Evidence map›Paper›PMID 41928827›Full record

ArticleDigital health

Feasibility of autonomous medication delivery robots considering elevator utilization in high-traffic hospital environments.

Yourack Lee, Song-Ee Kim, Jeong Su Kim, Hyeong Guk Son, Sung Hyeon Lee, Hyung-Joo Lee, Hyobeen Jeong, Jin Woo Lim, Sug Young Park, Yong Joo Lee and 8 more

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

18 authors.

Yourack LeeBiomedical Research Center, Korea University Guro Hospital, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0001-7674-0329
Song-Ee KimMedical Device Usability Test Center, Korea University Guro Hospital, Seoul, Republic of Korea.ORCID https://orcid.org/0009-0006-8855-472X
Jeong Su KimMedical Device Usability Test Center, Korea University Guro Hospital, Seoul, Republic of Korea.ORCID https://orcid.org/0009-0006-0445-1257
Hyeong Guk SonMedical Device Usability Test Center, Korea University Guro Hospital, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0002-9352-9039
Sung Hyeon LeeBiomedical Research Center, Korea University Guro Hospital, Seoul, Republic of Korea.
Hyung-Joo LeeDepartment of Nursing, Korea University Guro Hospital, Seoul, Republic of Korea.
Hyobeen JeongDepartment of Nursing, Korea University Guro Hospital, Seoul, Republic of Korea.
Jin Woo LimDepartment of Pharmacy, Korea University Guro Hospital, Seoul, Republic of Korea.
Sug Young ParkDepartment of Pharmacy, Korea University Guro Hospital, Seoul, Republic of Korea.
Yong Joo LeeDepartment of Nursing, Korea University Guro Hospital, Seoul, Republic of Korea.
So Jung KangDepartment of Nursing, Korea University Guro Hospital, Seoul, Republic of Korea.
Chanho ParkDepartment of Pathology, Korea University Guro Hospital, Seoul, Republic of Korea.
Jung Boone KimDepartment of Pathology, Korea University Guro Hospital, Seoul, Republic of Korea.ORCID https://orcid.org/0009-0003-2290-5564
Minsun KimDepartment of Nursing, Korea University Guro Hospital, Seoul, Republic of Korea.
Jang Pyo YuResearch and Development Center, DOGU Co., Ltd., Seoul, Republic of Korea.
Quoc Huy TranResearch and Development Center, DOGU Co., Ltd., Seoul, Republic of Korea.
Jun Seo ParkResearch and Development Center, DOGU Co., Ltd., Seoul, Republic of Korea.
Il-Ho ParkMedical Device Usability Test Center, Korea University Guro Hospital, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0002-7011-6071

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Autonomous medication delivery robots can streamline hospital logistics. However, their feasibility under elevator congestion remains uncertain. Objective: To evaluate the feasibility of medication delivery robots in a tertiary hospital and quantify how the elevator operating rate (EOR, %) affects delivery success, delay, and user experience. Methods: A prospective feasibility study was conducted in a tertiary hospital where a robot is used for delivering medicine. We analyzed 122 non-urgent missions from June 18-29, 2025, spanning weekdays and weekends. Data included the Elevator Operating Rate (EOR), passenger and cargo counts, Elevator Waiting Time, and Elevator Travel Time. The delivery outcomes were recorded, and a Monte Carlo simulation was used to model the failure probabilities under different congestion scenarios. The staff usability and workload were assessed using the System Usability Scale (SUS) and NASA Task Load Index (NASA-TLX). Results: A Higher EOR was strongly associated with more delivery failures. Most failures resulted from physical obstruction by passengers or cargo. The data also confirmed that a high EOR coincided with greater elevator occupancy. Simulations incorporating space occupancy reproduced failure patterns similar to the in situ observations. An increased EOR also prolonged the delivery time. The staff reported relatively high usability, but the NASA-TLX scores indicated that frequent robot users felt greater time-related pressure, likely reflecting delays during congestion. Conclusions: Autonomous medication delivery is feasible. However, its performance is sensitive to elevator congestion. Effective deployment requires consideration of elevator usage rates, and robotic medication delivery should be scheduled when congestion is below critical thresholds to ensure reliability and minimize the staff burden.

Indexed as

delivery robotdigital healthfeasibility studyhospital automation

Identifiers

PMID41928827
PMCPMC13039597

What Socratic holds

Textmetadata
LicenceCC BY-NC
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.