Clinical nursing information recording method and equipment of smart medical system, and medium

Abstract

The invention relates to the technical field of medical care, in particular to a clinical care information recording method and equipment of an intelligent medical system and a medium. The method comprises the following steps: acquiring a patient category of a current patient, taking other patients in the patient category as target patients, and acquiring a nursing effectiveness degree according to a target achievement condition and a physical sign stability condition of the target patients; screening low-score patients through nursing plan satisfaction scoring, obtaining contrast patients of the low-score patients, and obtaining reference weights of the low-score patients according to differences between the low-score patients and the contrast patients; and presetting reference weights of other target patients except the low-score patient, obtaining a nursing evaluation degree according to the reference weights, the nursing effectiveness degree and nursing plan satisfaction scoring, and judging whether the nursing plan of the current patient is reasonable or not. By acquiring the nursing assessment degree, whether the nursing plan of the current patient is reasonable or not is accurately judged, and the nursing plan of the current patient is adjusted in time, so that the current patient is effectively treated.

Description

Technical Field

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

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

[0003] Normally, the nursing plans for patients with the same disease are the same. Therefore, the existing method directly determines whether the nursing plan assigned to the current patient is reasonable by analyzing the evaluation results of the nursing plans of historical patients with the same disease as the current patient. However, in actual situations, the evaluation results of the nursing plans of historical patients are subjective, resulting in inaccurate evaluation results of the nursing plans. It is therefore impossible to accurately analyze the rationality of the nursing plan of the current patient based on the evaluation results of the nursing plans of historical patients with the same disease as the current patient, resulting in an inability to accurately judge whether the nursing plan of the current patient is reasonable, affecting the timely adjustment and optimization of the nursing plan of the current patient. Summary of the invention

[0004] In order to solve the technical problem that the evaluation results of the nursing plans of historical patients are subjective, resulting in inaccurate evaluation results of the nursing plans, and thus it is impossible to accurately judge whether the nursing plans of the current patients are reasonable, the purpose of the present invention is to provide a clinical nursing information recording method, device and medium of a smart medical system, and the technical solutions adopted are as follows: In a first aspect, an embodiment of the present invention provides a method for recording clinical nursing information of a smart medical system, the method comprising 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 level of nursing assessment.

[0005] Furthermore, 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.

[0006] Furthermore, 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.

[0007] Furthermore, 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; The nursing effectiveness level of the target patient is obtained according to the target achievement degree value and the second reference effectiveness level.

[0008] Furthermore, 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.

[0009] Furthermore, the reference weight is obtained by: 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.

[0010] Furthermore, the method for obtaining the nursing assessment degree 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.

[0011] Furthermore, 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.

[0012] 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, and when the processor executes the computer program, the steps of any one of the above methods are implemented.

[0013] 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.

[0014] The present invention has the following beneficial effects: The present invention first obtains the patient category to which the current patient belongs, which is conducive to subsequently improving the efficiency of analyzing whether the nursing plan for the current patient is reasonable; then other patients in the patient category to which the current patient belongs are taken as target patients, which is conducive to a clearer description; and then according to the goal achievement and physical sign stability of each target patient, the nursing effectiveness of each target patient is obtained, accurately reflecting the effect of each target patient after treatment through 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 score of each target patient is obtained to screen out low-scoring patients, and determine the target patients who may have deviations in the evaluation results of the nursing plan; 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 the analysis of the nursing plan of the current patient, and then according to the comparison between each low-scoring patient and each other target patient taking medicine and the medicine Based on the similarity of the components, a control patient for each low-scoring patient is obtained, which is conducive to the subsequent accurate analysis of the reference degree of each low-scoring patient; further, based on the difference in the nursing plan satisfaction score and the difference in medication between each low-scoring patient and his control patient, the reference weight of each low-scoring patient is obtained, which accurately reflects the reference degree of each low-scoring patient when analyzing the rationality of the current patient's nursing plan; in order to make each target patient participate in the analysis of whether the current patient's nursing plan is reasonable, the reference weights of other target patients except low-scoring patients are preset, and finally, based on the reference weight, nursing effectiveness and nursing plan satisfaction score of each target patient, the nursing evaluation degree of the current patient is accurately obtained, which accurately reflects the rationality of the current patient's nursing plan; then, based on the nursing evaluation degree, whether the current patient's nursing plan is reasonable is accurately judged, which is conducive to timely adjustment and optimization of the current patient's nursing plan, to ensure that the current patient receives effective treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] 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 embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A schematic flow chart of a clinical nursing information recording method of a smart medical system provided by an embodiment of the present invention; Figure 2 A flow chart of a method for obtaining a patient category provided by an embodiment of the present invention; Figure 3A flow chart of a method for obtaining a control patient provided by one embodiment of the present invention; Figure 4 A schematic diagram of a computer device provided by an embodiment of the present invention; Figure 5 A structural diagram of a clinical nursing information recording system of a smart medical system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the clinical nursing information recording method, device and medium of the smart medical system proposed by the present invention, its specific implementation method, structure, characteristics and effects are described in detail as follows in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] The following is a detailed description of the clinical nursing information recording method, device and medium of the smart medical system provided by the present invention in conjunction with the accompanying drawings.

