CN111180027A - Patient portrait correlation rule screening method and device based on medical big data - Google Patents
Patient portrait correlation rule screening method and device based on medical big data Download PDFInfo
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Abstract
The invention provides a method and a device for screening patient portrait correlation rules based on medical big data, wherein the method comprises the following steps: acquiring medical data corresponding to a plurality of fields to be checked in a specified time period from a pre-configured medical database; grouping the medical data according to diagnosis, extracting a treatment item corresponding to each diagnosis, and calculating a correlation value between the diagnosis and the corresponding treatment item according to a pre-configured patient image correlation rule method; acquiring hospitalization records of all patients in an area to be screened, and extracting diagnosis and treatment items of each patient in the hospitalization process; calculating a confidence value for each patient hospitalization; patients with confidence values below the set value are flagged as suspect. The invention provides a method for constructing patient portrait correlation rules by utilizing big data, and provides a method for screening the patient portrait correlation rules based on medical big data based on the method, so that the range of people can be quickly locked, suspicious people can be found, and the screening efficiency is improved.
Description
Technical Field
The invention relates to medical data screening, in particular to a patient portrait correlation rule screening method based on medical big data.
Background
In some areas, some people use false hospitalization, false diagnosis, false treatment and the like to collect medical insurance funds, which is not beneficial to long-term and stable development of medical insurance and causes harm to patients who need normal medical insurance, so that the deceptive insurance people need to be screened.
At present, the screening and judgment of target people mainly adopt a manual mode, because the manual inspection efficiency is extremely low, the progress of discovering cheating and insurance people can not be kept up with under the condition that massive diagnosis information is generated every year, and a large number of professional doctors are employed for auditing, so that the method is unlikely to be used in the modern shortage of medical resources.
Disclosure of Invention
The invention provides a method for constructing patient portrait correlation rules based on big data, and provides a method for screening patient portrait correlation rules based on medical big data based on the method, so that the range of people can be quickly locked, suspicious people can be found, and the screening efficiency is improved.
The technical scheme of the invention is realized as follows:
a patient portrait correlation rule screening method based on medical big data comprises the following steps:
step 1: acquiring medical data corresponding to a plurality of fields to be checked in a specified time period from a pre-configured medical database;
step 2: grouping the medical data according to diagnosis, extracting a treatment item corresponding to each diagnosis, and calculating a correlation value between the diagnosis and the corresponding treatment item according to a pre-configured patient image correlation rule method;
and step 3: acquiring hospitalization records of all patients in an area to be screened, and extracting diagnosis and treatment items of each patient in the hospitalization process; calculating a credible value of each patient hospitalization according to the correlation value calculated in the step 2;
and 4, step 4: patients with confidence values below the set value are flagged as suspect.
Further, in step 2, the method for patient representation correlation rule includes:
and taking the treatment item corresponding to each diagnosis as the diagnosis characteristic of the diagnosis, taking the diagnosis characteristic as an analysis value, and in a specified time period, the frequency of occurrence of the diagnosis characteristic corresponding to the diagnosis is in direct proportion to the correlation value.
Further, in step 3, the method for calculating the confidence value of each patient hospitalization comprises: and extracting the correlation value of each treatment item and diagnosis of the patient, and obtaining the authenticity correlation value of the patient in the hospitalization process according to the correlation values of all the treatment items and the diagnoses.
Further, the method for obtaining the authenticity correlation value during the hospitalization of the patient according to the correlation values of all treatment items and diagnoses comprises the following steps:
all treatment items were added to the diagnostic correlation values and then averaged to determine the true correlation value for the patient during hospitalization.
Further, in step 1, the field to be verified includes: age, medical care, diagnosis, number of hospitalizations, and testing, examination, treatment, medication during hospitalization.
Further, the medical database is created by:
in the acquisition of medical data
Basic data and service data, and performing corresponding filling on the contents of the fields based on a data model; wherein the base data comprises: institution information, medical personnel information, diagnostic information, medical service items, medical service prices, the business data includes: patient staff information, visit information, charge details, prescription information, medical advice information.
