KR102549638B1 - Method, device, and system for providing service for classifying non-reimbursement data - Google Patents

Abstract

Disclosed is a method of providing a service for classifying non-payroll data, performed by an apparatus. The method may include acquiring non-salary data generated by a terminal device used by a plurality of medical institutions; Filtering by performing verification work on the non-salary data; classifying the filtered non-payroll data into non-payroll reporting data and selected non-payroll data according to a first classification method; additionally classifying each item according to a second classification method for each of the non-payroll report data and the selected non-payroll data; and updating at least one of the first classification method and the second classification method based on the non-payroll report data and the selected non-payroll data additionally classified for each item, wherein the first classification method includes the filtered non-payroll data. Includes a method of classifying data having a price disclosure item and an item corresponding to a predefined analysis required item among data into the non-payroll reporting data, and the second classification method includes the non-payroll reporting data and each of the selected non-payroll data A method of classifying by item based on the medical field and medical treatment type may be included.

Description

Method, device, and system for providing a service that classifies non-salary data

The present disclosure relates to a method, apparatus, and system for providing a service of classifying non-salary data. Specifically, the present disclosure relates to a method, apparatus, and system for providing a service of classifying non-payroll reporting data and selected non-payroll data from non-payroll data.

A medical institution can manage non-salary-related information (eg, type of non-salary medical service and price information according to the corresponding medical service, etc.) for each specific code. In addition, each medical institution may write a prescription or a medical record related to medical treatment by using the corresponding non-salary information.

Medical institutions and computer development companies have attempted to manage non-coverage reporting data more efficiently and identify reportable items from the non-coverage data by developing non-coverage data extraction programs suitable for each computer environment. However, since the computer environment and services related to non-payroll data management are different for each company, an efficient non-payroll report data management method has not been developed.

Registered Patent Publication No. 10-1380459, 2014.03.26

The present disclosure has been made to solve the above-described problems, and an object of the present disclosure is to provide a method, apparatus, and system for providing a service for classifying non-payroll data into non-payroll reporting data and selected non-payroll data.

The problems to be solved by the present disclosure are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description below.

According to an embodiment of the present disclosure, a method for providing a service for classifying non-salary data, performed by an apparatus, includes acquiring non-salary data generated by a terminal device used by a plurality of medical institutions; Filtering by performing verification work on the non-salary data; classifying the filtered non-payroll data into non-payroll reporting data and selected non-payroll data according to a first classification method; additionally classifying each item according to a second classification method for each of the non-payroll report data and the selected non-payroll data; and updating at least one of the first classification method and the second classification method based on the non-payroll report data and the selected non-payroll data additionally classified for each item, wherein the first classification method includes the filtered non-payroll data. Includes a method of classifying data having a price disclosure item and an item corresponding to a predefined analysis required item among data into the non-payroll reporting data, and the second classification method includes the non-payroll reporting data and each of the selected non-payroll data A method of classifying by item based on the medical field and medical treatment type may be included.

The filtering may include filtering at least one of data in which a predefined item is missing from among the non-salary data or data in which a predefined non-salary related content ratio is less than a threshold value.

In addition, the first classification method reports the non-payroll data including data having an item matching the price disclosure item code among the filtered non-payroll data and a keyword related to a predefined analysis required item among the filtered non-payroll data. It may include a method of classifying data and classifying the remaining data except for the non-payroll reporting data among the filtered non-payroll data as the selected non-payroll data.

Also, the treatment type may include an action item, a treatment material item, and a drug item.

The method may include: identifying at least one item not classified according to the action item, the treatment material item, and the drug item among the items included in the non-salary report data and the selected non-salary benefit data; and grouping the identified at least one item by item having a similar keyword or similar property.

In addition, the updating step calculates a first ratio of actual non-payroll reporting data among the non-payroll reporting data additionally classified for each item, and calculates a second ratio of the actual selected non-payroll reporting data among the selected non-payroll reporting data additionally classified for each item. steps may be included.

