Outpatient service auxiliary examination optimization scheduling system and method based on artificial intelligence

Abstract

The invention discloses an outpatient service auxiliary examination optimization scheduling system and method based on artificial intelligence, relates to the field of medical treatment, and solves the problem that an existing outpatient service auxiliary examination optimization scheduling system is poor in optimization scheduling effect. The scheduling type module is used for carrying out preliminary examination duration prediction on each item to be examined and obtaining patient scheduling type division data according to a prediction result to obtain preliminary examination prediction data, and the scheduling analysis module is used for carrying out examination sequence analysis on patients of a first scheduling type to obtain patient examination analysis data and sending the patient examination analysis data to the first scheduling type; and the project scheduling module is used for performing project scheduling and project inspection time length prediction on the first scheduling type of patients and the second scheduling type of patients according to the inspection preliminary prediction data and the patient inspection analysis data. According to the invention, the pertinence and scientificity of the outpatient auxiliary inspection optimization scheduling system can be improved.

Description

Technical Field

[0001] The present invention belongs to the medical field and relates to artificial intelligence technology. Specifically, it is an outpatient auxiliary examination optimization scheduling system and method based on artificial intelligence. Background Art

[0002] When the existing outpatient auxiliary examination optimization scheduling system conducts outpatient auxiliary examination optimization scheduling, it has the following specific defects:

[0003] 1. The existing outpatient auxiliary examination optimization scheduling system cannot analyze the commonalities of the examination items of the patient's outpatient examination items and patients with the same historical diseases, and does not classify the scheduling types of patients according to the analysis results, resulting in a lack of pertinence in the examination optimization scheduling method and being unable to fully meet the examination optimization needs of patients;

[0004] 2. The existing outpatient auxiliary examination optimization scheduling system cannot combine the historical examination records of the corresponding examination items to predict the number of people queuing for the items in the future period, so it cannot provide an accurate scheduling plan and the duration required for the examination, resulting in low efficiency of the examination optimization scheduling and being unable to effectively save the patient's examination waiting time.

[0005] Therefore, we propose an outpatient auxiliary examination optimization scheduling system and method based on artificial intelligence. Summary of the Invention

[0006] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide an outpatient auxiliary examination optimization scheduling system and method based on artificial intelligence, aiming to improve the pertinence and scientificity of the outpatient auxiliary examination optimization scheduling system.

[0007] To achieve the above purpose, the present invention adopts the following technical solutions: An outpatient auxiliary examination optimization scheduling system based on artificial intelligence, comprising:

[0008] A patient data module: used to respectively obtain a plurality of items to be examined corresponding to the target patient to be examined and the item treatment correlation coefficient corresponding to each item to be examined, and obtain patient medical collection data;

[0009] A scheduling type module: used to respectively obtain the waiting duration and single - examination duration corresponding to each item to be examined by analyzing the patient medical collection data, make a preliminary examination duration prediction for the target patient to be examined, and obtain patient scheduling type classification data according to the prediction result to obtain preliminary examination prediction data;

[0010] A scheduling analysis module: used to analyze the examination order of the first - scheduling - type patients according to the preliminary examination prediction data to obtain patient examination analysis data;

[0011] Project scheduling module: used to perform project scheduling and predict the required duration of project inspections for patients of the first scheduling type and the second scheduling type according to the preliminary prediction data of inspections and the patient inspection analysis data respectively.

[0012] Furthermore, the patient data module obtains the patient medical collection data as follows:

[0013] Obtain outpatient patients who need to undergo medical project inspections in real time, and select a target patient to be inspected from the obtained outpatient patients;

[0014] Obtain the medical record of the target patient to be inspected, and obtain the inspection items that the target patient needs to complete according to the medical record, obtain multiple inspection items to be inspected, and sequentially mark the obtained inspection items as J1 inspection item to Ja inspection item in the order of the time of issuance of the items;

[0015] Obtain the treatment correlation coefficient between the J1 inspection item and the patient's disease condition, and obtain the J1 item treatment correlation coefficient;

[0016] Obtain the item treatment correlation coefficients corresponding to the J2 inspection item to the Ja inspection item respectively, and obtain the J2 item treatment correlation coefficient to the Ja item treatment correlation coefficient;

[0017] Define the J1 inspection item to the Ja inspection item and the J1 item treatment correlation coefficient to the Ja item treatment correlation coefficient as the patient medical collection data.

[0018] Furthermore, the patient data module obtains the J1 item treatment correlation coefficient as follows:

[0019] Obtain the medical record of the target patient to be inspected, and obtain the clinical diagnosis of the patient according to the medical record, and obtain the target clinical diagnosis;

[0020] Obtain the historical treatment records of the medical institution, screen out several historical clinical patients diagnosed as the target clinical diagnosis from the historical treatment records of the medical institution, count the number of historical clinical patients, and obtain the historical clinical patient quantity value;

[0021] Obtain the outpatient inspection items of several historical clinical patients respectively, mark the historical clinical patients with the J1 inspection item in the outpatient inspection items as J1 historical clinical patients, count the number of J1 historical clinical patients, and obtain the J1 clinical patient quantity value;

[0022] Calculate the ratio of the J1 clinical patient quantity value to the historical clinical patient quantity value, obtain the inspection common ratio corresponding to the J1 inspection item, and name it the J1 inspection common ratio;

[0023] Obtain the examination results of each J1 historical clinical patient for the items to be examined for J1 respectively. Mark the J1 historical clinical patients with abnormal examination results as J1 item abnormal patients, and count the number of J1 item abnormal patients to obtain the number value of J1 item abnormal patients;

[0024] Calculate the ratio of the number value of J1 item abnormal patients to the number value of J1 clinical patients to obtain the J1 examination abnormality ratio;

[0025] Count the number of times each J1 historical clinical patient undergoes the items to be examined for J1 during the treatment cycle respectively to obtain multiple completion times of the items to be examined for J1, and calculate the average of the obtained multiple completion times of the items to be examined for J1 to obtain the J1 examination cycle repetition times;

[0026] Calculate the J1 examination common ratio, J1 examination abnormality ratio, and J1 examination cycle repetition times to obtain the item treatment correlation coefficient corresponding to the items to be examined for J1, and name it the J1 item treatment correlation coefficient;

[0027] Calculate the J1 item treatment correlation coefficient, and the specific formula is as follows:

[0028] Xgj1 = Gj1 + Yj1 2 + Fj1;

[0029] Wherein, Xgj1 is the J1 item treatment correlation coefficient, Gj1 is the J1 examination common ratio, Yj1 is the J1 examination abnormality ratio, and Fj1 is the J1 examination cycle repetition times.

[0030] Furthermore, the scheduling type module obtains the preliminary examination prediction data as follows:

[0031] Obtain the patient medical collection data, and respectively obtain the items to be examined for J1 to Ja and the J1 item treatment correlation coefficient to Ja item treatment correlation coefficient according to the patient medical collection data;

[0032] Arrange the J1 item treatment correlation coefficient to Ja item treatment correlation coefficient in descending order according to the numerical value. According to the sorting result, re-mark the item to be examined corresponding to the item treatment correlation coefficient ranked first as the P1 item to be examined, and re-mark the item to be examined corresponding to the item treatment correlation coefficient ranked second as the P2 item to be examined, and so on. According to the sorting result, re-mark the item to be examined corresponding to the item treatment correlation coefficient ranked a-th as the Pa item to be examined;

[0033] Obtain the average inspection duration of a single patient corresponding to each of the inspection items from P1 to Pa, and obtain the single inspection duration from P1 to Pa;

[0034] Mark the time point corresponding to the current moment as the P1 characteristic time point;

[0035] Obtain the number of queuing patients corresponding to the P1 inspection item at the P1 characteristic time point to obtain the P1 queuing patient number value, and calculate the product of the P1 single inspection duration and the P1 queuing patient number value to obtain the waiting duration of the P1 inspection item;

[0036] Obtain the waiting duration of the P2 inspection item;

[0037] Obtain the waiting durations corresponding to each of the inspection items from P3 to Pa, and obtain the waiting durations from P3 to Pa;

[0038] Sum up the waiting durations of the inspection items from P1 to Pa to obtain the preliminary predicted inspection duration corresponding to the target inspection item;

[0039] Obtain the threshold of the preliminary predicted inspection duration, compare the preliminary predicted inspection duration with the threshold of the preliminary predicted inspection duration, and obtain the patient scheduling type classification data according to the comparison result;

[0040] Specifically as follows:

[0041] When the preliminary predicted inspection duration is greater than or equal to the threshold of the preliminary predicted inspection duration, it is determined that the target patient to be inspected is a patient of the first scheduling type;

[0042] When the preliminary predicted inspection duration is less than the threshold of the preliminary predicted inspection duration, it is determined that the target patient to be inspected is a patient of the second scheduling type.