Further, after the step 4, the method further comprises the following steps:
and sorting the suspicious patients from small to large according to the credibility value, and outputting a sorting result.
A big-data based medical data detection apparatus, comprising:
an acquisition unit: the system comprises a medical database, a data acquisition module, a data processing module and a data processing module, wherein the medical database is used for acquiring medical data corresponding to a plurality of fields to be detected in a specified time period from the medical database; grouping the medical data according to diagnosis, and extracting a treatment item corresponding to each diagnosis;
a determination unit: the system is used for calculating a correlation numerical value of diagnosis and a corresponding treatment item according to a preset patient image correlation rule script; analyzing the credible value of hospitalization of each patient in the area to be screened;
a detection unit: the system is used for marking the patients with the credibility values lower than the set value as suspicious, sorting the suspicious patients according to the credibility values and outputting sorting results.
Furthermore, the patient image correlation rule script takes the treatment item corresponding to each diagnosis as the diagnosis characteristic of the diagnosis, and takes the diagnosis characteristic as an analysis value to acquire the frequency of occurrence of the diagnosis characteristic corresponding to the diagnosis in a specified time period.
Further, the system also comprises a manual auditing module which is used for manually auditing the suspicious patients.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the disclosure and together with the description serve to explain the principles of the disclosure.
FIG. 1 is a flow chart of a method according to a first embodiment of the disclosure;
FIG. 2 is a block diagram of a second embodiment of the disclosure;
Detailed Description
The present disclosure will be described in further detail with reference to the drawings and embodiments. It is to be understood that the specific embodiments described herein are for purposes of illustration only and are not to be construed as limitations of the present disclosure. It should be further noted that, for the convenience of description, only the portions relevant to the present disclosure are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
The first embodiment is as follows:
a patient portrait correlation rule screening method based on medical big data comprises the following steps:
step 1: acquiring medical data corresponding to a plurality of fields to be checked in a specified time period from a pre-configured medical database;
wherein the specified time period may be a natural year time period, such as: 1, 1-12 and 31 days per year are taken as a specified time period;
step 2: grouping the medical data according to diagnosis, extracting a treatment item corresponding to each diagnosis, and calculating a correlation value between the diagnosis and the corresponding treatment item according to a pre-configured patient image correlation rule method; in this step, the calculation result is a correlation value between a certain treatment item and the corresponding diagnosis;
in this step, the medical data should be preprocessed, including: the medicine and the examination are replaced by the classification name of the national medical insurance, the traditional Chinese medicine in the medicine and the examination is deleted, and non-medical insurance items such as hospitalization cost and garbage disposal cost are deleted.
And step 3: acquiring hospitalization records of all patients in an area to be screened, and extracting diagnosis and treatment items of each patient in the hospitalization process; calculating a credible value of each patient hospitalization according to the correlation value calculated in the step 2;
and 4, step 4: patients with confidence values below the set value are flagged as suspect.
Wherein the medical data includes a diagnosis of the patient and treatment items prescribed by the doctor according to the diagnosis, the treatment items including: drug, examination, assay, treatment, etc.;
the field to be inspected includes: age, medical care, diagnosis, number of hospitalizations, and testing, examination, treatment, medication during hospitalization.
In this embodiment, the medical database is created as follows:
in the acquisition of medical data
Basic data and service data, and performing corresponding filling on the contents of the fields based on a data model; wherein the base data comprises: institution information, medical personnel information, diagnostic information, medical service items, medical service prices, the business data includes: patient staff information, visit information, charge details, prescription information, medical advice information.
According to the method, the rule for finding the data characteristics of the cheat insurance people is compiled by taking clinical experience as guidance, the corresponding credibility value of the patient to be diagnosed is calculated by combining statistical learning and machine learning based on the database, and the people in the range are quickly locked and the suspect is found according to the ranking of the credibility value.
