Multiple chronic conditions (MCC) drive high symptom burden and utilization of healthcare resources. Person-centered palliative symptom management approaches are underused in primary care.
Narrative synthesis of major policy recommendations and practice literature to propose a primary-care-implementable framework (screening, triage, and management using Edmonton Symptom Assessment System).
Early, protocolized symptom screening and management in MCC is associated with fewer exacerbations, hospitalizations, improved physical functioning, and quality of life; feasible to deliver by primary care teams with targeted training.
Shift palliative skills upstream in primary care; adopt standardized tools; and measure quality outcomes.
Unique challenges of data quality, standardization, and interoperability face non-traditional sources of real world data (RWD), those outside of the common RWD source, the electronic health record (EHR). The experience of our clinical, research, and community group in addressing the endangered school-based physical fitness testing (SB-PFT) data set may be useful as a paradigm for a variety of nontraditional instances of health-related RWD. Until recently, SB-PFT was mandated annually within public schools in ~60% of U.S. children and adolescents representing one of the most robust and inclusive data sets available in youth. When implemented rigorously, SB-PFT produced data that improved pediatric health and student learning. Driven by perceived and real gaps in data quality, inconsistent and insensitive school-site implementation, and uneven evidence for health-relevance, calls for revamping (and, in some cases, ending) school testing are growing.
To preserve and enhance this unique resource, SB-PFT must incorporate emerging science and technology. We describe here the rationale, structure, and process involved in our building an HL7-associated Domain Analysis Model for SB-PFT. Our team consisted of clinicians, exercise scientists, school personnel, publish health officials, parents and students. The structure of the DAM was facilitated by data standard experts and skilled Unified Modeling Language professionals. Administrative support was rendered by our institution's NIH-funded Clinical Translational Science Award.
The DAM will serve as a necessary first step in integrating Fast Healthcare Interoperability Resources (FHIR) and Learning Health Systems (LHS) models essential to realize the full potential of SB-PFT data in advancing child health.
The experience presented here can serve as a roadmap for addressing critical issues of data standardization and RWE generation in nontraditional instances of RWD. The DAM can be deployed in SB-PFT data exchange in real world settings.
To analyze the barriers, enablers, and strategies affecting the implementation of learning health systems (LHS) projects across individual (micro), organizational (meso), and system (macro) levels within Australian healthcare settings
Semistructured interviews were conducted with 26 clinicians undertaking LHS projects in their organizations, as part of a LHS Academy fellowship program. Interviews were completed at two discrete time points: early in project implementation and again at project completion. They explored fellows' experiences with implementing LHS projects in real-world organizational contexts. Data were analyzed using a hybrid deductive-inductive approach guided by a multilevel ecological codebook developed by Shaw et al.
Participants' early interviews highlighted limited confidence, emerging leadership skills, and uncertainty in navigating implementation challenges, while later interviews described greater confidence, stronger leadership capability, and more refined adaptive strategies at the micro level. Organizational influences were described at both time points as slow and uneven, though later interviews included examples of small cultural shifts and early collaborative efforts alongside persistent constraints such as limited resources, siloed cultures, and inadequate digital infrastructure. At the system level, barriers including regulatory complexity, policy misalignment, and limited funding were reported consistently across both interviews and were perceived as largely unchanged.
Building individual capability was necessary but insufficient for successful LHS project implementation. Alignment across micro-, meso-, and macrolevels is critical, requiring parallel investment in organizational readiness, digital infrastructure, and policy reform. Fellowship programs may support individual change and foster proof-of-concept projects, but scaling impact demands coordinated, multilevel strategies.
Learning Health Systems (LHSs) depend on the ability to effectively evaluate programs and translate data into practice. Within the Veterans Affairs (VA) health system, formal mandates require programmatic decision-making to be data-driven; however, opportunities for staff to develop applied program evaluation skills that support LHS learning cycles remain limited.
To address this need, the synchronous virtual Evaluation Bootcamp Training (EBcT) was developed to equip learners with practical, applied evaluation skills. This manuscript describes the design rationale, structure, and core concepts of the training, emphasizing design decisions intended to support evaluation capacity building within a large and complex health system.
EBcT was delivered over 4 days across 2 training cohorts (8 learners in 4 project teams from Cohort 1; 12 learners in 5 project teams from Cohort 2). Project teams addressed a range of clinical and operational initiatives within VA. Learners represented diverse professional roles, including lead evaluators, clinical subject matter experts, project managers, operational leaders, and analysts. Learners reported high satisfaction throughout the training, with full attendance at application sessions and strong participation in didactic components. A majority reported meeting predefined evaluation learning targets at the end of the training.