[0043] Define the inspection items from P1 to Pa, the waiting durations of the inspection items from P1 to Pa, the single inspection durations from P1 to Pa, and the patient scheduling type classification data as the preliminary predicted inspection data.

[0044] Furthermore, the scheduling type module obtains the waiting duration of the P2 inspection item, specifically as follows:

[0045] Obtain the number of queuing patients corresponding to each of the inspection items from P2 to Pa at the P1 characteristic time point to obtain the P2 queuing patient number value to Pa queuing patient number value;

[0046] Set a P2 project queuing number interval with the P2 queuing patient number value as the middle value of the interval to obtain the P2 project queuing number interval;

[0047] Obtain the historical inspection records corresponding to the items to be inspected for P2. Among the historical inspection records, mark the dates when the number of queuing patients corresponding to the items to be inspected for P2 at the P1 characteristic time point on the current day is within the P2 item queuing number range as the P2 characteristic dates, and obtain multiple P2 characteristic dates;

[0048] Mark the time points of Pd1 + 1 P1 single inspection durations after the P1 characteristic time point as the P2 characteristic time points;

[0049] where Pd1 is the value of the number of queuing patients for P1;

[0050] Respectively obtain the P2 characteristic time points within each P2 characteristic date and the value of the number of queuing patients corresponding to the items to be inspected for P2, obtain multiple values of the number of queuing patients, and calculate the average of the obtained multiple values of the number of queuing patients to obtain the predicted queuing person-times for the P2 item. Calculate the product of the predicted queuing person-times for the P2 item and the P2 single inspection duration to obtain the waiting duration for the items to be inspected for P2.

[0051] Furthermore, the scheduling analysis module obtains the patient inspection analysis data as follows:

[0052] Obtain the preliminary inspection prediction data, obtain the patient scheduling type classification data based on the preliminary inspection prediction data, and obtain the patients of the first scheduling type according to the patient scheduling type classification data;

[0053] Obtain the patient medical collection data, and respectively obtain the items to be inspected from J1 to Ja corresponding to the patients of the first scheduling type and the treatment correlation coefficients of the J1 item to the Ja item based on the patient medical collection data;

[0054] Respectively obtain the average inspection duration of a single patient corresponding to the items to be inspected from J1 to Ja to obtain the J1 single inspection duration to the Ja single inspection duration;

[0055] Respectively obtain the values of the number of queuing patients corresponding to the items to be inspected from J1 to Ja at the J1 characteristic time point to obtain the J1 queuing patient number value to the Ja queuing patient number value;

[0056] Respectively obtain the item inspection positions corresponding to the items to be inspected from J1 to Ja to obtain the J1 item inspection position to the Ja item inspection position;

[0057] Obtain the real-time position of the patients of the first scheduling type in real time to obtain the real-time position of the scheduled patients, and obtain the commuting durations from the real-time position of the scheduled patients to the J1 item inspection position to the Ja item inspection position through the map program to obtain the J1 item commuting duration to the Ja item commuting duration;

[0058] Obtain the first-priority items to be examined for patients of the first scheduling type, and obtain the S1 items to be examined;

[0059] Respectively obtain the second-priority to the a-th priority items to be examined for patients of the first scheduling type, and obtain the S2 to Sa items to be examined;

[0060] Obtain the single-examination durations corresponding to the S1 to Sa items to be examined, and obtain the S1 to Sa single-examination durations;

[0061] Obtain the predicted queuing person-times of the S1 to Sa items to be examined, and obtain the S1 to Sa predicted queuing person-times of the items;

[0062] Define the S1 to Sa items to be examined, the S1 to Sa single-examination durations, and the S1 to Sa predicted queuing person-times of the items as patient examination analysis data.

[0063] Further, the scheduling analysis module obtains the S1 items to be examined as follows:

[0064] Mark the time point corresponding to the current moment as the J1 characteristic time point, obtain the number of people queuing for the J1 item to be examined at the J1 characteristic time point, and obtain the number of people queuing at the J1 moment;

[0065] Set a J1 item queuing number interval with the number of people queuing at the J1 moment as the intermediate value of the interval, and obtain the J1 item queuing number interval;

[0066] Obtain the historical examination records corresponding to the J1 item to be examined. In the historical examination records, mark the dates when the number of people queuing for the J1 item to be examined at the J1 characteristic time point is within the J1 item queuing number interval as J1 characteristic dates, and obtain multiple J1 characteristic dates;

[0067] Mark the time point corresponding to one J1 item commuting duration after the J1 characteristic time point as the J1 item examination time point;

[0068] Respectively obtain the J1 item examination time points within each J1 characteristic date and the number of queuing patients corresponding to the J1 item to be examined, obtain multiple values of the number of queuing patients, and calculate the average of the obtained multiple values of the number of queuing patients to obtain the predicted queuing person-times of the J1 item;

[0069] Calculate the scheduling priority coefficient corresponding to the J1 item to be examined through the predicted queuing person-times of the J1 item, the J1 single-examination duration, and the J1 item treatment correlation coefficient, and name it the J1 scheduling priority coefficient;

[0070] Calculate the scheduling priority coefficient of J1. The specific formula is as follows:

[0071]

[0072] Among them, Dyj1 is the scheduling priority coefficient of J1, Xgj1 is the treatment relevance coefficient of the J1 project, Jsj1 is the duration of a single inspection of J1, and Psj1 is the predicted queuing population of the J1 project;

[0073] Repeat the process of obtaining the scheduling priority coefficient corresponding to the items to be inspected of J1, and obtain the scheduling priority coefficients corresponding to the items to be inspected of J2 to Ja respectively to obtain the scheduling priority coefficients of J2 to Ja;

[0074] Compare the numerical sizes of the scheduling priority coefficients of J1 to Ja, and mark the item to be inspected corresponding to the scheduling priority coefficient with the largest numerical value as the item to be inspected of S1.

[0075] Furthermore, the item scheduling module performs item scheduling as follows:

[0076] Obtain the preliminary inspection prediction data, and respectively obtain the items to be inspected from P1 to Pa, the waiting times of the items to be inspected from P1 to Pa, the durations of a single inspection from P1 to Pa, and the patient scheduling type classification data according to the preliminary inspection prediction data;

[0077] Obtain the first type of patients and the second type of patients according to the patient scheduling type classification data;

[0078] Obtain the second item scheduling sequence data and the duration required for the second patient's inspection, and upload the second item scheduling sequence data and the duration required for the second patient's inspection to the electronic medical record corresponding to the second type of patients;

[0079] Specifically as follows:

[0080] Schedule the item to be inspected of P1 as the first-sequence inspection item of the second type of patients, schedule the item to be inspected of P2 as the second-sequence inspection item of the second type of patients, and so on, schedule the item to be inspected of Pa as the a-sequence inspection item of the second type of patients to obtain the second item scheduling sequence data;

[0081] Calculate the duration required for the second patient's inspection from the waiting times of the items to be inspected from P1 to Pa and the durations of a single inspection from P1 to Pa;

[0082] Calculate the duration required for the second patient's inspection. The specific formula is as follows:

[0083]

[0084] Among them, Hjs2 is the duration required for the second patient's examination, Ddpi is the waiting duration of the items to be examined for Pi, and Jcpi is the duration of a single examination for Pi.

