Quantitative methods, devices and media for evaluating the quality of disease registry data

Abstract

This application discloses a quantitative method for evaluating the data quality of a disease registration database, the method comprising: obtaining a data dictionary and a backup data set of a disease registration database; referring to the description and method of the medical database data quality evaluation in previous literature, this patent defines at least five dimensions to quantitatively evaluate the data quality of the disease registration database, the dimensions at least including: the corrected disease registration database case volume, the number of variables collected by the disease registration database data, the case data integrity, the case data reliability, and the annual follow-up completion rate; based on the combination of the five dimensions, a data quality evaluation index and its calculation formula are defined, and the data quality of the disease registration database is comprehensively evaluated based on the data quality index. This application is beneficial to cancer research based on databases, and can systematically and flexibly evaluate different databases, improve the effectiveness and comparability of databases, and promote their optimal use.

Description

Technical Field

[0001] The present application relates to the field of databases, and in particular to a quantitative method, device and medium for evaluating the data quality of a disease registration database. Background Art

[0002] High-quality medical data is a prerequisite for obtaining reliable clinical research results. Data quality affects the effectiveness and repeatability of medical research. At present, there are various medical databases of different sizes (cancer, chronic diseases) at home and abroad, so it is necessary to evaluate the data quality of the database.

[0003] There are a variety of data quality frameworks and assessment methods in the related art, but most of them are aimed at disease registry databases of electronic medical records, and there is no relevant data quality assessment framework and unified standards. Considering the available data and research questions of interest, the data quality framework of electronic medical records cannot be fully applied to disease registry databases. Standardized assessment cannot be applied to all data environments, and a data quality framework specific to disease registry databases is needed. In addition, the definition and standards of quality domains are unclear, making comparison between databases difficult.

[0004] Therefore, the above-mentioned technical problems existing in the related technology need to be solved urgently. Summary of the invention

[0005] The present application aims to solve one of the technical problems in the related art. To this end, the present application embodiment provides a quantitative method, device and medium for evaluating the data quality of a disease registration database, which can evaluate the quality of the disease registration database under clear standards.

[0006] According to one aspect of the present application, a quantitative method for evaluating the data quality of a disease registration database is provided, the method comprising:

[0007] Obtain data dictionary and backup data sets of disease registry databases;

[0008] Based on the qualitative evaluation of the data quality of disease registration databases in previous literature, five dimensions were defined to quantitatively evaluate the disease registration database, including: the corrected sample size of the disease registration database, the total number of variables in the disease registration database, the completeness of case data, the reliability of case data, and the follow-up completion rate;

[0009] A database data quality assessment index is defined according to a combination of the five dimensions, and a comprehensive assessment of the data quality of the disease registration database is performed based on the database data quality assessment index.

[0010] In one embodiment, the corrected disease registration database case volume is the ratio of the total number of cases in the disease registration database to the number of new cases of the disease in the country or region, and the calculation formula for the corrected disease registration database case volume is:

[0011]

[0012] Among them, aTNRs (Adjusted Total Number of Records) is the adjusted case volume in the disease registration database.

[0013] In one embodiment, the case data integrity is the data integrity of all case records in the disease registration database, and the calculation formula of the case data integrity is:

[0014]

[0015] Among them, Record Completeness refers to the completeness of case data.

[0016] In one embodiment, the reliability of the case data is the degree to which the case data conforms to the actual situation, and the calculation formula of the case data reliability is:

[0017]

[0018] Among them, Record Reliability refers to the reliability of case data.

[0019] In one embodiment, the annual follow-up completion rate is the completeness of the follow-up prognosis data of patients in the disease registration database, and the formula for the annual follow-up completion rate is:

[0020]

[0021] CRFu (Completion Rate of Follow-up) is the annual follow-up completion rate. The conditions for the completion of the annual follow-up include: the follow-up status is dead, or the time interval between the last visit date and the backup database date is less than or equal to 12 months.

[0022] In one embodiment, defining a data quality assessment index according to a combination of the five dimensions, and comprehensively assessing the data quality of the disease registration database according to the data quality assessment index includes:

[0023] The combination of the five dimensions is used to define the data quality assessment index according to the requirements. The formula is:

[0024] DRQI=aTNRs×TNVs×Record Completeness×Record Reliability×CRFu

[0025] Among them, DRQI is the database data quality assessment index, aTNRs is the corrected sample size of the disease registration database, TNVs (Total Number of Variables) is the total number of variables in the disease registration database, RecordCompleteness is the completeness of case data, Record Reliability is the reliability of case data, and CRFu is the annual follow-up completion rate.

