Medical data processing system and method
By integrating the data entry permission module, comparison module, and data processing module, the problems of data entry errors and untimely updates of medical knowledge in existing medical data processing systems are solved, enabling efficient and accurate data updates and timely decision support.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-03-23
- Publication Date
- 2026-04-03
AI Technical Summary
Existing medical data processing systems are inadequate in terms of data entry accuracy and efficiency, timeliness of medical knowledge updates, and decision support, leading to data errors and inaccurate clinical decisions.
It employs a data entry permission module, a data entry module, a cloud database, a detection result comparison module, and a data processing module. It corrects spelling errors through an edit distance algorithm, automatically updates detection items and normal value ranges, and provides comprehensive decision support.
It improves the accuracy and efficiency of data entry, ensures timely data updates and accuracy, provides timely medical advice, and enhances support for clinical decision-making.
Smart Images
Figure CN121789922A_ABST
Abstract
Description
Technical Field
[0001] This invention proposes a medical data processing system and method, belonging to the field of medical data management systems. Background Technology
[0002] A medical data processing system is a computer system specifically designed for managing and processing medical information. The core objective of this type of system is to efficiently and securely process and store various types of data related to patient care and medical services.
[0003] Existing data processing systems for patient medical testing information often have the following drawbacks:
[0004] Issues regarding the accuracy and efficiency of data entry:
[0005] Current technology lacks a sufficiently effective mechanism to identify and correct spelling errors or name discrepancies during data entry. This leads to data entry errors that require manual review and correction, thus reducing the efficiency and accuracy of data processing.
[0006] The response to updates in medical knowledge is not timely enough.
[0007] Existing systems are not flexible enough in handling updates to medical knowledge (such as normal ranges for test results). As medical knowledge and clinical practice continuously advance, the failure to update the database in a timely manner leads to outdated medical data, thereby affecting the accuracy of clinical decisions.
[0008] Inadequacy of decision support:
[0009] Current technologies lack sufficient algorithmic linkage mechanisms to provide comprehensive decision support. For example, the analysis of medical data, the review of historical data, and the medical recommendations based on this information are not comprehensive or timely enough, which limits the flexibility and responsiveness of medical institutions in providing medical services. Summary of the Invention
[0010] This invention provides a medical data processing system and method, the technical solution of which is as follows:
[0011] A medical data processing system, comprising:
[0012] Data entry permission module;
[0013] Data entry module;
[0014] Cloud database;
[0015] Detection result comparison module;
[0016] Data processing module;
[0017] The data entry permission module is used to review the data entry permissions of medical institutions;
[0018] The data entry module is used to input the patient's medical test information, which includes test items and test data.
[0019] The cloud database includes a patient test data storage library, a test item library, and a test item normal value range database;
[0020] The test result comparison module is used to verify the input patient medical test information;
[0021] The data processing module is used to update the test item library and the normal value range database for test items, re-examine the patient's medical test information, and provide feedback on the reviewed tests.
[0022] Preferably, the patient testing database is used to store patient medical testing information that has been correctly detected by the testing result comparison module through comparison with the testing item database and the normal value range database of the testing items;
[0023] The testing item database includes the names of common medical testing items;
[0024] The database of normal value ranges for the test items includes normal value ranges for common medical test items;
[0025] The test item library and the test item normal value range database are updated periodically to include new test items and update the normal value ranges of medical test items derived from experiments.
[0026] A medical data processing method, comprising the aforementioned medical data processing system, wherein the method for obtaining permissions for the data entry permission module includes:
[0027] Medical institutions apply to the cloud database administrator for access rights and provide the necessary institutional qualifications and certification information;
[0028] The cloud database administrator reviews the organization's qualifications to confirm its legality and eligibility;
[0029] After the medical institution is verified by the database administrator, the system administrator creates an institutional account for the medical institution and sets institutional-level access permissions;
[0030] Healthcare institutions obtain a unique authentication key or digital certificate for authentication and data encryption;
[0031] Database administrators grant access to the data input module to verified medical institutions;
[0032] Medical institutions install and configure data input modules on their internal networks;
[0033] Medical institutions enter patients' medical test information into the data input module;
[0034] The entered medical test information is encrypted using an authentication key and uploaded to the cloud database via a secure transmission protocol.