[0085] Obtain the first project scheduling sequence data and the duration required for the first patient's examination according to the patient examination analysis data, and upload the first project scheduling sequence data and the duration required for the first patient's examination to the electronic medical record corresponding to the first scheduling type of patients.

[0086] Furthermore, the project scheduling module obtains the first project scheduling sequence data and the duration required for the first patient's examination, specifically as follows:

[0087] Obtain the patient examination analysis data, and obtain the items to be examined from S1 to Sa, the duration of a single examination from S1 to Sa, and the predicted queuing number of people for the S1 project to the Sa project according to the patient examination analysis data;

[0088] Schedule the items to be examined in S1 as the first-order examination items for the first scheduling type of patients, schedule the items to be examined in S2 as the second-order examination items for the first scheduling type of patients, and so on, schedule the items to be examined in Sa as the a-order examination items for the first scheduling type of patients, to obtain the first project scheduling sequence data;

[0089] Calculate the duration required for the first patient's examination from the duration of a single examination from S1 to Sa and the predicted queuing number of people for the S1 project to the Sa project;

[0090] Calculate the duration required for the first patient's examination, and the specific formula is as follows:

[0091]

[0092] Among them, Hjs1 is the duration required for the first patient's examination, Pdsi is the predicted queuing number of people for the Si project, and Djsi is the duration of a single examination for Si.

[0093] An outpatient auxiliary examination optimization scheduling method based on artificial intelligence includes the following specific steps:

[0094] Step S1: Obtain several items to be examined corresponding to the target patient to be examined and the project treatment correlation coefficient corresponding to each item to be examined respectively, to obtain the patient medical collection data;

[0095] Step S2: By analyzing the patient's medical collection data, obtain the waiting time and single inspection time corresponding to each item to be inspected respectively, predict the preliminary inspection time for the target patient to be inspected, and obtain the patient scheduling type classification data according to the prediction result to obtain the preliminary inspection prediction data;

[0096] Step S3: Analyze the inspection order of the patients of the first scheduling type according to the preliminary inspection prediction data to obtain the patient inspection analysis data;

[0097] Step S4: Predict the required time for item scheduling and item inspection for the patients of the first scheduling type and the patients of the second scheduling type respectively according to the preliminary inspection prediction data and the patient inspection analysis data.

[0098] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are as follows:

[0099] 1. By analyzing the commonalities of the inspection items between the patient's outpatient inspection items and historical patients with the same disease, and classifying the scheduling types of patients according to the analysis results, and adopting different scheduling optimization methods for different types of patients, the present invention improves the pertinence of the inspection optimization scheduling method and can further meet the inspection optimization needs of patients;

[0100] 2. By combining the historical inspection records of the corresponding inspection items to predict the number of people queuing for items in the future period, and providing scheduling suggestions and the required inspection time for patients according to the prediction results, the present invention can improve the inspection optimization scheduling efficiency and effectively shorten the inspection waiting time of patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings.

[0102] Figure 1 It is the overall system block diagram of the present invention;

[0103] Figure 2 [[ID=—28]]It is the implementation step diagram of the present invention;

[0104] Figure 3 It is the schematic diagram of the project sorting result of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0105] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0106] Embodiment 1

[0107] Please refer to Figure 1

[0108] The patient data module respectively obtains a plurality of items to be examined corresponding to the target patient to be examined and the project treatment correlation coefficient corresponding to each item to be examined, and obtains patient medical collection data;

[0109] Specifically as follows:

[0110] Obtain outpatient patients who need to undergo medical item examinations in real time, and select a target patient to be examined from the obtained outpatient patients;

[0111] It should be noted here that:

[0112] In this application, the target patient to be examined involved here is the object of outpatient auxiliary examination optimization scheduling by the outpatient auxiliary examination optimization scheduling system;

[0113] In this application, the outpatient patients involved here are all general outpatient patients, excluding emergency department patients and critically ill department patients.

[0114] Obtain the medical record of the target patient to be examined, and obtain the examination items that the target patient to be examined needs to complete according to the medical record, obtain a plurality of items to be examined, and sequentially mark the obtained items to be examined in the order of the item issuance time as item J1 to be examined to item Ja to be examined;

[0115] It should be noted here that:

[0116] In this application, J involved here is the identifier corresponding to the item to be examined, a is the numerical value corresponding to the item to be examined of the target patient to be examined, and a is an integer greater than 0.

[0117] The item issuance time involved here is specifically the time when the doctor uploads the issued examination item to the medical record;

[0118] In this application, the specific examination items corresponding to item J1 to be examined to item Ja to be examined are subject to the medical record. The item J1 to be examined involved here can be gastroscopy, the item J2 to be examined involved here can be CT, and the item J3 to be examined involved here can be blood routine;

[0119] <000Obtain the treatment correlations between the J1 to be examined items to the Ja to be examined items and the patient's symptoms respectively, and obtain the treatment correlation coefficients of the J1 items to the Ja items;

[0120] Obtain the medical records of the target patient to be examined, obtain the patient's clinical diagnosis based on the medical records, and obtain the target clinical diagnosis;

[0121] Obtain historical treatment records of the medical institution, screen out a number of historical clinical patients diagnosed with the target clinical diagnosis from the historical treatment records of the medical institution, count the number of historical clinical patients, and obtain a number value of historical clinical patients;

[0122] Obtain outpatient examination items of several historical clinical patients respectively, mark the historical clinical patients with J1 to-be-examined items in their outpatient examination items as J1 historical clinical patients, count the number of J1 historical clinical patients, and obtain the number value of J1 clinical patients;

[0123] Calculate the ratio of the number of clinical patients in J1 to the number of historical clinical patients to obtain the examination commonality ratio corresponding to the J1 examination items and name it as the J1 examination commonality ratio;

[0124] Obtain the J1 examination results of each J1 historical clinical patient for the item to be examined, mark the J1 historical clinical patients with abnormal examination results as J1 item abnormal patients, count the number of J1 item abnormal patients, and obtain the number value of J1 item abnormal patients;

[0125] The ratio of the number of patients with abnormal J1 items to the number of patients with clinical J1 was calculated to obtain the J1 examination abnormality ratio;

[0126] Obtain statistics on the number of J1 pending examination items performed by each J1 historical clinical patient during the treatment cycle to obtain the number of completions of multiple J1 pending examination items, and average the obtained number of completions of multiple J1 pending examination items to obtain the number of repetitions of the J1 examination cycle;

[0127] The J1 inspection commonality ratio, J1 inspection abnormality ratio and J1 inspection cycle repetition times are calculated to obtain the item treatment correlation coefficient corresponding to the J1 to-be-inspected item, and named it the J1 item treatment correlation coefficient;

[0128] The treatment correlation coefficient of the J1 project is calculated using the following formula:

[0129] Xgj1=Gj1+Yj1 2 +Fj1;

[0130] Among them, Xgj1 is the treatment correlation coefficient of Project J1, Gj1 is the common ratio of J1 examinations, Yj1 is the abnormal ratio of J1 examinations, and Fj1 is the number of repetitions of the J1 examination cycle;

[0131] Repeat the process of obtaining the treatment correlation coefficient of the item corresponding to the item to be examined in J1, and respectively obtain the treatment correlation coefficients of the items corresponding to the items to be examined in J2 to Ja, so as to obtain the treatment correlation coefficients of Project J2 to Project Ja;

[0132] Define the items to be examined in J1 to Ja and the treatment correlation coefficients of Project J1 to Project Ja as the patient medical collection data;

