Medical record online supervision and remote cooperation management system based on random sampling

Abstract

The invention discloses a medical record online supervision and remote cooperation management system based on random sampling, and the system is characterized in that the system comprises a data random sampling module; a medical record generation module; a clinical supervision module; a nursing supervision module; and a result output module. The invention discloses a fully-informationized medical history supervision management system which replaces a traditional offline manual medical history supervision process, realizes random sampling and online supervision of medical history through an informationized means, supports remote cooperation, comprehensively innovates the medical history supervision process, and improves the medical history supervision efficiency. Random sampling of medical records, online supervision and review, dynamic scoring table configuration and automatic summarization and storage of scoring data are realized, the working efficiency and accuracy of medical record supervision and inspection are comprehensively improved, and the whole supervision and inspection process is optimized.

Description

Technical Field

[0001] The present invention relates to a medical record online supervision and remote collaboration management system. Background Art

[0002] The existing manual medical record inspection model is: every month, the Medical Statistics Section of the Medical Affairs Office randomly selects discharge medical records from each department to form an inspection list; the National Examination Office randomly sorts the departments for mutual inspection based on the list; each clinical department and nursing department selects a quality control specialist to go to the Medical Statistics Section of the Medical Affairs Office to review the paper medical records on site and manually score and inspect each other; reviewers from the Medical Affairs Office and the Finance Office accompany the whole process and review the scores of the clinical departments; after the inspection of the month is completed, a dedicated person will summarize the scores and prepare a table.

[0003] The above-mentioned medical record inspection method has the following problems:

[0004] (1) Preliminary preparations take a long time: the management department needs to confirm whether the extracted medical record list is appropriate and whether the medical record information is complete. The relevant information needs to be sorted out in advance and placed separately;

[0005] (2) It consumes manpower and has low work efficiency: Quality control specialists in clinical departments and nursing departments need to interrupt their clinical work to go to the site to review medical records and conduct scoring. There are many temporary staff changes and scoring interruptions for emergency rescue. Quality control specialists may also lack patience when encountering medical records with long hospitalization time, which may lead to missed judgments and wrong judgments.

[0006] (3) The subsequent input of the manual evaluations of each quality control specialist into the computer is time-consuming and prone to input errors due to problems such as illegible handwriting;

[0007] (4) The inspection site also serves as an office, and the inspection involves nearly 70 people, including quality control specialists and reviewers. The intensive personnel turnover not only affects daily work but also increases the potential risks of misplacement and loss of medical records.

[0008] (5) There is waste of office resources such as paper and ink. Summary of the Invention

[0009] The purpose of the present invention is to solve the problems existing in the manual medical record supervision mode and to propose a fully informationized medical record supervision management solution.

[0010] To achieve the above-mentioned objectives, the technical solution of the present invention is to disclose an online medical record supervision and remote collaboration management system based on random sampling. Each patient has a unique treatment number, and all patient information corresponding to the same treatment number is stored in different information subsystems within the hospital. The system is characterized in that it includes:

[0011] A data random sampling module, which is used to randomly obtain the treatment numbers of N patients as the treatment numbers to be supervised, where N≥1, and obtain all the patient information within a certain time length corresponding to each treatment number to be supervised from different information subsystems in the hospital. Among them, it is ensured that at least one treatment number to be supervised is drawn from each ward area in the hospital;

[0012] A medical record generation module, which is used to generate N medical records to be supervised based on the treatment numbers of N patients to be supervised obtained by the data random sampling module and all the patient information of each sub-information system in the hospital;

[0013] A clinical supervision module, which is used to generate a front page score sheet of the medical record and a medical history score sheet. After obtaining the scores of the inpatient medical record front page information and other information except the inpatient medical record front page information in N medical records to be supervised based on the front page score sheet of the medical record and the medical history score sheet respectively, generate a clinical supervision result based on the scoring results;

[0014] A nursing supervision module, which is used to generate a nursing score sheet. After obtaining the scores of each item of information in N medical records to be supervised based on the nursing score sheet, generate a nursing supervision result based on the scoring results;

[0015] A result output module, which is used to visually output the clinical supervision result obtained by the clinical supervision module and the nursing supervision result obtained by the nursing supervision module.