And, the updating may include modifying the price disclosure item code and keywords related to the predefined item requiring analysis based on that at least one of the first ratio or the second ratio is less than a threshold value. can include

And, the second classification method includes a method of classifying the selected non-coverage data by item based on the medical field and the medical treatment type, and further classifying the selected non-coverage data classified by item based on a benefit classification system. can do.

In another embodiment of the present disclosure, an apparatus for providing a service for classifying non-salary data includes a memory for storing one or more instructions; and one or more processors, wherein the one or more processors obtain non-salary data generated by terminal devices used by a plurality of medical institutions; filtering the non-salary data through verification; Classifying the filtered non-payroll data into non-payroll report data and selected non-payroll data according to a first classification scheme; additionally classifying each of the non-payroll reporting data and the selected non-payroll data according to a second classification method for each item; and based on the non-payroll report data and selected non-payroll data additionally classified for each item, at least one of the first classification method and the second classification method is set to be updated, wherein the first classification method includes the filtered non-payroll data. A method of classifying data corresponding to price disclosure items and predefined items requiring analysis as the non-coverage reporting data, and the second classification method includes the non-coverage reporting data and the selected non-coverage data, respectively, in the field of treatment and the type of treatment It may include a method of classifying by item based on.

In addition to this, a computer program stored in a computer readable recording medium for execution to implement the present disclosure may be further provided.

In addition to this, a computer readable recording medium recording a computer program for executing a method for implementing the present disclosure may be further provided.

According to the above-described problem solving means of the present disclosure, a method, apparatus, and system for providing a service for classifying non-payroll data into non-payroll reporting data and selected non-payroll data may be provided.

In addition, according to the above-mentioned problem solving means of the present disclosure, medical institutions placed in various computer environments can efficiently classify, extract, and verify non-reimbursement report data and report them.

The effects of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below.

1 is a schematic diagram of a system for implementing a method for providing a service of classifying non-salary data according to an embodiment of the present disclosure.
Figure 2 is a block diagram for explaining the configuration of an apparatus for providing a service for classifying non-salary data according to an embodiment of the present disclosure.
3 is a flowchart illustrating a method of providing a service for classifying non-salary data according to an embodiment of the present disclosure.
4 is a diagram for explaining a process of additionally classifying selected non-salary data according to an embodiment of the present disclosure.
5 is a diagram for explaining a process of classifying non-salary data according to an embodiment of the present disclosure.
6 is a diagram for explaining a process of sub-classifying selected non-salary data according to an embodiment of the present disclosure.
7 is a diagram for illustrating an example of classified non-salary data according to an embodiment of the present disclosure.

Advantages and features of the present disclosure, and methods of achieving them, will become clear with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below and may be implemented in various different forms, but only the present embodiments make the disclosure of the present disclosure complete, and are common in the art to which the present disclosure belongs. It is provided to fully inform the person skilled in the art of the scope of the present disclosure, which is only defined by the scope of the claims.

Terminology used herein is for describing the embodiments and is not intended to limit the present disclosure. In this specification, singular forms also include plural forms unless specifically stated otherwise in a phrase. As used herein, "comprises" and/or "comprising" does not exclude the presence or addition of one or more other elements other than the recited elements.

Like reference numerals throughout the specification refer to like elements, and “and/or” includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various components, these components are not limited by these terms, of course. These terms are only used to distinguish one component from another. Accordingly, it goes without saying that the first element mentioned below may also be the second element within the technical spirit of the present disclosure.

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used with meanings commonly understood by those skilled in the art to which this disclosure belongs. In addition, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless explicitly specifically defined.

The spatially relative terms "below", "beneath", "lower", "above", "upper", etc. It can be used to easily describe a component's correlation with other components. Spatially relative terms should be understood as encompassing different orientations of elements in use or operation in addition to the orientations shown in the drawings.