[0020] Embodiment 1: The specific scenario of this embodiment is: in order to more accurately analyze whether the nursing plan of the current patient is reasonable, this embodiment first obtains the patient category of the current patient, wherein other patients in the patient category of the current patient have the same disease as the current patient, and other patients in the patient category of the current patient are all historical patients. It is known that the nursing plans of patients with the same disease are the same, and then the nursing plan of the current patient is determined according to the nursing plans of historical patients in the patient category of the current patient. All historical patients in the patient category of the current patient are taken as target patients, and the nursing plan of the current patient can be indirectly analyzed based on the evaluation results of the nursing plans of the target patients. Then, each target patient is analyzed, and the reference weight of each target patient is accurately obtained, and the interference of the evaluation results of the target patients whose condition has not improved due to their own tolerance to drugs on the rationality of the nursing plan of the current patient is reduced. Finally, the nursing evaluation degree of the current patient is accurately obtained according to the reference weight, nursing effectiveness and nursing plan satisfaction of each target patient, and then the nursing evaluation degree of the current patient is accurately judged based on the nursing evaluation degree. Whether the nursing plan of the current patient is reasonable is conducive to timely adjustment and optimization of the nursing plan of the current patient, ensuring that the current patient receives comprehensive and effective care.

[0021] The present invention proposes a method for recording clinical nursing information in a smart medical system. Figure 1 , which shows a schematic flow chart of a clinical nursing information recording method of a smart medical system provided by an embodiment of the present invention, the method comprising the following steps: Step S1: Obtain the patient category of the current patient, and use other patients in the patient category as target patients, wherein the target patient has the same disease as the current patient, and the nursing plan of the target patient is the nursing plan of the current patient.

[0022] Specifically, this embodiment takes a department in a hospital as an example for analysis. The hospital's information system will store the diagnosis and treatment records of each patient who comes to the department for care. One patient corresponds to one diagnosis and treatment record, wherein the diagnosis and treatment record contains various diagnosis and treatment information such as the patient's basic information, medical history information, symptom description, and medication record. The patient's diagnosis and treatment records 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 to three years, wherein the end time of the current time period must be the current time. The implementer can set the size of the current time period according to the actual situation, which is not limited here, and obtains the diagnosis and treatment records of each patient in the current time period. It should be noted that this embodiment has only one current patient, therefore, there is one current patient and multiple historical patients in the current time period.

[0023] It is known that the nursing plans for patients with the same disease are the same. In order to determine the nursing plan for the current patient, we first find historical patients with the same disease as the current patient. Patients with the same disease have the same diagnosis and treatment characteristics. Therefore, we classify patients according to the diagnosis and treatment information in the diagnosis and treatment records, and then determine the historical patients of the same category as the current patient. In actual situations, there are many types of diagnosis and treatment information in the diagnosis and treatment records, and some types of diagnosis and treatment information have little to do with the patient's pathological characteristics. For example, the patient's basic information such as occupation, income and other diagnosis and treatment information. If all the diagnosis and treatment information in the diagnosis and treatment records are analyzed, it will increase the amount of calculation, waste resources, and may cause inaccurate classification results. It is known that the four types of medical information, namely, family history, past medical history, symptom description and medication status, can fully reflect the patient's pathological characteristics. Therefore, this embodiment sets the four types of medical information, namely, family history, past medical history, symptom description and medication status, as designated types of medical information. The more similar the same designated type of medical information is between any two patients, the more likely the two patients are to be patients of the same category. Therefore, this embodiment divides patients into patient categories according to the similarity of the designated type of medical information between different patients.

[0024] Preferably, in one possible implementation of this embodiment, the method for obtaining the patient category is as follows: Figure 2, which shows a flow chart of a method for obtaining a patient category provided in this embodiment, the method comprising the following steps: Step S101: For any two patients, according to the similarity of the specified type of diagnosis and treatment information between the two patients, obtain the degree of similarity of the diagnosis and treatment between the two patients.

