Method for performing quality control on archive data in public health system of existing primary medical institution based on RPA technology

Abstract

The invention relates to a method for performing quality control on archive data in a public health system of an existing primary medical institution based on an RPA technology, and belongs to the technical field of medical information and data processing. Comprising the following steps: S1, constructing a quality control rule establishing an electronic rule base containing multi-dimensional data verification rules according to business logic and data specifications of public health services; s2, data acquisition and automatic verification; s3, performing man-machine cooperative processing; and S4, automatically writing back the data. According to the method, the RPA technology is utilized, the preset data quality inspection rule in the basic-level public-guard system is automatically executed, the problems of data errors, data missing, logic contradictions and data inconsistency are efficiently recognized, marked or corrected, repeated manual operation is reduced, and therefore the integrity, accuracy, consistency and timeliness of data quality are remarkably improved, and the working efficiency is improved. And the burden of primary medical personnel on repeated quality control work is greatly reduced, and the working efficiency of the public health department of the primary medical institution is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to a method for quality control of archive data in an existing public health system of primary medical institutions based on RPA technology, and belongs to the technical field of medical information and data processing. BACKGROUND

[0002] With the deepening of the national basic public health service project, the management of residents' health archives (covering chronic diseases, pregnant women, children, the elderly, and preventive vaccination) in primary medical institutions has shown explosive growth.

[0003] These data are not only large in quantity, but also scattered in sources (such as HIS, LIS, physical examination system, follow-up records, and paper archives digitized input), and have different formats and standards (structured, semi-structured, and unstructured). The traditional quality control method relying on manual checking and spot checks is not enough in the face of such a large amount of heterogeneous data. Primary medical institutions generally face the pressure of insufficient staff and heavy workload. Medical staff (especially family doctors and public health personnel) spend a lot of time and effort on data entry, checking, and correcting repetitive and transactional work, which squeezes the time they should invest in residents' health services, disease intervention, and personalized management, and causes low efficiency and limited coverage, making it difficult to meet the strict requirements for data "completeness, accuracy, consistency, and timeliness". Based on this, the present application improves the method for quality control of archive data in the existing public health system of primary medical institutions based on RPA technology. SUMMARY

[0004] Therefore, the present application provides a method for quality control of archive data in the existing public health system of primary medical institutions based on RPA technology, which significantly reduces the workload of the public health department of primary medical institutions, reduces repetitive manual operations, ensures the completeness, consistency, accuracy, and timeliness of data, and improves the work efficiency of the public health department of primary medical institutions.

[0005] The present application provides a method for quality control of archive data in the existing public health system of primary medical institutions based on RPA technology, and the technical solution proposed is as follows:

[0006] S1: Construct a quality control rule library: according to the business logic and data specifications of public health services, establish an electronic rule library containing multi-dimensional data verification rules;

[0007] S2: Data Acquisition and Automated Verification: Deploy an RPA robot, which is configured to automatically log into the public health system of one or more primary healthcare institutions and retrieve resident health record data to be quality controlled; the RPA robot has a built-in rule engine, which loads and executes the rules in the quality control rule base to automatically verify the retrieved record data item by item in order to identify record data that does not conform to the rules;

[0008] S3: Human-machine collaborative processing: The RPA robot will centrally display the non-compliant archive data and the reasons for non-compliance on an independent intelligent quality control interactive interface for public health personnel to manually review, modify and confirm.

[0009] S4: Automated Data Write-back: After public health personnel complete the modification and confirmation of the data on the intelligent quality control interactive interface and trigger the submission command, the RPA robot is activated and automatically writes back or uploads the modified and confirmed standard data to the corresponding original public health system, completing the closed-loop process of data quality control.

[0010] Furthermore, in step S1, the construction of the quality control rule base covers quality control rules for the entire life cycle of resident health records, specifically including at least one of the following: completeness rules: checking whether there are any missing key fields in the records; accuracy and format rules: verifying the correctness of the data format; consistency rules: comparing the same record data between different systems or different modules to ensure information consistency; logical rules: judging whether the internal logical relationship between data is reasonable; and timeliness rules: monitoring the update frequency of the records.

[0011] Furthermore, the primary healthcare institution public health system includes a homepage interactive interface for displaying task status, a statistical analysis interactive interface, an intelligent quality control interactive interface, an automated operation interactive interface, a data comparison interactive interface, an import / export interactive interface, and a log center interactive interface.

[0012] Furthermore, the statistical analysis interactive interface includes an elderly management data module, a hypertension patient management data module, a diabetes patient management data module, and a data report module; the intelligent quality control interactive interface includes one or more of the following modules: a resident health record management module, an elderly health management module, a hypertension patient health management module, and a type 2 diabetes patient health management module.

[0013] Furthermore, in step S3, the intelligent quality control interactive interface has statistical analysis functions, which can classify, summarize and visualize data that does not conform to the rules, and generate statistical reports. The visualization includes displaying various reasons for non-standard files and their corresponding quantities in the form of charts.

