Automatic analysis device and automatic analysis system

Abstract

The purpose of the present invention is to provide an automatic analysis device that reduces erroneous samples caused by a pretreatment step and reduces the workload of an operator. To this end, the present invention is an automatic analysis device for analyzing a specimen, the automatic analysis device being provided with: a preprocessing registration information acquisition unit for reading or receiving preprocessing registration information for each specimen, the preprocessing registration information specifying a preprocessing execution source or preprocessing date / time at which preprocessing for the specimen has been executed; an error determination unit that determines, for each of the specimens, whether or not an error caused by the preprocessing has occurred; and a statistical data output unit that statistically outputs the occurrence state of an error for each of the preprocessing execution source or the preprocessing date and time.

Description

Technical Field

[0001] The present invention relates to an automatic analysis device and an automatic analysis system. Background Art

[0002] An automatic analysis device causes a specimen (sample) such as blood or urine to react with a reagent, and optically and electrically detects the reaction generated between the specimen and the reagent. In such an automatic analysis device, in the case where the amount of serum in the specimen is small or there is hemolysis in the serum, an error may occur during analysis. Therefore, it is desirable to perform countermeasures such as removing such specimens before analysis. For example, Patent Document 1 discloses the following analysis device: by analyzing an image captured by a photographing unit, as the state inside a specimen container before being put into the analysis device, for example, the volume of serum, centrifugation, the presence or absence of hemolysis, etc. are determined.

[0003] Prior Art Documents

[0004] Patent Documents

[0005] Patent Document 1: Japanese Unexamined Patent Application Publication No. 2012-159318 Summary of the Invention

[0006] Problems to be Solved by the Invention

[0007] If the technique described in Patent Document 1 or the like is used, it is possible to mechanically determine an abnormal specimen in the pretreatment stage without relying on the visual judgment of an operator, but it does not achieve the solution of the fundamental problem, that is, suppressing the occurrence of errors caused by the method in the pretreatment process itself.

[0008] An object of the present invention is to provide an automatic analysis device and an automatic analysis system that reduce erroneous specimens caused by a pretreatment process and reduce the work burden on an operator.

[0009] Means for Solving the Problems

[0010] To solve the above problems, the automatic analysis device of the present invention includes: a pretreatment registration information acquisition unit that reads or receives pretreatment registration information for the specimen, the pretreatment registration information being used to determine a pretreatment execution source or a pretreatment date and time for which pretreatment has been performed on the specimen; an error determination unit that determines whether an error caused by the pretreatment has occurred for the specimen; and a statistical data output unit that statistically outputs the occurrence status of errors according to the pretreatment execution source or the pretreatment date and time.

[0011] Alternatively, the automatic analysis system of the present invention includes: a plurality of automatic analysis devices that analyze specimens; and an analysis computer that is connected to the plurality of automatic analysis devices via a communication line. The plurality of automatic analysis devices send analysis results associated with a specimen ID attached to a specimen container containing the specimen to the analysis computer, and the analysis computer statistically outputs the occurrence status of errors according to the preprocessing execution source or preprocessing date and time that has performed preprocessing on the specimen.

[0012] Advantages of the Invention

[0013] According to the present invention, it is possible to provide an automatic analysis device and an automatic analysis system that reduce misidentified specimens caused by the preprocessing process and reduce the workload of operators. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is an overall structural diagram of the automatic analysis system of Embodiment 1.

[0015] Figure 2 is a perspective view showing the structure of the analysis unit.

[0016] Figure 3 is a block diagram showing the structure of the control computer of the automatic analysis device of Embodiment 1.

[0017] Figure 4 An example of the specimen preprocessing association information table stored in the specimen preprocessing association information database.

[0018] Figure 5 is a diagram showing examples of normal specimens and misidentified specimens when the specimen is blood.

[0019] Figure 6 is an example of the total result of misidentified specimens.

[0020] Figure 7 is an example of a chart showing the trend of the error rate.

[0021] Figure 8 is a block diagram showing the overall structure of the automatic analysis system of Embodiment 2. DETAILED DESCRIPTION OF THE INVENTION

[0022] Hereinafter, embodiments of the present invention will be described with reference to the drawings.

