Tumor patient follow-up visit data processing service system
By using an automated data processing system, the quality of follow-up data and health status of cancer patients can be assessed and adjusted, solving the problems of low efficiency and difficulty in ensuring data quality in manual registration. This enables personalized follow-up and efficient data processing, supporting personalized care and clinical decision-making.
Patent Information
- Application Number
- CN202410975958.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-07-19
- Publication Date
- 2025-12-30
AI Technical Summary
Current cancer follow-up mainly relies on manual registration, which results in low data collection efficiency, difficulty in ensuring data quality, inability to provide personalized services, and lack of in-depth analysis and mining, thus failing to support scientific research and clinical decision-making.
The system employs modules for data collection and preprocessing, comprehensive computation and processing, follow-up adjustment and optimization, and follow-up data management. Through algorithm units, it evaluates the quality and health status of follow-up data and adjusts follow-up strategies, thereby achieving automated and intelligent data processing.
It improves the efficiency of follow-up data processing, provides personalized follow-up services, ensures data quality and individual differences, supports personalized care plans, and improves care efficiency and decision-making accuracy.
Smart Images
Figure CN121237289A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of medical information processing, in particular to a tumor patient follow-up data processing service system. BACKGROUND
[0002] Tumor registration is a standard method for collecting information about tumor patients, and tumor follow-up registration is an important way to understand the disease of tumor patients, so as to facilitate nursing staff and medical staff to pay attention to the disease of tumor patients in time and take corresponding measures.
[0003] The existing tumor follow-up is mainly registered by artificial registration, which is greatly affected by subjective factors of the registration personnel and is prone to miss registration, re-registration, time-consuming and laborious, and the follow-up registration data collection efficiency of tumor patients is low and the data quality is difficult to guarantee, which is difficult to meet the demand of large-scale data processing, and the traditional follow-up method cannot provide personalized follow-up service according to the individual differences and disease characteristics of patients, and the follow-up data is often simply stored, lacking in-depth and detailed analysis and mining, and cannot provide support for scientific research and clinical decision-making. SUMMARY
[0004] The purpose of the present application is to provide a tumor patient follow-up data processing service system, which solves the problems raised in the background art.
[0005] To achieve the above purpose, the present application provides the following technical scheme: a tumor patient follow-up data processing service system, which adopts:
[0006] Data collection and preprocessing module, comprehensive calculation and processing module, follow-up adjustment and optimization module and follow-up data management module are realized;
[0007] The specific implementation process is as follows:
[0008] Data collection:
[0009] The follow-up data of tumor patients is collected from the hospital case system, medical equipment and patient communication through the data collection and preprocessing module, and the follow-up data includes tumor detection data, basic information of patients, life habits of patients and dietary habits of patients;
[0010] Data processing:
[0011] The collected follow-up data is cleaned and arranged by using the data collection and preprocessing module, and the repeated, incorrect and invalid data is removed;
[0012] Follow-up data management:
[0013] The comprehensive calculation and processing module is realized, which includes:
[0014] a tumor patient follow-up data quality evaluation algorithm unit, a tumor patient health status evaluation algorithm unit, and a follow-up strategy adjustment and patient health status evaluation algorithm unit;
[0015] First, the completeness and accuracy of the collected follow-up data are evaluated, and a follow-up data completeness score index and a follow-up data accuracy score index are obtained based on the number of follow-up items and the follow-up situation;
[0016] Next, the follow-up data completeness score index and the follow-up data accuracy score index are substituted into the tumor patient follow-up data quality evaluation algorithm unit together to calculate a tumor patient follow-up quality evaluation index;
[0017] Then, the tumor patient follow-up quality evaluation index and the follow-up data completeness score index are substituted into the tumor patient health status evaluation algorithm unit together to calculate a patient health status index;
[0018] And the tumor patient follow-up quality evaluation index and the patient health status index are substituted into the follow-up strategy adjustment and patient health status evaluation algorithm unit together to calculate a patient health status change early warning index value;
[0019] After that, the calculated patient health status change early warning index value is further operated to obtain a new patient follow-up plan adherence adjustment factor, which replaces the original patient follow-up plan adherence adjustment factor in the tumor patient follow-up data quality evaluation algorithm unit to affect the calculation values of the tumor patient follow-up quality evaluation index and the patient health status index;
[0020] Follow-up adjustment:
[0021] According to the patient health status change early warning index value, the follow-up strategy is adjusted based on the follow-up data management module, specifically including adjusting the follow-up frequency, follow-up content and follow-up mode;
[0022] Data uploading:
[0023] After the follow-up data is sorted, it is uploaded to the hospital case system.
