Personalized nursing information management method and system for elderly people with multiple diseases

Abstract

The present invention provides a personalized nursing information management method and system for elderly people with multiple coexisting diseases, which relates to the field of nursing information management technology, wherein the method includes: obtaining the patient's medical information, behavioral information and initial nursing plan information; performing pharmacokinetic conflict, nursing timing conflict detection and biorhythm matching detection respectively, and selecting a pharmacokinetic conflict resolution strategy, a nursing timing optimization strategy and a biorhythm adaptation strategy according to the test results; generating a personalized nursing plan based on the pharmacokinetic conflict resolution strategy, the nursing timing optimization strategy and the nursing timing optimization strategy. Through the method of the present invention, not only can the three major problems of pharmacokinetic conflict, nursing timing conflict and biorhythm interference be effectively solved, the nursing effect can be improved, and it can be adapted to the personalized needs of different patients, but it can also have good versatility on the basis of ensuring medical safety.

Description

Technical Field

[0001] The present invention relates to the technical field of nursing information management, and in particular to a personalized nursing information management method and system for elderly people with multiple co-existing diseases. Background Art

[0002] With the accelerating aging of the population, multimorbidity has become a major health issue for the elderly. Multimorbidity refers to the simultaneous presence of multiple medical conditions, such as cardiovascular disease, diabetes, and chronic respiratory illnesses. According to incomplete statistics, the majority of elderly people experience multimorbidity, leading to a growing demand for elderly care and related chronic disease care.

[0003] For elderly patients with multiple diseases, nursing care is almost as important as direct treatment. However, in current nursing management, nursing plans are generally integrated in a linear superposition manner. For example, all nursing plans corresponding to different diseases of the same patient are directly superimposed to form an overall nursing plan. However, this linear superposition nursing plan will have the following problems:

[0004] First, pharmacokinetic conflicts. That is, differences in parameters such as half-life and plasma protein binding rate between drugs used for different diseases will cause metabolic interference, which is not conducive to the patient's recovery effect.

[0005] Second, there are conflicts in nursing timing. The time windows for specialized nursing operations corresponding to different diseases may overlap, which will lead to conflicts in the execution of nursing operations in some specialties and is also detrimental to the patient's recovery effect.

[0006] Third, biological rhythm interference. The overall nursing plan formed by superposition may not be synchronized with the patient's life rhythm, which may cause the patient to resist nursing and affect the patient's recovery effect. Summary of the Invention

[0007] In order to solve the problems in the related art, the present invention provides a personalized nursing information management method and system for elderly people with multiple diseases.

[0008] In order to achieve the above object, the technical solution adopted by the present invention includes:

[0009] According to a first aspect of the present invention, a method for managing personalized nursing information for elderly people with multiple diseases is provided, comprising the following steps:

[0010] Step S1: Obtain the patient's medical information, behavioral information, and initial nursing plan information, wherein the medical information includes the patient's diagnosis data and medication data, the behavioral information includes the activity type and its corresponding activity time, and the initial nursing plan information includes the nursing operation type data and its corresponding execution time data;

[0011] Step S2: performing pharmacokinetic conflict detection based on the pharmacokinetic parameters, the drug interaction database, and the biopharmacokinetic model; and selecting a pharmacokinetic conflict resolution strategy based on the results of the pharmacokinetic conflict detection;

[0012] Step S3: Nursing operations are divided into emergency nursing operations, routine nursing operations, and preventive nursing operations in descending order of priority according to urgency and importance, and nursing sequence conflict detection is performed based on the time window overlap and nursing operation complexity. A nursing sequence optimization strategy is selected based on the results of the nursing sequence conflict detection;

[0013] Step S4: Analyze the patient's behavioral information, extract the patient's activity time and activity intensity, and perform a biorhythm matching test between the nursing plan and the patient based on the time matching and intensity matching; and select a biorhythm adaptation strategy based on the results of the biorhythm matching test;

[0014] Step S5: Generate a personalized nursing plan based on the pharmacokinetic conflict resolution strategy obtained in step S2, the nursing timing optimization strategy obtained in step S3, and the nursing timing optimization strategy obtained in step S4.

[0015] Optionally, in step S2, performing pharmacokinetic conflict detection based on pharmacokinetic parameters specifically includes:

[0016] Calculate the sum of the half-life difference and plasma protein binding rate between each two drugs one by one. When the following conditions are met, the two drugs are considered to have a metabolic interference risk:

[0017]

[0018] Where, and are the half-lives of drug A and drug B, respectively. is the half-life difference threshold, and are the plasma protein binding rates of drug A and drug B, respectively. is the threshold value of the total plasma protein binding rate.

[0019] Optionally, in step S2, performing pharmacokinetic conflict detection based on a drug interaction database specifically includes: using the drug interaction database to query whether there are known interactions between drugs.

[0020] Optionally, in step S2, performing pharmacokinetic conflict detection based on a biopharmacokinetic model specifically includes:

[0021] Develop a PBPK model;

[0022] Simulate the distribution and metabolism of drugs in patients;

[0023] Predict potential drug interactions.

