Rehabilitation training management method and system based on big data
By using a big data-based rehabilitation training management method and a rule engine to determine patient access priorities and generate a rehabilitation follow-up list, the problem of insufficient rehabilitation therapists has been solved, and the efficiency and effectiveness of rehabilitation treatment have been improved.
Patent Information
- Application Number
- CN202411411300.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-10
- Publication Date
- 2025-02-14
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
In rehabilitation hospitals, when there is a shortage of rehabilitation therapists, how can we reasonably allocate time to handle the rehabilitation plans of multiple patients at the same time and ensure that the rehabilitation treatment effect is not affected?
We adopt a big data-based rehabilitation training management approach. By acquiring patients' rehabilitation plans and rehabilitation therapists' access records, we use a rule engine to determine the priority of patient access data and generate a patient rehabilitation follow-up list. This ensures that rehabilitation therapists follow up with each patient in an orderly manner to answer questions and provide assistance.
This improved the treatment capabilities of rehabilitation therapists, ensured the effectiveness of rehabilitation therapy, and increased the efficiency of rehabilitation therapy for patients.
Smart Images
Figure CN119446573A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of rehabilitation training management, and in particular to a rehabilitation training management method and system based on big data. Background Art
[0002] Rehabilitation therapists play a very important role in the patient's rehabilitation training process. Rehabilitation therapists will adopt different treatment methods for patients with different conditions and rehabilitation plans. In some cases, rehabilitation therapists will accompany patients throughout the whole process to help them with rehabilitation treatment. In some cases, a rehabilitation therapist will be responsible for multiple patients at the same time. During the patient's rehabilitation treatment, they will collect the patient's daily treatment data, examine the patient at key points, and adjust the rehabilitation plan. For some rehabilitation hospitals, there are fewer rehabilitation therapists, and it is often the case that one rehabilitation therapist is responsible for multiple patients. In this case, it is very important to arrange the rehabilitation therapist's time reasonably. Summary of the invention
[0003] In order to solve the above technical problems, the present application provides a rehabilitation training management method and system based on big data.
[0004] In a first aspect, the present application provides a rehabilitation training management method based on big data. The method comprises:
[0005] Obtain patient rehabilitation plans and rehabilitation therapists' records of each patient's rehabilitation visits;
[0006] Based on a rule engine, determining patient access priority data according to the patient rehabilitation plan and the rehabilitation access record;
[0007] A patient rehabilitation return visit list is obtained according to the patient visit priority data and a preset patient rehabilitation return visit time threshold.
[0008] By adopting the above technical solution, when the rehabilitation therapist needs to handle the rehabilitation plans of multiple patients at the same time, there is a better screening method for the patient's follow-up selection; based on this solution, the rehabilitation therapist can follow up each patient in an orderly manner through the patient rehabilitation follow-up list, answer possible problems in the rehabilitation process of each patient, and help the patient's rehabilitation; in this way, it is ensured that the rehabilitation therapist can handle multiple rehabilitation patients at the same time without any problems, and that the rehabilitation therapist handles the patient's rehabilitation problems according to the necessity of the patient's follow-up, thereby improving the rehabilitation therapist's handling ability and improving the patient's rehabilitation treatment effect.
[0009] Furthermore, the determining of the patient access priority data based on the rule engine according to the patient rehabilitation plan and the rehabilitation access record includes:
[0010] Screen and determine the data of patients who need to be revisited according to the patient rehabilitation plan;
[0011] Determine the patient revisit necessity data according to the patient revisit data and the rehabilitation visit record;
[0012] Patient visit priority data is determined according to the patient return visit necessity data.
[0013] Furthermore, the determining of the patient revisit necessity data according to the patient revisit data and the rehabilitation visit record includes:
[0014] For patients who need to be revisited, assume that there are n revisit records of rehabilitation therapists within the preset time, and the doctor-patient communication time of the i-th revisit record of rehabilitation therapists is t 1i , t i The final time is T 1i , the current time is T0, T 11 The patient return visit necessity data closest to the current moment is W1, then
[0015]
[0016]
[0017] Where, t al is the first preset rehabilitation therapist accompanying training time threshold, indicating that the rehabilitation therapist needs to accompany the patient all the time in the rehabilitation plan of the corresponding patient; t no is the second preset rehabilitation therapist accompanying training time threshold, indicating that the rehabilitation therapist does not need to accompany the patient in the rehabilitation plan of the corresponding patient; k1, k2, k3, k4, and k5 are all preset constants greater than zero.
