Presentation Details
| A Steward-Led Framework for Preserving Clinical Judgment and the Therapeutic Relationship in Perioperative Care Eva H.Berry. BerryWell Holdings, Inc., Orlando, FL, USA |
Abstract
BACKGROUND: Perioperative systems face increasing strain from workforce shortages, rising patient complexity, and administrative saturation. Highly trained clinicians—particularly registered nurses, advanced practice registered nurses (APRNs), physician assistants (PAs), and anesthesia professionals—are frequently tasked with repetitive, low-complexity data collection. This misalignment of expertise erodes cognitive bandwidth, diminishes professional role integrity, weakens the clinician–patient relationship, and contributes to burnout and attrition. Over time, therapeutic connection has been displaced at the altar of efficiency, documentation, and throughput.
PURPOSE: This work proposes a steward-led framework in which artificial intelligence (AI) functions strictly as a preoperative data-collection extender. The system is designed to gather and organize standardized, low-risk information (e.g., medication history, relevant diagnostics, prior medical events, and standardized screening inputs) through asynchronous, patient-centered interaction. AI systems are intentionally restricted from interpreting data, making recommendations, or influencing clinical judgments. All synthesis, risk stratification, and perioperative planning remain the responsibility of licensed clinicians.
RESULTS: By reassigning clerical burden away from clinicians, this framework restores time and cognitive capacity for meaningful patient engagement, clinical reasoning, and individualized perioperative planning. The clinician–patient encounter is refocused on education, partnership, and trust rather than form completion, strengthening both safety and therapeutic presence. Proposed governance principles include transparent patient consent, clinician oversight of atypical findings, and strict adherence to professional scope boundaries.
CONCLUSIONS: A steward-led approach to AI integration has the potential to reduce clinician burnout, improve data completeness, restore integrity to the therapeutic relationship, and preserve human judgment in perioperative care. This framework represents a deliberate, ethical response to workforce strain that prioritizes stewardship of both clinicians and patients during a period of accelerating technological change.
No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the author.
PURPOSE: This work proposes a steward-led framework in which artificial intelligence (AI) functions strictly as a preoperative data-collection extender. The system is designed to gather and organize standardized, low-risk information (e.g., medication history, relevant diagnostics, prior medical events, and standardized screening inputs) through asynchronous, patient-centered interaction. AI systems are intentionally restricted from interpreting data, making recommendations, or influencing clinical judgments. All synthesis, risk stratification, and perioperative planning remain the responsibility of licensed clinicians.
RESULTS: By reassigning clerical burden away from clinicians, this framework restores time and cognitive capacity for meaningful patient engagement, clinical reasoning, and individualized perioperative planning. The clinician–patient encounter is refocused on education, partnership, and trust rather than form completion, strengthening both safety and therapeutic presence. Proposed governance principles include transparent patient consent, clinician oversight of atypical findings, and strict adherence to professional scope boundaries.
CONCLUSIONS: A steward-led approach to AI integration has the potential to reduce clinician burnout, improve data completeness, restore integrity to the therapeutic relationship, and preserve human judgment in perioperative care. This framework represents a deliberate, ethical response to workforce strain that prioritizes stewardship of both clinicians and patients during a period of accelerating technological change.
No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the author.
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