For example, if you flip a component that is shown in a drawing, a component described as "below" or "beneath" another component will be placed "above" the other component. can Thus, the exemplary term “below” may include directions of both below and above. Components may also be oriented in other orientations, and thus spatially relative terms may be interpreted according to orientation.

In describing the present disclosure, "medical institution" refers to clinics, hospitals, general hospitals, and tertiary hospitals that provide various medical services, and refers to doctors, nurses, and office workers working in the medical institution. And, "computing development company" refers to a company that provides a corresponding service by receiving logic related to extraction of non-salary reporting items and reporting service from a device. Further, the "threshold value" refers to a value preset by statistics, experiments, etc., but can be changed by a user using the device, of course. And, "selective non-payroll data" may refer to the remaining data except for non-payroll reporting items among the entire non-payroll data.

Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

1 is a schematic diagram of a system 1000 for implementing a method for providing a service for classifying non-payroll data, according to one embodiment of the present disclosure.

As shown in FIG. 1, a system 1000 for implementing a method of providing a service for classifying non-salary data according to an embodiment of the present disclosure includes an apparatus 100, a terminal device used by a plurality of medical institutions ( A(1), ? A(n)) (n is a natural number greater than or equal to 2), and a terminal device B used by other organizations.

Here, the other institution may mean the Health Insurance Review and Assessment Service, but is not limited thereto, and may mean an institution that manages/examines/evaluates non-benefit data and related costs.

The device 100, terminal devices A(1), ? A(n) used by a plurality of medical institutions, and terminal devices B used by other institutions can communicate with each other using a network W. .

Here, the network W may include a wired network and a wireless network. For example, the wireless network may include various networks such as a local area network (LAN), a metropolitan area network (MAN), and a wide area network (WAN).

However, the network according to an embodiment of the present disclosure is not limited to the networks listed above, and may include at least a part of a known wireless data network, a known telephone network, and a known wired/wireless television network.

1 shows a case where the device 100, terminal devices A(1), ? A(n) used by a plurality of medical institutions, and terminal devices B used by other institutions are desktops, but are limited thereto. it is not going to be Each device may be implemented as various types of electronic devices such as smart phones, tablet PCs, laptops, wearable devices, and separate server devices.

In addition, the device 100, the terminal devices A(1), ? A(n) used by a plurality of medical institutions, and the terminal device B used by other institutions are not a single electronic device, but a plurality of types of electronic devices. It may mean a device group consisting of. Here, each of a plurality of types of electronic devices constituting the device group may be connected wirelessly/wired.

The device 100 is a terminal device (A(1), ? A(n)) using a logic (or program) that enables a service for classifying non-salary data to be used by a plurality of medical institutions or/and other computer systems. It can be provided to a terminal device used by a developer.

The apparatus 100 may acquire non-salary data generated by terminal devices A(1) and ? A(n) used by a plurality of medical institutions in order to build a service and system for classifying non-salary data. The device 100 may filter the corresponding non-salary data as it performs verification work. Then, the apparatus 100 may classify the non-payroll data into non-payroll reporting data and selected non-payroll data.

As another example, the device 100 may provide a logic (or program) capable of classifying non-salary data to a terminal device used by a medical institution or a computer development company. In addition, the terminal device used by the medical institution or computer development company may filter the non-payroll data using the corresponding logic (or program), and classify the filtered non-payroll data into non-payroll reporting items and selected non-payroll data.

Then, the apparatus 100 may further classify each item for each non-salary reporting data and selected non-salary data. For example, the apparatus 100 may classify each of the non-payroll report data and selected non-payroll data for each item based on the medical field and medical treatment type.

And, the device may update the pre-applied classification method based on the result to be classified. That is, the device may continuously update the method of classifying non-payroll data into non-payroll reporting data and selected non-payroll data.

A process of classifying non-salary data by the apparatus 100 and a method of updating the classification method of non-salary data will be described in detail with reference to FIGS. 3 to 6 .

