Clinical Nursing Information Recording Method, Device and Medium of Smart Medical System

Abstract

The present invention relates to the technical field of medical care, and particularly to a clinical care information recording method, device, and medium for an intelligent medical system. The method obtains the patient category where the current patient is located, takes other patients within the patient category as target patients, and obtains the nursing effectiveness according to the goal achievement situation and physical sign stability situation of the target patients; screens out low-score patients through the nursing plan satisfaction score, obtains the control patients of the low-score patients, and obtains the reference weight of the low-score patients according to the differences between the low-score patients and the control patients; presets the reference weights of other target patients except the low-score patients, and obtains the nursing evaluation degree according to the reference weights, nursing effectiveness, and nursing plan satisfaction score, and judges whether the nursing plan of the current patient is reasonable. By obtaining the nursing evaluation degree, the present invention accurately judges whether the nursing plan of the current patient is reasonable, timely adjusts the nursing plan of the current patient, so that the current patient can receive effective treatment.

Description

Technical Field

[0001] The present invention relates to the technical field of medical care, and specifically relates to a method, device and medium for recording clinical care information in a smart medical system. Background Art

[0002] Clinical care information is the information related to patient care generated, recorded and used during the process of nursing practice. Medical staff can formulate corresponding care plans for current patients based on the clinical care information of historical patients to help patients receive comprehensive and effective care. In order to ensure that the care plan provided to the current patient is accurate and reliable, it is necessary to analyze the care plan of the current patient and accurately evaluate whether the care plan of the current patient is reasonable.

[0003] Under normal circumstances, the care plans for patients with the same disease are the same. Therefore, in the existing methods, by analyzing the evaluation results of the care plans of historical patients with the same disease as the current patient, it is directly determined whether the care plan designated for the current patient is reasonable. However, in actual situations, there are subjective human intentions in the evaluation results of the care plans of historical patients, resulting in inaccurate evaluation results of the care plans. Furthermore, it is impossible to accurately analyze the reasonableness of the care plan of the current patient based on the evaluation results of the care plans of historical patients with the same disease as the current patient, resulting in the inability to accurately judge whether the care plan of the current patient is reasonable and affecting the timely adjustment and optimization of the care plan of the current patient. Summary of the Invention

[0004] In order to solve the technical problem that there are subjective human intentions in the evaluation results of the care plans of historical patients, resulting in inaccurate evaluation results of the care plans and thus unable to accurately judge whether the care plan of the current patient is reasonable, the purpose of the present invention is to provide a method, device and medium for recording clinical care information in a smart medical system. The specific technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present invention provides a method for recording clinical care information in a smart medical system. The method includes the following steps:

[0006] Obtain the patient category to which the current patient belongs, and use other patients within the patient category as target patients. Among them, the target patients have the same disease as the current patient, and the care plan of the target patients is the care plan of the current patient;

[0007] According to the goal achievement situation and physical sign stability situation of each target patient, obtain the care effectiveness degree of each target patient; the goal achievement situation indicates the number of diagnostic and treatment indicators that the target patient reaches as required by the doctor after the care plan ends;

[0008] Obtain the satisfaction scores of the care plans for each target patient, screen out the patients with low scores, and obtain the control patients for each low-score patient according to the similarity of the medications and their ingredients taken by each low-score patient and each other target patient.

[0009] According to the difference in the satisfaction scores of the care plans between each low-score patient and their control patients, as well as the difference in the medications taken, obtain the reference weight for each low-score patient; preset the reference weights for other target patients except the low-score patients, and obtain the nursing evaluation degree of the current patient according to the reference weight, nursing effectiveness degree, and satisfaction score of the care plan of each target patient.

[0010] Based on the nursing evaluation degree, determine whether the care plan of the current patient is reasonable.

[0011] Furthermore, the method for obtaining the patient categories is as follows:

[0012] For any two patients, obtain the diagnostic similarity degree between the two patients according to the similarity of the specified type of diagnostic information between the two patients.

[0013] Through the hierarchical clustering algorithm, divide the patients into patient categories according to the diagnostic similarity degree.

[0014] Furthermore, the method for obtaining the diagnostic similarity degree is as follows:

[0015] For any specified type of diagnostic information and any two patients, take the cosine similarity of the word segmentation sequences of the specified type of diagnostic information of the two patients as the first similarity degree.

[0016] According to the difference in the number of word segments between the word segmentation sequences of the specified type of diagnostic information of the two patients, obtain the second similarity degree; among them, the difference in the number of word segments and the second similarity degree are negatively correlated.

[0017] According to the first similarity degree and the second similarity degree, obtain the reference similarity degree of the word segmentation sequences of the specified type of diagnostic information of the two patients.

[0018] Take the average value of the reference similarity degrees of the word segmentation sequences of all the same specified type of diagnostic information of the two patients as the diagnostic similarity degree of the two patients.

[0019] Furthermore, the method for obtaining the nursing effectiveness degree is as follows:

[0020] For any one target patient, take the ratio of the number of diagnostic indicators achieved by the target patient after the care plan to the total number of diagnostic indicators required by the doctor as the target achievement degree value of the target patient.

[0021] Obtain the second reference effectiveness degree of the target patient according to the average change value of each physical sign within a preset time period after the end of the target patient care plan; wherein, the average change value and the second reference effectiveness degree have a negative correlation;

[0022] Obtain the care effectiveness degree of the target patient according to the target achievement degree value and the second reference effectiveness degree.

[0023] Further, the method for obtaining the control patient is as follows:

[0024] For any low-score patient and any target patient other than the low-score patient, take the cosine similarity between the word segmentation sequence of all the drugs taken by the low-score patient and the word segmentation sequence of all the drugs taken by the target patient as the first drug similarity degree;

[0025] For any drug taken by the low-score patient, take the maximum reference similarity degree among the reference similarity degrees between the word segmentation sequence of the components of this drug and the word segmentation sequence of the components of each drug taken by the target patient as the local similarity degree of this drug;

[0026] Take the average value of the local similarity degrees of all the drugs taken by the low-score patient as the second drug similarity degree;

[0027] Obtain the drug similarity degree between the low-score patient and the target patient according to the first drug similarity degree and the second drug similarity degree;

[0028] When the drug similarity degree is greater than the preset drug similarity degree threshold, the target patient is the control patient of the low-score patient.