[0025] For any two patients, when the same specified type of diagnosis and treatment information between the two patients is also similar, it means that the more similar the diseases of the two patients are, the more similar the treatment processes are, and the more likely the two patients are of the same category. Therefore, this embodiment obtains the degree of similarity of diagnosis and treatment between the two patients based on the similarity of the specified type of diagnosis and treatment information between the two patients. The greater the degree of similarity of diagnosis and treatment, the more likely the two patients are of the same category.

[0026] In one possible implementation of this embodiment, the method for obtaining the degree of similarity of diagnosis and treatment is as follows: for any specified type of diagnosis and treatment information and any two patients, this embodiment obtains the segmentation sequence of the specified type of diagnosis and treatment information of the two patients through Jieba segmentation, wherein Jieba segmentation is a well-known technology and will not be described in detail. The cosine similarity of the segmentation sequence of the specified type of diagnosis and treatment information of the two patients is obtained as the first degree of similarity; the greater the first degree of similarity, the more similar the specified type of diagnosis and treatment information of the two patients is, which indirectly reflects that the two patients are more likely to be of the same category, wherein the method for obtaining the cosine similarity of the segmentation sequence is well-known and will not be described in detail. In order to further analyze the similarity of the specified type of medical information of the two patients, and then obtain the difference in the number of segmentation words between the segmentation sequences of the specified type of medical information of the two patients, the smaller the difference in the number of segmentation words between the segmentation sequences of the specified type of medical information of the two patients, the more similar the specified type of medical information of the two patients is. Therefore, according to the difference in the number of segmentation words between the segmentation sequences of the specified type of medical information of the two patients, the second similarity is obtained; wherein, the difference in the number of segmentation words is negatively correlated with the second similarity; the greater the second similarity, the more similar the specified type of medical information of the two patients is. Then, according to the first similarity and the second similarity, the reference similarity of the segmentation sequence of the specified type of medical information of the two patients is obtained; the greater the reference similarity, the more similar the specified type of medical information of the two patients is accurately explained. In order to accurately analyze whether the two patients are of the same category, the mean of the reference similarity of the segmentation sequences of all the same specified type of medical information of the two patients is obtained as the medical similarity of the two patients.

[0027] In order to accurately represent the degree of similarity of diagnosis and treatment between any two patients, this embodiment specifically quantifies the degree of similarity of diagnosis and treatment into a value of similarity of diagnosis and treatment. The larger the value of similarity of diagnosis and treatment, the more likely the two patients are of the same category. The calculation formula of the value of similarity of diagnosis and treatment is: ; In the formula, is the diagnosis and treatment similarity value between the i-th patient and the j-th patient; V is the number of types of diagnosis and treatment information of the specified type, which is set to 4 in this embodiment; The word segmentation sequence of the vth specified type of medical information for the i-th patient; The word segmentation sequence of the vth specified type of medical information for the jth patient; is the cosine function; The number of segmented words in the segmented word sequence of the vth specified type of medical information for the i-th patient; The number of segmented words in the segmented word sequence of the vth specified type of medical information for the jth patient; is the absolute value function; exp is the exponential function with a natural constant as the base; is the first similarity value; is the second similarity value; The reference similarity value of the word segmentation sequence of the vth specified type of medical information for the i-th patient and the j-th patient.

[0028] At this point, the diagnosis and treatment similarity values ​​of any two patients are obtained.

[0029] Step S102: using a hierarchical clustering algorithm, the patients are divided into patient categories according to the similarity of their diagnosis and treatment.

[0030] According to the diagnosis and treatment similarity values, all patients are divided into patient categories by a hierarchical clustering algorithm. Among them, patients in the same patient category have the same disease and are treated with the same nursing plan. Among them, the hierarchical clustering algorithm is a well-known technology and will not be described in detail.

[0031] Determine the patient category of the current patient. In order to clearly describe it later, all historical patients in the patient category of the current patient are taken as target patients, where the nursing plan of the target patient is the nursing plan of the current patient.

[0032] Step S2: Obtain the nursing effectiveness of each target patient based on the target achievement and physical sign stability of each target patient; the target achievement indicates the number of diagnosis and treatment indicators required by the doctor that the target patient achieves after the nursing plan is completed.

