A Learning Health System Infrastructure for Precision Rehabilitation After Stroke

Relevance of learning health systems to physiatrists and its synergy with implementation science: A commentary

As health care attempts to bridge the gap between evidence and practice, the concept of the learning health system (LHS) is becoming increasingly relevant. LHS integrates evidence with health systems data, driving health care quality and outcomes through updates in policy, practice, and care delivery. In addition, LHS research is becoming critically important as there are several initiatives underway to increase research capacity, expertise, and implementation, including attempts to stimulate increasing numbers of LHS researchers. Physical Medicine & Rehabilitation (PM&R) physicians (physiatrists), nurses, therapists (physical therapists, occupational therapists, speech therapists, clinical psychologists), and scientists are affiliated with LHSs. As LHS research expands in health care systems, better awareness and understanding of LHSs and LHS research competencies are key for rehabilitation professionals including physiatrists. To address this need, the Agency of Healthcare Research and Quality (AHRQ) identified 33 core competencies, grouped into eight domains, for training LHS researchers. The domains are: (1) Systems Science; (2) Research Questions and Standards of Scientific Evidence; (3) Research Methods; (4) Informatics; (5) Ethics of Research and Implementation in Health Systems; (6) Improvement and Implementation Science; (7) Engagement, Leadership, and Research Management; and the recently added (8) Health and Healthcare Equity and Justice. The purpose of this commentary is to define LHS and its relevance to physiatrists, present the role of implementation science (IS) in LHSs and application of IS principles to design LHSs, illustrate current LHS research in rehabilitation, and discuss potential solutions to improve awareness and to stimulate interest in LHS research and IS among physiatrists in LHSs.

Transforming modeling in neurorehabilitation: clinical insights for personalized rehabilitation

Practicing clinicians in neurorehabilitation continue to lack a systematic evidence base to personalize rehabilitation therapies to individual patients and thereby maximize outcomes. Computational modeling- collecting, analyzing, and modeling neurorehabilitation data- holds great promise. A key question is how can computational modeling contribute to the evidence base for personalized rehabilitation? As representatives of the clinicians and clinician-scientists who attended the 2023 NSF DARE conference at USC, here we offer our perspectives and discussion on this topic. Our overarching thesis is that clinical insight should inform all steps of modeling, from construction to output, in neurorehabilitation and that this process requires close collaboration between researchers and the clinical community. We start with two clinical case examples focused on motor rehabilitation after stroke which provide context to the heterogeneity of neurologic injury, the complexity of post-acute neurologic care, the neuroscience of recovery, and the current state of outcome assessment in rehabilitation clinical care. Do we provide different therapies to these two different patients to maximize outcomes? Asking this question leads to a corollary: how do we build the evidence base to support the use of different therapies for individual patients? We discuss seven points critical to clinical translation of computational modeling research in neurorehabilitation- (i) clinical endpoints, (ii) hypothesis- versus data-driven models, (iii) biological processes, (iv) contextualizing outcome measures, (v) clinical collaboration for device translation, (vi) modeling in the real world and (vii) clinical touchpoints across all stages of research. We conclude with our views on key avenues for future investment (clinical-research collaboration, new educational pathways, interdisciplinary engagement) to enable maximal translational value of computational modeling research in neurorehabilitation.

Leveraging Emerging Technologies to Expand Accessibility and Improve Precision in Rehabilitation and Exercise for People with Disabilities

Physical rehabilitation and exercise training have emerged as promising solutions for improving health, restoring function, and preserving quality of life in populations that face disparate health challenges related to disability. Despite the immense potential for rehabilitation and exercise to help people with disabilities live longer, healthier, and more independent lives, people with disabilities can experience physical, psychosocial, environmental, and economic barriers that limit their ability to participate in rehabilitation, exercise, and other physical activities. Together, these barriers contribute to health inequities in people with disabilities, by disproportionately limiting their ability to participate in health-promoting physical activities, relative to people without disabilities. Therefore, there is great need for research and innovation focusing on the development of strategies to expand accessibility and promote participation in rehabilitation and exercise programs for people with disabilities. Here, we discuss how cutting-edge technologies related to telecommunications, wearables, virtual and augmented reality, artificial intelligence, and cloud computing are providing new opportunities to improve accessibility in rehabilitation and exercise for people with disabilities. In addition, we highlight new frontiers in digital health technology and emerging lines of scientific research that will shape the future of precision care strategies for people with disabilities.