[0029] Further, the method for obtaining the reference weight is as follows:

[0030] For any low-score patient, take all the control patients of the low-score patient as reference patients, and obtain the control patients of each reference patient according to the similarity of the drugs taken and the drug components between each reference patient and each other target patient;

[0031] Sort the care plan satisfaction scores of all the control patients of the low-score patient from small to large to obtain the target score sequence of the low-score patient;

[0032] Sort the care plan satisfaction scores of all the control patients of each reference patient from small to large to obtain the reference score sequence of each reference patient;

[0033] For any reference patient, obtain the difference between the care plan satisfaction score of the low-score patient and that of the reference patient as the first difference;

[0034] Obtain the cosine similarity between the target scoring sequence and the reference scoring sequence of the reference patient as the first eigenvalue;

[0035] According to the first difference and the first eigenvalue, obtain the reference influence degree of the reference patient on the low-score patient; wherein, the first difference and the reference influence degree are negatively correlated, and the first eigenvalue and the reference influence degree are positively correlated;

[0036] Take the normalized result of the mean value of the reference influence degrees of all reference patients on the low-score patient as the reference weight of the low-score patient.

[0037] Further, the method for obtaining the nursing evaluation degree is as follows:

[0038] For any target patient, obtain the mean value of the nursing effectiveness degree and the normalized satisfaction score of the target patient as the overall nursing effectiveness degree of the target patient;

[0039] Modify the overall nursing effectiveness degree through the reference weight of the target patient to obtain the true overall nursing effectiveness degree of the target patient;

[0040] Take the mean value of the true overall nursing effectiveness degrees of all target patients as the nursing evaluation degree of the current patient.

[0041] Further, the method for determining whether the nursing plan of the current patient is reasonable based on the nursing evaluation degree is as follows:

[0042] When the nursing evaluation degree is greater than the preset nursing evaluation degree threshold, it is determined that the nursing plan of the current patient is reasonable;

[0043] When the nursing evaluation degree is less than or equal to the preset nursing evaluation degree threshold, it is determined that the nursing plan of the current patient is unreasonable.

[0044] In a second aspect, another embodiment of the present invention provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of any one of the above methods are implemented.

[0045] In a third aspect, another embodiment of the present invention provides a computer-readable storage medium, in which a computer processing program is stored. When the computer processing program is executed by a processor, the steps of any one of the above methods are implemented.

[0046] The present invention has the following beneficial effects:

[0047] The present invention first obtains the patient category to which the current patient belongs, which is beneficial to improving the efficiency of subsequent analysis of whether the nursing plan for the current patient is reasonable; then takes other patients within the patient category to which the current patient belongs as target patients, which is beneficial for clearer description; and then obtains the nursing effectiveness of each target patient according to the goal achievement situation and physical sign stability situation of each target patient, accurately reflecting the effect of each target patient after treatment with the nursing plan, and indirectly reflecting the rationality of the nursing plan for the current patient; in order to more accurately analyze the accuracy of the evaluation results of each target patient on the nursing plan, the nursing plan satisfaction scores of each target patient are obtained to screen out low-scoring patients, and the target patients who may have deviations in the evaluation results of the nursing plan are determined; in order to determine the reference degree of each low-scoring patient for analyzing the nursing plan of the current patient and improve the accuracy of analyzing the nursing plan of the current patient, the control patients of each low-scoring patient are obtained according to the similarity of the medications and drug ingredients taken by each low-scoring patient and each other target patient, which is beneficial for accurately analyzing the reference degree of each low-scoring patient subsequently; further, according to the difference in the nursing plan satisfaction scores between each low-scoring patient and its control patient, as well as the difference in the medications taken, the reference weight of each low-scoring patient is obtained, accurately reflecting the reference degree of each low-scoring patient when analyzing the rationality of the nursing plan of the current patient; in order to enable each target patient to participate in the analysis of whether the nursing plan for the current patient is reasonable, the reference weights of other target patients except low-scoring patients are preset, and finally, according to the reference weights, nursing effectiveness and nursing plan satisfaction scores of each target patient, the nursing evaluation degree of the current patient is accurately obtained, accurately reflecting the rationality of the nursing plan of the current patient; and then based on the nursing evaluation degree, it is accurately judged whether the nursing plan for the current patient is reasonable, which is beneficial for timely adjusting and optimizing the nursing plan for the current patient to ensure that the current patient receives effective treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0049] Figure 1 It is a schematic flowchart of a clinical nursing information recording method for a smart medical system provided by an embodiment of the present invention;

[0050] Figure 2 It is a flowchart of a method for obtaining a patient category provided by an embodiment of the present invention;

[0051] Figure 3 Flowchart of a method for obtaining control patients provided by an embodiment of the present invention;

[0052] Figure 4 Schematic diagram of a computer device provided by an embodiment of the present invention;

[0053] Figure 5 Structural diagram of a clinical nursing information recording system of a smart healthcare system provided by an embodiment of the present invention. Detailed implementation manners

[0054] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following specifically describes, with reference to the accompanying drawings and preferred embodiments, the clinical nursing information recording method, device, and medium of the smart healthcare system proposed according to the present invention, including its specific implementation manners, structures, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0056] The following specifically describes the specific solutions of the clinical nursing information recording method, device, and medium of the smart healthcare system provided by the present invention with reference to the accompanying drawings.

[0057] Embodiment 1:

[0058] The specific scenario of this embodiment is as follows: In order to more accurately analyze whether the current patient's care plan is reasonable, this embodiment first obtains the patient category to which the current patient belongs. Among them, other patients in the patient category to which the current patient belongs have the same disease as the current patient, and all other patients in the patient category to which the current patient belongs are historical patients. Since the care plans of patients with the same disease are the same, the care plan of the current patient is determined based on the care plans of the historical patients in the patient category to which the current patient belongs. All historical patients in the patient category to which the current patient belongs are used as target patients. Based on the evaluation results of the care plans of the target patients, it is possible to indirectly analyze whether the care plan of the current patient is reasonable. Then, each target patient is analyzed to accurately obtain the reference weight of each target patient, reducing the interference of the evaluation results of target patients whose conditions have not improved due to their own drug tolerance on the evaluation of the rationality of the current patient's care plan. Finally, based on the reference weight, care effectiveness, and care plan satisfaction score of each target patient, the care evaluation degree of the current patient is accurately obtained. Furthermore, based on the care evaluation degree, it is accurately determined whether the care plan of the current patient is reasonable, which is conducive to timely adjusting and optimizing the care plan of the current patient to ensure that the current patient receives comprehensive and effective care.

