Evidence map›Paper›PMID 39313808›Full record

ArticleBMC health services research2024

Implementing telemedicine with 5G technologies in a nursing home for reducing emergency admissions- study protocol of a mixed-methods study.

Johanna Sophie Lubasch, Patrick Andreas Eder, Christian Kaiser, Andrea Diana Klausen, Daniel Overheu, Anja Partheymüller, Asarnusch Rashid, Simon Thomas Schäfer, Maximilian Scharonow, Insa Seeger

Abstract read
In one paragraph

Article in BMC health services research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
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

10 authors.

Johanna Sophie LubaschResearch Network Emergency and Intensive Care Medicine, School of Medicine and Health Sciences, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany. johanna.lubasch@uol.de.
Patrick Andreas EderInnovation Management, ZTM, Bad Kissingen, Germany.
Christian KaiserEmergency Department, St. Josefs-Hospital Cloppenburg, Cloppenburg, Germany.
Andrea Diana KlausenResearch Network Emergency and Intensive Care Medicine, School of Medicine and Health Sciences, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany.
Daniel OverheuEvangelisches Krankenhaus Oldenburg, Oldenburg, Germany.
Anja PartheymüllerInnovation Management, ZTM, Bad Kissingen, Germany.
Asarnusch RashidInnovation Management, ZTM, Bad Kissingen, Germany.
Simon Thomas SchäferClinic of Anesthesiology/Intensive Care Medicine/Emergency Medicine/Pain Therapy, Department of Human Medicine, School of Medicine and Health Sciences, University of Oldenburg, Klinikum Oldenburg AöR, Oldenburg, Oldenburg, Germany.
Maximilian ScharonowAnesthesia & intensive care medicine, St. Josefs-Hospital Cloppenburg, Cloppenburg, Germany.
Insa SeegerResearch Network Emergency and Intensive Care Medicine, School of Medicine and Health Sciences, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBy transmitting various types of data, telemedical care enables the provision of care where physicians and patients are physically separated. In nursing homes, telemedicine has the potential to reduce hospital admissions in nonemergency situations. In this study, telemedicine devices were implemented with the new 5G mobile communications standard in selected wards of a large nursing home in Northwest Germany. The main aim of this study is to investigate which individual and organizational factors are associated with the use of telemedicine devices and how users perceive the feasibility and implementation of such devices. Moreover, it is investigated whether the telemedical devices help to reduce the number of emergency admissions.

methodsTelemedicine devices are implemented over an 18-month period using a private 5G network, and all users receive training. This study uses qualitative and quantitative methods: To assess the individual and organizational factors associated with the use of telemedicine devices, survey data from employees before and after the implementation of these devices are compared. To assess the perception of the implementation process as well as the feasibility and usability of the telemedical devices, the nursing staff, physicians, medical assistants and residents are interviewed individually. Moreover, every telemedicine consultation is evaluated with a short survey. To assess whether the number of emergency admissions decreased, data from one year before implementation and one year after implementation are compared. The data are provided by the integrated dispatch centre and emergency medical services (EMS) protocols. The interview data are analysed via structured qualitative content analysis according to Kuckartz. Survey data are analysed using multivariable regression analysis. DISCUSSION: Learnings from the implementation process will be used to inform future projects implementing telemedicine in care organizations, making the final telemedicine implementation and care concept available to more nursing homes and hospitals. Moreover, the study results can be used to provide use cases for appropriate and targeted application of telemedicine in nursing homes and to define the role of 5G technologies in these use cases. If the intervention is proven successful, the results will be used to promote 5G network rollout.

trial registrationGerman Clinical Trials Register - trial registration number: DRKS00030598.

Indexed as

Nursing HomesTelemedicineEmergency Service, HospitalFemaleGermanyHumansMalePatient AdmissionQualitative ResearchSurveys and QuestionnairesAmbulatory careAvoiding hospital admissionEmergency Care Information Systems (MeSH Unique ID: D007256)ImplementationNursing Homes (MeSH Unique ID: D009735)Organizational developmentPrimary careTelemedicine (MeSH unique ID: D017216)

Identifiers

PMID39313808
PMCPMC11421173

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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.