[0023] Embodiment 1

[0024] <Automatic Analysis System>

[0025] Figure 1 is an overall structural diagram of the automatic analysis system of Embodiment 1. As Figure 1As shown in the figure, the automatic analysis system of Embodiment 1 is composed of an automatic analysis device 1, a LIS (Laboratory Information System) 2, a HIS (Hospital Information System) 3, and a first communication line 4. Here, the LIS is a superior system of the automatic analysis device and controls the whole of the automatic analysis device. In addition, the HIS is a system used on the clinical side and is a superior system of the LIS. The first communication line 4 is a wired or wireless line for mutual communication between the automatic analysis device and the LIS, and between the LIS and the HIS.

[0026] The automatic analysis device 1 includes a loading section 11, a storage section 12, a conveying line 13, an identifier reading section 14, a camera 15, a second communication line 16, an analysis section 17, and a control computer 20. The loading section 11 is a part for loading a specimen rack on which a specimen container containing a specimen is mounted into the automatic analysis device 1. The storage section 12 is a part for retrieving and storing the specimen rack. The conveying line 13 conveys the specimen rack from the loading section 11 to the analysis section 17, or conveys the specimen rack from the analysis section 17 to the storage section 12. The identifier reading section 14 reads a specimen rack identifier attached to the specimen rack loaded from the loading section 11, or a specimen identifier attached to the specimen container mounted on the specimen rack. The identifier reading section 14 is, for example, a barcode reader when the identifier is a barcode, and an RFID reader when the identifier is an RFID tag. In addition, since the reading of the identifier needs to be performed before analysis, the installation location of the identifier reading section 14 is preferably near the upstream of the conveying line 13 or near the loading section 11. The camera 15 photographs the inside of the specimen container loaded into the loading section 11, and is, like the identifier reading section 14, installed, for example, near the upstream of the conveying line 13 or near the loading section 11. The camera 15 may also have the function of the identifier reading section 14. The second communication line 16 is a wired or wireless line for mutual communication between the respective parts (mechanisms) in the automatic analysis device 1. The information read by the identifier reading section 14 and the image photographed by the camera 15 are sent to the control computer 20 via the second communication line 16 and stored in a storage device or the like of the control computer 20.

[0027] <Analysis section>

[0028] The analysis section causes the specimen and a reagent to react in a reaction container, measures the reaction solution after the reaction, and thereby determines the concentration of the biological component contained in the specimen. Figure 2 is a perspective view showing the structure of the analysis section. As Figure 2As shown in the figure, the analysis unit, as the main structure, includes a reagent disk 101, a reaction disk 102, a specimen transfer mechanism 103, specimen dispensing mechanisms 104, 105, reagent dispensing mechanisms 106-109, a spectrophotometer 110, a stirring mechanism 111, and cleaning tanks 112-115. In addition, each mechanism is connected to a control computer 20 described later through a second communication line 16, and its operation is controlled by the control computer 20.

[0029] The reagent disk 101 is arranged inside a reagent cold storage (not shown), and a plurality of reagent containers 116 containing reagents can be placed on its upper surface in a circular shape.

[0030] On the reaction disk 102, a plurality of reaction containers 117 for accommodating a mixture of a specimen and a reagent are arranged in a circular shape. Near the reaction disk 102, a specimen transfer mechanism 103 for transferring a specimen rack 119 carrying a specimen container 118 is arranged.

[0031] Between the reaction disk 102 and the specimen transfer mechanism 103, specimen dispensing mechanisms 104, 105 that can rotate and move up and down are arranged, and are respectively provided with specimen dispensing probes 104a, 105a. Although not shown in the figure, specimen syringes are respectively connected to the specimen dispensing probes 104a, 105a via dispensing flow paths. The specimen syringes suck specimens from the specimen container 118 or discharge the sucked specimens into the reaction container 117 via the specimen dispensing probes 104a, 105a. In addition, a pressure sensor (not shown) for detecting the pressure in the dispensing flow path is also provided in the middle of the dispensing flow path.

[0032] Between the reaction disk 102 and the reagent disk 101, reagent dispensing mechanisms 106-109 that can rotate and move up and down are arranged, and are respectively provided with reagent dispensing probes 106a-109a. The reagent dispensing probes 106a-109a move up and down and horizontally through the reagent dispensing mechanisms 106-109. The reagent dispensing probes 106a-109a are respectively connected to reagent syringes (not shown). Through the reagent syringes, reagents, detergents, diluents, pretreatment reagents, etc. sucked from the reagent containers 116 are dispensed into the reaction container 117 via the reagent dispensing probes 106a-109a.