[0059] The present invention proposes a clinical care information recording method for a smart medical system. Please refer to Figure 1 , which shows a schematic flowchart of a clinical care information recording method for a smart medical system provided by an embodiment of the present invention. The method includes the following steps:

[0060] Step S1: Obtain the patient category to which the current patient belongs, and use other patients in the patient category as target patients. Among them, the target patients have the same disease as the current patient, and the care plan of the target patients is the care plan of the current patient.

[0061] Specifically, this embodiment takes a department in a hospital as an example for analysis. The hospital's information system stores the diagnosis and treatment records of each patient who comes to this department for care. One patient corresponds to one diagnosis and treatment record, and the diagnosis and treatment record includes various diagnosis and treatment information such as the patient's basic information, medical history information, symptom description, and medication record. Through the patient's diagnosis and treatment record, it can help doctors make diagnoses and formulate reasonable care plans for patients. In order to analyze whether the care plan formulated for the current patient is reasonable, this embodiment sets the current time period as three years. Among them, the end time of the current time period must be the current time, and the implementer can set the size of the current time period according to the actual situation, which is not limited here. Obtain the diagnosis and treatment records of each patient within the current time period. It should be noted that there is only one current patient in this embodiment. Therefore, within the current time period, there is one current patient and multiple historical patients.

[0062] It is known that the care plans for patients with the same disease are the same. To determine the care plan for the current patient, historical patients with the same disease as the current patient are first found. Patients with the same disease have the same diagnosis and treatment characteristics. Therefore, patients are classified based on the diagnosis and treatment information in the medical records, and then historical patients in the same category as the current patient are determined. In actual situations, there are many types of diagnosis and treatment information in the medical records. Some types of diagnosis and treatment information have little relation to the pathological characteristics of the patient, such as the patient's basic information like occupation, income, etc. If all the diagnosis and treatment information in the medical records is analyzed, it will increase the computational workload, waste resources, and may also result in inaccurate classification results. It is known that the four types of diagnosis and treatment information, namely the family history, past medical history, symptom description, and medication situation of the patient, can fully reflect the pathological characteristics of the patient. Therefore, in this embodiment, the four types of diagnosis and treatment information, namely the family history, past medical history, symptom description, and medication situation, are set as the specified types of diagnosis and treatment information. When the same specified types of diagnosis and treatment information between any two patients are more similar, it indicates that the two patients are more likely to be patients of the same category. Therefore, in this embodiment, patients are classified into patient categories according to the similarity of the specified types of diagnosis and treatment information between different patients.

[0063] Preferably, in a realizable manner of this embodiment, for the method of obtaining patient categories, please refer to Figure 2 , which shows a flowchart of a method for obtaining patient categories provided in this embodiment. The method includes the following steps:

[0064] Step S101: For any two patients, obtain the diagnostic similarity degree of the two patients according to the similarity of the specified types of diagnosis and treatment information between the two patients.

[0065] For any two patients, when the same specified types of diagnosis and treatment information between the two patients are also similar, it indicates that the diseases of the two patients are more similar, the treatment processes are more similar, and the two patients are more likely to be of the same category. Therefore, in this embodiment, the diagnostic similarity degree of the two patients is obtained according to the similarity of the specified types of diagnosis and treatment information between the two patients. The greater the diagnostic similarity degree, the more likely the two patients are to be of the same category.

[0066] In an implementable manner of this embodiment, the method for obtaining the similarity degree of diagnosis and treatment is as follows: For any specified type of diagnosis and treatment information and any two patients, in this embodiment, the Jieba word segmentation is used to obtain the word segmentation sequences of the specified type of diagnosis and treatment information of the two patients. Among them, Jieba word segmentation is a well-known technology and will not be elaborated here. The cosine similarity of the word segmentation sequences of the specified type of diagnosis and treatment information of the two patients is obtained as the first similarity degree; the greater the first similarity degree, the more similar the specified type of diagnosis and treatment information of the two patients, indirectly reflecting that the two patients are more likely to be of the same category. Among them, the method for obtaining the cosine similarity of the word segmentation sequences is well-known and will not be elaborated here. In order to further analyze the similarity of the specified type of diagnosis and treatment information of the two patients, and then obtain the difference in the number of word segments between the word segmentation sequences of the specified type of diagnosis and treatment information of the two patients. When the difference in the number of word segments between the word segmentation sequences of the specified type of diagnosis and treatment information of the two patients is smaller, it indicates that the specified type of diagnosis and treatment information of the two patients is more similar. Therefore, according to the difference in the number of word segments between the word segmentation sequences of the specified type of diagnosis and treatment information of the two patients, the second similarity degree is obtained; among them, the difference in the number of word segments and the second similarity degree are negatively correlated; the greater the second similarity degree, the more similar the specified type of diagnosis and treatment information of the two patients. Then, according to the first similarity degree and the second similarity degree, the reference similarity degree of the word segmentation sequences of the specified type of diagnosis and treatment information of the two patients is obtained; the greater the reference similarity degree, the more accurately it indicates that the specified type of diagnosis and treatment information of the two patients is more similar. In order to accurately analyze whether the two patients are of the same category, and then obtain the average value of the reference similarity degrees of the word segmentation sequences of all the same specified types of diagnosis and treatment information of the two patients as the diagnosis and treatment similarity degree of the two patients.

[0067] In order to accurately represent the diagnosis and treatment similarity degree of any two patients, in this embodiment, the diagnosis and treatment similarity degree is specifically quantified as a diagnosis and treatment similarity degree value. The greater the diagnosis and treatment similarity degree value, the more likely the corresponding two patients are of the same category. Among them, the calculation formula of the diagnosis and treatment similarity degree value is: ; In the formula, is the diagnosis and treatment similarity degree value of the i-th patient and the j-th patient; V is the number of types of specified diagnosis and treatment information, which is set to 4 in this embodiment; is the word segmentation sequence of the v-th type of specified diagnosis and treatment information of the i-th patient; is the word segmentation sequence of the v-th type of specified diagnosis and treatment information of the j-th patient; is the cosine function; is the number of word segments in the word segmentation sequence of the v-th type of specified diagnosis and treatment information of the i-th patient; is the number of word segments in the word segmentation sequence of the v-th type of specified diagnosis and treatment information of the j-th patient; is the absolute value function; exp is the exponential function with the natural constant as the base; is the first similarity degree value; is the second similarity degree value; is the reference similarity degree value of the word segmentation sequence of the v-th specified type of diagnosis and treatment information of the i-th patient and the j-th patient.