[0018] Furthermore, determining the patient visit priority data according to the patient return visit necessity data includes arranging the patient return visit necessity data in descending order, and determining the patient visit priority data according to the arrangement order.
[0019] Furthermore, obtaining the patient rehabilitation return visit list according to the patient access priority data and a preset patient rehabilitation return visit time threshold includes:
[0020] Determining a patient return visit time according to the patient visit priority data;
[0021] Adjusting the patient return visit sequence according to the patient return visit time and the preset patient rehabilitation return visit time threshold;
[0022] Determine the patient rehabilitation follow-up list based on the adjusted patient follow-up order.
[0023] In a second aspect, the present application provides a rehabilitation training management system based on big data, the system comprising:
[0024] An acquisition module is used to obtain the patient's rehabilitation plan and the rehabilitation visit record of each patient by the rehabilitation therapist;
[0025] a determination module, configured to determine patient access priority data based on a rule engine and according to the patient rehabilitation plan and the rehabilitation access record;
[0026] The output module is used to obtain a patient rehabilitation return visit list according to the patient access priority data and a preset patient rehabilitation return visit time threshold.
[0027] Furthermore, the determination module is further configured to determine the patient access priority data based on the rule engine according to the patient rehabilitation plan and the rehabilitation access record, including:
[0028] Screen and determine the data of patients who need to be revisited according to the patient rehabilitation plan;
[0029] Determine the patient revisit necessity data according to the patient revisit data and the rehabilitation visit record;
[0030] Patient visit priority data is determined according to the patient return visit necessity data.
[0031] Furthermore, the determination module is further configured to determine the patient return visit necessity data according to the patient return visit data and the rehabilitation visit record, including:
[0032] For patients who need to be revisited, assume that there are n revisit records of rehabilitation therapists within the preset time, and the doctor-patient communication time of the i-th revisit record of rehabilitation therapists is t 1i , t i The final time is T 1i , the current time is T0, T 11 The patient return visit necessity data closest to the current moment is W1, then
[0033]
[0034]
[0035] Where, t al is the first preset rehabilitation therapist accompanying training time threshold, indicating that the rehabilitation therapist needs to accompany the patient all the time in the rehabilitation plan of the corresponding patient; t no is the second preset rehabilitation therapist accompanying training time threshold, indicating that the rehabilitation therapist does not need to accompany the patient in the rehabilitation plan of the corresponding patient; k1, k2, k3, k4, and k5 are all preset constants greater than zero.
[0036] Furthermore, the determination module is further configured such that determining the patient visit priority data according to the patient return visit necessity data includes arranging the patient return visit necessity data in order from large to small, and determining the patient visit priority data according to the arrangement order.
[0037] Furthermore, the output module is further configured to obtain the patient rehabilitation return visit list according to the patient access priority data and the preset patient rehabilitation return visit time threshold, including:
[0038] Determining a patient return visit time according to the patient visit priority data;
[0039] Adjusting the patient return visit sequence according to the patient return visit time and the preset patient rehabilitation return visit time threshold;
[0040] Determine the patient rehabilitation follow-up list based on the adjusted patient follow-up order.
[0041] It should be understood that the contents described in the Summary of the Invention are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS
[0042] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:
[0043] Figure 1 A flowchart of a rehabilitation training management method based on big data in an embodiment of the present application is shown;
[0044] Figure 2 A block diagram of a rehabilitation training management system based on big data in an embodiment of the present application is shown. DETAILED DESCRIPTION
[0045] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0046] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.
[0047] The present application provides a rehabilitation training management method and system based on big data, which can help rehabilitation therapists reasonably arrange rehabilitation plans for patients, ensure that rehabilitation therapists can handle the rehabilitation plans of multiple patients at the same time, and ensure that the patients' rehabilitation experience is not affected.
[0048] In the first aspect, the present application provides a rehabilitation training management method based on big data. Figure 1 The specific steps of the method are as follows.
[0049] Step S110: Obtain the patient's rehabilitation plan and the rehabilitation therapist's rehabilitation visit records for each patient.