[0024] Optionally, the tumor patient follow-up data quality evaluation algorithm unit is as follows:
[0025] A1 = LX * a;
[0026] A2 = LC * b;
[0027] A3 = LV * c;
[0028] Wherein:
[0029] LZ is a tumor patient follow-up quality evaluation index;
[0030] LX is a follow-up data integrity score index;
[0031] LC is a follow-up data accuracy score index;
[0032] LV is a follow-up data timeliness score index;
[0033] LB is a patient compliance with follow-up plan adjustment factor;
[0034] a, b and c are weight factors of LX, LC and LV respectively;
[0035] A1 is a data integrity contribution degree value, A1 reflects the comprehensiveness of follow-up data;
[0036] A2 is a data accuracy contribution degree value, A2 reflects the accuracy of follow-up data;
[0037] A3 is a data timeliness contribution degree value, A3 reflects the timeliness of follow-up data. Optionally, the tumor patient health status evaluation algorithm unit is as follows:
[0038]
[0039] B1 = a * KX;
[0040] B2 = β * KC;
[0041] Wherein:
[0042] KZ is a patient health status index;
[0043] KX is a patient age number;
[0044] KC is a tumor staging number, that is, a first to fourth stage of tumor;
[0045] B1 is an age health contribution degree value;
[0046] B2 is a tumor disease severity value;
[0047] a, β and γ are weight degree values of age, tumor staging and follow-up data integrity respectively.
[0048] Optionally, the follow-up strategy adjustment and patient health status evaluation algorithm unit is as follows:
[0049]
[0050] Q1 = KZ - KZ n ;
[0051] Q2 = LZ - LZ n ;
[0052] Wherein:
[0053] HZ is the patient health state change early warning index value;
[0054] KZ n is the patient health state index of the last round of calculation, wherein the initial value is zero;
[0055] LZ n is the tumor patient follow-up quality evaluation index of the last round of calculation, wherein the initial value is zero;
[0056] Q1 is the patient health state change value;
[0057] Q2 is the quality and completeness of follow-up data change value.
[0058] Optionally, the follow-up strategy adjustment and patient health state evaluation algorithm unit is further as follows:
[0059] S1, set the value of HTD to 0.4 and set the value of LTD to -0.25;
[0060] S2, when HZ>HTD, it is considered that the health state of the patient and the follow-up quality have a positive change, and the follow-up strategy needs to be adjusted to actively follow up, thereby outputting LB new =LB*1.1;
[0061] S3, when HZ new =LB*0.9;
[0062] S4, when LTD≤HZ<HTD, the output LB value remains unchanged.
[0063] Optionally, the data collection and preprocessing module comprises:
[0064] a data collection unit, a data cleaning unit and a data arrangement unit, the data collection unit collects follow-up data of tumor patients from hospital case systems and medical equipment to cover basic information of tumor patients, related indexes of tumors and living habits.
[0065] Optionally, the data arrangement unit centrally arranges the data processed by the data cleaning unit and marks and estimates the missing data.
[0066] Optionally, the data collection unit uses devices such as CT scanning devices, blood analysis equipment and hospital electronic medical record systems.
[0067] Compared with the prior art, the present application has the following beneficial effects:
[0068] The tumor patient follow-up quality evaluation index is calculated by the tumor patient follow-up data quality evaluation algorithm unit, and the calculation of the tumor patient follow-up quality evaluation index can comprehensively judge the integrity, accuracy and timeliness of the follow-up data, thereby guaranteeing the follow-up quality of the nursing personnel and improving the follow-up efficiency and effect of the nursing personnel.
[0069] Then, the patient health state index is calculated by the tumor patient health condition evaluation algorithm unit, and the calculation of the patient health state index is based on the age and tumor stage of the patient to comprehensively evaluate the overall health state of the patient, thereby providing a quantitative index of the current health condition of the patient for the nursing personnel, and helping the nursing personnel to formulate a personalized nursing plan.
