A case allocation method and device based on subspecialty matching, a terminal and a medium

Abstract

This invention discloses a method, device, terminal, and medium for case allocation based on subspecialty matching. The method includes: acquiring pathological information corresponding to several cases to be processed, including specimen type and number of slides; determining the target subspecialty physician group corresponding to each case and the workload corresponding to each case; identifying the allocated workload of each member in the target subspecialty physician group, and planning an optimal allocation path so that the difference between the number of cases and the difference between the number of slides allocated to each member in the target subspecialty physician group are within a preset difference range; and obtaining the case allocation result of each member in the target subspecialty physician group according to the optimal allocation path. This application improves the matching degree and efficiency of case allocation, reduces the risk of misdiagnosis and missed diagnosis, and converts the number of cases and slides into workload, plans the optimal allocation path for cases and slides, and thus rationally allocates the workload of each doctor.

Description

Technical Field

[0001] This invention relates to the field of pathology technology, and more particularly to a method, device, terminal, and medium for case allocation based on subspecialty matching. Background Technology

[0002] With the increasing sophistication of modern medicine, pathological diagnosis, as the "gold standard" for disease confirmation, demands ever more stringent professionalism and precision. This has led to the emergence of pathology subspecialties, which are further subdivided into numerous fields based on different human systems, organs, and disease types, such as gynecological, gastroenterological, neurological, dermatological, bone, and renal pathology. The pathological slides of different subspecialties each have their own characteristics, requiring significantly different diagnostic experience and professional knowledge.

[0003] Currently, hospital pathology departments are facing numerous obstacles in their progress towards subspecialization. On the one hand, full subspecialization means precisely assigning corresponding subspecialty slides to personnel with matching expertise. This allocation process is complex, tedious, and consumes significant manpower. Most pathology departments still use the traditional manual allocation model, where the department head simply counts the cases and slides delivered that day and assigns them to doctors according to case and slide numbers. Subspecialty divisions are not considered during slide allocation; different types of slides, such as those from cervical biopsies, liver punctures, and lymphohematopoietic procedures, are arbitrarily assigned, leading to frequent cases of mismatched expertise and compromising diagnostic quality.

[0004] In other words, the existing manual segmentation method is difficult to meet the needs of pathology subspecialties. Department staff need to spend a lot of time and energy to verify information, assign tasks, and communicate and coordinate repeatedly. In most cases, it is impossible to reasonably allocate the workload of each pathologist.

[0005] Therefore, existing technologies have shortcomings and need to be improved and developed. Summary of the Invention

[0006] This application provides a method, device, terminal, and medium for case allocation based on subspecialty matching, in order to solve the technical problems of low efficiency and inability to reasonably allocate the workload of each pathologist in related technologies.

[0007] To achieve the above objectives, this application adopts the following technical solution:

[0008] A subspecialty-matched case allocation method, wherein the method includes:

[0009] Obtain pathological information corresponding to several cases to be processed, including specimen type and number of slides;

[0010] The target subspecialist group corresponding to each case is determined based on the specimen type, and the workload corresponding to each case is determined based on the specimen type and the number of slides.

[0011] Identify the assigned workload of each member in the target subspecialty physician group, and plan the optimal allocation path based on the workload corresponding to each case and the assigned workload, so that the difference between the number of cases assigned to each member in the target subspecialty physician group and the difference between the number of slides are within a preset difference range.

[0012] The case allocation results for each member in the target subspecialty physician group are obtained based on the optimal allocation path.

[0013] In one embodiment of this application, determining the target subspecialty physician group corresponding to each of the cases based on the specimen type includes:

[0014] Obtain a preset subspecialty matching table, which includes a first correspondence between specimen types and subspecialty physician groups, wherein the subspecialty physician groups are obtained by grouping physicians based on preset subspecialty types;

[0015] Based on the specimen type of the case, the subspecialty matching table is searched, and the target subspecialty physician group corresponding to the specimen type of the case is obtained according to the first correspondence.

[0016] In one embodiment of this application, determining the workload corresponding to each case based on the specimen type and the number of slides includes:

[0017] Locate the preset workload conversion table, which includes a second correspondence between specimen types and workload measurement values;

[0018] Based on the second correspondence, the target workload measurement value corresponding to the specimen type of each case is obtained;

[0019] The workload for each case is determined based on the target workload measurement value and the number of slices for each case.

[0020] In one embodiment of this application, the allocated workload of each member in the target subspecialist physician group is identified, and an optimal allocation path is planned based on the workload corresponding to each case and the allocated workload, so that the difference between the number of cases allocated to each member in the target subspecialist physician group and the difference between the number of slides are both within a preset difference range, including:

[0021] Calculate the sum of the workloads for all cases based on the workloads corresponding to each case.

[0022] If the sum of the workloads is less than or equal to a preset workload threshold, then the assigned workload of each member in the target subspecialty physician group is identified.

[0023] Based on the workload corresponding to each case and the allocated workload, an optimal allocation path is planned so that the difference between the number of cases allocated to each member of the target subspecialist physician group and the difference between the number of slides are within a preset range.

[0024] In one embodiment of this application, after calculating the sum of the workloads for all cases based on the workloads corresponding to each of the cases, the method further includes:

[0025] If the sum of the workloads is greater than the preset workload threshold, then the current case to be assigned is determined according to the preset allocation priority and the preset workload threshold, and the workload corresponding to the current case to be assigned is obtained.

[0026] Based on the workload corresponding to the current cases to be assigned and the already assigned workload, an optimal assignment path is planned so that the difference between the number of cases assigned to each member of the target subspecialist physician group and the difference between the number of slides are both within a preset range.

