CN113807657A - Method for realizing hospital medicine data standardization - Google Patents
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Abstract
The invention discloses a method for realizing hospital medicine data standardization, which relates to the technical field of computer application, and comprises single-field matching and medicine product matching, wherein a single-field matching result is applied to medicine product matching; the drug product matching comprises intelligent matching and manual matching, drug data gathered in each hospital and each project are mapped to a standard drug dictionary, and the intelligent matching process is as follows: 1) matching the relevant single field of the hospital medicine field with a standard value; 2) matching the standard value of the hospital drug single field with the field of the standard drug dictionary according to the matching rule; 3) and if the standard medicine dictionary data are matched, comparing the specifications, and taking the first piece of recommended data according to the similarity of the specifications. The invention can unify the standards of the hospital medicine data, efficiently and accurately standardize the hospital medicine data and save the labor cost.
Description
Technical Field
The invention relates to the technical field of computer application, in particular to a method for realizing hospital medicine data standardization.
Background
With the development of medical service, the introduction and use of hospital information systems, and the quality management of hospital drugs are gradually scientific and standardized. However, the standard of each hospital is different during standardized treatment, and the national drug standard management has not yet formed a system, so that multiple writing methods exist for the same meaning of a drug field, and drug data barriers exist among hospitals. Secondly, the medicine circulation in the hospital mainly relates to four links of purchase, storage, distribution and use. Some hospital medicines are not standard enough in maintenance, and information of the same medicine in four links may be inconsistent, for example, multiple writing methods of medicine names and manufacturer names exist simultaneously, as shown in table 1. In addition, some information of the medicine itself may also change, for example, the approved code has a service life, the pharmaceutical manufacturer may change the product, and the like, which all result in the change of the information of the medicine.
TABLE 1 examples of the writing methods of different pharmaceutical manufacturers for the same pharmaceutical in a certain hospital
Disclosure of Invention
The technical task of the invention is to provide a method for realizing hospital medicine data standardization aiming at the defects, which can unify the standard of the hospital medicine data, standardize the hospital medicine data efficiently and accurately and save labor cost.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a method for realizing hospital medicine data standardization comprises single-field matching and medicine product matching, wherein single-field matching results are applied to medicine product matching; the drug product matching comprises intelligent matching and manual matching, drug data gathered in each hospital and each project are mapped into a standard drug dictionary,
the intelligent matching process is as follows:
1) matching the relevant single field of the hospital medicine field with a standard value;
2) matching the standard value of the hospital drug single field with the field of the standard drug dictionary according to the matching rule;
3) if a plurality of pieces of standard medicine dictionary data are matched, comparing the specifications, and taking a first piece of recommended data according to the similarity of the specifications;
and the manual matching is used for checking whether the intelligently matched data is correct or not, and mapping the data which is subjected to intelligent matching and is wrong according to subjective awareness to form an empirical value after the data is successfully mapped.
The method can be used for mapping the hospital medicine data in a standardized manner in the data management process, so that the matching accuracy is improved while the labor is saved.
The method supports the standardization of single fields of medicine data of hospitals, lays a foundation for efficient intelligent matching, has various writing methods for each field of medicines, and determines that medicine products need to be determined by a plurality of fields together;
multi-level intelligent matching recommendation of hospital drug product data is supported, and matching efficiency is improved; the manual matching mode of the hospital medicine product data is supported, and the matching accuracy is improved; the intelligent matching is supported to refer to the manually checked data, the more the manually checked data is, the higher the matching rate is, and the matching speed and accuracy are improved. And the manual matching is referred based on the result obtained by intelligent matching, and whether the mapping is successful or not is subjectively judged to form an empirical value. The intelligent matching is recommended according to the experience value, the intelligent matching result can be more accurate along with the increase of the experience value, and a mapping tool for assisting the intelligent matching and releasing manpower is created.
Preferably, the single-field matching comprises medicine name matching, medicine formulation matching and medicine manufacturer name matching,
drug name matching standardizes the original name of the drug, including:
1) correcting the original name of the medicine to be a standard medicine name,
2) matching the correction name with the standard universal name of the medicine;
the matching of the drug dosage forms comprises the steps of matching the hospital drug dosage forms with the standard drug dosage forms;
drug manufacturer name matching the hospital drug manufacturer name is matched with a standard drug manufacturer.
