Automated analyzers and automated analysis systems

Abstract

The purpose of the present invention is to provide an automated analysis device that reduces erroneous samples originating at a preprocessing step and reduces the workload on an operator. The present invention is an automated analysis device that analyzes samples and comprises: a preprocessing registration information acquisition unit that, for each sample, reads or receives preprocessing registration information that specifies a preprocessing source or a preprocessing date and time at which preprocessing was performed on the sample; an error determination unit that, for each sample, determines whether there was an error during the preprocessing; and a statistical data output unit that statistically outputs an error situation for each preprocessing source or preprocessing date and time.

Description

Technical Field

[0001] The present invention relates to an automatic analyzer and an automatic analysis system.

Background Art

[0002] An automatic analyzer reacts a specimen (sample) such as blood or urine with a reagent, and optically or electrically detects the reaction that occurs between the specimen and the reagent. In such an automatic analyzer, when the amount of serum in the specimen is small or when there is hemolysis or the like in the serum, an error may occur during analysis. Therefore, it is desirable to take measures such as removing such specimens before analysis. For example, Patent Document 1 discloses an analyzer that determines, as the internal state of a specimen container before being introduced into an analyzer, for example, the volume of serum, the presence or absence of centrifugation or hemolysis, etc., by analyzing an image captured by an imaging means.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Using the technology described in Patent Document 1, etc., it is possible to mechanically identify abnormal specimens in the pretreatment stage without relying on visual judgment by an operator, but it does not lead to the solution of fundamental problems, that is, the suppression of the occurrence of errors itself caused by the procedures in the pretreatment process.

[0005] An object of the present invention is to provide an automatic analyzer and an automatic analysis system that reduce error specimens caused by the pretreatment process and reduce the work burden of an operator.

Means for Solving the Problems

[0006] To solve the aforementioned problems, the automated analyzer of the present invention comprises: a pre-processing registration information acquisition unit that reads or receives pre-processing registration information for each sample, which identifies the source or date and time of the pre-processing that performed the pre-processing on the sample; an error determination unit that determines for each sample whether or not an error caused by the pre-processing has occurred; and a statistical data output unit that statistically outputs the error occurrence status for each pre-processing source or date and time of the pre-processing.

[0007] Alternatively, the present invention provides an automated analysis system comprising: a plurality of automated analyzers for analyzing a sample; and an analysis computer connected to the plurality of automated analyzers via a communication line, wherein the plurality of automated analyzers transmit analysis results linked to a sample ID attached to a sample container containing the sample to the analysis computer, and the analysis computer statistically outputs the error occurrence status for each pre-processing source or pre-processing date and time that performed the pre-processing on the sample. [Effects of the Invention]

[0008] According to the present invention, it is possible to provide an automated analyzer and automated analysis system that reduce erroneous samples caused by the pre-processing step and reduce the workload of the operator. [Brief explanation of the drawing]

[0009] [Figure 1] Overall configuration diagram of the automated analysis system according to Example 1. [Figure 2] A perspective view showing the configuration of the analysis section. [Figure 3] A block diagram showing the configuration of the control computer for the automated analyzer according to Example 1. [Figure 4] An example of a sample preprocessing-related information table stored in the sample preprocessing-related information database. [Figure 5] A diagram showing examples of normal and erroneous samples when the sample is blood. [Figure 6] An example of aggregated results for erroneous samples. [Figure 7] An example of a graph showing the trend in error rates. [Figure 8] A block diagram showing the overall configuration of the automated analysis system according to Example 2. [Modes for carrying out the invention]

[0010] Embodiments of the present invention will be described below with reference to the drawings. [Examples]

[0011] <Automated Analysis System> Figure 1 is an overall configuration diagram of the automated analysis system according to Example 1. As shown in Figure 1, the automated analysis system of Example 1 consists of an automated analyzer 1, a Laboratory Information System (LIS) 2, a Hospital Information System (HIS) 3, and a first communication line 4. Here, the LIS is a higher-level system of the automated analyzer and controls the entire automated analyzer. The HIS is a system used on the clinical side and is a higher-level system of the LIS. The first communication line 4 is a wired or wireless line that enables mutual communication between the automated analyzer and the LIS, and between the LIS and the HIS.

