Method and system for digitally building files of patient rehabilitation information based on electronic medical records

Abstract

The present invention relates to the field of rehabilitation information archiving and recording technology, and specifically to a method and system for digitally archiving patient rehabilitation information based on electronic medical records. The present invention first obtains the rehabilitation file of the patient to be rehabilitated at the current time of the follow-up visit; then obtains the recovery evaluation parameters of the patient to be rehabilitated at the current time of the follow-up visit; screens out all reference rehabilitation patients of the patient to be rehabilitated to obtain the recovery favorable coefficient of the patient to be rehabilitated, and then adjusts the preset fixed follow-up cycle to obtain the next future follow-up time; iterates the follow-up visit to update the rehabilitation file, and finally obtains the digital rehabilitation file of the patient to be rehabilitated. The present invention evaluates the rehabilitation favorable coefficient of the patient to be rehabilitated based on the rehabilitation information of all historical rehabilitation patients with similar disease conditions as the patient to be rehabilitated; then evaluates the current recovery situation of the patient to be rehabilitated in combination with the changes in the physiological functions of the patient to be rehabilitated, so as to reasonably adjust the follow-up cycle, thereby improving the digital archiving effect of the patient's rehabilitation information.

Description

Technical Field

[0001] The present invention relates to the technical field of rehabilitation information archiving and recording, and in particular to a method and system for digitally archiving patient rehabilitation information based on electronic medical records. Background Art

[0002] The establishment of a patient's rehabilitation information file is a crucial link in the medical process. It not only concerns the management of the patient's personal health information, but also involves many aspects such as medical quality, legal compliance, and scientific research development. The patient's rehabilitation information file can help medical staff quickly obtain patient information, and is also conducive to tracking the condition and dynamically monitoring the rehabilitation process, so as to accurately formulate treatment plans and improve rehabilitation efficiency.

[0003] At present, the rehabilitation information of patients is usually initially filed using the patient's electronic medical record, and then the relevant medical staff will conduct regular follow-up visits to evaluate the patient's rehabilitation status in order to continuously record and improve the rehabilitation file, thereby helping medical staff to better formulate personalized rehabilitation plans; however, regular follow-up visits have certain limitations. For patients who have recovered well, a fixed follow-up cycle may be a waste of medical resources; for patients who have recovered slowly, a fixed follow-up cycle may result in medical staff being unable to detect potential rehabilitation problems in a timely manner; therefore, an inappropriate fixed follow-up cycle will affect the effect of digital filing of patient rehabilitation information. Summary of the invention

[0004] In order to solve the technical problem that the inappropriate fixed return visit cycle in the prior art will lead to poor effect of digital archiving of patient rehabilitation information, the purpose of the present invention is to provide a method and system for digital archiving of patient rehabilitation information based on electronic medical records. The technical solution adopted is as follows:

[0005] The present invention proposes a method for digitally filing patient rehabilitation information based on electronic medical records, the method comprising:

[0006] Obtaining the rehabilitation file of the patient to be rehabilitated at the current time of the follow-up visit; the rehabilitation file includes the electronic medical record information of the patient to be rehabilitated before the rehabilitation treatment and the rehabilitation record information of each follow-up visit during the historical rehabilitation process; the electronic medical record information includes physical sign assessment information and symptom description information;

[0007] According to the changes in the rehabilitation record information between adjacent follow-up visits in the rehabilitation file, the recovery assessment parameters of the patient to be rehabilitated at the current follow-up time are obtained; according to the similarity of the symptom description information between patients, all reference rehabilitation patients of the patient to be rehabilitated are screened out from all the historical rehabilitation patients of the rehabilitation therapist to whom the patient to be rehabilitated belongs; according to the physical sign assessment information and rehabilitation process of all the reference rehabilitation patients, the recovery favorable coefficient of the patient to be rehabilitated is obtained;

[0008] At the current follow-up time, according to the recovery assessment parameters and the recovery favorable coefficient of the patient to be rehabilitated, the preset fixed follow-up cycle is adjusted to obtain the next future follow-up time; all future follow-up times in the future rehabilitation process of the patient to be rehabilitated and the future rehabilitation record information at each future follow-up time are iteratively obtained until the patient is recovered; all future rehabilitation records are added to the rehabilitation file to obtain a digital rehabilitation file of the patient to be rehabilitated.

[0009] Furthermore, the rehabilitation record information includes at least the Barthel index and the functional parameters under each functional recovery index; the physical sign assessment information includes at least the functional independence measurement score and the physical sign parameters under each physical sign index; and the disease description information includes at least the disease vector and several symptom vectors.

[0010] Furthermore, the method for obtaining the recovery assessment parameter includes:

[0011] According to the parameter difference between the functional parameters under the same functional recovery index between adjacent return visits of the patient to be rehabilitated, the functional recovery parameter of the patient to be rehabilitated at the current return visit time is obtained; the parameter difference is positively correlated with the functional recovery parameter;

[0012] Between the current return visit of the patient to be rehabilitated and the previous return visit, according to the index difference corresponding to the Barthel index and the deviation of the Barthel index corresponding to the current return visit relative to the preset standard index, the life ability recovery parameter of the patient to be rehabilitated at the current return visit time is obtained; the index difference is positively correlated with the life ability recovery parameter, and the deviation is negatively correlated with the life ability recovery parameter;

[0013] According to the functional recovery parameters and the life ability recovery parameters, the recovery assessment parameters of the patient to be rehabilitated at the current follow-up time are obtained; the functional recovery parameters and the life ability recovery parameters are both positively correlated with the recovery assessment parameters.

[0014] Furthermore, the method for obtaining the disease vector and the symptom vector includes:

[0015] The disease description information includes disease diagnosis result segmentations and several disease symptom segmentations; the disease vector of the disease diagnosis result segmentation and the symptom vector of each disease symptom segmentation are obtained based on the Word2Vec model.

[0016] Furthermore, the similarity acquisition method includes:

[0017] Normalizing the cosine similarities of the disease vectors between different patients, and using the normalized result as a first similarity parameter;

[0018] Obtain all binary combinations of the symptom vectors between different patients; use the cosine similarity between the two symptom vectors in each binary combination as a second similarity sub-parameter, normalize the mean of the second similarity sub-parameters of all the binary combinations, and use the normalized sum result as the second similarity parameter;

[0019] The first similarity parameter and the second similarity parameter are integrated to obtain the similarity of the symptom description information between the patient to be rehabilitated and each historical rehabilitated patient; the first similarity parameter and the second similarity parameter are both positively correlated with the similarity.

