Artificial intelligence evaluation method and system for patient data and application

Abstract

The invention provides an artificial intelligence evaluation method and system for patient data and application, and relates to the technical field of data processing. The method comprises the following steps: acquiring doctor-seeing waiting information of a patient user, and intelligently matching question information according to estimated doctor-seeing time; according to the question information, collecting response information provided by the patient; performing data evaluation on the response information based on an artificial intelligence technology to generate a pre-diagnosis evaluation report; the pre-diagnosis evaluation report is sent to a doctor user to be diagnosed, so that the doctor user can make corresponding medical examination items and / or medication prescriptions for the patient user. According to the invention, related information related to the condition of the patient can be acquired in advance by using the waiting time of the patient, so that the interrogation process and the interrogation time of the doctor are saved, and the interrogation efficiency of the hospital doctor is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an artificial intelligence evaluation method, system and application for patient data. Background Art

[0002] At present, when patients visit the hospital for treatment, the different treatment needs of the patients and the uncertainty of the treatment time often lead to the work arrangements of the hospital departments not being able to handle the treatment needs in a timely manner.

[0003] Considering that hospital visits are primarily divided into emergency and outpatient settings, emergency needs are often more time-sensitive. In contrast, outpatient visits are more relaxed, but still require patients to deal with more specific emergencies, such as fractures and sprains. Furthermore, patients may have personal preferences for hospitals and departmental doctors depending on the situation.

[0004] Based on this, under the blessing of various situations, there may be situations where some departments are overcrowded with patient users and the waiting time has to be extended. At the same time, considering the level of detail in the doctor's consultation process, it often takes a lot of time to ask the patient, such as asking the patient's basic condition, drug allergy information and other basic information. In addition, the doctor and the patient will spend a lot of time in communication, which leads to low consultation efficiency. In this case, it is more intuitive to prepare text, images and other information in advance and present it directly to the doctor.

[0005] Currently, the mainstream procedure for patients before visiting a hospital is for nurses to perform pre-examination and triage to roughly determine the patient's basic condition, in an attempt to achieve the effect of rationally allocating medical resources by quickly screening the patient's urgency. However, nurses do not have the authority to perform more detailed pre-examinations, nor do they have relevant operating experience. In addition, in actual operation, the doctor-patient ratio is unbalanced. Therefore, at this stage, it is difficult for hospital departments to perform pre-examination inquiries after pre-examination and triage and before the consultation to reduce the pressure of subsequent consultations. Therefore, the pre-examination inquiries before the patient's medical treatment also need to be configured with corresponding interactive operations and intelligent operations.

[0006] Based on this, the present invention provides an artificial intelligence evaluation method, system and application for patient data to provide intelligent pre-diagnosis inquiry operations after the patient undergoes pre-examination and triage and before the patient sees the doctor. This ensures that the doctor is efficiently aware of the patient's condition before and during the patient's visit, while shortening the consultation time. This is a technical problem that urgently needs to be solved. Summary of the Invention

[0007] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide an artificial intelligence evaluation method, system and application for patient data. The present invention can utilize the time patients wait for medical treatment to obtain relevant information related to the patient's condition in advance, thereby saving the doctor's consultation process and consultation time, and improving the diagnostic efficiency of hospital doctors.

[0008] In order to solve the existing technical problems, the present invention provides the following technical solutions: An artificial intelligence evaluation method for patient data, specifically comprising: Obtain patient user's consultation waiting information and intelligently match the inquiry information based on the estimated consultation time; Based on the aforementioned question information, collecting response information provided by the patient user; The aforementioned response information is subjected to data evaluation based on artificial intelligence technology to generate a pre-diagnosis evaluation report; the pre-diagnosis evaluation report is sent to the doctor user to be seen so that the doctor user can prescribe corresponding medical examination items and / or medication prescriptions for the patient user.

[0009] Furthermore, the waiting information for consultation includes department information, doctor information, number of patients waiting in the queue, and estimated waiting time for consultation; The question information involves the patient user's basic personal information, past medical history information, and current chief complaint information; The response information includes information provided by the patient user in the form of text, symbols, links, pictures, videos and / or audio.

[0010] Furthermore, the intelligent matching refers to filtering question information from a preset information question database based on the patient user's medical department and disease association; Corresponding to the aforementioned question information, the intelligent matching can also adjust the question information that appears later according to the content of the patient user's response information.

[0011] Furthermore, when performing intelligent matching, the following steps are also included: Count the current number of departments to which the patient user is registered; When the current number of departments to which the patient user is registered is 1, the patient user's past medical history information is obtained, and matching question information is provided to the patient user based on the disease association relationship between the past medical history information and the diseases involved in the departments to which the patient user is registered; When the current number of departments to which the patient user is registered is greater than or equal to 2, while obtaining the patient user's past medical history information, matching question information is provided to the aforementioned patient user based on the disease association relationship between the past medical history information and the diseases involved in the different departments to which the patient user is registered.