[0026] According to one aspect of the present application, a device for evaluating and quantifying the quality of disease registration database data is provided, the device comprising:

[0027] An acquisition module is used to obtain the data dictionary and backup data set of the disease registration database;

[0028] A definition module, for performing a qualitative evaluation of the disease registration database based on previous literature, and defining at least five dimensions for quantitative evaluation of the disease registration database, wherein the dimensions at least include: the corrected sample size of the disease registration database, the total number of variables in the disease registration database, the completeness of case data, the reliability of case data, and the annual follow-up completion rate;

[0029] An evaluation module is used to define a data quality evaluation index according to a combination of the five dimensions, and to conduct a comprehensive evaluation of the data quality of the disease registration database according to the data quality evaluation index.

[0030] According to one aspect of the present application, a device for evaluating and quantifying the quality of disease registration database data is provided, the device comprising:

[0031] at least one processor;

[0032] at least one memory, the memory being used to store at least one program;

[0033] When at least one of the programs is executed by at least one of the processors, the quantitative method for evaluating the data quality of the disease registration database as described in the previous embodiment is implemented.

[0034] According to one aspect of an embodiment of the present application, a medium is provided, wherein the medium stores a program executable by a processor, and when the program executable by the processor is executed by the processor, the quantitative method for evaluating the data quality of a disease registration database as described in the previous embodiment is implemented.

[0035] The beneficial effects of a quantitative method, device and medium for evaluating the data quality of a disease registration database provided in an embodiment of the present application are as follows: the present application obtains a data dictionary and a backup data set of a disease registration database; based on a qualitative evaluation of the disease registration database in previous literature, at least five dimensions are defined to quantitatively evaluate the disease registration database, and the dimensions include at least: the corrected sample size of the disease registration database, the total number of variables in the disease registration database, the completeness of case data, the reliability of case data, and the annual follow-up completion rate; a data quality evaluation index is defined based on a combination of the five dimensions, and a comprehensive evaluation of the data quality of the disease registration database is performed based on the data quality evaluation index. The present application is beneficial to cancer research based on databases, and can systematically and flexibly evaluate different databases, improve the effectiveness and comparability of databases, and promote their optimal use.

[0036] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0038] Figure 1 A flowchart of a quantitative method for evaluating the data quality of a disease registration database provided in an embodiment of the present application;

[0039] Figure 2 A schematic diagram of evaluation parameters of a quantitative method for evaluating the data quality of a disease registration database provided in an embodiment of the present application;

[0040] Figure 3 A schematic diagram of a device for evaluating and quantifying the quality of disease registration database data provided in an embodiment of the present application;

[0041] Figure 4 Another schematic diagram of a device for evaluating and quantifying the data quality of a disease registration database provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0043] The terms "first", "second", "third" and "fourth" etc. in the specification, claims and drawings of the present application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0044] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0045] High-quality medical data is a prerequisite for obtaining reliable clinical research results. Data quality affects the effectiveness and repeatability of medical research. At present, there are various medical databases of different sizes (cancer, chronic diseases) at home and abroad, so it is necessary to evaluate the data quality of the database.

[0046] There are a variety of data quality frameworks and assessment methods in the related art, but most of them are aimed at disease registry databases of electronic medical records, and there is no relevant data quality assessment framework and unified standards. Considering the available data and research questions of interest, the data quality framework of electronic medical records cannot be fully applied to disease registry databases. Standardized assessment cannot be applied to all data environments, and a data quality framework specific to disease registry databases is needed. In addition, the definition and standards of quality domains are unclear, making comparison between databases difficult.

[0047] In order to solve the above problems, this application proposes a quantitative method for evaluating the data quality of a disease registration database.

[0048] Figure 1 A flowchart of a quantitative method for evaluating the data quality of a disease registration database provided in an embodiment of the present application, such as Figure 1 As shown, the quantitative method for evaluating the data quality of the disease registration database proposed in this application includes:

[0049] S101. Obtain a data dictionary and a backup data set of a disease registration database.

[0050] S102. Based on the qualitative evaluation of the disease registration database in previous literature, define at least five dimensions to conduct a quantitative evaluation of the disease registration database.