[0024] Optionally, in step S2, the pharmacokinetic conflict resolution strategy includes a dosing time optimization strategy, a dose adjustment strategy, a drug substitution strategy, and a special population medication recommendation strategy;

[0025] Among them, the specific strategy for optimizing the administration time is: according to the half-life of the drug and plasma protein binding , calculate the optimal dosing time interval according to the following formula :

[0026]

[0027] Where, is the adjustment factor, and ;

[0028] To stagger the administration of different drugs and reduce metabolic interference;

[0029] The specific dosage adjustment strategy is: Based on the drug interaction, adjust the dosage according to the following formula :

[0030]

[0031] Where, For the initial dose, is the dose adjustment factor, and Determined by the severity of the drug interaction;

[0032] The specific drug substitution strategy is as follows: based on the drug database, automatically recommending alternative drugs to the initial drug, wherein the interaction between the alternative drug and other currently used drugs is smaller than the interaction between the initial drug and other currently used drugs;

[0033] The medication recommendation strategy for special populations specifically includes: providing medication dosage and dose adjustment recommendations based on the patient's age and diagnostic data.

[0034] Optionally, in step S3, performing nursing sequence conflict detection based on time window overlap and nursing operation complexity specifically includes:

[0035] Step S3-1: For all nursing operations Set the corresponding execution time window respectively and complexity parameters ;

[0036] Step S3-2: According to the execution time window of the nursing operation Calculate the overlap of time windows: If the execution time window of the nursing operation If there is a partial overlap between , then it is considered that there is an unadjustable timing conflict, where is the preset time threshold, , For all nursing operations The overlapping time, For the duration of each nursing procedure;

[0037] Step S3-3: Based on the complexity parameter of the nursing operation , evaluate the compatibility of each nursing operation: if the complexity parameters of two nursing operations adjacent in time are If the values ​​are both greater than the preset complexity threshold, the two adjacent nursing operations are considered incompatible.

[0038] Optionally, in step S3, selecting a nursing sequence optimization strategy according to the result of the nursing sequence conflict detection specifically includes:

[0039] In step S3-2,

[0040] When there is only an adjustable timing conflict, the corresponding nursing operations are executed one by one according to the priority, and the partially overlapping time in the nursing operations is divided according to the preset ratio;

[0041] When there is an irreconcilable timing conflict, high-priority nursing operations are given priority and fully executed;

[0042] In step S3-3, if two adjacent nursing operations are not compatible, the two adjacent nursing operations are arranged in descending order of priority, and another complexity parameter is set to A nursing operation with a complexity less than or equal to a preset complexity threshold is performed between the two nursing operations.

[0043] Optionally, in step S4, performing the matching degree detection between the nursing plan and the patient's biorhythm based on the time matching degree and the intensity matching degree specifically includes:

[0044] Step S4-1: Calculate the matching degree between nursing operation time and patient activity time according to the following formula :

[0045]

[0046] Where, is the total number of nursing operations, is the activity weight, For the The time of nursing operation, The patient's activity time, Match tolerance to time;

[0047] Step S4-2: Calculate the matching degree between nursing operation intensity and patient activity intensity according to the following formula: :

[0048]

[0049] Where, For the The operational intensity of each nursing operation, is the patient's activity intensity during the corresponding period;

[0050] Step S4-3: Calculate the matching degree between the nursing plan and the patient's biorhythm according to the following formula :

[0051]

[0052] Where, and is the weight coefficient, and .

[0053] Optionally, in step S4, the biorhythm adaptation strategy is selected according to the result of the biorhythm matching test, specifically including: adjusting the time and arrangement sequence of the nursing operation so that the nursing plan matches the patient's biorhythm. Less than the preset matching threshold.

[0054] According to a second aspect of the present invention, there is further provided a personalized nursing information management system for elderly people with multiple co-morbidities, which is applied to the personalized nursing information management method for elderly people with multiple co-morbidities described in any technical solution of the first aspect of the present invention, and the personalized nursing information management system for elderly people with multiple co-morbidities comprises:

[0055] Data collection module, used to obtain patients' medical information, behavioral information and initial care plan information;

[0056] The pharmacokinetic conflict identification module is used to perform pharmacokinetic conflict detection based on pharmacokinetic parameters, drug interaction database and biopharmacokinetic model, and select pharmacokinetic conflict resolution strategies based on the results of pharmacokinetic conflict detection;

[0057] The nursing sequence conflict identification module is used to classify nursing operations into emergency nursing operations, routine nursing operations, and preventive nursing operations in descending order of priority according to urgency and importance, and to perform nursing sequence conflict detection based on the overlap of time windows and the complexity of nursing operations. The nursing sequence optimization strategy is selected based on the results of the nursing sequence conflict detection;

[0058] The biorhythm matching recognition module is used to extract the patient's activity time and activity intensity, and detect the matching between the nursing plan and the patient's biorhythm based on the time matching and intensity matching; and select the biorhythm adaptation strategy based on the results of the biorhythm matching test;

[0059] The personalized nursing plan generation module is used to generate personalized nursing plans based on the pharmacokinetic conflict resolution strategy, nursing timing optimization strategy and nursing timing optimization strategy.

[0060] Beneficial effects:

[0061] 1. Through the above technical solution, first, the present invention can systematically solve the three-dimensional collaborative mechanism of nursing conflicts for coexisting multiple diseases. Specifically, the present invention detects and optimizes pharmacokinetic conflicts, nursing timing conflicts, and biorhythm interference respectively to generate personalized nursing plans, which can not only meet the nursing needs of patients with multiple diseases, but also effectively fit the patients' work and rest patterns. Compared with the direct superposition nursing plan generation method in the existing related technology, it can effectively break through the traditional plan's mechanical arrangement of simply superimposing nursing operations, which can not only effectively improve the nursing effect, but also effectively meet the personalized needs of different patients.

[0062] Second, the present invention integrates medical information, behavioral information and nursing information (initial nursing plan information), which can more effectively meet the patient's personalized needs compared to the traditional plan that simply superimposes nursing operations.