[0070] Finally, the patient health state change early warning index value is calculated by the follow-up strategy adjustment and patient health state evaluation algorithm unit, the calculation of the patient health state change early warning index value is convenient for the user to understand whether the follow-up strategy needs to be adjusted and the degree of change of the patient's health state during follow-up, and the patient health state change early warning index value can be further calculated and substituted into the tumor patient follow-up data quality evaluation algorithm unit, thereby adjusting the calculation of the tumor patient follow-up quality evaluation index, so that the three algorithm units in the system form a form of cyclic iteration, so as to continuously adjust and adapt to the tumor patient follow-up data processing service in various situations. BRIEF DESCRIPTION OF DRAWINGS
[0071] Figure 1 The method flowchart of the tumor patient follow-up data processing service system is shown in the figure.
[0072] Figure 2 The overall structure diagram of the tumor patient follow-up data processing service system is shown in the figure.
[0073] Figure 3 The structure diagram of the data collection and preprocessing module of the present application is shown in the figure.
[0074] Figure 4 The structure diagram of the comprehensive calculation and processing module of the present application is shown in the figure. DETAILED DESCRIPTION
[0075] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.
[0076] Compared with the existing tumor patient follow-up data processing service system, the existing tumor patient follow-up data processing service system mainly relies on manual operation, which is not only low in efficiency, but also easily affected by human factors, resulting in inaccurate and incomplete data. With the increasing number of tumor patients, the amount of follow-up data is also increasing rapidly, and the traditional processing method is difficult to meet the demand of modern medicine for efficient and accurate follow-up data processing. The algorithm unit can significantly improve the processing efficiency of follow-up data through automatic and intelligent data processing technology to meet the demand of large-scale data processing, and can automatically match the best follow-up time and examination items according to the individual differences and disease characteristics of patients to realize personalized follow-up service.
[0077] Embodiment one, please refer to Figures 1 to 4 The embodiment provides a tumor patient follow-up data processing service system, which realizes by adopting a data collection and preprocessing module, a comprehensive calculation and processing module, a follow-up adjustment and optimization module and a follow-up data management module.
[0078] The specific implementation process is as follows:
[0079] Data collection: the follow-up data of tumor patients are collected from hospital case systems, medical equipment and patient communication through the data collection and preprocessing module, and the follow-up data includes tumor detection data, basic information of patients, living habits of patients and dietary habits of patients.
[0080] Data processing: the collected follow-up data are cleaned and arranged by the data collection and preprocessing module, and the repeated, incorrect and invalid data are removed.
[0081] Follow-up data management: realized by the comprehensive calculation and processing module, the comprehensive calculation and processing module includes:
[0082] A tumor patient follow-up data quality evaluation algorithm unit, a tumor patient health status evaluation algorithm unit and a follow-up strategy adjustment and patient health status evaluation algorithm unit.
[0083] Firstly, the completeness and accuracy of the collected follow-up data are evaluated, and the follow-up data completeness score index and the follow-up data accuracy score index are obtained based on the number of follow-up items and the follow-up situation.
[0084] Then, the follow-up data completeness score index and the follow-up data accuracy score index are substituted into the tumor patient follow-up data quality evaluation algorithm unit together, and the tumor patient follow-up quality evaluation index is calculated.
[0085] Then, the tumor patient follow-up quality evaluation index and the follow-up data completeness score index are substituted into the tumor patient health status evaluation algorithm unit together, and the patient health status index is calculated.
[0086] Furthermore, the follow-up quality assessment index and the patient health status index of cancer patients are substituted into the follow-up strategy adjustment and patient health status assessment algorithm unit to calculate the early warning index value of changes in patient health status.
[0087] Subsequently, the calculated patient health status change warning index value is further calculated to obtain a new patient follow-up plan adjustment factor. This patient follow-up plan adjustment factor replaces the original patient follow-up plan adjustment factor in the tumor patient follow-up data quality assessment algorithm unit to affect the calculated values of the tumor patient follow-up quality assessment index and the patient health status index.
[0088] Follow-up adjustments are made based on the patient's health status change warning index value and the follow-up data management module to adjust the follow-up strategy, specifically including adjusting the follow-up frequency, follow-up content and follow-up method;
[0089] Data upload: The follow-up data is organized and uploaded to the hospital's medical record system.