[0035] Preferably,
[0036] The method for the test result comparison module to review the patient's test items includes:
[0037] The test result comparison module searches the test item database for medical test items that exactly match the input name;
[0038] When a perfectly matching medical test item is found, the test result comparison module automatically accepts the input and allows the medical test item to be directly entered into the patient test data storage database.
[0039] When no perfectly matching item is found, the detection result comparison module uses the edit distance algorithm to calculate the similarity score between the input medical test item name and each medical test item name in the database;
[0040] Set a similarity threshold;
[0041] When the highest similarity score is found and exceeds the similarity threshold, the detection result comparison module will prompt the input user whether it is the closest match.
[0042] The user can confirm and select the suggested item name after the input is judged;
[0043] When the similarity scores of all items are below the threshold, the detection result comparison module prompts the doctor to check and re-enter the names of the detection items.
[0044] Preferably,
[0045] The distance editing algorithm includes:
[0046] Let L(i, j) be the edit distance between the first i characters of the input string a and the first j characters of the reference string b;
[0047] 0≤i≤m,L(i,0)=i;
[0048] 0≤j≤n, L(j,0)=J;
[0049]
[0050] Where 'a' represents the input string, 'b' represents the reference string, L(i-1, j)+1 represents a deletion operation on the input string 'a', L(i, j-1)+1 represents an insertion operation on the input string 'a', and l(i-1, j-1)+1 represents a insertion operation on the input string 'a'. (ai≠bj) This indicates that for input string a, in a i Not equal to b i Perform a replacement operation at a time or in a i equals b i No operation is performed at this time, where m and n are the lengths of the input string a and the reference string b, respectively.
[0051] 1 (ai≠bj) This is an indicator function; it takes the value 1 if ai is not equal to bi, and 0 otherwise.
[0052] The formula for the similarity score S is as follows:
[0053]
[0054] Where max(m, n) represents the string with the larger length between the input string a and the reference string b; S is the similarity score.
[0055] Preferably,
[0056] The method for the test result comparison module to verify the normal range of medical test items includes:
[0057] Search the database of normal value ranges for the test items for the corresponding normal value ranges.
[0058] Set the normal value range as the detection value threshold range;
[0059] Determine whether the input detection data is within the detection value threshold range;
[0060] When the detection data is within the detection value threshold range, the detection result comparison module automatically accepts the input and allows the medical detection data to be directly entered into the patient detection data storage library;
[0061] The test result comparison module alerts the user to abnormal data. After the user confirms the medical test data again, the data is allowed to be entered into the patient test data storage database and marked as abnormal data.
[0062] Preferably, the method by which the data processing module processes the updated database of normal value ranges for detection items includes:
[0063] The data processing module continuously monitors the database of normal value ranges for the detected items for any updates.
[0064] When the data processing module detects an update to the normal value range of a specific item, it re-audits it.
[0065] The data processing module retrieves all historical detection data related to the update range;
[0066] The data processing module re-evaluates each retrieved data item using the new normal value range for the detection items;
[0067] Determine whether each test data point falls within or outside the new normal range;
[0068] For test data that was previously abnormal but is now normal, the data processing module sends a reminder to the relevant medical institutions, suggesting that they reduce their focus on this aspect for patients.
[0069] For data that was previously normal but is now abnormal, the data processing module sends a warning to the relevant medical institution and suggests paying attention to the patient's potential disease in this area; at the same time, it suggests recalling the patient for retesting.
[0070] The present invention has the following beneficial effects:
[0071] 1. By using the edit distance algorithm employed in the item name comparison module, the system can effectively identify and correct spelling errors or minor name discrepancies that may occur during data entry by medical professionals. This not only improves the accuracy of data entry but also enhances overall efficiency, as it reduces the need for manual review and correction.
[0072] 2. As medical knowledge advances and clinical practice changes, the normal ranges for test results may be adjusted. The data processing module monitors these changes and updates the database promptly, while also reviewing historical data. This ensures that the data stored in the system is always up-to-date and accurate, helping healthcare institutions adhere to the latest medical standards and guidelines.
[0073] 3. The interplay of various algorithms within the system provides robust decision support. For instance, when the database of normal ranges for test items is updated, the data processing module not only re-examines the relevant historical test data but also provides key medical recommendations to medical institutions based on the review results. This collaborative mechanism ensures that medical institutions can adjust their focus on patients in a timely manner, thereby providing more timely medical interventions when necessary, such as re-testing or addressing potential health issues. Attached Figure Description
[0074] Figure 1 , one A schematic diagram illustrating the main workflow of a medical data processing module for updating the database of normal value ranges for test items. Detailed Implementation
[0075] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.