[0033] It is known that the current patient and the target patient have the same disease condition and the same nursing plan. If the target patient's condition has improved significantly or recovered after the end of the nursing plan, it indirectly reflects that the nursing plan for the current patient is more reasonable. When a target patient reaches a greater number of diagnostic and treatment indicators required by the doctor after the end of the nursing plan, it means that the nursing effect of the target patient is better; when the physical signs of the target patient are more stable after the end of the nursing plan, it also means that the nursing effect of the target patient is better. Then, according to the goal achievement and physical sign stability of each target patient, the nursing effectiveness of each target patient is obtained. The greater the nursing effectiveness, the better the recovery of the corresponding target patient, which indirectly indicates that the nursing plan of the current patient is more reasonable.

[0034] Preferably, in a method that can be implemented in this embodiment, the method for obtaining the effectiveness of nursing care is: for any target patient, the ratio of the number of diagnostic and treatment indicators required by the doctor after the target patient's nursing plan to the total number of diagnostic and treatment indicators required by the doctor is used as the target patient's target achievement value; the larger the target achievement 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 raised, after the nursing plan treatment, one of the diagnostic and treatment indicators required by the doctor is that the target patient's arm can be raised by 30 cm. If after the nursing plan is completed, the height that the target patient's arm can be raised is greater than or equal to 30 cm, then the target patient has achieved a diagnostic and treatment indicator required by the doctor after the nursing plan. Among them, the number of treatment indicators required by the doctor after the target patient's nursing plan and the total number of treatment indicators required by the doctor can be directly obtained in the patient's diagnosis and treatment record; in order to further analyze the nursing situation of the target patient, the average change value of each sign in the preset time period after the end of the nursing plan of the target patient is further obtained. The smaller the average change value, the more stable the corresponding 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 repeated. Then, according to the average change value of each sign in the preset time period after the end of the nursing plan of the target patient, the second reference effectiveness of the target patient is obtained; wherein, 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 end of the nursing plan of the target patient to the current moment is less than 3 months, the time period from the end of the nursing plan of the target patient 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, which is not limited here. Among them, the physical signs in this embodiment include pulse, blood pressure and body temperature. The implementer can set the type of physical signs according to actual conditions, and there is no limitation here.

[0035] In order to accurately standardize the nursing effect of each target patient, this embodiment specifically quantifies the nursing effectiveness as a nursing effectiveness value. The larger the nursing effectiveness value, the better the nursing effect of the corresponding target patient, which indirectly indicates that the nursing plan of the current patient is more reasonable. The calculation formula of the nursing effectiveness value is: ; In the formula, is the nursing effectiveness value of the tth target patient; The target number of patients who meet the target after the completion of the nursing plan for the tth target patient; is the target number of targets set by the doctor before nursing the tth 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 kth physical sign within the preset time period after the end of the nursing plan for the tth target patient; is the second preset constant, greater than 0; norm is the normalization function; is the degree of goal achievement; is the second reference validity value.

[0036] This embodiment will Set to 1 to avoid the denominator being 0. Implementers can set it according to actual conditions. The size is not limited here.

[0037] At this point, the nursing effectiveness value of each target patient is obtained.

[0038] Step S3: 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.

[0039] It is known that the evaluation result of the nursing plan of the current patient is evaluated based on the evaluation result of the nursing plan of the target patient, wherein the evaluation result of the nursing plan of the target patient is jointly determined according to the nursing effectiveness of the target patient and the satisfaction score of the target patient to the nursing plan. In this embodiment, the satisfaction score of the target patient to the nursing plan is set to 0 to 10, where 0 is the lowest score indicating that the target patient is very dissatisfied with the nursing plan, and 10 is the highest score indicating that the target patient is very satisfied with the nursing plan. After the nursing plan is completed, the target patients will manually score the nursing plan based on their overall feeling about the nursing plan, thereby obtaining the nursing plan satisfaction score of each target patient.

[0040] In actual situations, there are special target patients whose bodies develop tolerance to drugs. For example, when a target patient develops tolerance to drugs, that is, the target patient's condition improves slowly or does not improve after taking drugs according to the nursing plan, the target patient's nursing plan satisfaction score is low. The reason for the low nursing plan satisfaction score is the ineffectiveness of the drugs, not the unreasonable formulation of the nursing plan. In order to accurately analyze the reasons for the low nursing plan satisfaction score, this embodiment first screens out low-scoring patients according to the nursing plan satisfaction score of each target patient, and then analyzes the improvement of the condition of each low-scoring patient compared with other target patients taking the same drugs, and determines whether each low-scoring patient is scored for nursing plan satisfaction due to physical tolerance to drugs or unreasonable nursing plans. In this way, when subsequently calculating the overall evaluation results of the target patients, the reference level of each low-scoring patient is accurately adjusted to reduce the accuracy of the analysis of the current patient's nursing plan due to the patient's own tolerance to drugs, so as to more accurately analyze whether the current patient's nursing plan is reasonable. Taking into account the different prescribing preferences and habits of different doctors, there are cases where target patients with the same symptoms take drugs with different names but similar drug ingredients. In order to more accurately analyze the reasons why each low-scoring patient scores satisfaction with the nursing plan, this embodiment obtains a control patient for each low-scoring patient based on the similarity between the drugs taken and the drug ingredients of each low-scoring patient and each other target patient.

