CN114724693A - Method and device for detecting abnormal diagnosis and treatment behaviors, electronic equipment and storage medium - Google Patents
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Abstract
The disclosure discloses a method and a device for detecting abnormal diagnosis and treatment behaviors, an electronic device and a storage medium, which relate to the field of data processing, and the main technical scheme comprises the following steps: acquiring diagnosis and treatment items to be detected, wherein the diagnosis and treatment items comprise at least one of medicines, inspection, chemical examination, diagnosis and treatment and consumables; sequentially converting at least one type of diagnosis and treatment items into item details corresponding to a national medical insurance item table; judging whether the converted item details are abnormal or not based on a preset treatment path, wherein the preset treatment path is obtained in advance through learning; if the abnormality exists, an alarm prompt that the diagnosis and treatment behavior is abnormal is triggered. Compared with the related technology, firstly, the diagnosis and treatment items are converted into item details corresponding to a national medical insurance item table, secondly, whether the item details are abnormal or not is determined by detecting the difference between the item details and a preset treatment path, and further, whether the diagnosis and treatment items are abnormal or not is determined, so that the detection of abnormal diagnosis and treatment behaviors is realized, and the diagnosis and treatment behaviors of doctors are standardized.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a method and an apparatus for detecting an abnormal diagnosis and treatment behavior, an electronic device, and a storage medium.
Background
At present, no effective management method for doctor diagnosis and treatment behaviors exists, so that many doctors have abnormal diagnosis and treatment behaviors, such as: the medicine is randomly prescribed, high-price medicine is prescribed, the treatment of small diseases is performed greatly, the treatment is performed randomly, and the like, so that the problem that the abnormal diagnosis and treatment behaviors of doctors need to be detected is urgently solved at present.
Disclosure of Invention
The disclosure provides a method and a device for detecting abnormal diagnosis and treatment behaviors, an electronic device and a storage medium. The method and the device for detecting the abnormal diagnosis and treatment behaviors mainly aim at solving the problem that a method for detecting the abnormal diagnosis and treatment behaviors does not exist in the related technology, and the method and the device for detecting the abnormal diagnosis and treatment behaviors can effectively detect the abnormal diagnosis and treatment behaviors.
According to a first aspect of the present disclosure, a method for detecting abnormal diagnosis and treatment behaviors is provided, including:
acquiring diagnosis and treatment items to be detected, wherein the diagnosis and treatment items comprise at least one of medicines, inspection, assay, diagnosis and treatment and consumables;
sequentially converting at least one type of diagnosis and treatment items into item details corresponding to a national medical insurance item table; the national medical insurance project table comprises at least three levels of directory formats;
judging whether the converted item details are abnormal or not based on a preset treatment path, wherein the preset treatment path is obtained in advance through learning;
if the abnormality exists, an alarm prompt that the diagnosis and treatment behavior is abnormal is triggered.
Optionally, before determining whether the converted item details are abnormal based on the preset treatment path, the method further includes:
acquiring diagnosis and treatment items for training, and performing data clustering processing on the diagnosis and treatment items for training to obtain at least one type of medicines, inspection, assay, diagnosis and treatment and consumables included in the diagnosis and treatment items for training;
performing word segmentation processing on the diagnosis and treatment items for training, and determining a national medical insurance project table matched with the diagnosis and treatment items according to word segmentation results;
matching the detail of the training items corresponding to the national medical insurance item table by adopting a preset similarity algorithm for the training diagnosis and treatment items;
training the detail of the training item to obtain a training treatment path;
and determining the training treatment path exceeding the preset use frequency threshold value as a preset treatment path.
Optionally, the acquiring the diagnosis and treatment items to be detected includes:
processing the diagnosis and treatment items to be detected by adopting data clustering;
and determining the category of diagnosis and treatment items corresponding to the disease diagnosis related group according to the data clustering result, wherein the disease diagnosis related group comprises medicines, inspection, assay, diagnosis and treatment and consumables.
Optionally, the sequentially converting at least one of the diagnosis and treatment items into item details corresponding to the national medical insurance item table respectively includes:
sequentially performing word segmentation processing on at least one type of diagnosis and treatment items, and determining a national medical insurance project table matched with the word segmentation processing according to the word segmentation result;
matching at least three levels of catalogs in a national medical insurance project table step by adopting a preset similarity algorithm for the diagnosis and treatment items until the lowest level of catalogs in the national medical insurance project table are matched, wherein each level of catalogs comprises a classification name and a classification code;
and structuring the diagnosis and treatment items into item details corresponding to the national medical insurance item table according to the matching result.