[0016] Preferably, the data random sampling module adopts the following method to achieve the extraction of N treatment numbers to be supervised and the acquisition of corresponding patient information:

[0017] Step 101: Obtain the treatment numbers of all inpatients in the hospital;

[0018] Step 102: Divide all the treatment numbers into N sets according to the value of the "discharged ward area" field corresponding to each treatment number obtained from the inpatient sub-information system in the hospital, where N is the number of ward areas in the hospital and N≥1;

[0019] Step 103: Traverse all the treatment numbers in each set in turn, at least extract and obtain the treatment numbers of one patient to be supervised from each set, and obtain all the patient information within a certain time length corresponding to each treatment number to be supervised.

[0020] Preferably, in step 103, when traversing the current set:

[0021] Step 1031: Obtain the current treatment number;

[0022] Step 1032: Obtain the total cost information corresponding to the current treatment number from the inpatient sub-information system in the hospital. If the total cost is not greater than the preset cost threshold, go to step 1033, otherwise, go to step 1034;

[0023] Step 1033: Obtain the discharge method information corresponding to the current treatment number from the in-hospital inpatient sub-information system. If the discharge method information is "death" or "discharge without doctor's advice", then proceed to Step 1034; otherwise, take the next treatment number in the current set as the current treatment number, return to Step 1031, and until all treatment numbers in the current set are traversed, proceed to Step 1035;

[0024] Step 1034: After taking the current treatment number as the treatment number to be extracted, take the next treatment number in the current set as the current treatment number, return to Step 1031, and until all treatment numbers in the current set are traversed, proceed to Step 1036;

[0025] Step 1035: Randomly extract M treatment numbers from the current set as the treatment numbers to be extracted, where M≥1, and proceed to Step 1036;

[0026] Step 1036: Obtain the hospitalization time information corresponding to all treatment numbers to be extracted from the in-hospital inpatient sub-information system. After excluding the treatment numbers to be extracted whose hospitalization time is not greater than a pre-set hospitalization time threshold, randomly extract at least one treatment number to be extracted from the remaining treatment numbers to be extracted as the treatment number to be supervised, and proceed to Step 1037;

[0027] Step 1037: Obtain all patient information corresponding to the current treatment number to be supervised within a certain time period from the in-hospital inpatient information sub-system and each specialty information sub-system.

[0028] Preferably, the medical record generation module includes:

[0029] A medical record generation unit, configured to generate N supervised medical records based on the treatment numbers to be supervised of N patients obtained by the data random sampling module and all patient information of each sub-information system in the hospital;

[0030] A verification unit, configured to verify the integrity of the N supervised medical records generated by the medical record generation unit. If the verification fails, the medical record generation unit regenerates the supervised medical records to replace the supervised medical records that fail the verification, where the generated supervised medical records also need to be verified by the verification unit until all N supervised medical records pass the verification.

[0031] A tagging unit, configured to tag the N supervised medical records with different tags.

[0032] Preferably, the supervised medical record includes at least the following field information:

[0033] The information on the first page of the inpatient medical record further includes patient information, diagnosis and treatment information, hospitalization information, and expense information; admission record; progress record; preoperative discussion record; operation record; postoperative progress record; discharge record; death record; death case discussion record; consultation record; pathological data; auxiliary examination report form; medical imaging examination data; doctor's order sheet; operation consent form; anesthesia consent form; preoperative anesthesia visit record; surgical safety verification record; surgical count record; anesthesia record; postoperative anesthesia visit record; informed consent form for blood transfusion therapy; consent form for special examination (special treatment); critical illness (severe illness) notice; temperature sheet; nursing record for critically ill (critical) patients; expense settlement list.

[0034] Preferably, the labels include "operation", "non-operation", "observation", "death", and "discharge against medical advice".

[0035] Preferably, based on the label of each medical record to be inspected, the clinical inspection module generates a medical record front page score sheet and a medical history score sheet corresponding to the label type for each medical record to be inspected.