[0041] In one possible implementation of this embodiment, the method for obtaining low-scoring patients is as follows: when the nursing plan satisfaction score is less than a preset satisfaction score threshold, the corresponding target patient is a low-scoring patient. In this embodiment, the preset satisfaction score threshold is set to 3, and the implementer can set the preset satisfaction score threshold according to the actual situation, which is not limited here. At this point, low-scoring patients among the target patients are screened out.

[0042] Preferably, in one possible implementation of this embodiment, the method for obtaining the control patient is as follows: Figure 3 , which shows a flow chart of a method for obtaining a control patient provided in this embodiment, the method comprising the following steps: Step S301: For any low-score patient and any target patient except 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 drug similarity.

[0043] As an example, the b-th low-score patient and the q-th target patient except the b-th low-score patient are taken as examples for analysis. The segmentation sequence of all drugs taken by the b-th low-score patient and the segmentation sequence of all drugs taken by the q-th target patient are obtained through Jieba word segmentation. The cosine similarity of the segmentation sequence of all drugs taken by the b-th low-score patient and the segmentation sequence of all drugs taken by the q-th target patient is obtained as the first similarity value of the drugs. The larger the first similarity value of the drugs, 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 to be the control patient of the b-th low-score patient.

[0044] Step S302: For any drug taken by the low-scoring patient, the maximum reference similarity between the component segmentation sequence of the drug and the reference similarities of the component segmentation sequence of each drug taken by the target patient is used as the local similarity of the drug.

[0045] It is known that a patient may take multiple drugs. Therefore, this embodiment compares the ingredients of each drug taken by the b-th low-score patient with the ingredients of each drug taken by the q-th target patient to more accurately analyze the similarities between 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, the component segmentation sequence of the h-th drug taken by the b-th low-score patient and the component segmentation sequence of each drug taken by the q-th target patient are obtained through Jieba segmentation. The reference similarity between the component segmentation sequence of the h-th drug and the component segmentation sequence of each drug taken by the q-th target patient is obtained by the method of obtaining the reference similarity in step S2.

[0046] Taking the fth drug taken by the qth target patient as an example, the calculation formula for obtaining the reference similarity value between the component segmentation sequence of the hth drug and the component segmentation sequence of the fth drug is: ; In the formula, The reference similarity value between the component word sequence of the hth drug taken by the bth low-scoring patient and the component word sequence of the fth drug taken by the qth target patient; The component segmentation sequence of the hth drug taken by the bth low-scoring patient; The component segmentation sequence of the fth drug taken by the qth target patient; The number of segmentations in the component segmentation sequence of the hth drug taken by the bth low-scoring patient; The number of segmentations in the component segmentation sequence of the fth drug taken by the qth target patient; is the absolute value function; is the cosine function; exp is the exponential function with a natural constant as the base.

[0047] According to the method of obtaining the reference similarity value of the component word segmentation sequence of the hth drug taken by the bth low-score patient and the component word segmentation sequence of the fth drug taken by the qth target patient, the reference similarity value of the component word segmentation sequence of the hth drug taken by the bth low-score patient and the component word segmentation sequence of each drug taken by the qth target patient is obtained, and the maximum reference similarity value among the reference similarity values ​​of the component word segmentation sequence of the hth drug taken by the bth low-score patient and the component word segmentation sequence of each drug taken by the qth target patient is used as the local similarity value of the hth drug taken by the bth low-score patient.

[0048] According to the method for obtaining the local similarity value of the h-th drug taken by the b-th low-score patient, the local similarity value of each drug taken by the b-th low-score patient is obtained.

[0049] Step S303: taking the average of the local similarities of all the drugs taken by the low-scoring patient as the second drug similarity.

[0050] In order to accurately obtain the similarity between the medications taken by the b-th low-score patient and the q-th target patient, the average of the local similarity values ​​of all the drugs taken by the b-th low-score patient is taken as the second similarity value of the drugs between the b-th low-score patient and the q-th target patient; the larger the second similarity value of the drugs, 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 to be the control patient for the b-th low-score patient.