[0051] In step S102, the dimensions include at least: the corrected sample size of the disease registration database, the total number of variables in the disease registration database, the completeness of the case data, the reliability of the case data, and the annual follow-up completion rate.

[0052] S103. Define a data quality assessment index according to a combination of the five dimensions, and conduct a comprehensive assessment of the data quality of the disease registration database according to the data quality assessment index.

[0053] This application is beneficial to database-based cancer research and can systematically and flexibly evaluate different databases, improve the validity and comparability of databases, and promote their optimal use.

[0054] The specific definitions and calculation methods of the five dimensions proposed in this embodiment are as follows:

[0055] (1) The corrected disease registration database case volume is the ratio of the disease registration database case volume to the number of new cancer cases in the country or region. The calculation formula for the corrected disease registration database case volume is:

[0056]

[0057] Among them, aTNRs (Adjusted Total Number of Records) is the number of cases in the disease registration database after correction. The number of new cancer cases in the country or region in this embodiment can be obtained through the Global Health Data (GHDx), the Global Cancer Observatory (GCO), and published and searchable literature. GHDx is a directory of global health and population data. Its goal is to help people find data cataloging data information including topics, by providing links to data providers or explaining how to obtain data, and with our permission, provide direct downloads of data. GHDx research includes census data, surveys, registrations, indicators and estimates, administrative health data, and health-related financial data. GCO is a web-based interactive platform that displays global cancer statistics and provides information for cancer control and cancer research. The platform focuses on visualizing cancer indicators, cancer incidence in five continents, international childhood cancer incidence, and several cancer survival benchmark projects using data from the Cancer Surveillance Department of the World Health Organization's International Agency for Research on Cancer.

[0058] (2) Case data integrity refers to the degree of data integrity of all case records in the disease registration database. The calculation formula for case data integrity is:

[0059]

[0060] Among them, Record Completeness refers to the completeness of case data.

[0061] (3) Case data reliability refers to the degree to which case data conforms to the actual situation. The calculation formula for case data reliability is:

[0062]

[0063] Among them, Record Reliability refers to the reliability of the case data. The reliability of case data can be quantitatively evaluated using the following sampling strategy: In principle, every case of data in the database must be quality controlled and reliability evaluated; if there is a lot of case data but insufficient quality control manpower, we recommend sampling 5%-10% of the total cases for reliability quality control.

[0064] (4) The annual follow-up completion rate refers to the completeness of the follow-up prognosis data of patients in the disease registration database. The formula for the annual follow-up completion rate is:

[0065]

[0066] CRFu (Completion Rate of Follow-up) is the annual follow-up completion rate. The conditions for the completion of the annual follow-up include: the follow-up status is dead or the time interval between the last access date and the backup database date is less than or equal to 12 months.

[0067] (5) The total amount of all variables (TNVs) collected by the disease registration database refers to the number of variables contained in each case record.

[0068] Reference Figure 2 In step S103, a data quality evaluation index is defined according to a combination of the five dimensions, and a comprehensive evaluation of the data quality of the disease registration database is performed according to the data quality evaluation index. Specifically, the data quality evaluation index is defined according to the combination of the five dimensions according to the requirements, and the formula is:

[0069] DRQI=aTNRs×TNVs×RecordCompleteness×Record Reliability×CRFu

[0070] CRFu (Completion Rate of Follow-up) is the annual follow-up completion rate. The conditions for the completion of the annual follow-up include: the follow-up status is dead, or the time interval between the last visit date and the backup database date is less than or equal to 12 months.

[0071] This embodiment develops a set of data quality assessment standards that can objectively and repeatably assess data quality, which is crucial for database-based research. Researchers can describe data quality deviations based on data quality assessment indicators, such as whether the data meets the expected data structure and distribution, and can systematically and flexibly evaluate different databases, improve the effectiveness and comparability of databases, and promote their optimal use.

[0072] Reference Figure 3 The embodiment of the present invention also provides a quantitative evaluation device for disease registration database data quality, including:

[0073] An acquisition module 301 is used to acquire a data dictionary and a backup data set of a disease registration database;

[0074] A definition module 302 is used to define at least five dimensions for quantitatively evaluating the disease registration database based on the qualitative evaluation of the disease registration database in previous literature, wherein the dimensions at least include: the corrected case volume of the disease registration database, the number of variables collected in the disease registration database data, the completeness of case data, the reliability of case data, and the annual follow-up completion rate;

[0075] The evaluation module 303 is used to define a data quality evaluation index according to a combination of the five dimensions, and to perform a comprehensive evaluation on the data quality of the disease registration database according to the data quality evaluation index.