[0050] In an embodiment of the present application, taking any rehabilitation hospital as an example, there are few rehabilitation therapists but many patients in the rehabilitation hospital; in this case, a rehabilitation therapist is required to handle the rehabilitation plans of multiple patients at the same time. When handling multiple rehabilitation patients at the same time, in order to ensure the rehabilitation treatment effect of the rehabilitation patients, the necessity of follow-up visits to each rehabilitation patient can be determined based on this solution, thereby determining the priority and time of follow-up visits by the rehabilitation therapist to each patient; it can be understood that there are three situations for the patient's rehabilitation plan. The first is that the rehabilitation therapist is required to accompany the patient throughout the rehabilitation treatment. The second is that the patient does not need to be accompanied by a rehabilitation therapist, and the patient can recover on his own based on the established rehabilitation plan. The third is that the rehabilitation therapist is required to conduct irregular follow-up visits based on the patient's daily rehabilitation training.
[0051] It can be understood that the rehabilitation visit record here is the rehabilitation therapist's follow-up record of the patient in the third case mentioned above. Based on the visit record, the rehabilitation therapist can understand the patient's rehabilitation training and physical recovery; the rehabilitation visit record also includes the communication time between the rehabilitation therapist and the patient.
[0052] It is understandable that the above-mentioned patient rehabilitation plan and rehabilitation therapist's rehabilitation visit records for each patient and other information can be obtained in a variety of ways. The specific acquisition method is not limited here, as long as the corresponding information and data can be obtained.
[0053] Step S120: Based on the rule engine, determine the patient access priority data according to the patient rehabilitation plan and the rehabilitation access record.
[0054] In an embodiment of the present application, the rule-based engine determines the patient visit priority data according to the patient rehabilitation plan and the rehabilitation visit record, specifically including: screening and determining the patient data requiring a follow-up visit according to the patient rehabilitation plan; determining the patient follow-up necessity data according to the patient data requiring a follow-up visit and the rehabilitation visit record; and determining the patient visit priority data according to the patient follow-up necessity data.
[0055] It can be understood that the screening of patients who need to be revisited based on the patient rehabilitation plan is what is mentioned in step S110. Among the three types of patients, it is necessary to screen out patients in different conditions and adopt different treatment methods.
[0056] The step of determining the patient revisit necessity data based on the patient revisit data and the rehabilitation visit record specifically includes:
[0057] For patients who need to be revisited, assume that there are n revisit records of rehabilitation therapists within the preset time, and the doctor-patient communication time of the i-th revisit record of rehabilitation therapists is t 1i , t i The final time is T 1i , the current time is T0, T 11 The patient return visit necessity data closest to the current moment is W1, then
[0058]
[0059]
[0060] Where, t al is the first preset rehabilitation therapist accompanying training time threshold, indicating that the rehabilitation therapist needs to accompany the patient all the time in the rehabilitation plan of the corresponding patient; t no is the second preset rehabilitation therapist accompanying training time threshold, indicating that the rehabilitation therapist does not need to accompany the patient in the corresponding patient's rehabilitation plan; k1, k2, k3, k4, k5 are all preset constants greater than zero; T0-T 11 Reflects the time between the current time and the last time of the data module's most recent self-test maintenance record; F is an intermediate symbol and is not explained. It can be understood that the larger i is, the larger T 1i The greater the time distance from the current moment.
[0061] Based on the methods corresponding to the above-mentioned steps, the obtained patient return visit necessity data can reflect the degree of necessity of the patient's rehabilitation return visit. The higher the degree of necessity of the patient's rehabilitation return visit, the greater the patient return visit necessity data.
[0062] The determining of the patient visit priority data according to the patient return visit necessity data specifically includes arranging the patient return visit necessity data in descending order, and determining the patient visit priority data according to the arrangement order.
[0063] Step S130: obtaining a patient rehabilitation return visit list according to the patient access priority data and a preset patient rehabilitation return visit time threshold.
[0064] In an embodiment of the present application, obtaining a patient rehabilitation return visit list based on the patient access priority data and a preset patient rehabilitation return visit time threshold specifically includes: determining a patient return visit time based on the patient access priority data; adjusting a patient return visit order based on the patient return visit time and the preset patient rehabilitation return visit time threshold; and determining a patient rehabilitation return visit list based on the adjusted patient return visit order.
