CN109524069B - Medical data processing method and device, electronic equipment and storage medium - Google Patents
Medical data processing method and device, electronic equipment and storage medium Download PDFInfo
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- CN109524069B CN109524069B CN201811334200.5A CN201811334200A CN109524069B CN 109524069 B CN109524069 B CN 109524069B CN 201811334200 A CN201811334200 A CN 201811334200A CN 109524069 B CN109524069 B CN 109524069B
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Abstract
The disclosure relates to the technical field of medical big data processing, in particular to a medical data processing method and device. The method comprises the following steps: reading the medical record from the electronic medical record system in the hospital; extracting patient information and medical behavior data and measured subject information of the hospital from the medical record; acquiring first-class statistical index data of measured subjects in a hospital aiming at each disease; determining a classification standard threshold value for an extension index classification through statistical analysis based on first-class statistical index data of a plurality of measured subjects of a hospital; and determining extension index data according to the classification standard threshold value so as to determine the medical efficiency of the measured subject of the hospital. The method extracts actual key information through the medical record in the hospital information system, and determines the classification standard through a statistical analysis mode, so that the extension index data of the measured subject of the hospital is obtained to determine the medical efficiency of the measured subject of the hospital, the objectivity, the comprehensiveness and the accuracy are achieved, and the research, development and use costs are reduced.
Description
Technical Field
The disclosure relates to the technical field of medical big data processing, in particular to a medical data processing method and device, electronic equipment and a storage medium.
Background
With the improvement of the comprehensive development level of the society, people pay more attention to health, and the demand for medical resources is increasing day by day. Meanwhile, the objective reality of uneven distribution of high-quality medical resources exists in China, and on the basis, various factors are superposed, so that high-quality medical institutions represented by the third Hospital are in an overload running state for a long time. To alleviate this problem, the state has proposed a reform idea of "grading diagnosis" from the government level. In the process, the efficiency of treating a certain disease in each hospital, department and doctor is objectively and accurately measured, and the method plays a key role in allocating medical resources.
On one hand, most of the traditional evaluation modes need manual participation to formulate various assessment indexes, but in the face of mass disease classification of modern medicine, a large amount of time resources are required to be input by a large number of clinical medical and hospital management experts, and the cost is high. For example, the Beijing DRGs (disease diagnosis related group) covers 600 kinds of DRGs, but the DRGs have the defects that the human resource investment of medical experts and hospital management experts is excessive, and meanwhile, the DRGs require whole personnel to participate in the implementation process of hospital landing and require a front-line medical staff to invest partial energy to learn and master.
On the other hand, from the perspective of patients, some internet medical platforms provide similar diagnosis guidance or recommendation services at present, taking a certain website as an example, a page of the website is marked with "XXXX patients recommend YYYY good appraisal doctors" for comment, which indicates that the way of measuring the medical efficiency is based on feedback of patients, but the recommendation is based on lack of clinical data support and mostly comes from patient recommendations, and the data quality hardly supports serious medical requirements.
Therefore, how to objectively, accurately and inexpensively measure medical efficiency data and improve processing efficiency is a technical problem to be solved in the field of medical big data processing, and is also a practical and objective demand of society and market.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The invention aims to provide a medical data processing method, a medical data processing device, a storage medium and an electronic device, which can improve the accuracy and efficiency of determining the medical efficiency of a medical subject.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to a first aspect of the present disclosure, there is provided a medical data processing method comprising: reading the medical record from the electronic medical record system in the hospital; extracting patient information and medical behavior data from the medical record, and acquiring measured subject information of the hospital associated with the medical behavior; for each disease diagnosed in the visit record, obtaining first-class statistical index data of a measured subject of the hospital for the disease; determining a classification standard threshold value for an extended index classification through statistical analysis based on first-class statistical index data of a plurality of measured subjects of a hospital for the disease; and determining extension index data according to the classification standard threshold value so as to determine medical efficiency data of the measured subject of the hospital.
In one embodiment, the patient information includes patient underlying physiological information, past medical history, treatment records, and/or complaints;
and/or
The medical behavioral data includes examination, diagnosis, surgery, medication, hospitalization, and/or discharge;
and/or
The hospital measured subject comprises a hospital, a department and a doctor;
and/or
The first type of statistical index data comprises the number of cases in consultation, the number of cases in hospital, the distribution of length of time of stay in hospital, the distribution of cost of stay in hospital, or the distribution of types of discharge hospital.
In one embodiment, before reading the visit record from the HIS, the method further comprises: the data of the treatment is read in from each information system of the hospital, and all the treatment data generated in one treatment is stored in one treatment record.