Optionally, the determining whether the converted item details are abnormal based on the preset treatment path includes:
if the item detail is inconsistent with the preset treatment path, determining that the diagnosis and treatment item is abnormal;
if the item detail is consistent with the preset treatment path and the item detail with the same level of directory price lower than that of the third level directory of the item detail exists, determining that the diagnosis and treatment item is abnormal;
and if the item detail is consistent with the preset treatment path and the item detail with the same level of directory price being relatively lower does not exist in the third level directory of the item detail, determining that the diagnosis and treatment item is normal.
According to a second aspect of the present disclosure, there is provided an abnormal diagnosis and treatment behavior detection apparatus, including:
the diagnosis and treatment system comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for acquiring diagnosis and treatment items to be detected, and the diagnosis and treatment items comprise at least one of medicines, examination, assay, diagnosis and treatment and consumables;
the conversion unit is used for sequentially converting at least one type of diagnosis and treatment items acquired by the first acquisition unit into item details corresponding to a national medical insurance item table; the national medical insurance project table comprises at least three levels of directory formats;
the judging unit is used for judging whether the item details converted by the converting unit are abnormal or not based on a preset treatment path, wherein the preset treatment path is obtained in advance through learning;
and the alarm unit is used for triggering an alarm prompt that the diagnosis and treatment behavior is abnormal when the judgment unit determines that the abnormality exists.
Optionally, the apparatus further comprises:
the second acquisition unit is used for acquiring diagnosis and treatment items for training before the judgment unit judges whether the converted item details are abnormal or not based on the preset treatment path, and performing data clustering processing on the diagnosis and treatment items for training to obtain at least one type of medicines, inspection, assay, diagnosis and treatment and consumables included in the diagnosis and treatment items for training;
a word segmentation unit, configured to perform word segmentation processing on the diagnosis and treatment items for training acquired by the second acquisition unit;
the first determining unit is used for determining a matched national medical insurance project table according to the word segmentation result;
the matching unit is used for matching the details of the training items corresponding to the national medical insurance item table by adopting a preset similarity algorithm for the training diagnosis and treatment items;
the training unit is used for training the details of the training items matched by the matching unit to obtain a training treatment path;
and the second determining unit is used for determining the training treatment path exceeding the preset use frequency threshold value as the preset treatment path.
Optionally, the first obtaining unit includes:
the processing module is used for processing the diagnosis and treatment items to be detected by adopting data clustering;
and the determining module is used for determining the category of diagnosis and treatment items corresponding to the disease diagnosis related group according to the data clustering result, wherein the disease diagnosis related group comprises medicines, inspection, assay, diagnosis and treatment and consumables.
Optionally, the conversion unit includes:
the determining module is used for sequentially carrying out word segmentation processing on at least one type of diagnosis and treatment items and determining a national medical insurance project table matched with the word segmentation processing according to the word segmentation result;
the matching module is used for matching at least three levels of catalogs in the national medical insurance project table step by adopting a preset similarity algorithm on the diagnosis and treatment items until the catalogs are matched with the lowest level of the national medical insurance project table, wherein each level of catalogs comprises a classification name and a classification code;
and the structuring module is used for structuring the diagnosis and treatment items into item details corresponding to the national medical insurance item table according to the matching result of the matching module.
Optionally, the determining unit includes:
the first determining module is used for determining that the diagnosis and treatment items are abnormal when the item details are inconsistent with the preset treatment path;
the second determining module is used for determining that the diagnosis and treatment item is abnormal when the item detail is consistent with the preset treatment path and the item detail with the relatively lower price of the same-level directory exists in the third-level directory of the item detail;
and the third determining module is used for determining that the diagnosis and treatment item is normal when the item detail is consistent with the preset treatment path and the item detail with the relatively lower price of the same-level directory does not exist in the third-level directory of the item detail.
According to a third aspect of the present disclosure, there is provided an electronic device comprising:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect.
According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of the aforementioned first aspect.
According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method as set forth in the preceding first aspect.
According to the detection method, the detection device, the electronic equipment and the storage medium for the abnormal diagnosis and treatment behaviors, diagnosis and treatment items to be detected are obtained, wherein the diagnosis and treatment items comprise at least one of medicines, inspection, assay, diagnosis and treatment and consumables; sequentially converting at least one type of diagnosis and treatment items into item details corresponding to a national medical insurance item table; the national medical insurance project table comprises at least three levels of directory formats; judging whether the converted item details are abnormal or not based on a preset treatment path, wherein the preset treatment path is obtained in advance through learning; if the abnormality exists, an alarm prompt that the diagnosis and treatment behavior is abnormal is triggered. Compared with the prior art, the diagnosis and treatment items are converted into the item details corresponding to the national medical insurance item table, and then whether the item details are abnormal or not is determined by detecting the difference between the item details and the preset treatment path, so that the abnormal diagnosis and treatment behaviors are detected, and the diagnosis and treatment behaviors of doctors are standardized.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present application, nor do they limit the scope of the present application. Other features of the present application will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
fig. 1 is a schematic flow chart of a method for detecting abnormal diagnosis and treatment behaviors provided in the embodiment of the present disclosure;
fig. 2 is a schematic flow chart of a method for obtaining a predetermined treatment path through training according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an abnormal diagnosis and treatment behavior detection apparatus provided by the present disclosure;
fig. 4 is a schematic structural diagram of another abnormal diagnosis and treatment behavior detection device provided by the present disclosure;
FIG. 5 shows a schematic block diagram of an example electronic device 400 that may be used to implement embodiments of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
A method, an apparatus, an electronic device, and a storage medium for detecting abnormal diagnosis and treatment behaviors according to embodiments of the present disclosure are described below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a method for detecting an abnormal diagnosis and treatment behavior according to an embodiment of the present disclosure. As shown in fig. 1, the method comprises the following steps:
101, acquiring diagnosis and treatment items to be detected, wherein the diagnosis and treatment items comprise at least one of medicines, inspection, tests, diagnosis and treatment and consumables.
