Evidence map›Paper›PMID 38561774›Full record

ArticleBMC health services research2024

Workforce strategies during the first wave of the COVID-19 pandemic: a retrospective online survey at intensive care units in Germany.

Lara C Stroth, Franziska Jahns, Berit Bode, Maike Stender, Michelle Schmidt, Heiko Baschnegger, Nurith Epstein, Benedikt Sandmeyer, Carla Nau

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 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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.

Lara C StrothDepartment of Anaesthesiology and Intensive Care, University Medical Centre Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538, Lübeck, Germany.
Franziska JahnsDepartment of Anaesthesiology and Intensive Care, University Medical Centre Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538, Lübeck, Germany.
Berit BodeDepartment of Anaesthesiology and Intensive Care, University Medical Centre Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538, Lübeck, Germany.
Maike StenderDepartment of Anaesthesiology and Intensive Care, University Medical Centre Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538, Lübeck, Germany.
Michelle SchmidtDepartment of Anaesthesiology, Medical Faculty, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany.
Heiko BaschneggerInstitut für Notfallmedizin und Medizinmanagement (INM), Klinikum der Universität München, LMU München, Schillerstr. 53, 80336, Munich, Germany.
Nurith EpsteinInstitute for Medical Education, University Hospital, LMU Munich, Ziemssenstraße 5, 80336, Munich, Germany.
Benedikt SandmeyerInstitut für Notfallmedizin und Medizinmanagement (INM), Klinikum der Universität München, LMU München, Schillerstr. 53, 80336, Munich, Germany.
Carla NauDepartment of Anaesthesiology and Intensive Care, University Medical Centre Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538, Lübeck, Germany. Carla.Nau@uksh.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs the COVID-19 pandemic swept across the globe at the beginning of 2020, healthcare systems were forced to rapidly adapt and expand to meet the sudden surge in demand for intensive care services. This study is the first systematic analysis of the strategies employed by German hospitals to recruit personnel and expand bed capacities during the first wave of the pandemic, and to evaluate the effectiveness of those recruitment measures.

methods152 German hospitals with intensive care capacities were selected and invited to participate in an online-based retrospective survey. Factors like the geographic distribution, individual COVID burden and level of care were considered for inclusion in the sample. The data were analyzed descriptively.

resultsA total of 41 hospitals participated in the survey. The additional demand for intensive care beds was met primarily by activating intensive care beds that were previously considered as non-operational in existing intensive care units (81% of respondents) and by upgrading recovery rooms (73%). The physician staffing requirements were met at approximately 75%, while the nursing staffing requirements were only met by about 45%. Staffing needs were met through reallocations/transfers (85%), staff recruitment from parental leave or retirement (49%), increased hours worked by internal staff (49%), new staff hiring (44%) and increased use of temporary staff (32%). Staff reallocations/transfers to critical care within a hospital were rated as the most effective measure. In this context, specialized personnel mostly from anesthesiology departments were appointed to intensive care medicine.

conclusionsDespite multiple recruitment efforts, the pandemic has exacerbated the nursing staff shortage. The reallocation of existing staff within hospitals was a key element in covering the staffing needs. However, additional measures and efforts are required in order to ensure that critically ill patients can be cared for without compromise. The results of this study may have important implications for healthcare providers and policymakers, offering an evidence-based foundation for responding to future public health emergencies with agility, efficiency, and success.

Indexed as

COVID-19Critical CareHumansIntensive Care UnitsPandemicsRetrospective StudiesSurveys and QuestionnairesWorkforceCOVID-19 pandemicICU-Response surveyICU staffIntensive care unit personnelOrganizational resilienceRecruitmentStaff reallocationWorkforce planning

Identifiers

PMID38561774
PMCPMC10985885

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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.