[0020] Furthermore, the method for obtaining the reference rehabilitation patients includes:

[0021] When the similarity of the disease description information between the patient to be rehabilitated and each historical rehabilitated patient is greater than a preset threshold, the corresponding historical rehabilitated patient will be used as a reference rehabilitated patient for the patient to be rehabilitated.

[0022] Furthermore, the method for obtaining the recovery benefit coefficient includes:

[0023] According to the satisfaction of each of the reference rehabilitation patients with the rehabilitation process and the duration of the rehabilitation process, a rehabilitation effect coefficient of each of the reference rehabilitation patients is obtained; the satisfaction is positively correlated with the rehabilitation effect coefficient, and the duration of the rehabilitation process is negatively correlated with the rehabilitation effect coefficient;

[0024] According to the functional independence measurement score of each of the reference rehabilitation patients and the abnormal deviation degree of each of the physical sign parameters relative to the preset standard physical sign range under the corresponding physical sign index, the rehabilitation difficulty coefficient of each of the reference rehabilitation patients is obtained; the functional independence measurement score is negatively correlated with the rehabilitation difficulty coefficient, and the abnormal deviation degree is positively correlated with the rehabilitation difficulty coefficient;

[0025] The rehabilitation effect coefficients and rehabilitation difficulty coefficients of all the reference rehabilitation patients of the patient to be rehabilitated are comprehensively considered to obtain a recovery favorable coefficient for the patient to be rehabilitated; the rehabilitation effect coefficient and the rehabilitation difficulty coefficient are both positively correlated with the recovery favorable coefficient.

[0026] Furthermore, the method for obtaining the abnormal deviation degree includes:

[0027] Under each physical sign index, if the physical sign parameter is within the corresponding preset standard physical sign range, the abnormal deviation parameter of the physical sign parameter is set to 0; if the physical sign parameter is not within the corresponding preset standard physical sign range, the midpoint of the interval of the preset standard physical sign range is used as the standard reference coefficient, and the difference between the physical sign parameter and the corresponding standard reference coefficient is used as the deviation sub-parameter of the physical sign parameter;

[0028] The deviation sub-parameters of the physical sign parameters of each reference rehabilitation patient under all physical sign indicators are integrated to obtain the abnormal deviation degree of each reference rehabilitation patient; the deviation sub-parameter is positively correlated with the abnormal deviation degree.

[0029] Furthermore, the method for obtaining the next future return visit time includes:

[0030] Obtaining a recovery gain weight according to the recovery advantage coefficient of the patient to be rehabilitated, weighting the recovery assessment parameter using the recovery gain weight, normalizing the weighted result of the recovery assessment parameter, and using the normalized result as the recovery ability coefficient of the corresponding patient at the current return visit time;

[0031] The recovery capacity coefficient of the patient to be rehabilitated is added with a constant 1 as a period adjustment weight, and the preset fixed return visit period is weighted using the period adjustment weight. The weighted result of the preset fixed return visit period is used as the return visit interval between the current return visit time and the next future return visit time of the corresponding patient; and the next future return visit time is obtained using the return visit interval.

[0032] The present invention also proposes a system for digitally filing patient rehabilitation information based on electronic medical records. The system 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 the method for digitally filing patient rehabilitation information based on electronic medical records are implemented.

[0033] The present invention has the following beneficial effects:

[0034] The present invention first obtains the rehabilitation file of the patient to be rehabilitated at the current return visit time; the rehabilitation file is the first edition of the digital rehabilitation file updated at each return visit, and also provides a data analysis basis for subsequent evaluation and adjustment of the return visit cycle; according to the changes in the rehabilitation record information, the recovery evaluation parameters of the patient to be rehabilitated at the current return visit time are obtained, which are used to evaluate the recovery of the disease; further, all reference rehabilitation patients of the patient to be rehabilitated are screened out to provide a reference basis for evaluating the rehabilitation skills of the rehabilitation therapists to determine the rehabilitation benefit coefficient; according to the physical sign evaluation information of all reference rehabilitation patients and the rehabilitation process, the recovery benefit coefficient of the patient to be rehabilitated is obtained, which is used to evaluate the rehabilitation benefit of the rehabilitation therapist to the corresponding patient to be rehabilitated, and also reflects the rehabilitation skill level of the rehabilitation therapist; at the current return visit time, according to the recovery evaluation parameters and the recovery benefit coefficient of the patient to be rehabilitated, the preset fixed return visit cycle is adjusted to obtain the next future return visit time; all future return visit times of the patient to be rehabilitated in the future rehabilitation process and the future rehabilitation record information at each future return visit time are iteratively obtained until the patient is recovered; all future rehabilitation records are added to the rehabilitation file to obtain the digital rehabilitation file of the patient to be rehabilitated. The present invention is based on the rehabilitation information of all historical rehabilitation patients with similar disease conditions to the patient to be rehabilitated, and evaluates the rehabilitation skills of their fellow rehabilitation therapists to determine the rehabilitation favorable coefficient of the patient to be rehabilitated; then, the current recovery status of the patient to be rehabilitated is evaluated in combination with the changes in the patient's physiological functions, so as to reasonably adjust the follow-up cycle, thereby improving the digital archiving effect of the patient's rehabilitation information. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] 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.

[0036] Figure 1 A flowchart of a method for digitally filing patient rehabilitation information based on electronic medical records provided by one embodiment of the present invention;

[0037] Figure 2 A flow chart of a method for obtaining recovery evaluation parameters provided by an embodiment of the present invention;

[0038] Figure 3 A flow chart of a method for obtaining a recovery benefit coefficient provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a patient rehabilitation information digital archiving method and system based on electronic medical records proposed by the present invention 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.

[0040] 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.

[0041] The following is a detailed description of a method and system for digitally filing patient rehabilitation information based on electronic medical records provided by the present invention in conjunction with the accompanying drawings.