[0012] Furthermore, when collecting the response information provided by the patient user, the process also includes confirming the response information previously provided by the patient user; the confirmation can be used to organize the response information previously provided by the patient user into different presentation formats and then feed it back to the patient user; the presentation formats can include setting specific options for the patient user to perform at least one of selection, judgment, fill-in-the-blank, matching, sorting, and / or case analysis questions; When performing confirmation, at least one confirmation operation is performed on the response information previously provided by the patient user; wherein, the confirmation includes multiple confirmations of the same content in the response information previously provided by the patient user, confirmation of the same batch of different contents, and multiple confirmations of different contents based on pathological association relationships.

[0013] Furthermore, the pre-diagnosis evaluation report obtained by the doctor user includes laboratory test recommendation information, imaging examination recommendation information, disease association analysis information, treatment recommendation information, lifestyle and diet recommendation information, prevention recommendation information and at least one of the medical terms that match the patient user's pathological condition; before the patient user visits the doctor, the doctor user can prescribe corresponding medical examination items for the patient user based on the laboratory test recommendation information and / or imaging examination recommendation information.

[0014] Furthermore, when generating the pre-diagnosis evaluation report, it also includes judging whether the department where the patient user registered is correct based on the response information previously provided by the patient user. If it is judged to be not, a prompt message to change the registered department is sent to the patient user.

[0015] Furthermore, corresponding to the number of visits to the department where the patient user registered, the pre-diagnosis evaluation reports in the same department are marked in sequence as the first pre-diagnosis evaluation report, the second pre-diagnosis evaluation report, ..., the Nth pre-diagnosis evaluation report, where N is a positive integer; When a patient user has at least one follow-up visit to the same department, the current question information is dynamically matched based on the previous pre-diagnosis evaluation report, the medical advice information previously provided by the doctor user, and the response information provided by the patient user; After the patient user registers for different departments, the question information provided by different departments is combined with the patient user's pre-diagnosis evaluation report in other departments and the medical order information provided by the doctor user, and the current question information is dynamically matched according to the pathological correlation of diseases in different departments.

[0016] An artificial intelligence evaluation system for patient data, comprising: Network nodes, used to send question information and receive response information; An information processing module processes the aforementioned question information and response information based on artificial intelligence technology; A system server, the system server connecting the network nodes and the information processing module; The system server is configured to: obtain the patient user's consultation waiting information, and intelligently match the question information according to the estimated consultation time; collect the response information provided by the patient user based on the aforementioned question information; perform data evaluation on the aforementioned response information based on artificial intelligence technology to generate a pre-diagnosis evaluation report; and send the pre-diagnosis evaluation report to the doctor user to be seen, so that the doctor user can prescribe corresponding medical examination items and / or medication prescriptions for the patient user.

[0017] A computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the implementation steps of any of the above methods.

[0018] Based on the above advantages and positive effects, the advantages of the present invention are: providing intelligent pre-diagnosis inquiry operations after the patient user undergoes pre-examination and triage and before the patient sees the doctor, ensuring that the doctor is efficiently aware of the patient user's condition before and during the patient's visit, while reasonably shortening the consultation time. At the same time, it can also shorten the waiting time for patients to queue for medical examination items and the waiting time for doctors to prescribe medication. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flowchart provided for an embodiment of the present invention.

[0020] Figure 2 A schematic diagram of the structure of a system provided in an embodiment of the present invention.

[0021] Description of reference numerals: System 200, network node 201, information processing module 202, system server 203. DETAILED DESCRIPTION

[0022] The following is a further detailed description of an artificial intelligence evaluation method, system and application of patient data disclosed in the present invention in conjunction with the accompanying drawings and specific embodiments. It should be noted that the technical features or combinations of technical features described in the following embodiments should not be considered isolated, and they can be combined with each other to achieve better technical effects. In the drawings of the following embodiments, the same reference numerals appearing in each drawing represent the same features or components, which can be applied to different embodiments. Therefore, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0023] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not intended to limit the conditions under which the invention can be implemented. Any structural modification, change in proportional relationship, or adjustment of size should fall within the scope of the technical content disclosed in the invention without affecting the efficacy and purpose of the invention. The scope of the preferred embodiments of the present invention includes alternative implementations, in which the functions can be performed in a non-described or discussed order, including performing the functions in a substantially simultaneous manner or in a reverse order according to the functions involved, which should be understood by those skilled in the art of the art to which the embodiments of the present invention belong.

[0024] Technologies, methods, and apparatus known to persons of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such technologies, methods, and apparatus should be considered part of the specification. In all examples shown and discussed herein, any specific values should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values. Example

[0025] In medical treatment institutions, mainly hospitals, when patients come for treatment, the actual number of patients and the complexity of their illness often determine the workload of the doctor on that day.