[0090] In this embodiment, the system, through the cooperation of three algorithm units, enables nursing staff to better understand the patient's health status, follow-up quality, and potential areas for improvement. Nursing staff can also develop more effective care plans, improving treatment outcomes and quality of life. Combining the results of LZ, KZ, and HZ calculations, a comprehensive and effective follow-up data processing service system is formed. This system can assess the quality of follow-up data, the patient's health status, and whether follow-up strategies need adjustment, while improving the accuracy and objectivity of decision-making. This helps improve the efficiency and quality of follow-up management for cancer patients. Compared to traditional follow-up systems, this system has significant advantages in data quality assessment, patient health status assessment, and follow-up strategy adjustment. LZ is the cancer patient follow-up quality assessment index, which ensures the quality of follow-up by nursing staff through a comprehensive judgment of the completeness, accuracy, and timeliness of follow-up data, thereby improving the efficiency of follow-up by nursing staff. The KZ algorithm, representing the patient's health status index, comprehensively assesses the patient's overall health status by combining the patient's age, tumor stage, and LZ. It provides nurses with quantitative indicators of the patient's current health status, helping them develop personalized care plans and employing a systematic assessment and management approach. This allows for a comprehensive and objective evaluation of the quality of follow-up data and the patient's health status, providing valuable decision support for nurses. The HZ algorithm serves as an early warning indicator for changes in the patient's health status, helping to ensure timely adjustments to follow-up strategies when data quality is poor. It allows users to understand whether adjustments to follow-up strategies are necessary, such as increasing follow-up frequency, changing follow-up content, or strengthening patient education. Furthermore, the calculation results of HZ can iteratively influence the calculation of LZ, resulting in a high degree of correlation and entanglement among the three algorithms in this system. This enables the overall algorithm system to automatically iterate and optimize based on actual conditions, making it more realistic.
[0091] Please see Figures 1 to 4 The algorithm unit for assessing the quality of follow-up data of cancer patients is as follows:
[0092]
[0093] A1 = LX * a;
[0094] A2 = LC * b;
[0095] A3 = LV * c;
[0096] Among them, LZ is the follow-up quality assessment index for cancer patients;
[0097] LX is the follow-up data integrity score index;
[0098] LC is a score index for the accuracy of follow-up data;
[0099] LV is the timeliness score index for follow-up data;
[0100] LB adjusts factors for patients following the follow-up plan;
[0101] a, b, and c are the weighting factors for LX, LC, and LV, respectively;
[0102] A1 represents the contribution to data integrity, reflecting the comprehensiveness of the follow-up data.
[0103] A2 represents the contribution of data accuracy to the accuracy of the follow-up data. A2 reflects the accuracy of the follow-up data.
[0104] A3 represents the contribution value to the timeliness of the data, reflecting the timeliness of the follow-up data.
[0105] In this embodiment: First, LX, LC, and LV in this algorithm unit are all corresponding scoring indices. In actual calculation, LX is the follow-up data integrity scoring index. For example, if there are 10 necessary follow-up items in the follow-up system, the integrity score of each item is 1 point. If a patient's follow-up record has 8 complete items, then his integrity score is 8 points. The same applies to LC and LV. Finally, the scores of LX, LC, and LV are quantified and calculated as a whole. A1 is the contribution value of data integrity. Its calculation ensures that doctors have enough information to assess the patient's health status, thereby making more accurate medical decisions. A2 is the contribution value of data accuracy. Its calculation can improve the reliability of medical decisions and reduce the risk of misdiagnosis or mistreatment. A3 is the contribution value of data timeliness. Its calculation is crucial for early detection of changes in the condition and timely adjustment of treatment strategies. Paying attention to the timeliness of follow-up data can ensure that patients receive timely medical attention and treatment.
[0106] This algorithm unit measures the completeness, accuracy, and timeliness of follow-up data, as well as the degree to which patients adhere to the follow-up plan. These values involved in the calculation are key factors in assessing follow-up quality. For nursing staff, understanding follow-up quality helps them determine the reliability of the data and make clinical decisions accordingly. Furthermore, through automated and standardized data processing workflows, this system can quickly and accurately collect and process large amounts of follow-up data, reducing the workload of medical staff. Existing follow-up systems often lack a comprehensive assessment of follow-up data quality. This algorithm unit can comprehensively assess multiple aspects, thereby improving the accuracy and comprehensiveness of the assessment.