[0068] So far, the diagnosis and treatment similarity degree values of any two patients are obtained.

[0069] Step S102: Through the hierarchical clustering algorithm, divide the patients into patient categories according to the diagnosis and treatment similarity degree.

[0070] According to the diagnosis and treatment similarity degree values, all patients are divided through the hierarchical clustering algorithm to obtain patient categories. Among them, the diseases of the patients within the same patient category are the same, and the same nursing plan is sampled for treatment. Among them, the hierarchical clustering algorithm is a well-known technology and will not be elaborated here.

[0071] Determine the patient category where the current patient is located. For the sake of clear description later, all the historical patients within the patient category where the current patient is located are regarded as target patients. Among them, the nursing plan of the target patient is the nursing plan of the current patient.

[0072] Step S2: According to the goal achievement situation and physical sign stability situation of each target patient, obtain the nursing effectiveness degree of each target patient; the goal achievement situation represents the number of diagnosis and treatment indicators that the target patient reaches the doctor's requirements after the nursing plan ends.

[0073] It is known that the disease conditions of the current patient and the target patient are the same and the nursing plans are the same. If the condition of the target patient has improved significantly or recovered after the nursing plan ends, it indirectly reflects that the nursing plan formulated for the current patient is more reasonable. When the number of diagnosis and treatment indicators that a certain target patient reaches the doctor's requirements after the nursing plan ends is larger, it indicates that the nursing effect of the target patient is better; when the physical signs of the target patient are more stable after the nursing plan ends, it also indicates that the nursing effect of the target patient is better. Therefore, according to the goal achievement situation and physical sign stability situation of each target patient, obtain the nursing effectiveness degree of each target patient. The greater the nursing effectiveness degree, the better the recovery of the corresponding target patient, and indirectly indicates that the nursing plan of the current patient is more reasonable.

[0074] Preferably, in a feasible implementation manner of this embodiment, the method for obtaining the effectiveness of nursing is as follows: For any target patient, the ratio of the number of treatment indicators that the target patient reaches after the nursing plan to the total number of treatment indicators required by the doctor is used as the target achievement degree value of the target patient; the larger the target achievement degree value, the better the nursing effect of the target patient. As an example, taking orthopedics as an example, for any target patient, assuming that the target patient's arm cannot be lifted, after treatment with the nursing plan, one of the treatment indicators required by the doctor is that the target patient's arm can be lifted 30 centimeters. If, after the nursing plan ends, the height that the target patient's arm can be lifted is greater than or equal to 30 centimeters, then the target patient reaches one treatment indicator required by the doctor after the nursing plan. Among them, the number of treatment indicators that the target patient reaches after the nursing plan to the total number of treatment indicators required by the doctor can be directly obtained from the patient's medical record; in order to further analyze the nursing situation of the target patient, the average change value of each physical sign within a preset time period after the end of the target patient's nursing plan is further obtained. The smaller the average change value, the more stable the corresponding physical sign, and the better the nursing effect of the target patient. Among them, the method for obtaining the average change value is a well-known technology and will not be elaborated here. Furthermore, according to the average change value of each physical sign within a preset time period after the end of the target patient's nursing plan, the second reference effectiveness of the target patient is obtained; among them, the average change value and the second reference effectiveness are negatively correlated; the larger the second reference effectiveness, the better the nursing effect of the target patient. Finally, according to the target achievement degree value and the second reference effectiveness, the nursing effectiveness of the target patient is obtained. In this embodiment, the preset time period is set to 3 months. If the time from the end of the target patient's nursing plan to the current moment is less than 3 months, then the time period from the end of the target patient's nursing plan to the current moment is set as the preset time period. The implementer can set the size of the preset time period according to the actual situation and is not limited here. Among them, the physical signs in this embodiment include pulse, blood pressure, and body temperature. The implementer can set the types of physical signs according to the actual situation and is not limited here.

[0075] To accurately measure the nursing effect of each target patient, in this embodiment, the nursing effectiveness is specifically quantified as a nursing effectiveness value. The larger the nursing effectiveness value, the better the nursing effect of the corresponding target patient, indirectly indicating that the current patient's nursing plan is more reasonable. Among them, the calculation formula for the nursing effectiveness value is: ; In the formula, is the nursing effectiveness value of the t-th target patient; is the number of achieved targets after the end of the nursing plan for the t-th target patient; is the number of target achievement preset by the doctor before the care for the t-th target patient; K is the number of types of physical signs, which is set to 3 in this embodiment; is the average change value of the k-th physical sign within the preset time period after the care plan for the t-th target patient ends; is the second preset constant, greater than 0; norm is the normalization function; is the target achievement degree value; is the second reference effectiveness value.

[0076] In this embodiment, is set to 1 to avoid a zero denominator. The implementer can set the size of according to the actual situation, which is not limited here.

[0077] Thus, the care effectiveness value of each target patient is obtained.

[0078] Step S3: Obtain the satisfaction scores of the care plans for each target patient, screen out the patients with low scores, and obtain the control patients for each low-score patient according to the similarity of the medications and their ingredients taken by each low-score patient and each other target patient.

[0079] It is known that the evaluation result of the care plan for the current patient is evaluated based on the evaluation results of the care plans for the target patients. Among them, the evaluation result of the care plan for the target patient is jointly determined by the care effectiveness of the target patient and the satisfaction score of the target patient for the care plan. In this embodiment, the satisfaction score of the target patient for the care plan is set to 0 to 10, where 0 is the lowest score indicating that the target patient is very dissatisfied with the care plan, and 10 is the highest score indicating that the target patient is very satisfied with the care plan; after the care plan ends, each target patient will manually score the care plan based on their overall feeling of the care plan, so as to obtain the satisfaction score of the care plan for each target patient.