[0076] It can be seen that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0077] Reference Figure 4 The present application embodiment provides a quantitative device for evaluating the quality of disease registration database data, including:

[0078] at least one processor 401;

[0079] At least one memory 402, used to store at least one program;

[0080] When the at least one program is executed by the at least one processor 401 , the at least one processor 401 implements the quantitative method for evaluating the data quality of the disease registration database of the aforementioned embodiment.

[0081] Similarly, the contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0082] An embodiment of the present invention further provides a medium storing a program, and the program is used to implement the method of the above embodiment when executed by a processor.

[0083] Similarly, the contents of the above method embodiments are all applicable to the present storage medium embodiments. The functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0084] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the application is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part described as a larger operation is performed independently.

[0085] In addition, although the present application is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present application. More specifically, in view of the properties, functions, and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional techniques of the engineer. Therefore, those skilled in the art can implement the present application set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present application, which is determined by the full scope of the attached claims and their equivalents.

[0086] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0087] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0088] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0089] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0090] In the above description of this specification, the description with reference to the terms "one embodiment / example", "another embodiment / example" or "certain embodiments / examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0091] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present application, and that the scope of the present application is defined by the claims and their equivalents.

[0092] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A quantitative method for evaluating the quality of disease registration database data, characterized in that: The following steps are involved: Obtain data dictionary and backup data sets of disease registry databases; Based on the qualitative evaluation of the quality of disease registration database data in previous literature, five quality assessment dimensions were defined to quantitatively evaluate the disease registration database. The assessment dimensions included: the corrected sample size of the disease registration database, the total number of variables in the disease registration database, the completeness of case data, the reliability of case data, and the annual follow-up completion rate; A comprehensive database data quality assessment index was defined by combining five quality assessment dimensions, and the data quality of the disease registration database was comprehensively assessed based on the database data quality assessment index; The adjusted sample size of the disease registration database is the ratio of the total number of cases in the disease registration database to the number of new cases of the disease in the country or region. The calculation formula for the adjusted sample size of the disease registration database is: Among them, aTNRs is the corrected case volume in the disease registration database; Case data integrity refers to the degree of data integrity of all cases in the disease registration database. The calculation formula for case data integrity is: Among them, Record Completeness refers to the completeness of case data; The reliability of case data refers to the degree to which the case data conforms to the actual situation. The calculation formula for the reliability of case data is: Among them, Record Reliability refers to the reliability of case data; The annual follow-up completion rate is the completeness of the follow-up data of patients in the disease registration database. The formula for the annual follow-up completion rate is: Among them, CRFu is the annual follow-up completion rate; the conditions for the completion of the annual follow-up include: the follow-up status is dead, or the time interval between the last visit date and the backup database date is less than or equal to 12 months; A comprehensive database data quality assessment index is defined by combining five data quality assessment dimensions. The data quality of the disease registration database is comprehensively assessed based on the database data quality assessment index, including: The formula for the database data quality assessment index is: DRQI=aTNRs×TNVs×Record Completeness×Record Reliability×CRFu Among them, DRQI is the database data quality assessment index, aTNRs is the corrected sample size of the disease registration database, TNVs is the total number of variables in the disease registration database, Record Completeness is the completeness of case data, Record Reliability is the reliability of case data, and CRFu is the annual follow-up completion rate.

2. A quantitative device for evaluating the quality of disease registration database data, applied to the method of claim 1, characterized in that: include: An acquisition module is used to obtain the data dictionary and backup data set of the disease registration database; The definition module is used to conduct a qualitative assessment of the quality of disease registry data based on previous literature, and to define five quality assessment dimensions for quantitative assessment of disease registry data. The assessment dimensions include: the corrected sample size of disease registry database, the total number of variables in the disease registry database, case data completeness, case data reliability, and annual follow-up completion rate; The evaluation module defines a comprehensive database data quality evaluation index by combining five quality evaluation dimensions, and conducts a comprehensive evaluation of the data quality of the disease registration database based on the database data quality evaluation index.

3. A quantitative device for evaluating the quality of disease registration database data, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the program is executed by a processor, the method according to claim 1 is implemented.

4. A medium, characterized in that The medium stores a program executable by a processor, and when the program executable by the processor is executed by the processor, the method according to claim 1 is implemented.

Images