[0107] Please see Figures 1 to 4 The algorithm unit for assessing the health status of cancer patients is as follows:
[0108]
[0109] B1 = α * KX;
[0110] B2 = β * KC;
[0111] KZ represents the patient's health status index;
[0112] KX represents the patient's age;
[0113] KC stands for tumor stage number, which refers to stages one through four of the tumor.
[0114] B1 represents the contribution of age to health;
[0115] B2 represents the severity of the tumor condition;
[0116] α, β, and γ are the weight values for age, tumor stage, and follow-up data completeness, respectively.
[0117] In this embodiment, B1 is the contribution value of age to health. Age is an important factor in assessing a patient's health status and can provide a more comprehensive assessment of the patient's health status, as age is closely related to the occurrence, development, and treatment effects of many diseases. B2 is the severity value of the tumor. By taking into account the tumor stage, the patient's health status and treatment effects can be assessed more accurately, thereby providing a basis for developing personalized treatment plans.
[0118] This algorithm unit combines the patient's age, tumor stage, and LZ (lower circumference) to comprehensively assess the patient's overall health status. This data is crucial for healthcare professionals, providing quantitative indicators of the patient's current health condition. This helps caregivers develop personalized care plans. Furthermore, by employing a systematic assessment and computational management approach, it can comprehensively and objectively evaluate the quality of follow-up data and the patient's health status, providing valuable decision support for caregivers. In contrast, existing traditional follow-up systems often focus only on the patient's tumor stage and certain physiological indicators, neglecting factors such as the quality of follow-up data and the age of the cancer patient. This algorithm unit, by integrating multiple dimensions, provides a more comprehensive perspective for assessing the patient's health status. This algorithm unit has a high correlation in effectiveness and computational calculation with the tumor patient follow-up data quality assessment algorithm unit, thus enabling comprehensive and multi-faceted assessment and calculation to reduce errors.
[0119] Please see Figures 1 to 4 The algorithm units for follow-up strategy adjustment and patient health status assessment are as follows:
[0120]
[0121] Q1 = KZ - KZ n ;
[0122] Q2 = LZ - LZ n ;
[0123] Among them, HZ is the early warning index value for changes in the patient's health status;
[0124] KZ n This is the patient health status index calculated in the previous round, with an initial value of zero;
[0125] LZ n This is the cancer patient follow-up quality assessment index calculated in the previous round, with an initial value of zero;
[0126] Q1 represents the change in the patient's health status;
[0127] Q2 represents the change in the quality and completeness of the follow-up data.
[0128] In this embodiment, the calculation of Q1 by this algorithm unit has a substantial effect on the cancer patient follow-up data processing service system. Q1 represents the change in the patient's health status. This change can be used to assess whether the patient's health status is improving or deteriorating. If the cancer patient follow-up quality assessment index increases, it indicates that the patient's health status may have deteriorated; if the cancer patient follow-up quality assessment index decreases, it indicates that the patient's health status may have improved. This change is very important for determining whether the follow-up strategy needs to be adjusted.
[0129] Q2 represents the change in the quality and completeness of the follow-up data. This change is used to assess whether the quality of the follow-up data is improving or decreasing. If the follow-up quality assessment index for cancer patients increases, it indicates that the quality and completeness of the follow-up data may have improved. If the follow-up quality assessment index for cancer patients decreases, it indicates that the quality and completeness of the follow-up data may have decreased. This change is also of great significance in determining whether the follow-up strategy needs to be adjusted, because the quality of the follow-up data can directly affect the accuracy of the assessment of the patient's health status.
[0130] This algorithm unit calculates based on the cancer patient follow-up quality assessment index and the patient health status index. Therefore, this algorithm unit is an integrated operation of the cancer patient follow-up data quality assessment algorithm unit and the cancer patient health status assessment algorithm unit. By comparing the patient health status and the quality and completeness of the follow-up data in the current round and the previous round, it helps users understand whether the follow-up strategy needs to be adjusted, such as increasing the follow-up frequency, changing the follow-up content, and strengthening patient education. This algorithm unit judges whether the follow-up strategy needs to be adjusted based on the cancer patient follow-up quality assessment index, which helps to ensure that the follow-up strategy is adjusted in a timely manner when the data quality is poor. Therefore, it can avoid making wrong medical decisions based on inaccurate data. At the same time, this algorithm unit also judges whether the follow-up strategy needs to be adjusted based on changes in the patient health status index, which helps to detect fluctuations in the patient's health status in a timely manner and adjust the follow-up strategy accordingly to ensure that the patient receives timely and effective treatment.