[0033] Around the reaction disk 102, a spectrophotometer 110 for measuring the absorbance of light passing through the mixture in the reaction container 117, a stirring mechanism 111 for mixing the specimen and the reagent dispensed into the reaction container 117, a cleaning mechanism (not shown) for cleaning the inside of the reaction container 117, etc. are arranged. In addition, cleaning tanks 112-115 for the reagent dispensing probes 106a-109a are respectively arranged in the operation ranges of the reagent dispensing mechanisms 106-109.

[0034] Next, a general description of the analysis process of the analysis unit will be given. First, the specimens in the specimen containers 118 on the specimen rack 119 transported by the specimen transport mechanism 103 to the vicinity of the reaction plate 102 are dispensed onto the reaction containers 117 on the reaction plate 102 by the specimen dispensing probes 104a and 105a of the specimen dispensing mechanisms 104 and 105. Next, the reagent dispensing mechanisms 106 to 109 dispense the reagents used in the analysis from the reagent containers 116 on the reagent tray 101 into the reaction containers 117 that have been previously dispensed with specimens through the reagent dispensing probes 106a to 109a. Next, the stirring mechanism 111 stirs the mixture of the specimen and the reagent in the reaction container 117.

[0035] Then, the light generated from the light source passes through the reaction container 117 containing the mixture, and the photometric intensity of the transmitted light is measured by the spectrophotometer 110. The photometric intensity measured by the spectrophotometer 110 is transmitted to the control computer 20 via the A / D converter and the second communication line 16. Then, the control computer 20 performs calculations to obtain the concentration of the predetermined component in the specimen, and displays the result on the display unit (refer to Figure 3 ), etc. In addition, in this specification, an automatic analysis device that uses a spectrophotometer 110 to obtain the concentration of a predetermined component is taken as an example for description, but the technology disclosed in this specification can also be used for an immuno-automatic analysis device or a coagulation automatic analysis device that uses other photometers to measure specimens.

[0036] <Control computer>

[0037] Figure 3 is a block diagram showing the structure of the control computer of the automatic analysis device according to Embodiment 1. As Figure 3 shown, the control computer 20 includes a communication interface 21, a display unit 22, an input unit 23, a processor 24, a memory 25, and a storage device 26.

[0038] The communication interface 21 receives the analysis request content (measurement items, etc.) from the LIS through the first communication line 4, and sends the inspection results to the LIS through the first communication line 4. The display unit 22 outputs information such as the analysis request content and the analysis result, and is, for example, a display. The input unit 23 selects a predetermined part in the screen displayed on the display unit 22, or inputs predetermined information, and is, for example, a keyboard, a mouse, etc. The processor 24 executes each function by reading each program stored in the memory 25, or stores the information received via the communication interface 21 in the storage device 26.

[0039] In the memory 25, programs corresponding to the respective functions executed by the processor 24 are stored as an operation control unit 25a, an analysis operation unit 25b, an error determination unit 25c, and a statistical data output unit 25d. The operation control unit 25a controls the operations of various units such as the input unit 11, the analysis unit 17, the storage unit 12, and the transfer line 13. The analysis operation unit 25b calculates the concentration of the biological components contained in the specimen. The error determination unit 25c determines whether there is an error caused by the pretreatment for each specimen. Here, the pretreatment refers to the processing performed before the automatic analyzer executes the analysis. For example, when the specimen is blood, it also includes the processing such as blood collection by a nurse or a clinical laboratory technician, inversion and mixing of the blood collection tube (specimen container), and centrifugation. The statistical data output unit 25d statistically outputs the occurrence status of errors according to the pretreatment execution source (e.g., the department of diagnosis and treatment) that has performed the pretreatment or the execution date and time of the pretreatment.

[0040] In addition, the program can also be provided by being pre-assembled in a ROM or the like, or recorded in a computer-readable recording medium in an installable form or an executable form of a file, or distributed. Also, the program can be stored on a computer connected to a network such as the Internet and provided or distributed by downloading via the network.