Findings suggest that structured, team-based training models such as EBcT can strengthen evaluation capacity and support LHS functioning by equipping project teams to engage in repeated cycles of measurement, learning, and performance improvement.
Artificial intelligence is increasingly embedded in healthcare delivery, yet existing Learning Health System (LHS) models do not fully account for the lifecycle management and continuous assurance requirements of AI systems. This gap limits health systems' ability to safely and sustainably integrate AI as a learning component of care.
We conducted a conceptual system modeling investigation grounded in LHS theory and contemporary AI governance frameworks. Through structured theoretical integration, we aligned the classical LHS learning cycle with an action-oriented AI lifecycle and five continuous assurance dimensions, developing a unified framework to support operational implementation within health systems.
The resulting Health AI Learning and Oversight (HALO) model specifies how AI functions as a dynamic knowledge artifact within an LHS. Application of the model illustrates how integrating lifecycle stages and continuous assurance instantiates iterative learning loops, enables adaptive governance, and supports operational lifecycle management, including ongoing monitoring of performance, safety, equity, transparency, and security across clinical environments.
By extending LHS theory to incorporate AI lifecycle and assurance requirements explicitly, the HALO framework operationalizes continuous learning and oversight for AI-enabled health systems. This model provides a foundation for designing, governing, and sustaining responsible and adaptive AI deployment as healthcare environments evolve.
Active post-marketing surveillance of prescribing behavior of high-risk drugs may provide early warning of unforeseen issues in a population, yet analysis approaches for surveillance using real-world data are underdeveloped. This paper evaluates a modified statistical process control (SPC) method for surveillance of risk minimization measures derived from administrative claims data. The approach detects changes in population-level prescribing behaviors and informs investigators of the timing and nature of any detected changes.
We investigated prescription drug claims for tapentadol extended release (ER) from the Colorado All-Payers Claims Database (2012–2019). The cohort was 2702 unique patients receiving their first prescriptions of tapentadol ER (an opiate with an FDA Risk Evaluation and Mitigation Strategy). The risk minimization measures were the prescribing rate and the proportion of prescriptions with appropriate dosing. A statistical model was fitted to data from January–December 2012 to establish a stable baseline and then updated biweekly through June 2019. We conducted surveillance with our modified SPC method and a classical SPC method, controlling the false alarm rate to 0.005 for each, and compared how detections aligned with external policy actions.
Our method detected two periods of unusual prescribing behavior beginning in March 2015 (p < 0.005) and April 2016 (p < 0.005). The classical method detected a single period in October 2016 (p < 0.005). Our detections aligned with risk minimization activities in Colorado; the classical method aligned with only the second activity, 6 months later.
Our modified SPC method, which monitors model misspecification rather than raw prescribing data, detects more periods of changing behavior that better temporally align with risk minimization actions. This tool may be useful for regulatory agencies to independently monitor for emerging risks and prescribing trends to complement REMS assessments.
Stakeholder engagement is a core element of a learning health system (LHS). Meaningful engagement of stakeholders can improve learning within a health system by informing program development and service delivery and by increasing buy-in and sustainability of evidence-based practices. While health systems recognize the value of stakeholder engagement, conducting stakeholder engagement activities requires time, resources, and the support of health system leadership. The objective of this pilot study was to obtain preliminary data on the value of stakeholder engagement from the perspective of rehabilitation directors.
We conducted a general qualitative study utilizing 90-min focus groups. The main outcomes were domains identified from analysis of the focus group transcripts.
Six individuals from 4 health systems participated in one of two focus groups. Seven domains were identified: (1) how stakeholder engagement supports organizational mission and priorities; (2) types of stakeholders working with health systems; (3) past stakeholder engagement experiences; (4) engagement with stakeholders with lived experiences; (5) sustaining stakeholder relationships; (6) challenges with stakeholder engagement; and (7) future stakeholder engagement goals.
This qualitative pilot study of the perspectives of rehabilitation LHS leaders on stakeholder engagement provides preliminary data that can be used to inform future research. While we found consistent themes recognizing the value of stakeholder engagement and participation in stakeholder engagement, respondents noted challenges in engaging stakeholders and using the information gained to effect change.
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