[0065] It is understandable that after obtaining the priority of the patient visit, the patient's follow-up time can be determined based on the priority; when formulating the patient's follow-up time, it is also necessary to consider the follow-up interval time since the last follow-up of the patient, that is, the preset patient recovery follow-up time threshold mentioned above, and it is necessary to ensure that the patient's follow-up time is not greater than the preset patient recovery follow-up time threshold.
[0066] It should be understood that this method is essentially a machine learning model. The parameters in the machine learning model can be trained according to the rehabilitation therapist's patient follow-up records. The trainable parameters include, for example, the first preset rehabilitation therapist's accompanying training time threshold, the second preset rehabilitation therapist's accompanying training time threshold, the preset patient rehabilitation follow-up time threshold, k1, k2, k3, k4, k5, etc. During long-term use, the parameters of the machine learning model are trained, so that the method can better judge the degree of necessity of the patient's follow-up, and can more reasonably obtain the patient's follow-up priority, and can better guarantee the patient's rehabilitation treatment effect and improve the rehabilitation therapist's rehabilitation treatment efficiency for the patient.
[0067] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the embodiments of the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.
[0068] In the second aspect, the present application provides a rehabilitation training management system based on big data. Figure 2As shown, the system includes an acquisition module 210 for acquiring a patient rehabilitation plan and a rehabilitation therapist's rehabilitation visit record for each patient; a determination module 220 for determining patient visit priority data based on a rule engine according to the patient rehabilitation plan and the rehabilitation visit record; and an output module 230 for obtaining a patient rehabilitation follow-up list according to the patient visit priority data and a preset patient rehabilitation follow-up time threshold.
[0069] Furthermore, the determination module 220 is further configured to determine the patient visit priority data based on the rule engine according to the patient rehabilitation plan and the rehabilitation visit record, including: screening and determining the patient data requiring a follow-up visit according to the patient rehabilitation plan; determining the patient follow-up necessity data according to the patient data requiring a follow-up visit and the rehabilitation visit record; and determining the patient visit priority data according to the patient follow-up necessity data.
[0070] Furthermore, the determination module 220 is further configured to determine the patient return visit necessity data according to the patient return visit data and the rehabilitation visit record, including, for the patient return visit, assuming that there are n rehabilitation therapist return visit records within a preset time, and the doctor-patient communication time of the i-th rehabilitation therapist return visit record is t 1i , t i The final time is T 1i , the current time is T0, T 11 The patient return visit necessity data closest to the current moment is W1, then
[0071]
[0072]
[0073] Where, t al is the first preset rehabilitation therapist accompanying training time threshold, indicating that the rehabilitation therapist needs to accompany the patient all the time in the rehabilitation plan of the corresponding patient; t no is the second preset rehabilitation therapist accompanying training time threshold, indicating that the rehabilitation therapist does not need to accompany the patient in the rehabilitation plan of the corresponding patient; k1, k2, k3, k4, and k5 are all preset constants greater than zero.
[0074] Furthermore, the determination module 220 is further configured to determine the patient visit priority data according to the patient return visit necessity data, including arranging the patient return visit necessity data in descending order, and determining the patient visit priority data according to the arrangement order.
[0075] Furthermore, the output module 230 is further configured to obtain a patient rehabilitation return visit list based on the patient access priority data and a preset patient rehabilitation return visit time threshold, including: determining the patient return visit time based on the patient access priority data; adjusting the patient return visit order based on the patient return visit time and the preset patient rehabilitation return visit time threshold; and determining the patient rehabilitation return visit list based on the adjusted patient return visit order.
[0076] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described device can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.
[0077] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.
Claims
1. A rehabilitation training management method based on big data, characterized in that: include: Obtain patient rehabilitation plans and rehabilitation therapists' records of each patient's rehabilitation visits; Based on a rule engine, determining patient access priority data according to the patient rehabilitation plan and the rehabilitation access record; A patient rehabilitation return visit list is obtained according to the patient visit priority data and a preset patient rehabilitation return visit time threshold.
2. The rehabilitation training management method based on big data according to claim 1, characterized in that: The determining of the patient access priority data based on the rule engine according to the patient rehabilitation plan and the rehabilitation access record includes: Screen and determine the data of patients who need to be revisited according to the patient rehabilitation plan; Determine the patient revisit necessity data according to the patient revisit data and the rehabilitation visit record; Patient visit priority data is determined according to the patient return visit necessity data.