The terminal device (A(1), ? A(n)) used by a plurality of medical institutions may store and manage non-salary data generated in each medical institution. In addition, terminal devices A (1), ? A (n) used by a plurality of medical institutions are stored and managed by the device 100 so that the device 100 can develop logic for a service that classifies non-salary data. Non-salary data can be transmitted. However, this is only one embodiment, and the device 100 may obtain non-salary data from other institutions that store/manage non-salary data generated by a plurality of medical institutions.

In addition, terminal devices A(1) and ? A(n) used by a plurality of medical institutions can efficiently classify non-payroll reporting items by using the non-payroll data classification service developed/provided by the device 100.

The terminal device B used by another institution may provide non-salary data from the device 100 . The terminal device B used by another institution may provide a classification template/classification criterion to the device 100 so that the device 100 can classify the non-salary data.

The terminal device B used by another institution can verify price disclosure data extracted from non-salary reporting items. The terminal device B used by another institution may disclose the verified price disclosure data. Here, the operation of disclosing the price disclosure data may include an operation of uploading the price disclosure data on a website or an application so that anyone can view it.

2 is a block diagram for explaining a configuration of an apparatus 100 according to an embodiment of the present disclosure. As shown in FIG. 2 , the device 100 may include a memory 110 , a communication module 120 , a display 130 and a processor 140 . However, it is not limited thereto, and the apparatus 100 may include software and hardware configurations within a range obvious from the point of view of those skilled in the art according to necessary operations.

The memory 110 may store one or more instructions for performing various operations. That is, the memory 110 may store data supporting various functions of the device 100 and a program for operating the processor 140, and input/output data (eg, non-salary data, non-salary report) data, optional non-salary data, etc.)

The communication module 120 may include one or more modules for communicating with other terminal devices. The communication module 120 may exchange data and messages with other devices through a network. The communication module 120 may include one or more wireless communication modules including circuitry, but is not limited thereto, and may include a wired communication module.

In addition to Wi-Fi modules and WiBro modules, wireless communication modules include GSM (global System for Mobile Communication), CDMA (Code Division Multiple Access), WCDMA (Wideband Code Division Multiple Access), 4G, 5G, etc. A wireless communication module supporting various wireless communication methods may be included.

The display 130 is a component for displaying various data. The display 130 may display data generated by the processor 140 or received through the communication module 120 (eg, non-payroll data, non-payroll report data, selected non-payroll data, etc.).

The processor 140 may be electrically connected to the memory 110 to control overall operations and functions of the device 100 . The processor 140 may perform overall operations and functions of the device 100 by executing one or more instructions stored in the memory 110 . The processor 140 may control the memory 110, the communication module 120, and the display 130 to provide a classification service for non-salary reporting items.

Overall operations and functions of the apparatus 100 described with reference to FIGS. 3 to 5 may be implemented by the processor 140 .

3 is a flowchart for explaining a method of extracting non-salary reporting items and providing a reporting service according to an embodiment of the present disclosure.

The device may obtain non-salary data generated by terminal devices used by a plurality of medical institutions (S310).

Specifically, the device may obtain non-salary (details) data from an institution containing the non-salary data. At this time, non-salary (detailed) data may be generated and managed by terminal devices used by a plurality of medical institutions.

The device may filter the non-salary data by performing verification work (S320).

First, the device may filter out data in which a predefined item is missing among non-salary data. For example, the device may exclude items that are essential to be included in non-salary data and data in which item-related contents are missing among non-salary data.

As another example, the device may exclude data having format errors among non-salary data. For example, the device may exclude data in which letters (or numbers) are written in items in which numbers (or letters) are to be written among non-salary data.

Thereafter, the device may filter out data in which a predefined non-salary related content ratio is less than a threshold value among non-salary data. For example, the device may exclude data in which content related to non-salary items (eg, inpatient-related content, etc.) is less than a threshold value among non-salary data.

And, the device may transmit a message requesting retransmission of the non-salary data to the medical institution that has transmitted the filtered data among the non-salary data.

