Evidence map›Paper›PMID 35635840›Full record

Observational studyJournal of medical Internet research2022

Nursing Workflow Change in a COVID-19 Inpatient Unit Following the Deployment of Inpatient Telehealth: Observational Study Using a Real-Time Locating System.

Stacie Vilendrer, Mary E Lough, Donn W Garvert, Monique H Lambert, Jonathan Hsijing Lu, Birju Patel, Nigam H Shah, Michelle Y Williams, Samantha M R Kling

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in Journal of medical Internet research, 2022. 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
1.4field-weighted citation impact, top 20% of its field
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, 7 citations in OpenAlex.

  1. Article
  2. Sleep Quality and Poor Sleep-related Factors Among Healthcare Workers During the COVID-19 Pandemic in Vietnam.Journal of preventive medicine and public health = Yebang Uihakhoe chi · 2023
    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

9 authors at 2 institutions in 1 country.

Stacie VilendrerEvaluation Sciences Unit, Division of Primary Care and Population Health, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States.ORCID 0000-0003-2317-5973
Mary E LoughEvaluation Sciences Unit, Division of Primary Care and Population Health, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States.ORCID 0000-0003-1155-1556
Donn W GarvertEvaluation Sciences Unit, Division of Primary Care and Population Health, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States.ORCID 0000-0002-0827-9611
Monique H LambertOffice of Research Patient Care Services, Stanford Health Care, Palo Alto, CA, United States.ORCID 0000-0002-3491-1481
Jonathan Hsijing LuCenter for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, United States.ORCID 0000-0002-1302-4875
Birju PatelCenter for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, United States.ORCID 0000-0001-7673-6143
Nigam H ShahCenter for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, United States.ORCID 0000-0001-9385-7158
Michelle Y WilliamsEvaluation Sciences Unit, Division of Primary Care and Population Health, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States.ORCID 0000-0001-6476-354X
Samantha M R KlingEvaluation Sciences Unit, Division of Primary Care and Population Health, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States.ORCID 0000-0001-9169-763X
Stanford University · USStanford Health Care · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic prompted widespread implementation of telehealth, including in the inpatient setting, with the goals to reduce potential pathogen exposure events and personal protective equipment (PPE) utilization. Nursing workflow adaptations in these novel environments are of particular interest given the association between nursing time at the bedside and patient safety. Understanding the frequency and duration of nurse-patient encounters following the introduction of a novel telehealth platform in the context of COVID-19 may therefore provide insight into downstream impacts on patient safety, pathogen exposure, and PPE utilization.

objectiveThe aim of this study was to evaluate changes in nursing workflow relative to prepandemic levels using a real-time locating system (RTLS) following the deployment of inpatient telehealth on a COVID-19 unit.

methodsIn March 2020, telehealth was installed in patient rooms in a COVID-19 unit and on movable carts in 3 comparison units. The existing RTLS captured nurse movement during 1 pre- and 5 postpandemic stages (January-December 2020). Change in direct nurse-patient encounters, time spent in patient rooms per encounter, and total time spent with patients per shift relative to baseline were calculated. Generalized linear models assessed difference-in-differences in outcomes between COVID-19 and comparison units. Telehealth adoption was captured and reported at the unit level.

resultsChange in frequency of encounters and time spent per encounter from baseline differed between the COVID-19 and comparison units at all stages of the pandemic (all P<.001). Frequency of encounters decreased (difference-in-differences range -6.6 to -14.1 encounters) and duration of encounters increased (difference-in-differences range 1.8 to 6.2 minutes) from baseline to a greater extent in the COVID-19 units relative to the comparison units. At most stages of the pandemic, the change in total time nurses spent in patient rooms per patient per shift from baseline did not differ between the COVID-19 and comparison units (all P>.17). The primary COVID-19 unit quickly adopted telehealth technology during the observation period, initiating 15,088 encounters that averaged 6.6 minutes (SD 13.6) each.

conclusionsRTLS movement data suggest that total nursing time at the bedside remained unchanged following the deployment of inpatient telehealth in a COVID-19 unit. Compared to other units with shared mobile telehealth units, the frequency of nurse-patient in-person encounters decreased and the duration lengthened on a COVID-19 unit with in-room telehealth availability, indicating "batched" redistribution of work to maintain total time at bedside relative to prepandemic periods. The simultaneous adoption of telehealth suggests that virtual care was a complement to, rather than a replacement for, in-person care. However, study limitations preclude our ability to draw a causal link between nursing workflow change and telehealth adoption. Thus, further evaluation is needed to determine potential downstream implications on disease transmission, PPE utilization, and patient safety.

Indexed as

COVID-19Nursing CareTelemedicineHospital UnitsHumansPandemicsWorkflowCOVID-19health care professionalhealth care staffhealth riskinformaticsnursesnursingpandemicpathogen exposurepatient outcomespatient safetyPPEreal-time locating systemtelehealthtelemedicinevirtual care

Identifiers

PMID35635840
PMCPMC9208574
OpenAlexW4281742039

What OpenQuestion 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 OpenQuestion graph.