A quality control decision support system for infant incubators

Abstract

The present invention discloses a quality control decision support system for an infant incubator, comprising: a detector, a wireless network module, a FMEA risk analysis module and a CDSS module; various parameters of the infant incubator are detected by the detector, and the FMEA risk analysis module compares various parameters with a pre-stored data risk feature library to obtain a risk coefficient value, and then matches the risk coefficient value with a risk strategy database pre-stored in the CDSS module to obtain a risk response decision, thereby achieving automated detection and making a decision, with high efficiency, realizing risk control of the infant incubator, and being convenient and practical; the present invention can be used to achieve quality control of the infant incubator.

Description

Technical Field

[0001] The present invention relates to the technical field of medical equipment, and in particular to a quality control decision support system for an infant incubator. Background Art

[0002] In recent years, many adverse medical events such as neonatal infection and equipment loss of control have attracted widespread attention from the society. The health and hygiene of newborns is of vital importance. Therefore, infant incubators have gradually been widely used. However, in the current application of infant incubators, only the corresponding temperature and humidity parameters are monitored, and a simple alarm function is provided. There is no management of other physical safety risks related to the quality of patient treatment, such as noise risk, oxygen concentration risk, alarm failure risk, temperature control disorder risk, and electrical safety risk. In addition, daily equipment quality management can only rely on manual recording and monitoring by medical staff, which is a large workload, low efficiency, irregular management, and uneven standards, which cannot meet the requirements of neonatal equipment quality and safety management. Summary of the invention

[0003] The purpose of the present invention is to provide an infant incubator quality control decision support system to solve one or more technical problems existing in the prior art and at least provide a beneficial choice or create conditions.

[0004] The technical solution adopted to solve the above technical problems is as follows: a quality control decision support system for an infant incubator, comprising: a detector, a wireless network module, a FMEA risk analysis module and a CDSS module; the detector is used to detect various data of the incubator; the wireless network module is used to provide a wireless network; the FMEA risk analysis module receives various data detected by the detector through the wireless network and compares them with the data risk feature library pre-stored in the FMEA risk analysis module to obtain a risk coefficient value; the CDSS module receives the risk coefficient value of the FMEA risk analysis module through the wireless network, compares it with the risk strategy database pre-stored in the CDSS module to obtain a risk response decision.

[0005] The various parameters of the infant incubator are tested by the detector, and the FMEA risk analysis module compares the various parameters with the pre-stored data risk feature library to obtain the risk coefficient value, which is matched with the risk strategy database pre-stored in the CDSS module to obtain the risk response decision, thereby realizing automated detection and making decisions, which is efficient, realizes risk control of the infant incubator, and is convenient and practical.

[0006] As a further improvement of the above technical solution, the detector includes: a temperature sensor, a humidity sensor, a noise sensor, an oxygen detector, an air velocity meter and a vibration monitor.

[0007] Used to detect various data.

[0008] As a further improvement of the above technical solution, a background server is also included, and the data information of the detector, the FMEA risk analysis module and the CDSS module are sent to the background server through a wireless network for storage, so as to realize background control.

[0009] As a further improvement of the above technical solution, it also includes a memory, which is respectively connected to the detector, the FMEA risk analysis module and the CDSS module for storing data.

[0010] As a further improvement of the above technical solution, the wireless network module is a ZigBee wireless network module to achieve short-distance and fast network communication.

[0011] As a further improvement of the above technical solution, the CDSS module includes an expert system module and a decision support module, and the expert system module and the decision support module are connected to realize the combination of system decision support and human control decision support.

[0012] Beneficial effects of the present invention: The present invention detects various parameters of the infant incubator through a detector, and the FMEA risk analysis module compares various parameters with a pre-stored data risk feature library to obtain a risk coefficient value, which is matched with a risk strategy database pre-stored in the CDSS module to obtain a risk response decision, thereby realizing automated detection to make decisions, with high efficiency, and realizing risk control of the infant incubator, which is convenient and practical.