[0063] Third, the present invention adopts progressive detection optimization from step S2 to step S4, first resolving drug conflicts (life safety priority), then dealing with nursing sequence conflicts (optimizing nursing efficiency), and finally adapting to the patient's biological rhythm (ensuring adaptive improvement of patient nursing compliance), thereby effectively achieving the gradual optimization of nursing quality and improving nursing effects while ensuring medical safety.

[0064] Fourth, the present invention can achieve a smooth transition from group standards to individualized solutions based on the step-by-step detection and strategy selection from step S2 to step S4, and can have good versatility while ensuring medical safety.

[0065] Fifth, the present invention establishes a systematic solution of multi-dimensional data collection, multi-dimensional standard detection, and multi-dimensional solution optimization. It can not only effectively solve the three major conflict problems of pharmacokinetic conflict, nursing timing conflict, and biological rhythm interference, but also creates a new intelligent decision-making solution based on quantitative models and dynamic optimization in the field of elderly care, which has good clinical application value.

[0066] 2. Other beneficial effects or advantages of the present invention will be described in detail in the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative labor.

[0068] in:

[0069] Figure 1 It is a flowchart of the steps of a personalized nursing information management method for elderly people with multiple diseases provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0071] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are intended to fall within the scope of protection of the present invention.

[0072] In addition, the terms "including" and "having" and any variations thereof mentioned in the description of the present invention are intended to cover non-exclusive inclusions. At the same time, in the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete way.

[0073] In order to facilitate relevant technical personnel to have a clearer and more accurate understanding of the technical solution of the present invention, the following first provides a more detailed explanation of the technical problems existing in the existing related technologies with reference to some specific examples.

[0074] In the current nursing management of elderly patients with multiple diseases, care plans are generally integrated in a linear superposition manner, that is, all care plans corresponding to different conditions of the same patient are directly superimposed to form an overall care plan. However, this simple superposition method has the following three major problems:

[0075] First, pharmacokinetic conflicts. Differences in parameters such as half-life and plasma protein binding between medications used for different diseases can lead to metabolic interference. For example, many drugs compete for plasma protein binding sites, leading to increased free drug concentrations and enhanced toxicity risks. Furthermore, pharmacokinetics in the elderly, particularly in drug metabolism and excretion, vary significantly. These factors combine to complicate and increase the risk of drug therapy in elderly patients with multiple coexisting conditions.

[0076] Second, there are conflicts in the timing of nursing care. The time windows for specialized nursing procedures corresponding to different diseases may partially overlap, leading to conflicts in the execution of nursing procedures in some specialties. For example, an elderly person with both cardiovascular disease and diabetes may need to undergo nursing activities such as blood pressure monitoring, blood sugar monitoring, and diet control at different times. If these activities are not arranged properly, it may make nursing procedures difficult to execute (for example, the patient may not understand or the patient may need to perform repeated invasive blood sugar monitoring), affecting the effectiveness of nursing care.

[0077] Third, there's the issue of biorhythm interference. The resulting overall care plan may be out of sync with the patient's biorhythm, potentially leading to resistance and compromised recovery. Elderly individuals often have fixed routines and life rhythms. If a care plan doesn't align with their daily routines, compliance can decline, significantly compromising care effectiveness.

[0078] However, my country's current medical system is still dominated by specialized diagnosis and treatment, which does not match the characteristics of elderly patients with multiple co-existing diseases. Although some medical institutions have begun to explore multidisciplinary collaboration models, for example, the nursing MDT (multidisciplinary collaborative diagnosis and treatment) established by Peking Union Medical College Hospital is to meet the nursing needs of critically ill patients with "one patient with multiple diseases" and "multiple diseases coexisting" involving multiple disciplines, multiple systems, and multiple organs, these explorations mostly focus on the macro level of drug management and lack the support of intelligent and personalized information management systems.

[0079] The technical solution of the present invention is described in detail below with reference to the accompanying drawings.

[0080] like Figure 1 As shown, this embodiment provides a personalized nursing information management method for elderly people with multiple diseases according to the first aspect of the present invention, including the following steps:

[0081] Step S1: Obtain the patient's medical information, behavioral information, and initial nursing plan information, wherein the medical information includes the patient's diagnosis data and medication data, the behavioral information includes the activity type and its corresponding activity time, and the initial nursing plan information includes the nursing operation type data and its corresponding execution time data;

[0082] Step S2: performing pharmacokinetic conflict detection based on the pharmacokinetic parameters, the drug interaction database, and the biopharmacokinetic model; and selecting a pharmacokinetic conflict resolution strategy based on the results of the pharmacokinetic conflict detection;

[0083] Step S3: Nursing operations are divided into emergency nursing operations (e.g., acute symptom management, vital sign monitoring, etc.), routine nursing operations (daily medication, dietary guidance, etc.), and preventive nursing operations (e.g., vaccination, health education, etc.) in descending order of priority according to urgency and importance. Nursing sequence conflicts are detected based on the time window overlap and nursing operation complexity, and a nursing sequence optimization strategy is selected based on the results of the nursing sequence conflict detection.

[0084] Step S4: Analyze the patient's behavioral information, extract the patient's activity time and activity intensity, and perform a biorhythm matching test between the nursing plan and the patient based on the time matching and intensity matching; and select a biorhythm adaptation strategy based on the results of the biorhythm matching test;

[0085] Step S5: Generate a personalized nursing plan based on the pharmacokinetic conflict resolution strategy obtained in step S2, the nursing timing optimization strategy obtained in step S3, and the nursing timing optimization strategy obtained in step S4.

