Acute pancreatitis doctor-patient communication risk management system based on interactive data analysis

Abstract

The invention relates to the technical field of doctor-patient communication, in particular to an acute pancreatitis doctor-patient communication risk management system based on interactive data analysis, which comprises a patient condition dynamic database, a condition deterioration prediction module, a communication closeness evaluation module and a resource dynamic allocation module, the storage module is used for storing patient physiological indexes, behavior data and medical records; the condition deterioration prediction module is used for predicting the condition deterioration probability of a patient, identifying a high-risk time period and guiding resource pre-allocation; the communication closeness evaluation module is used for quantifying the communication demand closeness between the patient and the medical team and generating a patient priority; and the resource dynamic allocation module is used for automatically allocating resources according to the predicted deterioration probability and the patient priority.

Description

Technical Field

[0001] The present invention relates to the technical field of doctor-patient communication, and in particular to an acute pancreatitis doctor-patient communication risk management system based on interactive data analysis. Background Art

[0002] Acute pancreatitis is a serious illness with an acute onset and rapid progression. A patient's condition can deteriorate rapidly, placing extremely high demands on the medical team for real-time monitoring, accurate prediction, and rapid response. Traditional methods of doctor-patient communication and resource allocation often rely on manual experience and lack the dynamic integration and analysis of multi-dimensional data. This can lead to information lags, inaccurate risk assessments, rigid communication priorities, and uneven resource allocation, potentially delaying treatment for high-risk patients.

[0003] Existing systems often fail to integrate patients' physiological indicators (such as blood creatinine and CRP), behavioral data (medication compliance, dietary records) and medical care records (disease progression, recurrence history, and imaging results), resulting in one-sided risk assessment; predictions of disease worsening often rely on static historical data or a single real-time indicator, without combining historical trends (such as time decay factors) and real-time symptom feedback (such as self-assessed symptom intensity), resulting in insufficient timeliness and accuracy of prediction results; patient priority is often based on fixed rules or single assessments, without dynamically combining the abnormal frequency of physiological indicators, urgency weights, and communication intervals, resulting in a disconnect between resource allocation and communication needs; resource allocation relies on the subjective judgment of medical staff and lacks an automated mechanism, making it difficult to match the real-time risk of disease worsening with the patient's urgency, which can easily lead to resource mismatch or waste. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this paper proposes an acute pancreatitis doctor-patient communication risk management system based on interactive data analysis. This system integrates physiological indicators, behavioral data, and medical records through a patient's dynamic condition database, breaking down data silos and providing a comprehensive basis for risk assessment. The dynamic resource allocation module automatically allocates resources based on predicted probabilities and priorities, reducing delays in manual intervention and improving response efficiency and treatment success rates. Each module utilizes quantitative formulas, priorities, and parameter design, optimized for medical scenarios, enhancing system transparency and adjustability, and facilitating clinical validation and iteration.

[0005] To achieve the above object, the technical solution of the present invention is as follows:

[0006] An acute pancreatitis doctor-patient communication risk management system based on interactive data analysis, the system includes a patient condition dynamic database, a condition deterioration prediction module, a communication closeness assessment module and a resource dynamic allocation module. The patient condition dynamic database is used to store patients' physiological indicators, behavioral data and medical care records; the physiological indicators include real-time monitoring data of blood amylase, CRP and bilirubin; the behavioral data include diet records, medication compliance and symptom self-assessment; the medical care records include disease progression, recurrence history, imaging examination results and nursing observation and intervention measures records; the condition deterioration prediction module is used to predict the probability of patient condition deterioration, identify high-risk periods, and guide resource pre-allocation; the communication closeness assessment module is used to quantify the closeness of communication needs between patients and medical teams and generate patient priorities; the resource dynamic allocation module is used to automatically allocate resources according to the predicted deterioration probability and patient priority.

[0007] A further improvement of the present invention is that the condition worsening prediction module includes a data analysis unit and a comprehensive prediction unit; the data analysis unit is used to run the condition worsening calculation strategy to calculate the probability distribution of the patient's condition worsening at different time periods in the past week and the probability distribution of the patient's condition worsening at different time periods on the same day; the comprehensive prediction unit is used to comprehensively calculate the probability of the patient's condition worsening at different time periods.