Furthermore, the medicine dosage form matching is realized by using a hospital value range name, a target value range code and a target value range name, wherein the hospital value range name refers to the medicine dosage form of the hospital, the target value range code refers to a standard medicine dosage form code, and the target value domain name refers to a standard medicine dosage form name;
the drug manufacturer name matching also uses a hospital value field name, a target value field code and a target value field name to realize matching, wherein the hospital value field name refers to the name of a hospital drug manufacturer, the target value field code refers to the code of a standard drug manufacturer, and the target value field name refers to the name of the standard drug manufacturer.
Preferably, the intelligent matching recommends standard drug data according to matching rules, which are as follows:
first-stage: accurately matching three fields of ' medicine name ', approved document number ' and ' manufacturer ' with experience value data;
and (2) second stage: accurately matching two fields of the 'medicine name' and the approved letter number with a standard medicine dictionary;
third-stage: matching the approved document number with a standard medicine dictionary;
and (4) fourth stage: the "drug name" is accurately matched with the standard drug dictionary, and the "manufacturer" is fuzzy matched with the standard drug dictionary.
Further, the medicine name is an abstract noun, if the hospital dictionary has a common name, the medicine name is the common name and a standard dosage form, otherwise, the medicine name is a correction name;
the 'manufacturer' is an abstract noun, if the hospital dictionary has a standard manufacturer name, the 'manufacturer' is the standard manufacturer name, otherwise, the 'manufacturer' is the original manufacturer name;
sorting the plurality of pieces according to the specification similarity during recommendation;
and if the universal name exists in the four-level matching, firstly, inquiring by using the universal name, the standard dosage form and the name of the manufacturer, and if the recommended data is not inquired, inquiring by using the corrected name and the name of the manufacturer.
Preferably, the manual matching comprises:
analyzing the intelligently matched data and confirming whether the intelligently matched data is correct or not;
matching on a manual mapping page: double-clicking a certain piece of data, the mapping tool automatically recommends a set of standard data with higher matching degree, and the matching process is as follows:
1) matching the relevant single field of the hospital medicine field with a standard value;
2) matching the standard value of the hospital drug single field with the field of the standard drug dictionary according to the matching rule;
3) sorting the recommended data according to the specification similarity and displaying the recommended data;
4) if the correct item exists in the recommended medicine, the correct item is selected, and matching is finished; and if the recommended medicine does not have the correct item, manually searching the standard medicine, selecting the correct standard medicine, and finishing matching.
Further, the manual matching recommends standard drug data according to matching rules, which are as follows:
first-stage: accurately matching three fields of ' medicine name ', approved document number ' and ' manufacturer ' with experience value data;
and (2) second stage: accurately matching two fields of the 'medicine name' and the approved letter number with a standard medicine dictionary;
third-stage: matching the approved document number with a standard medicine dictionary;
and (4) fourth stage: the "drug name" is accurately matched with the standard drug dictionary, and the "manufacturer" is fuzzy matched with the standard drug dictionary.
And (5) fifth stage: inquiring the trade name of the standard medicine which is the corrected name;
and a sixth stage: and precisely matching the medicine name with a standard medicine dictionary.
The invention also claims a system for realizing the hospital medicine data standardization, which comprises a single-word-segment matching module and a medicine product matching module, and the system realizes the method for realizing the hospital medicine data standardization.
The invention also claims a device for realizing hospital medicine data standardization, which comprises: at least one memory and at least one processor;
the at least one memory to store a machine readable program;
the at least one processor is used for calling the machine readable program to execute the method for realizing the hospital drug data standardization.
The invention also claims a computer readable medium having stored thereon computer instructions which, when executed by a processor, cause the processor to perform the above-described method of achieving hospital drug data standardization.
Compared with the prior art, the method for realizing the hospital drug data standardization has the following beneficial effects:
the method provides standardization of single character segments, and improves the accuracy and matching efficiency of medicine standardization;
the matching method has complete matching flow and matching rules, and supports intelligent matching of machines, so that the matching result is more accurate and rapid, and the function of relieving the manpower is achieved;
the data to be matched can be matched with manually matched data, so that the manual matching efficiency is greatly improved;
recommendation standard data are provided, and reference can be made manually during matching, so that the accuracy is improved;
the standardized mapping of the medical and pharmaceutical data plays a great role in the data management process.
Drawings
FIG. 1 is a block diagram of a method for standardizing hospital drug data according to one embodiment of the present invention;
FIG. 2 is a diagram of an example drug name correction provided by one embodiment of the present invention;
FIG. 3 is a diagram of an example of a drug calibration name match provided by an embodiment of the invention;
FIG. 4 is a diagram of an example of a drug dosage form match provided by an embodiment of the present invention;
FIG. 5 is a diagram of an example of pharmaceutical manufacturer name matching provided by an embodiment of the invention;
FIG. 6 is a flow diagram of intelligent matching provided by one embodiment of the invention;
FIG. 7 is a flow diagram of manual matching provided by one embodiment of the invention.