[0076] Example 1:
[0077] A medical data processing system, comprising:
[0078] Data entry permission module;
[0079] Data entry module;
[0080] Cloud database;
[0081] Detection result comparison module;
[0082] Data processing module;
[0083] The data entry permission module is used to review the data entry permissions of medical institutions;
[0084] The data entry module is used to input the patient's medical test information, which includes test items and test data.
[0085] The cloud database includes a patient test data storage library, a test item library, and a test item normal value range database;
[0086] The test result comparison module is used to verify the input patient medical test information;
[0087] The data processing module is used to update the test item library and the normal value range database for test items, re-examine the patient's medical test information, and provide feedback on the reviewed tests.
[0088] Specifically, the patient testing database is used to store patient medical testing information that has been correctly detected by the testing result comparison module through comparison with the testing item database and the normal value range database of the testing items;
[0089] The testing item database includes the names of common medical testing items;
[0090] The database of normal value ranges for the test items includes normal value ranges for common medical test items;
[0091] The test item library and the test item normal value range database are updated periodically to include new test items and update the normal value ranges of medical test items derived from experiments.
[0092] A medical data processing method, comprising the aforementioned medical data processing system, wherein the method for obtaining permissions for the data entry permission module includes:
[0093] Medical institutions apply to the cloud database administrator for access rights and provide the necessary institutional qualifications and certification information;
[0094] The cloud database administrator reviews the organization's qualifications to confirm its legality and eligibility;
[0095] After the medical institution is verified by the database administrator, the system administrator creates an institutional account for the medical institution and sets institutional-level access permissions;
[0096] Healthcare institutions obtain a unique authentication key or digital certificate for authentication and data encryption;
[0097] Database administrators grant access to the data input module to verified medical institutions;
[0098] Medical institutions install and configure data input modules on their internal networks;
[0099] Medical institutions enter patients' medical test information into the data input module;
[0100] The entered medical test information is encrypted using an authentication key and uploaded to the cloud database via a secure transmission protocol.
[0101] By requiring healthcare institutions to provide qualification and certification information to apply for access, and through the cloud database administrator's review of this information, the system ensures that only legitimate and qualified healthcare institutions can access and input data. Furthermore, the use of unique authentication keys or digital certificates for authentication and data encryption further enhances data security and privacy protection, preventing unauthorized access and data leaks.
[0102] Ensure data integrity and accuracy:
[0103] The database administrator sets up institution-level access permissions for each verified healthcare institution, granting them access to the data entry module. This arrangement ensures the formalization and standardization of the data entry process, helping to maintain data integrity and accuracy. Healthcare institutions can install and configure the data entry module within their internal network, thus ensuring data quality at the source.
[0104] The use of cloud databases allows different healthcare institutions to securely share and access data, promoting the interconnection of medical information. This data sharing is of great significance for improving the quality of healthcare services, strengthening clinical research, and promoting collaboration among healthcare institutions. Meanwhile, the use of encrypted and secure transmission protocols ensures the security of data during transmission.
[0105] Specifically,
[0106] The method for the test result comparison module to review the patient's test items includes:
[0107] The test result comparison module searches the test item database for medical test items that exactly match the input name;
[0108] When a perfectly matching medical test item is found, the test result comparison module automatically accepts the input and allows the medical test item to be directly entered into the patient test data storage database.
[0109] When no perfectly matching item is found, the detection result comparison module uses the edit distance algorithm to calculate the similarity score between the input medical test item name and each medical test item name in the database;
[0110] Set a similarity threshold;
[0111] When the highest similarity score is found and exceeds the similarity threshold, the detection result comparison module will prompt the input user whether it is the closest match.
[0112] The user can confirm and select the suggested item name after the input is judged;
[0113] When the similarity scores of all items are below the threshold, the detection result comparison module prompts the doctor to check and re-enter the names of the detection items.
[0114] Specifically,
[0115] The distance editing algorithm includes:
[0116] Let L(i, j) be the edit distance between the first i characters of the input string a and the first j characters of the reference string b;
[0117] 0≤i≤m,L(i,0)=i;
[0118] 0≤j≤n, L(j,0)=J;
[0119]
[0120] Where 'a' represents the input string, 'b' represents the reference string, L(i-1, j)+1 represents a deletion operation on the input string 'a', and L(i, j-1)+1 represents an insertion operation on the input string 'a'. (ai≠bj) This indicates that for input string a, in a i Not equal to b i Perform a replacement operation at a time or in a i equals b i No operation is performed at this time, where m and n are the lengths of the input string a and the reference string b, respectively.
