Evidence map›Paper›PMID 37287022›Full record

ArticleBMC health services research2023

Matching medical staff to long term care facilities to respond to COVID-19 outbreak.

Hamid Reza Zarei, Mahsa Ghanbarpour Mamaghani, Ozlem Ergun, Patricia Yu, Leanne Winchester, Elizabeth Chen

Open access · goldAbstract read
In one paragraph

Article in BMC health services research, 2023. 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
0.4field-weighted citation impact, top 33% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  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

6 authors at 3 institutions in 1 country.

Hamid Reza Zarei *Department of Mechanical & Industrial Engineering, Northeastern University, Boston, MA, USA. zarei.h@northeastern.edu.
Mahsa Ghanbarpour Mamaghani *Department of Mechanical & Industrial Engineering, Northeastern University, Boston, MA, USA.
Ozlem ErgunDepartment of Mechanical & Industrial Engineering, Northeastern University, Boston, MA, USA.
Patricia YuExecutive Office of Health and Human Services, Boston, MA, USA.
Leanne WinchesterGraduate School of Nursing, University of MA Chan Medical School - Commonwealth Medicine, Worcester, MA, USA.
Elizabeth ChenExecutive Office of Elder Affairs, Boston, MA, USA.
Northeastern University · USMassachusetts Executive Office of Health and Human Services · USUniversity of Massachusetts Chan Medical School · US

Funding

National Science Foundation, United States 2038421
6 · The paper itself

Abstract

backgroundStaff shortage is a long-standing issue in long term care facilities (LTCFs) that worsened with the COVID-19 outbreak. Different states in the US have employed various tools to alleviate this issue in LTCFs. We describe the actions taken by the Commonwealth of Massachusetts to assist LTCFs in addressing the staff shortage issue and their outcomes. Therefore, the main question of this study is how to create a central mechanism to allocate severely limited medical staff to healthcare centers during emergencies.

methodsFor the Commonwealth of Massachusetts, we developed a mathematical programming model to match severely limited available staff with LTCF demand requests submitted through a designed portal. To find feasible matches and prioritize facility needs, we incorporated restrictions and preferences for both sides. For staff, we considered maximum mileage they are willing to travel, available by date, and short- or long-term work preferences. For LTCFs, we considered their demand quantities for different positions and the level of urgency for their demand. As a secondary goal of this study, by using the feedback entries data received from the LTCFs on their matches, we developed statistical models to determine the most salient features that induced the LTCFs to submit feedback.

resultsWe used the developed portal to complete about 150 matching sessions in 14 months to match staff to LTCFs in Massachusetts. LTCFs provided feedback for 2,542 matches including 2,064 intentions to hire the matched staff during this time. Further analysis indicated that nursing homes and facilities that entered higher levels of demand to the portal were more likely to provide feedback on the matches and facilities that were prioritized in the matching process due to whole facility testing or low staffing levels were less likely to do so. On the staffing side, matches that involved more experienced staff and staff who can work afternoons, evenings, and overnight were more likely to generate feedback from the facility that they were matched to.

conclusionDeveloping a central matching framework to match medical staff to LTCFs at the time of a public health emergency could be an efficient tool for responding to staffing shortages. Such central approaches that help allocate a severely limited resource efficiently during a public emergency can be developed and used for different resource types, as well as provide crucial demand and supply information in different regions and/or demographics.

Indexed as

COVID-19Disease OutbreaksHumansLong-Term CareMedical StaffNursing HomesCOVID-19Long term care facilitiesOptimal assignmentResource allocationStaff shortages

Identifiers

PMID37287022
PMCPMC10244086
OpenAlexW4379769325

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.