[0013] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0015] Figure 1 The present invention provides an optical path diagram of an infant incubator quality control decision support system. DETAILED DESCRIPTION

[0016] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0017] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., and orientations or positional relationships indicated are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0018] In the description of the present invention, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0019] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0020] FMEA (Failure Mode and Effects Analysis): Failure mode and effects analysis is also known as "potential failure mode and consequences analysis", abbreviated as FMEA. FMEA is a systematic activity that analyzes the subsystems and parts that constitute the product and each process of the constituent process one by one during the product design stage and process design stage to find out all potential failure modes and analyze their possible consequences, so as to take necessary measures in advance to improve the quality and reliability of the product.

[0021] CDSS (Clinical Decision Support System): Clinical decision support system generally refers to any computer system that can provide support for clinical decision-making. This system makes full use of available and appropriate computer technology to improve and enhance decision-making efficiency through human-computer interaction for semi-structured or unstructured medical problems.

[0022] RPN (risk priority number): is the product of the frequency, severity and detection level of an event. It is called the risk factor or risk priority number. The larger the value, the more serious the potential problem. It is used to measure possible process defects so that possible preventive measures can be taken to reduce key process changes and make the process more reliable. Correction of the process should first focus on those links that are of greatest concern and have the highest risk. RPN values ​​range from 1 (absolute best) to 1000 (absolute worst). The best way to determine where to start is to use the RPN Pareto chart to screen out projects with cumulative levels far below 80%.

[0023] CDR (clinical data center): clinical data center.

[0024] Reference Figure 1 Embodiment 1, a quality control decision support system for an infant incubator, comprising: a detector, a wireless network module, a FMEA risk analysis module, a CDSS module, a background server and a memory; the detector comprises: a temperature sensor, a humidity sensor, a noise sensor, an oxygen detector, an air velocity meter and a vibration monitor.

[0025] The backend server and storage are respectively connected to the detector, the FMEA risk analysis module and the CDSS module; the detector is connected to the FMEA risk analysis module; and the FMEA risk analysis module is connected to the CDSS module.

[0026] The wireless network module is used to provide a wireless network.

[0027] The detector is used to detect various data of the incubator, including the internal environment temperature, humidity, noise, oxygen, air flow rate, vibration and other data of the infant incubator.

[0028] The FMEA risk analysis module receives various data detected by the detector through the wireless network and compares it with the data risk feature library pre-stored in the FMEA risk analysis module to obtain the risk coefficient value; that is, the alarm information of the temperature, humidity, noise, oxygen, air flow rate, and vibration of the infant incubator is classified into risk indicator data, the specific risk information of the alarm code is screened out, and the level is sorted to establish a data risk feature library. Through the expert system evaluation case, the RPN (risk coefficient) value is obtained. If the data of the internal environment temperature, humidity, noise, oxygen, air flow rate, vibration, etc. of the infant incubator deviates from the set range, the higher the risk coefficient.

[0029] The CDSS module receives the risk coefficient value of the FMEA risk analysis module through the wireless network, compares it with the risk strategy database pre-stored in the CDSS module, and obtains the risk response decision. The CDSS module, combined with the professional knowledge of infant incubator quality control and the infant incubator usage data of various hospitals, builds a data platform, including: architecture design, database CDR construction, process design and function design. The risk data is loaded into the CDSS module system, and by establishing a knowledge base CDR based on the infant incubator quality control knowledge base, through the interface call method, the risk analysis and identification of positions at all levels are realized, the integration and sorting of risk information are realized, and the risk response of multiple strategies is implemented to achieve decision support. The CDSS module system adopts the browser / server mode design, which consists of human-computer interaction, knowledge base subsystem, and decision support service.

[0030] Preferably, the decision support part is applied as follows: when the quality control monitor collects abnormal data of each infant incubator, the various alarms generated can display the response measures on the service interface, including: when the temperature deviation alarm occurs, it prompts that the parameters need to be adjusted, the air duct temperature sensor, the box sensor is faulty, etc.; when the humidity deviation alarm occurs, it prompts water shortage, fan abnormality or water tank is overfull, etc.; when the noise deviation occurs, it prompts that the baby is abnormal, the fan is faulty, the bed is faulty or the equipment parameters need to be adjusted, etc.; when the oxygen concentration deviation alarm occurs, it prompts that the box performance is abnormal or the oxygen concentration parameters need to be adjusted, etc.; when the air flow rate is abnormal, it prompts that the fan is abnormal, the box performance is abnormal, etc.; when the vibration is abnormal, it prompts that the bed is abnormal, the fan is abnormal, the box is abnormal, etc.