In one embodiment, the method further comprises: the clinic data read from each information system of the hospital is structured and output according to the preset rule.
In one embodiment, the patient information is desensitized patient information.
In one embodiment, determining by statistical analysis a classification criterion threshold for an extended index classification based on a first class of statistical indicator data for the disease for a plurality of measured subjects of the hospital comprises: and determining a threshold value for applying index classification as a classification standard by a clustering method based on the first class of statistical index data of a plurality of measured subjects of the hospital for the disease.
According to another aspect of the present invention, there is provided a medical data processing apparatus comprising: the medical record reading module is used for reading medical records from an electronic medical record system in a hospital; the key data extraction module is used for extracting patient information and medical behavior data from the visit record and acquiring measured subject information of the hospital related to the medical behavior; the direct index acquisition module is used for acquiring first-class statistical index data of a measured subject of the hospital for each disease diagnosed in a clinic record; a classification standard determination module for determining a classification standard threshold value for reiterating an index classification through statistical analysis based on a plurality of first-class statistical index data of measured subjects of the hospital for the disease; and the extension index acquisition module is used for determining extension index data according to the classification standard threshold so as to determine the medical efficiency of the measured subject of the hospital.
In one embodiment, the apparatus further comprises: the system comprises a treatment record generation module, a treatment record generation module and a treatment record generation module, wherein the treatment record generation module is used for reading treatment data from each information system of a hospital and storing all emergency treatment data generated in one treatment record.
According to a further aspect of the present invention, there is provided a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, realizes the above-mentioned medical data processing method.
According to still another aspect of the present invention, there is provided an electronic apparatus including:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the medical data processing method described above via execution of the executable instructions.
According to the medical data processing method and device provided by the embodiment of the disclosure, the actual key information is extracted through the medical record in the hospital information system, and the classification standard is determined in a statistical analysis mode, so that the extension index of the measured subject of the hospital is obtained, the medical efficiency of the measured subject of the hospital is determined, objectivity and accuracy are achieved, and efficiency is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
FIG. 1 shows a flow diagram of one embodiment of a medical data processing method according to the invention;
FIG. 2 shows a flow chart of another embodiment of a medical data processing method according to the invention;
FIG. 3 shows a flow chart of yet another embodiment of a medical data processing method according to the invention;
FIG. 4 shows a block diagram of one embodiment of a medical data processing apparatus according to the invention;
FIG. 5 shows a block diagram of another embodiment of a medical data processing apparatus according to the invention;
FIG. 6 shows a schematic view of an electronic device for use in the medical data processing method of the invention; and
fig. 7 shows a schematic view of a storage medium storing a program for executing the medical data processing method of the present invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
In recent years, medical informatization has been advanced, and a plurality of hospitals have built in-hospital information systems including various systems such as HIS (hospital information system), PACS (picture archiving and communication system), LIS (laboratory testing information system), and the like. The aggregated data of these hospital information systems is collectively referred to as "electronic medical records" herein. At present, automatic collection, arrangement and storage systems of electronic medical records are established in a plurality of hospitals, and a large amount of clinical diagnosis and treatment data are accumulated. The electronic medical record data is directly acquired from clinical diagnosis and treatment behaviors, and the acquisition process is fused with the clinical diagnosis and treatment process, so that no additional burden is caused to medical staff. The medical efficiency is measured by utilizing the data, so that the method has the advantages of small medical resource investment, credible data authority and the like. The progress of medical information systems and the accumulation of massive clinical medical data make it possible.
Fig. 1 shows a flow chart of an embodiment of a medical data processing method according to the invention.
As shown in fig. 1, in step S102, a medical record is read from an in-hospital electronic medical record system.
In step S104, patient information and medical action data are extracted from the medical record, and hospital measured subject information associated with the medical action is acquired.
In step S106, for each disease diagnosed in the visit record, first-class statistical index data for the disease of the measured subject of the hospital is obtained. The first type of index data may also be referred to as direct index data, i.e., may be directly obtained through the visit data of one hospital.
In step S108, a classification criterion threshold value for use in reiterating the index classification is determined by a statistical analysis method based on the first-class statistical index data for diseases of a plurality of hospital measured subjects. The extended index may also be referred to as a second type of statistical index, which refers to index data obtained by combining direct index data of a plurality of hospitals.
In step S110, extension index data is determined according to the classification criterion threshold value so as to determine the medical efficiency of the measured subject of the hospital. The reiterated index data represents the relative medical treatment conditions among hospitals, so that the medical efficiency of the main body of the hospital can be represented.