[0014] Furthermore, in step S3, when the intelligent quality control interactive interface displays non-compliant archive data, it will clearly indicate the specific reasons why each piece of data does not meet the standards, so as to guide public health personnel to make targeted modifications.

[0015] Furthermore, in step S4, the public health personnel trigger the submission instruction by clicking the upload or submit button on the intelligent quality control interactive interface.

[0016] The beneficial effects of this invention are:

[0017] This invention utilizes RPA technology to automatically execute preset data quality inspection rules in primary public health systems, efficiently identifying, marking, or correcting data errors, missing data, logical contradictions, and inconsistencies. This reduces repetitive manual operations, thereby significantly improving the integrity, accuracy, consistency, and timeliness of data quality, greatly reducing the burden on primary healthcare staff in repetitive quality control work, and improving the work efficiency of public health departments in primary healthcare institutions. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the homepage interactive interface of the present invention.

[0019] Figure 2 This is a schematic diagram of the statistical analysis interactive interface of the present invention. Figure 1 .

[0020] Figure 3 This is a schematic diagram of the statistical analysis interactive interface of the present invention. Figure 2 .

[0021] Figure 4 This is a schematic diagram of the statistical analysis interactive interface of the present invention. Figure 3 .

[0022] Figure 5 This is a schematic diagram of the intelligent quality control interactive interface of the present invention. Figure 1 .

[0023] Figure 6 This is a schematic diagram of the intelligent quality control interactive interface of the present invention. Figure 2 .

[0024] Figure 7 This is a schematic diagram of the intelligent quality control interactive interface of the present invention. Figure 3 . Detailed Implementation

[0025] The preferred embodiments of the present invention will now be described in detail.

[0026] This invention provides a method for quality control of archival data in existing primary healthcare institutions' public health systems based on RPA technology, comprising the following steps:

[0027] S1: Building a Quality Control Rule Base: Based on the business logic and data standards of public health services, we will conduct in-depth analysis of public health business processes and record data structures. In collaboration with clinical experts and public health managers, we will develop quality control rules covering the entire lifecycle, establishing an electronic rule base containing multi-dimensional data verification rules. The quality control rule base covers quality control rules for the entire lifecycle of resident health records, specifically including at least one of the following: Completeness rules: Checking for missing key fields in the records, such as ID number, contact information, and diagnostic information; Accuracy and format rules: Verifying the correctness of data formats, such as identity... The rules include: the number of digits and check digits of the certificate number, the format of the telephone number, and the valid range of the date; consistency rules: comparing the same file data across different systems or modules to ensure information consistency, such as whether the diagnostic information in the HIS system matches the diagnostic information in the chronic disease file in the public health system; logical rules: judging whether the internal logical relationship between data is reasonable, such as whether medication records match diagnoses, whether follow-up dates are later than the file creation date, and whether the age and examination items in children's health records correspond; timeliness rules: monitoring the update frequency of the files, such as whether the follow-up records of chronic disease patients are completed and entered within the specified time.

[0028] S2: Data Acquisition and Automated Verification: Deploy an RPA robot, configured to automatically log into one or more primary healthcare institution public health systems and retrieve resident health record data to be quality controlled. The RPA robot has a built-in rule engine that loads and executes rules from the quality control rule base to automatically verify each item of the retrieved record data, identifying data that does not conform to the rules. The primary healthcare institution public health system includes a homepage interactive interface for displaying task status, a statistical analysis interactive interface, an intelligent quality control interactive interface, an automated operation interactive interface, a data comparison interactive interface, an import / export interactive interface, and a log center interactive interface. The statistical analysis interactive interface includes modules for elderly management data, hypertension patient management data, diabetes patient management data, and a data report module. The intelligent quality control interactive interface includes one or more of the following modules: resident health record management module, elderly health management module, hypertension patient health management module, and type 2 diabetes patient health management module.

[0029] like Figure 1 The system homepage shown allows you to initiate a quality control task, such as controlling all hypertension records from the previous month; for example... Figure 2 , 3 The statistical analysis interactive page shown in Figure 4, Figure 2 It allows for a direct view of the reasons and quantity of non-standard files; Figure 3 , 4 It can export monthly statistical reports;

[0030] S3: Human-Machine Collaborative Processing: The RPA robot will centrally display the non-compliant archival data and the reasons for the non-compliance on an independent intelligent quality control interactive interface, which will be used by public health personnel for manual review, modification, and confirmation. The intelligent quality control interactive interface has statistical analysis functions, which can classify, summarize, and visualize the non-compliant data, and generate statistical reports. The visualization includes charts showing the various reasons for non-compliance and their corresponding quantities. When displaying non-compliant archival data, the intelligent quality control interactive interface will clearly indicate the specific reasons for each non-compliance to guide public health personnel to make targeted modifications.