[0008] A further improvement of the present invention is that the communication closeness evaluation module includes a physiological correlation calculation unit and a priority calculation unit. The physiological correlation calculation unit is used to evaluate the patient's urgency based on the abnormal frequency of physiological indicators; the priority calculation unit is used to run the priority calculation strategy and dynamically adjust the patient's priority according to the urgency and communication interval.

[0009] A further improvement of the present invention is that the data analysis unit runs a disease progression calculation strategy, and the disease progression calculation strategy includes the following specific steps:

[0010] S11. Calculate the probability distribution of the patient's condition worsening at different times in the past week. The calculation formula is:

[0011]

[0012] Among them, P hist (t) represents the probability of worsening of the patient's condition in time period t in the past week, C(t) is the number of worsening events in time period t in the past week, ∑C(t) represents the sum of the number of worsening events in all time periods of the patient in the past week, λ is the time attenuation factor, which is 0.3, t mean The average time it takes for aggravation events to occur in the past week;

[0013] S12. Calculate the probability distribution of the patient's condition worsening at different times of the day using the following formula:

[0014]

[0015] Among them, P real (t) represents the probability of the patient's condition worsening at time t on the day, C day (t) represents the number of exacerbation events of the patient in time period t on the same day, F(t) represents the symptom feedback intensity of the patient in time period t on the same day, and ρ is the real-time symptom weight factor.

[0016] A further improvement of the present invention is that the comprehensive prediction unit is used to comprehensively calculate the probability of worsening of the patient's condition at different time periods, and the calculation formula is:

[0017] P(t)=αP hist (t)+βP real (t);

[0018] Among them, P(t) is the probability of the patient's condition worsening in time period t, α is the weight factor of the patient's condition worsening probability in time period t within the past week, and β is the weight factor of the patient's condition worsening probability in time period t on the same day.

[0019] A further improvement of the present invention is that the physiological correlation calculation unit is used to evaluate the patient's urgency based on the abnormal frequency of physiological indicators, and the calculation formula is:

[0020]

[0021] Among them, R i Indicates the urgency of the i-th patient, R ij represents the correlation between the jth physiological index and the ith patient, V ij represents the number of abnormal physiological indicators of the i-th patient, L ij Indicates the severity of abnormality of the jth physiological indicator of the i-th patient, with a value of 1-5, w i It represents the urgency weight of the patient's condition of the i-th patient, with a value between 0 and 1, i between 1 and n, and j between 1 and m.

[0022] A further improvement of the present invention is that the priority calculation unit is used to run a priority calculation strategy, and the specific formula of the priority calculation strategy is:

[0023]

[0024] Among them, P i represents the priority of the i-th patient, Δt i It represents the time interval between the i-th patient and the last effective communication, and T represents the time sensitivity coefficient.

[0025] The technical effects of the present invention are as follows:

[0026] This invention integrates physiological indicators, behavioral data, and medical records through a patient condition dynamic database, breaking down data silos and providing a comprehensive basis for risk assessment. The dynamic resource allocation module automatically allocates resources based on predicted probabilities and priorities, reducing delays in manual intervention and improving response efficiency and treatment success rates. Each module utilizes quantitative formulas, priorities, and parameter design, optimized for medical scenarios, enhancing system transparency and adjustability, and facilitating clinical validation and iteration. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0028] Figure 1 This is a structural diagram of the acute pancreatitis doctor-patient communication risk management system based on interactive data analysis of the present invention. DETAILED DESCRIPTION

[0029] Example 1

[0030] This example proposes an acute pancreatitis doctor-patient communication risk management system based on interactive data analysis. This system integrates physiological indicators, behavioral data, and medical care records through a patient's dynamic condition database, breaking down data silos and providing a comprehensive basis for risk assessment. The dynamic resource allocation module automatically allocates resources based on predicted probabilities and priorities, reducing delays in manual intervention and improving response efficiency and treatment success rates. Each module utilizes quantitative formulas, priorities, and parameter design, optimized for medical scenarios, enhancing system transparency and adjustability, and facilitating clinical validation and iteration.