[0080] In actual situations, the bodies of special target patients may develop drug tolerance. For example, when the body of a certain target patient develops drug tolerance, that is, when the condition of the target patient improves slowly or does not improve after taking the drug according to the care plan, the satisfaction score of the care plan for this target patient is relatively low. The reason for the low satisfaction score of the care plan is the ineffectiveness of the drug, rather than the unreasonable formulation of the care plan. To accurately analyze the reasons for the low satisfaction score of the care plan, in this embodiment, first, low-score patients are selected according to the satisfaction scores of the care plans of each target patient. Then, the improvement conditions of each low-score patient and other target patients taking the same drug are analyzed to determine whether each low-score patient's satisfaction score of the care plan is due to drug tolerance of the body or unreasonable care plan, so as to accurately adjust the reference degree of each low-score patient when calculating the overall evaluation result of the target patient in the subsequent process, reduce the accuracy of analyzing the care plan of the current patient due to the patient's own drug tolerance, and enable a more accurate analysis of whether the care plan of the current patient is reasonable. Considering that different doctors have different prescribing preferences and habits, there are cases where the names of the drugs taken by target patients with the same disease are different, but their drug components are similar. To more accurately analyze the reasons for the satisfaction scores of the care plans of each low-score patient, in this embodiment, according to the similarity of the drugs and drug components taken by each low-score patient and each other target patient, the control patients of each low-score patient are obtained.

[0081] In a feasible implementation manner of this embodiment, the method for obtaining low-score patients is as follows: When the satisfaction score of the care plan is less than the preset satisfaction score threshold, the corresponding target patient is a low-score patient. In this embodiment, the preset satisfaction score threshold is set to 3, and the implementer can set the size of the preset satisfaction score threshold according to the actual situation, which is not limited here. Thus, the low-score patients among the target patients are selected.

[0082] Preferably, in a feasible implementation manner of this embodiment, for the method of obtaining control patients, please refer to Figure 3 , which shows a flowchart of a method for obtaining control patients provided in this embodiment. The method includes the following steps:

[0083] Step S301: For any low-score patient and any target patient other than the low-score patient, the cosine similarity between the word segmentation sequences of all the drugs taken by the low-score patient and the word segmentation sequences of all the drugs taken by the target patient is used as the first similarity degree of the drugs.

[0084] As an example, the b-th low-score patient and the q-th target patient other than the b-th low-score patient are taken for analysis. By using Jieba word segmentation, the word segmentation sequences of all the drugs taken by the b-th low-score patient and the word segmentation sequences of all the drugs taken by the q-th target patient are obtained. The cosine similarity between the word segmentation sequences of all the drugs taken by the b-th low-score patient and the word segmentation sequences of all the drugs taken by the q-th target patient is used as the first drug similarity degree value. The larger the first drug similarity degree value is, the more similar the drugs taken by the b-th low-score patient and the q-th target patient are, and the more likely the q-th target patient is the control patient of the b-th low-score patient.

[0085] Step S302: For any drug taken by the low-score patient, the maximum reference similarity degree among the reference similarity degrees between the word segmentation sequence of the components of this drug and the word segmentation sequences of the components of each drug taken by the target patient is used as the local similarity degree of this drug.

[0086] Since there may be multiple drugs taken by a patient, in this embodiment, the components of each drug taken by the b-th low-score patient are compared with the components of each drug taken by the q-th target patient, so as to more accurately analyze the similarity of the drugs taken by the b-th low-score patient and the q-th target patient. Taking the h-th drug taken by the b-th low-score patient as an example, by using Jieba word segmentation, the word segmentation sequence of the components of the h-th drug taken by the b-th low-score patient and the word segmentation sequences of the components of each drug taken by the q-th target patient are obtained. According to the method for obtaining the reference similarity degree in step S2, the reference similarity degree between the word segmentation sequence of the components of the h-th drug and the word segmentation sequences of the components of each drug taken by the q-th target patient is obtained.

[0087] Taking the f-th drug taken by the q-th target patient as an example, the calculation formula for obtaining the reference similarity degree value between the word segmentation sequence of the components of the h-th drug and the word segmentation sequence of the components of the f-th drug is as follows: ; In the formula, is the reference similarity degree value between the word segmentation sequence of the components of the h-th drug taken by the b-th low-score patient and the word segmentation sequence of the components of the f-th drug taken by the q-th target patient; is the word segmentation sequence of the components of the h-th drug taken by the b-th low-score patient; is the word segmentation sequence of the components of the f-th drug taken by the q-th target patient; is the number of word segments in the word segmentation sequence of the components of the h-th drug taken by the b-th low-score patient; is the number of word segments in the word segmentation sequence of the components of the f-th drug taken by the q-th target patient; is the absolute value function; is the cosine function; exp is the exponential function with the natural constant as the base.

[0088] According to the method of obtaining the reference similarity degree value between the component word segmentation sequence of the h-th drug taken by the b-th low-score patient and the component word segmentation sequence of the f-th drug taken by the q-th target patient, obtain the reference similarity degree value between the component word segmentation sequence of the h-th drug taken by the b-th low-score patient and the component word segmentation sequence of each drug taken by the q-th target patient, and take the maximum reference similarity degree value among the reference similarity degree values between the component word segmentation sequence of the h-th drug taken by the b-th low-score patient and the component word segmentation sequence of each drug taken by the q-th target patient as the local similarity degree value of the h-th drug taken by the b-th low-score patient.

[0089] According to the method of obtaining the local similarity degree value of the h-th drug taken by the b-th low-score patient, obtain the local similarity degree value of each drug taken by the b-th low-score patient.

[0090] Step S303: Take the average value of the local similarity degrees of all drugs taken by this low-score patient as the second drug similarity degree.

[0091] In order to accurately obtain the similarity situation of the drugs taken by the b-th low-score patient and the q-th target patient, and further take the average value of the local similarity degree values of all drugs taken by the b-th low-score patient as the second drug similarity degree value between the b-th low-score patient and the q-th target patient; the larger the second drug similarity degree value, the more similar the drugs taken by the b-th low-score patient and the q-th target patient, and the more likely the q-th target patient is the control patient of the b-th low-score patient.

[0092] Step S304: Obtain the drug similarity degree between this low-score patient and this target patient according to the first drug similarity degree and the second drug similarity degree.

[0093] It is known that when both the first drug similarity degree and the second drug similarity degree are larger, it indicates that the drugs taken by the b-th low-score patient and the q-th target patient are more similar. Furthermore, obtain the drug similarity degree between the b-th low-score patient and the q-th target patient according to the first drug similarity degree and the second drug similarity degree.