Detailed Description
The invention is further described with reference to the following figures and specific examples.
Hospital's medicine data are mixed and disorderly and many, and it is loaded down with trivial details consuming time and be full of challenging work only to rely on the manual work to check matching standard medicine data, simultaneously, because the writing method of medicine field is diversified, it is not good to directly carry out product intelligence matching effect, consequently, this method standardizes hospital's medicine data through the mode of two-stage matching.
The embodiment of the invention provides a method for realizing hospital drug data standardization, which comprises single-field matching and drug product matching, wherein a single-field matching result is applied to the drug product matching.
The writing methods of each field of the medicine are various, a plurality of fields are required to be determined together for determining the medicine product, important single-word segments of the medicine are matched firstly in order to reduce the workload of product matching, the single-word segment matching can improve the intelligent recommendation accuracy of medicine product matching, and meanwhile, the workload of product matching is reduced.
The single-word section matching comprises medicine name matching, medicine formulation matching and medicine manufacturer name matching,
A. the drug name matching standardizes the original name of the drug, and comprises the following two steps:
1) name correction, correcting the original name of the medicine to be a standard medicine name, and referring to the example shown in fig. 2;
2) matching the correction name with a drug standard universal name, referring to the example shown in fig. 3;
B. matching the drug dosage form, namely matching the hospital drug dosage form with a standard drug dosage form, referring to the example shown in fig. 4, wherein the hospital domain name refers to the hospital drug dosage form, the target domain code refers to the standard drug dosage form code, and the target domain name refers to the standard drug dosage form name;
C. matching the names of the pharmaceutical manufacturers, namely matching the names of the hospital pharmaceutical manufacturers with the names of the standard pharmaceutical manufacturers, referring to the example shown in fig. 5, the hospital value range name refers to the name of the hospital pharmaceutical manufacturer, the target value range code refers to the code of the standard pharmaceutical manufacturer, and the target value range name refers to the name of the standard pharmaceutical manufacturer.
The drug product matching adopts an intelligent matching and manual matching mode, and drug data gathered in each hospital and each project are mapped to a standard drug dictionary.
Referring to fig. 6, the intelligent matching process is as follows:
1) matching the relevant single field of the hospital medicine field with a standard value;
2) matching the standard value of the hospital drug single field with the field of the standard drug dictionary according to the matching rule;
3) if a plurality of pieces of standard medicine dictionary data are matched, comparing the specifications, and taking a first piece of recommended data according to the similarity of the specifications;
the intelligent matching recommends standard medicine data according to matching rules, wherein the matching rules are as follows:
first-stage: accurately matching three fields of ' medicine name ', approved document number ' and ' manufacturer ' with experience value data;
and (2) second stage: accurately matching two fields of the 'medicine name' and the approved letter number with a standard medicine dictionary;
third-stage: matching the approved document number with a standard medicine dictionary;
and (4) fourth stage: the "drug name" is accurately matched with the standard drug dictionary, and the "manufacturer" is fuzzy matched with the standard drug dictionary.
The medicine name is an abstract noun, if the hospital dictionary has a common name, the medicine name is the common name and a standard dosage form, otherwise, the medicine name is a correction name;
the 'manufacturer' is an abstract noun, if the hospital dictionary has a standard manufacturer name, the 'manufacturer' is the standard manufacturer name, otherwise, the 'manufacturer' is the original manufacturer name;
sorting the plurality of pieces according to the specification similarity during recommendation;
and if the universal name exists in the four-level matching, firstly, inquiring by using the universal name, the standard dosage form and the name of the manufacturer, and if the recommended data is not inquired, inquiring by using the corrected name and the name of the manufacturer.
The purpose of manual matching is to check whether the intelligently matched data is correct or not, intelligently match the wrong data through subjective awareness mapping, and form an empirical value after the data is successfully mapped.
Manual matching provides two ways:
1. analyzing the intelligently matched data and confirming whether the intelligently matched data is correct or not;
2. matching on a manual mapping page: double-clicking a certain piece of data, the mapping tool automatically recommends a set of standard data with a higher matching degree. Because the method can recommend a plurality of pieces of standard data, the method has two levels of recommendation compared with the recommendation of intelligent matching, namely:
and (5) fifth stage: inquiring the trade name of the standard medicine which is the corrected name;
and a sixth stage: and precisely matching the medicine name with a standard medicine dictionary.
