Presentation Details
| Patient Engagement and Experience with AI-Enabled Preoperative Instruction Calls: Analysis of Pilot Implementation Data Kristen M.Lund, Avital Y.O'Glasser. Oregon Health & Science University, Portland, OR, USA |
Abstract
BACKGROUND: Clear and consistent delivery of preoperative instructions is essential to ensure surgical readiness and reduce day-of-surgery cancellations. Manual outreach can be time-intensive and variable, leading to inconsistencies. To enhance communication efficiency and patient experience, an artificial intelligence (AI) conversational assistant was implemented to conduct automated outbound phone calls that deliver preoperative instructions, verify readiness, and answer common patient questions through natural dialogue.
PURPOSE: To date, more than 400 AI-driven preoperative instruction calls have been completed, with analysis underway and total call volume expected to reach approximately 1,000 by the time of presentation. The AI assistant uses natural-language processing to confirm readiness details, review fasting and infection prevention strategies, and flag unresolved issues for scheduling teams to follow up on. Call data were analyzed for completion and comprehension rates, Net Promoter Score (NPS), and demographic trends in engagement and follow-up requests.
RESULTS: Findings demonstrated high patient engagement and satisfaction with the automated calls. The average NPS was 8.1, indicating strong approval of clarity, tone, and convenience. Greater than 85% of patients completed their calls without requiring human assistance. Requests for live follow-up were most common among Generation X patients (ages 40s–50s), suggesting variation in comfort with AI-based communication by generation. Common positive feedback themes included appreciation for clear instructions, time savings, and reduced anxiety about surgical preparation.
CONCLUSIONS: AI-enabled phone outreach for preoperative instructions demonstrates strong patient acceptance and the potential to improve readiness communication while reducing staff administrative workload. Continued analysis of call data will further evaluate engagement patterns, demographic differences, and impacts on readiness compliance and surgical cancellation rates.
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: To date, more than 400 AI-driven preoperative instruction calls have been completed, with analysis underway and total call volume expected to reach approximately 1,000 by the time of presentation. The AI assistant uses natural-language processing to confirm readiness details, review fasting and infection prevention strategies, and flag unresolved issues for scheduling teams to follow up on. Call data were analyzed for completion and comprehension rates, Net Promoter Score (NPS), and demographic trends in engagement and follow-up requests.
RESULTS: Findings demonstrated high patient engagement and satisfaction with the automated calls. The average NPS was 8.1, indicating strong approval of clarity, tone, and convenience. Greater than 85% of patients completed their calls without requiring human assistance. Requests for live follow-up were most common among Generation X patients (ages 40s–50s), suggesting variation in comfort with AI-based communication by generation. Common positive feedback themes included appreciation for clear instructions, time savings, and reduced anxiety about surgical preparation.
CONCLUSIONS: AI-enabled phone outreach for preoperative instructions demonstrates strong patient acceptance and the potential to improve readiness communication while reducing staff administrative workload. Continued analysis of call data will further evaluate engagement patterns, demographic differences, and impacts on readiness compliance and surgical cancellation rates.
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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