Evidence map›Paper›PMID 42224675›Full record

Observational studyJournal of medical Internet research2026

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

Gina L Georgadarellis, Ellen Benjamin, Shannon C Roberts, Cidalia J Vital, Frank C Sup Iv

Abstract readObservational Study
In one paragraph

Observational study in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Gina L GeorgadarellisMechanical and Industrial Engineering, Riccio College of Engineering, University of Massachusetts Amherst, Engineering Laboratory, 160 Governors Drive, Amherst, MA, United States, 1 413-545-2946.ORCID http://orcid.org/0009-0004-3351-8882
Ellen BenjaminDonna M and Robert J Manning College of Nursing and Health Sciences, University of Massachusetts Boston, Boston, MA, United States.ORCID http://orcid.org/0000-0003-3287-3802
Shannon C RobertsMechanical and Industrial Engineering, Riccio College of Engineering, University of Massachusetts Amherst, Engineering Laboratory, 160 Governors Drive, Amherst, MA, United States, 1 413-545-2946.ORCID http://orcid.org/0000-0002-0052-7801
Cidalia J VitalBaystate Health, Springfield, MA, United States.ORCID http://orcid.org/0000-0003-2753-2683
Frank C Sup IvMechanical and Industrial Engineering, Riccio College of Engineering, University of Massachusetts Amherst, Engineering Laboratory, 160 Governors Drive, Amherst, MA, United States, 1 413-545-2946.ORCID http://orcid.org/0000-0002-6290-9805

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Robotic technology has the potential to assist nurses, but the complexity and unpredictability of health care environments cannot be replicated in a laboratory setting. Furthermore, 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 use 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 use an observational study of active hospital units to gain a deeper understanding of nursing tasks, workflow, and the health care setting; 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 framework; and synthesize the work system components of the PETT scan into themes. Methods: We used the practice-oriented model of the Systems Engineering Initiative for Patient Safety, 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, medical and surgical intensive care unit, preop or postanesthesia care unit, 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 total of 53 hours across 16 sessions were recorded. Multiple rounds of inductive and deductive coding were conducted. Briefly, a 3-phase iterative data analysis process was used-initial inductive content analysis, a deductive phase to organize emergent categories into a PETT scan, and finalization of the PETT scan with the identification of overarching themes. Results: Observations across all units yielded a broad set of barriers to integrating robotic and other health care technologies. Using the PETT scan, 78 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 robotic technologies are presently best suited for auxiliary and background roles. Broader integration into patient care workflows will depend on designs that align with clinical workflows, support interoperability and robustness, and address ethical, accountability, and coordination challenges inherent in nursing care, as well as maintained organizational support.

Indexed as

NursingRoboticsHumansWorkflowergonomicshealthcarehospital unitshuman factorsnursingpatient safetyroboticstechnologyworkflow

Identifiers

PMID42224675
PMCPMC13225718

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.