governance and operational layer that enables safe, efficient, and speed‐appropriate translation from T ₄ to T ₅ , where competing translational pathways converge. The proposed phenotyping schema is aimed at optimizing LHS configuration and translational performance. Methods: We synthesize prior work on translational science, LHS design, and organizational capability into an analytic typology (LHS ₁ –LHS ₄ ) based on innovation generation and adoption, and their specific investigative capabilities. This new organizational typology shifts the LHS concept from a descriptive model to an actionable design and governance tool, enabling organizational leaders to align infrastructure, strategy, and translational intent. Results: Health systems differ in how they generate, test, and adopt new knowledge, yet all require explicit governance, prioritization, data infrastructure, stakeholder engagement, and improvement capacity to move evidence into routine care and population impact. The LHS ₁ –LHS ₄ are presented as descriptive phenotypes with distinct capability profiles and operationalizations, highlighting the central role of LHS governance in aligning priorities, resources, and implementation pathways. Conclusions: By explicitly linking system phenotype to translational function, leaders can build a robust foundation for operational decision‐making and targeted investments, while clarifying the rights, rules, and permissions needed for safe knowledge movement, and offering a basis for evaluation, implementation, and further empirical testing.

Background: Learning Health Systems (LHS) aim to accelerate generation, implementation, and dissemination of knowledge to improve population health. Community engagement is widely recognized as an essential component of LHS, yet methods for evaluating community empowerment and its impact on translational performance remain limited. Most assessments focus on participation metrics rather than structural influence on governance, research, and care delivery. Results: We proposed a conceptual and quantitative evaluation framework in which the Clinical and Translational Science Award (CTSA) hub serves as the translational science engine of the system, while community empowerment serves as the axle and the variable transmission (drivetrain) regulating the conversion of institutional resources into translational performance. System outputs are expressed through four coupled LHS cycles (clinical care, education, research, and governance), each characterized by translational velocity, innovation throughput, and economic performance. Community empowerment is quantified using the Community Transmission Index (CTI), a structured instrument that evaluates engagement maturity across eight domains including shared governance, participatory data governance, co-production, trust capital, and equity integration. The CTI generates a standardized 0–1 score that reflects the extent to which community partners shape institutional decision-making and system operations. Translational performance across the four LHS cycles can be evaluated using measures such as velocity (inverse latency to sustainment), innovation output, return on investment, and translational efficiency indices. We hypothesized that higher CTI scores will increase translational velocity, improve balance across learning cycles, and enhance innovation uptake by reducing sociotechnical friction and strengthening the legitimacy and sustainability of change processes. Conclusions: This framework offers a structured approach for evaluating how community empowerment influences translational performance in LHS and how it can be assessed using complementary metrics. By linking engagement measures to operational and economic outcomes, the model enables LHS and academic institutions to assess whether community partnerships function as advisory mechanisms or as integral components of the translational infrastructure.

Background: Over the past two decades, biomedical research has made extraordinary progress, at amazing speeds and with astounding computational capabilities. Even in very agile organizations, these fast-paced advances represent significant barriers to nimble and successful implementation, scaling, integration, cultural shaping, and ongoing education. Learning at individual, team, organization or system levels is paramount to the good functioning of all health systems. Main Body: Academic health institutions are uniquely positioned to drive innovation, improve patient outcomes, and cultivate future leaders by integrating well-managed clinical operations, breakthrough research, and outstanding education. The Learning Health System (LHS) framework provides a powerful model of integration, leveraging iterative cycles of discovery, learning and improvement by employing robust operational management, analytical capabilities, implementation and dissemination capacity and functionalities. Central to this model are three interconnected phases: Practice to Data, Data to Knowledge, and Knowledge to Practice, which form the ‘implementation arc’ or the LHS cycle. At each junction between these phases, the incorporation of external evidence and the active engagement of community members or stakeholders serve as critical pillars, ensuring that learning is both rigorous and relevant. Conclusions: First, we offer a modified design and conceptualization of LHS as a four-cycle, mission-based model centered by its community, and strengthened by external evidence and data as distinct inputs, at multiple points of the cycle. Second, we propose that the Clinical and Translational Science Award (CTSA) hubs’ structures and functions can represent the strong operational engine of the LHS plant, while community represents the central axle and transmission of movement in the central mechanism of the LHS. As such, the CTSA hubs can serve as a robust machinery for the clinical, research, and educational cycles in learning health systems. Randomized control trial number: Not applicable.

The biomedical discovery journey from laboratory to population often encompasses meandering paths, iterative cycles, replication and validation efforts, complex trade-offs between adaptability and fidelity, long lag times and often high costs. The path is sometimes described as a translational spectrum or continuum, with linear or serial-parallel segments and specific transitions. The starting point is usually represented by the inception, ideation or funding opportunity (T0), progressing to laboratory studies (T1), toward human specimen-based studies (T2) and clinic or hospital-based trials (T3), then to community (T4), and populations at large (T5). The latter translation is often accomplished through new policies, laws, regulations or societal normative changes. An innovation-friendly ecosystem requires powerful, effective, and efficient platforms to accelerate the adoption of innovations and discoveries not only in academic organizations that generate new knowledge or provide cutting-edge education, but also in organizations trying to maintain their relevance and competitive advantage by embracing novelty, incremental or breakthrough discoveries, swiftly and efficiently. Local research and development hubs that provide nurture and upkeep of traditional, ‘secular’ innovation trees often must create de novo, ‘engineered’ trees to compensate for gaps in the investigative capability portfolio, and sometimes need to graft ‘engineered’ branches to create ‘hybrid’ trees, able to fulfill successfully and completely the discovery journey. This type of innovation orchard framework may be a powerful environment, ecosystem or milieu, regulating the growth and development of all these types of innovation trees, all with the purpose of agile functionality in implementing research and discovery in population health and clinical care.

Rationale: Area under expiratory flow-volume curve (AEX) has been shown to be a valuable functional measurement in respiratory physiology. Area under inspiratory flow-volume loop (AIN) also shows promise in characterizing upper and/or lower airflow obstruction. Objectives: we aimed here to develop normative reference values for AIN, able to ascertain deviations from normal. Methods: We analyzed AIN in 4,980 spirometry tests recorded in non-smoking, healthy individuals in the Pulmonary Function Testing Laboratory. Results: The mean (95% confidence interval, CI), standard deviation and median (25^th-75^th interquartile range) AIN were 16.05 (15.79–16.31), 9.08 and 14.72 (9.12–21.42) L²·sec^-1, respectively. The mean (95% CI) and standard deviation of the best-trial measurements for square root of AIN (Sqrt AIN) were 3.84 (3.81–3.87) and 1.14; 4.15 (4.12–4.18) and 1.03 in men, and 2.68 (2.63–2.72) and 0.72 L·sec^-1/2 in women. The mean (standard deviation) of pre- and post-bronchodilator Sqrt AIN were 3.71 (1.17) and 3.81 (1.19) L·sec^-1/2, respectively. The mean (95% CI), standard deviation and lowest 5^th percentile (lower limit of normal, LLN) of Sqrt AIN/Sqrt AEX (%) were 101.3 (100.82–101.88), 18.7, and 71.8%; stratified by gender, it was 102.2 (101.6–102.8), 18.6, and 72.8% in men, and 98 (96.9–99.2), 18.8, and 68.6% in women, respectively. Conclusions: The availability of area under the inspiratory flow-volume curve (AIN) and the derived indices offers a promising opportunity to assess upper airway disease (e.g., involvement of larynx, trachea or major bronchi), especially because some of these measurements appear to be independent of age, race, height, and weight.

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