[0027] In one embodiment of this application, the pathological information further includes: case number, slide number, and paraffin block information corresponding to each slide; the case number is set as a consecutive number, and the optimal allocation path is obtained by segmenting the consecutive number, with the goal of minimizing the number of segments of the consecutive number;

[0028] After obtaining the case allocation results for each member of the target subspecialty physician group based on the optimal allocation path, the following steps are also included:

[0029] Based on the pathological information and case allocation results, a record table is obtained showing the case number, slide number, and paraffin block information corresponding to each slide assigned to each member of the target subspecialty physician group;

[0030] Determine the workload of each member in the target subspecialty physician group and calculate the average workload of each member.

[0031] In one embodiment of this application, before planning the optimal allocation path based on the workload corresponding to each case and the allocated workload, the method further includes:

[0032] If the case is pre-labeled with a frozen section tag, then find the diagnosing physician corresponding to the intraoperative frozen section diagnosis report of the case;

[0033] The slides corresponding to the postoperative routine report of the case were assigned to the diagnosing physician.

[0034] This application also provides a subspecialty-based case allocation device, wherein the device includes:

[0035] The acquisition module is used to acquire pathological information corresponding to several cases to be processed, including specimen type and number of slides.

[0036] The determination module is used to determine the target subspecialist physician group corresponding to each case based on the specimen type, and to determine the workload corresponding to each case based on the specimen type and the number of slides;

[0037] The planning module is used to identify the assigned workload of each member in the target subspecialty physician group, and plan the optimal allocation path according to the workload corresponding to each case and the assigned workload, so that the difference between the number of cases and the number of slides assigned to each member in the target subspecialty physician group are within a preset difference range.

[0038] The allocation module is used to obtain the case allocation results for each member in the target subspecialty physician group according to the optimal allocation path.

[0039] This application also provides a terminal, comprising: a memory, a processor, and a subspecialty-matching-based case allocation program stored in the memory and executable on the processor, wherein the subspecialty-matching-based case allocation program, when executed by the processor, implements the steps of the subspecialty-matching-based case allocation method as described above.

[0040] This application also provides a computer-readable storage medium storing a computer program that can be executed to implement the steps of the subspecialty-matched case allocation method described above.

[0041] The beneficial effects of this invention are as follows: The method of this embodiment acquires pathological information corresponding to several cases to be processed, including specimen type and number of slides; determines the target subspecialty physician group corresponding to each case based on the specimen type, and determines the workload corresponding to each case based on the specimen type and number of slides; identifies the assigned workload of each member in the target subspecialty physician group, and plans an optimal allocation path based on the workload corresponding to each case and the assigned workload, so that the difference between the number of cases and the number of slides assigned to each member in the target subspecialty physician group are within a preset difference range; and obtains the case allocation result of each member in the target subspecialty physician group based on the optimal allocation path. This application accurately and automatically allocates different types of cases and slides to the target subspecialty physician group according to specimen type, improving the matching degree and efficiency of case allocation, and reducing the risk of misdiagnosis and missed diagnosis; and converts the number of cases and slides into workload, plans the optimal allocation path for cases and slides, and thus reasonably allocates the workload of each pathologist. Attached Figure Description

[0042] Figure 1 This is a flowchart of a preferred embodiment of the case allocation method based on subspecialty matching in this invention.

[0043] Figure 2 This is a schematic diagram illustrating the principle of planning the optimal allocation path in this invention.

[0044] Figure 3 This is a flowchart of a specific embodiment of the case allocation method based on subspecialty matching of the present invention.

[0045] Figure 4 This is a functional principle block diagram of a preferred embodiment of the case allocation device based on subspecialty matching in this invention.

[0046] Figure 5 This is a functional principle block diagram of a preferred embodiment of the terminal in this invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0048] With the increasing sophistication of modern medicine, pathological diagnosis, as the "gold standard" for disease confirmation, demands ever more stringent professionalism and precision. This has led to the emergence of pathology subspecialties, which are further subdivided into numerous fields based on different human systems, organs, and disease types, such as gynecological, gastroenterological, neurological, dermatological, bone, and renal pathology. The pathological slides of different subspecialties each have their own characteristics, requiring significantly different diagnostic experience and professional knowledge.

[0049] For example, the gynecology subspecialty focuses on diseases of the female reproductive system. Cervical biopsy, as an important pathological examination method, suffers from significant subjective judgments among different pathologists due to differences in experience and professional focus, resulting in highly subjective diagnostic results and low consistency and reproducibility in multiple diagnoses of the same case. The gastroenterology subspecialty involves multiple organs such as the gastrointestinal tract, esophagus, liver, and pancreas. Physicians outside the gastroenterology subspecialty often lack sufficient knowledge of non-cancerous diseases of the digestive system, leading to the missed or misdiagnosis of many non-cancerous diseases. In addition, liver biopsies are highly difficult to diagnose due to the complex tissue structure and potential lesion types of the liver, and require a high level of professional expertise from the physician. The pathological manifestations of nervous system diseases are complex and unique, with diverse tumor types, and cases are usually few, making pathological diagnosis highly difficult.

[0050] However, hospital pathology departments are currently facing numerous obstacles in their progress towards subspecialization. On the one hand, full subspecialization means precisely assigning slides to personnel with matching expertise for each subspecialty. This allocation process is complex, tedious, and consumes significant manpower. Department staff must constantly verify case information and review doctor schedules and specialties; even slight errors can lead to misallocation or omissions, and subsequent adjustments are time-consuming and labor-intensive. Even with spreadsheets recording daily workloads and case types, while convenient for data viewing, it remains essentially manual, suffers from delayed updates, cumbersome calculations, and an inability to intelligently allocate slides based on subspecialty expertise. Adjusting allocation schemes for special cases is extremely inconvenient. On the other hand, achieving full subspecialization places a significant demand on the number of pathologists. Currently, there is a substantial shortage of pathologists, and the number of pathologists is limited, making it difficult to gather sufficient personnel for each subspecialty. The inability of most departments to achieve subspecialization results in insufficient diagnostic expertise, increasing the risk of misdiagnosis and missed diagnosis. However, many hospitals are trying to make up for this deficiency by "partial subspecialization" because of the reality that the number of pathologists is insufficient in the short to medium term. This means that they will temporarily subspecialize some subspecialties that are easy to miss or misdiagnose, and assign these subspecialty slides to the corresponding subspecialty doctors. Meanwhile, the subspecialty slides that have not been subspecialized will still be assigned to all doctors.