[0036] Preferably, the clinical inspection module adopts the following steps to generate the clinical inspection results of the medical records to be inspected:

[0037] Step 201: The clinical inspection module obtains the label of the medical record to be inspected and generates a corresponding medical record front page score sheet and a medical history score sheet for each medical record to be inspected based on different label types;

[0038] Step 202: The clinical quality control specialist uses the clinical inspection module to score the information on the first page of the inpatient medical record and the information other than the information on the first page of the inpatient medical record for N medical records to be inspected respectively based on the medical record front page score sheet and the medical history score sheet;

[0039] Step 203: The clinical review specialist uses the clinical inspection module to review the scoring results of the information on the first page of the inpatient medical record and the scoring results of the information other than the information on the first page of the inpatient medical record for N medical records to be inspected: If the review is passed, go to Step 205; if the review is not passed, go to Step 204;

[0040] Step 204: The clinical inspection module sends the medical records to be inspected that fail the review and the scoring results of the information on the first page of the inpatient medical record or the scoring results of the information other than the information on the first page of the inpatient medical record to a pre-designated third-party department within the hospital for review: If the review is passed, go to Step 205; otherwise, the clinical inspection module records the number of times the clinical quality control specialist fails the review. When the number of times of failing the review reaches Threshold 1, the clinical quality control specialist will be retrained. If the number of times of failing the review reaches Threshold 2, the clinical quality control qualification of the clinical quality control specialist will be deprived, and Threshold 1 is less than Threshold 2;

[0041] Step 205: Aggregate all the scoring results of all the medical records to be supervised that have passed the review as the final clinical supervision result.

[0042] Preferably, based on the labels of each medical record to be supervised, the nursing supervision module generates a nursing scoring form corresponding to the label type for each medical record to be supervised.

[0043] Preferably, the nursing supervision module generates the clinical supervision result of the medical records to be supervised by the following steps:

[0044] Step 301: The nursing supervision module obtains the labels of the medical records to be supervised and generates a corresponding nursing scoring form for each medical record to be supervised based on different label types.

[0045] Step 302: The nursing quality control specialist uses the nursing supervision module to score each piece of information of N medical records to be supervised based on the nursing scoring form.

[0046] Step 303: The nursing review specialist uses the clinical supervision module to review the scoring results of each piece of information of N medical records to be supervised: If the review passes, proceed to Step 305; if the review fails, proceed to Step 304.

[0047] Step 304: The nursing supervision module sends the medical records to be supervised that have failed the review and the scoring results of each piece of information to a pre-designated third-party department within the hospital for review: If the review passes, proceed to Step 305; otherwise, the nursing supervision module records the number of times the nursing quality control specialist has failed the review. When the number of times of failing the review reaches Threshold 1, the nursing quality control specialist will be retrained. If the number of times of failing the review reaches Threshold 2, the nursing quality control qualification of the nursing quality control specialist will be revoked. Threshold 1 is less than Threshold 2.

[0048] Step 305: Aggregate all the scoring results of all the medical records to be supervised that have passed the review as the final nursing supervision result.

[0049] Alternatively, the nursing supervision module generates the clinical supervision result of the medical records to be supervised by the following steps:

[0050] Step 3-1: The nursing supervision module obtains the labels of the medical records to be supervised and generates a corresponding nursing scoring form for each medical record to be supervised based on different label types.

[0051] Step 3-2: The nursing quality control specialist uses the nursing supervision module to score each piece of information of N medical records to be supervised based on the nursing scoring form.

[0052] Step 3-3: Aggregate all the scoring results of all the medical records to be supervised as the final nursing supervision result.

[0053] The present invention discloses a fully informatized medical history supervision and management system, which replaces the traditional offline manual medical record supervision process. Through informatization means, it realizes random sampling of medical records, online supervision, and supports remote collaboration, comprehensively innovating the medical record supervision process, achieving random sampling of medical records, online supervision and review, dynamic scoring form configuration, and automatic summary and sealing of scoring data, comprehensively improving the work efficiency and accuracy of medical record supervision, and optimizing the entire supervision process.

[0054] Compared with the existing manual medical record supervision mode, the present invention specifically has the following beneficial effects:

[0055] 1. Improve the efficiency of medical record supervision

[0056] The traditional manual medical record supervision process is cumbersome and time-consuming, requiring a large amount of human resources for tasks such as extraction, preparation, supervision, and scoring of medical records. However, the present invention realizes random sampling of medical records, online supervision, and remote collaboration through automated and informatized means, greatly improving the efficiency of medical record supervision and reducing the waste of human resources.

[0057] 2. Enhance the accuracy of medical record supervision

[0058] During the manual supervision process, due to the existence of human factors, problems such as subjective scoring and data recording errors often easily occur. However, the present invention incorporates rich quality control rules and scoring standards, which can automatically score and verify medical records, effectively avoiding errors caused by human factors and improving the accuracy of medical record supervision.