[0121] 1 (ai≠bj) This is an indicator function; it takes the value 1 if ai is not equal to bi, and 0 otherwise.
[0122] The formula for the similarity score S is as follows:
[0123]
[0124] Where max(m, n) represents the string with the larger length between the input string a and the reference string b; S is the similarity score.
[0125] When the entered medical test name matches exactly the name in the test database, the test result comparison module automatically accepts the input and allows direct entry, which greatly improves the accuracy of data entry. For perfect matches, automated processing reduces the possibility of human error and ensures data reliability.
[0126] For items that do not find a perfect match, the detection result comparison module uses an edit distance algorithm to calculate a similarity score and suggests the closest match when the score exceeds a preset threshold. This intelligent suggestion not only optimizes the user experience and avoids users manually searching through a large number of items, but also improves the efficiency of data entry. Even if the user's initial input has slight errors, the system can provide the closest correct option, reducing the time spent on error correction.
[0127] If the similarity scores of all items are below the set threshold, the detection result comparison module will prompt the doctor to check and re-enter the names of the tested items. This step is to prevent mis-entry due to spelling or input errors. When the input differences are too large, the system ensures data quality by requiring re-entry and avoids potential problems caused by misunderstandings or incorrect data entry.
[0128] Specifically,
[0129] The method for the test result comparison module to verify the normal range of medical test items includes:
[0130] Search the database of normal value ranges for the test items for the corresponding normal value ranges.
[0131] Set the normal value range as the detection value threshold range;
[0132] Determine whether the input detection data is within the detection value threshold range;
[0133] When the detection data is within the detection value threshold range, the detection result comparison module automatically accepts the input and allows the medical detection data to be directly entered into the patient detection data storage library;
[0134] The test result comparison module alerts the user to abnormal data. After the user confirms the medical test data again, the data is allowed to be entered into the patient test data storage database and marked as abnormal data.
[0135] Specifically, the method by which the data processing module processes the updated database of normal value ranges for detection items includes:
[0136] The data processing module continuously monitors the database of normal value ranges for the detected items for any updates.
[0137] When the data processing module detects an update to the normal value range of a specific item, it re-audits it.
[0138] The data processing module retrieves all historical detection data related to the update range;
[0139] The data processing module re-evaluates each retrieved data item using the new normal value range for the detection items;
[0140] Determine whether each test data point falls within or outside the new normal range;
[0141] For test data that was previously abnormal but is now normal, the data processing module sends a reminder to the relevant medical institutions, suggesting that they reduce their focus on this aspect for patients.
[0142] For data that was previously normal but is now abnormal, the data processing module sends a warning to the relevant medical institution and suggests paying attention to the patient's potential disease in this area; at the same time, it suggests recalling the patient for retesting.
[0143] By searching the database of normal ranges for corresponding test items and setting thresholds, the test result comparison module can accurately determine whether the input medical test data falls within the normal range. This method effectively ensures the accuracy and reliability of medical test data, thus providing solid data support for subsequent clinical decisions.
[0144] When the normal range for a test item is updated, the data processing module not only re-examines the relevant historical test data, but also provides crucial feedback to medical institutions based on the re-examination results. For example, for data previously marked as abnormal but now falling within the new normal range, the system will remind medical institutions that they may not need to overemphasize that aspect of the condition. This helps medical institutions adjust their focus on patients and treatment plans in a timely manner.
[0145] For test data that changes from normal to abnormal due to updates to the normal range, the system not only issues a warning but also suggests that healthcare institutions pay attention to potential underlying conditions in the patient and consider recalling the patient for retesting. This proactive health management strategy is crucial for early identification and intervention of potential health problems, thereby enhancing overall patient care and safety.
Claims
1. A medical data processing system, characterized in that, include: Data entry permission module; Data entry module; Cloud database; Detection result comparison module; Data processing module; The data entry permission module is used to review the data entry permissions of medical institutions; The data entry module is used to input the patient's medical test information, which includes test items and test data. The cloud database includes a patient test data storage library, a test item library, and a test item normal value range database; The test result comparison module is used to verify the input patient medical test information; The data processing module is used to update the test item library and the normal value range database for test items, re-examine the patient's medical test information, and provide feedback on the reviewed tests.