[0026] This embodiment takes into account the varying lengths of time patients wait for their appointments. While waiting, patients can still clearly describe their current condition and medical history, thereby shortening the consultation time with the doctor, improving the doctor's diagnosis and treatment efficiency, and subsequent treatment arrangements. Therefore, it is essential to provide comprehensive personal medical information within the shortest possible time.

[0027] See Figure 1 FIG. 1 is a flow chart of the present invention. The implementation step S100 of the method is as follows: S101, obtaining the patient user's consultation waiting information, and intelligently matching the question information according to the estimated consultation time.

[0028] The consultation waiting information includes information about the department the patient user plans to visit, information about the doctor who will consult the patient user, the number of people waiting in the queue, and the estimated waiting time for the consultation.

[0029] The intelligent matching refers to filtering question information from a preset information question database based on the patient user's treatment department and disease association; and corresponding to the aforementioned question information, the intelligent matching can also adjust the subsequent question information based on the content of the patient user's response information.

[0030] The question information involves the patient user's basic personal information (such as age, home address, contact number), past medical history information, and current chief complaint information (such as dizziness, accompanied by nausea and vomiting).

[0031] In practice, patients often register with different departments on the same day to minimize wait times, hoping to address multiple medical conditions through consultations with doctors from different departments. Therefore, it's necessary to intelligently match patient information based on the estimated consultation time.

[0032] As one of the preferred implementations of this embodiment, when performing intelligent matching, step S110 is further included: S111, counting the current number of departments to which the patient user is registered.

[0033] S112, when the current number of departments to which the patient user is registered is 1, obtain the patient user's past medical history information, and provide matching question information for the aforementioned patient user based on the disease association relationship between the past medical history information and the diseases treated in the departments to which the patient user is registered.

[0034] S113, when the current number of departments to which the patient user is registered is greater than or equal to 2, while obtaining the patient user's past medical history information, according to the disease association relationship between the past medical history information and the diseases treated in different departments to which the patient user is registered, matching question information is provided to the aforementioned patient user.

[0035] As an example, not a limitation, consider Patient 1, who registered for both orthopedics and endocrinology appointments on the same day. The hospital's intelligent system then estimated the time it would take for Patient 1 to see Doctor B in the orthopedics department and Doctor C in the endocrinology department. Based on the correlation between the departments and the illnesses, the system then provided Patient 1 with intelligently matched question information.

[0036] Here is an example of the question received by patient user 1 before visiting the orthopedic department: First, check the current chief complaint information, such as: What is your main bone or joint problem? (For example: back pain, knee pain, shoulder pain, etc.); the time of symptom onset, frequency, and changes; Do you have a history of fractures or other orthopedic diseases? Have you had any surgery, treatment history, or ongoing treatment? Second, record your symptoms, for example: please describe the specific location of the pain, the degree of pain (using a score of 1-10), the type of pain (stabbing, dull pain, etc.); whether there are other accompanying symptoms (such as swelling, stiffness, limited movement, etc.).

[0037] Third, lifestyle habits, such as: your occupation and daily activity level (whether you sit for a long time, whether you often do physical labor); your exercise habits (whether you exercise regularly, specific exercise types and frequency).

[0038] For example, the question information received by patient user 1 before visiting the endocrinology department: First, review the current chief complaint, such as: What is your primary endocrine problem (e.g., thyroid problems, diabetes, osteoporosis, etc.); the onset, frequency, and changes of symptoms; do you have a history of endocrine-related diseases (e.g., thyroid disease, diabetes, etc.)? Have you had any related surgeries, treatments, or are you currently undergoing treatment? Second, record your symptoms. For example, please describe the main symptoms and their specific manifestations (such as fatigue, weight changes, abnormal sweating, etc.); whether there are other accompanying symptoms (such as palpitations, hand tremors, skin changes, etc.).

[0039] Third, lifestyle habits, such as: your eating habits (whether it is a high-sugar or high-fat diet) and weight changes; whether there is a family history of endocrine diseases.

[0040] Among them, it is worth mentioning that, considering the necessity of information collection, the intelligent matching can also match the question information after preliminarily determining the treatment needs of the patient user through the registration information of the patient user 1, and combining it with the pathological correlation between the disease involved in the corresponding department.

[0041] For example, considering that both orthopedic surgeons and endocrinologists are concerned about patient 1's osteoporosis, we pre-announced questions about osteoporosis in patient 1's question information. When the orthopedic surgeon asks about osteoporosis, the endocrinologist's subsequent questions will not ask the same information about osteoporosis, but instead ask about other aspects of osteoporosis. This avoids duplication between orthopedic and endocrinological questions while digging deeper into different aspects of osteoporosis.

[0042] By way of example and not limitation, patient user 1 has consulted both an orthopedic specialist and an endocrinologist, and her osteoporosis condition has already been mentioned in the orthopedic specialist's inquiry. To avoid repeated questions, further discussion of other osteoporosis-related content will be included in the endocrinology specialist's inquiry.