[0042] See also Figure 1 , which shows a flow chart of a method for digitally creating a file of patient rehabilitation information based on electronic medical records provided by an embodiment of the present invention, specifically comprising:

[0043] Step S1, obtaining the rehabilitation file of the patient to be rehabilitated at the current time of the follow-up visit; the rehabilitation file includes the electronic medical record information of the patient to be rehabilitated before the rehabilitation treatment and the rehabilitation record information of each follow-up visit during the historical rehabilitation process; the electronic medical record information includes physical sign assessment information and symptom description information.

[0044] It should be noted that the patients to be recovered targeted by the embodiments of the present invention are patients who have not recovered and need follow-up observation; the analysis method for each patient to be recovered at each follow-up time is the same, and here only the construction of a digital rehabilitation file of any patient to be recovered at the current follow-up time is taken as an example to illustrate.

[0045] The construction process of digital rehabilitation files can be briefly described as follows: obtaining the electronic medical records of patients to be recovered when they are admitted to the hospital for treatment from the medical information system. The electronic medical records can provide the basic information and physical signs of the patients before rehabilitation treatment, which can provide certain data support for the subsequent evaluation of the difficulty or effect of rehabilitation; obtaining the required electronic medical record information from the electronic medical records to construct the initial rehabilitation file of the patients to be recovered during the entire rehabilitation process, and then revisiting the patients to be recovered to obtain rehabilitation record information, and after each revisit, adding the rehabilitation record information obtained during this revisit process to the initial rehabilitation file, so as to obtain the rehabilitation file after this revisit; continuously revisiting and improving the rehabilitation file of the patients to be recovered until they are discharged from the hospital, so as to obtain a digital rehabilitation file corresponding to a complete rehabilitation process.

[0046] In one embodiment of the present invention, firstly, based on the above-mentioned digital rehabilitation file construction process method, the rehabilitation file of the patient to be rehabilitated at the current follow-up time is obtained, that is, the rehabilitation file after the current follow-up process is completed; the rehabilitation file includes the electronic medical record information of the patient to be rehabilitated before rehabilitation treatment and the rehabilitation record information of each follow-up during the historical rehabilitation process.

[0047] Among them, the electronic medical record information corresponds to the initial rehabilitation file, including physical sign assessment information and symptom description information; the physical sign assessment information is used for subsequent assessment of the physical fitness of the patient to be rehabilitated before rehabilitation treatment; the symptom description information is used to assess the similarity of symptoms between different patients, so as to facilitate the subsequent screening of all recovered patients with similar symptoms corresponding to the same rehabilitation therapist, and thus facilitate the subsequent beneficial influence of the rehabilitation therapist on the recovery of the patient to be rehabilitated;

[0048] The rehabilitation record information is the rehabilitation assessment record information of the rehabilitation therapist for the rehabilitation patient at the current follow-up time, mainly including the evaluation of the independent ability of the rehabilitation patient in daily life activities and the evaluation of the degree of recovery of the rehabilitation patient in various physiological functions, etc., which is used to evaluate the recovery status of the patient to be rehabilitated at the current follow-up time.

[0049] Considering that the Barthel index can comprehensively and objectively reflect the patient's daily living activities, it can be used to evaluate the patient's rehabilitation effect and quality of life; the functional recovery index can reflect the recovery of the physiological function of the patient to be rehabilitated; therefore, in a preferred embodiment of the present invention, the rehabilitation record information at least includes the Barthel index and the functional parameters under each functional recovery index; as an example, the functional recovery index at least includes joint range of motion, muscle strength and vital capacity. It should be noted that the Barthel parameter is a prior art well known to those skilled in the art and will not be described here; in other examples, the implementer can also set the type and number of functional recovery indicators by himself, such as adding reaction speed, balance ability and tactile sensitivity, etc.; other self-care ability assessment tools such as functional independence measurement score and disability assessment can also be used instead of the Barthel index.

[0050] Considering that the functional independence measurement score can be used to assess the severity of the patient's inability to take care of themselves, it indirectly reflects the patient's basic physical fitness and the severity of the disease; the physical sign index can reflect the basic physical sign of the patient to be rehabilitated; therefore, in a preferred embodiment of the present invention, the physical sign assessment information at least includes the functional independence measurement score and the physical sign parameters under each physical sign index; as an example, the physical sign index at least includes resting heart rate, respiratory rate and blood pressure. It should be noted that the functional independence measurement score is a prior art well known to those skilled in the art and will not be described in detail here; in other examples, the implementer can also set the type and number of physical sign indicators by himself, such as adding body temperature, blood oxygen and pupil shape, etc.; other self-care ability assessment tools such as the Barthel index and disability assessment scale can also be used instead of the functional independence measurement score.

[0051] Taking into account that the symptom description information usually involves relevant information used to describe the patient's condition in the electronic medical record, in a preferred embodiment of the present invention, the diagnosis results of the symptoms of the rehabilitation patients treated by the rehabilitation therapists and other relevant medical staff and the patients' main complaints and symptoms are first obtained in the electronic medical record; since the diagnosis results and the patients' main complaints and symptoms are text information, the text information needs to be segmented first. Specifically, the text information is preprocessed first, and then the jieba segmentation tool is used to segment the text, and stop words are removed. The disease diagnosis result segmentations and all disease symptom segmentations in the main complaints and symptoms are extracted from all segmentations, and then the disease vector of the disease diagnosis result segmentation and the symptom vector of each disease symptom segmentation are obtained based on the Word2Vec model.

[0052] It should be noted that using the jieba word segmentation tool to obtain text segmentation and the corresponding vectors of word segmentation based on the Word2Vec model are both existing technologies well known to those skilled in the art and will not be described in detail here.

[0053] Step S2, based on the changes in adjacent follow-up rehabilitation record information in the rehabilitation file, obtain the recovery assessment parameters of the patient to be rehabilitated at the current follow-up moment; based on the similarity of the symptom description information between patients, screen out all reference rehabilitation patients of the patient to be rehabilitated from all the historical rehabilitation patients of the rehabilitation therapist to whom the patient to be rehabilitated belongs; based on the rehabilitation process of the physical sign assessment information of all reference rehabilitation patients, obtain the recovery favorable coefficient of the patient to be rehabilitated.