Specifically, the patient image correlation rule method includes:
taking the treatment item corresponding to each diagnosis as the visit characteristic of the diagnosis, taking the visit characteristic as an analysis value, and within a specified time period, the number of times of appearance of the visit characteristic corresponding to the diagnosis is in direct proportion to the correlation value, that is, the greater the number of times of appearance of the visit characteristic corresponding to the diagnosis is, the higher the correlation value is, for convenience of understanding, this embodiment may set that, every 1 more the number of times of appearance of the visit characteristic corresponding to the diagnosis is, the greater the correlation value is, the 1 is added to the correlation value;
the method for calculating the confidence value of each patient hospitalization comprises the following steps: and extracting the correlation value of each treatment item and diagnosis of the patient, and obtaining the authenticity correlation value of the patient in the hospitalization process according to the correlation values of all the treatment items and the diagnoses.
In this embodiment, the method for obtaining the authenticity correlation value during the hospitalization of the patient according to the correlation values of all treatment items and diagnoses includes:
all treatment items were added to the diagnostic correlation values and then averaged to determine the true correlation value for the patient during hospitalization.
If a certain patient relevance numerical value is lower, the lower the relevance numerical value is, the more the treatment items adopted by diagnosis in the hospitalization of the patient are different from most of patients, the more suspicious the authenticity of the hospitalization is, the smaller the numerical value is to the larger the ranking is, the ranking of risk groups can be obtained, then manual key ranking is carried out, the labor intensity of workers can be greatly reduced, the labor is saved, and the dual advantages of accuracy and speed are achieved.
Therefore, this embodiment further includes, after step 4:
and sorting the suspicious patients from small to large according to the credibility value, and outputting a sorting result. The smaller the confidence value, the more suspect is the authenticity of its hospitalization, sorted by numerical value.
The method for screening the patient portrait correlation rule based on the medical big data provided by the embodiment divides medical data of all patients in a region to be screened into groups according to diagnosis, extracts specific treatment measures corresponding to the diagnosis according to various treatment items of medicines, examinations, tests, treatments and the like prescribed by the doctor according to the diagnosis, takes the specific treatment measures as general diagnosis characteristics of the diagnosis, takes the general diagnosis characteristics as analysis values, obtains the medical data corresponding to fields in a specified time period from a medical database, calculates the medical behavior characteristics of all people, screens by using software, sorts credible values obtained by calculation results, and ranks the lower the credible value, the higher the risk people needing key monitoring, and then manually performs key investigation.
Example two:
the present embodiment screens medical data according to a medical data detection device.
A big-data based medical data detection apparatus, comprising:
an acquisition unit: the system comprises a medical database, a data acquisition module, a data processing module and a data processing module, wherein the medical database is used for acquiring medical data corresponding to a plurality of fields to be detected in a specified time period from the medical database; grouping the medical data according to diagnosis, and extracting a treatment item corresponding to each diagnosis;
a determination unit: the system is used for calculating a correlation numerical value of diagnosis and a corresponding treatment item according to a preset patient image correlation rule script; analyzing the credible value of hospitalization of each patient in the area to be screened;
a detection unit: the system is used for marking the patients with the credibility values lower than the set value as suspicious, sorting the suspicious patients according to the credibility values and outputting sorting results.
The patient image correlation rule script takes a treatment item corresponding to each diagnosis as a diagnosis characteristic of the diagnosis, and takes the diagnosis characteristic as an analysis value to acquire the frequency of occurrence of the diagnosis characteristic corresponding to the diagnosis in a specified time period.
The medical data detection device also comprises a manual auditing module which is used for manually auditing the suspicious patients, and after the manual auditing is finished, the manual auditing result can be input into the medical data detection device.
The medical data detection device of the embodiment can be applied to computers with various configurations and can be adapted to computer operating systems with various configurations.
It will be understood by those skilled in the art that the foregoing embodiments are merely for clarity of description and are not intended to limit the scope of the invention. It will be apparent to those skilled in the art that other variations or modifications may be made on the above invention and still be within the scope of the invention.