[0051] Step S304: According to the first drug similarity and the second drug similarity, the drug similarity between the low-scoring patient and the target patient is obtained.

[0052] It is known that when the first drug similarity and the second drug similarity are larger, it means that the drugs taken by the b-th low-score patient and the q-th target patient are more similar. Then, based on the first drug similarity and the second drug similarity, the drug similarity between the b-th low-score patient and the q-th target patient is obtained.

[0053] In order to accurately represent the degree of similarity between the drugs of the bth low-scoring patient and the qth target patient, this embodiment specifically quantifies the degree of similarity as a drug similarity value. The larger the drug similarity value, the more similar the drugs taken by the bth low-scoring patient and the qth target patient are. The calculation formula of the drug similarity value is: ; In the formula, is the drug similarity value between the bth low-score patient and the qth target patient; is the first similarity value of the drug between the bth low-score patient and the qth target patient; is the second similarity value of the drug between the bth low-score patient and the qth target patient; norm is a normalization function. In other embodiments, Get , which is not limited here.

[0054] Step S305: When the drug similarity is greater than a preset drug similarity threshold, the target patient is a control patient for the low-scoring patient.

[0055] It is known that the greater the similarity of the drugs, the more similar the drugs taken by the b-th low-scoring patient and the q-th target patient are, and the more likely the q-th target patient is a control patient for the b-th low-scoring patient. Therefore, this embodiment sets the preset drug similarity threshold to 0.5, and the implementer can set the preset drug similarity threshold according to actual conditions, which is not limited here. When the drug similarity value between the b-th low-scoring patient and the q-th target patient is greater than the preset drug similarity threshold, the q-th target patient is a control patient for the b-th low-scoring patient.

[0056] Obtain the drug similarity value between the b-th low-score patient and each target patient except the b-th low-score patient, and then determine each control patient corresponding to the b-th low-score patient.

[0057] At this point, a control patient is obtained for each low-scoring patient.

[0058] Step S4: Obtain the reference weight of each low-scoring patient based on the difference in nursing plan satisfaction scores between each low-scoring patient and their control patients, as well as the difference in medication taken; preset reference weights for other target patients except low-scoring patients, and obtain the nursing assessment level of the current patient based on the reference weight, nursing effectiveness and nursing plan satisfaction score of each target patient.

[0059] Specifically, when the difference between the nursing plan satisfaction score of a low-scoring patient and each of its control patients is greater, it means that the low-scoring patient is more special, the nursing plan satisfaction score of the low-scoring patient is less correlated with the unreasonable nursing plan, and the correlation with the low-scoring patient's own drug resistance is greater, which indirectly indicates that the evaluation result of the nursing plan of the low-scoring patient is less accurate. When evaluating the rationality of the nursing plan of the current patient, the reference degree should be smaller to avoid affecting the accuracy of the analysis of whether the nursing plan of the current patient is reasonable. At the same time, in order to more accurately analyze the reference degree of the low-scoring patient based on the control patient of the low-scoring patient, further analyze the similarity between the medication situation of the control patient of the low-scoring patient and the medication situation of the control patient of each control patient of the low-scoring patient. When the medication situation is more similar, the reference degree of the analyzed low-scoring patient is more accurate. Then, according to the difference in the nursing plan satisfaction score of each low-scoring patient and its control patient, and the difference in the medication taken, the reference weight of each low-scoring patient is obtained. Among them, the smaller the reference weight, the less accurate the evaluation result of the nursing plan of the corresponding low-scoring patient.

[0060] It is known that by analyzing the overall evaluation results of the nursing plans of all target patients, it is possible to infer whether the nursing plan of the current patient is reasonable. Therefore, in order to allow each target patient to participate, this embodiment presets the reference weight of each target patient except the low-scoring patient to be 1. The implementer can preset the reference weight of each target patient except the low-scoring patient according to the actual situation, which is not limited here. It is known that the nursing effectiveness and nursing plan satisfaction scores of the target patients can reflect the evaluation results of the nursing plan. Considering that some target patients have drug resistance, which leads to deviations in the evaluation results of the nursing plan, this embodiment adjusts the nursing effectiveness and nursing plan satisfaction scores of the corresponding target patients through the reference weight of each target patient to reduce the impact of inaccurate nursing plan evaluation results. In addition, this embodiment accurately obtains the nursing evaluation degree of the current patient according to the reference weight, nursing effectiveness and nursing plan satisfaction scores of each target patient. Among them, the greater the nursing evaluation degree, the more reasonable the nursing plan formulated for the current patient.

