医院手术室医疗设备故障分析数据
收藏资源简介：
在医院手术室医疗设备故障分析场景中,通过对医院的手术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手术室医疗设备故障的异常波动分析.从而分析手术室医疗设备故障的变化幅度与时间范围的关系.以此来管控手术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手术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: 采用公式计算该手术室医疗设备异常预警 DEC= (DET-DIT)-DAC; 4:数据应用: 在医院手术室医疗设备故障分析场景中,该手术室医疗设备异常预警 DEC( 小于0,该手术室医疗设备异常预警为异常, 大于等于0该手术室医疗设备异常预警为正常 );
In the scenario of medical equipment failure analysis for hospital operating rooms, the data generated by mining the correlation between operating room medical equipment failures and time dimensions in hospitals can assist models in enabling hospital supervisors to conduct abnormal fluctuation analysis of such failures, thereby exploring the relationship between the change amplitude of operating room medical equipment failures and time ranges. This enables the control of abnormal fluctuations of operating room medical equipment failures, problem localization, and targeted intervention, control and adjustment. This model is generally applicable to all major hospitals and medical institutions. 1. Data Source: The data is based on the failure records of operating room medical equipment from the fixed asset systems of multiple hospitals, and processed via systematic algorithms. 2. Data Processing: - Equipment Name: PN - Specification and Model: SPEC - Equipment Unit Price: DP (procurement unit price of the equipment) - Equipment Purchase Time: DIT (procurement time of the equipment) - Failure Time: DET (time when the equipment malfunctions) - Equipment Failure Cause: DER (cause of the equipment's malfunction) - Predefined Equipment Life Cycle: DAC (the time span from the equipment's purchase to its scrappage) - Abnormal Early Warning Indicator: DEC (indicates whether the equipment malfunctions within the predefined life cycle) - Data Creation Time: CD (date when the data is created) 3. Algorithm Analysis: The abnormal early warning indicator DEC for operating room medical equipment is calculated using the formula: DEC = (DET - DIT) - DAC; 4. Data Application: In the scenario of medical equipment failure analysis for hospital operating rooms, the judgment rule for the abnormal early warning indicator DEC is as follows: if DEC < 0, the operating room medical equipment is in an abnormal state; if DEC ≥ 0, the operating room medical equipment is in a normal state.