The embodiment of the application is applied to settlement data of all hospitals, diagnosis and treatment items are analyzed according to the settlement data, and whether abnormal diagnosis and treatment items exist is determined.
The abnormal diagnosis and treatment items include but are not limited to irregular phenomena such as random medicine opening, high-price medicine opening, big small disease treatment, random treatment and the like, so the abnormal diagnosis and treatment items can be reflected from settlement data, for example, the small disease is slightly treated, low-price medicines for treatment are provided, but high-price medicines are provided; for example, a medicine for treating a male disease or an abnormal diagnosis and treatment item such as an examination item is prescribed.
The above example is exemplified by the detection and the drug category in the medical matters, and in practical applications, the medical matters include: medicine, inspection, assay, diagnosis and treatment, and consumable materials. In a specific application process, at least one type of diagnosis and treatment items can be detected.
In order to simplify and unify the diagnosis and treatment items, data clustering is adopted to process the diagnosis and treatment items to be detected, and the diagnosis and treatment items can be generated by but not limited to settlement between a medical insurance center and a hospital; according to the data clustering result, determining the category of the diagnosis and treatment items corresponding to the disease diagnosis related group, where the disease diagnosis related group includes drugs, examinations, assays, diagnoses, and consumables, it should be understood that the embodiments of the present application are not limited to the above embodiments.
In order to detect the irregular phenomena of random prescription, high-price prescription, big treatment of small diseases, random treatment and the like, the embodiment of the application defines the diagnosis and treatment behaviors which are separated from most doctors as abnormal diagnosis and treatment, and the diagnosis and treatment items acquired in the step 101 need to be corresponding to the national medical insurance projects in terms of data implementation, so that the diagnosis and treatment items in different settlement data are structurally processed.
The national medical insurance project table comprises at least a three-level catalog format, and the embodiment of the application takes the example that the national medical insurance project table comprises a four-level catalog format as an example for explanation, but it should be clear that the explanation mode is not intended to limit that the national medical insurance project table can be only four levels, and the division of the specific catalog needs to conform to the relevant regulations in the medical insurance overall planning area.
For example, to facilitate understanding of the directory format of the national medical insurance project table, please refer to table 1, where table 1 includes: first class name, second class name, third class name, fourth class name, and project name.
It should be noted that table 1 is only an exemplary example, and the directory format of the national medical insurance project table is not limited in the embodiment of the present application.
As can be seen from the above embodiments, the diagnosis and treatment items include at least one type of drugs, examinations, assays, diagnoses, and consumables, and when the execution is converted into the item details corresponding to the national medical insurance item table, different types correspond to different item tables, for example, drugs included in the diagnosis and treatment items are mapped to the medical insurance drug classification information table, or consumables included in the diagnosis and treatment items are mapped to the medical insurance consumable classification information table and mapped according to the corresponding relationship.
In a specific implementation process, the process of converting the diagnosis and treatment items into the item details includes, but is not limited to, performing word segmentation on at least one type of diagnosis and treatment items in sequence, determining a national medical insurance item table matched with the diagnosis and treatment items according to word segmentation results, and structuring (mapping) the diagnosis and treatment items into the item details corresponding to the national medical insurance item table according to matching results.
And 103, judging whether the converted item details are abnormal or not based on a preset treatment path, wherein the preset treatment path is obtained in advance through learning.
It should be noted that the preset treatment path described in the embodiment of the present application is obtained by learning the sample settlement data in advance, and is essentially a preset treatment path generated by deeply mining the sample settlement data, and is used for determining whether the item details of the patient have an abnormality.
The preset treatment path includes the proportion of the items used by the doctor, and if the proportion of the items used is smaller, the items can be directly ignored and do not need to be used as the preset treatment path.