3. The rehabilitation training management method based on big data according to claim 2, characterized in that: The determining of the patient revisit necessity data according to the patient revisit data and the rehabilitation visit record includes: For patients who need to be revisited, assume that there are n revisit records of rehabilitation therapists within the preset time, and the doctor-patient communication time of the i-th revisit record of rehabilitation therapists is t 1i , t i The final time is T 1i , the current time is T0, T 11 The patient return visit necessity data closest to the current moment is W1, then Where, t al is the first preset rehabilitation therapist accompanying training time threshold, indicating that the rehabilitation therapist needs to accompany the patient all the time in the rehabilitation plan of the corresponding patient; t no is the second preset rehabilitation therapist accompanying training time threshold, indicating that the rehabilitation therapist does not need to accompany the patient in the rehabilitation plan of the corresponding patient; k1, k2, k3, k4, and k5 are all preset constants greater than zero.
4. The rehabilitation training management method based on big data according to claim 3 is characterized in that: Determining the patient visit priority data according to the patient return visit necessity data includes arranging the patient return visit necessity data in descending order, and determining the patient visit priority data according to the arrangement order.
5. The rehabilitation training management method based on big data according to claim 4 is characterized in that: The step of obtaining a patient rehabilitation return visit list according to the patient access priority data and a preset patient rehabilitation return visit time threshold comprises: Determining a patient return visit time according to the patient visit priority data; Adjusting the patient return visit sequence according to the patient return visit time and the preset patient rehabilitation return visit time threshold; Determine the patient rehabilitation follow-up list based on the adjusted patient follow-up order.
6. A rehabilitation training management system based on big data, characterized in that: include: An acquisition module (210) is used to acquire the patient's rehabilitation plan and the rehabilitation visit record of each patient by the rehabilitation therapist; A determination module (220) for determining patient access priority data based on a rule engine according to the patient rehabilitation plan and the rehabilitation access record; The output module (230) is used to obtain a patient rehabilitation return visit list according to the patient visit priority data and a preset patient rehabilitation return visit time threshold.
7. The rehabilitation training management method based on big data according to claim 6, characterized in that: The determination module (220) is further configured to determine the patient access priority data based on the rule engine according to the patient rehabilitation plan and the rehabilitation access record, including: Screen and determine the data of patients who need to be revisited according to the patient rehabilitation plan; Determine the patient revisit necessity data according to the patient revisit data and the rehabilitation visit record; Patient visit priority data is determined according to the patient return visit necessity data.
8. The rehabilitation training management system based on big data according to claim 7, characterized in that: The determination module (220) is further configured to determine the patient return visit necessity data according to the patient return visit data and the rehabilitation visit record, including: For patients who need to be revisited, assume that there are n revisit records of rehabilitation therapists within the preset time, and the doctor-patient communication time of the i-th revisit record of rehabilitation therapists is t 1i , t i The final time is T 1i , the current time is T0, T 11 The patient return visit necessity data closest to the current moment is W1, then Where, t al is the first preset rehabilitation therapist accompanying training time threshold, indicating that the rehabilitation therapist needs to accompany the patient all the time in the rehabilitation plan of the corresponding patient; t no is the second preset rehabilitation therapist accompanying training time threshold, indicating that the rehabilitation therapist does not need to accompany the patient in the rehabilitation plan of the corresponding patient; k1, k2, k3, k4, and k5 are all preset constants greater than zero.
9. The rehabilitation training management system based on big data according to claim 8, characterized in that: The determination module (220) is further configured to determine the patient visit priority data according to the patient return visit necessity data, including arranging the patient return visit necessity data in descending order, and determining the patient visit priority data according to the arrangement order.
10. The rehabilitation training management system based on big data according to claim 9, characterized in that: The output module (230) is further configured to obtain a patient rehabilitation return visit list according to the patient access priority data and a preset patient rehabilitation return visit time threshold, including: Determining a patient return visit time according to the patient visit priority data; Adjusting the patient return visit sequence according to the patient return visit time and the preset patient rehabilitation return visit time threshold; Determine the patient rehabilitation follow-up list based on the adjusted patient follow-up order.