[0051] Therefore, the existing technology has the following drawbacks:

[0052] First, there is a lack of accurate subspecialty matching. Current technology makes precise subspecialty matching almost impossible. For example, many slides that should be diagnosed by dermatology subspecialty pathologists are assigned to other non-dermatology subspecialty pathologists, leading to inaccurate diagnoses. Slides that should be diagnosed by gynecology subspecialty pathologists are assigned to doctors lacking gynecological pathology experience, resulting in misdiagnosis or missed diagnosis (such as misdiagnosing cervical atrophy as high-grade squamous intraepithelial lesion, or missing cervical adenocarcinoma). Liver biopsies, inflammatory bowel disease, and non-tumor gastrointestinal pathology slides from the digestive pathology subspecialty are not being distributed to doctors with expertise in these areas, affecting diagnostic professionalism and accuracy, and increasing the risk to patients.

[0053] Secondly, the labor costs are high and the efficiency is low. The extensive manual slicing method cannot meet the needs of pathology subspecialties. Department staff need to invest a lot of time and energy to verify information, assign tasks, and communicate and coordinate repeatedly. In most cases, it is impossible to reasonably allocate the workload of each pathologist, and it is also easy to cause internal conflicts among staff. Although spreadsheets seem convenient, data entry, calculation and adjustment are labor-intensive and the overall efficiency is low.

[0054] Third, it lacks dynamic adaptability. When faced with the hospital's emergency allocation of pathologists, or when doctors suddenly take leave or fall ill, the existing slide distribution technology is unable to quickly adjust the allocation plan, causing departmental work to stagnate or become disordered, resulting in delays in slide distribution time, affecting the issuance of subsequent reports and postponing the execution time of auxiliary testing projects (such as immunohistochemistry, molecular pathology testing, etc.).

[0055] This invention can balance workload and precise matching of subspecialties, and can reduce labor costs.

[0056] The following describes a case allocation method, apparatus, terminal, and medium based on subspecialty matching according to embodiments of this application, with reference to the accompanying drawings. Addressing the issues of low efficiency and inability to reasonably allocate workload among pathologists using subspecialty matching in the related technologies mentioned in the background, this application provides a case allocation method based on subspecialty matching. In this method, pathological information corresponding to several cases to be processed is obtained, including specimen type and number of slides; a target subspecialty physician group corresponding to each case is determined based on the specimen type, and the workload corresponding to each case is determined based on the specimen type and number of slides; the allocated workload of each member in the target subspecialty physician group is identified; an optimal allocation path is planned based on the workload corresponding to each case and the allocated workload, so that the difference between the number of cases allocated to each member in the target subspecialty physician group and the difference between the number of slides are both within a preset difference range; and the case allocation result for each member in the target subspecialty physician group is obtained according to the optimal allocation path. This application accurately and automatically assigns different categories of cases and slides to target subspecialty physician groups based on specimen type, improving the matching accuracy and efficiency of case allocation and reducing the risk of misdiagnosis and missed diagnosis; and converts the number of cases and slides into workload, plans the optimal allocation path for cases and slides, and thus reasonably allocates the workload of each physician.

[0057] Please see Figure 1 The case allocation method based on subspecialty matching described in this embodiment of the invention includes the following steps:

[0058] Step S100: Obtain pathological information corresponding to several cases to be processed, wherein the pathological information includes specimen type and number of slides.

[0059] Specifically, workload comprises two key elements: first, the number of cases, which is equivalent to the number of pathology request forms assigned to each person, with one request form corresponding to one case and registered as a pathology number; and second, the number of slides, which may include one to dozens of slides per case. The two are closely related. The number of slides is equal to the number of paraffin blocks. A case or request form may contain one to a dozen specimens, so the number of slides corresponding to a case or request form varies, and the number of slides reflects the workload.

[0060] Case information also includes: case number, slide number, department, etc. That is, each case's pathology number corresponds to a specific department. Specimen types usually correspond to clinical departments, such as dermatology for dermatological specimens, gastroenterology for gastrointestinal pathology specimens, etc. However, there are a few exceptions, such as the infectious disease department submitting liver biopsy specimens or dermatological specimens.

[0061] For example, Case 1 has a pathology number of 1, and 1 paraffin block is produced after specimen collection; Case 2 has a pathology number of 2, and 35 paraffin blocks are produced after specimen collection; Case 3 has a pathology number of 3, and 6 paraffin blocks are produced after specimen collection. In addition to assigning subspecialty and frozen section specimens to corresponding doctors and adding a difficulty coefficient for individual specimens, the slide division software of this invention ensures that each doctor's daily workload is approximately the same, meaning that each doctor receives a similar number of cases and a similar number of slides.

[0062] After the specimens are sent to the pathology department, the first day involves specimen processing and sampling, generally between 8:00 AM and 5:00 PM. After sampling, the final number of paraffin blocks is obtained. The specimens are then dehydrated overnight in the machine. The next day, technicians process the specimens to prepare slides. The slides for the second day are generally prepared between 10:00 AM and 2:00 PM. Therefore, the slides assigned by the system are those corresponding to the specimens and paraffin blocks from the previous day. The case to which the slide belongs is assigned as a pending case, and all slides from the same case are assigned to the same physician.