Referring to fig. 7, the manual matching process is as follows:
1) matching the relevant single field of the hospital medicine field with a standard value;
2) matching the standard value of the hospital drug single field with the field of the standard drug dictionary according to the matching rule;
3) sorting the recommended data according to the specification similarity and displaying the recommended data;
4) if the correct item exists in the recommended medicine, the correct item is selected, and matching is finished; and if the recommended medicine does not have the correct item, manually searching the standard medicine, selecting the correct standard medicine, and finishing matching.
The method can be used for mapping the hospital medicine data in a standardized manner in the data management process, so that the matching accuracy is improved while the labor is saved. The method supports the standardization of the hospital medicine data single field and lays a foundation for efficient and intelligent matching; multi-level intelligent matching recommendation of hospital drug product data is supported, and matching efficiency is improved; the manual matching mode of the hospital medicine product data is supported, and the matching accuracy is improved; the intelligent matching is supported to refer to the manually checked data, the more the manually checked data is, the higher the matching rate is, and the matching speed and accuracy are improved.
And the manual matching is referred based on the result obtained by intelligent matching, and whether the mapping is successful or not is subjectively judged to form an empirical value. The intelligent matching is recommended according to the experience value, the intelligent matching result can be more accurate along with the increase of the experience value, and a mapping tool for assisting the intelligent matching and releasing manpower is created.
The invention also claims a system for realizing the hospital medicine data standardization, which comprises a single-word-segment matching module and a medicine product matching module, and the system realizes the method for realizing the hospital medicine data standardization.
The embodiment of the invention also provides a device for realizing hospital drug data standardization, which comprises: at least one memory and at least one processor;
the at least one memory to store a machine readable program;
the at least one processor is used for calling the machine readable program to execute the method for realizing the hospital drug data standardization described in the above embodiment.
The present invention also claims a computer readable medium having stored thereon computer instructions which, when executed by a processor, cause the processor to execute the method for realizing hospital drug data standardization described in the above embodiments. Specifically, a system or an apparatus equipped with a storage medium on which software program codes that realize the functions of any of the above-described embodiments are stored may be provided, and a computer (or a CPU or MPU) of the system or the apparatus is caused to read out and execute the program codes stored in the storage medium.
In this case, the program code itself read from the storage medium can realize the functions of any of the above-described embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.
Examples of the storage medium for supplying the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (e.g., CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD + RW), a magnetic tape, a nonvolatile memory card, and a ROM. Alternatively, the program code may be downloaded from a server computer via a communications network.
Further, it should be clear that the functions of any one of the above-described embodiments may be implemented not only by executing the program code read out by the computer, but also by causing an operating system or the like operating on the computer to perform a part or all of the actual operations based on instructions of the program code.
Further, it is to be understood that the program code read out from the storage medium is written to a memory provided in an expansion board inserted into the computer or to a memory provided in an expansion unit connected to the computer, and then causes a CPU or the like mounted on the expansion board or the expansion unit to perform part or all of the actual operations based on instructions of the program code, thereby realizing the functions of any of the above-described embodiments.
While the invention has been shown and described in detail in the drawings and in the preferred embodiments, it is not intended to limit the invention to the embodiments disclosed, and it will be apparent to those skilled in the art that various combinations of the code auditing means in the various embodiments described above may be used to obtain further embodiments of the invention, which are also within the scope of the invention.
Claims (10)
1. A method for realizing hospital drug data standardization is characterized by comprising single field matching and drug product matching, wherein the single field matching result is applied to the drug product matching; the drug product matching comprises intelligent matching and manual matching, drug data gathered in each hospital and each project are mapped into a standard drug dictionary,
the intelligent matching process is as follows:
1) matching the relevant single field of the hospital medicine field with a standard value;
2) matching the standard value of the hospital drug single field with the field of the standard drug dictionary according to the matching rule;
3) if a plurality of pieces of standard medicine dictionary data are matched, comparing the specifications, and taking a first piece of recommended data according to the similarity of the specifications;
and the manual matching is used for checking whether the intelligently matched data is correct or not, and mapping the data which is subjected to intelligent matching and is wrong according to subjective awareness to form an empirical value after the data is successfully mapped.
