Reimagining how we develop leaders for healthcare’s evolving digital/data ecosystem: Implications for graduate programs in health administration
Bibliographic record
Abstract
Objective: The evolving healthcare landscape, driven by digital transformation and increasing reliance on emerging Artificial Intelligence-derived tools, calls for a reassessment of the competencies required for effective healthcare leadership. Traditional healthcare administration and informatics programs may no longer meet the current and future complexity of the contemporary healthcare system. This study examines how graduate healthcare administration programs could adapt to better equip future leaders with leadership, management, and technical skills.Methods: The research draws on three sources of information that were analyzed by the authors: (1) a comparison of the National Center for Health Leadership Competency Model 3.0TM and the American Medical Informatics Association Health Informatics Core Competencies; (2) analyses of opportunities to integrate health informatics in general and artificial intelligence (AI), in particular - into healthcare administration education competencies; and (3) insights from interviews with 55 C suite executives from 33 U.S. nonprofit health systems.Results: There are areas for integrating and synthesizing competencies from health care and informatics disciplines. In addition, AI may be integrated across a variety of competencies and learning activities. Future executives will require the ability to integrate technology and informatics knowledge and skills into management and leadership competencies, skills, and behaviors.Conclusions: To prepare healthcare leaders for the digital age, educational programs must integrate informatics and AI-driven technologies into their curricula. This includes a focus on data analytics, financial training, regulatory knowledge, and change management. The study calls for a reimagined approach to healthcare education that ensures leaders are equipped to thrive in an increasingly data-driven and regulated environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".