[0133] The patient data module obtains the patient medical collection data and transports it to the scheduling type module, the scheduling analysis module, and the project scheduling module;

[0134] The scheduling type module respectively obtains the waiting duration and the single examination duration corresponding to each item to be examined by analyzing the patient medical collection data, makes a preliminary prediction of the examination duration of the target patient to be examined, and obtains the patient scheduling type classification data according to the prediction result to obtain the preliminary examination prediction data;

[0135] Specifically as follows:

[0136] Obtain the patient medical collection data, and respectively obtain the items to be examined in J1 to Ja and the treatment correlation coefficients of Project J1 to Project Ja according to the patient medical collection data;

[0137] Arrange the treatment correlation coefficients of Project J1 to Project Ja in descending order according to the numerical values. According to the sorting result, re-mark the item to be examined corresponding to the treatment correlation coefficient ranked first as the item to be examined in P1, and according to the sorting result, re-mark the item to be examined corresponding to the treatment correlation coefficient ranked second as the item to be examined in P2, and so on. According to the sorting result, re-mark the item to be examined corresponding to the treatment correlation coefficient ranked a-th as the item to be examined in Pa;

[0138] Respectively obtain the average examination duration of a single patient corresponding to the items to be examined in P1 to Pa to obtain the single examination durations of P1 to Pa;

[0139] Mark the time point corresponding to the current moment as the P1 characteristic time point;

[0140] Obtain the value of the number of queuing patients corresponding to the item to be inspected at the P1 characteristic time point for P1, obtain the P1 queuing patient number value, and calculate the product of the P1 single inspection duration and the P1 queuing patient number value to obtain the waiting duration of the item to be inspected for P1;

[0141] Obtain the waiting duration of the item to be inspected for P2;

[0142] Specifically as follows:

[0143] Respectively obtain the values of the number of queuing patients corresponding to the item to be inspected from P2 to Pa at the P1 characteristic time point, and obtain the P2 queuing patient number value to the Pa queuing patient number value;

[0144] Set a P2 project queuing number interval with the P2 queuing patient number value as the middle value of the interval to obtain the P2 project queuing number interval;

[0145] It should be noted here that:

[0146] Assume that the P2 queuing patient number value is 40, then the set P2 project queuing number interval can be [35, 45];

[0147] Obtain the historical inspection records corresponding to the item to be inspected for P2. In the historical inspection records, mark the dates when the number of queuing people for the item corresponding to the P1 characteristic time point of the item to be inspected for P2 on the same day is within the P2 project queuing number interval as P2 characteristic dates, and obtain multiple P2 characteristic dates;

[0148] Mark the time point of Pd1 + 1 P1 single inspection durations after the P1 characteristic time point as the P2 characteristic time point;

[0149] Among them, Pd1 is the P1 queuing patient number value;

[0150] Respectively obtain the P2 characteristic time points within each P2 characteristic date, the values of the number of queuing patients corresponding to the item to be inspected for P2, obtain multiple values of the number of queuing patients, and calculate the average of the obtained multiple values of the number of queuing patients to obtain the predicted queuing person-times for the P2 project. Calculate the product of the predicted queuing person-times for the P2 project and the P2 single inspection duration to obtain the waiting duration of the item to be inspected for P2;

[0151] Repeat the process of obtaining the waiting duration of the item to be inspected for P2, and respectively obtain the waiting durations corresponding to the items to be inspected from P3 to Pa to obtain the waiting duration of the item to be inspected for P3 to the waiting duration of the item to be inspected for Pa;

[0152] Sum up the waiting durations of the item to be inspected from P1 to Pa to obtain the preliminary predicted inspection duration corresponding to the target item to be inspected;

[0153] Obtain the preliminary prediction duration threshold for the examination, compare the preliminary prediction duration of the examination with the preliminary prediction duration threshold for the examination numerically, and obtain the patient scheduling type classification data according to the numerical comparison result;

[0154] It should be noted here that:

[0155] In this application, the preliminary prediction duration threshold for the examination involved here is the maximum preliminary prediction duration threshold corresponding to the patients of the second scheduling type.

[0156] Specifically as follows:

[0157] When the preliminary prediction duration of the examination is greater than or equal to the preliminary prediction duration threshold for the examination, it is determined that the target patient to be examined is a patient of the first scheduling type;

[0158] When the preliminary prediction duration of the examination is less than the preliminary prediction duration threshold for the examination, it is determined that the target patient to be examined is a patient of the second scheduling type.

[0159] Define the items to be examined from P1 to Pa, the waiting durations of the items to be examined from P1 to Pa, the single-examination durations from P1 to Pa, and the patient scheduling type classification data as the preliminary prediction data for the examination;

[0160] The scheduling type module obtains the preliminary prediction data for the examination and transports it to the scheduling analysis module and the project scheduling module.

[0161] The scheduling analysis module analyzes the examination order of the patients of the first scheduling type according to the preliminary prediction data for the examination to obtain the patient examination analysis data;

[0162] Obtain the preliminary prediction data for the examination, obtain the patient scheduling type classification data according to the preliminary prediction data for the examination, and obtain the patients of the first scheduling type according to the patient scheduling type classification data;

[0163] Obtain the patient medical collection data, and respectively obtain the items to be examined from J1 to Ja corresponding to the patients of the first scheduling type and the treatment relevance coefficients of the items from J1 to Ja according to the patient medical collection data;

[0164] Respectively obtain the average examination duration of a single patient corresponding to the items to be examined from J1 to Ja to obtain the single-examination durations from J1 to Ja;

[0165] Respectively obtain the number of queuing patients corresponding to the items to be examined from J1 to Ja at the J1 characteristic time point to obtain the number of queuing patients from J1 to Ja;

[0166] Obtain the project inspection positions corresponding to the items to be inspected from J1 to Ja respectively, and obtain the project inspection positions from J1 to Ja;

[0167] Obtain the real-time position of the patients of the first scheduling type in real time to get the real-time position of the scheduled patients, and obtain the commuting time between the real-time position of the scheduled patients and the project inspection positions from J1 to Ja through the map program, so as to obtain the commuting time from J1 project to Ja project;

[0168] Obtain the item to be inspected with the first priority for the patients of the first scheduling type to get the item S1 to be inspected;

[0169] Specifically as follows:

[0170] Mark the time point corresponding to the current moment as the J1 characteristic time point, and obtain the queuing number of the item to be inspected at the J1 characteristic time point for the item to be inspected at J1 to get the queuing number at the J1 moment;

[0171] Set a queuing number interval for the J1 project with the queuing number at the J1 moment as the intermediate value of the interval to get the queuing number interval for the J1 project;

[0172] It should be noted here that:

[0173] Assume that the value of the number of queuing patients at J1 is 40, then the set queuing number interval for the J1 project can be [35, 45];

[0174] Obtain the historical inspection records corresponding to the item to be inspected at J1. In the historical inspection records, mark the dates when the queuing number of the item to be inspected at the J1 characteristic time point is within the queuing number interval of the J1 project as the J1 characteristic dates to get multiple J1 characteristic dates;

[0175] Mark the time point corresponding to one commuting time of the J1 project after the J1 characteristic time point as the J1 project inspection time point;

[0176] Obtain the inspection time points of the J1 project within each J1 characteristic date and the value of the number of queuing patients corresponding to the item to be inspected at J1 respectively, to get multiple values of the number of queuing patients, and calculate the average of the obtained multiple values of the number of queuing patients to get the predicted queuing times of the J1 project;

[0177] Calculate the scheduling priority coefficient corresponding to the item to be inspected at J1 through the predicted queuing times of the JI project, the single inspection duration of J1 and the treatment correlation coefficient of the J1 project, and name it the J1 scheduling priority coefficient;

[0178] Calculate the J1 scheduling priority coefficient, and the specific formula is as follows:

[0179]