Claims (10)
1. A method for screening patient portrait correlation rules based on medical big data is characterized by comprising the following steps:
step 1: acquiring medical data corresponding to a plurality of fields to be checked in a specified time period from a pre-configured medical database;
step 2: grouping the medical data according to diagnosis, extracting a treatment item corresponding to each diagnosis, and calculating a correlation value between the diagnosis and the corresponding treatment item according to a pre-configured patient image correlation rule method;
and step 3: acquiring hospitalization records of all patients in an area to be screened, and extracting diagnosis and treatment items of each patient in the hospitalization process; calculating a credible value of each patient hospitalization according to the correlation value calculated in the step 2;
and 4, step 4: patients with confidence values below the set value are flagged as suspect.
2. The method for screening the patient image correlation rule based on the medical big data as claimed in claim 1, wherein in step 2, the method for screening the patient image correlation rule comprises:
and taking the treatment item corresponding to each diagnosis as the diagnosis characteristic of the diagnosis, taking the diagnosis characteristic as an analysis value, and in a specified time period, the frequency of occurrence of the diagnosis characteristic corresponding to the diagnosis is in direct proportion to the correlation value.
3. The method for screening the patient image correlation rule based on the medical big data as claimed in claim 1, wherein in the step 3, the method for calculating the confidence value of each hospitalized patient comprises: and extracting the correlation value of each treatment item and diagnosis of the patient, and obtaining the authenticity correlation value of the patient in the hospitalization process according to the correlation values of all the treatment items and the diagnoses.
4. The method for screening the patient image correlation rule based on the medical big data as claimed in claim 3, wherein the method for obtaining the authenticity correlation value during the hospitalization of the patient according to the correlation values of all the treatment items and the diagnoses comprises:
all treatment items were added to the diagnostic correlation values and then averaged to determine the true correlation value for the patient during hospitalization.
5. The method for screening the patient image correlation rule based on the medical big data as claimed in claim 1, wherein in step 1, the field to be checked comprises: age, medical care, diagnosis, number of hospitalizations, and testing, examination, treatment, medication during hospitalization.
6. The method for screening patient representation correlation rules based on medical big data as claimed in claim 1, wherein the medical database is created by:
in the acquisition of medical data
Basic data and service data, and performing corresponding filling on the contents of the fields based on a data model; wherein the base data comprises: institution information, medical personnel information, diagnostic information, medical service items, medical service prices, the business data includes: patient staff information, visit information, charge details, prescription information, medical advice information.
7. The method for screening patient representation correlation rules based on medical big data as claimed in claim 1, further comprising after step 4:
and sorting the suspicious patients from small to large according to the credibility value, and outputting a sorting result.
8. A big-data-based medical data detection device, comprising:
an acquisition unit: the system comprises a medical database, a data acquisition module, a data processing module and a data processing module, wherein the medical database is used for acquiring medical data corresponding to a plurality of fields to be detected in a specified time period from the medical database; grouping the medical data according to diagnosis, and extracting a treatment item corresponding to each diagnosis;
a determination unit: the system is used for calculating a correlation numerical value of diagnosis and a corresponding treatment item according to a preset patient image correlation rule script; analyzing the credible value of hospitalization of each patient in the area to be screened;
a detection unit: the system is used for marking the patients with the credibility values lower than the set value as suspicious, sorting the suspicious patients according to the credibility values and outputting sorting results.
9. The big-data-based medical data detection apparatus according to claim 8, wherein the patient image correlation rule script takes a treatment item corresponding to each diagnosis as a diagnosis characteristic of the diagnosis, and obtains the number of times of occurrence of the diagnosis characteristic corresponding to the diagnosis in a predetermined time period by using the diagnosis characteristic as an analysis value.
10. The big-data based medical data detection apparatus according to claim 8 or 9, further comprising a manual review module for performing manual review on the suspicious patient.
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