Evidence map›Paper›PMID 41855475›Full record

ArticleJMIR nursing2026

Virtual Nursing Pilot in the Inpatient Setting: Qualitative Evaluation.

Ksenia Gorbenko, Maria Bailon, Jordan Randall, James Viskochil, Clara Cualing, Patrick Healy, Madhu Mazumdar, Robert Freeman

Abstract read
In one paragraph

Article in JMIR nursing, 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

8 authors.

Ksenia GorbenkoDepartment of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, One Gustav Levy Place, Box 1077, New York, NY, 10029, United States, 1 212-659-1470.ORCID 0000-0003-1010-2257
Maria BailonMedical/Surgical Unit, Mount Sinai Hospital, Mount Sinai Health System, New York, NY, United States.ORCID 0009-0003-6528-987X
Jordan RandallClinical Operations and Analytics, New York-Presbyterian Hospital, New York, NY, United States.ORCID 0009-0003-3510-749X
James ViskochilMedical/Surgical Unit, Mount Sinai Hospital, Mount Sinai Health System, New York, NY, United States.ORCID 0009-0008-6714-1517
Clara CualingMedical/Surgical Unit, Mount Sinai Hospital, Mount Sinai Health System, New York, NY, United States.ORCID 0009-0009-9235-1476
Patrick HealyTechnology, Innovation, and Transformation, Rush University System for Health, Chicago, IL, United States.ORCID 0000-0002-1784-3958
Madhu MazumdarDepartment of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, One Gustav Levy Place, Box 1077, New York, NY, 10029, United States, 1 212-659-1470.ORCID 0000-0003-3965-5146
Robert FreemanMedical/Surgical Unit, Mount Sinai Hospital, Mount Sinai Health System, New York, NY, United States.ORCID 0000-0003-4946-6533

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Global nursing shortages require innovative care delivery models. Virtual nursing is a cutting-edge model being explored. Objective: This study aimed to examine the perspectives of clinical and administrative staff involved in a virtual nursing pilot on a medical-surgical unit and to identify best practices for future adoption. Methods: We conducted a qualitative evaluation using individual semistructured interviews with virtual and bedside nurses, nurse executives, and IT project managers implementing a virtual nursing pilot program at a medical-surgical unit with 35 private and semiprivate beds at the Mount Sinai Hospital, a 1110-bed acute care hospital in New York, NY. Interviews took place in the spring of 2024 and were completed via phone or Zoom, audio recorded, and professionally transcribed. Participants were selected using purposive sampling. The authors applied an iterative thematic analysis to transcripts using Dedoose. Claude.ai was used to generate code summaries for select codes. Results: Of 33 individuals approached, 16 (48.5%) consented to participate. Nine participants were clinical staff (virtual and bedside nurses and nurse managers), and 7 were executive leaders or managers in nursing and informatics. Our analysis identified the following themes: (1) staff attitudes toward virtual nursing shifted from resistance to acceptance over time, (2) direct communication channels between virtual and bedside nurses were critical for efficient care coordination and model adoption, (3) admission and discharge processes evolved throughout the pilot implementation, and (4) adaptable staffing allocations were necessary to accommodate fluctuating patient census and unit demands. Conclusions: The main beneficiaries of this intervention, bedside nurses, found their virtual counterparts helpful following a few adjustments. Participants reported a perceived reduction in administrative burden, uninterrupted completion of clinical tasks, and they felt their overtime was reduced, which all increased their buy-in for this care model. There are several opportunities for improvement, such as real-time communication, unit-specific virtual nurse training, and flexible staffing for high-volume units. Our findings suggest that virtual nursing can address staffing challenges. Calculating the true return on investment for virtual nursing programs will require comprehensive mixed methods evaluations of such outcomes as care team and patient satisfaction, length of stay, readmission prevention, completion of nursing tasks, and reduction in overtime.

Indexed as

TelenursingAdultAttitude of Health PersonnelFemaleHumansInpatientsMaleMiddle AgedPilot ProjectsQualitative Researchcare coordinationcare delivery modelchange managementdigital transformationevaluationqualitativestaff satisfactionsystems integrationvirtual nursing

Identifiers

PMID41855475
PMCPMC13001998

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Registered trials

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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 OpenQuestion graph.