[0063] like Figure 1 As shown, the case allocation method based on subspecialty matching further includes the following steps:

[0064] Step S200: Determine the target subspecialist physician group corresponding to each case based on the specimen type, and determine the workload corresponding to each case based on the specimen type and the number of slides.

[0065] In this embodiment of the application, step S200, "determining the target subspecialist physician group corresponding to each case based on the specimen type," specifically includes:

[0066] Obtain a preset subspecialty matching table, which includes a first correspondence between specimen types and subspecialty physician groups, wherein the subspecialty physician groups are obtained by grouping physicians based on preset subspecialty types;

[0067] Based on the specimen type of the case, the subspecialty matching table is searched, and the target subspecialty physician group corresponding to the specimen type of the case is obtained according to the first correspondence.

[0068] Specifically, the slicing software used in this application embodiment can be developed using existing software, such as Rhino, Python, Ruby, or various software programs that support programming functions in C, and the allocation is based on a comprehensive consideration of the number of cases and the number of slides. Priority is given to subspecialty classification rules, precisely assigning specific cases to corresponding subspecialty doctors; for example, dermatology slides are assigned to dermatology subspecialists, cervical biopsies to gynecology subspecialists, and liver biopsies and inflammatory bowel disease slides to gastroenterology subspecialists. This application can also utilize an artificial intelligence deep learning framework to train a neural network model to process pathological data. By learning from a large amount of historical pathological data, it automatically determines the case-doctor matching pattern and workload allocation, rather than relying on preset rule algorithms.

[0069] This application breaks away from the traditional pathology department's experience-based, rough allocation model. With the help of a software system, different types of cases and slides, such as cervical biopsy, liver puncture, and dermatopathology, are accurately and automatically allocated to relevant subspecialists such as gynecologists, gastroenterologists, and dermatopathologists based on specimen type. This significantly improves diagnostic professionalism, avoids the drawbacks of cross-disciplinary diagnosis, and reduces the risk of misdiagnosis and missed diagnosis.

[0070] In one embodiment of this application, step S200, "determining the workload corresponding to each case based on the specimen type and the number of slides," specifically includes:

[0071] Locate the preset workload conversion table, which includes a second correspondence between specimen types and workload measurement values;

[0072] Based on the second correspondence, the target workload measurement value corresponding to the specimen type of each case is obtained;

[0073] The workload for each case is determined based on the target workload measurement value and the number of slices for each case.

[0074] Specifically, since one liver biopsy section is equivalent to 20 ordinary sections in terms of difficulty, this application uses a workload conversion table to increase the workload measurement value for special cases with high diagnostic complexity. For example, the workload is measured in units of one ordinary section, stipulating that one liver biopsy section is equivalent to 20 ordinary sections, so that the workload statistics are closer to the actual work difficulty and to prevent unreasonable allocation of special cases.

[0075] This application's embodiments abandon the practice of manually estimating workload and introduce quantitative thinking, converting the number of cases and slides into workload quantification values. Intelligent algorithms are used to compare the workload of each pathologist, strictly controlling the difference in workload per person after allocation to within 10 cases / slides, achieving a relatively balanced workload for department staff and ensuring overall work efficiency and the doctors' working condition.

[0076] like Figure 1 As shown, the case allocation method based on subspecialty matching further includes the following steps:

[0077] Step S300: Identify the assigned workload of each member in the target subspecialist physician group, and plan the optimal allocation path based on the workload corresponding to each case and the assigned workload, so that the difference between the number of cases assigned to each member in the target subspecialist physician group and the difference between the number of slides are within a preset difference range.

[0078] Understandably, during the allocation process, the technician inputs the number of members from the target subspecialty physician group who are participating in the allocation for that day, in order to exclude members who are not working that day.

[0079] like Figure 2 As shown, when planning the optimal allocation path, the number of cases is used as the x-axis, and the workload per case is used as the y-axis to divide the workload. During division, three conditions are met: First, the area of ​​each segment is similar, ensuring an average workload; second, the length of each segment (i.e., the number of cases) is similar, ensuring an average display workload; third, the total number of divisions is minimized to reduce additional burden. This is to ensure that after subspecialty cases and slides are assigned to subspecialties, the remaining cases are allocated with as continuous numbering as possible, reducing the difficulty and error rate of subsequent slide division for technicians due to multiple breaks in numbering. In other words, while dividing the numbering, continuity between segments must be maintained. Using software programs, annealing algorithms, genetic algorithms, and other methods are employed to calculate a scheme that simultaneously satisfies the above three conditions as the optimal allocation path.

[0080] Manual calculation is almost impossible to achieve this kind of segmentation. Currently, only a few doctors can complete this segmentation within 1 hour, but they can only ensure that the number of cases and the number of wax blocks are slightly similar and cannot maintain continuity. Others need several hours, and some people are completely unable to calculate it manually.

[0081] In this embodiment of the application, step S300 specifically includes:

[0082] Step S310: Calculate the sum of the workloads for all cases based on the workloads corresponding to each case;

[0083] Step S320a: If the sum of the workloads is less than or equal to a preset workload threshold, then identify the assigned workload of each member in the target subspecialty physician group.

[0084] Step S330a: Based on the workload corresponding to each case and the allocated workload, plan the optimal allocation path so that the difference between the number of cases allocated to each member of the target subspecialist physician group and the difference between the number of slides are within a preset difference range.

[0085] Specifically, the preset difference range refers to the fact that the number of cases and slides allocated to each person does not exceed a certain value set by the user, such as 5 or 10. For example, the system monitors the number of cases and slides received by each person in real time and automatically adjusts the allocation to ensure that each person's workload is basically the same, and the difference between the number of cases and slides is controlled within 10 cases or 10 slides.