[0012] The automated analyzer 1 comprises an input unit 11, a storage unit 12, a transport line 13, an identifier reading unit 14, a camera 15, a second communication line 16, an analysis unit 17, and a control computer 20. The input unit 11 is the part for inputting sample racks, on which sample containers containing samples are mounted, into the automated analyzer 1. The storage unit 12 is the part for collecting and storing the sample racks. The transport line 13 transports the sample racks from the input unit 11 to the analysis unit 17, and transports the sample racks from the analysis unit 17 to the storage unit 12. The identifier reading unit 14 reads the rack identifier attached to the sample racks input from the input unit 11, or the sample identifier attached to the sample containers mounted on the racks. The identifier reading unit 14 is, for example, a barcode reader if the identifier is a barcode, and an RFID reader if the identifier is an RFID tag. Since the identifier must be read before analysis, it is desirable to install the identifier reading unit 14 near the upstream of the transport line 13 or near the input unit 11. The camera 15 images the inside of the sample containers that have been placed in the input unit 11, and like the identifier reading unit 14, it is installed, for example, near the upstream of the transport line 13 or near the input unit 11. The camera 15 may also perform the function of the identifier reading unit 14. The second communication line 16 is a wired or wireless line for communication between the various parts (mechanisms) within the automatic analyzer 1. The information read by the identifier reading unit 14 and the images captured by the camera 15 are transmitted to the control computer 20 via the second communication line 16 and stored in the control computer 20's storage, etc.

[0013] <Analysis Department> The analysis unit reacts the sample with reagents in a reaction vessel and measures the resulting reaction solution to determine the concentration of biological components contained in the sample. Figure 2 is a perspective view showing the configuration of the analysis unit. As shown in Figure 2, the analysis unit has the following main components: reagent disk 101, reaction disk 102, sample transport mechanism 103, sample dispensing mechanisms 104, 105, reagent dispensing mechanisms 106-109, spectrophotometer 110, stirring mechanism 111, and washing tanks 112-115. Each mechanism is connected to a control computer 20 (described later) via a second communication line 16, and its operation is controlled by the control computer 20.

[0014] The reagent disk 101 is disposed inside a reagent cold storage (not shown), and a plurality of reagent containers 116 containing reagents can be placed circumferentially on its upper surface.

[0015] On the reaction disk 102, a plurality of reaction containers 117 for accommodating a mixture of a specimen and a reagent are arranged circumferentially. Near the reaction disk 102, a specimen transport mechanism 103 for transporting a specimen rack 119 on which a specimen container 118 is mounted is arranged.

[0016] Between the reaction disk 102 and the specimen transport mechanism 103, specimen dispensing mechanisms 104, 105 capable of rotating and moving up and down are arranged, each having a specimen dispensing probe 104a, 105a. Although not shown, specimen syringes are connected to the specimen dispensing probes 104a, 105a via dispensing channels. 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. A pressure sensor (not shown) for detecting the pressure in the dispensing channel is also provided in the middle of the dispensing channel.

[0017] Between the reaction disk 102 and the reagent disk 101, reagent dispensing mechanisms 106 to 109 capable of rotating and moving up and down are installed, each having a reagent dispensing probe 106a to 109a. The reagent dispensing probes 106a to 109a are moved up and down and horizontally by the reagent dispensing mechanisms 106 to 109. Reagent syringes (not shown) are connected to the reagent dispensing probes 106a to 109a respectively. By means of this reagent syringe, 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 to 109a.