2. The method of claim 1, wherein the single field matching comprises drug name matching, drug formulation matching, and drug manufacturer name matching,
drug name matching standardizes the original name of the drug, including:
1) correcting the original name of the medicine to be a standard medicine name,
2) matching the correction name with the standard universal name of the medicine;
the matching of the drug dosage forms comprises the steps of matching the hospital drug dosage forms with the standard drug dosage forms;
drug manufacturer name matching the hospital drug manufacturer name is matched with a standard drug manufacturer.
3. The method of claim 2, wherein the drug dosage form matching uses a hospital domain name, a target domain code and a target domain name, wherein the hospital domain name refers to the hospital drug dosage form, the target domain code refers to the standard drug dosage form code, and the target domain name refers to the standard drug dosage form name;
the drug manufacturer name matching also uses a hospital value field name, a target value field code and a target value field name to realize matching, wherein the hospital value field name refers to the name of a hospital drug manufacturer, the target value field code refers to the code of a standard drug manufacturer, and the target value field name refers to the name of the standard drug manufacturer.
4. The method for realizing hospital drug data standardization according to claim 2, wherein said intelligent matching recommends standard drug data according to matching rules as follows:
first-stage: accurately matching three fields of ' medicine name ', approved document number ' and ' manufacturer ' with experience value data;
and (2) second stage: accurately matching two fields of the 'medicine name' and the approved letter number with a standard medicine dictionary;
third-stage: matching the approved document number with a standard medicine dictionary;
and (4) fourth stage: the "drug name" is accurately matched with the standard drug dictionary, and the "manufacturer" is fuzzy matched with the standard drug dictionary.
5. The method for realizing the hospital drug data standardization according to the claim 4, wherein the drug name is an abstract noun, if the hospital dictionary has a common name, the drug name is a common name and a standard dosage form, otherwise, the drug name is a corrected name;
the 'manufacturer' is an abstract noun, if the hospital dictionary has a standard manufacturer name, the 'manufacturer' is the standard manufacturer name, otherwise, the 'manufacturer' is the original manufacturer name;
sorting the plurality of pieces according to the specification similarity during recommendation;
and if the universal name exists in the four-level matching, firstly, inquiring by using the universal name, the standard dosage form and the name of the manufacturer, and if the recommended data is not inquired, inquiring by using the corrected name and the name of the manufacturer.
6. The method for standardizing hospital drug data according to any one of claims 1-5 wherein the manual matching comprises:
analyzing the intelligently matched data and confirming whether the intelligently matched data is correct or not;
matching on a manual mapping page: double-clicking a certain piece of data, the mapping tool automatically recommends a set of standard data with higher matching degree, and the matching process is as follows:
1) matching the relevant single field of the hospital medicine field with a standard value;
2) matching the standard value of the hospital drug single field with the field of the standard drug dictionary according to the matching rule;
3) sorting the recommended data according to the specification similarity and displaying the recommended data;
4) if the correct item exists in the recommended medicine, the correct item is selected, and matching is finished; and if the recommended medicine does not have the correct item, manually searching the standard medicine, selecting the correct standard medicine, and finishing matching.
7. The method for standardizing hospital drug data according to claim 6, wherein the manual matching recommends standard drug data according to matching rules as follows:
first-stage: accurately matching three fields of ' medicine name ', approved document number ' and ' manufacturer ' with experience value data;
and (2) second stage: accurately matching two fields of the 'medicine name' and the approved letter number with a standard medicine dictionary;
third-stage: matching the approved document number with a standard medicine dictionary;
and (4) fourth stage: the "drug name" is accurately matched with the standard drug dictionary, and the "manufacturer" is fuzzy matched with the standard drug dictionary.
And (5) fifth stage: inquiring the trade name of the standard medicine which is the corrected name;
and a sixth stage: and precisely matching the medicine name with a standard medicine dictionary.
8. A system for standardizing hospital drug data, comprising a single field matching module and a drug product matching module, the system implementing the method for standardizing hospital drug data according to any one of claims 1 to 7.
9. An apparatus for realizing hospital drug data standardization, comprising: at least one memory and at least one processor;
the at least one memory to store a machine readable program;
the at least one processor, configured to invoke the machine readable program to perform the method of any of claims 1 to 7.
10. A computer readable medium having stored thereon computer instructions which, when executed by a processor, cause the processor to perform the method of any of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114388142A (en) * | 2022-03-23 | 2022-04-22 | 成都瑞华康源科技有限公司 | Value domain code mapping rapid processing method |
| CN114880430A (en) * | 2022-05-10 | 2022-08-09 | 马上消费金融股份有限公司 | Name processing method and device |
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