The device may classify the filtered non-payroll data into non-payroll reporting data and selected non-payroll data according to a first classification method (S330).

Here, in the first classification method, among the filtered non-payroll data, data having a price disclosure item and an item corresponding to a predefined analysis-required item may be classified as non-payroll report data. That is, among the non-salary data filtered according to the first classification method, the remaining data except for the non-salary reporting data may be classified as optional non-salary data (ie, data other than non-salary reporting data).

Specifically, the first classification method classifies data having an item matching the price disclosure item code among filtered non-salary data and data including keywords related to predefined items requiring analysis among filtered non-salary data as non-salary reporting data. can do. Then, according to the first classification method, the remaining data except for the non-payroll reporting data among the filtered non-payroll data may be classified as selected non-payroll data.

A keyword related to a predefined analysis (or survey) required item may refer to a keyword that appears in the predefined analysis required item more than a predetermined number of times. For example, if the word 'cola temp' appears more than a predetermined number of times in items included in a plurality of non-salary data (ie, items requiring analysis), the device uses the word 'cola temp' as a keyword related to the item requiring analysis. can be identified.

The device may additionally classify each item according to the second classification method for each of the non-payroll reporting data and the selected non-payroll data (S340).

For example, the second classification method may include a method of classifying each of the non-coverage report data and the selected non-coverage data for each item based on the medical field and medical treatment type. Here, the treatment type may include an action item, a treatment material item, and a drug item.

Specifically, each of the non-coverage report data and the selected non-coverage data may be classified by item based on the treatment field and treatment type according to the second classification method. In addition, the optional non-salary data classified by category according to the second classification method may be additionally classified based on a salary classification system (ie, a fee per action) (eg, a chapter, a section, a subsection, etc.).

For example, as shown in FIG. 4 , the device may classify selected non-salary data into a major category (chapter), a middle category (section), and a sub-category (sub-section) using a pay classification system of fee-for-action. In addition, the device may be classified into three categories by representative name (eg, product or general name).

In one embodiment, the device may identify at least one item that is not classified according to an action item, a treatment material item, and a drug item among items included in the non-salary report data and the selected non-salary benefit data. In this case, the device may group the identified at least one item by item having a similar keyword or similar property.

For example, the device may group each item having the same keyword among at least one identified item. Alternatively, the device may be grouped by item having the same medical field or medical treatment type among at least one identified item.

For example, the device may additionally classify non-salary data in the form of a table as shown in FIG. 7 .

The device may update at least one of the first classification method and the second classification method based on the non-salary reporting data and the selected non-salary reporting data additionally classified for each item (S350).

The device can verify non-payroll reporting data and selected non-payroll data additionally classified by category. For example, the device may calculate a first ratio of actual non-payroll reporting data among non-payroll reporting data additionally classified for each item. That is, the device may calculate the first ratio by comparing the final classified non-payroll reporting data and the actual non-payroll reporting data.

Then, the device may calculate a second ratio of the actual selected non-salary data among the selected non-salary data additionally classified for each item. That is, the device may calculate the second ratio by comparing the finally classified non-payroll selection data with the actual selection non-payroll data.

Based on that at least one of the first rate or the second rate is less than the threshold value, the device may modify the price disclosure item code and the keywords associated with the predefined analysis needs item. If at least one of the first ratio or the second ratio is less than the threshold value, it may mean that the device did not accurately classify non-payroll reporting data and selected non-payroll data from non-payroll data.

Accordingly, the device may rearrange item codes of non-salary data included in the non-salary data set. Alternatively, the device may recalculate keywords related to analysis-required items included in the non-salary data. In addition, the device may modify a price disclosure item code and a keyword related to a predefined necessary item by using the item code of the rearranged non-salary data and the recalculated keyword.

5 is a diagram for explaining a process of classifying non-salary data according to an embodiment of the present disclosure.

The device may obtain non-salary data generated by a plurality of medical institutions 510 . For example, a plurality of medical institutions 510 may upload the generated non-salary data on a portal site (or application) 511 provided by the device.