[0094] In order to accurately represent the drug similarity degree between the b-th low-score patient and the q-th target patient, in this embodiment, the drug similarity degree is specifically quantified as a drug similarity degree value. The larger the drug similarity degree value, the more similar the drugs taken by the b-th low-score patient and the q-th target patient. Among them, the calculation formula of the drug similarity degree value is: ; In the formula, is the drug similarity degree value between the b-th low-score patient and the q-th target patient; is the first drug similarity degree value between the b-th low-score patient and the q-th target patient; is the second drug similarity degree value between the b-th low-score patient and the q-th target patient; norm is a normalization function. In other embodiments, it can be obtained through obtained , which is not limited herein.

[0095] Step S305: When the drug similarity degree is greater than a preset drug similarity degree threshold, the target patient is the control patient of the low-score patient.

[0096] It is known that the greater the drug similarity degree, the more similar the drugs taken by the b-th low-score patient and the q-th target patient are, and the more likely the q-th target patient is the control patient of the b-th low-score patient. Therefore, in this embodiment, the preset drug similarity degree threshold is set to 0.5, and the implementer can set the size of the preset drug similarity degree threshold according to the actual situation, which is not limited herein. When the drug similarity degree value between the b-th low-score patient and the q-th target patient is greater than the preset drug similarity degree threshold, the q-th target patient is the control patient of the b-th low-score patient.

[0097] Obtain the drug similarity degree values between the b-th low-score patient and each other target patient except the b-th low-score patient, and then determine each control patient corresponding to the b-th low-score patient.

[0098] So far, the control patients of each low-score patient are obtained.

[0099] Step S4: According to the differences in the satisfaction scores of the nursing plans between each low-score patient and their control patients, as well as the differences in the drugs taken, obtain the reference weight of each low-score patient; preset the reference weights of other target patients except the low-score patients, and obtain the nursing evaluation degree of the current patient according to the reference weight, nursing effectiveness degree and satisfaction score of the nursing plan of each target patient.

[0100] Specifically, when the difference in the satisfaction scores of the care plans between a low-score patient and each of their control patients is greater, it indicates that the low-score patient is more special. The lower the association between the satisfaction score of the care plan of the low-score patient and an unreasonable care plan, and the greater the association with the low-score patient's own drug resistance. Indirectly, it shows that the evaluation result of the care plan for the low-score patient is less accurate. When evaluating the reasonableness of the care plan for the current patient, the degree of reference should be smaller to avoid affecting the accuracy of the analysis of whether the care plan for the current patient is reasonable. At the same time, in order to more accurately analyze the degree of reference of the low-score patient based on the control patients of the low-score patient, further analyze the similarity between the medication conditions of the control patients of the low-score patient and the medication conditions of the control patients of each control patient of the low-score patient. When the medication conditions are more similar, the analyzed degree of reference of the low-score patient is more accurate. Furthermore, in this embodiment, according to the difference in the satisfaction scores of the care plans between each low-score patient and their control patients, as well as the difference in the medications taken, the reference weight of each low-score patient is obtained. Among them, the smaller the reference weight, the less accurate the evaluation result of the care plan corresponding to the low-score patient.

[0101] It is known that by analyzing the overall evaluation results of the care plans of all target patients, it is possible to infer whether the care plan for the current patient is reasonable. Therefore, in order to involve each target patient, in this embodiment, the reference weight of each target patient other than the low-score patients is preset to 1. The implementer can preset the reference weight of each target patient other than the low-score patients according to the actual situation, which is not limited here. It is known that the care effectiveness and the satisfaction score of the care plan of the target patient can reflect the evaluation result of the care plan. Considering that there are some target patients whose evaluation results of the care plan are deviated due to their own drug resistance, therefore, in this embodiment, the care effectiveness and the satisfaction score of the care plan of the corresponding target patient are adjusted by the reference weight of each target patient to reduce the impact of inaccurate evaluation results of the care plan. Furthermore, in this embodiment, according to the reference weight, care effectiveness, and satisfaction score of the care plan of each target patient, the care evaluation degree of the current patient is accurately obtained. Among them, the greater the care evaluation degree, the more reasonable the care plan formulated for the current patient.

[0102] Preferably, in an implementable manner of this embodiment, the method for obtaining the reference weight is as follows: for any low-score patient, all control patients of this low-score patient are used as reference patients. Through the method for obtaining control patients in step S3, according to the similarity of the drugs and drug ingredients taken by each reference patient and each other target patient, the control patients of each reference patient are obtained; the satisfaction scores of the care plans of all control patients of this low-score patient are sorted from small to large to obtain the target score sequence of this low-score patient; the satisfaction scores of the care plans of all control patients of each reference patient are sorted from small to large to obtain the reference score sequence of each reference patient; for any reference patient, the absolute value of the difference between the satisfaction score of the care plan of this low-score patient and that of this reference patient is obtained as the first difference; the greater the first difference, the more special the satisfaction score of the care plan of this low-score patient, indirectly indicating that the satisfaction score of the care plan of this low-score patient has a greater association with the development of drug resistance to the drug by oneself, and the smaller the reference significance; further, the cosine similarity between the target score sequence and the reference score sequence of this reference patient is obtained as the first eigenvalue; the greater the first eigenvalue, the more accurate the first difference, which is beneficial to more accurately analyzing the reference degree of this low-score patient; in order to accurately determine the influence of this reference patient on the reference degree of this low-score patient, the reference influence degree of this reference patient on this low-score patient is obtained based on the first difference and the first eigenvalue; among them, the first difference and the reference influence degree are negatively correlated, and the first eigenvalue and the reference influence degree are positively correlated;

[0103] Among them, the calculation formula for the reference influence degree is: ; in the formula, is the reference influence degree of the ath reference patient of the bth low-score patient on the bth low-score patient; is the satisfaction score of the care plan of the bth low-score patient; is the satisfaction score of the care plan of the ath reference patient of the bth low-score patient; is the absolute value function; is the first difference; is the third preset constant, greater than 0; is the first eigenvalue; among them, in this embodiment, is set to 1 to avoid the denominator being 0. The implementer can set the size of according to the actual situation, which is not limited here;

[0104] In order to represent the reference degree of this low-score patient as a whole, the normalized result of the mean value of the reference influence degrees of all reference patients on this low-score patient is used as the reference weight of this low-score patient. It should be noted that in this embodiment, the mean value of the reference influence degrees of all reference patients on this low-score patient is normalized through the norm normalization function.

[0105] Thus, the reference weight of each low-score patient is obtained. Among them, the reference weight of each target patient other than the low-score patient is 1.