[0054] Considering that if the changes in the rehabilitation record information of the patient to be rehabilitated during the current follow-up visit compared with the previous follow-up visit can be used to evaluate whether his or her condition has improved or worsened, the embodiment of the present invention will obtain the recovery evaluation parameters of the patient to be rehabilitated at the current follow-up time based on this; the recovery evaluation parameter is used to evaluate the recovery of the patient to be rehabilitated relative to the previous adjacent follow-up visit. The larger the recovery evaluation parameter, the better the condition has recovered, and in the subsequent process, it can be considered to increase the follow-up interval to a greater extent to reduce the waste of medical resources; the smaller the recovery evaluation parameter, the smaller the degree of recovery or even worse, and in the subsequent process, the follow-up interval should be changed to a smaller extent to avoid reducing the rehabilitation tracking effect due to excessively large follow-up intervals, thereby generating medical risks.

[0055] Preferably, in one embodiment of the present invention, the method for obtaining the recovery assessment parameter includes:

[0056] Please refer to this Figure 2 , which shows a method for obtaining recovery assessment parameters provided by an embodiment of the present invention, specifically comprising:

[0057] Step S201, obtaining the functional recovery parameters of the patient to be rehabilitated at the current time of the follow-up visit according to the parameter difference between the functional parameters under the same functional recovery index between adjacent follow-up visits of the patient to be rehabilitated; the parameter difference is positively correlated with the functional recovery parameter.

[0058] Considering that if the functional parameters under each functional recovery index become larger between each follow-up visit and the previous follow-up visit, it means that the physical function of the patient to be recovered is gradually recovering and improving. Based on this, the functional recovery parameters are obtained to quantify the degree of recovery of the physical function of the patient to be recovered.

[0059] As an example, the calculation formula of the functional recovery parameter is: ;in, Functional recovery parameters of patients waiting for rehabilitation at the current time of follow-up; is the serial number of the functional recovery index; is the total number of functional recovery indicators; is the standard normalization function; is the return visit serial number; The total number of return visits up to the current return visit time, including the current return visit; For patients waiting to recover During the first visit, Functional parameters under the functional recovery index; For patients waiting to recover During the first visit, Functional parameters under the functional recovery index; For patients waiting to recover The first visit and During the first visit, The parameter difference under the functional recovery index.

[0060] In the calculation formula of functional recovery parameters, the parameter differences are normalized by linear normalization, so that when the parameter difference is greater than 0 and the larger it is, the larger the normalized value is, which means that the patient's physical function has recovered better and the degree of recovery is greater at each follow-up visit relative to the previous follow-up visit; on the contrary, when the parameter difference is less than 0 and the smaller it is, the smaller the normalized value is, which means that the patient's physical function has not recovered well and the condition has worsened; then the mean of the normalized results of the parameter differences between all adjacent follow-up visits under all functional recovery indicators is used to comprehensively evaluate the functional recovery parameters.

[0061] In other embodiments of the present invention, the implementer may first fit the parameter curves of the functional parameters corresponding to all follow-up moments under each functional recovery indicator in chronological order at the current follow-up moment, and then evaluate the overall slope of the parameter curve based on the two-point equal curve slope evaluation method, and linearly normalize the overall slopes of the corresponding parameter curves under all functional recovery indicators and then calculate the average. The larger the slope is positive, the more likely the condition is to improve and the larger the functional recovery evaluation parameter is.

[0062] Step S202, between the current follow-up visit of the patient to be rehabilitated and the previous follow-up visit, according to the index difference of the corresponding Barthel index, and the deviation of the corresponding Barthel index of the current follow-up relative to the preset standard index, obtain the life ability recovery parameter of the patient to be rehabilitated at the current follow-up time; the index difference is positively correlated with the life ability recovery parameter, and the deviation is negatively correlated with the life ability recovery parameter.

[0063] Considering that between the current follow-up visit and the previous follow-up visit, if the Barthel index becomes larger and gets closer to the full score of the Barthel index, it also means that the self-care ability of the patient to be recovered becomes stronger and the condition gradually recovers and improves. Based on this, the life ability recovery parameter is obtained to quantify the degree of recovery of the self-care ability of the patient to be recovered.

[0064] As an example, the calculation formula of the vitality recovery parameter is: ;in, The life ability recovery parameters of the patients waiting for rehabilitation at the current time of the follow-up visit; It is the standard normalization method; The Barthel index of the patient waiting for recovery at the current time of the follow-up visit; The Barthel index of the patient to be recovered at the last visit time adjacent to the current visit time; is the index difference; The deviation of the Barthel index corresponding to the current return visit relative to the preset standard index; is a preset standard index; To preset a non-zero positive constant, 1 is taken in this example to prevent the denominator from being meaningless if it is zero.

[0065] It should be noted that the preset standard index is the total score of the Barthel index, which is usually 100. The total score of the Barthel index may be different in different versions or applications, and implementers can also set it according to actual conditions. In the calculation formula of the life ability recovery parameter, the larger the index difference is, the better the self-care ability of the patient to be rehabilitated has improved. The smaller the deviation is, the closer the self-care ability is to the full score, that is, the preset standard index. After adding the preset non-zero normal number, the inverse operation is performed to adjust the logic, so that the larger the index difference is, the smaller the deviation is, the larger the corresponding ratio is. The ratio is linearly normalized, and the larger the normalized value is, the larger the life ability recovery parameter is.

[0066] In other examples, implementers may also normalize the deviation using other negative correlation mapping methods, such as using the deviation as the x in an exponential function exp(-x) with the natural constant e as the base; other normalization methods may also be used, which will not be described in detail here.

[0067] Step S203, obtaining the recovery assessment parameters of the patient to be rehabilitated at the current time of the follow-up visit according to the functional recovery parameters and the life ability recovery parameters; the functional recovery parameters and the life ability recovery parameters are both positively correlated with the recovery assessment parameters.

[0068] As an example, the mean of the functional recovery parameter and the living ability recovery parameter is used as the recovery assessment parameter of the patient to be rehabilitated at the current follow-up time; in other examples, the implementer can also weight the two to find the average, such as setting different weights to adjust the focus on different recovery parameters, which will not be elaborated here.