In the embodiment of the application, when the converted item detail is consistent with the item in the preset treatment path, determining the diagnosis and treatment item to be detected as normal diagnosis and treatment data, and continuing to execute a corresponding reimbursement process; when the converted item detail is not consistent with the item in the preset treatment path, it is determined that the diagnosis and treatment item to be detected is abnormal diagnosis and treatment data, and the step 104 is continuously executed.
And 104, if the abnormality exists, triggering an alarm prompt that the diagnosis and treatment behavior is abnormal.
When the abnormity exists, the alarm prompt prompts abnormal diagnosis and treatment behaviors in the forms of popup, short message, vibration, alarm, highlight and the like. The purpose of the alarm prompt is not limited in the embodiment of the application.
As a feasible manner of the embodiment of the present application, after the abnormal diagnosis and treatment behavior is confirmed, statistics and alarms may be performed, for example, statistics on abnormal diagnosis and treatment behaviors occurring in one doctor, statistics on abnormal diagnosis and treatment behaviors occurring in a certain hospital, and the like are performed, and specifically, the embodiment of the present application does not limit this.
According to the method for detecting the abnormal diagnosis and treatment behaviors, diagnosis and treatment items to be detected are obtained, wherein the diagnosis and treatment items comprise at least one of medicines, inspection, assay, diagnosis and treatment and consumables; sequentially converting at least one type of diagnosis and treatment items into item details corresponding to a national medical insurance item table; the national medical insurance project table comprises at least three levels of directory formats; judging whether the converted item details are abnormal or not based on a preset treatment path, wherein the preset treatment path is obtained in advance through learning; if the abnormality exists, an alarm prompt that the diagnosis and treatment behavior is abnormal is triggered. Compared with the prior art, the diagnosis and treatment items are converted into the item details corresponding to the national medical insurance item table, and then whether the item details are abnormal or not is determined by detecting the difference between the item details and the preset treatment path, so that the abnormal diagnosis and treatment behaviors are detected, and the diagnosis and treatment behaviors of doctors are standardized.
Whether the converted item details are abnormal or not is judged based on the preset treatment path, and abnormal diagnosis and treatment behaviors can be effectively detected.
Fig. 2 is a flowchart illustrating a method for training a predetermined treatment path according to an embodiment of the present disclosure. As shown in fig. 2, the method comprises the following steps:
The diagnosis and treatment items for training in the embodiment of the application are derived from settlement data of all medical insurance centers and all hospitals, and are obtained by grouping and clustering a plurality of settlement data of the medical insurance centers and the hospitals.
In a specific implementation process, the diagnosis and treatment items for training are processed by data clustering, and the diagnosis and treatment items for training can be generated by but not limited to settlement of a medical insurance center and a hospital; and determining the category of diagnosis and treatment items corresponding to the disease diagnosis related group according to the data clustering result, wherein the disease diagnosis related group comprises medicines, inspection, assay, diagnosis and treatment and consumables.
The format of the training item detail is the same as that of the item detail in step 102, and for the description of the training item detail, reference may be made to step 102, and details of the training item detail format will not be repeated in this step 202.
Matching the medical items for training with detailed items for training corresponding to the national medical insurance item table, for example: and adopting the ending word segmentation, finding the national medical service items with the same word for the first time through the word segmentation, and then adopting an edit distance similarity algorithm to find the corresponding item detail in the most similar national medical insurance item table. It should be understood that the above description is not intended to limit the word segmentation method to be the final word segmentation and the similarity algorithm to be the edit distance only.
And step 203, training the details of the training items to obtain a training treatment path.
The training is trained with item detail, which essentially is the training treatment path obtained by deep mining of sample settlement data.
And step 204, determining the training treatment path exceeding the preset use frequency threshold as a preset treatment path.
The preset number of times threshold is an empirical value, and when the preset number of times threshold is set, the mode may be adopted for determination, for example, the preset number of times threshold is 10, that is, when the number of times of using the training treatment path exceeds 10, the training treatment path is determined to be the preset treatment path.
In practical applications, when the preset treatment path is confirmed, the preset treatment path may also be determined through a preset usage ratio threshold, for example, when the usage ratio of the training treatment path exceeds 20%, the training treatment path is determined as the preset treatment path. Specifically, the embodiment of the present application does not limit the way of confirming the preset treatment path.
As a refinement of the embodiment of the present application, when the step 101 is executed to acquire the medical items to be detected, the following implementation manners may be adopted, but are not limited to: and processing the diagnosis and treatment items to be detected by adopting data clustering, and determining the classification of the diagnosis and treatment items corresponding to disease diagnosis related groups according to data clustering results, wherein the disease diagnosis related groups comprise medicines, inspection, assay, diagnosis and treatment and consumable materials.