[0086] Through the above technical solution, first, the present invention can systematically solve the three-dimensional collaborative mechanism of nursing conflicts in patients with coexisting multiple diseases. Specifically, the present invention detects and optimizes pharmacokinetic conflicts, nursing timing conflicts, and biorhythm interference respectively to generate personalized nursing plans, which can not only meet the nursing needs of patients with multiple diseases, but also effectively conform to the patients' daily routines. Compared with the direct superposition nursing plan generation method in the existing related technology, it can effectively break through the traditional mechanical arrangement of simply superimposing nursing operations, which can not only effectively improve the nursing effect, but also effectively meet the personalized needs of different patients.

[0087] Second, the present invention integrates medical information, behavioral information and nursing information (initial nursing plan information), which can more effectively meet the patient's personalized needs compared to the traditional plan that simply superimposes nursing operations.

[0088] Third, the present invention adopts progressive detection optimization from step S2 to step S4, first resolving drug conflicts (life safety priority), then dealing with nursing sequence conflicts (optimizing nursing efficiency), and finally adapting to the patient's biological rhythm (ensuring adaptive improvement of patient nursing compliance), thereby effectively achieving the gradual optimization of nursing quality and improving nursing effects while ensuring medical safety.

[0089] Fourth, the present invention can achieve a smooth transition from group standards to individualized solutions based on the step-by-step detection and strategy selection from step S2 to step S4, and can have good versatility while ensuring medical safety.

[0090] Fifth, the present invention establishes a systematic solution of multi-dimensional data collection, multi-dimensional standard detection, and multi-dimensional solution optimization. It can not only effectively solve the three major conflict problems of pharmacokinetic conflict, nursing timing conflict, and biological rhythm interference, but also creates a new intelligent decision-making solution based on quantitative models and dynamic optimization in the field of elderly care, which has good clinical application value.

[0091] It can be understood that, first, in the present invention, the initial nursing plan information is a nursing plan that directly integrates all nursing operations in the existing related technology, and the present invention is improved based on this.

[0092] Second, the drug interaction database in the present invention is relatively mature in the existing related technologies, for example, DrugBank, Micromedex, etc., and the present invention will not elaborate on this.

[0093] In one embodiment of the present invention, in step S2 of the present invention, performing pharmacokinetic conflict detection based on pharmacokinetic parameters may specifically include:

[0094] Calculate the sum of the half-life difference and plasma protein binding rate between each two drugs one by one. When the following conditions are met, the two drugs are considered to have a metabolic interference risk:

[0095]

[0096] Where, and are the half-lives of drug A and drug B, respectively. is the half-life difference threshold, and are the plasma protein binding rates of drug A and drug B, respectively. is the threshold value of the total plasma protein binding rate.

[0097] In this embodiment, first, the present invention defines the half-life difference ( ) and the sum of plasma protein binding rate ( ) can make the risk of drug metabolism interference have a quantifiable standard, which can effectively improve the accuracy of judgment compared with the traditional empirical judgment method.

[0098] Second, this implementation can also be effectively adapted to elderly patients. Specifically, the half-life difference threshold can be dynamically adjusted based on the characteristics of liver and kidney function decline in elderly patients. range (for example, for elderly patients with renal insufficiency, the half-life difference threshold can be relaxed to 1.5 times the standard value) to avoid excessively restricting medication choices.

[0099] Third, through the implementation method, data updates can be combined with real-time medication, and rolling testing can be performed on new drug combinations, thereby effectively reducing the possibility of the "prescription waterfall" phenomenon.

[0100] In this embodiment, it is understood that the half-life difference threshold and plasma protein binding threshold The standard value (or reference value) can be selected and set according to the actual situation of different patients, and the present invention does not make any specific limitations on this.

[0101] In one embodiment of the present invention, in step S2 of the present invention, performing pharmacokinetic conflict detection based on a drug interaction database may specifically include: using the drug interaction database to query whether there are known interactions between drugs.

[0102] First, in this embodiment, authoritative drug interaction databases (these databases generally integrate pharmacokinetic, pharmacodynamic interaction and clinical case data and are highly referenceable) can be directly called instead of relying on algorithmic inference. This can cover known clinical high-risk drug combinations, avoid the limitations of parameter calculation alone, and effectively ensure the clinical reliability of the test.

[0103] Second, in this embodiment, since these drug interaction databases have a regular update mechanism, the detection method of the present invention can indirectly have the ability to self-iterate and upgrade, and can adapt to the evolution of the medical knowledge base without manual remodeling.

[0104] In this embodiment, it should be noted that relying solely on parameter calculations may lead to the omission of some drug risk combinations. For example, the half-life difference in the above technical solution may miss some complex scenarios. For example, the combination of beta-blockers and insulin may lead to an increased risk of hypoglycemia, and such risks cannot be reflected by half-life parameters; for another example, the risk of rhabdomyolysis caused by the combination of CYP3A4 inhibitors (such as clarithromycin) and statins needs to rely on the metabolic pathway annotations in the database.

[0105] In one embodiment of the present invention, in step S2 of the present invention, performing pharmacokinetic conflict detection based on a biopharmacokinetic model may specifically include:

[0106] Develop a PBPK model;

[0107] Simulate the distribution and metabolism of drugs in patients;

[0108] Predict potential drug interactions.