[0031] like Figure 1 As shown, an acute pancreatitis doctor-patient communication risk management system based on interactive data analysis, the system includes a patient condition dynamic database, a condition deterioration prediction module, a communication closeness assessment module and a resource dynamic allocation module, the patient condition dynamic database is used to store patients' physiological indicators, behavioral data and medical care records; the physiological indicators include real-time monitoring data of blood amylase, CRP and bilirubin, the behavioral data include diet records, medication compliance and symptom self-assessment, the medical care records include disease progression, recurrence history, imaging examination results and nursing observation and intervention measures records; the condition deterioration prediction module is used to predict the probability of patient condition deterioration, identify high-risk periods, and guide resource pre-allocation; the communication closeness assessment module is used to quantify the closeness of communication needs between patients and medical teams and generate patient priorities; the resource dynamic allocation module is used to automatically allocate resources according to the predicted deterioration probability and patient priority.

[0032] In this embodiment, the condition worsening prediction module includes a data analysis unit and a comprehensive prediction unit; the data analysis unit is used to run the condition worsening calculation strategy to calculate the probability distribution of the patient's condition worsening at different time periods in the past week and the probability distribution of the patient's condition worsening at different time periods on the same day; the comprehensive prediction unit is used to comprehensively calculate the probability of the patient's condition worsening at different time periods.

[0033] In this embodiment, the communication closeness assessment module includes a physiological correlation calculation unit and a priority calculation unit. The physiological correlation calculation unit is used to assess the patient's urgency based on the abnormal frequency of physiological indicators; the priority calculation unit is used to run the priority calculation strategy and dynamically adjust the patient's priority according to the urgency and communication interval.

[0034] Example 2

[0035] In this embodiment, the data analysis unit executes a disease progression calculation strategy, which includes the following specific steps:

[0036] S11. Calculate the probability distribution of the patient's condition worsening at different times in the past week. The calculation formula is:

[0037]

[0038] Among them, P hist (t) represents the probability of worsening of the patient's condition in time period t in the past week, C(t) is the number of worsening events in time period t in the past week, ∑C(t) represents the sum of the number of worsening events in all time periods of the patient in the past week, λ is the time attenuation factor, which is 0.3, t mean The average time it takes for aggravation events to occur in the past week;

[0039] S12. Calculate the probability distribution of the patient's condition worsening at different times of the day using the following formula:

[0040]

[0041] Among them, P real (t) represents the probability of the patient's condition worsening at time t on the day, C day (t) represents the number of exacerbation events of the patient in time period t on the same day, F(t) represents the symptom feedback intensity of the patient in time period t on the same day, and ρ is the real-time symptom weight factor.

[0042] Example 3

[0043] In this embodiment, the comprehensive prediction unit is used to comprehensively calculate the probability of worsening of the patient's condition in different time periods, and the calculation formula is:

[0044] P(t)=αP hist (t)+βPreal (t);

[0045] Among them, P(t) is the probability of the patient's condition worsening in time period t, α is the weight factor of the patient's condition worsening probability in time period t within the past week, and β is the weight factor of the patient's condition worsening probability in time period t on the same day.

[0046] Example 4

[0047] In this embodiment, the physiological correlation calculation unit is used to evaluate the patient's urgency based on the abnormal frequency of physiological indicators. The calculation formula is:

[0048]

[0049] Among them, R i Indicates the urgency of the i-th patient, R ij represents the correlation between the jth physiological index and the ith patient, V ij represents the number of abnormal physiological indicators of the i-th patient, L ij Indicates the severity of abnormality of the jth physiological indicator of the i-th patient, with a value of 1-5, w i It represents the urgency weight of the patient's condition of the i-th patient, with a value between 0 and 1, i between 1 and n, and j between 1 and m.

[0050] In this embodiment, the priority calculation unit is used to execute a priority calculation strategy. The specific formula of the priority calculation strategy is:

[0051]

[0052] Among them, P i represents the priority of the i-th patient, Δt i It represents the time interval between the i-th patient and the last effective communication, and T represents the time sensitivity coefficient.

[0053] It's important to note that the patient's dynamic condition database integrates physiological indicators, behavioral data, and medical records, breaking down data silos and providing a comprehensive basis for risk assessment. The dynamic resource allocation module automatically allocates resources based on predicted probabilities and priorities, reducing delays in manual intervention and improving response efficiency and treatment success rates. Each module utilizes quantitative formulas, priorities, and parameter design, optimized for medical scenarios, enhancing system transparency and adjustability, and facilitating clinical validation and iteration.

[0054] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0055] It should be understood that determining B based on A does not mean determining B based solely on A. B can also be determined based on A and / or other information.