[0086] In one embodiment of this application, step S310 is followed by:

[0087] Step S320b: If the sum of the workloads is greater than the preset workload threshold, then the current case to be assigned is determined according to the preset allocation priority and the preset workload threshold, and the workload corresponding to the current case to be assigned is obtained.

[0088] Step S330b: Based on the workload corresponding to the current cases to be assigned and the already assigned workload, plan the optimal allocation path so that the difference between the number of cases assigned to each member of the target subspecialist physician group and the difference between the number of slides are both within the preset difference range.

[0089] This application fully considers the practical difficulties of insufficient staff in pathology departments and the difficulty of complete subspecialization. It can ensure the professionalism of subspecialty case allocation and flexibly allocate physician resources, helping hospitals to gradually achieve partial subspecialization and improving the universality and adaptability of pathology diagnostic services.

[0090] like Figure 1 As shown, the case allocation method based on subspecialty matching further includes the following steps:

[0091] Step S400: Obtain the case allocation results for each member in the target subspecialist physician group according to the optimal allocation path.

[0092] In one specific embodiment, the slicing software is integrated into the hospital's Laboratory Information System (LIS). Since the number of cases and slides is known one day in advance (i.e., after specimen collection), technicians can export all the previous day's data—the cases to be processed—from the LIS system at a fixed time each morning. The raw pathology data obtained from the LIS needs to be converted into a format that the slicing software can process. Based on a pre-set subspecialty matching table and workload conversion table, a reasonable slicing plan for each pathologist is quickly calculated, achieving the dual goals of balanced workload and professional alignment. Finally, a detailed allocation guidance table is output to assist technicians in efficiently completing the actual allocation work. The specific process is as follows: Figure 3 As shown.

[0093] Specifically, every morning at work (e.g., 8:00 AM) or the day before (after sample collection), technicians log into the LIS system on time and initiate the data export operation. The LIS system's built-in filtering and export module accurately captures key information such as all case numbers, slide numbers, departments, number of paraffin blocks, and specimen types from the previous day, quickly generating a raw data file in Excel format. Special case types are marked in the Excel spreadsheet, such as PF (dermatology), GC (liver biopsy), FK (gynecological pathology), and BD (frozen section). The entire process takes approximately 2 minutes, ensuring data timeliness and completeness.

[0094] If the slicing software is developed based on Rhino, the accompanying data conversion tool can be used to convert the Excel file to a .csv format that Rhino can directly read. After conversion, the data is seamlessly imported into the software's temporary cache, awaiting slicing calculation. Upon receiving the imported data, Rhino immediately activates the subspecialty identification module and workload statistics module. First, based on the specimen type, it accurately matches the subspecialty physician groups corresponding to each case and slide. For physicians within the group, it simultaneously calculates the number of cases and slides they have currently received, and combines this with special case conversion rules (e.g., 1 liver biopsy = 20 slides) to convert it into workload values. The software's built-in balancing algorithm is workload-balanced, comprehensively considering factors such as each physician's subspecialty and workload to plan the optimal allocation path for all cases and slides. This process fully adheres to the subspecialty-specific allocation principle, ensuring that special cases are assigned to physicians with corresponding specialties. The entire process takes approximately 5-8 minutes.

[0095] This application embodiment establishes a fast data channel from the hospital pathology laboratory information system (LIS) to the slicing software, enabling technicians to export key data from the previous day from the LIS on time, quickly convert the format, and import it into the software. The process is standardized, the operation is simple, human error is reduced, and the data preparation work in the early stage of allocation is accelerated.

[0096] In practice, after exporting the Excel spreadsheet, the subspecialty specimen type is marked by the technician. Next, the Excel spreadsheet is quickly converted to a .csv file and opened in the software. First, the total number of diagnostic physicians participating in the slicing that day is entered. Second, the number of physicians assigned to a specific subspecialty that day is selected (for example, there may be three physicians in the gastroenterology subspecialty, but the number of physicians participating in the slicing that day could be one, two, or three). Third, the slicing calculation module is accessed to plan and select the optimal allocation scheme. In addition, personalized slicing rules can be developed based on departmental needs, such as: assigning routine frozen specimens and routine frozen specimens obtained through overtime freezing to the corresponding frozen section diagnostic physicians, subspecialty + frozen section, subspecialty + overtime frozen section, and in special cases, a physician only slicing 1 / 2 slide on that day, etc.

[0097] This application can set up a case allocation procedure in the hospital pathology laboratory information system to improve timeliness and accuracy; it can also cooperate with third-party medical data platforms to develop a new case allocation system and optimize allocation strategies, but this is costly.

[0098] This application integrates encryption technology and access control systems into the data acquisition, transmission, storage, and processing processes. Data exported from the LIS system is encrypted during transmission to ensure data security in the network environment. Strict user permissions are set in the sharding software and related storage devices, allowing only authorized personnel to access and process pathology data, preventing data leakage and malicious tampering, and protecting patient privacy and hospital information security. When clinical departments have urgent needs or special questions regarding the diagnosis of a case, they can promptly report this to the case allocation system, which can adjust the allocation priority of that case accordingly, accelerating the diagnostic process. This application can also establish an internal quality monitoring module to track and evaluate case allocation results and subsequent diagnostic processes. It statistically analyzes indicators such as diagnostic accuracy and diagnosis time for different doctors in case allocation, examining the rationality and effectiveness of the sharding scheme. Based on monitoring data, potential problems can be identified and corrected in a timely manner, such as persistently high or low workloads for certain subspecialists, or poor diagnostic results after special case allocation, continuously optimizing case allocation technology and rules to achieve self-improvement and enhancement.