[0106] Preferably, in an implementable manner of this embodiment, the method for obtaining the nursing evaluation degree is as follows: for any target patient, obtain the mean value of the nursing effectiveness degree and the normalized nursing plan satisfaction score of the target patient as the overall nursing effectiveness degree of the target patient; the greater the overall nursing effectiveness degree, the better the treatment effect of the target patient through the nursing plan, indirectly indicating that the current patient's nursing plan is more reasonable. Considering that the evaluation results of different target patients on the nursing plan have different interference degrees of their own, therefore, in order to more accurately analyze the evaluation results of the target patient on the nursing plan, in this embodiment, the overall nursing effectiveness degree is modified by the reference weight of the target patient to obtain the true overall nursing effectiveness degree of the target patient; the greater the true overall nursing effectiveness degree, the more accurately it indicates that the treatment effect of the target patient through the nursing plan is better. Then, the mean value of the true overall nursing effectiveness degrees of all target patients is used as the nursing evaluation degree of the current patient.

[0107] To accurately represent the nursing evaluation degree of the current patient, in this embodiment, the nursing evaluation degree is specifically quantified as a nursing evaluation degree value. The greater the nursing evaluation degree value, the more reasonable the nursing plan of the current patient. Among them, the calculation formula of the nursing evaluation degree value is: ; in the formula, is the nursing evaluation degree value of the current patient; W is the total number of target patients; is the reference weight of the w-th target patient; is the nursing effectiveness degree value of the w-th target patient; is the nursing plan satisfaction score of the w-th target patient; norm is the normalization function; is the overall nursing effectiveness degree value of the w-th target patient; is the true overall nursing effectiveness degree value of the w-th target patient.

[0108] Step S5: Judge whether the nursing plan of the current patient is reasonable based on the nursing evaluation degree.

[0109] It is known that the greater the nursing evaluation degree, the more reasonable the nursing plan of the current patient. Therefore, in this embodiment, it is judged whether the nursing plan of the current patient is reasonable based on the nursing evaluation degree.

[0110] In this embodiment, the preset threshold for the degree of nursing assessment is set to 0.6. The implementer can set the size of the preset threshold for the degree of nursing assessment according to the actual situation, which is not limited here. When the value of the nursing assessment degree is greater than the preset threshold for the degree of nursing assessment, it indicates that the current patient's nursing plan is reasonable and does not need to be adjusted; when the value of the nursing assessment degree is less than or equal to the preset threshold for the degree of nursing assessment, it indicates that the current patient's nursing plan is unreasonable, and medical staff need to adjust and optimize the current patient's nursing plan in a timely manner to ensure that the current patient receives effective treatment according to the adjusted nursing plan.

[0111] In summary, this embodiment obtains the patient category to which the current patient belongs, takes other patients within the patient category as target patients, and obtains the effectiveness of nursing according to the goal achievement situation and physical sign stability situation of the target patients; screens out low-score patients through the satisfaction score of the nursing plan, obtains the control patients of the low-score patients, and obtains the reference weight of the low-score patients according to the differences between the low-score patients and the control patients; preset the reference weights of other target patients except the low-score patients, and obtain the degree of nursing assessment according to the reference weights, the effectiveness of nursing, and the satisfaction score of the nursing plan, and judge whether the current patient's nursing plan is reasonable. By obtaining the degree of nursing assessment, the present invention accurately judges whether the current patient's nursing plan is reasonable and adjusts the current patient's nursing plan in a timely manner, so that the current patient receives effective treatment.

[0112] Embodiment 2:

[0113] The present invention also proposes a computer device. Please refer to Figure 4 , which includes a memory 401, a processor 402, and a computer program 403 stored in the memory 401 and running on the processor 402. Among them, when the processor 402 executes the computer program 403, the computer device can execute any one of the clinical nursing information recording methods of the intelligent medical system described above.

[0114] Embodiment 3:

[0115] This embodiment also provides a computer-readable storage medium, in which computer program code is stored. When the computer program code runs on a computer, the computer is enabled to execute the above-related method steps to implement the clinical nursing information recording method of an intelligent medical system provided in the above embodiment.

[0116] Embodiment 4:

[0117] The present invention also proposes a clinical nursing information recording system for an intelligent medical system. Please refer to Figure 5, which shows the structural diagram of a clinical nursing information recording system of a smart medical system provided by an embodiment of the present invention. The system includes: an acquisition module 10, a nursing effectiveness acquisition module 20, a control patient acquisition module 30, a nursing evaluation degree acquisition module 40, and a judgment module 50.

[0118] The acquisition module 10 is used to acquire the patient category where the current patient is located, and regard other patients within the patient category as target patients. Among them, the target patients have the same disease as the current patient, and the nursing plan of the target patients is the nursing plan of the current patient.

[0119] The nursing effectiveness acquisition module 20 is used to acquire the nursing effectiveness of each target patient according to the goal achievement situation and physical sign stability situation of each target patient; the goal achievement situation represents the number of diagnostic and treatment indicators that the target patient reaches the requirements of the doctor after the nursing plan ends.

[0120] The control patient acquisition module 30 is used to obtain the satisfaction score of the nursing plan of each target patient, screen out low-score patients, and obtain the control patients of each low-score patient according to the similarity of the medications and drug ingredients taken by each low-score patient and other target patients.

[0121] The nursing evaluation degree acquisition module 40 is used to obtain the reference weight of each low-score patient according to the difference in the satisfaction scores of the nursing plans of each low-score patient and their control patients, and the difference in the medications taken; preset the reference weights of other target patients except low-score patients, and obtain the nursing evaluation degree of the current patient according to the reference weights, nursing effectiveness, and satisfaction scores of the nursing plans of each target patient.

[0122] The judgment module 50 is used to judge whether the nursing plan of the current patient is reasonable based on the nursing evaluation degree.

[0123] It should be noted that: for the system provided in the above embodiment, only the above-mentioned division of each functional module is used for illustration. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, a clinical nursing information recording system of a smart medical system and an embodiment of a clinical nursing information recording method of a smart medical system provided in the above embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0124] Example 5:

[0125] The present invention also provides a clinical care information recording device for a smart healthcare system. The device includes a memory and a processor. The memory stores executable program code, and the processor is configured to call and execute the executable program code to perform a clinical care information recording method provided in an embodiment of the present application. The device may specifically be a chip, a component, or a module. The chip may include a processor and a memory connected to each other. The memory is used to store instructions. When the processor calls and executes the instructions, the chip can perform the clinical care information recording method provided in the above embodiment.