When the step 102 sequentially converts at least one of the diagnosis and treatment items into the item details corresponding to the national medical insurance item table, the following implementation manners may be adopted, but are not limited to:
sequentially performing word segmentation processing on at least one type of diagnosis and treatment items, determining a national medical insurance project table matched with the diagnosis and treatment items according to word segmentation results, matching at least three levels of catalogs in the national medical insurance project table step by adopting a preset similarity algorithm on the diagnosis and treatment items until the lowest level of catalogs in the national medical insurance project table are matched, wherein each level of catalogs comprises a classification name and a classification code, and structuring the diagnosis and treatment items into project details corresponding to the national medical insurance project table according to matching results.
As a refinement to the above embodiment, when determining whether there is an abnormality in the converted item details based on the preset treatment path is performed in step 103, the following implementation manners may be adopted, but are not limited to, for example: traversing the diagnosis and treatment items, and giving a prompt when the third-level directory of the diagnosis and treatment items is not in the third-level directory to which the preset treatment path belongs: the category to which the diagnosis and treatment item belongs is not in the common category; when the third-level directory is in the third-level directory to which the preset treatment path belongs, whether diagnosis and treatment items with lower similar prices exist or not is judged, and a prompt is given: the item is in the common national medical insurance category, but the price is too high, and the same category of three-level catalog categories of medicine/examination/consumable/assay/diagnosis and treatment (for example, the price of the A medicine is 200 yuan, the price of the B medicine is 245 yuan and the price of the C medicine is 300 yuan) can be selected.
It should be noted that, when traversing the diagnosis and treatment items, the third-level directory is not necessarily limited, but a first-level directory, a second-level directory or a fourth-level directory may be adopted, but the traversal result is less when traversing the first-level directory or the second-level directory; when the four-level directory is traversed, the traversal result is more, so that a mode of traversing the three-level directory is adopted in practical application, and the flexible limitation is particularly required according to a specific application scene during retrieval.
To sum up, the embodiment of the present application can achieve the following effects:
1. according to the embodiment of the application, diagnosis and treatment items are converted into item details corresponding to a national medical insurance item table, and then whether the item details are abnormal or not is determined by detecting the difference between the item details and a preset treatment path, so that whether the diagnosis and treatment items are abnormal or not is determined, the detection of abnormal diagnosis and treatment behaviors is realized, and the diagnosis and treatment behaviors of doctors are standardized.
2. According to the embodiment of the application, firstly, diagnosis and treatment items for training are converted into the details for the training corresponding to the national medical insurance project table, then the details for the training are trained to obtain the treatment path for training, and then the treatment path for training exceeding the preset use frequency threshold is determined as the preset treatment path, so that the preset treatment path training is realized.
3. And processing the diagnosis and treatment items to be detected by adopting data clustering, determining the classification of the diagnosis and treatment items corresponding to the disease diagnosis related groups according to the data clustering result, and realizing the acquisition of the diagnosis and treatment items.
4. Sequentially performing word segmentation processing on at least one type of diagnosis and treatment items, determining a national medical insurance project table matched with the diagnosis and treatment items according to word segmentation results, matching at least three levels of catalogs in the national medical insurance project table step by adopting a preset similarity algorithm on the diagnosis and treatment items until the lowest level of catalogs in the national medical insurance project table are matched, wherein each level of catalogs comprises a classification name and a classification code, structuring the diagnosis and treatment items into project details corresponding to the national medical insurance project table according to matching results, realizing accurate conversion of the diagnosis and treatment items and the project details corresponding to the national medical insurance project table, and obtaining standard format structured project details.
5. And judging whether the converted item detail is abnormal or not based on a preset treatment path, so as to judge whether the item detail is abnormal or not.
6. And the abnormal diagnosis and treatment behaviors are effectively and timely prompted through alarm prompting.
Corresponding to the method for detecting the abnormal diagnosis and treatment behaviors, the invention also provides a device for detecting the abnormal diagnosis and treatment behaviors. Since the device embodiment of the present invention corresponds to the method embodiment described above, details that are not disclosed in the device embodiment may refer to the method embodiment described above, and are not described again in the present invention.
Fig. 3 is a schematic structural diagram of an abnormal diagnosis and treatment behavior detection device provided by the present disclosure, as shown in fig. 3, including:
the first acquiring unit 31 is configured to acquire medical items to be detected, where the medical items include at least one of drugs, examinations, assays, diagnoses, and consumables;
a conversion unit 32, configured to sequentially convert at least one type of the diagnosis and treatment items acquired by the first acquisition unit into item details corresponding to a national medical insurance item table; the national medical insurance project table comprises at least three levels of directory formats;
a judging unit 33, configured to judge whether the item details converted by the converting unit are abnormal based on a preset treatment path, where the preset treatment path is obtained in advance through learning;
and the alarm unit 34 is used for triggering an alarm prompt that the diagnosis and treatment behaviors are abnormal when the judgment unit determines that the abnormalities exist.