[0180] Among them, Dyj1 is the scheduling priority coefficient of J1, Xgj1 is the treatment relevance coefficient of the J1 project, Jsj1 is the duration of a single inspection of J1, and Psj1 is the predicted queuing population of the J1 project;

[0181] Repeat the process of obtaining the scheduling priority coefficient corresponding to the item to be inspected of J1, and respectively obtain the scheduling priority coefficients corresponding to the items to be inspected of J2 to Ja, to obtain the scheduling priority coefficients of J2 to Ja;

[0182] Compare the numerical magnitudes of the scheduling priority coefficients of J1 to Ja, and mark the item to be inspected corresponding to the scheduling priority coefficient with the largest numerical value as the item to be inspected of S1;

[0183] Repeat the process of obtaining the item to be inspected of S1, and respectively obtain the second to a-th items to be inspected of the first scheduling type patients, to obtain the items to be inspected of S2 to Sa;

[0184] Obtain the duration of a single inspection corresponding to the items to be inspected of S1 to Sa, to obtain the duration of a single inspection of S1 to Sa;

[0185] Obtain the predicted queuing population of the items corresponding to the items to be inspected of S1 to Sa, to obtain the predicted queuing population of the S1 project to the Sa project;

[0186] Define the items to be inspected of S1 to Sa, the duration of a single inspection of S1 to Sa, and the predicted queuing population of the S1 project to the Sa project as patient inspection analysis data;

[0187] The scheduling analysis module obtains the patient inspection analysis data and conveys it to the project scheduling module;

[0188] The project scheduling module predicts the project scheduling and the required duration of project inspection for the first scheduling type patients and the second scheduling type patients respectively according to the preliminary inspection prediction data and the patient inspection analysis data;

[0189] Specifically as follows:

[0190] Please refer to Figure 3 , obtain the preliminary inspection prediction data, and respectively obtain the items to be inspected of P1 to Pa, the waiting duration of the items to be inspected of P1 to Pa, the duration of a single inspection of P1 to Pa, and the patient scheduling type classification data according to the preliminary inspection prediction data;

[0191] Divide the data according to the patient scheduling type, and obtain the patients of the first scheduling type and the second scheduling type;

[0192] Obtain the second project scheduling sequence data and the time required for the second patient's examination, and upload the second project scheduling sequence data and the time required for the second patient's examination to the electronic medical record corresponding to the second scheduling type patient;

[0193] Specifically as follows:

[0194] Schedule the items to be examined in P1 as the first-sequence examination items for the second scheduling type patients, schedule the items to be examined in P2 as the second-sequence examination items for the second scheduling type patients, and so on, schedule the items to be examined in Pa as the a-sequence examination items for the second scheduling type patients, to obtain the second project scheduling sequence data;

[0195] Calculate the time required for the second patient's examination from the waiting time of the items to be examined in P1 to the waiting time of the items to be examined in Pa and the single-examination time of P1 to the single-examination time of Pa;

[0196] Calculate the time required for the second patient's examination, and the specific formula is as follows:

[0197]

[0198] Among them, Hjs2 is the time required for the second patient's examination, Ddpi is the waiting time of the Pi item to be examined, and Jcpi is the single-examination time of the Pi item;

[0199] It should be noted here that:

[0200] In this application, the waiting time of the Pi item to be examined involved here can be the waiting time of any item to be examined from the waiting time of the item to be examined in P1 to the waiting time of the item to be examined in Pa, and the single-examination time of the Pi item involved here can be the single-examination time of any item from the single-examination time of P1 to the single-examination time of Pa;

[0201] Obtain the first project scheduling sequence data and the time required for the first patient's examination according to the patient examination analysis data, and upload the first project scheduling sequence data and the time required for the first patient's examination to the electronic medical record corresponding to the first scheduling type patient;

[0202] Specifically as follows:

[0203] Obtain the patient examination analysis data, and obtain the items to be examined from S1 to Sa, the single-examination time from S1 to Sa, and the predicted queuing times of the S1 project to the Sa project according to the patient examination analysis data;

[0204] Schedule the item to be inspected S1 as the first-order inspection item for patients of the first scheduling type, schedule the item to be inspected S2 as the second-order inspection item for patients of the first scheduling type, and so on, schedule the item to be inspected Sa as the a-order inspection item for patients of the first scheduling type, to obtain the first project scheduling order data;

[0205] Calculate the duration required for the first patient's examination from the single-inspection duration of S1 to Sa and the predicted queuing number of people for the S1 project to the Sa project;

[0206] Calculate the duration required for the first patient's examination, and the specific formula is as follows:

[0207]

[0208] Among them, Hjs1 is the duration required for the first patient's examination, Pdsi is the predicted queuing number of people for the Si project, and Djsi is the single-inspection duration of the Si project;

[0209] It should be noted here that:

[0210] In this application, the predicted queuing number of people for the Si project involved here can be any one of the predicted queuing numbers of people for the S1 project to the Sa project, and the single-inspection duration of the Si project involved here can be any one of the single-inspection durations from the S1 single-inspection duration to the Sa single-inspection duration.

[0211] In this application, if there are corresponding calculation formulas, the above calculation formulas are all dimensionless and take their numerical values for calculation. In the formulas,

[0212] Existing coefficient such as weight coefficient and proportionality coefficient, the size of its setting is a result value obtained by quantifying each parameter. Regarding the size of the weight coefficient and proportionality coefficient, as long as it does not affect the proportional relationship between the parameter and the result value.

[0213] Embodiment 2

[0214] Please refer to Figure 2 , based on another concept of the same invention, a method for optimizing the outpatient auxiliary examination scheduling based on artificial intelligence is proposed, including the following steps:

[0215] Step S1: Respectively obtain a plurality of items to be inspected corresponding to the target patient to be inspected and the project treatment correlation coefficient corresponding to each item to be inspected, to obtain the patient medical collection data;

[0216] Step S11: Obtain in real time the outpatient patients who need to undergo medical item examinations, and select a target patient to be inspected from the obtained plurality of outpatient patients;

[0217] Step S12: Obtain the medical records of the target patient to be examined. Based on the medical records, obtain the examination items that the target patient to be examined needs to complete, resulting in multiple examination items to be examined. Then, sequentially label the obtained several examination items to be examined as Examination Item J1 to Examination Item Ja according to the chronological order of item issuance time;

[0218] Step S13: Obtain the treatment correlation coefficient between Examination Item J1 and the patient's disease condition to obtain the J1 item treatment correlation coefficient;

[0219] Step S131: Obtain the medical records of the target patient to be examined. Based on the medical records, obtain the target clinical diagnosis of the patient;

[0220] Step S132: Obtain the historical treatment records of the medical institution. Screen out several historical clinical patients diagnosed with the target clinical diagnosis from the historical treatment records of the medical institution, and count the number of historical clinical patients to obtain the historical clinical patient quantity value;

[0221] Step S133: Respectively obtain the outpatient examination items of several historical clinical patients. Mark the historical clinical patients with Examination Item J1 in the outpatient examination items as J1 historical clinical patients, and count the number of J1 historical clinical patients to obtain the J1 clinical patient quantity value;

[0222] Step S134: Calculate the ratio of the J1 clinical patient quantity value to the historical clinical patient quantity value to obtain the examination common ratio corresponding to Examination Item J1, and name it the J1 examination common ratio;

[0223] Step S135: Respectively obtain the examination results of Examination Item J1 for each J1 historical clinical patient. Mark the J1 historical clinical patients with abnormal examination results as J1 item abnormal patients, and count the number of J1 item abnormal patients to obtain the J1 item abnormal patient quantity value;

[0224] Step S136: Calculate the ratio of the J1 item abnormal patient quantity value to the J1 clinical patient quantity value to obtain the J1 examination abnormal ratio;

[0225] Step S137: Respectively count the number of times each J1 historical clinical patient undergoes Examination Item J1 during the treatment cycle to obtain multiple J1 examination item completion times, and calculate the average of the obtained multiple J1 examination item completion times to obtain the J1 examination cycle repetition times;