In the embodiment, the actual key information is extracted through the medical record in the hospital information system, and the classification standard is determined in a statistical analysis mode, so that the reiteration index of the measured subject in the hospital is obtained, the medical efficiency of the measured subject in the hospital is determined, and the objectivity and the accuracy are achieved; the expert intervention and workload are reduced, and the research and development cost is reduced.
Fig. 2 shows a flow chart of another embodiment of a medical data processing method according to the invention.
As shown in fig. 2, in step 201, the data of the medical treatment is read in from each information system of the hospital and is structured and output according to a predetermined rule. The information systems used by different hospitals may differ, and different information systems of the same hospital may differ in data format and the like. The doctor seeing data read in from different information systems is structurally output according to a preset rule, so that the unification of data formats can be realized, and the follow-up processing is facilitated.
And 202, organizing all the data according to each visit to form visit data for the structurally output visit data, and generating electronic medical record data. "in terms of each visit" means: and storing the treatment data generated in one treatment record. The patient may have a plurality of symptoms in one visit and may also have a past medical history, and the symptoms and the medical history have internal correlation, so that the information is integrated in a visit record, and medical behaviors can be extracted more accurately and counted.
The "desensitized patient information" includes, but is not limited to, basic physiological information of the patient, past medical history and treatment records, and chief complaints; "medical performance data" includes, but is not limited to, examination, test, diagnosis, surgery, medication, hospitalization, discharge, etc.
The "subject to be measured in hospital" mentioned herein refers to a doctor, or a department, or a subject such as a hospital, who needs the medical efficiency to be measured; "direct index" may also be referred to as "first-class statistical index", and refers to an index that can be counted by using data of a measured subject of a single hospital, for example: the number of cases receiving a consultation, the number of cases in hospital, the distribution of the length of time of hospital stay, the distribution of the cost of hospital stay, the distribution of the type of discharge and the like. The indicators listed herein are for example only and may include other indicators.
And step 207, combining direct index data of a plurality of measured subjects of the hospital, and definitely extending the classification standard required by the indexes by adopting a statistical analysis method.
The "reiterated index" may also be referred to as "second-class statistical index" herein, and refers to an index obtained after data comparison and statistics of a plurality of measured subjects in a hospital are required. For example, the number of cases in the same disease that are in doubt. By classification criterion, in connection with this example, it can be interpreted as: the cases can be classified into problematic cases according to what criteria. This will be described in detail with reference to fig. 3. The recitation of indicators herein is merely exemplary and other indicators can be included.
And step 208, determining an extension index of the measured subject of the hospital based on the classification standard.
And step 210, outputting the extension indexes of the measured subject of the hospital, and determining the medical efficiency of the measured subject of the hospital. And outputting the measurement result.
In the above embodiment, all the visit data generated by one visit are combined into one visit record. In another embodiment, the visit data is not organized by visit, i.e., the visit data generated by a visit may be stored in multiple visit records, all the visit data is stacked together, and the categories and amounts of diseases diagnosed in the visit records are directly counted, regardless of whether the recipes belong to the same visit.
Fig. 3 shows a flow chart of an application example of the medical data processing method according to the invention. As shown in fig. 3, in step S302, electronic medical records of all patients for a diagnosis of a disease at each hospital in a region are obtained.
In step S304, the distribution of the number of hospital stay and medical expenses for the patient with the diagnosed disease in different hospitals is obtained.
Step S306, defining the threshold values of the number of hospitalization days and medical expenses by using statistical analysis methods such as clustering and classification, and the threshold values are used as classification standards of difficult cases.
Step S308, determining the hospital stay days of the patient with the diagnosed disease and the case with the medical expense exceeding the threshold as the difficult case.
Step S310, determining the medical efficiency of each hospital according to the number of difficult cases of the diagnosed diseases of each hospital.
Compared with the prior art such as DRGs, the method provides a lightweight medical efficiency measurement scheme, and a computer system is used for carrying out computer analysis by using a data analysis and statistical technology, so that the research and development cost is reduced; and data are read from the electronic medical record to finish measurement, so that extra energy of medical personnel at the same time is not needed, and the implementation cost is reduced.
Compared with the current Internet medical platform medical guidance service, the method starts from the electronic medical record in a hospital, and the data rigor and the reliability are improved.