[0041] The storage device 26 includes an analysis order content database 26a, an analysis result database 26b, a specimen pretreatment related information database 26c, etc. In the analysis order content database 26a, analysis order source information, measurement items, specimen reception date and time, the name or ID of the examinee (patient), blood collection tube information (blood collection tube manufacturer, lot number, expiration date), etc. registered according to the specimen ID are stored. Here, the analysis order source information is, for example, information such as the name or ID of the department of diagnosis and treatment that has ordered the analysis, whether it is an outpatient clinic or a ward (inpatient), the blood collector, and the blood collection date and time. In the analysis result database 26b, in addition to storing the measurement results for the measurement items included in the analysis order content, it also stores whether there is a blockage in the specimen dispensing probe described later.

[0042] Figure 4 It is an example of the specimen pretreatment related information table stored in the specimen pretreatment related information database. As Figure 4As shown, in the sample pretreatment related information table, in addition to the pretreatment registration information for determining the pretreatment execution source and the pretreatment date and time, the presence or absence of an error caused by the pretreatment is stored in a form associated with the sample ID. Here, the pretreatment registration information is information extracted from the analysis order content database 26a. For example, the pretreatment execution source can be the medical department that is the source of the analysis order, or the blood collector who actually performs blood collection as the pretreatment. In addition, the pretreatment date and time can be the sample reception date and time when the analysis order was received, or the blood collection date and time. Examples of errors caused by the pretreatment include abnormalities such as the flatness of the sample interface, the adhesion of blood clots to the wall surface of the blood collection tube, the precipitation of fibrin in the blood collection tube, the hemolysis of the sample, and the amount of the sample. In addition, although not shown in Figure 4 it can also include blood collection tube information and images inside the blood collection tube in the sample pretreatment related information table.

[0043] <Error determination>

[0044] Next, a method for the error determination unit 25c to determine the presence or absence of an error caused by the pretreatment will be described. Figure 5 is a diagram showing examples of normal samples and error samples when the sample is serum. In the case of a normal sample, if the blood is centrifuged, it is clearly divided into three layers: a serum layer, a separating agent layer, and a blood clot layer from above. On the other hand, in the case of an error sample where the pretreatment is not properly performed, it is possible for blood cell components to adhere to the tube wall, fibrin to precipitate, blood cell components to mix into the separating agent layer, or the interface of each layer to be uneven. In addition, examples of improper pretreatment include insufficient inversion mixing of the blood collection tube, insufficient amount of the sample (blood), insufficient standing time, and insufficient centrifugation (error in centrifugation conditions). Insufficient inversion mixing causes uneven coagulation due to uneven dispersion of agents such as coagulation promoters coated inside the blood collection tube, for example, resulting in blood clots adhering to the tube wall and fibrin precipitating inside the blood collection tube. Insufficient standing time and centrifugation, for example, result in a decrease in the flatness of the sample interface. Insufficient blood volume, for example, may cause hemolysis due to the negative pressure inside the blood collection tube. When the sample is plasma, it is also necessary to evenly disperse the anticoagulant inside the blood collection tube and centrifuge it. After centrifugation, it is divided into a plasma layer, a blood cell layer (a platelet + white blood cell layer and a red blood cell layer) from above.

[0045] Here, as a method for determining an error sample, two methods will be described as examples: a first determination method using the camera 15 before analysis and a second determination method during analysis.

[0046] 《First determination method》

[0047] The first determination method is a method for determining whether there is an error before analyzing the specimen based on the image captured by the camera 15. When an erroneous specimen is extracted by this method, an operator such as a clinical examination technician can remove the specimen itself of the object, or remove fibrin or the like in the specimen, or perform centrifugation again, thus improving the analysis efficiency. Hereinafter, a specific description will be given.

[0048] First, the camera 15 acquires RGB data within the viewing angle including the specimen container. Next, the error determination unit 25c determines the interface based on changes in the signal amounts of the respective colors of RGB, changes in the ratios of the signal amounts of the respective colors of RGB, etc. When the flatness of the interface between the serum layer and the separation agent layer or the interface between the separation agent layer and the blood clot layer does not satisfy a predetermined condition, the error determination unit 25c considers that there is a deviation at the interface, and there is a possibility of insufficient standing time, centrifugation, etc., and determines that there is an error (NG) (refer to Figure 4 ). In addition, when the position of the upper end of the serum layer is lower than a predetermined height, the error determination unit 25c determines that there is an error (NG) due to insufficient specimen volume (refer to Figure 4 ). Moreover, when the error determination unit 25c determines foreign substances such as blood cell components and fibrin on the tube wall based on the signal amounts of the respective colors of RGB, etc., it also considers that there is a possibility of insufficient inversion mixing, etc., and determines that there is an error (NG) (refer to Figure 4 ). Then, when the signal amount of R exceeds a predetermined threshold value, there is a possibility of hemolysis, so the error determination unit 25c determines that there is an error (NG) (refer to Figure 4 ).