[0056] The above embodiments can be implemented in whole or in part through software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0057] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0058] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0059] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only one type. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not performed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0060] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0061] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0062] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0063] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An acute pancreatitis doctor-patient communication risk management system based on interactive data analysis, characterized by: The system includes a patient condition dynamics database, a condition deterioration prediction module, a communication closeness assessment module, and a resource dynamic allocation module. The patient condition dynamics database is used to store patient physiological indicators, behavioral data, and medical care records; the physiological indicators include real-time monitoring data of blood amylase, CRP, and bilirubin; the behavioral data include dietary records, medication compliance, and symptom self-assessment; the medical care records include disease progression, recurrence history, imaging examination results, and nursing observations and intervention measures; the condition deterioration prediction module is used to predict the probability of patient condition deterioration, identify high-risk periods, and guide resource pre-allocation; the communication closeness assessment module is used to quantify the closeness of communication needs between patients and medical teams and generate patient priorities; The dynamic resource allocation module is used to automatically allocate resources based on the predicted probability of deterioration and patient priority.

2. The acute pancreatitis doctor-patient communication risk management system based on interactive data analysis according to claim 1, characterized in that: The disease deterioration prediction module includes a data analysis unit and a comprehensive prediction unit; the data analysis unit is used to run the disease deterioration calculation strategy to calculate the probability distribution of the patient's disease deterioration at different time periods in the past week and the probability distribution of the patient's disease deterioration at different time periods on the same day; the comprehensive prediction unit is used to comprehensively calculate the probability of the patient's disease deterioration at different time periods.

3. The acute pancreatitis doctor-patient communication risk management system based on interactive data analysis according to claim 2, characterized in that: The communication closeness evaluation module includes a physiological correlation calculation unit and a priority calculation unit. The physiological correlation calculation unit is used to evaluate the patient's urgency based on the abnormal frequency of physiological indicators; The priority calculation unit is used to run the priority calculation strategy and dynamically adjust the patient priority according to the urgency and communication interval.

4. The acute pancreatitis doctor-patient communication risk management system based on interactive data analysis according to claim 3, characterized in that: The data analysis unit executes a disease progression calculation strategy, which includes the following specific steps: S11. Calculate the probability distribution of the patient's condition worsening at different times in the past week. The calculation formula is: Among them, P hist (t) represents the probability of worsening of the patient's condition in time period t in the past week, C(t) is the number of worsening events in time period t in the past week, ∑C(t) represents the sum of the number of worsening events in all time periods of the patient in the past week, λ is the time attenuation factor, which is 0.3, t mean The average time it takes for aggravation events to occur in the past week; S12. Calculate the probability distribution of the patient's condition worsening at different times of the day using the following formula: Among them, P real (t) represents the probability of the patient's condition worsening at time t on the day, C day (t) represents the number of exacerbation events of the patient in time period t on the same day, F(t) represents the symptom feedback intensity of the patient in time period t on the same day, and ρ is the real-time symptom weight factor.

5. The acute pancreatitis doctor-patient communication risk management system based on interactive data analysis according to claim 4 is characterized in that: The comprehensive prediction unit is used to comprehensively calculate the probability of worsening of the patient's condition at different time periods. The calculation formula is: P(t)=αP hist (t)+βP real (t); Among them, P(t) is the probability of the patient's condition worsening in time period t, α is the weight factor of the patient's condition worsening probability in time period t within the past week, and β is the weight factor of the patient's condition worsening probability in time period t on the same day.

6. The acute pancreatitis doctor-patient communication risk management system based on interactive data analysis according to claim 5, characterized in that: The physiological correlation calculation unit is used to evaluate the patient's urgency based on the abnormal frequency of physiological indicators. The calculation formula is: Among them, R i Indicates the urgency of the i-th patient, R ij represents the correlation between the jth physiological index and the ith patient, V ij represents the number of abnormal physiological indicators of the i-th patient, L ij Indicates the severity of abnormality of the jth physiological indicator of the i-th patient, with a value of 1-5, w i It represents the urgency weight of the patient's condition of the i-th patient, with a value between 0 and 1, i between 1 and n, and j between 1 and m.

7. The acute pancreatitis doctor-patient communication risk management system based on interactive data analysis according to claim 6, characterized in that: The priority calculation unit is used to run the priority calculation strategy. The specific formula of the priority calculation strategy is: Among them, P i represents the priority of the i-th patient, Δt i It represents the time interval between the i-th patient and the last effective communication, and T represents the time sensitivity coefficient.

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