[0018] Around the reaction disk 102, a spectrophotometer 110 for measuring the absorbance of light that has passed through the mixture in the reaction vessel 117, a stirring mechanism 111 for mixing the sample and reagent dispensed into the reaction vessel 117, a cleaning mechanism (not shown) for cleaning the inside of the reaction vessel 117, etc. are arranged. Also, on the operating ranges of the reagent dispensing mechanisms 106 to 109, cleaning tanks 112 to 115 for the reagent dispensing probes 106a to 109a are respectively arranged.

[0019] Next, an overview of the analysis process by the analysis unit will be described. First, the sample in the sample container 118 placed on the sample rack 119 transported near the reaction disk 102 by the sample transport mechanism 103 is dispensed into the reaction vessel 117 on the reaction disk 102 by the sample dispensing probes 104a and 105a of the sample dispensing mechanisms 104 and 105. Next, the reagent dispensing mechanisms 106 to 109 dispense the reagents used for the analysis from the reagent containers 116 on the reagent disk 101 to the reaction vessel 117 where the sample was previously dispensed, by means of the reagent dispensing probes 106a to 109a. Subsequently, the stirring mechanism 111 stirs the mixture of the sample and reagent in the reaction vessel 117.

[0020] After that, the light generated from the light source passes through the reaction vessel 117 containing the mixture, and the light intensity of the transmitted light is measured by the spectrophotometer 110. The light 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 a predetermined component in the sample, and displays the result on a display unit (see Figure 3) etc. In this specification, an automatic analyzer that uses a spectrophotometer 110 to obtain the concentration of a predetermined component is described as an example, but the technology disclosed in this specification may also be used in an immuno - automatic analyzer or a coagulation - automatic analyzer that measures a sample using other photometers.

[0021] <Control computer> Figure 3 is a block diagram showing the configuration of the control computer of the automated analyzer according to Embodiment 1. As shown in Figure 3, 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 unit 26.

[0022] The communication interface 21 receives analysis request details (measurement items, etc.) from the LIS via the first communication line 4 and transmits the test results to the LIS via the first communication line 4. The display unit 22 outputs information such as analysis request details and analysis results, and is, for example, a display. The input unit 23 is used to select a predetermined part of the screen displayed on the display unit 22 or to input predetermined information, and is, for example, a keyboard or mouse. The processor 24 executes various functions by reading each program stored in the memory 25 and stores the information received via the communication interface 21 in the storage 26.

[0023] Memory 25 stores programs corresponding to each function executed by the processor 24, as an operation control unit 25a, an analysis calculation unit 25b, an error determination unit 25c, and a statistical data output unit 25d. The operation control unit 25a controls the operation of each part, such as the input unit 11, the analysis unit 17, the storage unit 12, and the transport line 13. The analysis calculation unit 25b calculates the concentration of biological components contained in the sample. The error determination unit 25c determines whether there are any errors caused by pre-processing for each sample. Here, pre-processing refers to the processing performed before the automated analyzer performs the analysis, and for example, if the sample is blood, it includes processing such as blood collection, inversion and mixing of the blood collection tube (sample container), and centrifugation performed by a nurse or clinical laboratory technician. The statistical data output unit 25d statistically outputs the error occurrence status for each pre-processing source (e.g., clinical department) or for each pre-processing execution date and time.

[0024] Furthermore, the program may be provided pre-installed on ROM or similar media, or provided or distributed as a file in an installable or executable format recorded on a computer-readable storage medium. In addition, the program may be stored on a computer connected to a network such as the Internet and provided or distributed by downloading it via the network.

[0025] The storage 26 includes an analysis request database 26a, an analysis results database 26b, a sample pre-processing related information database 26c, etc. The analysis request database 26a stores information such as the source of the analysis request, measurement items, date and time of sample receipt, subject (patient) name or ID, and blood collection tube information (blood collection tube manufacturer, lot number, expiration date), which are registered for each sample ID. Here, the source of the analysis request information is, for example, the name or ID of the medical department that requested the analysis, whether it was an outpatient or inpatient (hospitalized) examination, the person who collected the blood, and the date and time of blood collection. The analysis results database 26b stores measurement results for the measurement items included in the analysis request, as well as information such as whether or not there was clogging of the sample dispensing probe, which will be described later.