[0049] In addition, the error determination unit 25c can perform processing based on the signal amounts in other color spaces such as L*a*b* in addition to performing processing based on the signal amounts in the RGB color space. In addition, the error determination unit 25c can also output whether there is an error by inputting the image data captured by the camera 15 into a pre-made learning model. Moreover, in the Figure 4 shown specimen pretreatment-related information table, an image ID is also stored. If a predetermined image ID is selected, the captured image that is the basis for this error determination can also be actually confirmed.

[0050] 《Second Determination Method》

[0051] The second determination method is a method for determining whether a specimen is an erroneous specimen based on the result of analyzing the specimen transported to the analysis unit. In this method, even without a camera 15 or the like that captures the inside of the specimen container, an erroneous specimen can be extracted. Hereinafter, a specific description will be given.

[0052] The error determination unit 25c determines whether the specimen dispensing probe is clogged based on the detection result of the pressure sensor when the specimen dispensing probe aspirates the specimen from the specimen container (see Figure 4 ). However, since the main cause of clogging may also be due to long-term use of the specimen dispensing probe, etc., the error determination unit 25c can also determine that there is an error (NG) at a stage where clogging caused by pretreatment can be determined.

[0053] In addition, the error determination unit 25c also determines whether the LD (lactate dehydrogenase) included in the analysis result is higher than a predetermined threshold (see Figure 4 ). However, the main cause of LD becoming a high value may also be due to abnormalities such as hepatocytes, etc., so the error determination unit 25c can also regard it as having an error (NG) at a stage where a false high value caused by pretreatment can be determined. In addition, regarding whether it is a false high value, the specimen is centrifuged again, or a specimen is collected again from the subject of the specimen, and as a result of re-examination, it is comprehensively determined whether LD is abnormal by comparing with the previous value of this patient, etc. As the main cause of LD becoming a false high value, inappropriate centrifugation conditions, insufficient standing time, insufficient inversion and mixing, etc. are considered.

[0054] In addition, in the case of hemolysis, in addition to LD, AST (aspartate aminotransferase) etc. may also become false high values. In addition, when the hemolysis rate is included in the analysis result, it is also possible to determine whether there is an error based on the hemolysis rate. Moreover, when the HBs antigen included in the analysis result is a false positive, there is also a possibility of inappropriate pretreatment of this specimen, so it can also be regarded as having an error (NG).

[0055] <Statistical processing>

[0056] Next, the statistical processing of the statistical data output unit 25d is described using Figure 6 and Figure 7 .

[0057] Figure 6 is an example of the total result of error specimens.

[0058] First, as an operator who is a user, conditions such as the period of data to be output and the source of the analysis request to be output are input using the display unit 22 and the input unit 23. In Figure 6In the example, the period from 6:00 on September 1, 2021 to 20:00 on December 31, 2021 is specified, and Department A, Department B, and Department D on the 3rd floor of Building C are specified as the sources of analysis entrustment. In addition, the date and time of the data are assumed to be counted based on the preprocessing date and time, but they can also be counted based on other date and times such as the specimen reception date and time. In addition, in Figure 6 In the example, the number of errors and the error rate of the specified source of analysis entrustment are output, but the output method is not limited to this. For example, when the types of errors are specified, it can also be the method of outputting the number of errors and the error rate according to the types of errors.

[0059] The statistical data output unit 25d refers to the specimen preprocessing-related information database 26c, extracts the information of the specimens that meet the specified conditions, and totals the number of errors by the source of analysis entrustment. In Figure 6 In the example, as the types of errors, the errors determined by the above-mentioned first determination method (image analysis) are shown, but the errors determined by the above-mentioned second determination method can also be shown. In addition, in Figure 6 In the example, the number of errors in the interface flatness during the specified period of Department B is as many as 150, and it can be seen that in the preprocessing process, the placement time and centrifugation of the blood collection tube may be insufficient.