[0099] In this embodiment of the application, the pathological information further includes: case number, slide number, and paraffin block information corresponding to each slide; the case number is set as a consecutive number, and the optimal allocation path is obtained by segmenting the consecutive numbers, with the segmentation aiming to minimize the number of segments of the consecutive numbers. After step S400, the method further includes:

[0100] Based on the pathological information and case allocation results, a record table is obtained showing the case number, slide number, and paraffin block information corresponding to each slide assigned to each member of the target subspecialty physician group;

[0101] Determine the workload of each member in the target subspecialty physician group and calculate the average workload of each member.

[0102] Specifically, after completing the partitioning calculation, the software integrates the allocation plan into a standard Excel spreadsheet, clearly listing the case number, slide number, and corresponding paraffin block information that each doctor should be assigned, along with workload statistics and comparisons with the average, facilitating intuitive review and verification by technicians. After receiving the spreadsheet, technicians strictly follow the content and enter it into the LIS system; the entire allocation process takes approximately 5 minutes. Technicians then accurately select the corresponding data from the pathology request form storage area and slide storage rack, distributing them one by one to each pathologist.

[0103] In practical implementation, this application can also construct a fully automated sample distribution system, using robots and automated track transport devices to automatically transport slides and application forms to doctors' workstations according to case allocation results, reducing human error, improving efficiency, and replacing traditional manual methods with intelligent methods.

[0104] In one embodiment, the slides can also be electronic slides, allowing for direct distribution of electronic slides on the system instead of manual distribution. However, electronic slides currently suffer from drawbacks such as inferior observation of larger slides compared to traditional slides, lack of three-dimensional depth, potential for unclear visualization of fine intracellular structures, and data security management risks. With technological advancements, once these drawbacks are overcome, it will be possible to distribute electronic slides on a system. This method can then disregard slide continuity, as system distribution does not need to consider the segmentation errors caused by manual distribution.

[0105] In one embodiment, due to staff shortages, a significant number of pathologists may need to cover 2-4 subspecialties. Therefore, slicing software can also address this issue by performing more detailed and complex slice allocation.

[0106] In this embodiment of the application, before planning the optimal allocation path based on the workload corresponding to each case and the already allocated workload, the method further includes:

[0107] If the case is pre-labeled with a frozen section tag, then find the diagnosing physician corresponding to the intraoperative frozen section diagnosis report of the case;

[0108] The slides corresponding to the postoperative routine report of the case were assigned to the diagnosing physician.

[0109] Specifically, one request form or case corresponds to one pathology number (e.g., pathology number 2505333 corresponds to a specific request form). One request form can contain one or more surgical specimens, resulting in one or more pathology slides. The patient information is the same for each request form. A characteristic of intraoperative frozen section cases is that an intraoperative frozen section diagnostic report is generated within 30 minutes of specimen receipt. A second report (i.e., the routine postoperative report) is generated for the same case 2-3 days later. The pathology number / request form is the same for both reports. During slide division, the pathology number is marked "frozen" and assigned to the same reviewing physician responsible for that intraoperative frozen section report.

[0110] In other words, during pathological diagnosis, the same surgical specimen will generate both an intraoperative frozen section report and a routine postoperative report. The intraoperative frozen section report aims to provide the surgeon with a rapid reference to determine the scope and method of the procedure, while the routine postoperative report provides a more detailed and precise diagnostic result. If these two reports are issued by different diagnostic physicians, diagnostic discrepancies may arise because different physicians may have varying levels of experience and different criteria for judging lesions. Therefore, having the same diagnostic physician responsible for both reports ensures the highest possible consistency in diagnosis. This physician has a more coherent understanding of the overall condition of the specimen, incorporating both the morphological characteristics presented in frozen sections and the more detailed histological features in routine sections into a unified judgment system. This avoids diagnostic disagreements caused by differences in understanding between different physicians, thus providing clinicians and patients with more accurate and stable diagnostic information. When intraoperative frozen section results differ from expectations, a single physician can more readily detect this discrepancy and adjust subsequent diagnostic approaches accordingly. This allows for a more reasonable diagnosis in the postoperative report, avoiding communication breakdowns or misunderstandings that may arise from differing diagnoses and ensuring consistency in clinical decision-making throughout the surgical procedure and subsequent treatment. Having the same diagnosing physician handle both the intraoperative frozen section and the postoperative report for the same specimen reduces the time costs associated with handover and communication between different physicians. When the same physician is familiar with the specimen, subsequent handling of routine specimens can be more targeted. For example, when selecting observation areas or applying auxiliary diagnostic techniques such as special staining or immunohistochemistry, the initial assessment from the intraoperative frozen section allows for more precise operations, improving efficiency and avoiding duplication of work, thus reducing resource waste. Patients typically have high expectations for the consistency and accuracy of diagnostic results. When two reports for the same specimen are issued by the same diagnosing physician, it fosters greater trust in the patient, avoiding the confusion and anxiety caused by discrepancies in results from different physicians, and reducing the risk of medical disputes arising from diagnostic differences. For pathologists, being responsible for both the intraoperative frozen section and the routine postoperative report of the same specimen allows them to refine their diagnosis based on the rapid assessment of the intraoperative frozen section. By comparing the differences and connections between the two diagnostic results, they can continuously reflect on and improve their diagnostic abilities, accumulate more experience, and gain a deeper understanding of special cases or difficult cases. This helps to improve their professional level and comprehensive judgment of diseases.

[0111] For intraoperative frozen specimens from the same day, to ensure timely fixation, most specimens are usually fixed on the same day. However, frozen specimens submitted later typically require overnight fixation, resulting in a one-day delay in slide preparation. Both sets of slides need to be assigned to the same surgeon performing the frozen section diagnosis.

[0112] In addition, when special cases such as liver biopsy are involved, if the lead physician in charge of the gastroenterology subspecialty is not on duty that day, or if the physician is also responsible for routine frozen sectioning that day and there are a large number of such sections, and the liver biopsy sections cannot be deducted at a ratio of 1:20, the option of "shelving" can be selected. That is, the corresponding number of liver biopsy sections will not be deducted on that day, or only a certain number of sections will be deducted. The corresponding workload will be deducted on the next shift when the physician reviews the sections, and then the physician can participate in the allocation process normally, so as to maintain the orderly allocation of work and seamlessly connect with the daily work arrangements.