[0069] Taking into account that each rehabilitation therapist may be good at different rehabilitation skills, which has an impact on the recovery of the patient to be rehabilitated; considering that the rehabilitation process of all the rehabilitated patients historically treated by each rehabilitation therapist can be analyzed, and then the skill level of the rehabilitation therapist or the impact on rehabilitation can be quantitatively evaluated indirectly; considering that before rehabilitation, the rehabilitation difficulty of patients with similar disease conditions should be similar, and the impact of the same rehabilitation therapist on their rehabilitation should also be similar; therefore, the embodiment of the present invention first screens out all reference rehabilitation patients of the patient to be rehabilitated from all historical rehabilitation patients of the rehabilitation therapist to whom the patient to be rehabilitated belongs based on the similarity of the symptom description information between the patients; the reference rehabilitation patients are recovered patients with similar disease conditions or rehabilitation difficulty as the patient to be rehabilitated before rehabilitation, providing reference support for the subsequent analysis of the recovery favorable coefficient of each rehabilitation therapist for the rehabilitation patient.

[0070] Preferably, in one embodiment of the present invention, considering that the text segmentation vector can reflect its lexical semantics and contextual relationship, and the cosine similarity can reflect the similarity between vectors, and thus the similarity between text segmentations can be evaluated; therefore, the method for obtaining similarity includes:

[0071] Normalizing the cosine similarity of corresponding disease vectors between different patients, and using the normalized result as the first similarity parameter;

[0072] Obtain all binary combinations of symptom vectors between different patients, where the two symptom vectors contained in the binary combination are from different patients; use the cosine similarity between the two symptom vectors in each binary combination as the second similarity sub-parameter, normalize the mean of the second similarity sub-parameters of all binary combinations, and use the normalized sum result as the second similarity parameter;

[0073] The first similarity parameter and the second similarity parameter are integrated to obtain the similarity of the symptom description information between the patient to be rehabilitated and each historical rehabilitated patient; the first similarity parameter and the second similarity parameter are both positively correlated with the similarity.

[0074] To facilitate the understanding of binary combinations, assume that the symptom vectors corresponding to patient A are a1, a2, and a3, and the symptom vectors corresponding to B are b1 and b2; then all binary combinations are: (a1, b1), (a1, b2), (a2, b1), (a2, b2), (a3, b1), (a3, b2).

[0075] As an example, the similarity is calculated as:

[0076] ;in, The serial number of the historical rehabilitation patients to which the rehabilitation therapist belongs for the patient to be rehabilitated; The first patient to be rehabilitated among all the patients who have been rehabilitated by their respective rehabilitation therapists. similarities between historically recovered patients; is the standard normalization function; is the cosine similarity function; Disease vectors for patients to be recovered; Among all the historical rehabilitation patients who are affiliated with rehabilitation therapists, the first Disease vectors of historical recovered patients; is the first similarity parameter; is the serial number of the binary combination; is the total number of binary combinations; For the The binary combination corresponds to the second similarity sub-parameter; is the second similarity parameter.

[0077] In the similarity calculation formula, the normalization function is set to ; The larger the cosine similarity between disease vectors of different patients when taken as x and the closer it is to 1, the more similar the semantics of the text segmentation words corresponding to the disease vectors are, the greater the possibility of being the same disease, the larger the corresponding normalized value, and the larger the first similarity parameter; the larger the mean of the second similarity sub-parameters of all binary combinations corresponding to different patients when taken as x, the more similar the semantics of the text segmentation words corresponding to the symptom vectors are, the greater the possibility of being similar disease symptoms or severity, the larger the corresponding normalized value, and the larger the second similarity parameter; then the first similarity parameter and the second similarity parameter are averaged to obtain the similarity; in other examples, the implementer may also use other basic mathematical operations or normalization methods, which are not repeated here.

[0078] By obtaining the similarity between the patient to be rehabilitated and each of the historical rehabilitated patients who belong to the same rehabilitation therapist, the reference rehabilitated patients can be selected based on the similarity; in a preferred embodiment of the present invention, when the similarity of the symptom description information between the patient to be rehabilitated and each of the historical rehabilitated patients is greater than a preset threshold, the corresponding historical rehabilitated patient will be used as a reference rehabilitated patient for the patient to be rehabilitated. The preset threshold is 0.5, and the implementer can also set it according to the actual situation.

[0079] In other embodiments of the present invention, implementers may also extract feature vectors based on electronic medical records, such as using symptom vectors and disease vectors as feature vectors, and constructing a classification model to train and identify patients with similar diseases; they may also evaluate the similarity between feature vectors to obtain a metric distance, and then use a clustering algorithm to obtain patients with similar diseases, which are all existing technologies and will not be elaborated here.

[0080] After obtaining all the reference rehabilitation patients, the recovery favorable coefficient of the patient to be rehabilitated can be obtained based on the physical sign evaluation information of all the reference rehabilitation patients and the rehabilitation process of the reference rehabilitation patients. The recovery favorable coefficient combines the recovery information of the reference rehabilitation patients who belong to the same rehabilitation therapist and have similar disease symptoms to evaluate the rehabilitation favorable situation of each rehabilitation therapist for the corresponding patient to be rehabilitated, and also reflects the rehabilitation skill level of each rehabilitation therapist.

[0081] Preferably, in one embodiment of the present invention, the method for obtaining the recovery favorable coefficient includes:

[0082] See also Figure 3 , which shows a flow chart of a method for obtaining a recovery favorable coefficient provided by an embodiment of the present invention, specifically comprising:

[0083] Step S301, obtaining the rehabilitation effect coefficient of each reference rehabilitation patient according to the satisfaction of each reference rehabilitation patient with the rehabilitation process and the duration of the rehabilitation process; the satisfaction is positively correlated with the rehabilitation effect coefficient, and the duration of the rehabilitation process is negatively correlated with the rehabilitation effect coefficient.

[0084] Considering that the shorter the rehabilitation time of the reference rehabilitation patient and the more satisfied he is with the rehabilitation process, the more obvious the beneficial effect of the rehabilitation therapist on the patient's rehabilitation is. Based on this, the rehabilitation effect coefficient of each reference rehabilitation patient can be obtained; the rehabilitation effect coefficient quantifies the rehabilitation effect based on the process duration and process evaluation of the historical rehabilitation process, so as to facilitate the subsequent evaluation of the recovery benefit coefficient.