[0031] Through the detector to detect the various parameters of the infant incubator, the FMEA risk analysis module compares the various parameters with the pre-stored data risk feature library to obtain the risk coefficient value and matches it with the risk strategy database pre-stored in the CDSS module to obtain the risk response decision, so as to achieve automated detection and decision-making, which is efficient and convenient to achieve risk control of the infant incubator. The CDSS module has always proposed auxiliary response countermeasures to clinical medical staff and engineering personnel, and users make emergency response measures according to the countermeasures. It is operational and convenient, reduces risk hazards, and ensures the stability, safety and reliability of the equipment in clinical applications. It can reduce the high risk of infant incubators, achieve efficient management, improve medical safety, reduce adverse events, and provide strong guarantees for the use and safe operation of equipment and the fragile lives of newborns.

[0032] In some embodiments, the wireless network module is a ZigBee wireless network module.

[0033] In some embodiments, the risk coefficient value of the FMEA risk analysis module is compared with the risk strategy database pre-stored in the CDSS module. If no corresponding risk decision plan is found, new data and risk analysis cases are added to the risk strategy database, thereby continuously improving the risk strategy database and achieving decision support.

[0034] In some embodiments, the FMEA risk analysis module includes a risk level database module and a risk guidance module, which performs risk level analysis and risk guidance on various test data to obtain a risk coefficient value.

[0035] In some embodiments, the CDSS module includes an expert system module and a decision support module. The risk coefficient value is obtained through the risk strategy database of the decision support module to obtain the risk strategy decision; at the same time, the auxiliary response countermeasures of clinical medical staff are obtained through the expert system module, ensuring the reliability, security and stability of the system.

[0036] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in the technical field without departing from the purpose of the present invention.

Claims

1. An infant incubator quality control decision support system, characterized in that: include: A detector, used to detect temperature data, humidity data, noise data, oxygen data, air flow rate data and vibration data of the internal environment of the infant incubator; A wireless network module, used for providing a wireless network; The FMEA risk analysis module receives various data detected by the detector through a wireless network and compares the data with the data risk feature library pre-stored in the FMEA risk analysis module to obtain a risk coefficient value; that is, the alarm information of the temperature, humidity, noise, oxygen, air flow rate, and vibration of the infant incubator is classified into risk indicator data, the risk information of the alarm code is screened out and ranked, a data risk feature library is established, and the case is evaluated by an expert system to obtain a risk coefficient value. If the internal environment temperature data, humidity data, noise data, oxygen data, air flow rate data, and vibration data of the infant incubator deviate more from the set range, the risk coefficient value is higher; The CDSS module receives the risk coefficient value of the FMEA risk analysis module through a wireless network, compares it with the risk strategy database pre-stored in the CDSS module, and obtains a risk response decision; Among them, the CDSS module includes an expert system module and a decision support module, the expert system module and the decision support module are connected, the risk coefficient value is passed through the risk strategy database of the decision support module to obtain the risk strategy decision, and at the same time, the auxiliary response countermeasures of clinical medical staff are obtained through the expert system module.

2. The infant incubator quality control decision support system according to claim 1, characterized in that: The detector includes: a temperature sensor, a humidity sensor, a noise sensor, an oxygen detector, an air velocity meter and a vibration monitor.

3. The infant incubator quality control decision support system according to claim 1, characterized in that: It also includes a background server, and the data information of the detector, the FMEA risk analysis module and the CDSS module are sent to the background server through a wireless network for storage.

4. The infant incubator quality control decision support system according to claim 1, characterized in that: It also includes a memory, which is respectively connected to the detector, the FMEA risk analysis module and the CDSS module.

5. The infant incubator quality control decision support system according to claim 1, characterized in that: The wireless network module is a ZigBee wireless network module.

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