[0131] This algorithm unit can be regarded as a warning indicator for changes in the health status. Through the evaluation of HZ, follow-up resources can be effectively allocated, and patients can be divided into different risk levels or management categories. Therefore, it helps nurses and doctors to more accurately identify high-risk patients, adopt targeted follow-up strategies and management measures, and can also be used as an evaluation tool for treatment effects. By comparing the HZ values at different treatment stages and under different treatment regimens, the advantages and disadvantages of treatment effects can be comprehensively evaluated, providing more personalized and effective treatment regimens for cancer patients. Finally, the application of HZ helps medical institutions continuously improve the quality of medical care. Through the change trend and influencing factors of the HZ value, problems and deficiencies in the follow-up process can be discovered, and corresponding measures can be quickly taken for improvement and optimization.
[0132] Please refer to Figures 1 to 4 , and the follow-up strategy adjustment and patient health status evaluation algorithm unit are further as follows:
[0133] S1. Set the value of HTD to 0.4 and the value of LTD to -0.25;
[0134] S2. When HZ > HTD, it is considered that the patient's health status and follow-up quality have changed positively, and thus the follow-up strategy needs to be adjusted to actively follow up, and then output LB new = LB * 1.1;
[0135] S3. When HZ < LTD, it is considered that the patient's health status and follow-up quality have changed negatively, and thus the follow-up strategy needs to be adjusted to cautiously follow up, and then output LB new = LB * 0.9;
[0136] S4. When LTD ≤ HZ < HTD, the output LB value remains unchanged.
[0137] In this embodiment, this algorithm unit can adjust the follow-up strategy in real time based on the changes in the follow-up quality evaluation index of cancer patients and the patient health status index, ensuring that patients receive timely and effective attention from medical resources. Therefore, it helps to improve the treatment effect and quality of life of patients and reduce the waste of medical resources. In the cancer patient follow-up data processing service system, through the follow-up quality evaluation index of cancer patients and the warning indicator value for evaluating changes in the patient health status, a closed-loop feedback iteration mechanism is formed as a whole. This mechanism helps nurses and doctors to more accurately evaluate the health status of patients based on high-quality follow-up data, and adjust the follow-up strategy accordingly, so as to optimize the treatment and management of patients. Through this iterative form, the integrity, accuracy, and timeliness of follow-up data can be quickly evaluated, thereby ensuring the quality of follow-up data collected by nurses. And high-quality follow-up data is the basis for subsequent analysis and decision-making;
[0138] Iteratively adjusting the values of early warning indicators for changes in patient health status can help optimize follow-up strategies, ensuring patients follow up according to the most suitable plan. This helps reduce unnecessary examinations or interventions. Furthermore, personalized follow-up plans enhance patients' trust and satisfaction with healthcare services when they receive necessary medical attention and support. When patients feel their needs are fully considered and met, they are more likely to follow the follow-up plan and actively participate in treatment, thus improving patient satisfaction and adherence. Iteratively adjusting the values of early warning indicators for changes in patient health status allows for the collection of more data on follow-up effectiveness. This data provides strong support for subsequent decision-making, enabling the medical team to better understand patients' needs and preferences, as well as the effectiveness of different follow-up strategies. Finally, iterative adjustment is a continuous process that can be continuously optimized and improved based on patient feedback and actual results, thereby continuously improving the quality and efficiency of medical services and providing better medical experiences and treatment outcomes for cancer patients.
[0139] HZ is an integrated calculation based on the changes in LZ and KZ. It quantitatively assesses whether adjustments to the follow-up strategy are needed, providing a scientific basis for such adjustments and avoiding the limitations of subjective judgment. As the quality of follow-up data improves, medical decisions based on high-quality data become more accurate. Furthermore, iterative adjustments to the follow-up strategy ensure patients receive timely and effective medical attention. This iterative and optimized follow-up strategy reduces unnecessary follow-up visits and examinations, thereby lowering medical costs. Medical decisions based on high-quality follow-up data also improve treatment efficiency, reducing patient suffering and inconvenience. In summary, this iterative approach of the algorithm unit plays a crucial role in the cancer patient follow-up data processing service system. By quantifying the quality of follow-up data and assessing the need for follow-up strategy adjustments, it provides nurses and treating physicians with a scientific and objective basis for optimizing patient treatment and management, improving medical service efficiency and promoting effective management of patient health.