[0059] 3. Optimize the medical record supervision process

[0060] In the traditional medical record supervision process, medical staff quality control specialists, medical records, medical history reviewers, etc. need to go to the medical statistics department in person for on-site supervision, which is not only time-consuming and laborious but also restricts the flexibility of personnel. However, this system supports remote collaboration, enabling relevant personnel to conduct medical record supervision and review work at different locations, optimizing the supervision process and improving the flexibility of work.

[0061] 4. Strengthen medical record management

[0062] Medical records are an important part of hospital management, and their integrity and accuracy are directly related to medical quality and safety. This system realizes standardized management of medical records through comprehensive informatization means, improves the integrity and accuracy of medical records, and provides strong support for the hospital's medical quality and safety management.

[0063] 5. Meet the needs of the hospital's informatization development

[0064] With the development of medical informatization, hospitals have increasingly higher requirements for medical record management. As an important part of the hospital informatization construction, this system can be seamlessly connected with other medical information systems, realizing information sharing and interaction, and enhancing the overall informatization level of the hospital. Brief Description of the Drawings

[0065] Figure 1 It is a system block diagram of a medical record online supervision and remote collaboration management system based on random sampling provided by the present invention;

[0066] Figure 2 It shows the implementation process of the data random sampling module;

[0067] Figure 3 It shows the implementation process of the medical record generation module;

[0068] Figure 4 It shows the process of the clinical supervision module generating supervision results;

[0069] Figure 5 It shows a process of the nursing supervision module generating supervision results;

[0070] Figure 6 It shows another process of the nursing supervision module generating supervision results

[0071] Figure 7 It shows the data output by a medical record online supervision and remote collaboration management system based on random sampling disclosed in the embodiment of the present invention. Among them, the displayed data is generated by the system and does not involve the privacy of patients;

[0072] Figure 8 It shows a visualization result output by a medical record online supervision and remote collaboration management system based on random sampling disclosed in the embodiment of the present invention.

[0073] The drawings only depict various embodiments of the disclosed technology for illustrative purposes. Those skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods shown in the drawings can be employed without departing from the principles of the disclosed technology described in the present invention. Detailed Embodiments

[0074] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of this application.

[0075] The terms used herein are merely for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprising", "including" and the like as used herein indicate the presence of the described features, steps, operations and / or components, but do not preclude the presence or addition of one or more other features, steps, operations or components.

[0076] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0077] In cases where expressions similar to "at least one of A, B, and C, etc." are used, generally, it should be interpreted according to the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include, but is not limited to, a system having only A, only B, only C, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.).

[0078] The detailed background technology may include other technical problems in addition to the technical problems solved by the independent claim.

[0079] In the present invention, each patient has a unique patient ID. In the embodiments of the present invention, this patient ID is named "treatment number", and all patient information corresponding to the same treatment number (including the patient's basic information, hospitalization information, treatment information, etc.) is stored in various different information subsystems within the hospital.

[0080] Based on the above background, one aspect of the embodiments of the present invention is to disclose a medical record online supervision and remote collaboration management system based on random sampling, as Figure 1 shown, specifically including the following modules:

[0081] I) Data random sampling module

[0082] For randomly obtaining the treatment numbers of N patients as the treatment numbers to be supervised, N≥1, and obtaining all patient information within a certain time length corresponding to each treatment number to be supervised from different information subsystems within the hospital, wherein, ensuring that at least one treatment number to be supervised is drawn from each ward area within the hospital.

[0083] As Figure 2 shown, in the embodiments of the present invention, the data random sampling module uses the following method to achieve the extraction of N treatment numbers to be supervised and the acquisition of the corresponding patient information:

[0084] Step 101, obtain the treatment numbers of all inpatients in the hospital;

[0085] Step 102: Divide all treatment numbers into N sets according to the values of the "discharge ward" fields corresponding to each treatment number obtained from the in-hospital inpatient information system, where N is the number of wards in the hospital and N≥1;

[0086] Step 103: Traverse all treatment numbers in each set in turn, extract at least one treatment number to be supervised for each patient from each set, and obtain all patient information corresponding to each treatment number to be supervised within a certain time period. When traversing the current set:

[0087] Step 1031: Obtain the current treatment number;

[0088] Step 1032: Obtain the total cost information corresponding to the current treatment number from the in-hospital inpatient information system. If the total cost is not greater than the preset cost threshold, go to Step 1033; otherwise, go to Step 1034. In an embodiment of the present invention, a preferred implementation is to set the cost threshold to 200,000;

