Reimagining Health Care and Education in the 21st Century: Lessons in Innovation from the Harvard Macy Institute.
Published by: Eboni Anderson, PhD, DHEd
Last week, from Sunday, June 1 through Friday, June 6, I stepped away from the familiar corridors of ATSU-SOMA to immerse myself in the Harvard Macy Institute’s Leading Innovation in Health Care and Education Program in Boston, MA. Balancing roles as a director, faculty member, researcher, and a medical student faculty advisor often means my days blur into back-to-back grading, student and faculty meetings, writing manuscripts, and research. This dedicated week offered a rare chance to pause and critically reflect on how we teach, and to gather fresh strategies for shaping the next generation of osteopathic physicians.
Innovation, artificial intelligence, and technology aren’t just buzzwords; they’re catalysts for rethinking how students learn and practice medicine. A recent scoping review of educational technologies found that AI modules, virtual-reality simulations, telemedicine training, and digital platforms collectively serve to improve knowledge, skills, and engagement, while preparing learners for modern clinical environments [5, 6]. Yet, this same body of work highlights that these tools should augment, not replace, the invaluable hands-on experiences that define osteopathic training. Addressing challenges such as infrastructure costs, methodological rigor, and digital fatigue is essential to ensure these innovations truly enhance learning rather than simply add complexity.
Concrete data bring these insights to life. In studies of modular AI courses, fourth-year medical students saw quiz performance jump by 97% and skill application rise by 89% [3]. Broader AI assessments reported 32% gains on multiple-choice exams and 70% improvements in imaging interpretation [4]. Simulation and virtual-reality interventions achieved Kirkpatrick Level 2b learning outcomes (knowledge and skills) in 73% of studies, demonstrating clear cognitive and psychomotor benefits [7]. Meanwhile, telemedicine training and digital-platform initiatives, even when not quantified, consistently describe enhanced competencies, greater access, and improved time management, albeit with noted issues such as uneven implementation and digital fatigue [8,1-2]. These figures provide a compelling evidence base for thoughtful integration into our curriculum.
At Harvard Macy Institute, I wasn’t merely a spectator; I was an active participant in workshops and design sprints that translated this evidence into practice. I honed a design-thinking approach to map learners’ journeys, pinpointing where adaptive AI quizzes could reinforce basic science concepts or where virtual-reality anatomy labs might deepen spatial understanding before students ever entered the cadaver suite. I explored evidence-based frameworks that guide the choice between high-fidelity mannequins and augmented-reality overlays, ensuring each technological adoption aligns with clear competency goals rather than novelty. Collaborative sessions with pharmacy, nursing, and allied-health educators revealed how shared resources, simulation centers, AI platforms, and standardized-patient programs, can foster seamless interprofessional learning. Additionally, leadership modules equipped me with storytelling techniques to reframe concerns about costs or digital fatigue as strategic investments in equity, quality, and future-proofing.
Returning to ATSU-SOMA, I’m energized by a concrete action plan. First, I’d like to propose a longitudinal course in care coordination that uses AI-enhanced assessment modules, embedding open-source, adaptive quizzes that flag individual knowledge gaps and guide personalized remediation. Next, I hope to work with the College of Graduate Health Studies to repurpose our MPH curriculum to introduce small-group virtual-reality public health sessions, letting students walk through epidemiology or biostatistics before entering the real world of public health practice. We should also explore weaving telemedicine training across clerkships, from digital-communication skills in preclinical labs to supervised remote consultations in clinical rotations. I hope to continue working on interprofessional digital health projects where medical, dental, nursing, public health, and social work (and other health professions students) will co-design patient-centered care plans leveraging AI decision-support, remote monitoring devices, and simulation case studies.
Measurement will be the linchpin of our work. Inspired by Harvard Macy Institute’s emphasis on robust evaluation, I hope to lead the development of tracking quantitative outcomes, quiz scores, OSCE performance, and pair them with rich qualitative data from student reflections and faculty observations. This mixed-methods approach will ensure that each innovation is iteratively refined, truly enhancing clinical competence rather than simply adding new tools.
Attending the Leading Innovation in Health Care and Education Program at Harvard Medical School was more than a professional retreat; it was a transformational reset. Armed with evidence-informed frameworks, practical design tools, and a global network of colleagues, I’m excited to help guide ATSU-SOMA into a new era of medical education, one where technology amplifies our osteopathic philosophy, fosters lifelong learning, and readies future physicians to meet the ever-evolving needs of their patients.
References
Neve G, Dutta N, Kumar S. Exploring the teaching and training needs of students and clinicians in digital health. BMJ Leader. 2020;4:A39.1-A39. doi:10.1136/leader-2020-FMLM.103.
Tan C, Cai C, Ithnin F, Lew E. Challenges and innovations in undergraduate medical education during the COVID-19 pandemic—a systematic review. Asia Pac Scholar. 2022;7(3):1.
Krive J, Isola M, Chang L, Patel T, Anderson M, Sreedhar R. Grounded in reality: artificial intelligence in medical education. JAMIA Open. 2023;6(2):ooad037. Published 2023 Jun 1. doi:10.1093/jamiaopen/ooad037
Varma JR, Fernando S, Ting BY, Aamir S, Sivaprakasam R. The global use of artificial intelligence in the undergraduate medical curriculum: a systematic review. Cureus. 2023;15(5):e39701. Published 2023 May 30. doi:10.7759/cureus.39701
Fung K. Otolaryngology-head and neck surgery in undergraduate medical education: advances and innovations. Laryngoscope. 2015;125:S1-S14.
Altintas L, Sahiner M. Transforming medical education: the impact of innovations in technology and medical devices. Expert Rev Med Devices. 2024;21:797-809. doi: 10.1080/17434440.2024.2400153
Wu Q, Wang Y, Lu L, Chen Y, Long H, Wang J. Virtual simulation in undergraduate medical education: a scoping review of recent practice. Front Med (Lausanne). 2022;9:855403. Published 2022 Mar 30. doi:10.3389/fmed.2022.855403
Waseh S, Dicker AP. Telemedicine training in undergraduate medical education: mixed-methods review. JMIR Med Educ. 2019;5(1):e12515. Published 2019 Apr 8. doi:10.2196/12515
