Evidence map›Paper›PMID 40070377›Full record

ArticleDelaware journal of public health2024

Integrating Collaborative Robots into a Complex Hospital Setting: A Qualitative Descriptive Study.

Susan Smith Birkhoff, Paige Merring, Amanda Spence, Wendy Bassett, Stephanie C Roth

Abstract read
In one paragraph

Article in Delaware journal of public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
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.

Susan Smith BirkhoffChristianaCare Health System.
Paige MerringChristianaCare Health System.
Amanda SpenceChristianaCare Health System.
Wendy BassettChristianaCare Health System.
Stephanie C RothChristianaCare Health System.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To describe the use, activities, and human interactions of cobots as a delivery system for medications, supplies, and equipment within a complex and multi-level 900-bed hospital setting. Integrating collaborative robots (cobots) into existing hospital workflows as a secure delivery transportation system is an early innovation and emerging area to explore. Methods: Guided by the Diffusion of Innovations theory, a qualitative descriptive design was used to build the foundational knowledge required to better understand and describe cobot implementation in the acute care hospital setting. The cobots were observed on all shifts, on different days of the week as they interacted with staff members, clinicians, and visitors while they traveled throughout the hospital completing deliveries. Data were analyzed among the study team members using an inductive coding approach followed by a qualitative content analysis level of interpretation. Results: For seven weeks from November 2022 - December 2022, 33 hours were collected from 23 individual cobot observation sessions. These observations included 89 end-to-end cobot deliveries. After analysis, four major themes emerged: 1) humanization of robots, 2) usability of robots, 3) cobots' autonomy, and 4) cobots' functionality within a dynamic hospital environment. Conclusions: Implementing cobots as a semi-autonomous delivery transporter is still in the early innovation phase. The cobots used in this study required human support to function adequately in a complicated and unpredictable environment. To sustainably augment current and future workflows exclusively performed by human, the cobots will need to transition toward greater model of autonomy and less human assistance.

Identifiers

PMID40070377
PMCPMC11892717

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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.