The device may perform a first verification 513 on the non-payroll data. For example, the device may exclude data in which a predefined item is missing from the non-salary data or data including a format error item.

Then, the device may perform the second verification 514 on the data for which the first verification 513 has been performed. For example, the device may exclude data having a predefined non-salary related content ratio less than a threshold value among non-salary data.

The device may transmit data subjected to the first verification (513) and the second verification (514) to a separate database or server. Then, the device may distribute (516) and classify (517) the data to classify the corresponding data. Since the method in which the device classifies the verified (or filtered) data into non-payroll report data and selected non-payroll data has been described above, duplicate descriptions will be omitted.

The device may transfer non-payroll reporting data to a separate database 518 . Then, the device may check (519) the classification result of the non-salary report data. The apparatus may analyze (520, 521) the classification result of non-payroll reporting data.

For example, the device may identify data whose medical expenses are higher than a preset value among non-payroll report data. As another example, the device may identify data in which the number of treatments is equal to or greater than a preset value among non-payroll report data. And, the device may transmit a message requesting review to the medical institution that provided the identified data.

Based on the analysis result, the device may update classification criteria ( 522 ) and generate classification guidelines ( 523 ). Since the process of updating the classification criterion by the device has been described above, redundant description will be omitted.

6 is a diagram for explaining a process of sub-classifying selected non-salary data according to an embodiment of the present disclosure.

The device may additionally classify data other than non-salary reporting data from the non-salary data, that is, the selected non-salary data (610).

Specifically, the device may remove redundant data based on the code (or code name) of the selected non-salary data (611). Thereafter, the device may classify (612) the selected non-coverage data by treatment field and treatment type.

The device may classify selected non-salary data by treatment field (eg, dentistry, oriental medicine, medicine, etc.) 613 or by treatment type (eg, action, treatment material, medicine, etc.) 614 .

The device may identify data 615 that matches health insurance treatment benefit data and data 616 that matches existing classification data from data classified by medical field and medical treatment type.

In addition, the device may review and adjust data classified by medical field and medical treatment type (617). For example, the device can review and adjust 1) health insurance care benefit data, 2) notification or screening criteria and administrative interpretation, and 3) data classified by treatment field and treatment type using the Internet and books. That is, the device may determine the identity of the submitted data based on the name submitted by the medical institution.

In addition, the device performs additional classification and grouping (622) by identifying data with matching (619) or similar (620) notification items and data (621) applicable to the notification classification in the data classified by medical field and treatment type. can do.

In addition, the device may obtain a representative name for additional classification and grouping by applying the notification classification system (623). Then, the device may complete the classification operation 625 by calculating a nursing care benefit registration item 624 from the additionally classified data.

Alternatively, the device may select items to be standardized from the list of nursing care benefits (626), and request consultation from a clinical expert on the selected items to be standardized (627).

Meanwhile, the above-described embodiments of the present disclosure can be written as a program that can be executed on a computer, and the written program can be stored in a medium.

The medium may continuously store programs executable by a computer or temporarily store them for execution or download. In addition, the medium may be various recording means or storage means in the form of a single or combined hardware, but is not limited to a medium directly connected to a certain computer system, and may be distributed on a network.

Examples of the medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical recording media such as CD-ROM and DVD, magneto-optical media such as floptical disks, and ROM, RAM, flash memory, etc. configured to store program instructions. In addition, examples of other media include recording media or storage media managed by an app store that distributes applications, a site that supplies or distributes various other software, and a server.

Above, the embodiments described in the present disclosure and the accompanying drawings merely illustrate some of the technical ideas included in the present disclosure by way of example. Therefore, since the embodiments disclosed in this specification are intended to explain rather than limit the technical spirit of the present disclosure, it is obvious that the scope of the technical spirit of the present disclosure is not limited by these embodiments.

All modifications and specific examples that can be easily inferred by those skilled in the art within the scope of the technical idea included in the specification and drawings of the present disclosure should be construed as being included in the scope of the present disclosure.