According to the detection device for the abnormal diagnosis and treatment behaviors, diagnosis and treatment items to be detected are obtained, wherein the diagnosis and treatment items comprise at least one of medicines, inspection, assay, diagnosis and treatment and consumables; sequentially converting at least one type of diagnosis and treatment items into item details corresponding to a national medical insurance item table; the national medical insurance project table comprises at least three levels of directory formats; judging whether the converted item details are abnormal or not based on a preset treatment path, wherein the preset treatment path is obtained in advance through learning; if the abnormality exists, an alarm prompt that the diagnosis and treatment behavior is abnormal is triggered. Compared with the prior art, the diagnosis and treatment items are converted into the item details corresponding to the national medical insurance item table, and then whether the item details are abnormal or not is determined by detecting the difference between the item details and the preset treatment path, so that the abnormal diagnosis and treatment behaviors are detected, and the diagnosis and treatment behaviors of doctors are standardized.
Further, in a possible implementation manner of this embodiment, as shown in fig. 4, fig. 4 is a schematic structural diagram of another abnormal diagnosis and treatment behavior detection apparatus provided by this disclosure, where the apparatus further includes:
a second obtaining unit 35, configured to obtain the diagnosis and treatment items for training before the determining unit 33 determines whether the converted item details are abnormal based on the preset treatment path, and perform data clustering on the diagnosis and treatment items for training to obtain at least one of drugs, examinations, assays, diagnoses, and consumables included in the diagnosis and treatment items for training;
a word segmentation unit 36, configured to perform word segmentation processing on the medical matters for training acquired by the second acquisition unit 35;
the first determining unit 37 is configured to determine a national medical insurance project table matched with the word segmentation result according to the word segmentation result;
the matching unit 38 is configured to match details of the training items corresponding to the national medical insurance item table by using a preset similarity algorithm for the training medical items;
a training unit 39, configured to train the details of the training items matched by the matching unit, so as to obtain a training treatment path;
a second determining unit 310, configured to determine the training therapy pathway exceeding the preset usage number threshold as the preset therapy pathway.
Further, in a possible implementation manner of this embodiment, as shown in fig. 4, the first obtaining unit 31 includes:
the processing module 311 is configured to process the diagnosis and treatment items to be detected by using data clustering;
the determining module 312 is configured to determine the category of the diagnosis and treatment items corresponding to the disease diagnosis related group according to the data clustering result, where the disease diagnosis related group includes drugs, examinations, assays, diagnoses, and consumables.
Further, in a possible implementation manner of this embodiment, as shown in fig. 4, the converting unit 32 includes:
the determining module 321 is configured to perform word segmentation processing on at least one type of diagnosis and treatment items in sequence, and determine a national medical insurance project table matched with the word segmentation result according to the word segmentation result;
the matching module 322 is configured to match at least three levels of directories in the national medical insurance project table step by using a preset similarity algorithm for the medical items until the lowest level directory in the national medical insurance project table is matched, where each level of directory includes a classification name and a classification code;
and the structuring module 323 is used for structuring the diagnosis and treatment items into item details corresponding to the national medical insurance item table according to the matching result of the matching module.
Further, in a possible implementation manner of this embodiment, as shown in fig. 4, the determining unit includes 33:
the first determining module 331, when the item detail is inconsistent with the preset treatment path, determining that the diagnosis and treatment item is abnormal;
a second determining module 332, configured to determine that the medical item is abnormal when the item detail is consistent with the preset treatment path and a relatively lower item detail of the same-level directory price exists in a third-level directory of the item detail;
the third determining module 333 determines that the medical item is normal when the item detail is consistent with the preset treatment path and no item detail with a relatively lower price in the same-level directory exists in the third-level directory of the item detail.
It should be noted that the foregoing explanation of the method embodiment is also applicable to the apparatus of the present embodiment, and the principle is the same, and the present embodiment is not limited thereto.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
FIG. 5 shows a schematic block diagram of an example electronic device 400 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 5, the device 400 includes a computing unit 401 that can perform various appropriate actions and processes in accordance with a computer program stored in a ROM (Read-only memory) 402 or a computer program loaded from a storage unit 408 into a RAM (random access memory) 403. In the RAM403, various programs and data necessary for the operation of the device 400 can also be stored. The computing unit 401, ROM402, and RAM403 are connected to each other via a bus 404. An I/O (Input/Output) interface 405 is also connected to the bus 404.
A number of components in device 400 are connected to I/O interface 405, including: an input unit 406 such as a keyboard, a mouse, or the like; an output unit 407 such as various types of displays, speakers, and the like; a storage unit 408 such as a magnetic disk, optical disk, or the like; and a communication unit 409 such as a network card, modem, wireless communication transceiver, etc.
The communication unit 409 allows the device 400 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
For example, in some embodiments, the method of detecting abnormal clinical behavior may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 408.