[0060] Here, when a specimen has multiple types of errors, it is counted as 1 error specimen. The error rate is obtained by dividing the number of error specimens by the total number of specimens by the source of analysis entrustment and is displayed in %. When the specified period is multiple months, the statistical data output unit 25d outputs the error rate as a monthly trend, thereby making it easy to establish an association with changes such as personnel changes in the source of analysis entrustment that may be the main cause of errors. In addition, other indicators indicating the frequency of error occurrence, such as the number of error specimens per month, can be used instead of the error rate.

[0061] Figure 7 is an example of a chart showing the trend of the error rate. In Figure 7 In the example, the error rate of Department A increased sharply from September to October 2021. Therefore, if there are blood collectors who re-preprocess during this period, it can also be speculated that there are problems with the techniques of these blood collectors. In addition, when there is a tendency that the error rate is high only on specific weekdays, it can also be speculated that there are problems with the techniques of the blood collectors responsible on these weekdays. If such objective statistical data is provided to the source of analysis entrustment to improve the techniques of blood collectors, etc., the number of error specimens caused by the preprocessing process can be reduced, that is, the workload of operators can be reduced. Furthermore, it may also contribute to a reduction in the re-examination rate and may also lead to a shortening of the time to the inspection result report required in many inspection rooms in recent years.

[0062] In addition, when the error rate increases above a certain level, a notification can also be issued at any timing (such as when the device is started at the end of the month). However, the threshold for issuing the notification can also vary depending on the source of the analysis request. This is because, in addition to the specimens caused by the pretreatment process, there are also specimens caused by the drugs administered to patients, and there are also sources of analysis requests (medical departments) where the error rate is inherently likely to be high. Regardless of the source of the analysis request, when the frequency of occurrence of errors caused by clogging of the specimen dispensing probe increases, it may be clogging caused by long-term use of the specimen dispensing probe rather than clogging caused by the pretreatment process. Therefore, a notification urging maintenance of the specimen dispensing probe can also be issued.

[0063] In addition, in Example 1, it was assumed that the automatic analysis device received the analysis request content from the LIS via the first communication line 4 for explanation. However, at least a part of the analysis request content can also be read by the automatic analysis device from an identifier. In particular, when the identifier attached to the specimen container or the like is an RFID tag or the like and can correspond to multiple pieces of information, not only the specimen ID can be read, but also the pretreatment registration information for determining the source of pretreatment execution and the pretreatment date and time can be read by an RFID reader or the like. That is, the pretreatment registration information acquisition unit is not limited to the communication interface 21 and can also be the identifier reading unit 14.

[0064] Moreover, the statistical data output unit 25d can also output the trend of the error rate according to the source of the analysis request, and extract the blood collection tube information from the specimen pretreatment-related information database 26c, and output the period when changes such as the manufacturer and lot number of the blood collection tube occurred according to the source of the analysis request. This is because the erroneous specimens may be caused not only by the pretreatment process but also by changes in the blood collection tubes (manufacturer, type, batch).

[0065] Example 2

[0066] Figure 8 is a block diagram showing the overall structure of the automatic analysis system of Example 2. As Figure 8 shown, the automatic analysis system of Example 2 includes, in addition to the automatic analysis device 1, the LIS, the HIS, and the first communication line 4, an analysis computer 30. Here, the analysis computer 30 is connected to a plurality of automatic analysis devices 1 via the first communication line 4, and is also connected to the LIS via the first communication line 4. In addition, in Figure 8 the automatic analysis device 1, the structure other than the control computer 20 is omitted from the illustration, but is the same as in Example 1.

[0067] First, the structure of the control computer 20 in the automatic analysis device of Embodiment 2 will be described. The control computer 20 of Embodiment 2 includes a communication interface 21, a display unit 22, an input unit 23, a processor 24, and a memory 25. That is, different from Embodiment 1, the control computer 20 of Embodiment 2 does not include a storage device for storing a sample pretreatment-related information database, etc. In addition, different from Embodiment 1, in the memory 25 of the control computer 20 of Embodiment 2, an error determination unit and a statistical data output unit are not stored.

