A Decision Support System for Rational Deployment of Medical Equipment Based on Real-world Data

Objectives: To inform judgments about the efficient and rational deployment of medical equipment in hospitals and give decision support.Methods: The information system for rational deployment of medical equipment (MERDIS) is based on ASP.NET MVC framework and designed with SQL Server database and C# language. The analysis methods are based on clinical pathway demand and multiple regression data statistics. It uses big data collected from hospitals, including current equipment deployment, clinical pathways, and other basic information, to calculate and provide each hospital with a recommended equipment deployment.Results: By analyzing the data of 52 hospitals through the MERDIS system, it is convenient, accurate, and intuitive to get the rational deployment plan, and suggestions of different types of hospitals affected by different factors can be given conveniently, accurately, and intuitively.Conclusions: The MERDIS system’s design provides the basis for the subsequent development of medical equipment macro data management. In the process of continuous improvement and supplementing of data, the software model will become more and more accurate and reliable.

Authors

Institutions

Publication Details

Journal
Global Clinical Engineering Journal
Published
2024-12-30
DOI
https://doi.org/10.31354/globalce.v6i4.203
Primary Topic
Quality and Safety in Healthcare
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Decision Support System for Rational Deployment of Medical Equipment Based on Real-world Data

Shunxin Qian, Dingding Jia, Qilin Tao, Heqing Lu et al.
Global Clinical Engineering Journal
Quality and Safety in Healthcare
article

A Decision Support System for Rational Deployment of Medical Equipment Based on Real-world Data

Shunxin Qian, Dingding Jia, Qilin Tao, Heqing Lu, Yiming Li, Haowei Zhang, You Yang, Qi Su
article en

Abstract

Objectives: To inform judgments about the efficient and rational deployment of medical equipment in hospitals and give decision support.Methods: The information system for rational deployment of medical equipment (MERDIS) is based on ASP.NET MVC framework and designed with SQL Server database and C# language. The analysis methods are based on clinical pathway demand and multiple regression data statistics. It uses big data collected from hospitals, including current equipment deployment, clinical pathways, and other basic information, to calculate and provide each hospital with a recommended equipment deployment.Results: By analyzing the data of 52 hospitals through the MERDIS system, it is convenient, accurate, and intuitive to get the rational deployment plan, and suggestions of different types of hospitals affected by different factors can be given conveniently, accurately, and intuitively.Conclusions: The MERDIS system’s design provides the basis for the subsequent development of medical equipment macro data management. In the process of continuous improvement and supplementing of data, the software model will become more and more accurate and reliable.

Global Clinical Engineering JournalVol. 6(4)
University of Shanghai for Science and Technology (CN), Shanghai Jiao Tong University (CN), Fudan University (CN), XinHua Hospital (CN), Shanghai First Maternity and Infant Hospital (CN), Children's Hospital of Fudan University (CN), Huashan Hospital (CN)
National Natural Science Foundation of China, Fudan University, Shanghai Hospital Development Center, Huashan Hospital, Ruijin Hospital
Openalex Percentile: Top 48%
Quality and Safety in Healthcare
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.