[0226] Step S138: Calculate the J1 item treatment correlation coefficient corresponding to Examination Item J1 through the J1 examination common ratio, the J1 examination abnormal ratio, and the J1 examination cycle repetition times, and name it the J1 item treatment correlation coefficient;

[0227] Calculate the treatment correlation coefficient of Project J1. The specific formula is as follows:

[0228] Xgj1 = Gj1 + Yj1 2 + Fj1;

[0229] Among them, Xgj1 is the treatment correlation coefficient of Project J1, Gj1 is the common ratio of J1 examinations, Yj1 is the abnormal ratio of J1 examinations, and Fj1 is the number of repetitions of the J1 examination cycle;

[0230] Step S14: Obtain the project treatment correlation coefficients corresponding to the items to be examined from Project J2 to Project Ja respectively, and obtain the treatment correlation coefficients of Project J2 to Project Ja;

[0231] Step S15: Define the items to be examined from Project J1 to Project Ja and the treatment correlation coefficients of Project J1 to Project Ja as the patient medical collection data;

[0232] Step S2: Analyze the patient medical collection data to obtain the waiting time and single examination time corresponding to each item to be examined respectively, predict the preliminary examination time of the target patient to be examined, and obtain the patient scheduling type classification data according to the prediction results to obtain the preliminary examination prediction data;

[0233] Step S21: Obtain the patient medical collection data, and obtain the items to be examined from Project J1 to Project Ja and the treatment correlation coefficients of Project J1 to Project Ja according to the patient medical collection data;

[0234] Step S22: Arrange the treatment correlation coefficients of Project J1 to Project Ja in descending order according to the numerical values. According to the sorting results, re-label the item to be examined corresponding to the treatment correlation coefficient ranked first as the item to be examined P1, and re-label the item to be examined corresponding to the treatment correlation coefficient ranked second as the item to be examined P2, and so on. Re-label the item to be examined corresponding to the treatment correlation coefficient ranked a-th according to the sorting results as the item to be examined Pa;

[0235] Step S23: Obtain the average single-patient examination time corresponding to the items to be examined from P1 to Pa respectively, and obtain the single examination times of P1 to Pa;

[0236] Step S24: Mark the time point corresponding to the current moment as the P1 characteristic time point;

[0237] Step S25: Obtain the number value of queuing patients corresponding to the item to be examined P1 at the P1 characteristic time point, obtain the P1 queuing patient number value, and calculate the product of the P1 single examination duration and the P1 queuing patient number value to obtain the waiting duration of the item to be examined P1;

[0238] Step S26: Obtain the waiting duration of the item to be examined P2;

[0239] Step S261: Respectively obtain the number value of queuing patients corresponding to the item to be examined P2 to the item to be examined Pa at the P1 characteristic time point, and obtain the P2 queuing patient number value to the Pa queuing patient number value;

[0240] Step S262: Set a P2 item queuing number interval with the P2 queuing patient number value as the middle value of the interval to obtain the P2 item queuing number interval;

[0241] Step S263: Obtain the historical examination records corresponding to the item to be examined P2. In the historical examination records, mark the dates when the item queuing number corresponding to the item to be examined P2 at the P1 characteristic time point on the current day is within the P2 item queuing number interval as the P2 characteristic dates to obtain multiple P2 characteristic dates;

[0242] Step S264: Mark the time point of Pd1 + 1 P1 single examination durations after the P1 characteristic time point as the P2 characteristic time point;

[0243] where Pd1 is the P1 queuing patient number value;

[0244] Step S265: Respectively obtain the P2 characteristic time points within each P2 characteristic date and the number value of queuing patients corresponding to the item to be examined P2 to obtain multiple queuing patient number values, and calculate the average of the obtained multiple queuing patient number values to obtain the predicted queuing person-times of the P2 item. Calculate the product of the predicted queuing person-times of the P2 item and the P2 single examination duration to obtain the waiting duration of the item to be examined P2;

[0245] Step S27: Respectively obtain the waiting durations corresponding to the items to be examined P3 to Pa to obtain the waiting durations of the items to be examined P3 to Pa. Sum up the waiting durations of the items to be examined P1 to Pa to obtain the preliminary predicted examination duration corresponding to the target item to be examined;

[0246] Step S28: Obtain the preliminary predicted examination duration threshold, compare the preliminary predicted examination duration with the preliminary predicted examination duration threshold numerically, and obtain the patient scheduling type classification data according to the numerical comparison result;

[0247] Step S281: When the preliminary inspection prediction duration is greater than or equal to the preliminary inspection prediction duration threshold, it is determined that the target patient to be inspected is a patient of the first scheduling type;

[0248] Step S282: When the preliminary inspection prediction duration is less than the preliminary inspection prediction duration threshold, it is determined that the target patient to be inspected is a patient of the second scheduling type.

[0249] Step S29: Define the items to be inspected from P1 to Pa, the waiting durations of the items to be inspected from P1 to Pa, the single-inspection durations from P1 to Pa, and the patient scheduling type classification data as preliminary inspection prediction data.

[0250] Step S3: Analyze the inspection order of the patients of the first scheduling type based on the preliminary inspection prediction data to obtain patient inspection analysis data;

[0251] Step S31: Obtain the preliminary inspection prediction data, obtain the patient scheduling type classification data according to the preliminary inspection prediction data, and obtain the patients of the first scheduling type according to the patient scheduling type classification data;

[0252] Step S32: Obtain the patient medical collection data, and respectively obtain the items to be inspected from J1 to Ja corresponding to the patients of the first scheduling type and the treatment relevance coefficients of the items from J1 to Ja according to the patient medical collection data;

[0253] Step S33: Respectively obtain the average inspection duration of a single patient corresponding to the items to be inspected from J1 to Ja to obtain the single-inspection durations from J1 to Ja;

[0254] Step S34: Respectively obtain the number of queuing patients corresponding to the items to be inspected from J1 to Ja at the J1 characteristic time point to obtain the number of queuing patients from J1 to Ja;

[0255] Step S35: Respectively obtain the inspection positions of the items to be inspected from J1 to Ja to obtain the inspection positions of the items from J1 to Ja;

[0256] Step S36: Obtain the real-time position of the patient of the first scheduling type to obtain the real-time position of the scheduled patient, and obtain the commuting durations from the real-time position of the scheduled patient to the inspection positions of the items from J1 to Ja through the map program to obtain the commuting durations of the items from J1 to Ja;

[0257] Step S37: Obtain the first item to be inspected of the patient of the first scheduling type to obtain the item S1 to be inspected;

[0258] Step S371: Mark the time point corresponding to the current moment as the J1 feature time point, obtain the queuing number of the J1 item to be inspected at the J1 feature time point, and get the queuing number at the J1 moment;

[0259] Step S372: Set a J1 item queuing number interval with the queuing number at the J1 moment as the middle value of the interval, and obtain the J1 item queuing number interval;

[0260] Step S373: Obtain the historical inspection records corresponding to the J1 item to be inspected. In the historical inspection records, mark the dates when the queuing number of the J1 item to be inspected at the J1 feature time point is within the J1 item queuing number interval as the J1 feature dates, and obtain multiple J1 feature dates;

[0261] Step S374: Mark the time point corresponding to the commuting duration of one J1 item after the J1 feature time point as the J1 item inspection time point;

[0262] Step S375: Respectively obtain the J1 item inspection time points within each J1 feature date and the queuing patient number values corresponding to the J1 item to be inspected, obtain multiple queuing patient number values, and calculate the average of the obtained multiple queuing patient number values to get the predicted queuing person-times of the J1 item;

[0263] Step S376: Calculate the scheduling priority coefficient corresponding to the J1 item to be inspected through the predicted queuing person-times of the J1 item, the single inspection duration of J1, and the treatment correlation coefficient of the J1 item, and name it the J1 scheduling priority coefficient;