In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 400 via the ROM402 and/or the communication unit 409.
When the computer program is loaded into RAM403 and executed by computing unit 401, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the aforementioned method of detecting abnormal clinical behavior in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above can be implemented in digital electronic circuitry, integrated circuitry, FPGAs (field programmable gate arrays), ASICs (Application-specific integrated circuits), ASSPs (Application specific standard products), SOCs (systems on chip), CPLDs (complex programmable logic devices), computer hardware, firmware, software, and/or combinations thereof.
These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a RAM, a ROM, an EPROM (erasable programmable read-Only-Memory) or flash Memory, an optical fiber, a CD-ROM (compact disc read-Only-Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (Cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer.
Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (local area network), WAN (wide area network), internet, and blockchain network.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("virtual privateserver", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.
It should be noted that artificial intelligence is a subject for studying a computer to simulate some human thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and includes both hardware and software technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing, and the like; the artificial intelligence software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, machine learning/deep learning, a big data processing technology, a knowledge map technology and the like.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (13)
1. A method for detecting abnormal diagnosis and treatment behaviors is characterized by comprising the following steps:
acquiring diagnosis and treatment items to be detected, wherein the diagnosis and treatment items comprise at least one of medicines, inspection, assay, diagnosis and treatment and consumables;
sequentially converting at least one type of diagnosis and treatment items into item details corresponding to a national medical insurance item table; the national medical insurance project table comprises at least three levels of directory formats;
judging whether the converted item details are abnormal or not based on a preset treatment path, wherein the preset treatment path is obtained in advance through learning;
and if the abnormity exists, triggering an alarm prompt that the diagnosis and treatment behaviors are abnormal.
2. The detection method according to claim 1, wherein before determining whether there is an abnormality in the converted item details based on a preset treatment path, the method further comprises:
acquiring diagnosis and treatment items for training, and performing data clustering processing on the diagnosis and treatment items for training to obtain at least one type of medicines, inspection, assay, diagnosis and treatment and consumables included in the diagnosis and treatment items for training;
performing word segmentation processing on the diagnosis and treatment items for training, and determining a national medical insurance project table matched with the diagnosis and treatment items according to word segmentation results;
matching the detail of the training items corresponding to the national medical insurance item table by adopting a preset similarity algorithm for the training diagnosis and treatment items;
training the detail of the training item to obtain a training treatment path;
and determining the training treatment path exceeding the preset use frequency threshold value as a preset treatment path.
3. The detection method according to claim 1, wherein the acquiring of the medical item to be detected comprises:
processing the diagnosis and treatment items to be detected by adopting data clustering;
and determining the category of diagnosis and treatment items corresponding to the disease diagnosis related group according to the data clustering result, wherein the disease diagnosis related group comprises medicines, inspection, assay, diagnosis and treatment and consumables.
4. The detection method according to claim 1, wherein the sequentially converting at least one of the medical items into item details corresponding to a national medical insurance item table comprises:
sequentially performing word segmentation processing on at least one type of diagnosis and treatment items, and determining a national medical insurance project table matched with the word segmentation processing according to the word segmentation result;
matching at least three levels of catalogs in a national medical insurance project table step by adopting a preset similarity algorithm for the diagnosis and treatment items until the lowest level of catalogs in the national medical insurance project table are matched, wherein each level of catalogs comprises a classification name and a classification code;
and structuring the diagnosis and treatment items into item details corresponding to the national medical insurance item table according to the matching result.
5. The detection method according to claim 1, wherein the determining whether the converted item details are abnormal or not based on the preset treatment path comprises:
if the item detail is inconsistent with the preset treatment path, determining that the diagnosis and treatment item is abnormal;
if the item detail is consistent with the preset treatment path and the item detail with the same level of directory price lower than that of the third level directory of the item detail exists, determining that the diagnosis and treatment item is abnormal;
and if the item detail is consistent with the preset treatment path and the item detail with the same level of directory price being relatively lower does not exist in the third level directory of the item detail, determining that the diagnosis and treatment item is normal.
6. A detection device for abnormal diagnosis and treatment behaviors is characterized by comprising:
the diagnosis and treatment system comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for acquiring diagnosis and treatment items to be detected, and the diagnosis and treatment items comprise at least one of medicines, examination, assay, diagnosis and treatment and consumables;
the conversion unit is used for sequentially converting at least one type of diagnosis and treatment items acquired by the first acquisition unit into item details corresponding to a national medical insurance item table; the national medical insurance project table comprises at least three levels of directory formats;
the judging unit is used for judging whether the item details converted by the converting unit are abnormal or not based on a preset treatment path, wherein the preset treatment path is obtained in advance through learning;
and the alarm unit is used for triggering an alarm prompt that the diagnosis and treatment behavior is abnormal when the judgment unit determines that the abnormality exists.