[0089] Step 1033: Obtain the discharge method information corresponding to the current treatment number from the in-hospital inpatient information system. If the discharge method information is "death" or "discharge without doctor's advice", go to Step 1034; otherwise, take the next treatment number in the current set as the current treatment number and return to Step 1031. After traversing all treatment numbers in the current set, go to Step 1035;

[0090] Step 1034: After taking the current treatment number as the treatment number to be extracted, take the next treatment number in the current set as the current treatment number and return to Step 1031. After traversing all treatment numbers in the current set, go to Step 1036;

[0091] Step 1035: Randomly extract M treatment numbers from the current set as the treatment numbers to be extracted, where M≥1, and go to Step 1036;

[0092] Step 1036: Obtain the hospitalization time information corresponding to all treatment numbers to be extracted from the in-hospital inpatient information system. After excluding the treatment numbers to be extracted whose hospitalization time is not greater than the preset hospitalization time threshold, randomly extract at least one treatment number to be extracted from the remaining treatment numbers to be extracted as the treatment number to be supervised, and go to Step 1037. In an embodiment of the present invention, a preferred implementation is to set the hospitalization time threshold to 7 days;

[0093] Step 1037: Obtain all patient information within a certain time period and corresponding to the current treatment number to be supervised from the in-hospital inpatient information system and each specialty information system. In an embodiment of the present invention, a preferred implementation is that the certain time period is all patient information within one month before the current time point.

[0094] In the embodiments of the present invention, when randomly extracting data, stepwise random extraction is performed according to the values of keyword fields such as "total cost", "discharge method", and "length of stay" corresponding to the treatment number. In the present invention, the selection of the foregoing keyword fields is based on the requirements of the national action plan for comprehensively improving medical care. First, multiple "problem medical record" predictors are screened out. Then, in the case system of Zhongshan Hospital affiliated to Fudan University, through a large amount of case data, whether each medical record is a "problem medical record" is set as the dependent variable, and the preliminarily screened "problem medical record" predictors are used as independent variables. Through neural network learning, layer-by-layer screening and step-by-step modeling are carried out, and finally, "total cost", "discharge method", and "length of stay" are selected as independent predictors of "problem medical records". Furthermore, based on these independent predictors, the foregoing data screening process is designed.

[0095] (2) Medical record generation module

[0096] It is used to generate N copies of medical records to be supervised based on the treatment numbers to be supervised of N patients obtained by the data random sampling module and all patient information in each sub-information system in the hospital.

[0097] In the embodiments of the present invention, a preferred implementation is that the medical records to be supervised are in PDF format.

[0098] Combined Figure 3 , in the embodiments of the present invention, another preferred implementation is that the medical record generation module further includes the following units:

[0099] Medical record generation unit, which is used to generate N copies of medical records to be supervised based on the treatment numbers to be supervised of N patients obtained by the data random sampling module and all patient information in each sub-information system in the hospital. In the implementation of the present invention, a preferred implementation is that the generated medical records to be supervised at least include the information of the following fields:

[0100] Information on the first page of the inpatient medical record, which further includes patient information, diagnosis and treatment information, hospitalization information, and cost information;

[0101] Admission record;

[0102] Course of disease record;

[0103] Preoperative discussion record;

[0104] Surgical record;

[0105] Postoperative course of disease record;

[0106] Discharge record;

[0107] Death record;

[0108] Death case discussion record;

[0109] Consultation record;

[0110] Pathological data;

[0111] Report of auxiliary examination;

[0112] Medical imaging examination data;

[0113] Doctor's advice sheet;

[0114] Operation consent form;

[0115] Anesthesia consent form;

[0116] Anesthesia preoperative visit record;

[0117] Surgical safety verification record;

[0118] Surgical inventory record;

[0119] Anesthesia record;

[0120] Anesthesia postoperative visit record;

[0121] Informed consent form for blood transfusion therapy;

[0122] Consent form for special examination (special treatment);

[0123] Critical illness (severe illness) notice;

[0124] Temperature sheet;

[0125] Nursing record for critically ill (critical) patients;

[0126] Expense settlement statement.

[0127] In the above fields, if the corresponding values are not obtained from the sub-information system, the values of these fields in the medical records to be inspected are "blank". For example, for non-surgical patients, the values of preoperative discussion record, operation record, postoperative progress record, operation consent form, anesthesia consent form, anesthesia preoperative visit record, surgical safety verification record, surgical inventory record, anesthesia record, and anesthesia postoperative visit record are "blank".