[0109] In this way, through this implementation, the established PBPK model can be combined with the parameter calculation (the sum of the half-life difference and the plasma protein binding rate) and database query (drug interaction database) described above, wherein the database query can cover known high-risk drug combinations, and the parameter calculation can detect potential metabolic interferences not included in the data (for example, drug accumulation caused by half-life differences). The established PBPK model can predict individualized metabolic differences (for example, special interactions in patients with abnormal liver and kidney function). In this way, the establishment of a multi-dimensional detection system (parameter calculation-database query-PBPK model) can be realized, realizing a three-layer prediction method of "known risk screening layer" (drug interaction database), "potential risk calculation layer" (parameter calculation) and "individualized prediction layer" (PBPK model), which can effectively improve the accuracy of conflict identification.

[0110] In this embodiment, it is understood that the PBPK model can be established by tools such as SimBiology to simulate the distribution and metabolic processes of drugs in the body and predict potential drug interactions. Since it is widely used in this field, it will not be described in detail in the present invention.

[0111] In one embodiment of the present invention, in step S2 of the present invention, the pharmacokinetic conflict resolution strategy includes a dosing time optimization strategy, a dose adjustment strategy, a drug substitution strategy, and a special population medication recommendation strategy;

[0112] Among them, the specific strategy for optimizing the administration time is: according to the half-life of the drug and plasma protein binding , calculate the optimal dosing time interval according to the following formula :

[0113]

[0114] Where, is the adjustment factor, and ;

[0115] To stagger the administration of different drugs and reduce metabolic interference;

[0116] The specific dosage adjustment strategy is: Based on the drug interaction, adjust the dosage according to the following formula :

[0117]

[0118] Where, For the initial dose, is the dose adjustment factor, and Determined by the severity of the drug interaction;

[0119] The specific drug substitution strategy is as follows: based on the drug database, automatically recommending alternative drugs to the initial drug, wherein the interaction between the alternative drug and other currently used drugs is smaller than the interaction between the initial drug and other currently used drugs;

[0120] The medication recommendation strategy for special populations specifically includes: providing medication dosage and dose adjustment recommendations based on the patient's age and diagnostic data.

[0121] In this embodiment, first, for the drug administration time optimization strategy, the calculation formula for the optimal drug administration time interval is ( ) The half-life is modified by introducing the plasma protein binding rate (for example, a high binding rate drug requires a longer dosing interval due to a lower free drug concentration). At the same time, the adjustment coefficient is set , allowing dynamic adjustment based on individual differences of patients (for example, liver and kidney function), which can effectively avoid drug accumulation that may be caused by traditional fixed intervals (for example, for elderly people with multiple coexisting diseases, their liver and kidney function is poor. If warfarin is used in combination with antibiotics, it may cause bleeding risks in the elderly due to differences in half-life).

[0122] Second, for the dosage adjustment strategy, the dosage of the present invention is Adjust the formula ( ) It is not a fixed value, but a dynamic value determined by the severity of the drug interaction (for example, for a case with a contraindication level, its It can be set to 0.5. For the situation with the severity level of warning, can be set to 0.3). This effectively avoids the tendency of traditional empirical regimens (i.e., dosage adjustments rely on physician experience) to overlook the nonlinear additive effects that may occur with the combined use of multiple drugs. In other words, the present invention can effectively avoid the "all or nothing" drawbacks of empirical medication (e.g., direct discontinuation of the drug or maintenance of the original dose) through a quantitative adjustment mechanism.

[0123] Third, for drug substitution strategies, traditional substitution schemes rely on manual queries, which are inefficient and prone to omissions. The present invention, through automated drug replacement recommendations, can quickly and comprehensively recommend more suitable alternative drugs.

[0124] Fourth, for medication recommendation strategies for special populations, medication dosage and dosage adjustment recommendations are dynamically adjusted based on patient age (age is matched with the physiological decline characteristics of general patients) and diagnostic data (to take into account individual differences, for example, a patient's glomerular filtration rate is lower than that of peers).

[0125] For example, in one exemplary embodiment, if a diabetic patient (using metformin) with chronic kidney disease (eGFR 45 mL / min) needs to use a contrast agent, traditional protocols may ignore the requirement to stop metformin before and after contrast agent use, leading to the risk of lactic acidosis. However, the present invention can automatically trigger renal function-related dosage adjustments through a special population medication recommendation strategy, and combined with a drug substitution strategy, recommend replacing insulin treatment 48 hours before the contrast examination. The time interval between insulin injection and contrast agent infusion is adjusted in combination with a dosing time strategy to minimize the patient's risk of lactic acidosis.

[0126] In one embodiment of the present invention, in step S3 of the present invention, performing nursing timing conflict detection based on time window overlap and nursing operation complexity may specifically include:

[0127] Step S3-1: For all nursing operations Set the corresponding execution time window respectively and complexity parameters ;

[0128] Step S3-2: According to the execution time window of the nursing operation Calculate the overlap of time windows: If the execution time window of the nursing operation If there is a partial overlap between , then it is considered that there is an unadjustable timing conflict, where is the preset time threshold, , For all nursing operations The overlapping time, For the duration of each nursing procedure;

[0129] Step S3-3: Based on the complexity parameter of the nursing operation , evaluate the compatibility of each nursing operation: if the complexity parameters of two nursing operations adjacent in time are If the values ​​are both greater than the preset complexity threshold, the two adjacent nursing operations are considered incompatible.