[0043] For example, in orthopedics questions about osteoporosis, patients are required to provide: First, symptoms and medical history: Have you ever suffered a fracture from a fall or minor external force? Please list any past fractures (e.g., spinal, hip, wrist, etc.). Do you experience back pain or sequelae of a fracture? Do you currently have difficulty walking or unstable standing? Second, medication and treatment history: Are you currently taking osteoporosis medications (such as bisphosphonates, estrogen, calcium and vitamin D supplements, etc.)? Have you ever had a bone density test? What were the test results? Third, lifestyle habits: Do you get enough calcium, vitamin D, and other nutrients needed for bone health in your daily diet? Do you engage in regular physical activity, especially weight training or weight-bearing exercise? Since the orthopedics question already includes information about osteoporosis symptoms, fracture history, and treatment, the endocrinology question will not repeat these questions. Instead, it will focus more on the endocrine background of osteoporosis and the risk factors associated with endocrine disorders. Therefore, after the patient answers the orthopedics question, the endocrinology question will require the patient to provide additional information related to osteoporosis.

[0044] As an example and not a limitation, based on the patient user's historical medical condition and orthopedic question information, the following question information applicable to the endocrinology department is screened out around osteoporosis.

[0045] First, hormone levels and metabolism: Have you used steroids long-term (e.g., for asthma, rheumatoid arthritis, etc.)? Have you ever had a thyroid disorder (e.g., hyperthyroidism, hypothyroidism, etc.) or received thyroid treatment? If you are female, have you experienced irregular menstruation or amenorrhea? What was your age at menopause? Do you have diabetes or take insulin long-term? Diabetic users are generally at higher risk of osteoporosis.

[0046] Second, endocrine risk factors for osteoporosis: Do you have a family history of osteoporosis, fractures, or other endocrine diseases (such as thyroid disease, diabetes, etc.)? Do you have a history of long-term low body weight or excessive weight loss? Being too low increases the risk of osteoporosis. Have you ever had adrenal problems or are you currently using hormone replacement therapy? Third, related examinations and treatments: Have you ever had a bone density test? The endocrinology department will assess the degree of osteoporosis based on the results of the bone density test. Are you currently taking vitamin D, calcium supplements, or other endocrine-regulating medications (such as estrogen replacement therapy)? It can be seen from this that through intelligent matching, the endocrinology department's questions will not ask about the symptoms of osteoporosis again (such as fracture history, pain, limited mobility, etc.). This information has already been obtained in the orthopedic consultation, but will focus more on endocrine factors related to osteoporosis, such as hormone levels, drug use, and family history.

[0047] Through intelligent matching, when the same patient registers in different departments, the question information of different departments can be referenced with each other, thus avoiding repeated questions and improving the efficiency of subsequent consultations.

[0048] S102: Collect response information provided by the patient user based on the aforementioned question information.

[0049] In actual operation, collecting the response information provided by the patient user to the aforementioned question information helps the hospital collect the patient user's response information to provide data support for the doctor to prescribe the corresponding medical examination items and / or medication prescriptions to the patient user.

[0050] The response information includes but is not limited to information provided by the patient user in the form of text, symbols, links, pictures, videos and / or audio.

[0051] Preferably, when collecting the response information provided by the patient user, it also includes confirming the response information previously provided by the patient user.

[0052] The confirmation can feed back the patient user the previously provided response information after organizing it into different presentation forms. The presentation forms include setting specific options for the patient user to perform at least one of selection, judgment, fill-in-the-blank, matching, sorting, and / or case analysis questions.

[0053] This is because patients may have different cultural levels, different language expression abilities, and conceal their medical conditions to maintain their self-esteem. These situations may lead to patients being unable to provide accurate response information.

[0054] Based on this, it is necessary to repeatedly and multiple times confirm the response information provided by the patient user to ensure that the doctor user can accurately obtain the patient user's actual medical condition through the pre-diagnosis evaluation report.

