Meeting the Need for Human Connection in Our Health Care Workforce
Bibliographic record
Abstract
To the Editor: Sustainable strategies to maintain human connection in our health care workforce are needed,1 especially amidst growing concerns about the potential for artificial intelligence (AI) to outperform human communication in areas that require genuine empathy and connection.2 These reports motivated us to share updates about our experience with an evidence-based experiential interprofessional medical improvisation program, the Alda Healthcare Experience (AHE), that focuses on building human connection, empathy, and team cohesion among our workforce. We believe approaches like this are critical to supporting health care team members’ resilience and ability to serve patients’ needs. The AHE is a series of brief workshops that draw upon the theater arts and the discipline of science communication to build health care communication skills with the explicit intent of promoting a positive organizational workforce culture. Others have demonstrated the positive impact of medical improvisational training on the ability to deliver messages effectively to colleagues and patients.3 The improvisational process—based on active listening, connectedness, and spontaneous collaboration with others—requires empathy and a willingness for an individual to adjust their own communication to meet another’s needs to build trust and foster a clear and accurate exchange of information. Effective team-based care and communication within and across health care organizations are well-established requirements to deliver safe, high-quality care. Supported by our organization’s leadership and the Health Resources and Services Administration, we are currently providing the AHE to 500 of our own health care professionals. Previously, our team was invited to deliver the AHE to health care professionals in partnership with other organizations (e.g., Gold Humanism Foundation, Planetree). Our team has delivered the AHE to health care professionals from the United States and Canada during a 2-day immersive medical improvisation training experience. We welcome the opportunity to partner with others to offer these workshops to their health care workforce to build a positive organizational culture that will enable all—patients and professionals alike—to thrive. Understanding that AI will play an evolving role in health care communication, we believe that AI should never fully replace human-to-human interactions. Leadership to create organizational cultures in health care that value and reward teamwork is urgently necessary. Susmita Pati, MD, MPHChief medical program advisor, Alan Alda Center for Communicating Science, professor of pediatrics and chief of the division of primary care pediatrics, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York; email: [email protected]Laura Lindenfeld, PhDExecutive director, Alan Alda Center for Communicating Science, and dean, School of Communication and Journalism, Stony Brook University, Stony Brook, New YorkStacy Gropack, PT, PhDDean and professor, School of Health Professions, Stony Brook University, Stony Brook, New YorkHarold L. Paz, MD, MSProfessor of medicine, Renaissance School of Medicine, and former executive vice president for health sciences and chief executive officer, Stony Brook University Medicine, Stony Brook University, Stony Brook, New York
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.018 | 0.026 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".