[0026] Figure 4 shows an example of a sample pre-processing related information table stored in the sample pre-processing related information database. As shown in Figure 4, the sample pre-processing related information table stores pre-processing registration information that identifies the source of pre-processing and the date and time of pre-processing, as well as whether or not there are any errors caused by pre-processing, linked to the sample ID. Here, the pre-processing registration information is extracted from the analysis request details database 26a. For example, the source of pre-processing may be the clinical department that requested the analysis, or the blood collector who actually performed the blood collection as part of the pre-processing. The date and time of pre-processing may be the date and time the sample was received for analysis, or the date and time of blood collection. Examples of errors caused by pre-processing include abnormalities in the flatness of the sample interface, the degree of blood clot adhesion to the blood collection tube wall, the degree of fibrin deposition inside the blood collection tube, the degree of hemolysis of the sample, and the volume of the sample. Although not shown in Figure 4, the sample pre-processing related information table may also include blood collection tube information and images of the inside of the blood collection tube.

[0027] <Error detection> Next, the method by which the error determination unit 25c determines whether or not there is an error due to pretreatment will be explained. Figure 5 shows examples of a normal sample and an error sample when the sample is serum. In the case of a normal sample, when the blood is centrifuged, it clearly separates into three layers from top to bottom: a serum layer, a separating agent layer, and a blood clot layer. On the other hand, in the case of an error sample in which pretreatment was not performed properly, blood cell components may adhere to the tube wall, fibrin may precipitate, blood cell components may be mixed into the separating agent layer, or the interfaces of each layer may not be flat. Examples of inadequate pretreatment include insufficient mixing by inverting the blood collection tube, insufficient sample (blood) volume, insufficient standing time, and insufficient centrifugation (incorrect centrifugation conditions). Insufficient mixing by inversion prevents the coagulation accelerator and other agents applied to the blood collection tube from dispersing uniformly, resulting in uneven coagulation. This can lead to problems such as blood clots adhering to the tube wall or fibrin precipitation inside the blood collection tube. Insufficient standing time or centrifugation can lead to a decrease in the surface flatness of the sample, and insufficient blood volume can lead to hemolysis due to negative pressure inside the blood collection tube. Similarly, when the sample is plasma, it is necessary to uniformly disperse the anticoagulant inside the blood collection tube and then centrifuge it. After centrifugation, the sample separates into a plasma layer and a blood cell layer (a layer of platelets + white blood cells and a layer of red blood cells).

[0028] Here, we will explain two methods for determining erroneous samples, using the camera 15 as an example: a first determination method performed before analysis and a second determination method performed during analysis.

[0029] ≪First Determination Method≫ The first determination method involves determining the presence or absence of errors in the sample before analysis, based on images captured by camera 15. If an erroneous sample is identified using this method, operators such as clinical laboratory technicians can remove the sample itself, remove fibrin or other substances from the sample, or re-centrifuge it, thereby improving analytical efficiency. A detailed explanation follows below.

[0030] First, camera 15 acquires RGB data within the field of view, including the sample container. Next, error determination unit 25c identifies the interface based on changes in the signal intensity of each RGB color, changes in the ratio of the signal intensity of each RGB color, etc. If the flatness of the interface between the serum layer and the separation agent layer, or between the separation agent layer and the blood clot layer, does not meet predetermined conditions, error determination unit 25c determines that there is an error (NG), indicating that there is variation in the interface and that there may be insufficient standing time or centrifugation (see Figure 4). Also, if the upper end of the serum layer is lower than a predetermined height, error determination unit 25c determines that there is an error (NG), indicating insufficient sample volume (see Figure 4). Furthermore, if error determination unit 25c identifies foreign substances such as blood cell components or fibrin on the tube wall based on the signal intensity of each RGB color, error determination unit 25c determines that there is an error (NG), indicating that there may be insufficient mixing by inversion, etc. (see Figure 4). The error determination unit 25c then determines that there is an error (NG) if the R signal amount exceeds a predetermined threshold, as there is a possibility of hemolysis (see Figure 4).