[0055] As an example and not a limitation, taking the question information as a multiple-choice question type and the judgment question type as an example during confirmation, the question information received by the electronic device of patient user 1 is: In the past year, have you ever sought medical treatment for a fracture or fall? And corresponding options are set: ① Yes, there was a fracture; ② Yes, there were multiple falls; ③ No, there were no fractures or falls; ④ I don’t remember. The response information of patient user 1, for example, the response information of patient user 1 is option ②, and when it comes to the confirmation stage, confirmation is made based on the answer selected by patient user 1: "You selected: 'No, there were no fractures or falls'. Please confirm whether it is correct." For example, if the question is a multiple-choice question and the confirmation is a true-or-false question, the question received by Patient User 1's electronic device might be: "When was your last fracture?" The corresponding fill-in-the-blank box requires a date or year. When confirming, Patient User 1's selected answer is: "The date you entered is 'May 2023'. Please confirm whether it is correct." For example, when it comes to more visual information such as body parts and symptoms, the question information preferably uses images, charts, or short animations to help the patient user understand a certain question. For example: The following are diagrams of different parts of the spine. Please select the area where you recently felt pain: ① Neck, ② Back, ③ Waist, ④ Lower limbs. Attached is a diagram: A diagram marking the different parts of the spine. When confirming, confirm according to the answer selected by patient user 1: "The pain area you selected is 'waist'. Please confirm whether it is correct." Another example is the situation where the question information will dynamically display new question information based on the patient user's previous response information. For example, the question information received by the electronic device of patient user 1 is: Question 1: Have you ever been to the doctor for low back pain? ①Yes, ②No; if "Yes" is selected: Question 2 is dynamically generated: "Have you ever had an X-ray or MRI examination?" ①Yes, ②No. When confirming this situation, it is preferred to confirm based on the answer selected by patient user 1: "You selected 'Yes' and have already had an X-ray examination. Please confirm whether it is correct." The purpose of the above adjustment of the display format is to ensure that the information provided by patient users is correct and complete through different interactive methods, while also reducing the difficulty for patient users to understand and answer. Furthermore, confirming the patient user's answer in a clear and concise manner can help improve the accuracy of the collected information and avoid misdiagnosis caused by patient user memory bias or misunderstanding. In actual use, it is preferred to select the appropriate display format according to the specific situation, and flexibly combine different question and answer methods to better serve patient users and enhance the medical experience.

[0056] In addition, considering that in actual operations, it may be difficult for patient users to realize that the information they provide is wrong, when performing confirmation, at least one confirmation operation is performed on the response information previously provided by the patient user; wherein, the confirmation includes multiple confirmations of the same content in the response information previously provided by the patient user, confirmation of the same batch of different contents, and multiple confirmations of different contents based on pathological association relationships.

[0057] When the patient denies the previous response information during confirmation or the newly provided content is inconsistent with the previous response information during confirmation, it is preferred to mark the previous response information as doubtful and perform multiple confirmations.

[0058] In actual operation, considering that the patient user answers the aforementioned question information in a short time before the consultation, a certain error tolerance is allowed for the response information provided by the patient user during confirmation. Once the error rate of the response information provided by the patient user reaches a preset error tolerance rate (for example, a preset error tolerance rate of 10%), it is preferred to mark these questionable response information and display them in the pre-diagnosis evaluation report when generating the pre-diagnosis evaluation report, so as to prompt the doctor user to confirm during the consultation process.

[0059] As another preferred implementation of this embodiment, taking into account the situation that the patient user himself is not proficient in operating electronic devices and the patient user's language expression ability is poor, at this time, it is preferred to request to obtain the patient user's address book contacts and / or WeChat contacts, such as relatives, friends, colleagues, and social work service providers, and apply for the above-mentioned address book contacts and / or WeChat contacts to help the patient user answer the question information. This still needs to consider the fault tolerance of the corresponding response information and confirm it. When the pre-diagnosis evaluation report is generated, it is preferred to mark in the pre-diagnosis evaluation report that the above-mentioned question information is answered by the patient user's address book contacts and / or WeChat contacts, and then suggest that the doctor user can let the patient user's address book contacts and / or WeChat contacts join the communication process with the patient user during the subsequent consultation, so that the address book contacts and / or WeChat contacts can also effectively follow up the patient user's subsequent medical treatment process and treatment process.

[0060] Among them, when the patient user's address book contacts and / or WeChat contacts join together to realize the consultation, it is preferred to establish a communication group (such as WeChat group, DingTalk group, etc.) for the doctor user, the patient user, and the patient user's address book contacts and / or WeChat contacts, and record the video call, voice call and / or text record information of the consultation in the group.

[0061] It is also worth noting that this embodiment preferably uses a pre-diagnosis inquiry operation to demonstrate the process of achieving pre-diagnosis interaction through question information and response information during the pre-diagnosis process in this embodiment. The pre-diagnosis inquiry operation is preferably performed by a doctor digital human, virtual doctor, or AI medical assistant to enhance the intelligent level of medical diagnosis in the hospital.

[0062] S103, performing data evaluation on the aforementioned response information based on artificial intelligence technology to generate a pre-diagnosis evaluation report.

[0063] The pre-diagnosis evaluation report is sent to the doctor user who is about to see the patient, so that the doctor user can prescribe corresponding medical examination items and / or medication prescriptions for the patient user.

[0064] The advantage of this operation is that it can facilitate doctors to prescribe corresponding medical examination items and / or medication prescriptions for patients before or during the consultation, thereby saving the doctor's consultation time and saving the patient's operational process of diagnosing the disease.

[0065] In this embodiment, the pre-diagnosis evaluation report can obtain the patient's main complaint information in the pre-diagnosis stage after evaluating the above-mentioned response information. Based on the above-mentioned patient's main complaint information, the doctor user can prescribe corresponding medical examination items and / or medication prescriptions for the patient user.

[0066] It is worth noting that, corresponding to the aforementioned patient complaint information, the pre-diagnosis evaluation report obtained by the doctor user also includes laboratory examination recommendation information, imaging examination recommendation information, disease association analysis information, treatment recommendation information, lifestyle and diet recommendation information, prevention recommendation information and at least one of the medical terms that match the patient user's pathological condition.