[0130] In this embodiment, first, the present invention sets an execution time window ( ) and introduce the time window overlap calculation formula ( ), which realizes the quantitative evaluation of nursing operation timing conflicts. Compared with the traditional manual experience judgment method, the present invention can accurately identify adjustable conflicts (partial overlap) and non-adjustable conflicts (overlap greater than or equal to the threshold) through mathematical modeling. ), which can effectively solve the operational contradictions caused by fuzzy judgment in traditional superposition solutions.

[0131] Second, introduce complexity parameters As an inherent attribute of nursing operations, objective evaluation criteria are established by presetting complexity thresholds. The present invention innovatively introduces a compatibility detection mechanism for adjacent nursing operations. When consecutive high-complexity operations ( When the complexity of the two nursing operations is greater than the preset complexity threshold, a conflict warning is triggered, and the two consecutive nursing operations are regarded as incompatible to avoid physiological burden on patients caused by continuous high-intensity nursing operations.

[0132] In one embodiment of the present invention, in step S3 of the present invention, selecting a nursing sequence optimization strategy according to the result of the nursing sequence conflict detection may specifically include: in step S3-2,

[0133] When there is only an adjustable timing conflict, the corresponding nursing operations are executed one by one according to the priority, and the partially overlapping time in the nursing operations is divided according to the preset ratio;

[0134] When there is an irreconcilable timing conflict, high-priority nursing operations are given priority and fully executed;

[0135] In step S3-3, if two adjacent nursing operations are not compatible, the two adjacent nursing operations are arranged in descending order of priority, and another complexity parameter is set to A nursing operation with a complexity less than or equal to a preset complexity threshold is performed between the two nursing operations.

[0136] In this embodiment, first, when only adjustable timing conflicts exist, a time-splitting strategy (split according to a preset ratio) is employed based on priority sorting to achieve efficient utilization of time resources while ensuring effective care. For example, in one exemplary embodiment, if there is a 30-minute overlap between nebulized treatment and blood glucose monitoring, this overlap can be segmented into 20 minutes of nebulized treatment and 10 minutes of blood glucose monitoring at a 2:1 ratio.

[0137] Second, ensure the complete execution of critical care operations by enforcing priority (e.g., urgent care operations take precedence over routine care operations) when there are non-adjustable timing conflicts.

[0138] Third, the present invention creatively proposes a buffering strategy of inserting low-complexity nursing operations between high-complexity nursing operations (for example, arranging low-intensity vital sign monitoring nursing operations after high-intensity posture adjustment nursing operations) to reduce the fluctuation amplitude of patients' physiological indicators and improve patients' acceptance of nursing operations, thereby improving nursing effects.

[0139] Overall, this implementation method achieves three-dimensional identification and hierarchical processing of nursing timing conflicts by establishing a dual detection mechanism (time overlap + complexity compatibility), which can effectively avoid the drawbacks of traditional linear superposition solutions and provide accurate decision-making basis for subsequent nursing optimization strategies.

[0140] In one embodiment of the present invention, in step S4 of the present invention, detecting the matching degree between the nursing plan and the patient's biorhythm based on the time matching degree and the intensity matching degree specifically includes:

[0141] Step S4-1: Calculate the matching degree between nursing operation time and patient activity time according to the following formula :

[0142]

[0143] Where, is the total number of nursing operations, is the activity weight, For the The time of nursing operation, The patient's activity time, Match tolerance to time;

[0144] Step S4-2: Calculate the matching degree between nursing operation intensity and patient activity intensity according to the following formula: :

[0145]

[0146] Where, For the The operational intensity of each nursing operation, is the patient's activity intensity during the corresponding period;

[0147] Step S4-3: Calculate the matching degree between the nursing plan and the patient's biorhythm according to the following formula :

[0148]

[0149] Where, and is the weight coefficient, and .

[0150] In this embodiment, first, the matching degree between nursing operation time and patient activity time is Calculation formula ( ), the present invention can shorten the nursing operation time Time spent with patients Specifically, the “bell curve” characteristic of the Gaussian function is used to allow the nursing time to be within a certain tolerance (given by For example, if a patient is accustomed to being active at 9 a.m., the system will consider nursing operations around 9 a.m. as a high match, thus avoiding conflicts caused by strict time requirements. Distinguish the importance of different activities (e.g., the importance of eating and walking) can be set to different values) to ensure that critical activity time is not disturbed by nursing operations, reduce patient resistance, and improve nursing effects.

[0151] Second, the matching degree between the intensity of nursing operation and the intensity of patient activity The calculation formula ( ) in terms of the present invention, the nursing operation intensity and patient activity intensity The difference is mapped to the matching degree. Specifically, when the intensity of the nursing operation is slightly different from the patient's activity intensity (for example, light activity corresponds to low-intensity nursing), the matching degree between the two is high; when the difference is large (for example, high-intensity nursing is arranged after strenuous activity), the matching degree drops sharply. This can give priority to intensity-adapted operations to prevent patients from affecting their recovery due to physical overdraft or overly easy operations. At the same time, it can also effectively avoid fatigue accumulation. For example, if the patient's activity intensity is low in the afternoon, low-intensity nursing (for example, blood pressure monitoring) can be implemented instead of high-intensity operations (for example, rehabilitation training), so that the nursing operation can effectively adapt to the patient's biological rhythm and avoid the patient's resistance caused by the conflict between the two.

[0152] Third, the matching degree between the nursing plan and the patient's biorhythm The calculation formula ( ), the present invention can integrate the matching degree of time and intensity through this formula, wherein, and It can be dynamically adjusted according to the actual situation of the patient. In this way, flexible priority allocation can be achieved (for example, for bedridden elderly patients, the time weight can be increased). , strictly match the fixed work and rest schedule; for patients with strong mobility, the intensity weight can be increased to ensure that the intensity of care is consistent with the rhythm of daily activities).