[0031] Furthermore, the error determination unit 25c may process based on signal amounts in other color spaces such as L*a*b*, in addition to processing based on signal amounts in the RGB color space. The error determination unit 25c may also output whether or not an error is present by inputting the image data captured by the camera 15 into a pre-created learning model. Additionally, the sample pre-processing related information table shown in Figure 4 also stores image IDs, and when a predetermined image ID is selected, it is possible to actually verify the captured image that formed the basis of the error determination.

[0032] ≪Second Determination Method≫ The second determination method involves determining whether a sample is an error sample based on the results of analysis performed on the sample transported to the analysis department. This method allows for the identification of error samples even without a camera 15 or similar device for imaging the inside of the sample container. This will be explained in detail below.

[0033] The error determination unit 25c determines whether or not the sample dispensing probe is clogged based on the detection result of the pressure sensor when the sample dispensing probe aspirates the sample from the sample container (see Figure 4). However, since the cause of clogging may also be the prolonged use of the sample dispensing probe, the error determination unit 25c may determine that there is an error (NG) once it has been confirmed that the clogging is due to pretreatment.

[0034] Furthermore, the error determination unit 25c also determines whether the LD (lactate dehydrogenase) included in the analysis results is higher than a predetermined threshold (see Figure 4). However, since high LD values ​​can also be caused by abnormalities in liver cells, etc., the error determination unit 25c may consider an error to exist (NG) once it has been determined that the high value is due to pretreatment. Whether or not a value is falsely high is determined by a combination of factors, such as whether or not there is an abnormality in LD after re-centrifugation of the sample or by taking another sample from the subject of the sample and performing a re-examination, and comparing it with the patient's previous value. Possible factors that can cause falsely high LD values ​​include inappropriate centrifugation conditions, insufficient standing time, and insufficient mixing by inversion.

[0035] Furthermore, if hemolysis occurs, in addition to LD, AST (aspartate aminotransferase) and other parameters may also show falsely high values. If the analysis results include the hemolysis rate, the presence or absence of an error may be determined based on the hemolysis rate. In addition, the error determination unit 25c may also consider an error present (NG) if the HBs antigen included in the analysis results is falsely positive, as this may indicate improper pretreatment of the sample.

[0036] <Statistical Processing> Next, the statistical processing of the statistical data output unit 25d will be explained using Figures 6 and 7. Figure 6 shows an example of the aggregated results for erroneous samples.

[0037] First, the user (operator) uses the display unit 22 and input unit 23 to input conditions such as the period for the data to be output and the source of the analysis request. In the example in Figure 6, the period is specified as from 6:00 AM on September 1, 2021 to 8:00 PM on December 31, 2021, and the analysis request sources are specified as Department A, Department B, and Department D on the 3rd floor of Building C. It is assumed that the data date and time will be counted by the pre-processing date and time, but it may also be counted by other dates and times such as the sample reception date and time. In the example in Figure 6, the number of errors and error rate for each specified analysis request source are output, but the output format is not limited to this. For example, if the type of error is specified, the number of errors and error rate may be output for each type of error.

[0038] The statistical data output unit 25d refers to the sample pre-processing related information database 26c, extracts information on samples that meet the specified conditions, and aggregates the number of errors for each analysis requester. In the example in Figure 6, the error types shown are those determined by the first determination method (image analysis) described above, but errors determined by the second determination method described above may also be shown. In addition, in the example in Figure 6, the number of interface flatness errors during the specified period for Department B is high at 150, indicating that there may be insufficient standing time for the blood collection tubes or insufficient centrifugation in the pre-processing step.