[0031] like Figure 5 As shown, after the RPA robot completes the verification, it will summarize all the data marked as non-compliant and present it in the pending list of the intelligent quality control interactive interface of this method. For example, when a public health worker logs into this system and enters the intelligent quality control interactive page, he / she can see a list displaying all the files with problems. When he / she clicks on a file, the right side of the interface will load the detailed information of that file, and at the same time... Figure 6 The system will highlight the specific problems identified by the RPA robot, such as the diastolic blood pressure not being empty during this follow-up visit or the follow-up doctor not signing the document.

[0032] Based on these clear prompts and the original information they have, whether it is paper records or communication with other doctors, public health personnel can directly edit on the interface, fill in the missing diastolic pressure, and select the correct follow-up doctor.

[0033] S4: Automated Data Write-back: After public health personnel complete the modification and confirmation of the data on the intelligent quality control interactive interface and trigger the submission command, the RPA robot is activated and automatically writes back or uploads the modified and confirmed standard data to the corresponding original public health system, completing the closed-loop process of data quality control. The operation of public health personnel to trigger the submission command is to click the upload or submit button on the intelligent quality control interactive interface.

[0034] like Figure 7 As shown, after public health personnel have modified all the problematic data on the interface, they can click the upload button located in the upper right corner of the interface, and the RPA robot will write the improved data back to the designated public health system.

[0035] This invention utilizes RPA technology to automatically execute preset data quality inspection rules in primary public health systems, efficiently identifying, marking, or correcting data errors, missing data, logical contradictions, and inconsistencies. This reduces repetitive manual operations, thereby significantly improving the integrity, accuracy, consistency, and timeliness of data quality, greatly reducing the burden on primary healthcare staff in repetitive quality control work, and improving the work efficiency of public health departments in primary healthcare institutions.

[0036] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A method for quality control of archival data in an existing public health system of primary medical institutions based on RPA technology, characterized by: The method comprises the following steps: S1: constructing a quality control rule library: according to the business logic and data specification of public health services, an electronic rule library containing multi-dimensional data checking rules is established; S2: data acquisition and automatic checking: deploying an RPA robot, which is configured to automatically log in to one or more public health systems of primary medical institutions, and grab the resident health record data to be quality controlled; the RPA robot has a built-in rule engine, which loads and executes the rules in the quality control rule library to automatically check the grabbed record data item by item to identify the record data that does not meet the rules; S3: human-machine collaborative processing: the RPA robot displays the identified record data that does not meet the rules and the reasons for not meeting the rules in a separate intelligent quality control interactive interface for public health personnel to manually review, modify and confirm; S4: automatic data backwriting: after the public health personnel complete the modification and confirmation of the data on the intelligent quality control interactive interface and trigger the submission instruction, the RPA robot is activated to automatically write or upload the modified and confirmed standard data to the corresponding original public health system, completing the closed-loop process of data quality control.

2. The method for quality control of archival data in existing primary medical institution public health systems based on RPA technology according to claim 1, characterized in that: In the step S1, the construction of the quality control rule library covers quality control rules for the whole life cycle of the resident health record, specifically including at least one of the following: completeness rule: checking whether the key fields in the record are missing; accuracy and format rule: checking the correctness of the data format; consistency rule: comparing the same record data in different systems or different modules to ensure consistency; logical rule: judging whether the internal logical relationship between the data is reasonable; timeliness rule: monitoring the update frequency of the record.

3. The method for quality control of archival data in existing primary medical institution public health systems based on RPA technology according to claim 1, characterized in that: The public health system of the primary medical institution includes a homepage interactive interface for displaying task status, a statistical analysis interactive interface, an intelligent quality control interactive interface, an automatic operation interactive interface, a data comparison interactive interface, an import and export interactive interface, and a log center interactive interface.

4. The method for quality control of archival data in existing primary medical institution public health systems based on RPA technology according to claim 3, characterized in that: The statistical analysis interactive interface includes an elderly management data module, a hypertension patient management data module, a diabetes patient management data module, and a data report module; the intelligent quality control interactive interface includes one or more of a resident health record management module, an elderly health management module, a hypertension patient health management module, and a type 2 diabetes patient health management module.

5. The method for quality control of archival data in existing primary medical institution public health systems based on RPA technology according to claim 1, characterized in that: In the step S3, the intelligent quality control interactive interface has a statistical analysis function, which can classify and visualize the data that does not meet the rules, and generate statistical reports; the visualization includes displaying the reasons for the record not being standardized and the corresponding quantities in the form of charts.

6. The method for quality control of archival data in existing primary medical institution public health systems based on RPA technology according to claim 1, characterized in that: In the step S3, when displaying the record data that does not meet the rules, the intelligent quality control interactive interface clearly displays the specific reasons for each piece of data not meeting the standard to guide the public health personnel to make targeted modifications.

7. The method for quality control of archival data in existing primary care public health systems based on RPA technology according to claim 1, characterized in that: In the step S4, the operation of the public health personnel triggering the submission instruction is clicking the upload or submit button on the intelligent quality control interactive interface.

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