[0061] Preferably, in an achievable manner of the present embodiment, the method for obtaining the reference weight is as follows: for any low-scoring patient, all control patients of the low-scoring patient are used as reference patients, and the method for obtaining control patients in step S3 is used to obtain control patients for each reference patient based on the similarity between the medications taken by each reference patient and each other target patient and the similarity of the medication ingredients; the nursing plan satisfaction scores of all control patients of the low-scoring patient are sorted from small to large to obtain a target score sequence for the low-scoring patient; the nursing plan satisfaction scores of all control patients of each reference patient are sorted from small to large to obtain a reference score sequence for each reference patient; for any reference patient, the absolute value of the difference between the nursing plan satisfaction scores of the low-scoring patient and the reference patient is obtained as the reference score sequence. is the first difference; the larger the first difference is, the more special the nursing plan satisfaction score of the low-scoring patient is, which indirectly indicates that the nursing plan satisfaction score of the low-scoring patient is more correlated with the drug resistance of the patient, and the smaller the reference significance is; further obtain the cosine similarity of the target score sequence and the reference score sequence of the reference patient as the first eigenvalue; the larger the first eigenvalue is, the more accurate the first difference is, which is conducive to more accurate analysis of the reference degree of the low-scoring patient; in order to accurately determine the influence of the reference patient on the reference degree of the low-scoring patient, the reference influence degree of the reference patient on the low-scoring patient is obtained according to the first difference and the first eigenvalue; wherein the first difference is negatively correlated with the reference influence degree, and the first eigenvalue is positively correlated with the reference influence degree; The calculation formula for the reference impact degree is: ; In the formula, The reference influence degree of the ath reference patient of the bth low-score patient on the bth low-score patient; Rate the satisfaction with the care plan for the bth low-scoring patient; Score the satisfaction with the care plan for the ath reference patient who is the bth low-scoring patient; is the absolute value function; is the first difference; is the third preset constant, which is greater than 0; is the first characteristic value; wherein, this embodiment sets 1 to avoid the denominator being 0. The implementer can set it according to the actual situation. The size is not limited here; In order to comprehensively represent the reference degree of the low-scoring patient, the mean of the reference influence degree of all reference patients on the low-scoring patient is normalized as the reference weight of the low-scoring patient. It should be noted that in this embodiment, the mean of the reference influence degree of all reference patients on the low-scoring patient is normalized by the norm normalization function.

[0062] At this point, the reference weight of each low-score patient is obtained. The reference weight of each target patient other than the low-score patient is 1.

[0063] Preferably, in a method that can be implemented in this embodiment, the method for obtaining the nursing evaluation degree is: for any target patient, the average of the nursing effectiveness degree and the normalized nursing plan satisfaction score of the target patient is obtained as the overall nursing effectiveness degree of the target patient; the greater the overall nursing effectiveness degree, the better the effect of the target patient's treatment through the nursing plan, which indirectly indicates that the nursing plan of the current patient is more reasonable. Considering that different target patients have different interference levels in the evaluation results of the nursing plan, in order to more accurately analyze the evaluation results of the target patient on the nursing plan, this embodiment modifies the overall nursing effectiveness degree through the reference weight of the target patient to obtain the real overall nursing effectiveness degree of the target patient; the greater the real overall nursing effectiveness degree, the more accurately it indicates that the target patient has a better effect of treatment through the nursing plan. Then the average of the real overall nursing effectiveness degrees of all target patients is used as the nursing evaluation degree of the current patient.

[0064] In order to accurately represent the nursing assessment degree of the current patient, this embodiment specifically quantifies the nursing assessment degree into a nursing assessment degree value. The larger the nursing assessment degree value, the more reasonable the nursing plan for the current patient. The calculation formula of the nursing assessment degree value is: ; In the formula, is the nursing assessment level of the current patient; W is the total number of target patients; is the reference weight of the wth target patient; is the nursing effectiveness value of the wth target patient; is the nursing plan satisfaction score of the wth target patient; norm is the normalization function; is the overall nursing effectiveness value of the wth target patient; is the actual overall nursing effectiveness value of the wth target patient.

[0065] Step S5: Determine whether the current nursing plan for the patient is reasonable based on the nursing assessment level.

[0066] It is known that the greater the nursing assessment degree, the more reasonable the nursing plan for the current patient is. Therefore, this embodiment determines whether the nursing plan for the current patient is reasonable based on the nursing assessment degree.