[0068] Next, the structure of the analysis computer 30 of Embodiment 2 will be described. The analysis computer 30 includes a communication interface 31, a processor 32, a memory 33, and a storage device 34. The communication interface 31 of the analysis computer 30 receives the analysis request content from the LIS through the first communication line 4, and sends the received analysis request content to the control computer 20 of the automatic analysis device through the first communication line 4. In addition, the communication interface 31 of the analysis computer 30 receives the analysis result from the control computer 20 of the automatic analysis device through the first communication line 4, and sends the received analysis result to the LIS. Furthermore, the processor 32 of the analysis computer 30 executes each function by reading out each program stored in the memory 33, or stores the information received via the communication interface 31 in the storage device 34.

[0069] In the memory 33 of the analysis computer 30, programs corresponding to the respective functions executed by the processor 32 are stored as an error determination unit 33a and a statistical data output unit 33b. In addition, the storage device 34 of the analysis computer 30 has an analysis request content database 34a, an analysis result database 34b, a sample pretreatment-related information database 34c, and the like.

[0070] That is, in Embodiment 2, the analysis computer 30 determines whether the sample for which analysis is requested to each automatic analysis device is an incorrect sample, and performs statistical processing of the occurrence status of errors.

[0071] Here, the method for determining an erroneous specimen in Example 2 is described. In the first determination method, each automatic analyzer sends an image of the inside of a specimen container captured by the camera 15 before analysis to the analysis computer 30, and the error determination unit 33a of the analysis computer 30 determines whether there is an error caused by preprocessing for each specimen based on the received image. The determination method of the error determination unit 33a in this case is the same as the first determination method in Example 1. In the second determination method, each automatic analyzer sends an analysis result including the clogging status of the specimen dispensing probe to the analysis computer 30, and the error determination unit 33a of the analysis computer 30 determines whether there is an error caused by preprocessing for each specimen based on the received analysis result. The determination method of the error determination unit 33a in this case is the same as the second determination method in Example 1.

[0072] Next, the error statistics processing method in Example 2 is described. The statistical data output unit 33b of the analysis computer 30 refers to the sample pre-processing related information database 34c stored in the storage device 34, extracts the information of the sample that meets the specified conditions, and counts the number of errors for each analysis request source. The processing method of the statistical data output unit 33b is the same as that of Example 1.

[0073] In the second embodiment, both the error determination and the statistical processing are performed by the analysis computer 30, but part or all of the error determination may be performed by the control computer 20 of the automatic analysis device. In this case, the result of the error determination performed by the control computer 20 of the automatic analysis device is sent to the analysis computer 30 and used for statistical processing by the statistical data output unit 33b of the analysis computer 30.

[0074] In addition, the present invention is not limited to the above-mentioned embodiments, and includes various modified examples. For example, in the above-mentioned embodiments, the control computer or the analysis computer performs an error judgment, but the error judgment can also be performed by the operator, and the control computer or the analysis computer performs statistical processing based on the judgment result input by the operator. In addition, the function of the analysis computer described in Example 2 can also be performed by the computer of the LIS. Furthermore, part of the structure of a certain embodiment can also be replaced with the structure of other embodiments, and the structure of other embodiments can also be added to the structure of a certain embodiment. In addition, for part of the structure of each embodiment, other structures can also be added, deleted, or replaced.

[0075] Explanation of symbols

[0076] 1…Automatic analysis device

[0077] 2…LIS

[0078] 3…HIS

[0079] 4…First communication line

[0080] 11…Input section

[0081] 12…Storage section

[0082] 13…Conveyor line

[0083] 14…Identifier reading section

[0084] 15…Camera

[0085] 16…Second communication line

[0086] 17…Analysis section

[0087] 20…Control computer

[0088] 21…Communication interface (control computer)

[0089] 22…Display section

[0090] 23…Input section

[0091] 24…Processor (control computer)

[0092] 25…Memory (control computer)

[0093] 25a…Motion control section

[0094] 25b…Analysis operation section

[0095] 25c…Error determination section

[0096] 25d…Statistical data output section

[0097] 26…Storage device

[0098] 26a…Analysis request content database

[0099] 26b…Analysis result database

[0100] 26c…Specimen pretreatment related information database

[0101] 30…Analysis computer

[0102] 31…Communication interface (analysis computer)