100: device
110: memory
120: communication module
130: display
140: processor

Claims (10)

A method for providing a service for classifying non-salary data, performed by an apparatus, the method comprising:
obtaining non-salary data generated by a terminal device used by a plurality of medical institutions;
Filtering by performing verification work on the non-salary data;
classifying the filtered non-payroll data into non-payroll reporting data and selected non-payroll data according to a first classification method;
additionally classifying each item according to a second classification method for each of the non-payroll report data and the selected non-payroll data; and
Updating at least one of the first classification method and the second classification method based on the non-payroll report data and the selected non-payroll data additionally classified for each item,
The first classification method includes a method of classifying data having a price disclosure item and an item corresponding to a predefined analysis required item among the filtered non-payroll data as the non-payroll report data,
The second classification method includes a method of classifying each of the non-coverage report data and the selected non-coverage data for each item based on the field of treatment and the type of treatment,
The filtering step is
Filtering at least one of data in which a predefined item is missing from among the non-salary data or data in which a predefined non-salary related content ratio is less than a threshold value,
The first classification method,
Among the filtered non-payroll data, data having an item matching a price disclosure item code and data including keywords related to a predefined analysis required item among the filtered non-payroll data are classified as the non-payroll report data,
And a method of classifying the remaining data except for the non-payroll reporting data among the filtered non-payroll data as the selected non-payroll data. delete delete According to claim 1,
wherein the care type includes an action item, a therapeutic material item, and a medication item. According to claim 4,
identifying at least one item that is not classified according to the action item, the treatment material item, and the drug item among items included in the non-salary report data and the selected non-salary benefit data; and
The method further comprises grouping the identified at least one item by item having a similar keyword or similar property. According to claim 5,
The updating step is
Calculate the first ratio of the actual non-payroll reporting data among the non-payroll reporting data additionally classified for each of the above items,
Comprising the step of calculating a second ratio of the actual selection non-salary data among the selected non-salary data additionally classified for each item. According to claim 6,
The updating step is
based on at least one of the first rate or the second rate being less than a threshold value, modifying the price disclosure item code and a keyword associated with the predefined analysis needs item. According to claim 7,
The second classification method,
Classify the selected non-coverage data by item based on the medical field and the medical treatment type,
A method comprising a method of additionally classifying the selected non-salary data classified by the item based on a salary classification system. An apparatus for providing a service for classifying non-salary data, the apparatus comprising:
a memory that stores one or more instructions; and
contains one or more processors;
The one or more processors,
obtaining non-salary data generated by terminal devices used by a plurality of medical institutions;
filtering the non-salary data through verification;
Classifying the filtered non-payroll data into non-payroll report data and selected non-payroll data according to a first classification scheme;
Additional classification according to a second classification method for each item for each of the non-payroll report data and the selected non-payroll data; and
At least one of the first classification method and the second classification method is set to be updated based on the non-salary reporting data and the selected non-salary reporting data additionally classified for each item,
The first classification method includes a method of classifying data corresponding to a price disclosure item and a predefined analysis required item among the filtered non-payroll data as the non-payroll report data,
The second classification method includes a method of classifying each of the non-coverage report data and the selected non-coverage data for each item based on the field of treatment and the type of treatment,
The filtering is
Filtering at least one of data in which a predefined item is missing from among the non-salary data or data in which a predefined non-salary related content ratio is less than a threshold value;
The first classification method,
Among the filtered non-payroll data, data having an item matching a price disclosure item code and data including keywords related to a predefined analysis required item among the filtered non-payroll data are classified as the non-payroll report data,
Apparatus comprising a method of classifying the remaining data except for the non-payroll report data among the filtered non-payroll data as the selected non-payroll data.
A computer program stored in a computer readable recording medium to perform a method of providing a service for classifying non-salary data according to any one of claims 1, 4 to 8, in combination with a hardware terminal device.

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