[0113] This invention achieves precise and efficient work allocation, significantly reducing the time and manpower costs of manual allocation, improving the overall work efficiency of the pathology department, and ensuring timely issuance of pathology reports. On the other hand, it enhances the accuracy of subspecialty matching, allowing specialist doctors to handle corresponding specialized cases, reducing the probability of misdiagnosis and missed diagnosis, and improving diagnostic quality. Furthermore, thanks to its rapid and flexible allocation adjustment mechanism, it seamlessly adapts to changes in departmental staff schedules, unexpected tasks, and other special circumstances, maintaining the continuity and stability of pathology department work.

[0114] In one embodiment, such as Figure 4 As shown, based on the above-described subspecialty-matched case allocation method, the present invention also provides a subspecialty-matched case allocation device, comprising:

[0115] The acquisition module 100 is used to acquire pathological information corresponding to several cases to be processed, the pathological information including specimen type and number of slides;

[0116] The determination module 200 is used to determine the target subspecialist physician group corresponding to each case based on the specimen type, and to determine the workload corresponding to each case based on the specimen type and the number of slides.

[0117] The planning module 300 is used to identify the assigned workload of each member in the target subspecialist physician group, and to plan the optimal allocation path based on the workload corresponding to each case and the assigned workload, so that the difference between the number of cases assigned to each member in the target subspecialist physician group and the difference between the number of slides are within a preset difference range.

[0118] The allocation module 400 is used to obtain the case allocation results of each member in the target subspecialty physician group according to the optimal allocation path.

[0119] It should be noted that the foregoing explanation of the subspecialty-based case allocation method embodiment also applies to the subspecialty-based case allocation device of this embodiment, and will not be repeated here.

[0120] This invention discloses a case allocation device based on subspecialty matching. It acquires pathological information corresponding to several cases to be processed, including specimen type and number of slides. Based on the specimen type, it determines the target subspecialty physician group corresponding to each case and the workload corresponding to each case based on the specimen type and number of slides. It identifies the assigned workload of each member in the target subspecialty physician group and plans an optimal allocation path based on the workload corresponding to each case and the assigned workload, ensuring that the difference between the number of cases and the number of slides assigned to each member in the target subspecialty physician group are within a preset range. The case allocation result for each member in the target subspecialty physician group is obtained based on the optimal allocation path. This application accurately and automatically allocates different categories of cases and slides to target subspecialty physician groups based on specimen type, improving the matching degree and efficiency of case allocation and reducing the risk of misdiagnosis and missed diagnosis. Furthermore, it converts the number of cases and slides into workload, plans the optimal allocation path for cases and slides, and thus rationally allocates the workload of each physician.

[0121] Figure 5 A schematic diagram of the structure of a terminal provided in an embodiment of this application. The terminal may include:

[0122] The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0123] When the processor 502 executes the program, it implements the case allocation method based on subspecialty matching provided in the above embodiments.

[0124] Furthermore, the terminal also includes:

[0125] Communication interface 503 is used for communication between memory 501 and processor 502.

[0126] The memory 501 is used to store computer programs that can run on the processor 502.

[0127] The memory 501 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0128] If the memory 501, processor 502, and communication interface 503 are implemented independently, they can be interconnected via a bus to communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, only one line is used in the diagram, but this does not imply that there is only one bus or one type of bus.

[0129] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0130] Processor 502 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0131] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described subspecialty-based case allocation method.

[0132] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0133] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0134] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0135] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can read and execute instructions from or in conjunction with such an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). In addition, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically by optically scanning paper or other media, followed by editing, interpreting or otherwise processing as necessary, and then stored in computer memory.

[0136] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0137] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium. When executed, the program includes one or a combination of the steps of the method embodiments.

[0138] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0139] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for case assignment based on subspecialty matching, the method comprising: The method comprises: acquiring pathological information corresponding to a plurality of cases to be processed, the pathological information comprising a specimen type and a slice quantity; determining a target sub-specialty doctor group corresponding to each of the cases according to the specimen type, and determining a workload corresponding to each of the cases according to the specimen type and the slice quantity; identifying an assigned workload of each member in the target sub-specialty doctor group, and planning an optimal allocation path according to the workload corresponding to each of the cases and the assigned workload, so that a difference between the number of cases allocated to each member in the target sub-specialty doctor group and a difference between the slice quantities are both within a preset difference range; obtaining a case allocation result of each member in the target sub-specialty doctor group according to the optimal allocation path; determining the workload corresponding to each of the cases according to the specimen type and the slice quantity comprises: searching for a preset workload conversion table, the workload conversion table comprising a second correspondence relationship between a specimen type and a workload measurement value; obtaining a target workload measurement value corresponding to the specimen type of each of the cases according to the second correspondence relationship; obtaining the workload corresponding to each of the cases according to the target workload measurement value corresponding to each of the cases and the slice quantity; identifying the assigned workload of each member in the target sub-specialty doctor group, and planning the optimal allocation path according to the workload corresponding to each of the cases and the assigned workload, so that the difference between the number of cases allocated to each member in the target sub-specialty doctor group and the difference between the slice quantities are both within the preset difference range, comprises: calculating a sum of the workloads of all the cases according to the workload corresponding to each of the cases; if the sum of the workloads is less than or equal to a preset workload threshold, identifying the assigned workload of each member in the target sub-specialty doctor group; planning the optimal allocation path according to the workload corresponding to each of the cases and the assigned workload, so that the difference between the number of cases allocated to each member in the target sub-specialty doctor group and the difference between the slice quantities are both within the preset difference range; after calculating the sum of the workloads of all the cases according to the workload corresponding to each of the cases, further comprising: if the sum of the workloads is greater than the preset workload threshold, determining a current case to be allocated according to a preset allocation priority and the preset workload threshold, and obtaining a workload corresponding to the current case to be allocated; planning the optimal allocation path according to the workload corresponding to the current case to be allocated and the assigned workload, so that the difference between the number of cases allocated to each member in the target sub-specialty doctor group and the difference between the slice quantities are both within the preset difference range; the pathological information further comprises: a case number, a slice number, and wax block information corresponding to each slice; the case number is set as a continuous number, the optimal allocation path is obtained by cutting the continuous number, and the minimum number of cutting times of the continuous number is taken as a target during cutting; after obtaining the case allocation result of each member in the target sub-specialty doctor group according to the optimal allocation path, further comprising: Based on the pathological information and the case allocation result, a record table of case numbers, slice numbers and wax block information corresponding to each slice allocated to each member in the target sub-specialty doctor group is obtained; The workloads of each member in the target sub-specialty doctor group are determined, and the average of the workloads of each member is calculated; All slices of the same case are allocated to the same doctor; When planning the optimal allocation path, the case number is taken as the horizontal coordinate, the workload of each case is taken as the vertical coordinate, the workload is divided, and three conditions are met during the division: average division workload, average division case number and minimum division times.