[0153] In one embodiment of the present invention, in step S4 of the present invention, the biorhythm adaptation strategy is selected according to the result of the biorhythm matching test, specifically including: adjusting the time and arrangement sequence of the nursing operation so that the nursing plan matches the patient's biorhythm. Less than the preset matching threshold.

[0154] In this way, the matching threshold can be quantified, that is, the preset threshold is used to determine whether the adjustment strategy is triggered ( Less than a preset matching threshold) to automatically identify unmatched care items and optimize their timing and arrangement order. This quantitative matching threshold can not only effectively reduce the judgment error of subjective adjustment, but also reduce the cost of manual intervention.

[0155] In addition, in this embodiment, first, the final optimization of timing conflicts can be effectively achieved. Specifically, after step S2 (pharmacokinetic conflict) and step S3 (nursing timing conflict) are resolved, this embodiment further eliminates conflicts at the biorhythm level. For example, in an exemplary embodiment, a diabetic patient needs to inject insulin before a meal (step S2 optimizes the administration time) and needs to perform post-meal blood glucose monitoring (step S3 optimizes the timing). This embodiment can further align these two operations with the patient's inherent meal time to form a complete conflict-free solution, further improving the nursing effect.

[0156] Second, it can effectively reduce complex side effects. Specifically, this implementation significantly reduces the risk of complications caused by inappropriate nursing plans by gradually resolving the triple conflict (pharmacokinetic conflict, timing conflict, and rhythm conflict). For example, it can avoid nocturnal hypoglycemia caused by the combined effects of drug metabolism interference (resolved in step S2) and nursing time conflicts (resolved in step S3).

[0157] According to a second aspect of the present invention, there is also provided a personalized nursing information management system for elderly people with multiple diseases, which is applied to the personalized nursing information management method for elderly people with multiple diseases described in any technical solution of the first aspect of the present invention. The personalized nursing information management system for elderly people with multiple diseases comprises:

[0158] Data collection module, used to obtain patients' medical information, behavioral information and initial care plan information;

[0159] The pharmacokinetic conflict identification module is used to perform pharmacokinetic conflict detection based on pharmacokinetic parameters, drug interaction database and biopharmacokinetic model, and select pharmacokinetic conflict resolution strategies based on the results of pharmacokinetic conflict detection;

[0160] The nursing sequence conflict identification module is used to classify nursing operations into emergency nursing operations, routine nursing operations, and preventive nursing operations in descending order of priority according to urgency and importance, and to perform nursing sequence conflict detection based on the overlap of time windows and the complexity of nursing operations. The nursing sequence optimization strategy is selected based on the results of the nursing sequence conflict detection;

[0161] The biorhythm matching recognition module is used to extract the patient's activity time and activity intensity, and detect the matching between the nursing plan and the patient's biorhythm based on the time matching and intensity matching; and select the biorhythm adaptation strategy based on the results of the biorhythm matching test;

[0162] The personalized nursing plan generation module is used to generate personalized nursing plans based on the pharmacokinetic conflict resolution strategy, nursing timing optimization strategy and nursing timing optimization strategy.

[0163] The personalized nursing information management system for elderly patients with multiple medical conditions presented in this invention not only systematically resolves the three-dimensional collaborative mechanism that creates nursing conflicts for these conditions, effectively improving care outcomes, but also effectively addresses the individual needs of different patients. Furthermore, it offers excellent versatility while ensuring medical safety. Furthermore, it pioneers a new intelligent decision-making solution in the field of elderly care based on quantitative models and dynamic optimization, demonstrating promising clinical application value.

[0164] The above are only specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A personalized nursing information management method for elderly people with multiple diseases, characterized by: The steps include: Step S1: Obtain the patient's medical information, behavioral information, and initial nursing plan information, wherein the medical information includes the patient's diagnosis data and medication data, the behavioral information includes the activity type and its corresponding activity time, and the initial nursing plan information includes the nursing operation type data and its corresponding execution time data; Step S2: performing pharmacokinetic conflict detection based on the pharmacokinetic parameters, the drug interaction database, and the biopharmacokinetic model; and selecting a pharmacokinetic conflict resolution strategy based on the results of the pharmacokinetic conflict detection; Step S3: Nursing operations are divided into emergency nursing operations, routine nursing operations, and preventive nursing operations in descending order of priority according to urgency and importance, and nursing sequence conflict detection is performed based on the time window overlap and nursing operation complexity. A nursing sequence optimization strategy is selected based on the results of the nursing sequence conflict detection; Step S4: Analyze the patient's behavioral information, extract the patient's activity time and activity intensity, and perform a biorhythm matching test between the nursing plan and the patient based on the time matching and intensity matching; and select a biorhythm adaptation strategy based on the results of the biorhythm matching test; Step S5: generating a personalized nursing plan based on the pharmacokinetic conflict resolution strategy obtained in step S2, the nursing timing optimization strategy obtained in step S3, and the nursing timing optimization strategy obtained in step S4; In step S2, performing pharmacokinetic conflict detection based on pharmacokinetic parameters specifically includes: Calculate the sum of the half-life difference and plasma protein binding rate between each two drugs one by one. When the following conditions are met, the two drugs are considered to have a metabolic interference risk: ； Where, and are the half-lives of drug A and drug B, respectively. is the half-life difference threshold, and are the plasma protein binding rates of drug A and drug B, respectively. is the threshold value of the sum of plasma protein binding rates; Pharmacokinetic conflict detection based on biopharmacokinetic models specifically includes: Develop a PBPK model; Simulate the distribution and metabolism of drugs in patients; predict potential drug interactions; Pharmacokinetic conflict resolution strategies include dosing time optimization strategies, dose adjustment strategies, drug substitution strategies, and medication recommendation strategies for special populations; Among them, the specific strategy for optimizing the administration time is: according to the half-life of the drug and plasma protein binding , calculate the optimal dosing time interval according to the following formula : ； Where, is the adjustment factor, and ; To stagger the administration of different drugs and reduce metabolic interference; The specific dosage adjustment strategy is: Based on the drug interaction, adjust the dosage according to the following formula : ； Where, For the initial dose, is the dose adjustment factor, and Determined by the severity of the drug interaction; The specific drug substitution strategy is as follows: based on the drug database, automatically recommending alternative drugs to the initial drug, wherein the interaction between the alternative drug and other currently used drugs is smaller than the interaction between the initial drug and other currently used drugs; The medication recommendation strategy for special populations specifically includes: providing medication dosage and dose adjustment recommendations based on the patient's age and diagnostic data.

