医院血透室医疗设备故障分析数据

在医院血透室医疗设备故障分析场景中,通过对医院的血透室医疗设备故障与时间维度的挖掘,生成的数据可作为模型辅助医院监管人员对医院血透室医疗设备故障的异常波动分析.从而分析血透室医疗设备故障的变化幅度与时间范围的关系.以此来管控血透室医疗设备故障异常波动.定位问题,针对性的做介入管控和调节.该模型普适于各大医院和医疗机构.1:数据来源: 以多个医院固定资产系统中血透室医疗设备故障数据为依据，经系统算法加工得出
2: 数据处理:
设备名称: PN ;
规格型号:SPEC ;
设备单价: DP 设备采购单价 ;
设备购入时间: DIT 设备采购时间 ;
故障时间: DET 设备出现故障的时间 ;
设备故障原因: DER 设备出现故障的原因 ;
设备设定生命周期: DAC 指该设备从采购到报废的时间;
设备异常预警: DEC 设备是否在设定的生命周期内出现故障
创建时间: CD 数据创建的日期
3:算法分析:
采用公式计算该血透室医疗设备异常预警 DEC= (DET-DIT)-DAC;
4:数据应用: 在医院血透室医疗设备故障分析场景中,该血透室医疗设备异常预警 DEC( 小于0,该血透室医疗设备异常预警为异常, 大于等于0该血透室医疗设备异常预警为正常 );

In the scenario of medical equipment failure analysis for hospital hemodialysis departments, by mining the fault data of hemodialysis room medical equipment and time-related dimensions in hospitals, the generated dataset can serve as a model to assist hospital supervisors in analyzing abnormal fluctuations of medical equipment failures in hemodialysis rooms, thereby exploring the relationship between the fluctuation magnitude of hemodialysis room medical equipment failures and time range, so as to control abnormal fluctuations of medical equipment failures in hemodialysis rooms, locate underlying issues, and implement targeted intervention control and adjustment. This model is universally applicable to major hospitals and medical institutions.

1. Data Source: Based on the fault data of medical equipment in hemodialysis rooms from multiple hospital fixed asset systems, processed via systematic algorithms.
2. Data Processing:
- Device Name: PN
- Specification and Model: SPEC
- Device Unit Price: DP, the procurement unit price of the device
- Device Purchase Time: DIT, the procurement time of the device
- Fault Time: DET, the time when the device malfunctions
- Device Fault Cause: DER, the cause of the device malfunction
- Set Device Lifecycle: DAC, referring to the time period from the device's purchase to its scrapping
- Abnormal Early Warning Indicator: DEC, indicating whether the device malfunctions within its set lifecycle
- Data Creation Time: CD, the date when the dataset is created
3. Algorithm Analysis:
The formula for calculating the abnormal early warning indicator DEC of hemodialysis room medical equipment is: DEC = (DET - DIT) - DAC;
4. Data Application:
In the scenario of medical equipment failure analysis for hospital hemodialysis departments, the abnormal early warning indicator DEC of hemodialysis room medical equipment is judged as follows: if DEC < 0, the medical equipment in the hemodialysis room is in an abnormal state; if DEC ≥ 0, the medical equipment in the hemodialysis room is in a normal state.

搜集汇总

背景与挑战

背景概述

该数据集包含846条医院血透室医疗设备故障数据，每月更新，用于分析设备故障异常波动及其与时间范围的关系。数据包括设备名称、规格型号、故障时间、故障原因等字段，通过算法计算设备异常预警，辅助医院监管人员定位问题并进行管控。