[0085] As an example, the calculation formula of the rehabilitation effect coefficient is:

[0086] ;in, The serial number of the patient who is to be recovered is the reference number of the patient who is to be recovered; For the The rehabilitation effect coefficient of a reference rehabilitation patient; For the The duration of the rehabilitation process for each reference rehabilitation patient; is the standard normalization function; For the Reference rehabilitation patients' satisfaction with the rehabilitation process.

[0087] In the rehabilitation effect coefficient, the rehabilitation process duration is linearly normalized and expressed as The x in the equation is adjusted to make the rehabilitation effect coefficient greater when the rehabilitation process is shorter. The greater the rehabilitation patient's satisfaction with the rehabilitation process, the greater the rehabilitation effect coefficient. The two are then averaged to obtain the rehabilitation effect coefficient. In other examples, the implementer may also use other basic mathematical operations to combine the two or other normalization methods, which will not be described here.

[0088] It should be noted that the duration of the rehabilitation process is the total time experienced by the reference rehabilitation patients from admission for rehabilitation treatment to complete discharge; the satisfaction of the reference rehabilitation patients with the rehabilitation process is determined by the satisfaction scores of the reference rehabilitation patients on the rehabilitation process in the form of a questionnaire after complete recovery. The total score is 1. The higher the satisfaction score, the greater the satisfaction.

[0089] Step S302, obtaining the rehabilitation difficulty coefficient of each reference rehabilitation patient according to the functional independence measurement score of each reference rehabilitation patient and the abnormal deviation degree of each sign parameter relative to the preset standard sign range under the corresponding sign index; the functional independence measurement score is negatively correlated with the rehabilitation difficulty coefficient, and the abnormal deviation degree is positively correlated with the rehabilitation difficulty coefficient.

[0090] Considering that the functional independence measurement score reflects the physical function level of the reference rehabilitation patients before rehabilitation, and the closer each physical sign parameter is to the preset standard physical sign range under the corresponding physical sign index, the healthier and more stable the basic physical signs of the reference rehabilitation patients before rehabilitation are; these two reflect the physical condition of the reference rehabilitation patients before rehabilitation, and also indirectly reflect the difficulty of their recovery. Based on this, the rehabilitation difficulty coefficient of each reference rehabilitation patient can be obtained. The larger the rehabilitation difficulty coefficient, the worse the physical condition of the reference rehabilitation patient, the greater the difficulty of recovery, and the greater the test of the rehabilitation skills of the rehabilitation therapist, which provides a reference for the subsequent evaluation of the rehabilitation therapist's impact on the recovery of the corresponding patients to be rehabilitated.

[0091] Among them, in a preferred embodiment of the present invention, considering that if the physical sign parameter is not within the corresponding preset standard physical sign range, the more it deviates from the midpoint of the range interval, the more abnormal it is; therefore, the method for obtaining the degree of abnormal deviation includes:

[0092] Under each physical sign index, if the physical sign parameter is within the corresponding preset standard physical sign range, the abnormal deviation parameter of the physical sign parameter is set to 0; if the physical sign parameter is not within the corresponding preset standard physical sign range, the midpoint of the preset standard physical sign range is used as the standard reference coefficient, and the difference between the physical sign parameter and the corresponding standard reference coefficient is used as the deviation sub-parameter of the physical sign parameter;

[0093] The deviation sub-parameters of the physical sign parameters of each reference rehabilitation patient under all physical sign indicators are combined to obtain the abnormal deviation degree of each reference rehabilitation patient; the deviation sub-parameters are positively correlated with the abnormal deviation degree.

[0094] As an example, when the physical sign parameter is not within the corresponding preset standard physical sign range, the calculation formula for the abnormal deviation degree is: ;in, The serial number of the patient who is to be recovered is the reference number of the patient who is to be recovered; For the The degree of abnormal deviation of the reference rehabilitation patients; is the standard normalization function; is the serial number of the physical sign indicator; is the total number of physical sign indicators; For the Reference recovered patients in Physical sign parameters under individual sign indicators; For the Standard reference coefficients under individual sign indicators; For the Reference recovered patients in Deviation sub-parameter of the sign parameter under the individual sign indicator.

[0095] It should be noted that in this example, linear normalization is specifically used, and the implementer may also use other normalization methods; the vital sign indicators mainly include resting heart rate, respiratory rate and blood pressure, among which blood pressure refers to systolic pressure; the resting heart rate corresponds to the preset standard vital sign range of 60-100 times / minute, the respiratory rate corresponds to the preset standard vital sign range of 12-20 times / minute, and the blood pressure range systolic pressure corresponds to the preset standard vital sign range of 90-139 mmHg.

[0096] As an example, the calculation formula for the rehabilitation difficulty coefficient is: ;in, The serial number of the patient who is to be recovered is the reference number of the patient who is to be recovered; For the The rehabilitation difficulty coefficient of the reference rehabilitation patients; For the Functional Independence Measure scores of reference rehabilitation patients; For the The degree of abnormal deviation of a reference rehabilitation patient.

[0097] In the calculation formula of the rehabilitation difficulty coefficient, the functional independence measurement score is used as The x in is normalized for negative correlation and the logic is adjusted so that the larger the functional independence measurement score is, the smaller the rehabilitation difficulty coefficient is. Then the negative correlation mapping result is averaged with the abnormal deviation degree to obtain the rehabilitation difficulty coefficient. In other examples, the implementer can also weight the two and average them, such as setting different weights to adjust the attention, which will not be elaborated here.

[0098] Step S303, comprehensively analyzing the rehabilitation effect coefficients and rehabilitation difficulty coefficients of all reference rehabilitation patients of the patient to be rehabilitated, and obtaining a recovery favorable coefficient for the patient to be rehabilitated; the rehabilitation effect coefficient and the rehabilitation difficulty coefficient are both positively correlated with the recovery favorable coefficient.

[0099] As an example, the recovery benefit factor is calculated as: ; The recovery factor for patients to be recovered; The serial number of the patient who is to be recovered is the reference number of the patient who is to be recovered; The total number of recovered patients is a reference for patients waiting to be recovered; For the The rehabilitation difficulty coefficient of the reference rehabilitation patients; For the The rehabilitation effect coefficient of a reference rehabilitation patient; is the standard normalization function.