Fig. 4 shows a block diagram of an embodiment of a medical data processing device according to the invention. As shown in fig. 4, the medical data processing apparatus includes: a visit record reading module 41, configured to read a visit record from the HIS; the key data extraction module 42 is used for extracting patient information and medical behavior data from the medical record and acquiring measured subject information of the hospital related to the medical behavior; a direct index obtaining module 43, configured to obtain, for each disease diagnosed in the medical record, first-class statistical index data of the measured subject of the hospital for the disease; a classification criterion determining module 44, configured to determine a classification criterion threshold value for reiterating the criterion classification through statistical analysis based on first-class statistical index data of a plurality of measured subjects of the hospital for a disease; and the extended index acquisition module 45 is used for determining extended index data according to the classification standard threshold so as to determine medical efficiency data of the measured subject in the hospital. In one embodiment, the patient information includes patient underlying physiological information, past medical history, treatment records, and/or complaints; and/or medical behavioral data including examination, diagnosis, surgery, medication, hospitalization, and/or discharge; and/or hospital subject being measured including hospital, department, doctor; and/or the direct index includes information on the number of cases received, the number of cases in hospital, the distribution of length of stay, the distribution of cost of stay, or the distribution of type of discharge.
In the embodiment, the actual key information is extracted through the key data extraction module based on the visit record in the hospital information system, and the classification standard is determined through the classification standard determination module in a statistical analysis mode, so that the reiteration index of the measured subject of the hospital is obtained and is used for determining the medical efficiency of the measured subject of the hospital, and the objectivity and the accuracy are achieved.
Fig. 5 shows a block diagram of another embodiment of a medical data processing device according to the invention. As shown in fig. 5, the medical data processing apparatus further includes: the medical record generating module 50 is used for reading medical data from each information system of the hospital and storing all the emergency data generated in one medical treatment in one medical record.
The technical scheme of the disclosure solves the problem of low-cost objective measurement of medical efficiency, and compared with the prior art, at least the following problems are solved
1. The comprehensiveness and the credibility of the data. By using electronic medical record data from a hospital, complete clinical diagnosis and treatment behaviors of the hospital within a period of time are comprehensively covered; the electronic medical record is objective data and has high reliability.
2. And the research and development cost is reduced. The technical scheme is mainly based on data statistics and analysis, so that the intervention and workload of experts are reduced, and the research and development cost is reduced.
3. The use cost is reduced. Data are directly read in from the electronic medical record system and are subjected to statistical analysis, and a medical worker is not required to invest energy specially, so that the use cost is reduced.
In an exemplary embodiment of the present disclosure, there is also provided an electronic device capable of implementing the medical data processing method described above.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 6. The electronic device 600 shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 6, the electronic device 600 is embodied in the form of a general purpose computing device. The components of the electronic device 600 may include, but are not limited to: the at least one processing unit 610, the at least one memory unit 620, and a bus 630 that couples the various system components including the memory unit 620 and the processing unit 610.
Wherein the storage unit stores program code that is executable by the processing unit 610 to cause the processing unit 610 to perform steps according to various exemplary embodiments of the present invention as described in the above section "exemplary methods" of the present specification. For example, the processing unit 610 may execute S102 shown in fig. 1, reading a medical record from a hospital information system; s104, extracting patient information and medical behavior data from the medical record, and acquiring measured subject information of the hospital related to the medical behavior; s106, obtaining a direct index of the measured subject of the hospital for each disease diagnosed in the visit record; s108, determining classification standards for extending index classification by a statistical method based on direct index data of a plurality of measured subjects of hospitals for the diseases; and S110, determining an extension index according to the classification standard so as to determine the medical efficiency of the measured subject in the hospital.
The storage unit 620 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)6201 and/or a cache memory unit 6202, and may further include a read-only memory unit (ROM) 6203.
The memory unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 600 may also communicate with one or more external devices (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 600, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 650. Also, the electronic device 600 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 660. As shown, the network adapter 660 communicates with the other modules of the electronic device 600 over the bus 630. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above section "exemplary methods" of the present description, when said program product is run on the terminal device.
Referring to fig. 7, a program product 700 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
Moreover, although the steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (10)
1. A medical data processing method, comprising:
reading the medical record from the electronic medical record system in the hospital;
extracting patient information and medical behavior data from the medical record, and acquiring measured subject information of the hospital associated with the medical behavior;
obtaining a first type of statistical index data of the diseases of the measured subjects of the hospital for each disease diagnosed in the visit record, wherein the first type of statistical index refers to an index counted by data of a single measured subject of the hospital;
determining a classification criterion threshold for an extended index classification by statistical analysis based on a first class of statistical indicator data for the disease for a plurality of the hospital measured subjects;
determining extension index data of the measured subjects of the hospital according to the classification standard threshold so as to determine medical efficiency data of the measured subjects of the hospital, wherein the extension index refers to an index obtained after data comparison and statistics of a plurality of measured subjects of the hospital, and the extension index comprises difficult cases.
