Evidence map›Paper›PMID 38997698›Full record

ArticleBMC health services research2024

Information management of full-cycle inpatient bed reservation for cancer patients under the normalised prevention and control of the COVID-19 pandemic.

Yang Yang, Yang Bin, Ma Yanping, Zhao Jinping, Zhou Xin, Cheng Chunjun, Zhai Zhenhua

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. 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

7 authors.

Yang Yang *Department of Breast, Head and Neck Oncology, First Affiliated Hospital of Jinzhou Medical University, No. 2 Duan 5, Renmin Street, Guta District, Jinzhou, 121001, Liaoning Province, China.
Yang Bin *Department of Breast, Head and Neck Oncology, First Affiliated Hospital of Jinzhou Medical University, No. 2 Duan 5, Renmin Street, Guta District, Jinzhou, 121001, Liaoning Province, China.
Ma YanpingDepartment of Breast, Head and Neck Oncology, First Affiliated Hospital of Jinzhou Medical University, No. 2 Duan 5, Renmin Street, Guta District, Jinzhou, 121001, Liaoning Province, China.
Zhao JinpingTumor Vascular and Microenvironment Laboratory, First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China.
Zhou XinDepartment of Breast, Head and Neck Oncology, First Affiliated Hospital of Jinzhou Medical University, No. 2 Duan 5, Renmin Street, Guta District, Jinzhou, 121001, Liaoning Province, China.
Cheng ChunjunCollege of International Education, Jinzhou Medical University, Jinzhou, China.
Zhai ZhenhuaDepartment of Breast, Head and Neck Oncology, First Affiliated Hospital of Jinzhou Medical University, No. 2 Duan 5, Renmin Street, Guta District, Jinzhou, 121001, Liaoning Province, China. zhenhua.zhai@vip.163.com.

Funding

Beijing Medical Award Foundation YXJL-2020-0941-0744Department of Education of Liaoning Province JYTJCZR2020047, 2020The funding of Scientific Research of The First Affiliated Hospital of Jinzhou Medical University FYQKR-202202The funding of Scientific Research of The First Affiliated Hospital of Jinzhou Medical University KYTD-2022001Wu Jieping Medical Foundation 320.6750.2023-18-126
6 · The paper itself

Abstract

backgroundDuring the prolonged COVID-19 pandemic, hospitals became focal points for normalised prevention and control. In this study, we investigated the feasibility of an inpatient bed reservation system for cancer patients that was developed in the department?s public WeChat account. We also explored its role in improving operational efficiency and nursing quality management, as well as in optimising nursing workforce deployment.

methodsWe utilised WeChat to facilitate communication between cancer patients and health care professionals. Furthermore, we collected data on admissions, discharges, average number of hospitalisation days, bed utilisation rate, and the number of bed days occupied by hospitalised patients through the hospital information system and nurses? working hours and competency levels through the nurse scheduling system. The average nursing hours per patient per day were calculated. Through the inpatient bed reservation system, the number of accepted admissions, denied admissions, and cancelled admissions from the reservation system were collected. The impact of the bed reservation system on the department?s operational efficiency was analysed by comparing the number of hospitalisation discharges before and after reservations, as well as the average hospitalisation and bed utilisation rates. By comparing nurses? working hours per month and average nursing hours per patient per day, the system?s impact on nurses? working hours and nursing quality indicators was analysed.

resultsThe average hospitalisation length, bed utilisation rate, and nurses? working hours were significantly lower, and the average number of nursing hours per patient per day was significantly higher after the implementation of the reservation system. The full-cycle bed information management model for cancer patients did not affect the number of discharged patients.

conclusionPatients? ability to reserve bed types from home in advance using the department?s official WeChat-based inpatient bed reservation system allowed nurses to prepare for their work ahead of time. This in turn improved the operational efficiency of the department and nursing quality, and it optimised the deployment of the nursing workforce.

Indexed as

COVID-19NeoplasmsBed OccupancyFemaleHospital Information SystemsHospitalizationHumansInpatientsMalePandemicsSARS-CoV-2Bed reservationsCancer patientsNormalisation of pandemic prevention and controlNursing managementOperational efficiency

Identifiers

PMID38997698
PMCPMC11241928

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.