[0128] The verification unit is used to verify the integrity of N medical records to be inspected generated by the medical record generation unit. If the verification fails, the medical record generation unit regenerates the medical records to be inspected to replace the medical records to be inspected that fail the verification. The generated medical records to be inspected also need to be verified by the verification unit until all N medical records to be inspected pass the verification.

[0129] It should be noted that there are various ways to verify the integrity of the medical records to be inspected. For example, the medical records to be inspected generated by the medical record generation unit can be compared with the template medical records pre-stored in the system to determine whether the medical records to be inspected contain all the fields listed above. If so, it is determined that the medical records to be inspected are complete; otherwise, it is determined that the medical records to be inspected are incomplete. In addition, algorithms such as MD5, SHA-256, or CRC32 can be used to further calculate the check value one of each field in the medical records to be inspected. At the same time, calculate the check value two of the original value of the corresponding field in the original information subsystem, and compare the check value one with the check value two to determine whether the corresponding field is complete. Only when all fields are complete, it is determined that the medical records to be inspected are complete; otherwise, it is determined that the medical records to be inspected are incomplete.

[0130] A labeling unit for labeling N medical records to be inspected with different labels. In an embodiment of the present invention, a preferred implementation manner is that the labels altogether include five types: "surgery", "non-surgery", "observation", "death", and "discharge without doctor's order". Among them: it can be determined whether the current medical record to be inspected belongs to "surgery" or "non-surgery" according to whether the value of the "surgical record" field in the medical record to be inspected is "empty". Those skilled in the art can also make judgments according to the values of other fields under the foregoing teachings. For example, it can be judged according to the value of the "preoperative discussion record" in the medical record to be inspected. Examples are not given one by one here, and the judgments of other labels are the same and will not be elaborated here; it can be determined whether the current medical record to be inspected belongs to "observation" according to the ward area information included in the information on the first page of the inpatient medical record. For example, if the information on the first page of the inpatient medical record shows that the current patient belongs to the observation ward area, then the current medical record to be inspected belongs to "observation"; otherwise, the current medical record to be inspected does not belong to "observation"; it can be determined whether the current medical record to be inspected belongs to "death" according to whether the value of the "death record" field in the medical record to be inspected is "empty"; it can be determined whether the current medical record to be inspected belongs to "discharge without doctor's order" according to the value of the "discharge record" field in the medical record to be inspected.

[0131] (III) Clinical inspection module

[0132] It is used to generate a score sheet for the first page of the medical record and a score sheet for the medical history. After obtaining the scores of the information on the first page of the inpatient medical record and other information except the information on the first page of the inpatient medical record in N medical records to be inspected based on the score sheet for the first page of the medical record and the score sheet for the medical history respectively, a clinical inspection result is generated based on the score results.

[0133] In an embodiment of the present invention, a preferred implementation manner is that the clinical inspection module, based on the label of each medical record to be inspected, generates a score sheet for the first page of the medical record and a score sheet for the medical history corresponding to the label type for each medical record to be inspected.

[0134] Combined with Figure 5, in an embodiment of the present invention, another preferred embodiment is that the clinical supervision module generates the clinical supervision results of the medical records to be supervised by the following steps:

[0135] Step 201: The clinical supervision module obtains the labels of the medical records to be supervised, and generates corresponding front-page score sheets and medical history score sheets for each medical record to be supervised based on different label types;

[0136] Step 202: The clinical quality control specialist uses the clinical supervision module to score the inpatient medical record front-page information and other information other than the inpatient medical record front-page information of N medical records to be supervised based on the front-page score sheet and the medical history score sheet respectively;

[0137] In step 202, assuming that there are a total of K clinical quality control specialists in the hospital, a preferred embodiment of distributing N medical records to be supervised is as follows:

[0138] If K = N, each clinical quality control specialist is assigned one medical record to be supervised;

[0139] If K > N, the following steps are used to distribute N medical records to be supervised:

[0140] Step 202-A: The clinical supervision module calculates the distribution factor for each clinical quality control specialist, and the distribution factor of the kth clinical quality control specialist is represented as Z k , Z k = α k × R k , where R k is a random number generated by the clinical supervision module for the kth clinical quality control specialist, and α k is the weight of the kth clinical quality control specialist, and its value is the total number of medical records to be supervised that the kth clinical quality control specialist has been assigned. The initial value of α k is 1;