[0264] Calculate the J1 scheduling priority coefficient, and the specific formula is as follows:

[0265]

[0266] Among them, Dyj1 is the J1 scheduling priority coefficient, Xgj1 is the treatment correlation coefficient of the J1 item, Jsj1 is the single inspection duration of J1, and Psj1 is the predicted queuing person-times of the J1 item;

[0267] Step S378: Respectively obtain the scheduling priority coefficients corresponding to the J2 item to be inspected to the Ja item to be inspected, and get the J2 scheduling priority coefficient to the Ja scheduling priority coefficient;

[0268] Step S379: Compare the numerical sizes of the J1 scheduling priority coefficient to the Ja scheduling priority coefficient, and mark the item to be inspected corresponding to the scheduling priority coefficient with the largest numerical value as the S1 item to be inspected;

[0269] Step S38: Obtain the second to the a-th items to be examined for the first scheduling type patients respectively, and obtain the S2 to Sa items to be examined;

[0270] Step S39: Obtain the single inspection durations corresponding to the S1 to Sa items to be examined, and obtain the S1 to Sa single inspection durations. Obtain the predicted queuing person-times for the S1 to Sa items to be examined, and obtain the S1 to Sa predicted queuing person-times. Define the S1 to Sa items to be examined, the S1 to Sa single inspection durations, and the S1 to Sa predicted queuing person-times as patient inspection analysis data;

[0271] The scheduling analysis module obtains the patient inspection analysis data and transports it to the project scheduling module;

[0272] Step S4: The block predicts the project scheduling and the required inspection duration for the first scheduling type patients and the second scheduling type patients respectively according to the preliminary inspection prediction data and the patient inspection analysis data;

[0273] Step S41: Obtain the preliminary inspection prediction data, and respectively obtain the P1 to Pa items to be examined, the waiting durations of the P1 to Pa items to be examined, the P1 to Pa single inspection durations, and the patient scheduling type classification data according to the preliminary inspection prediction data;

[0274] Step S42: Obtain the first scheduling type patients and the second scheduling type patients according to the patient scheduling type classification data;

[0275] Step S43: Obtain the second project scheduling sequence data and the second patient inspection required duration, and upload the second project scheduling sequence data and the second patient inspection required duration to the electronic medical record corresponding to the second scheduling type patients;

[0276] Step S431: Schedule the P1 item to be examined as the first sequence inspection item for the second scheduling type patients, schedule the P2 item to be examined as the second sequence inspection item for the second scheduling type patients, and so on, schedule the Pa item to be examined as the a-th sequence inspection item for the second scheduling type patients, to obtain the second project scheduling sequence data;

[0277] Step S432: Calculate the second patient inspection required duration from the waiting durations of the P1 to Pa items to be examined and the P1 to Pa single inspection durations;

[0278] Calculate the second patient inspection required duration, and the specific formula is as follows:

[0279]

[0280] Among them, Hjs2 is the time required for the second patient's examination, Ddpi is the waiting time for the examination items to be checked for Pi, and Jcpi is the time for a single examination of Pi;

[0281] Step S44: Obtain the first project scheduling sequence data and the time required for the first patient's examination based on the patient examination analysis data, and upload the first project scheduling sequence data and the time required for the first patient's examination to the electronic medical record corresponding to the first scheduling type of patients;

[0282] Step S441: Obtain the patient examination analysis data, and based on the patient examination analysis data, obtain the examination items to be checked from S1 to Sa, the time for a single examination from S1 to Sa, and the predicted queuing people for the S1 project to the Sa project;

[0283] Step S442: Schedule the examination items to be checked in S1 as the first-order examination items for the first scheduling type of patients, schedule the examination items to be checked in S2 as the second-order examination items for the first scheduling type of patients, and so on, schedule the examination items to be checked in Sa as the a-order examination items for the first scheduling type of patients, to obtain the first project scheduling sequence data;

[0284] Step S443: Calculate the time required for the first patient's examination from the time for a single examination from S1 to Sa and the predicted queuing people for the S1 project to the Sa project;

[0285] Calculate the time required for the first patient's examination, and the specific formula is as follows:

[0286]

[0287] Among them, Hjs1 is the time required for the first patient's examination, Pdsi is the predicted queuing people for the Si project, and Djsi is the time for a single examination of Si.

[0288] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only the specific implementation manners. Obviously, many modifications and changes can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principle and practical application of the present invention, so that those skilled in the relevant technical fields can understand and utilize the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An outpatient auxiliary examination optimization scheduling system based on artificial intelligence, characterized in that, Including: Patient Data Module: used to obtain multiple items to be examined corresponding to the target patient to be examined and the project treatment correlation coefficient corresponding to each item to be examined respectively, so as to obtain the patient medical acquisition data; Scheduling Type Module: used to obtain the waiting duration and single inspection duration corresponding to each item to be examined respectively, and conduct a preliminary inspection duration prediction for the target patient to be examined. According to the prediction results, the target patient to be examined is divided into the first scheduling type patients and the second scheduling type patients, so as to obtain the preliminary inspection prediction data for the examination; Scheduling Analysis Module: used to analyze the examination order of the first scheduling type patients according to the preliminary inspection prediction data for the examination, so as to obtain the patient examination analysis data; Project Scheduling Module: used to conduct project scheduling and predict the required duration for project inspection for the first scheduling type patients and the second scheduling type patients respectively according to the preliminary inspection prediction data for the examination and the patient examination analysis data.

2. The outpatient auxiliary examination optimization scheduling system based on artificial intelligence according to claim 1, characterized in that, Obtain the patient medical acquisition data as follows: Select the target patient to be examined; Obtain the examination items that the target patient to be examined needs to complete according to the medical record, and obtain the items to be examined J1 to Ja; Analyze the treatment correlation between the items to be examined J1 to Ja and the patient's disease condition, and obtain the project treatment correlation coefficients J1 to Ja; Obtain the patient medical acquisition data.

3. The outpatient auxiliary examination optimization scheduling system based on artificial intelligence according to claim 2, characterized in that, Obtain the project treatment correlation coefficient J1 as follows: Obtain the target clinical diagnosis according to the medical record of the patient; Select several historical clinical patients diagnosed as the target clinical diagnosis from the historical treatment records of the medical institution, count their numbers, and obtain the historical clinical patient quantity value; Obtain the outpatient examination items of each historical clinical patient, mark the historical clinical patients with the item to be examined J1 in the outpatient examination items as J1 historical clinical patients, and count their numbers to obtain the J1 clinical patient quantity value; Calculate the ratio of the J1 clinical patient quantity value to the historical clinical patient quantity value to obtain the J1 examination common ratio; Obtain the examination results of the item to be examined J1 of each J1 historical clinical patient, mark the J1 historical clinical patients with abnormal examination results as J1 project abnormal patients, and count their numbers to obtain the J1 project abnormal patient quantity value; Calculate the ratio of the J1 project abnormal patient quantity value to the J1 clinical patient quantity value to obtain the J1 examination abnormal ratio; Obtain the number of times each J1 historical clinical patient conducts the item to be examined J1 during the treatment cycle, count the statistical results, and calculate the average value to obtain the J1 examination cycle repetition times; The J1 inspection commonality ratio Gj1, the J1 inspection abnormality ratio Yj1, and the number of repetitions Fj1 of the J1 inspection cycle are used to calculate the J1 project treatment correlation coefficient Xgj1. The specific formula is: Xgj1 = Gj1 + Yj1 2 + Fj1.

