555: From Data to Decisions: Nursing Judgment in AI-Supported Outpatient Postoperative Care

Topic: Education, Societal Impact, and Others
Subtopic: Artificial Intelligence/Machine Learning

Background and Aims: Wearable technology and artificial intelligence advances have expanded use of patient-generated health data in clinical care . Nurses remain responsible for interpreting data, integrating caregiver reports, and making time-sensitive decisions to determine risk and need for escalation of concerns . The primary aim of this study is to understand nurses’ lived experiences and clinical decision making in outpatient postoperative care. A secondary aim is examining how AI outputs from sensor data are used, interpreted, and questioned in practice .

Methods: This qualitative study uses phenomenological design to examine lived experiences of nurses engaged in outpatient postoperative care. A purposive sample of nurses using patient-generated health data, including data from wearables or other sensors, will be recruited . Data will be collected through semi-structured interviews focused on clinical decision-making, risk assessment, escalation processes, and the use of predictive artificial intelligence. Interviews will be audio-recorded, transcribed, and analyzed using interpretive phenomenological analysis to identify themes . Rigor will be supported through reflexive journaling, use of direct quotes, and member checking.

Results: Recruitment is ongoing.

Conclusions: This study employs a qualitative phenomenological approach to examine how nurses engage in clinical decision-making within data-rich outpatient postoperative care environments. By focusing on nurses’ lived experiences, the study is methodologically positioned to capture elements of clinical judgment, data interpretation, and workflows not well represented in research. This approach is intended to generate insights that to inform the design and evaluation of predictive tools and alert systems that incorporate real-world workflows and nursing clinical practices. This methods-focused work contributes to interdisciplinary efforts to better align emerging technologies with real-world clinical decision processes.

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Presenting Author: Elizabeth Rose Davis
Submitting Author: Elizabeth Davis

Senior Author: Renee Manworren

IASP 2026 Global Year theme of Neuropathic Pain: No

• Renee CB Manworren: PhD - The University of Texas at Arlington
• Elizabeth Rose Davis: BSN - The University of Texas at Arlington
• Regina Urban: PhD - The University of Texas at Arlington