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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Dec 11, 2025
Date Accepted: Apr 30, 2026

The final, peer-reviewed published version of this preprint can be found here:

Evaluating Nursing Work Systems and Identifying Barriers for Robotic Technology Integration: Observational Study

Georgadarellis GL, Benjamin E, Roberts SC, Vital CJ, Sup FC IV

Evaluating Nursing Work Systems and Identifying Barriers for Robotic Technology Integration: Observational Study

J Med Internet Res 2026;28:e89409

DOI: 10.2196/89409

PMID: 42224675

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Evaluating nursing work systems: An observational study identifying barriers for robotic technology integration

  • Gina L Georgadarellis; 
  • Ellen Benjamin; 
  • Shannon C Roberts; 
  • Cidalia J Vital; 
  • Frank C Sup IV

ABSTRACT

Background:

Robotic technology has the potential to assist nurses, but the complexity and unpredictability of healthcare environments cannot be replicated in a laboratory setting. Further, there is a lack of experiential evidence that robotic technology will meaningfully impact nursing. Collaborative development of technology and real-world usability studies offers the ability to address problems early in the design process when functional changes can be implemented.

Objective:

The purpose of this study was to employ an observational study and systematically evaluate the work system of inpatient nurses to identify barriers to the integration of robotic technology. The objectives are to employ an observational study of active hospital units to gain a deeper understanding of nursing tasks, workflow, and the healthcare setting; systematically evaluate the work system of inpatient nurses to identify barriers to the integration of robotic technology using the People, Environment, Tools, and Tasks (PETT) Scan from the Systems Engineering Initiative for Patient Safety (SEIPS) framework; and synthesize the work system components of the PETT Scan into themes.

Methods:

We used the practice-oriented model of SEIPS, the PETT Scan, to identify barriers for robotic technology use and innovation. A convenience sample of nursing staff was observed as they worked. Units included the emergency department (ED), medical and surgical intensive care unit, pre-op/post-anesthesia care unit (PACU), and general medical-surgical floor. The total number of observation hours per unit was based on data saturation, which occurred at variable times during the day shift, and was arranged with unit management. A mixed-methods approach of inductive and deductive coding was employed. Briefly, a three-phase iterative data analysis process was used: initial inductive and deductive content analysis to create code books; a follow-up phase to review code books and translate them into a PETT Scan; and finalization of the PETT Scan. Codes were clustered into themes.

Results:

Observations across all units yielded a broad set of barriers to integrating robotic and other healthcare technologies. Using the PETT Scan, over 75 barriers were identified and were summarized into 20 themes with supporting subthemes and exemplars.

Conclusions:

By systematically observing nursing workflows and synthesizing barriers into themes, this study provides new insight into the conditions that enable or constrain robotic integration. Findings suggest that robotics is best integrated into auxiliary and background tasks. Designs must prioritize adaptability, workflow alignment, robustness, interoperability, and ethical clarity. Successful implementation will require organizational support and relational sensitivity.


 Citation

Please cite as:

Georgadarellis GL, Benjamin E, Roberts SC, Vital CJ, Sup FC IV

Evaluating Nursing Work Systems and Identifying Barriers for Robotic Technology Integration: Observational Study

J Med Internet Res 2026;28:e89409

DOI: 10.2196/89409

PMID: 42224675

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