2. The sub-specialty based matching case assignment method of claim 1, wherein, According to the specimen type, the target sub-specialty doctor group corresponding to each case is determined, including: A preset sub-specialty matching table is obtained, which includes a first correspondence relationship between the specimen type and the sub-specialty doctor group, and the sub-specialty doctor group is obtained by grouping doctors based on a preset sub-specialty type; Based on the specimen type of the case, the sub-specialty matching table is searched, and the target sub-specialty doctor group corresponding to the specimen type of the case is obtained according to the first correspondence relationship.

3. The sub-specialty based matching case assignment method of claim 1, wherein, Before planning the optimal allocation path according to the workload of each case and the allocated workload, it further includes: If the case is pre-marked with a frozen label, the diagnosis doctor corresponding to the intraoperative frozen diagnosis report of the case is searched; The slices corresponding to the postoperative routine report of the case are allocated to the diagnosis doctor.

4. A case assignment apparatus based on subspecialty matching, comprising: The device includes: An acquisition module is configured to acquire pathological information corresponding to a plurality of cases to be processed, wherein the pathological information includes a specimen type and a slice number; A determination module is configured to determine a target sub-specialty doctor group corresponding to each case according to the specimen type, and determine a workload of each case according to the specimen type and the slice number; A planning module is configured to identify an allocated workload of each member in the target sub-specialty doctor group, plan an optimal allocation path according to the workload of each case and the allocated workload, so that the difference between the case numbers and the difference between the slice numbers allocated to each member in the target sub-specialty doctor group are within a preset difference range; An allocation module is configured to obtain a case allocation result of each member in the target sub-specialty doctor group according to the optimal allocation path; According to the specimen type and the slice number, the workload of each case is determined, including: searching a preset workload conversion table, wherein the workload conversion table includes a second correspondence relationship between the specimen type and a workload measurement value; obtaining a target workload measurement value corresponding to the specimen type of each case according to the second correspondence relationship; and obtaining the workload of each case according to the target workload measurement value corresponding to each case and the slice number; The method comprises: identifying the assigned workload of each member in the target sub-specialty doctor group, and planning an optimal allocation path according to the workload of each case and the assigned workload, so that the difference between the number of cases allocated to each member in the target sub-specialty doctor group and the difference between the number of slices are within a preset difference range. The method comprises: calculating the sum of the workloads of all cases according to the workload of each case; if the sum of the workloads is less than or equal to a preset workload threshold, identifying the assigned workload of each member in the target sub-specialty doctor group; and planning an optimal allocation path according to the workload of each case and the assigned workload, so that the difference between the number of cases allocated to each member in the target sub-specialty doctor group and the difference between the number of slices are within a preset difference range. If the sum of the workloads is greater than the preset workload threshold, determining a current to-be-allocated case according to a preset allocation priority and a preset workload threshold, and obtaining the workload corresponding to the current to-be-allocated case; and planning an optimal allocation path according to the workload corresponding to the current to-be-allocated case and the assigned workload, so that the difference between the number of cases allocated to each member in the target sub-specialty doctor group and the difference between the number of slices are within a preset difference range. The pathological information further comprises: case numbers, slice numbers, and wax block information corresponding to each slice; the case numbers are set as consecutive numbers, and the optimal allocation path is obtained by cutting the consecutive numbers, and the number of cutting times of the consecutive numbers is minimized during cutting; Based on the pathological information and the case allocation result, a record table of the case numbers, slice numbers, and wax block information corresponding to each slice allocated to each member in the target sub-specialty doctor group is obtained; the workload corresponding to each member in the target sub-specialty doctor group is determined, and the average workload of each member is calculated; All slices of the same case are allocated to the same doctor. During planning of the optimal allocation path, the number of cases is taken as the horizontal coordinate, and the workload of each case is taken as the vertical coordinate, the workload is cut, and during cutting, three conditions are met: average cutting workload, average cutting case number, and minimum cutting times.

5. A terminal, characterized by comprising: The memory, the processor, and a case allocation program based on sub-specialty matching stored on the memory and executable on the processor, wherein the case allocation program based on sub-specialty matching, when executed by the processor, implements the steps of the case allocation method based on sub-specialty matching according to any one of claims 1-3. The computer readable storage medium stores a computer program, which can be executed to implement the steps of the case allocation method based on sub-specialty matching according to any one of claims 1-3.

6. A computer-readable storage medium, characterized in that, ​

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