[0039] Here, if a single sample has multiple types of errors, it is counted as one error sample. The error rate is calculated by dividing the number of error samples per analysis requestor by the total number of samples per analysis requestor and expressing it as a percentage. If the specified period is multiple months, the statistical data output unit 25d outputs the error rate as a monthly trend, making it easier to correlate it with changes that may cause errors, such as personnel changes at the analysis requestor. Note that other indicators showing the frequency of error occurrence, such as the number of error samples per month, may be used instead of the error rate.

[0040] Figure 7 is an example of a graph showing the trend in the error rate. In the example in Figure 7, the error rate in Department A increased sharply from September to October 2021. Therefore, if there were any blood collectors who started performing pre-processing during this period, it is possible to infer that there was a problem with the technique of those blood collectors. Also, if the error rate tends to be high only on specific days of the week, it is possible to infer that there was a problem with the technique of the blood collectors on duty on those days. If the objective statistical data output in this way is provided to the client requesting the analysis, and the techniques of the blood collectors are improved, it will be possible to reduce the number of erroneous samples caused by the pre-processing process, that is, reduce the workload of the operators. Furthermore, it may contribute to reducing the retest rate and can lead to a reduction in the time to report test results, which is required by many laboratories these days.

[0041] Furthermore, if the error rate increases above a certain level, a notification may be issued at any time (such as when the device is started up at the end of the month). However, the threshold for whether or not to issue a notification may differ for each requesting organization. This is because, in addition to errors caused by the sample preprocessing process, errors can also be caused by medications administered to patients, and some requesting organizations (clinical departments) are inherently more prone to high error rates. Regardless of the requesting organization, if the frequency of errors due to clogging of the sample dispensing probe increases, it may be possible to issue a notification prompting maintenance of the sample dispensing probe, as this could indicate that the clogging is due to long-term use of the sample dispensing probe rather than clogging caused by the sample preprocessing process.

[0042] In Example 1, the analysis request details were described assuming that the automated analyzer receives them from the LIS via the first communication line 4. However, at least a portion of the analysis request details may be read by the automated analyzer from the identifier. In particular, if the identifier attached to the sample container, etc., is an RFID tag or the like and can be associated with a lot of information, not only the sample ID but also pre-processing registration information that identifies the source of the pre-processing and the date and time of the pre-processing can be read by the RFID reader, etc. In other words, the pre-processing registration information acquisition unit is not limited to the communication interface 21, but may also be the identifier reading unit 14.

[0043] Furthermore, the statistical data output unit 25d may output the trend of the error rate for each analysis requester, and may also extract blood collection tube information from the sample pre-processing related information database 26c and output the time when there were changes to the manufacturer, lot number, etc. of the blood collection tube for each analysis requester. This is because erroneous samples may be caused not only by the pre-processing process but also by changes in the blood collection tube (manufacturer, type, lot). [Examples]

[0044] Figure 8 is a block diagram showing the overall configuration of the automated analysis system according to Embodiment 2. As shown in Figure 8, the automated analysis system of Embodiment 2 includes an automated analyzer 1, a LIS, a HIS, and a first communication line 4, in addition to an analysis computer 30. Here, the analysis computer 30 is connected to multiple automated analyzers 1 via the first communication line 4, and is also connected to the LIS via the first communication line 4. Note that in the automated analyzer 1 shown in Figure 8, the configuration other than the control computer 20 is omitted from the illustration, but it is the same as in Embodiment 1.

[0045] First, the configuration of the control computer 20 in the automated analyzer of Example 2 will be described. The control computer 20 of Example 2 includes a communication interface 21, a display unit 22, an input unit 23, a processor 24, and a memory 25. In other words, unlike Example 1, the control computer 20 of Example 2 does not have storage for storing a database of sample pre-processing related information, etc. Also, unlike Example 1, the memory 25 of the control computer 20 of Example 2 does not store an error determination unit and a statistical data output unit.

