Evidence map›Paper›PMID 39052994›Full record

ArticleJMIR formative research2024

Implementation of a Web-Based Chatbot to Guide Hospital Employees in Returning to Work During the COVID-19 Pandemic: Development and Before-and-After Evaluation.

Ozan Unlu, Aaron Pikcilingis, Jonathan Letourneau, Adam Landman, Rajesh Patel, Erica S Shenoy, Dean Hashimoto, Marvel Kim, Johnny Pellecer, Haipeng Zhang

Abstract read
In one paragraph

Article in JMIR formative research, 2024. 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

10 authors.

Ozan UnluDivision of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA, United States.ORCID https://orcid.org/0000-0002-0106-5004
Aaron PikcilingisBrigham Digital Innovation Hub, Brigham and Women's Hospital, Boston, MA, United States.ORCID https://orcid.org/0000-0001-8006-2938
Jonathan LetourneauBrigham Digital Innovation Hub, Brigham and Women's Hospital, Boston, MA, United States.ORCID https://orcid.org/0000-0002-3564-7037
Adam LandmanMass General Brigham, Boston, MA, United States.ORCID https://orcid.org/0000-0002-2166-0521
Rajesh PatelMass General Brigham, Boston, MA, United States.ORCID https://orcid.org/0000-0002-7043-4192
Erica S ShenoyHarvard Medical School, Boston, MA, United States.ORCID https://orcid.org/0000-0001-8086-1123
Dean HashimotoOccupational Health Services, Mass General Brigham, Boston, MA, United States.ORCID https://orcid.org/0009-0005-8289-4945
Marvel KimOccupational Health Services, Mass General Brigham, Boston, MA, United States.ORCID https://orcid.org/0009-0002-3004-2423
Johnny PellecerOccupational Health Services, Mass General Brigham, Boston, MA, United States.ORCID https://orcid.org/0009-0003-3338-3706
Haipeng ZhangHarvard Medical School, Boston, MA, United States.ORCID https://orcid.org/0000-0002-0205-9319

Funding

The Harvard Clinical and Translational Science CenterUL1TR002541 · NCATS · HARVARD MEDICAL SCHOOL · PI NADLER, LEE MARSHALL · 2018 to 2022
$93.0M
NCATS NIH HHS UL1 TR002541
6 · The paper itself

Abstract

backgroundThroughout the COVID-19 pandemic, multiple policies and guidelines were issued and updated for health care personnel (HCP) for COVID-19 testing and returning to work after reporting symptoms, exposures, or infection. The high frequency of changes and complexity of the policies made it difficult for HCP to understand when they needed testing and were eligible to return to work (RTW), which increased calls to Occupational Health Services (OHS), creating a need for other tools to guide HCP. Chatbots have been used as novel tools to facilitate immediate responses to patients' and employees' queries about COVID-19, assess symptoms, and guide individuals to appropriate care resources.

objectiveThis study aims to describe the development of an RTW chatbot and report its impact on demand for OHS support services during the first Omicron variant surge.

methodsThis study was conducted at Mass General Brigham, an integrated health care system with over 80,000 employees. The RTW chatbot was developed using an agile design methodology. We mapped the RTW policy into a unified flow diagram that included all required questions and recommendations, then built and tested the chatbot using the Microsoft Azure Healthbot Framework. Using chatbot data and OHS call data from December 10, 2021, to February 17, 2022, we compared OHS resource use before and after the deployment of the RTW chatbot, including the number of calls to the OHS hotline, wait times, call length, and time OHS hotline staff spent on the phone. We also assessed Centers for Disease Control and Prevention data for COVID-19 case trends during the study period.

resultsIn the 5 weeks post deployment, 5575 users used the RTW chatbot with a mean interaction time of 1 minute and 17 seconds. The highest engagement was on January 25, 2022, with 368 users, which was 2 weeks after the peak of the first Omicron surge in Massachusetts. Among users who completed all the chatbot questions, 461 (71.6%) met the RTW criteria. During the 10 weeks, the median (IQR) number of daily calls that OHS received before and after deployment of the chatbot were 633 (251-934) and 115 (62-167), respectively (U=163; P<.001). The median time from dialing the OHS phone number to hanging up decreased from 28 minutes and 22 seconds (IQR 25:14-31:05) to 6 minutes and 25 seconds (IQR 5:32-7:08) after chatbot deployment (U=169; P<.001). Over the 10 weeks, the median time OHS hotline staff spent on the phone declined from 3 hours and 11 minutes (IQR 2:32-4:15) per day to 47 (IQR 42-54) minutes (U=193; P<.001), saving approximately 16.8 hours per OHS staff member per week.

conclusionsUsing the agile methodology, a chatbot can be rapidly designed and deployed for employees to efficiently receive guidance regarding RTW that complies with the complex and shifting RTW policies, which may reduce use of OHS resources.

Indexed as

agile methodologychatbotconversational agentCOVID-19digital interventiondigital supportemployeehealth care deliveryhealth care personnelhospital staffoccupational healthreturn to worksupport servicework policy

Identifiers

PMID39052994
PMCPMC11310642

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.