[0100] In the calculation formula of the recovery favorable coefficient, the rehabilitation effect coefficient and the rehabilitation difficulty coefficient of each reference rehabilitation patient are multiplied and combined, the products are averaged and then linearly normalized to obtain the recovery favorable coefficient; in other examples, the implementer may also use basic mathematical operations such as addition or weighted summation or related mapping methods to combine the rehabilitation effect coefficient and the rehabilitation difficulty coefficient, or other normalization methods, which are not repeated here.

[0101] Step S3, at the current follow-up time, adjust the preset fixed follow-up cycle according to the recovery assessment parameters and recovery favorable coefficient of the patient to be rehabilitated, and obtain the next future follow-up time; iteratively obtain all future follow-up times of the patient to be rehabilitated in the future rehabilitation process and the future rehabilitation record information at each future follow-up time until the patient recovers; add all future rehabilitation records to the rehabilitation file to obtain a digital rehabilitation file of the patient to be rehabilitated.

[0102] Considering that if the recovery favorable coefficient of the patient to be rehabilitated is larger, it means that the rehabilitation therapist is more favorable to his recovery, and the recovery speed may be faster and the condition will show a trend of improvement; considering that if the recovery ability coefficient of the patient to be rehabilitated is larger, it further indicates that the current rehabilitation state of the patient to be rehabilitated is good; and then, based on the two, it is appropriate to consider increasing the follow-up interval to avoid wasting medical resources; on the contrary, if the recovery favorable coefficient and the recovery evaluation parameters are smaller, it is necessary to increase the follow-up interval as little as possible or consider reducing the follow-up interval so as to timely discover potential rehabilitation risks.

[0103] Based on this, in a preferred embodiment of the present invention, the method for obtaining the next future return visit time includes:

[0104] Obtaining a recovery gain weight according to the recovery benefit coefficient of the patient to be rehabilitated, using the recovery gain weight to weight the recovery assessment parameter, normalizing the weighted result of the recovery assessment parameter, and using the normalized result as the recovery ability coefficient of the corresponding patient at the current time of the return visit;

[0105] The recovery capacity coefficient of the patient to be rehabilitated plus the constant 1 is used as the period adjustment weight, and the preset fixed return visit period is weighted using the period adjustment weight. The weighted result of the preset fixed return visit period is used as the return visit interval between the current return visit time and the next future return visit time of the corresponding patient; the next future return visit time is obtained using the return visit interval.

[0106] As an example, the recovery advantage coefficient is added with a constant 1 as the recovery gain weight of the recovery assessment parameter, and then the recovery gain weight is multiplied by the recovery assessment parameter, and then the product is linearly normalized, and the normalized result is used as the recovery ability coefficient of the corresponding patient at the current follow-up time; thereby obtaining the periodic adjustment weight, multiplying the periodic adjustment weight by the preset fixed follow-up period, and then obtaining the follow-up interval, and determining the next future follow-up time.

[0107] It should be noted that after obtaining the follow-up interval between the current follow-up time and the next future follow-up time, the current follow-up time is added to the follow-up interval to obtain the next future follow-up time; the preset fixed follow-up period is the hospital's prescribed follow-up period for patients awaiting recovery, which is set to 1 week in this example, and the implementer can also set it according to actual conditions; the implementer can also use other normalization functions or related mapping functions to map the recovery-favorable coefficient to a value range of -1 to 1, thereby increasing or decreasing the follow-up interval, which will not be elaborated here.

[0108] In one embodiment of the present invention, considering that the rehabilitation file of the patient to be rehabilitated is improved through continuous follow-up visits, it is necessary to iteratively obtain all future follow-up moments of the patient to be rehabilitated in the future rehabilitation process and the future rehabilitation record information at each future follow-up moment until the patient is recovered; all future rehabilitation records are added to the rehabilitation file to obtain a digital rehabilitation file of the patient to be rehabilitated.

[0109] Among them, when time passes to the next future return visit moment, the next future return visit moment is taken as the new current moment. According to the method described in the above steps, the new next future return visit moment can be iterated continuously to obtain new rehabilitation record data, thereby continuously updating the rehabilitation file until the patient recovers, and a complete digital rehabilitation file can be obtained.

[0110] In summary, the present invention first obtains the rehabilitation file of the patient to be rehabilitated at the current time of the follow-up visit; then obtains the recovery assessment parameters of the patient to be rehabilitated at the current time of the follow-up visit; further combines the similar symptoms of the patients to screen out all reference rehabilitation patients of the patient to be rehabilitated; obtains the recovery favorable coefficient of the patient to be rehabilitated based on the physical sign assessment information and rehabilitation process of all reference rehabilitation patients, and then adjusts the preset fixed follow-up cycle at the current time of the follow-up visit to obtain the next future follow-up time; iterates the follow-up visit to update the rehabilitation file and obtains the digital rehabilitation file of the patient to be rehabilitated. The present invention is based on the rehabilitation information of all historical rehabilitation patients with similar disease conditions as the patient to be rehabilitated, evaluates the rehabilitation skills of the same rehabilitation therapists to determine the rehabilitation favorable coefficient of the patient to be rehabilitated; then evaluates the current recovery of the patient to be rehabilitated in combination with the changes in the physiological functions of the patient to be rehabilitated, so as to reasonably adjust the follow-up cycle, thereby improving the digital archiving effect of the patient's rehabilitation information.

[0111] The present invention also proposes a system for digitally filing patient rehabilitation information based on electronic medical records. The system 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, steps S1-S3 of a method for digitally filing patient rehabilitation information based on electronic medical records are implemented.

[0112] 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.