[0046] Next, the configuration of the analysis computer 30 in Example 2 will be described. The analysis computer 30 includes a communication interface 31, a processor 32, a memory 33, and a storage 34. The communication interface 31 of the analysis computer 30 receives the analysis request from the LIS via the first communication line 4 and transmits the received analysis request to the control computer 20 of the automatic analyzer via the first communication line 4. The communication interface 31 of the analysis computer 30 also receives the analysis results from the control computer 20 of the automatic analyzer via the first communication line 4 and transmits the received analysis results to the LIS. The processor 32 of the analysis computer 30 executes various functions by reading each program stored in the memory 33 and stores the information received via the communication interface 31 in the storage 34.

[0047] The memory 33 of the analysis computer 30 stores programs corresponding to each function executed by the processor 32, which are stored as an error determination unit 33a and a statistical data output unit 33b. The storage 34 of the analysis computer 30 also contains an analysis request database 34a, an analysis results database 34b, and a sample pre-processing related information database 34c, etc. In other words, in Example 2, the analysis computer 30 determines whether the sample sent to each automated analyzer for analysis is an error sample or not, and performs statistical processing on the occurrence of errors.

[0048] Here, we will describe the method for determining erroneous samples in Example 2. The first determination method involves each automated analyzer transmitting images of the sample container captured by the camera 15 before analysis to the analysis computer 30, and the error determination unit 33a of the analysis computer 30 determining, based on the received images, whether or not there are errors caused by pre-processing for each sample. In this case, the determination method by the error determination unit 33a is the same as the first determination method in Example 1. The second determination method involves each automated analyzer transmitting analysis results, including the clogging status of the sample dispensing probe, to the analysis computer 30, and the error determination unit 33a of the analysis computer 30 determining, based on the received analysis results, whether or not there are errors caused by pre-processing for each sample. In this case, the determination method by the error determination unit 33a is the same as the second determination method in Example 1.

[0049] Next, the method for processing error statistics in Example 2 will be 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 34, extracts information on samples that meet the specified conditions, and aggregates the number of errors for each analysis requester. The processing method by the statistical data output unit 33b is the same as in Example 1.

[0050] In Example 2, both error determination and statistical processing were performed by the analysis computer 30. However, some or all of the error determination may be performed by the control computer 20 of the automated analyzer. In that case, the results of the error determination performed by the control computer 20 of the automated analyzer are transmitted to the analysis computer 30 and used for statistical processing by the statistical data output unit 33b of the analysis computer 30.

[0051] It should be noted that the present invention is not limited to the embodiments described above, and various modifications are included. For example, in the embodiments described above, a control computer or analysis computer performed error determination, but an operator may perform error determination, and the control computer or analysis computer may perform statistical processing based on the determination result input by the operator. Also, the functions of the analysis computer described in Embodiment 2 may be performed by the computer of the LIS. Furthermore, it is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. In addition, it is possible to add, delete, or replace parts of the configuration of each embodiment with other configurations. [Explanation of Symbols]

[0052] 1...Automatic analyzer, 2...LIS, 3...HIS, 4...First communication line, 11...Input unit, 12...Storage unit, 13...Transport line, 14...Identifier reading unit, 15...Camera, 16...Second communication line, 17...Analysis unit, 20...Control computer, 21...Communication interface (control computer), 22...Display unit, 23...Input unit, 24...Processor (control computer), 25...Memory (control computer), 25a...Operation control unit, 25b...Analysis calculation unit, 25c...Error determination unit, 25d...Statistical data output unit, 26...Storage, 26a...Analysis request content database, 26b...Analysis result database, 26c...Sample pre-processing related information database, 30...Analysis computer, 31...Communication interface (for analysis) Computer), 32...Processor (analysis computer), 33...Memory (analysis computer), 33a...Error determination unit, 33b...Statistical data output unit, 34...Storage (analysis computer), 34a...Analysis request database, 34b...Analysis result database, 34c...Sample pretreatment related information database, 101...Reagent disk, 102...Reaction disk, 103...Sample transport mechanism, 104,105...Sample dispensing mechanism, 104a,105a...Sample dispensing probe, 106~109...Reagent dispensing mechanism, 106a~109a...Reagent dispensing probe, 110...Spectrophotometer, 111...Agitation mechanism, 112~115...Washing tank, 116...Reagent container, 117...Reaction vessel, 118...Sample container, 119...Sample rack.