[0140] In the specific implementation process, the three algorithm units in this system constitute the cancer patient follow-up data processing service system architecture. The cancer patient follow-up data quality assessment algorithm unit calculates the LZ value. LZ measures the completeness, accuracy, and timeliness of the follow-up data, as well as the patient's adherence to the follow-up plan. This helps nursing staff understand the follow-up quality and determine the reliability of the data. Then, LZ, along with the values used in the LZ calculation, are substituted into the cancer patient health status assessment algorithm unit to calculate KZ. KZ combines the patient's age, cancer stage, and LZ to comprehensively assess the patient's overall health status. KZ helps nursing staff develop personalized care plans. Furthermore, the use of a systematic assessment and calculation management method enables a comprehensive evaluation of the quality of follow-up data and the patient's health status. The values of LZ and KZ are then substituted into the follow-up strategy adjustment and patient health status assessment algorithm units to calculate the value of HZ. The calculation of the HZ value makes it easier for nursing staff to know in a timely manner whether the follow-up strategy needs to be adjusted, and the follow-up strategy can be adjusted in time when the data quality is poor. Therefore, it can avoid making wrong medical decisions based on inaccurate data. Furthermore, the further calculation of HZ can also iterate the value of LB in the tumor patient follow-up data quality assessment algorithm unit. LB is the patient adherence to the follow-up plan adjustment factor, which can improve the personalization of the follow-up plan, optimize the follow-up effect, and improve patient satisfaction and compliance. In this way, the three algorithm units can cooperate with each other in pairs and can also perform overall looping and iteration, so that the overall system has the effect of automatic optimization and updating, and thus has better adaptability.
[0141] Example 2, please refer to Figures 1 to 4 The data collection and preprocessing module includes a data collection unit, a data cleaning unit, and a data organization unit. The data collection unit collects follow-up data of cancer patients from the hospital's medical records system and medical equipment to cover the basic information of cancer patients, cancer-related indicators, and lifestyle habits.
[0142] The data preparation unit is responsible for centrally organizing the data cleaned and processed by the data cleaning unit, and marking and estimating any missing data.
[0143] The data collection unit uses equipment including CT scanners, blood analysis equipment, and hospital electronic medical record systems.
[0144] In this embodiment, CT scanning devices, blood analysis equipment, and MRI are used to collect patients' physiological indicators and medical imaging data in a comprehensive and detailed manner, thereby providing direct data support for the patient's disease and ensuring the accuracy and objectivity of the data. The hospital's electronic medical record system is used to retrieve and view the patient's basic information and historical medical records, thereby providing the patient's basic information and past medical history, which helps to comprehensively assess the patient's condition. Data processing software is used to collect and process statistical data, clean, organize, and analyze the data to improve the efficiency and accuracy of data processing.
[0145] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A tumor patient follow-up data processing service system, characterized in that: It is realized by using a data collection and preprocessing module, a comprehensive calculation and processing module, a follow-up adjustment and optimization module, and a follow-up data management module; The specific steps are as follows: Data collection: Collect the follow-up data of tumor patients from the hospital case system, medical equipment and patient communication through the data collection and preprocessing module; The follow-up data includes tumor detection data, patient basic information, patient lifestyle and patient diet habits; Data processing: Use the data collection and preprocessing module to clean and arrange the collected follow-up data, and remove duplicate, incorrect and invalid data; Follow-up data management: Through the comprehensive calculation and processing module; Follow-up adjustment: Adjust the follow-up strategy based on the follow-up data management module according to the patient health state change warning index value, including adjusting the follow-up frequency, follow-up content and follow-up method; Data upload: Upload the follow-up data after arrangement to the hospital case system.