[0103] 32…Processor (analysis computer)

[0104] 33…Memory (analysis computer)

[0105] 33a…Error determination section

[0106] 33b…Statistical data output section

[0107] 34…Storage device (analysis computer)

[0108] 34a…Analysis order content database

[0109] 34b…Analysis result database

[0110] 34c…Specimen pretreatment related information database

[0111] 101…Reagent tray

[0112] 102…Reaction tray

[0113] 103…Specimen transfer mechanism

[0114] 104, 105…Specimen dispensing mechanism

[0115] 104a, 105a…Specimen dispensing probe

[0116] 106~109…Reagent dispensing mechanism

[0117] 106a~109a…Reagent dispensing probe

[0118] 110…Spectrophotometer

[0119] 111…Stirring mechanism

[0120] 112~115…Washing tank

[0121] 116…Reagent container

[0122] 117…Reaction container

[0123] 118…Specimen container

[0124] 119…Specimen rack.

Claims

1. An automatic analysis device that analyzes a specimen, characterized in that, the automatic analysis device includes: a pretreatment registration information acquisition unit that reads or receives pretreatment registration information for the specimen, the pretreatment registration information being used to determine the pretreatment execution source or pretreatment date and time for which pretreatment has been performed on the specimen; an error determination unit that determines whether an error caused by the pretreatment has occurred for the specimen; and a statistical data output unit that statistically outputs the occurrence status of errors according to the pretreatment execution source or pretreatment date and time.

2. The automatic analysis device according to claim 1, characterized in that, the automatic analysis device includes: a loading unit into which a specimen container containing the specimen is loaded; and a camera that photographs the inside of the specimen container loaded into the loading unit, and the error determination unit determines whether there is an error before analyzing the specimen based on the image captured by the camera.

3. The automatic analysis device according to claim 2, characterized in that, the error determination unit determines whether there is an error by determining at least one of the interface flatness of the specimen, the blood clot attachment state to the wall surface of the specimen container, the fibrin precipitation state in the specimen container, the hemolysis state of the specimen, and the amount of the specimen through the image.

4. The automatic analysis device according to claim 1, characterized in that, the automatic analysis device further includes: a specimen dispensing probe that dispenses the specimen; and a clogging detection unit that detects clogging of the specimen dispensing probe, and the error determination unit determines whether there is an error based on the clogging state detected by the clogging detection unit.

5. The automatic analysis device according to claim 4, characterized in that, regardless of the pretreatment execution source, when the occurrence frequency of errors caused by clogging of the specimen dispensing probe increases, a maintenance reminder notice is issued.

6. The automatic analysis device according to claim 1, characterized in that, when the occurrence frequency of errors of a predetermined pretreatment execution source increases above a certain level, a notice is issued.

7. The automatic analysis device according to claim 1, characterized in that, when the analysis result of a predetermined item is a false high value or false positive, the error determination unit regards an error as having occurred.

8. The automatic analysis device according to claim 1, characterized in that, the automatic analysis device further includes: a reading unit that reads a specimen ID attached to a specimen container containing the specimen, and the pretreatment registration information acquisition unit is a communication interface that receives the pretreatment registration information associated with the specimen ID via a communication line.

9. The automatic analysis device according to claim 1, characterized in that, the pretreatment registration information is attached to the specimen container containing the specimen as an identifier together with the specimen ID, and the pretreatment registration information acquisition unit reads the identifier.

10. An automatic analysis system that includes: a plurality of automatic analysis devices that analyze specimens; and an analysis computer that is connected to the plurality of automatic analysis devices via a communication line, characterized in that, A plurality of the automatic analyzers send analysis results associated with a specimen ID attached to a specimen container containing the specimen to the analysis computer. The analysis computer statistically outputs the occurrence status of errors according to a preprocessing execution source or preprocessing date and time of the preprocessing performed on the specimen.

11. The automatic analysis system according to claim 10, characterized in that a plurality of the automatic analyzers determine whether an error caused by the preprocessing has occurred for the specimen, and send the determined result to the analysis computer.

12. The automatic analysis system according to claim 10, characterized in that a plurality of the automatic analyzers send an image inside the specimen container captured by a camera to the analysis computer, and the analysis computer determines whether an error caused by the preprocessing has occurred for the specimen according to the image.

Images