2. The method of claim 1,
the patient information comprises basic physiological information, past medical history, diagnosis and treatment records and/or main complaints of the patient;
and/or
The medical behavioral data includes examination, diagnosis, surgery, medication, hospitalization, and/or discharge;
and/or
The hospital measured subject comprises a hospital, a department and a doctor;
and/or
The first type of statistical index data comprises the number of cases in consultation, the number of cases in hospital, the distribution of length of time of stay in hospital, the distribution of cost of stay in hospital, or the distribution of types of discharge hospital.
3. The method of claim 1 or 2, further comprising, prior to reading the medical record from the in-hospital electronic medical record system:
the data of the treatment is read in from each information system of the hospital, and all the treatment data generated in one treatment is stored in one treatment record.
4. The method of claim 3, further comprising:
the clinic data read from each information system of the hospital is structured and output according to the preset rule.
5. The method of claim 1, wherein the patient information is desensitized patient information.
6. The method of claim 1, wherein said determining a classification threshold for an extended index classification by statistical analysis based on a first class of statistical indicator data for the disease for a plurality of measured subjects of the hospital comprises:
and determining a threshold value for applying index classification as a classification standard by a clustering method based on the first class of statistical index data of a plurality of measured subjects of the hospital for the disease.
7. A medical data processing apparatus, characterized by comprising:
the medical record reading module is used for reading medical records from an electronic medical record system in a hospital;
the key data extraction module is used for extracting patient information and medical behavior data from the visit record and acquiring measured subject information of the hospital related to the medical behavior;
the direct index acquisition module is used for acquiring first-class statistical index data of the measured subject of the hospital for the diseases aiming at each disease diagnosed in the visit record, wherein the first-class statistical index refers to an index counted by the data of the measured subject of a single hospital;
a classification standard determination module for determining a classification standard threshold value for reiterating an index classification through statistical analysis based on a plurality of first-class statistical index data of measured subjects of the hospital for the disease;
and the extension index acquisition module is used for determining extension index data of the measured main bodies of the hospital according to the classification standard threshold so as to determine medical efficiency data of the measured main bodies of the hospital, wherein the extension index refers to an index obtained after data of a plurality of measured main bodies of the hospital are compared and counted, and the extension index comprises difficult cases.
8. The apparatus of claim 7, further comprising:
the system comprises a treatment record generation module, a treatment record generation module and a treatment record generation module, wherein the treatment record generation module is used for reading treatment data from each information system of a hospital and storing all emergency treatment data generated in one treatment record.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a medical data processing method according to any one of claims 1 to 6.
10. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the medical data processing method of any one of claims 1-6 via execution of the executable instructions.
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| CN201811334200.5A CN109524069B (en) | 2018-11-09 | 2018-11-09 | Medical data processing method and device, electronic equipment and storage medium |
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| CN110459276B (en) * | 2019-08-15 | 2022-05-24 | 北京嘉和海森健康科技有限公司 | Data processing method and related equipment |
| CN110459285A (en) * | 2019-08-16 | 2019-11-15 | 济南大学 | Preprocess method, system, equipment and the medium of the medical data of disease |
| CN110706769A (en) * | 2019-09-20 | 2020-01-17 | 上海金仕达卫宁软件科技有限公司 | Method and device for DRGs grouping of medical insurance data and electronic equipment |
| CN111063446B (en) * | 2019-12-17 | 2023-06-16 | 医渡云（北京）技术有限公司 | Method, apparatus, device and storage medium for standardizing medical text data |
| CN111243756A (en) * | 2020-01-21 | 2020-06-05 | 杭州杏林信息科技有限公司 | Method and device for counting infection cases of type I incision operation part and storage medium |
| CN111554371A (en) * | 2020-03-30 | 2020-08-18 | 平安医疗健康管理股份有限公司 | Internet data processing method and device, computer equipment and storage medium |
| CN112420142A (en) * | 2020-11-20 | 2021-02-26 | 上海梅斯医药科技有限公司 | Data acquisition method and device, computer equipment and storage medium |
| CN113077857A (en) * | 2021-03-15 | 2021-07-06 | 医渡云（北京）技术有限公司 | Medical data correlation method and device |
| CN113724883B (en) * | 2021-08-31 | 2023-09-15 | 平安科技（深圳）有限公司 | Medical expense prediction method, medical expense prediction device, storage medium and computer equipment |
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