Claims

1. An automated analyzer for analyzing samples, A pre-processing registration information acquisition unit reads or receives pre-processing registration information for each sample, which identifies the source or date and time of the pre-processing that performed the pre-processing on the sample. An error determination unit that determines whether or not an error has occurred for each sample due to the aforementioned pre-processing, A statistical data output unit that statistically outputs the error occurrence status for each preprocessing execution source or preprocessing date and time, An automated analyzer equipped with the following features.

2. In the automated analyzer described in claim 1, An input section into which a sample container containing the aforementioned sample is inserted, The system includes a camera that captures images inside the sample container that has been placed into the input section, The error determination unit is characterized by determining whether or not an error is present in the sample before analysis, based on the image captured by the camera.

3. In the automated analyzer described in claim 2, The error determination unit is characterized by identifying at least one of the following using the image: the interface flatness of the sample, the state of blood clot adhesion to the sample container wall, the state of fibrin precipitation in the sample container, the state of hemolysis of the sample, and the amount of the sample, and determining whether or not an error is present.

4. In the automated analyzer described in claim 1, A sample dispensing probe for dispensing the aforementioned sample, The system further includes a blockage detection unit for detecting blockages in the sample dispensing probe, The automatic analysis device is characterized in that the error determination unit determines whether or not an error is present based on the blockage condition detected by the blockage detection unit.

5. In the automated analyzer according to claim 4, An automated analyzer characterized in that, regardless of the source of the pre-processing, a notification prompting maintenance is issued when the frequency of errors due to clogging of the sample dispensing probe increases.

6. In the automated analyzer described in claim 1, An automated analyzer characterized by issuing a notification when the frequency of errors occurring in a predetermined preprocessing source increases above a certain level.

7. In the automated analyzer described in claim 1, The automatic analyzer is characterized in that the error determination unit considers an error to have occurred if the analysis result of a predetermined item is a false high value or a false positive.

8. In the automated analyzer described in claim 1, The system further includes a reading unit for reading the sample ID attached to the sample container containing the aforementioned sample, The automatic analyzer is characterized in that the pre-processing registration information acquisition unit is a communication interface that receives the pre-processing registration information associated with the sample ID via a communication line.

9. In the automated analyzer described in claim 1, The aforementioned pre-processing registration information is attached as an identifier to the sample container containing the sample, along with the sample ID. The automatic analysis device is characterized in that the pre-processing registration information acquisition unit reads the identifier.

10. An automated analysis system comprising multiple automated analyzers for analyzing samples, and an analysis computer connected to the multiple automated analyzers via a communication line, Multiple automated analyzers transmit analysis results linked to the sample ID attached to the sample container containing the sample to the analysis computer. The aforementioned analysis computer is an automated analysis system that statistically outputs the error occurrence status for each pre-processing source or pre-processing date and time for the pre-processing performed on the sample.

11. In the automated analysis system according to claim 10, The automated analysis system is characterized in that multiple automated analyzers determine for each sample whether or not an error has occurred due to the pretreatment, and transmit the determined result to the analysis computer.

12. In the automated analysis system according to claim 10, Multiple automated analyzers transmit images of the sample container captured by the camera to the analysis computer. The aforementioned analysis computer is an automated analysis system characterized by determining, based on the image, whether or not there are errors caused by the preprocessing for each sample.

Images