[0067] The laboratory test recommendation information is information suggesting laboratory tests that the patient user needs to take. For example, if the patient user has provided a response indicating that they have abnormal symptoms such as high blood sugar, high cholesterol, or anemia, the laboratory test recommendation information in the pre-diagnosis evaluation report may recommend that the doctor user perform laboratory tests such as blood sugar monitoring, blood lipid testing, or a routine blood test.

[0068] The imaging examination recommendation information is information suggesting that the patient user should undergo imaging examinations. For example, if the patient user has provided a response indicating that the patient has chest pain, persistent cough, or joint swelling, the imaging examination recommendation information in the pre-diagnosis evaluation report may recommend that the doctor user undergo an imaging examination such as an X-ray, CT scan, or MRI.

[0069] Disease association analysis information suggests associations between certain diseases based on a patient's medical history and symptoms, and recommends further diagnostic analysis. For example, if the response information determines that a patient has a history of hypertension and experiences dizziness, the pre-diagnosis assessment report will preferably include disease association analysis information analyzing whether the patient's hypertension has a pathological association with cardiovascular or cerebrovascular disease.

[0070] The treatment recommendation information refers to the recommendation information that the patient user needs to adopt a corresponding disease treatment plan.

[0071] The lifestyle and diet advice information refers to the patient user's recommended lifestyle and diet management information. For example, if the patient's body mass index (BMI) is 30, it is recommended to lose weight, adopt a low-salt diet, and start regular exercise.

[0072] The preventive advice information is advice given from the perspective of disease prevention based on the patient's physical condition. For example, a 65-year-old osteoporosis patient may be advised to be careful about falls and injuries.

[0073] As for the medical terms that match the patient's pathological condition, let's take the example of a patient who fell (from a height of 2 meters) and experienced severe pain in the right hip and thigh, making it impossible to stand or walk. When the patient sought medical treatment at this hospital, they provided the following chief complaints based on the question: right hip swelling, significant tenderness, and localized tenderness; right thigh external rotation deformity, with inability to perform active and passive movements; no obvious open wounds on the skin, and no traumatic bleeding; tenderness in the right lower femur, with significantly increased pain; normal pulse and neurological function tests, and normal right lower limb arterial pulses. X-rays and CT scans taken at other hospitals also showed a right femoral neck fracture, a displaced fracture with mild fracture deformity; and a CT scan confirmed an oblique fracture line and slight displacement of the femoral head.

[0074] Based on this, in the pre-diagnosis evaluation report generated by collecting the patient's response information and generating the data evaluation, the medical terms and corresponding term explanations that match the patient's pathological conditions are as follows: Femoral neck fracture: A fracture of the femoral neck, usually between the femoral head and shaft.

[0075] Displaced fracture: The broken bone ends are significantly displaced and require surgical intervention to reduce them.

[0076] Oblique fracture: The fracture line is oblique and at an angle to the long axis of the bone, usually caused by an oblique external force.

[0077] Femoral head fixation nail: A surgical method for treating femoral neck fractures that uses nails to fix the two ends of the fracture to help the fracture heal.

[0078] As one of the preferred implementations of this embodiment, before the patient user visits the doctor, the doctor user can also prescribe corresponding medical examination items for the patient user based on the laboratory examination recommendation information and / or imaging examination recommendation information.

[0079] Considering that in actual medical consultation operations, the first meeting between doctors and patients is often only able to determine the patient's main complaint through a brief communication. In most cases, doctors cannot determine the treatment plan based solely on the patient's communication. Out of strict medical literacy, doctors should select necessary examination items to accurately determine the patient's disease without excessive diagnosis and treatment.

[0080] In order to save the patient's consultation time and avoid the doctor from repeating the consultation operation, after the patient user provides the response information, combined with the patient user's estimated remaining waiting time for the consultation, the doctor user will preferably be able to prescribe corresponding medical examination items for the patient user based on the laboratory examination recommendation information and / or imaging examination recommendation information in the generated pre-diagnosis evaluation report.

[0081] For example, if the remaining waiting time for a patient user's consultation is 30 minutes, during these 30 minutes, the patient user can undergo corresponding examinations based on the medical examination items prescribed by the doctor user, such as routine blood drawing, chest CT scan, etc.

[0082] The advantage of this operation is that it can use the time when patients are waiting for consultation to complete part of the operation process of the consultation interaction with the doctor, which simplifies the doctor's consultation process for patients. At the same time, it also executes some of the original arrangements after the consultation in advance while the patient is waiting for consultation, which also means saving the patient's consultation time.