2. The medical data processing system according to claim 1, characterized in that, The patient testing database is used to store patient medical testing information that has been correctly detected by the testing result comparison module through comparison with the testing item library and the normal value range database of the testing items. The testing item database includes the names of common medical testing items; The database of normal value ranges for the test items includes normal value ranges for common medical test items; The test item library and the test item normal value range database are updated periodically to include new test items and update the normal value ranges of medical test items derived from experiments.
3. A medical data processing method, characterized in that, The medical data processing system according to claim 2, wherein the method for obtaining permissions for the data entry permission module includes: Medical institutions apply to the cloud database administrator for access rights and provide the necessary institutional qualifications and certification information; The cloud database administrator reviews the organization's qualifications to confirm its legality and eligibility; After the medical institution is verified by the database administrator, the system administrator creates an institutional account for the medical institution and sets institutional-level access permissions; Healthcare institutions obtain a unique authentication key or digital certificate for authentication and data encryption; Database administrators grant access to the data input module to verified medical institutions; Medical institutions install and configure data input modules on their internal networks; Medical institutions enter patients' medical test information into the data input module; The entered medical test information is encrypted using an authentication key and uploaded to the cloud database via a secure transmission protocol.
4. The medical data processing method according to claim 3, characterized in that, The method for the test result comparison module to review the patient's test items includes: The test result comparison module searches the test item database for medical test items that exactly match the input name; When a perfectly matching medical test item is found, the test result comparison module automatically accepts the input and allows the medical test item to be directly entered into the patient test data storage database. When no perfectly matching item is found, the detection result comparison module uses the edit distance algorithm to calculate the similarity score between the input medical test item name and each medical test item name in the database; Set a similarity threshold; When the highest similarity score is found and exceeds the similarity threshold, the detection result comparison module will prompt the input user whether it is the closest match. The user can confirm and select the suggested item name after the input is judged; When the similarity scores of all items are below the threshold, the detection result comparison module prompts the doctor to check and re-enter the names of the detection items.
5. A medical data processing method according to claim 4, characterized in that, The distance editing algorithm includes: Let L(i, j) be the edit distance between the first i characters of the input string a and the first j characters of the reference string b; 0≤i≤m,L(i,0)=i; 0≤j≤n, L(j,0)=J; Where 'a' represents the input string, 'b' represents the reference string, L(i-1,j)+1 represents a deletion operation on the input string 'a', and L(i,j-1)+1 represents an insertion operation on the input string 'a'. (ai≠bj) This indicates that for input string a, in a i Not equal to b i Perform a replacement operation at a time or in a i equals b i No operation is performed at this time, where m and n are the lengths of the input string a and the reference string b, respectively. 1 (ai≠bj) This is an indicator function; it takes the value 1 if ai is not equal to bi, and 0 otherwise. The formula for the similarity score S is as follows: Where max(m, n) represents the string with the larger length between the input string a and the reference string b; S is the similarity score.
6. A medical data processing method according to claim 4, characterized in that, The method for the test result comparison module to verify the normal range of medical test items includes: Search the database of normal value ranges for the test items for the corresponding normal value ranges. Set the normal value range as the detection value threshold range; Determine whether the input detection data is within the detection value threshold range; When the detection data is within the detection value threshold range, the detection result comparison module automatically accepts the input and allows the medical detection data to be directly entered into the patient detection data storage library; The test result comparison module alerts the user to abnormal data. After the user confirms the medical test data again, the data is allowed to be entered into the patient test data storage database and marked as abnormal data.
7. A medical data processing method according to claim 3, characterized in that, The method by which the data processing module processes the updated database of normal value ranges for detection items includes: The data processing module continuously monitors the database of normal value ranges for the detected items for any updates. When the data processing module detects an update to the normal value range of a specific item, it re-audits it. The data processing module retrieves all historical detection data related to the update range; The data processing module re-evaluates each retrieved data item using the new normal value range for the detection items; Determine whether each test data point falls within or outside the new normal range; For test data that was previously abnormal but is now normal, the data processing module sends a reminder to the relevant medical institutions, suggesting that they reduce their focus on this aspect for patients. For data that was previously normal but is now abnormal, the data processing module sends a warning to the relevant medical institution and suggests paying attention to the patient's potential disease in this area; at the same time, it suggests recalling the patient for retesting.