Evidence map›Paper›PMID 37395160›Full record

ArticleNursing open2023

Lean management of nursing human resources during COVID-19 pandemic.

Jianghong Tan, Yanzhen Tian, Lang Wang, Yi He, Songli Cui, Chunyan Feng, Anlie Cai

Open access · goldAbstract read
In one paragraph

Article in Nursing open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.9field-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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. 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

7 authors at 1 institution in 1 country.

Jianghong TanNursing Department, Zhuzhou Central Hospital, Zhuzhou, China.ORCID 0000-0002-4970-4733
Yanzhen TianNursing Department, Zhuzhou Central Hospital, Zhuzhou, China.
Lang WangMetabolic Endocrinology Department, Zhuzhou Central Hospital, Zhuzhou, China.
Yi HeNursing Administration Department, Zhuzhou Central Hospital, Zhuzhou, China.
Songli CuiGynaecology Department, Zhuzhou Central Hospital, Zhuzhou, China.
Chunyan FengNursing Department, Zhuzhou Central Hospital, Zhuzhou, China.
Anlie CaiAdministration Department, Zhuzhou Central Hospital, Zhuzhou, China.
Zhuzhou Central Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimTo find a rapid, scientific, rational and accurate method of allocating nursing human resources during the COVID-19 pandemic.

designA longitudinal prospective study.

methodsLean management tool is used to implement four-level scheduling of nursing human resources, which is departmental level, district level, hospital level and city level, according to the daily reporting data of the whole hospital, such as Lianfan scheduling data, Dingding sensitive data and Hospital Information System daily report data.

resultsFifty batches of nursing manpower, 294 nurses and 3813 working days were deployed during the pandemic, and the nursing human resources allocation mathematical models of the hospital and all departments were constructed. Since COVID-19 occurred, the infection rate of nurses with novel coronavirus and the mortality rate of critical patients have been keeping 0%, and the cure rate of common patients has been 100%.

conclusionThe use of lean management tool to allocate nursing human resources plays a positive role in ensuring zero infection of nurses, improving the cure rate of common patients and reducing the mortality rate of critically ill patients with COVID-19.

Indexed as

COVID-19Nursing Staff, HospitalHumansPandemicsProspective StudiesWorkforceCOVID-19lean managementmathematical modelnursing human resources

Identifiers

PMID37395160
PMCPMC10416023
OpenAlexW4382918798

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.