2. The personalized nursing information management method for elderly people with multiple diseases according to claim 1 is characterized in that: In step S2, performing pharmacokinetic conflict detection based on the drug interaction database specifically includes: using the drug interaction database to query whether there are known interactions between drugs.

3. The personalized nursing information management method for elderly people with multiple diseases according to claim 1 is characterized in that: In step S3, performing nursing sequence conflict detection based on time window overlap and nursing operation complexity specifically includes: Step S3-1: For all nursing operations Set the corresponding execution time window respectively and complexity parameters ; Step S3-2: According to the execution time window of the nursing operation Calculate the overlap of time windows: If the execution time window of the nursing operation If there is a partial overlap between , then it is considered that there is an unadjustable timing conflict, where is the preset time threshold, , For all nursing operations The overlapping time, For the duration of each nursing procedure; Step S3-3: Based on the complexity parameter of the nursing operation , evaluate the compatibility of each nursing operation: if the complexity parameters of two nursing operations adjacent in time are If the values ​​are both greater than the preset complexity threshold, the two adjacent nursing operations are considered incompatible.

4. The personalized nursing information management method for elderly people with multiple diseases according to claim 3 is characterized in that: In step S3, selecting a nursing sequence optimization strategy according to the result of the nursing sequence conflict detection specifically includes: In the step S3-2, When there is only an adjustable timing conflict, the corresponding nursing operations are executed one by one according to the priority, and the partially overlapping time in the nursing operations is divided according to the preset ratio; When there is an irreconcilable timing conflict, high-priority nursing operations are given priority and fully executed; In step S3-3, if two adjacent nursing operations are not compatible, the two adjacent nursing operations are arranged in descending order of priority, and another complexity parameter is set to A nursing operation with a complexity less than or equal to a preset complexity threshold is performed between the two nursing operations.

5. The personalized nursing information management method for elderly people with multiple diseases according to claim 1 is characterized in that: In step S4, the matching degree detection between the nursing plan and the patient's biorhythm based on the time matching degree and the intensity matching degree specifically includes: Step S4-1: Calculate the matching degree between nursing operation time and patient activity time according to the following formula : ； Where, is the total number of nursing operations, is the activity weight, For the The time of nursing operation, The patient's activity time, Match tolerance to time; Step S4-2: Calculate the matching degree between nursing operation intensity and patient activity intensity according to the following formula: : ； Where, For the The operational intensity of each nursing operation, is the patient's activity intensity during the corresponding period; Step S4-3: Calculate the matching degree between the nursing plan and the patient's biorhythm according to the following formula : ； Where, and is the weight coefficient, and .

6. The personalized nursing information management method for elderly people with multiple diseases according to claim 5 is characterized in that: In step S4, the biorhythm adaptation strategy is selected according to the result of the biorhythm matching test, specifically including: adjusting the time and arrangement sequence of the nursing operation so that the nursing plan matches the patient's biorhythm. Less than the preset matching threshold.

7. A personalized nursing information management system for elderly people with multiple diseases, characterized by: A personalized nursing information management method for elderly people with multiple diseases as described in any one of claims 1 to 6, wherein the personalized nursing information management system for elderly people with multiple diseases comprises: Data collection module, used to obtain patients' medical information, behavioral information and initial care plan information; The pharmacokinetic conflict identification module is used to perform pharmacokinetic conflict detection based on pharmacokinetic parameters, drug interaction database and biopharmacokinetic model, and select pharmacokinetic conflict resolution strategies based on the results of pharmacokinetic conflict detection; The nursing sequence conflict identification module is used to classify nursing operations into emergency nursing operations, routine nursing operations, and preventive nursing operations in descending order of priority according to urgency and importance, and to perform nursing sequence conflict detection based on the overlap of time windows and the complexity of nursing operations. The nursing sequence optimization strategy is selected based on the results of the nursing sequence conflict detection; The biorhythm matching recognition module is used to extract the patient's activity time and activity intensity, and detect the matching between the nursing plan and the patient's biorhythm based on the time matching and intensity matching; and select the biorhythm adaptation strategy based on the results of the biorhythm matching test; The personalized nursing plan generation module is used to generate personalized nursing plans based on the pharmacokinetic conflict resolution strategy, nursing timing optimization strategy and nursing timing optimization strategy.

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