[0067] This embodiment sets the preset nursing assessment threshold to 0.6. The implementer can set the preset nursing assessment threshold according to the actual situation, which is not limited here. When the nursing assessment value is greater than the preset nursing assessment threshold, it means that the nursing plan of the current patient is reasonable and does not need to be adjusted; when the nursing assessment value is less than or equal to the preset nursing assessment threshold, it means that the nursing plan of the current patient is unreasonable, and the medical staff needs to adjust and optimize the nursing plan of the current patient in time to ensure that the current patient receives effective treatment according to the adjusted nursing plan.

[0068] In summary, this embodiment obtains the patient category of the current patient, takes other patients in the patient category as target patients, obtains the nursing effectiveness according to the target patient's goal achievement and physical sign stability; screens low-scoring patients through nursing plan satisfaction scoring, obtains control patients of low-scoring patients, and obtains the reference weight of low-scoring patients according to the difference between low-scoring patients and control patients; presets the reference weight of other target patients except low-scoring patients, obtains the nursing evaluation degree according to the reference weight, nursing effectiveness and nursing plan satisfaction score, and judges whether the nursing plan of the current patient is reasonable. The present invention accurately judges whether the nursing plan of the current patient is reasonable by obtaining the nursing evaluation degree, and adjusts the nursing plan of the current patient in time, so that the current patient can receive effective treatment.

[0069] Embodiment 2: The present invention also provides a computer device, see Figure 4 The computer device includes a memory 401, a processor 402, and a computer program 403 stored in the memory 401 and running on the processor 402, wherein 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 smart medical system introduced above.

[0070] Embodiment 3: This embodiment also provides a computer-readable storage medium, which stores computer program code. When the computer program code runs on a computer, the computer executes the above-mentioned related method steps to implement a clinical nursing information recording method for a smart medical system provided in the above embodiment.

[0071] Embodiment 4: The present invention also proposes a clinical nursing information recording system for a smart medical system, see Figure 5, which shows a structure 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 assessment degree acquisition module 40 and a judgment module 50.

[0072] The acquisition module 10 is used to obtain the patient category of the current patient and take 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.

[0073] The nursing effectiveness acquisition module 20 is used to obtain the nursing effectiveness of each target patient based on the target achievement and physical sign stability of each target patient; the target achievement indicates the number of diagnosis and treatment indicators required by the doctor that the target patient achieves after the nursing plan is completed.

[0074] The control patient acquisition module 30 is used to obtain the nursing plan satisfaction score of each target patient to screen out low-scoring patients, and obtain a control patient 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.

[0075] The nursing assessment level acquisition module 40 is used to obtain the reference weight of each low-scoring patient based on the difference in nursing plan satisfaction scores between each low-scoring patient and its control patient, as well as the difference in medication taken; the reference weights of other target patients except low-scoring patients are preset, and the nursing assessment level of the current patient is obtained based on the reference weight, nursing effectiveness and nursing plan satisfaction score of each target patient.

[0076] The judgment module 50 is used to judge whether the current nursing plan for the patient is reasonable based on the nursing assessment level.

[0077] It should be noted that the system provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, 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, the clinical nursing information recording system of a smart medical system provided in the above embodiment and the clinical nursing information recording method embodiment of a smart medical system belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0078] Embodiment 5: The present invention also proposes a clinical nursing information recording device for a smart medical system, the device comprising a memory and a processor, wherein the memory stores an executable program code, and the processor is used to call and execute the executable program code to execute a clinical nursing information recording method for a smart medical system provided in an embodiment of the present application. The device can be specifically a chip, a component or a module, and the chip may include a connected processor and a memory; wherein the memory is used to store instructions, and when the processor calls and executes the instructions, the chip can execute a clinical nursing information recording method for a smart medical system provided in the above embodiment.

[0079] Embodiment 6: This embodiment also provides a computer program product. When the computer program product runs on a computer, the computer executes the above-mentioned related steps to implement a clinical nursing information recording method of a smart medical system provided in the above embodiment.

[0080] Among them, the device, computer-readable storage medium, computer program product or chip provided in this embodiment is 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 repeated here.

[0081] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying 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.

[0082] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced 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; The nursing effectiveness level of the target patient is obtained according to the target achievement degree value and the second reference effectiveness level.

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. A clinical nursing information recording method for a smart medical system as claimed in claim 4, characterized in that: 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.

6. 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.

7. 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.

8. 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 7.

9. 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 7 are implemented.

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