Utilization of Generative AI-drafted Responses for Managing Patient-Provider Communication

Soumik Mandal; Batia M Wiesenfeld; Adam C Szerencsy; William R Small; Vincent Major; Safiya Richardson; Antoinette Schoenthaler; Devin Mann; Oded Nov

doi:10.1038/s41746-025-01972-w

Utilization of Generative AI-drafted Responses for Managing Patient-Provider Communication

NPJ Digit Med. 2025 Oct 2;8(1):591. doi: 10.1038/s41746-025-01972-w.

Authors

Soumik Mandal^{1

2

3}, Batia M Wiesenfeld⁴, Adam C Szerencsy^{5

6}, William R Small^{5

6}, Vincent Major^{7

5}, Safiya Richardson^{7

5}, Antoinette Schoenthaler^{7

8}, Devin Mann^{7

5}, Oded Nov⁹

Affiliations

¹ Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA. Soumik.mandal@nyulangone.org.
² Technology Management & Innovation, New York University Tandon School of Engineering, New York, NY, USA. Soumik.mandal@nyulangone.org.
³ Institute of Excellence in Health Equity, New York University Langone Health, New York, NY, USA. Soumik.mandal@nyulangone.org.
⁴ Department of Management, New York University Leonard N Stern School of Business, New York, NY, USA.
⁵ MCIT Department of Health Informatics, New York University Langone Health, New York, NY, USA.
⁶ Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA.
⁷ Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA.
⁸ Institute of Excellence in Health Equity, New York University Langone Health, New York, NY, USA.
⁹ Technology Management & Innovation, New York University Tandon School of Engineering, New York, NY, USA.

Abstract

The integration of generative AI (GenAI) in patient communication presents benefits and challenges. This retrospective observational study analyzed EHR audit logs to assess how 75 healthcare professionals (HCPs) utilized AI-generated drafts for patient messages from October 2023 to August 2024 at a large health system in New York City. Overall utilization was low (19.4%), though prompt refinements improved usage (from 12% to 20%), particularly among physicians. GenAI drafts were generated for all messages, including 80% that received no response, adding to the review burden and potentially undermining efficiency. Text analysis showed HCPs preferred concise, information-rich drafts, with role-based differences-physicians favored shorter drafts, while clinical support staff preferred more empathetic responses. AI-generated drafts reduced message turnaround time by 6.76% despite a marginal increase in required steps (InBasket actions). These findings highlight the need for targeted GenAI deployment strategies, better aligned with clinician workflows and optimized draft generation for improved efficiency.

Abstract

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