7. The detection device of claim 6, further comprising:
the second acquisition unit is used for acquiring diagnosis and treatment items for training before the judgment unit judges whether the converted item details are abnormal or not based on the preset treatment path, and performing data clustering processing on the diagnosis and treatment items for training to obtain at least one type of medicines, inspection, assay, diagnosis and treatment and consumables included in the diagnosis and treatment items for training;
a word segmentation unit, configured to perform word segmentation processing on the diagnosis and treatment items for training acquired by the second acquisition unit;
the first determining unit is used for determining a matched national medical insurance project table according to the word segmentation result;
the matching unit is used for matching the detail of the training items corresponding to the national medical insurance item table by adopting a preset similarity algorithm for the training diagnosis and treatment items;
the training unit is used for training the details of the training items matched by the matching unit to obtain a training treatment path;
and the second determining unit is used for determining the training treatment path exceeding the preset use frequency threshold value as the preset treatment path.
8. The detection apparatus according to claim 6, wherein the first acquisition unit includes:
the processing module is used for processing the diagnosis and treatment items to be detected by adopting data clustering;
and the determining module is used for determining the category of diagnosis and treatment items corresponding to the disease diagnosis related group according to the data clustering result, wherein the disease diagnosis related group comprises medicines, inspection, assay, diagnosis and treatment and consumables.
9. The detection apparatus according to claim 6, wherein the conversion unit includes:
the determining module is used for sequentially carrying out word segmentation processing on at least one type of diagnosis and treatment items and determining a national medical insurance project table matched with the word segmentation processing according to the word segmentation result;
the matching module is used for matching at least three levels of catalogs in the national medical insurance project table step by adopting a preset similarity algorithm on the diagnosis and treatment items until the catalogs are matched with the lowest level of the national medical insurance project table, wherein each level of catalogs comprises a classification name and a classification code;
and the structuring module is used for structuring the diagnosis and treatment items into item details corresponding to the national medical insurance item table according to the matching result of the matching module.
10. The detection apparatus according to claim 6, wherein the determination unit includes:
the first determining module is used for determining that the diagnosis and treatment items are abnormal when the item details are inconsistent with the preset treatment path;
the second determining module is used for determining that the diagnosis and treatment item is abnormal when the item detail is consistent with the preset treatment path and the item detail with the relatively lower price of the same-level directory exists in the third-level directory of the item detail;
and the third determining module is used for determining that the diagnosis and treatment item is normal when the item detail is consistent with the preset treatment path and the item detail with the relatively lower price of the same-level directory does not exist in the third-level directory of the item detail.
11. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.
12. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-5.
13. A computer program product, characterized in that it comprises a computer program which, when being executed by a processor, carries out the method according to any one of claims 1-5.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117316404A (en) * | 2023-09-11 | 2023-12-29 | 北京合源汇丰医药科技有限公司 | Medical information anomaly detection method and system based on AI algorithm |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109636642A (en) * | 2018-12-13 | 2019-04-16 | 平安医疗健康管理股份有限公司 | Recognition methods, device, equipment and the readable storage medium storing program for executing of Misuse adjuvant |
| CN111986038A (en) * | 2020-08-31 | 2020-11-24 | 平安医疗健康管理股份有限公司 | Examination method, device, equipment and storage medium for diagnosis and treatment behaviors of single disease |
| CN113707261A (en) * | 2021-08-30 | 2021-11-26 | 平安国际智慧城市科技股份有限公司 | Artificial intelligence-based medicine recommendation method and device and related equipment |
| CN114512228A (en) * | 2022-02-08 | 2022-05-17 | 吾征智能技术(北京)有限公司 | Traditional Chinese medicine disease auxiliary diagnosis system, equipment and storage medium |
-
2022
- 2022-06-07 CN CN202210636368.1A patent/CN114724693A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109636642A (en) * | 2018-12-13 | 2019-04-16 | 平安医疗健康管理股份有限公司 | Recognition methods, device, equipment and the readable storage medium storing program for executing of Misuse adjuvant |
| CN111986038A (en) * | 2020-08-31 | 2020-11-24 | 平安医疗健康管理股份有限公司 | Examination method, device, equipment and storage medium for diagnosis and treatment behaviors of single disease |
| CN113707261A (en) * | 2021-08-30 | 2021-11-26 | 平安国际智慧城市科技股份有限公司 | Artificial intelligence-based medicine recommendation method and device and related equipment |
| CN114512228A (en) * | 2022-02-08 | 2022-05-17 | 吾征智能技术(北京)有限公司 | Traditional Chinese medicine disease auxiliary diagnosis system, equipment and storage medium |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117316404A (en) * | 2023-09-11 | 2023-12-29 | 北京合源汇丰医药科技有限公司 | Medical information anomaly detection method and system based on AI algorithm |
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