[0083] It's also worth noting that disease associations are initially considered when generating the pre-diagnosis evaluation report. That is, the laboratory test recommendations and / or imaging test recommendations in the pre-diagnosis evaluation report are based on the disease association analysis information in the pre-diagnosis evaluation report and are determined after weighing the disease associations. Therefore, the doctor will prescribe the corresponding medical examination items for the patient based on the laboratory test recommendations and / or imaging test recommendations in the pre-diagnosis evaluation report after weighing the disease associations based on the disease association analysis information in the pre-diagnosis evaluation report.

[0084] In addition, considering the limited knowledge of the patient user about his or her own symptoms, when generating the pre-diagnosis evaluation report, it also includes judging whether the department where the patient user registered is correct based on the response information previously provided by the patient user. If it is not, a prompt message to change the registration department is sent to the patient user.

[0085] There are two main scenarios for determining if a patient has registered with the wrong department: first, when providing a response, the patient may discover through the content of the question that they misreported the department; second, after collecting the patient's response information and performing data evaluation based on the aforementioned response information using artificial intelligence technology, it may be discovered that the patient misreported the department. In these situations, a prompt message is sent to the patient to change the registered department to avoid complications with the patient's medical treatment due to misreporting the department.

[0086] In this embodiment, it is considered that the patient user may dynamically adjust the estimated consultation time due to the extension or shortening of the consultation time. This situation is related to the complexity and difficulty of the patient's physical illness.

[0087] To this end, while intelligently matching question information, it is preferred to intelligently estimate the patient's waiting time for consultation. The patient's expected consultation time is intelligently calculated based on the situation in which the previous patient answered the question information during the patient consultation sorting process.

[0088] By way of example and not limitation, a pre-defined AI medical diagnostic system can dynamically adjust the order of appointments based on the department and patient's appointment schedule. For example, if patient 1's orthopedics appointment takes longer, while the endocrinology appointment requires more time, the system can intelligently adjust the appointment time for patient 1 to avoid long queues and wait times due to time conflicts between different departments.

[0089] Preferably, corresponding to the number of visits to the department where the patient user registered, the pre-diagnosis evaluation reports in the same department are marked in sequence as the first pre-diagnosis evaluation report, the second pre-diagnosis evaluation report, ..., the Nth pre-diagnosis evaluation report, where N is a positive integer.

[0090] When a patient user has at least one follow-up visit to the same department, the current question information is dynamically matched based on the previous pre-diagnosis evaluation report, the medical advice information previously provided by the doctor user, and the response information provided by the patient user.

[0091] Specifically, based on the response information provided by the patient user, data evaluation is performed on the aforementioned response information based on artificial intelligence medical diagnosis technology, and the Nth pre-diagnosis evaluation report of the patient user in the corresponding department can be generated.

[0092] After the patient user registers for different departments, the question information provided by different departments is combined with the patient user's pre-diagnosis evaluation report in other departments and the medical order information provided by the doctor user, and the current question information is dynamically matched according to the pathological correlation of the diseases in different departments.

[0093] Among them, in order to facilitate doctor users to fully understand the patient's medical treatment situation, each pre-diagnosis evaluation report can sort out the previous pre-diagnosis evaluation report and the medical order information provided by the doctor user, and then confirm the patient user's patient complaint information in the latest pre-diagnosis evaluation report.

[0094] Other technical features are described in the previous embodiments and will not be repeated here.

[0095] In addition, see Figure 2 As shown, the present invention also provides an embodiment, which provides an artificial intelligence evaluation system 200 for patient data, including: The network node 201 is used to send question information and receive response information.

[0096] The information processing module 202 processes the aforementioned question information and response information based on artificial intelligence technology.

[0097] The system server 203 is connected to the network node 201 and the information processing module 202 .

[0098] The system server 203 is configured to: obtain the patient user's consultation waiting information, and intelligently match the question information according to the estimated consultation time; collect the response information provided by the patient user based on the aforementioned question information; perform data evaluation on the aforementioned response information based on artificial intelligence technology to generate a pre-diagnosis evaluation report; and send the pre-diagnosis evaluation report to the doctor user to be seen so that the doctor user can prescribe corresponding medical examination items and / or medication prescriptions for the patient user.

[0099] For other technical features, please refer to the previous embodiments and will not be repeated here.

[0100] In addition, an embodiment of the present invention also provides a computer-readable storage medium on which a program is stored, which is used in the aforementioned artificial intelligence evaluation system for patient data. When the program is executed by the processor, it can implement the steps of any of the above-mentioned artificial intelligence evaluation methods for patient data.

[0101] For other technical features, please refer to the previous embodiments and will not be repeated here.

[0102] In the above description, the components may be selectively and operatively combined in any number within the scope of the intended protection of the present disclosure. In addition, terms such as "include," "encompass," and "have" should be interpreted as inclusive or open-ended rather than exclusive or closed by default, unless expressly defined to the contrary. All technical, technological, or other terms have the meanings understood by those skilled in the art, unless they are defined to the contrary. Common terms found in dictionaries should not be interpreted in an overly idealized or unrealistic manner in the context of the relevant technical documentation, unless expressly defined to that extent by the present disclosure.