2. The tumor patient follow-up data processing service system according to claim 1, characterized in that: The comprehensive calculation and processing module includes: A tumor patient follow-up data quality evaluation algorithm unit, a tumor patient health status evaluation algorithm unit, and a follow-up strategy adjustment and patient health status evaluation algorithm unit; Firstly, the completeness and accuracy of the collected follow-up data are evaluated, and the follow-up data completeness score index and the follow-up data accuracy score index are obtained based on the number of follow-up items and the follow-up situation; Next, the follow-up data completeness score index and the follow-up data accuracy score index are substituted into the tumor patient follow-up data quality evaluation algorithm unit together to calculate the tumor patient follow-up quality evaluation index; Then, the tumor patient follow-up quality evaluation index and the follow-up data completeness score index are substituted into the tumor patient health status evaluation algorithm unit together to calculate the patient health status index; And the tumor patient follow-up quality evaluation index and the patient health status index are substituted into the follow-up strategy adjustment and patient health status evaluation algorithm unit together to calculate the patient health state change warning index value; After that, the calculated patient health state change warning index value is further calculated to obtain a new patient follow-up plan adjustment factor, which replaces the original patient follow-up plan adjustment factor in the tumor patient follow-up data quality evaluation algorithm unit to affect the calculation values of the tumor patient follow-up quality evaluation index and the patient health status index.
3. The tumor patient follow-up data processing service system according to claim 2, characterized by: The tumor patient follow-up data quality evaluation algorithm unit is as follows: A1=LX*a; A2=LC*b; A3=LV*c; Where: LZ is the tumor patient follow-up quality evaluation index; LX is the follow-up data completeness score index; LC is the follow-up data accuracy score index; LV is the follow-up data timeliness score index; LB is the patient follow-up plan adjustment factor; a, b and c are weight factors of LX, LC and LV respectively; A1 is the data completeness contribution degree value, which reflects the comprehensiveness of the follow-up data; A2 is a data accuracy contribution degree value, A2 reflects the accuracy of follow-up data; A3 is a data timeliness contribution degree value, A3 reflects the timeliness of follow-up data.
4. The tumor patient follow-up data processing service system according to claim 3, characterized by: The tumor patient health status evaluation algorithm unit is as follows: B1=α*KX; B2=β*KC; Wherein: KZ is the patient health state index; KX is the patient age number; KC is the tumor staging number, that is, the first to fourth stages of tumor; B1 is the age health contribution degree value; B2 is the tumor disease severity value; α, β and γ are the weight degree values of age, tumor staging and follow-up data integrity respectively.
5. The system for processing follow-up data of tumor patients according to claim 4, characterized in that: The follow-up strategy adjustment and patient health status evaluation algorithm unit is as follows: Q1 = KZ - KZ n ; Q2 = LZ - LZ n ; Wherein: HZ is the patient health state change early warning index value; KZ n PHSI = PHSI for previous round of calculations, with initial value of zero; LZ n Quality of follow-up assessment index for the tumor patient of the previous round of calculation, with an initial value of zero; Q1 is the patient health state change value; Q2 is the quality and integrity change value of follow-up data.
6. The tumor patient follow-up data processing service system according to claim 5, characterized by: The follow-up strategy adjustment and patient health status evaluation algorithm unit is further as follows: S1, set the value of HTD to 0.4 and set the value of LTD to-0.25; S2, when HZ > HTD, it is considered that the health status of the patient and the follow-up quality have positive changes, and then the follow-up strategy needs to be adjusted to actively follow up, so as to output LB new = LB * 1.1; S3, when HZ < LTD, it is considered that the health status of the patient and the quality of follow-up have a negative change, and then the follow-up strategy needs to be adjusted to follow up carefully, so as to output LB new = LB * 0.9; S4, when LTD≤HZ<HTD, the output LB value remains unchanged.
7. The tumor patient follow-up data processing service system according to claim 2, characterized by: The data collection and preprocessing module comprises: The data collection unit, the data cleaning unit and the data arrangement unit, the data collection unit collects the follow-up data of tumor patients from the hospital case system and medical equipment to cover the basic information of tumor patients, tumor related indicators and living habits.
8. The tumor patient follow-up data processing service system according to claim 7, characterized in that: The data arrangement unit is to centrally arrange the data processed by the data cleaning unit and mark and estimate the missing data.
9. The tumor patient follow-up data processing service system according to claim 7, characterized in that: The equipment used by the data collection unit includes CT scanning device, blood analysis equipment and hospital electronic medical record system.