[0113] 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 method for digitally filing patient rehabilitation information based on electronic medical records, characterized in that: The method comprises: Obtaining the rehabilitation file of the patient to be rehabilitated at the current time of the follow-up visit; the rehabilitation file includes the electronic medical record information of the patient to be rehabilitated before the rehabilitation treatment and the rehabilitation record information of each follow-up visit during the historical rehabilitation process; the electronic medical record information includes physical sign assessment information and symptom description information; According to the changes in the rehabilitation record information between adjacent follow-up visits in the rehabilitation file, the recovery assessment parameters of the patient to be rehabilitated at the current follow-up time are obtained; according to the similarity of the symptom description information between patients, all reference rehabilitation patients of the patient to be rehabilitated are screened out from all the historical rehabilitation patients of the rehabilitation therapist to whom the patient to be rehabilitated belongs; according to the physical sign assessment information and rehabilitation process of all the reference rehabilitation patients, the recovery favorable coefficient of the patient to be rehabilitated is obtained; At the current return visit time, according to the recovery assessment parameters and the recovery favorable coefficient of the patient to be rehabilitated, the preset fixed return visit cycle is adjusted to obtain the next future return visit time; all future return visit times in the future rehabilitation process of the patient to be rehabilitated and the future rehabilitation record information at each future return visit time are iteratively obtained until the patient is recovered; all future rehabilitation records are added to the rehabilitation file to obtain a digital rehabilitation file of the patient to be rehabilitated; The rehabilitation record information at least includes the Barthel index and the functional parameters under each functional recovery index; the physical sign assessment information at least includes the functional independence measurement score and the physical sign parameters under each physical sign index; the disease description information at least includes the disease vector and several symptom vectors; The method for obtaining the recovery assessment parameter includes: According to the parameter difference between the functional parameters under the same functional recovery index between adjacent return visits of the patient to be rehabilitated, the functional recovery parameter of the patient to be rehabilitated at the current return visit time is obtained; the parameter difference is positively correlated with the functional recovery parameter; Between the current return visit of the patient to be rehabilitated and the previous return visit, according to the index difference corresponding to the Barthel index and the deviation of the Barthel index corresponding to the current return visit relative to the preset standard index, the life ability recovery parameter of the patient to be rehabilitated at the current return visit time is obtained; the index difference is positively correlated with the life ability recovery parameter, and the deviation is negatively correlated with the life ability recovery parameter; According to the functional recovery parameter and the life ability recovery parameter, obtaining the recovery assessment parameter of the patient to be rehabilitated at the current time of the return visit; the functional recovery parameter and the life ability recovery parameter are both positively correlated with the recovery assessment parameter; The method for obtaining the recovery favorable coefficient includes: According to the satisfaction of each of the reference rehabilitation patients with the rehabilitation process and the duration of the rehabilitation process, a rehabilitation effect coefficient of each of the reference rehabilitation patients is obtained; the satisfaction is positively correlated with the rehabilitation effect coefficient, and the duration of the rehabilitation process is negatively correlated with the rehabilitation effect coefficient; According to the functional independence measurement score of each of the reference rehabilitation patients and the abnormal deviation degree of each of the physical sign parameters relative to the preset standard physical sign range under the corresponding physical sign index, the rehabilitation difficulty coefficient of each of the reference rehabilitation patients is obtained; the functional independence measurement score is negatively correlated with the rehabilitation difficulty coefficient, and the abnormal deviation degree is positively correlated with the rehabilitation difficulty coefficient; The rehabilitation effect coefficients and the rehabilitation difficulty coefficients of all the reference rehabilitation patients of the patient to be rehabilitated are comprehensively considered to obtain a recovery favorable coefficient of the patient to be rehabilitated; the rehabilitation effect coefficient and the rehabilitation difficulty coefficient are both positively correlated with the recovery favorable coefficient; The method for obtaining the next future return visit time includes: Obtaining a recovery gain weight according to the recovery advantage coefficient of the patient to be rehabilitated, weighting the recovery assessment parameter using the recovery gain weight, normalizing the weighted result of the recovery assessment parameter, and using the normalized result as the recovery ability coefficient of the corresponding patient at the current return visit time; The recovery capacity coefficient of the patient to be rehabilitated is added with a constant 1 as a period adjustment weight, and the preset fixed return visit period is weighted using the period adjustment weight. The weighted result of the preset fixed return visit period is used as the return visit interval between the current return visit time and the next future return visit time of the corresponding patient; and the next future return visit time is obtained using the return visit interval.

2. A method for digitally creating patient rehabilitation information based on electronic medical records according to claim 1, characterized in that: The method for obtaining the disease vector and the symptom vector includes: The disease description information includes disease diagnosis result segmentations and several disease symptom segmentations; the disease vector of the disease diagnosis result segmentation and the symptom vector of each disease symptom segmentation are obtained based on the Word2Vec model.

3. A method for digitally creating patient rehabilitation information based on electronic medical records according to claim 2, characterized in that: The method for obtaining the similarity includes: Normalizing the cosine similarities of the disease vectors between different patients, and using the normalized result as the first similarity parameter; Obtain all binary combinations of the symptom vectors between different patients; use the cosine similarity between the two symptom vectors in each binary combination as a second similarity sub-parameter, normalize the mean of the second similarity sub-parameters of all the binary combinations, and use the normalized sum result as the second similarity parameter; The first similarity parameter and the second similarity parameter are integrated to obtain the similarity of the symptom description information between the patient to be rehabilitated and each historical rehabilitated patient; the first similarity parameter and the second similarity parameter are both positively correlated with the similarity.

4. A method for digitally filing patient rehabilitation information based on electronic medical records according to claim 1 or 3, characterized in that: The method for obtaining the reference rehabilitation patient comprises: When the similarity of the disease description information between the patient to be rehabilitated and each historical rehabilitated patient is greater than a preset threshold, the corresponding historical rehabilitated patient will be used as a reference rehabilitated patient for the patient to be rehabilitated.

5. The method for digitally creating a file of patient rehabilitation information based on electronic medical records according to claim 1, characterized in that: The method for obtaining the abnormal deviation degree includes: Under each physical sign index, if the physical sign parameter is within the corresponding preset standard physical sign range, the abnormal deviation parameter of the physical sign parameter is set to 0; if the physical sign parameter is not within the corresponding preset standard physical sign range, the midpoint of the interval of the preset standard physical sign range is used as the standard reference coefficient, and the difference between the physical sign parameter and the corresponding standard reference coefficient is used as the deviation sub-parameter of the physical sign parameter; The deviation sub-parameters of the physical sign parameters of each reference rehabilitation patient under all physical sign indicators are integrated to obtain the abnormal deviation degree of each reference rehabilitation patient; the deviation sub-parameter is positively correlated with the abnormal deviation degree.

6. A digital filing system for patient rehabilitation information based on electronic medical records, characterized in that: The system 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 a method for digitally establishing patient rehabilitation information based on electronic medical records as described in any one of claims 1 to 5 are implemented.

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