4. The outpatient auxiliary examination optimization scheduling system based on artificial intelligence according to claim 1, characterized in that Obtain the preliminary inspection prediction data for the examination as follows: Arrange the project treatment correlation coefficients J1 to Ja in descending order according to the numerical values according to the patient medical acquisition data, and obtain the items to be examined P1 to Pa according to the sorting results; Obtain the average inspection duration of a single patient corresponding to the items to be examined P1 to Pa, and obtain the single inspection durations P1 to Pa; Mark the time point corresponding to the current moment as the P1 characteristic time point; Obtain the number value of queuing patients corresponding to the item to be examined P1 at the P1 characteristic time point, obtain the P1 queuing patient number value, and calculate the product of the P1 single examination duration and the P1 queuing patient number value to obtain the waiting duration of the item to be examined P1; Obtain the waiting durations corresponding to the items to be examined from P2 to Pa, and obtain the waiting durations of the items to be examined from P2 to Pa; Sum up the waiting durations of the items to be examined from P1 to Pa to obtain the preliminary predicted examination duration corresponding to the target item to be examined; Obtain the threshold of the preliminary predicted examination duration. When the preliminary predicted examination duration is greater than or equal to the threshold of the preliminary predicted examination duration, it is determined that the target patient to be examined is a first scheduling type patient. When the preliminary predicted examination duration is less than the threshold of the preliminary predicted examination duration, it is determined that the target patient to be examined is a second scheduling type patient; Obtain the preliminary predicted examination data.

5. The outpatient auxiliary examination optimization scheduling system based on artificial intelligence according to claim 4, characterized in that, Obtain the waiting duration of the item to be examined P2, specifically as follows: Respectively obtain the number values of queuing patients corresponding to the items to be examined from P2 to Pa at the P1 characteristic time point, and obtain the P2 queuing patient number values to Pa queuing patient number values; Set a P2 item queuing population interval with the P2 queuing patient number value as the middle value of the interval to obtain the P2 item queuing population interval; Obtain the historical examination records corresponding to the item to be examined P2. In the historical examination records, mark the dates when the item queuing population corresponding to the item to be examined P2 at the P1 characteristic time point on the current day is within the P2 item queuing population interval as P2 characteristic dates, and obtain multiple P2 characteristic dates; Mark the time point of Pd1 + 1 P1 single examination durations after the P1 characteristic time point as the P2 characteristic time point; Where Pd1 is the P1 queuing patient number value; Obtain the P2 characteristic time points within each P2 characteristic date and the number values of queuing patients corresponding to the item to be examined P2, obtain multiple number values of queuing patients, and calculate the average of the obtained multiple number values of queuing patients to obtain the predicted queuing person-times of the P2 item. Calculate the product of the predicted queuing person-times of the P2 item and the P2 single examination duration to obtain the waiting duration of the item to be examined P2.

6. The outpatient auxiliary examination optimization scheduling system based on artificial intelligence according to claim 1, characterized in that, Obtain the patient examination analysis data, specifically as follows: Respectively obtain the average examination durations of individual patients corresponding to the items to be examined from J1 to Ja, and obtain the J1 single examination duration to Ja single examination duration; Respectively obtain the number values of queuing patients corresponding to the items to be examined from J1 to Ja at the J1 characteristic time point, and obtain the J1 queuing patient number values to Ja queuing patient number values; Respectively obtain the item examination positions corresponding to the items to be examined from J1 to Ja, and obtain the J1 item examination position to Ja item examination position; Obtain the real-time position of the first scheduling type patients in real time to obtain the real-time position of the scheduled patients. Obtain the commuting durations between the real-time position of the scheduled patients and the J1 item examination position to Ja item examination position through the map program, and obtain the J1 item commuting duration to Ja item commuting duration; Respectively obtain the items to be examined from S1 to Sa; Obtain the single inspection duration corresponding to the inspection items from S1 to Sa to be inspected, and obtain the single inspection duration from S1 to Sa; Obtain the predicted queuing person-times corresponding to the inspection items from S1 to Sa to be inspected, and obtain the predicted queuing person-times from S1 to Sa; Define the inspection items from S1 to Sa to be inspected, the single inspection duration from S1 to Sa, and the predicted queuing person-times from S1 to Sa as patient inspection analysis data.

7. An outpatient auxiliary examination optimization scheduling system based on artificial intelligence according to claim 6, characterized in that, Obtain the inspection items to be inspected for S1, specifically as follows: Mark the time point corresponding to the current moment as the J1 characteristic time point, obtain the queuing number of the J1 inspection item at the J1 characteristic time point, and obtain the queuing number at the J1 moment; Set a J1 item queuing number interval with the queuing number at the J1 moment as the intermediate value of the interval, and obtain the J1 item queuing number interval; In the historical inspection records, mark the dates when the queuing number of the J1 inspection item at the J1 characteristic time point is within the J1 item queuing number interval as J1 characteristic dates, and obtain multiple J1 characteristic dates; Mark the time point corresponding to one J1 item commuting duration after the J1 characteristic time point as the J1 item inspection time point; Obtain the J1 item inspection time points within each J1 characteristic date and the queuing patient quantity values corresponding to the J1 inspection item to be inspected, obtain multiple queuing patient quantity values, and calculate the average of the obtained multiple queuing patient quantity values to obtain the predicted queuing person-times of the J1 item; The predicted queuing person-times Psj1, the single inspection duration Jsj1, and the treatment correlation coefficient Xgj1 of project J1 are calculated to obtain the scheduling priority coefficient Dyj1 of project J1. The specific formula is as follows: Obtain the scheduling priority coefficients from J2 to Ja respectively; Compare the numerical sizes of the scheduling priority coefficients from J1 to Ja, and mark the inspection item corresponding to the scheduling priority coefficient with the largest numerical value as the inspection item to be inspected for S1.

8. The outpatient auxiliary examination optimization scheduling system based on artificial intelligence according to claim 1, wherein The project scheduling process is specifically as follows: Obtain the second project scheduling order data and the second required patient inspection duration through analyzing the preliminary prediction data for inspection, and upload the second project scheduling order data and the second required patient inspection duration to the electronic medical record corresponding to the patients of the second scheduling type; Specifically as follows: Schedule the inspection items from P1 to Pa to be inspected as the first-order to a-order inspection items for the patients of the second scheduling type, and obtain the second project scheduling order data; Calculate the second required patient inspection duration from the waiting durations of the inspection items from P1 to Pa to be inspected and the single inspection durations from P1 to Pa; Obtain the first project scheduling order data and the first required patient inspection duration according to the patient inspection analysis data, and upload the first project scheduling order data and the first required patient inspection duration to the electronic medical record corresponding to the patients of the first scheduling type.

9. The outpatient auxiliary examination optimization scheduling system based on artificial intelligence according to claim 8, characterized in that, The project scheduling module obtains the first project scheduling order data and the first required patient inspection duration, specifically as follows: Schedule the inspection items from S1 to Sa to be inspected as the first-order to a-order inspection items for the patients of the first scheduling type, and obtain the first project scheduling order data; The duration required for the first patient's examination is obtained by calculating the single - check duration from S1 to Sa and the predicted queuing number of people for the S1 project to the predicted queuing number of people for the Sa project.

10. An outpatient auxiliary examination optimization scheduling method based on artificial intelligence, applicable to an outpatient auxiliary examination optimization scheduling system according to any one of claims 1-9, characterized in that, The outpatient auxiliary examination optimization scheduling method includes the following specific steps: Step S1: Obtain several items to be examined corresponding to the target patient to be examined and the project treatment correlation coefficient corresponding to each item to be examined respectively, so as to obtain the patient medical collection data; Step S2: Analyze the patient medical collection data to obtain the waiting duration and single - check duration corresponding to each item to be examined respectively, conduct a preliminary examination duration prediction for the target patient to be examined, and obtain the patient scheduling type classification data according to the prediction result to obtain the preliminary examination prediction data; Step S3: Analyze the examination order of the first - type scheduling patients according to the preliminary examination prediction data to obtain the patient examination analysis data; Step S4: Predict the project scheduling and the duration required for project examination for the first - type scheduling patients and the second - type scheduling patients respectively according to the preliminary examination prediction data and the patient examination analysis data.

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