[0126] Embodiment 6:

[0127] This embodiment also provides a computer program product. When the computer program product runs on a computer, it causes the computer to perform the above-related steps to implement a clinical care information recording method provided in the above embodiment.

[0128] The device, computer-readable storage medium, computer program product, or chip provided in this embodiment is all used to execute the corresponding method provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method provided above, and will not be elaborated here.

[0129] It should be noted that the above sequence of embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0130] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A clinical nursing information recording method for a smart medical system, characterized in that: The method comprises the following steps: Obtain the patient category of the current patient, and use other patients in the patient category as target patients, wherein the target patients have the same disease as the current patient, and the nursing plan of the target patients is the nursing plan of the current patient; According to the target achievement and physical sign stability of each target patient, the nursing effectiveness of each target patient is obtained; the target achievement means the number of diagnosis and treatment indicators required by the doctor that the target patient has achieved after the nursing plan is completed; Obtain the nursing plan satisfaction score of each target patient to screen out low-scoring patients, and obtain control patients for each low-scoring patient based on the similarity between each low-scoring patient and each other target patient in taking medication and the similarity of the medication ingredients; According to the difference in nursing plan satisfaction scores between each low-scoring patient and its control patient, as well as the difference in medication, the reference weight of each low-scoring patient is obtained; the reference weights of other target patients except low-scoring patients are preset, and the nursing evaluation degree of the current patient is obtained according to the reference weight, nursing effectiveness and nursing plan satisfaction score of each target patient; Determine whether the current patient care plan is reasonable based on the degree of nursing assessment; The method for obtaining the nursing effectiveness is: For any target patient, the ratio of the number of diagnosis and treatment indicators required by the doctor after the target patient's nursing plan to the total number of diagnosis and treatment indicators required by the doctor is used as the target achievement value of the target patient; Obtaining a second reference effectiveness level of the target patient according to the average change value of each physical sign within a preset time period after the end of the nursing plan of the target patient; wherein the average change value and the second reference effectiveness level are negatively correlated; According to the target achievement degree value and the second reference effectiveness degree, the nursing effectiveness degree of the target patient is obtained; The method for obtaining the reference weight is: For any low-score patient, all control patients of the low-score patient are used as reference patients, and control patients of each reference patient are obtained based on the similarity of medications taken and medication ingredients between each reference patient and each other target patient; The nursing plan satisfaction scores of all control patients of the low-scoring patient are sorted from small to large to obtain the target score sequence of the low-scoring patient; Sort the nursing plan satisfaction scores of all control patients of each reference patient from small to large to obtain a reference score sequence of each reference patient; For any reference patient, the difference in nursing plan satisfaction scores between the low-scoring patient and the reference patient is obtained as the first difference; Obtaining the cosine similarity between the target scoring sequence and the reference scoring sequence of the reference patient as the first eigenvalue; According to the first difference and the first eigenvalue, a reference influence degree of the reference patient on the low-scoring patient is obtained; wherein the first difference and the reference influence degree are negatively correlated, and the first eigenvalue and the reference influence degree are positively correlated; The result of normalizing the mean of the reference influence of all reference patients on the low-scoring patient is used as the reference weight of the low-scoring patient.

2. A clinical nursing information recording method for a smart medical system as claimed in claim 1, characterized in that: The method for obtaining the patient category is: For any two patients, according to the similarity of the diagnosis and treatment information of the specified type between the two patients, obtain the similarity degree of the diagnosis and treatment of the two patients; Through the hierarchical clustering algorithm, patients were divided into patient categories according to the similarity of diagnosis and treatment.

3. A clinical nursing information recording method for a smart medical system as claimed in claim 2, characterized in that: The method for obtaining the degree of diagnosis and treatment similarity is: For any specified type of medical information and any two patients, the cosine similarity of the word segmentation sequences of the specified type of medical information of the two patients is used as the first similarity degree; Obtaining a second similarity degree according to the difference in the number of segmented words between the segmented word sequences of the specified type of medical information of the two patients; wherein the difference in the number of segmented words is negatively correlated with the second similarity degree; According to the first similarity and the second similarity, obtaining a reference similarity of the word segmentation sequences of the specified type of diagnosis and treatment information of the two patients; The average of the reference similarities of the word segmentation sequences of all the same specified types of medical treatment information of the two patients is used as the medical treatment similarity of the two patients.

4. A clinical nursing information recording method for a smart medical system as claimed in claim 3, characterized in that: The method for obtaining the control patients is: For any low-score patient and any target patient other than the low-score patient, the cosine similarity between the word segmentation sequence of all drugs taken by the low-score patient and the word segmentation sequence of all drugs taken by the target patient is used as the first similarity degree of the drugs; For any drug taken by the low-scoring patient, the maximum reference similarity between the component segmentation sequence of the drug and the reference similarity between the component segmentation sequence of each drug taken by the target patient is used as the local similarity of the drug; The average of the local similarity of all the drugs taken by the low-scoring patient is taken as the second similarity of the drugs; According to the first drug similarity degree and the second drug similarity degree, the drug similarity degree between the low-scoring patient and the target patient is obtained; When the drug similarity level is greater than a preset drug similarity level threshold, the target patient becomes a control patient for the low-scoring patient.

5. The clinical nursing information recording method of a smart medical system according to claim 1, characterized in that: The method for obtaining the nursing assessment level is: For any target patient, the mean of the nursing effectiveness and normalized satisfaction score of the target patient is obtained as the overall nursing effectiveness of the target patient; The overall nursing effectiveness is modified by the reference weight of the target patient to obtain the real overall nursing effectiveness of the target patient; The average of the actual overall nursing effectiveness of all target patients is used as the nursing assessment level of the current patients.

6. The clinical nursing information recording method of a smart medical system according to claim 1, characterized in that: The method for judging whether the current patient's nursing plan is reasonable based on the nursing assessment degree is: When the nursing assessment level is greater than the preset nursing assessment level threshold, the current nursing plan for the patient is judged to be reasonable; When the nursing assessment level is less than or equal to the preset nursing assessment level threshold, it is judged that the current nursing plan for the patient is unreasonable.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When executing the computer program, the processor implements the steps of the clinical nursing information recording method of the smart medical system described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer processing program, and when the computer processing program is executed by the processor, the steps of the clinical nursing information recording method of the smart medical system described in any one of claims 1 to 6 are implemented.

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