[0103] Although example aspects of the present disclosure have been described for illustrative purposes, those skilled in the art will appreciate that the foregoing description is merely a description of preferred embodiments of the present invention and does not limit the scope of the present invention in any way. The scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order in which they appear or are discussed. Any changes or modifications made by those skilled in the art based on the foregoing disclosure are intended to fall within the scope of the claims.

Claims

1. An artificial intelligence evaluation method for patient data, characterized in that: Specifically include: Obtain patient user's consultation waiting information and intelligently match the inquiry information based on the estimated consultation time; Based on the aforementioned question information, collecting response information provided by the patient user; The aforementioned response information is subjected to data evaluation based on artificial intelligence technology to generate a pre-diagnosis evaluation report; the pre-diagnosis evaluation report is sent to the doctor user to be seen so that the doctor user can prescribe corresponding medical examination items and / or medication prescriptions for the patient user.

2. The method according to claim 1, characterized in that The waiting information for consultation includes department information, doctor information, number of patients waiting in the queue, and estimated waiting time for consultation; The question information involves the patient user's basic personal information, past medical history information, and current chief complaint information; The response information includes information provided by the patient user in the form of text, symbols, links, pictures, videos and / or audio.

3. The method according to claim 1, characterized in that The intelligent matching refers to filtering question information from a preset information question database based on the patient user's treatment department and disease association; Corresponding to the aforementioned question information, the intelligent matching can also adjust the question information that appears later according to the content of the patient user's response information.

4. The method according to claim 1, wherein When performing smart matching, the following steps are also included: Count the current number of departments to which the patient user is registered; When the current number of departments to which the patient user is registered is 1, the patient user's past medical history information is obtained, and matching question information is provided to the patient user based on the disease association relationship between the past medical history information and the diseases involved in the departments to which the patient user is registered; When the current number of departments to which the patient user is registered is greater than or equal to 2, while obtaining the patient user's past medical history information, matching question information is provided to the aforementioned patient user based on the disease association relationship between the past medical history information and the diseases involved in the different departments to which the patient user is registered.

5. The method according to claim 1, wherein When collecting the response information provided by the patient user, it also includes confirming the response information previously provided by the patient user; the confirmation can be fed back to the aforementioned patient user after arranging the response information previously provided by the patient user into different display forms; The display format includes setting specific options for the patient user to perform at least one of selection, judgment, fill-in-the-blank, matching, sorting and / or case analysis questions; When performing confirmation, at least one confirmation operation is performed on the response information previously provided by the patient user; wherein, the confirmation includes multiple confirmations of the same content in the response information previously provided by the patient user, confirmation of the same batch of different contents, and multiple confirmations of different contents based on pathological association relationships.

6. The method according to claim 1, characterized in that The pre-diagnosis evaluation report obtained by the doctor user includes laboratory test recommendation information, imaging examination recommendation information, disease association analysis information, treatment recommendation information, lifestyle and diet recommendation information, prevention recommendation information and at least one of the medical terms matching the patient user's pathological condition; before the patient user visits the doctor, the doctor user can prescribe corresponding medical examination items for the patient user based on the laboratory test recommendation information and / or imaging examination recommendation information.

7. The method according to claim 1, characterized in that When generating the pre-diagnosis evaluation report, it also includes judging whether the department where the patient user is registered is correct based on the response information previously provided by the patient user. If it is judged to be not, a prompt message to change the registered department is sent to the patient user.

8. The method according to claim 1, characterized in that Corresponding to the number of visits to the department where the patient user registered, the pre-diagnosis evaluation reports in the same department are marked in sequence as the first pre-diagnosis evaluation report, the second pre-diagnosis evaluation report, ..., the Nth pre-diagnosis evaluation report, where N is a positive integer; When a patient user has at least one follow-up visit to the same department, the current question information is dynamically matched based on the previous pre-diagnosis evaluation report, the medical advice information previously provided by the doctor user, and the response information provided by the patient user; After the patient user registers for different departments, the question information provided by different departments is combined with the patient user's pre-diagnosis evaluation report in other departments and the medical order information provided by the doctor user, and the current question information is dynamically matched according to the pathological correlation of diseases in different departments.

9. An artificial intelligence evaluation system for patient data according to the method of any one of claims 1 to 8, characterized in that include: Network nodes, used to send question information and receive response information; An information processing module processes the aforementioned question information and response information based on artificial intelligence technology; A system server, the system server connecting the network nodes and the information processing module; The system server is configured to: obtain the patient user's consultation waiting information and intelligently match the question information according to the estimated consultation time; Based on the aforementioned question information, collecting response information provided by the patient user; The aforementioned response information is subjected to data evaluation based on artificial intelligence technology to generate a pre-diagnosis evaluation report; the pre-diagnosis evaluation report is sent to the doctor user to be seen so that the doctor user can prescribe corresponding medical examination items and / or medication prescriptions for the patient user.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps of any one of claims 1 to 8 are implemented.

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