[0141] Step 202-B: The clinical supervision module sorts the K clinical quality control specialists from small to large according to the calculated distribution factors;

[0142] Step 202-C: The clinical supervision module selects the first N clinical quality control specialists from the K clinical quality control specialists, evenly distributes the current N medical records to be supervised to the selected N clinical quality control specialists, and at the same time, adds one to the weight α k corresponding to the N clinical quality control specialists;

[0143] If K < N, the following steps are used to distribute N medical records to be supervised:

[0144] Step 202-1: The clinical supervision module assigns K quantity labels to the K clinical quality control specialists. Among them, the quantity label assigned to the kth clinical quality control specialist is represented as Bk , B k The initial value of is 0, k = 1, 2, …, K;

[0145] Step 202-2: The clinical supervision module calculates the number of medical records to be supervised that each clinical quality control specialist needs to be assigned. Among the K clinical quality control specialists, only one clinical quality control specialist needs to be assigned the number of medical records to be supervised as The number of medical records to be supervised that the remaining clinical quality control specialists need to be assigned is where, represents rounding down;

[0146] Step 202-3: The clinical supervision module determines whether the values of all labels B k are all 1. If so, go to Step 202-5; otherwise, go to Step 202-4;

[0147] Step 202-4: Randomly select one clinical quality control specialist from all the clinical quality control specialists whose label B k value is 0, and assign pieces of medical records to be supervised to this clinical quality control specialist, and set the value of the corresponding label B k to 1, and assign pieces of medical records to be supervised to the remaining K-1 clinical quality control specialists respectively;

[0148] Step 202-5: After setting the values of all labels B k to 0, randomly select one clinical quality control specialist from the K clinical quality control specialists, and assign pieces of medical records to be supervised to this clinical quality control specialist, and set the value of the corresponding label B k to 1, and assign pieces of medical records to be supervised to the remaining K-1 clinical quality control specialists respectively;

[0149] Step 203: The clinical review specialist uses the clinical supervision module to review the scoring results of the front page information of the inpatient medical records and the scoring results of other information except the front page information of the N medical records to be supervised: If the review passes, go to Step 205; if the review fails, go to Step 204;

[0150] In Step 203, assuming that there are a total of L clinical review specialists in this hospital, a better implementation method for allocating the N medical records to be supervised is the same as the steps for allocating the N medical records to be supervised to the K clinical quality control specialists described in the above Step 202, which will not be elaborated here;

[0151] Step 204: The clinical supervision module sends the medical records to be supervised that fail the review and the scoring results of the information on the front page of the inpatient medical record or the scoring results of other information except the information on the front page of the inpatient medical record to a pre-designated third-party department within the hospital for review. If the review is passed, proceed to Step 205; otherwise, the clinical supervision module records the number of times the clinical quality control specialist fails the review. When the number of times of failing the review reaches Threshold 1, the clinical quality control specialist will be retrained. If the number of times of failing the review reaches Threshold 2, the clinical quality control qualification of the clinical quality control specialist will be revoked. Threshold 1 is less than Threshold 2.

[0152] Step 205: Aggregate all the scoring results of all the medical records to be supervised that pass the review as the final clinical supervision result.

[0153] Since both the clinical quality control specialists and the clinical review specialists are doctors with certain professional knowledge and have relatively heavy medical work, in order to avoid significantly increasing the workload of relevant doctors in the supervision work, in the above steps, the equal distribution algorithm designed by the present invention is adopted for the distribution of N medical records to be supervised, so that the N medical records to be supervised can be evenly distributed to different clinical quality control specialists or clinical review specialists as much as possible, enabling doctors to be more actively involved in the supervision work.

[0154] IV) Nursing Supervision Module

[0155] It is used to generate a nursing scoring form. After obtaining the scores of each item of information in N medical records to be supervised based on the nursing scoring form, a nursing supervision result is generated based on the scoring results.

[0156] In an embodiment of the present invention, a preferred implementation manner is that the nursing supervision module generates a nursing scoring form corresponding to each medical record to be supervised based on the label of each medical record to be supervised.

[0157] Combined with Figure 6 In an embodiment of the present invention, another preferred implementation manner is that the nursing supervision module generates a clinical supervision result of the medical records to be supervised by the following steps:

[0158] Step 301: The nursing supervision module obtains the labels of the medical records to be supervised and generates a corresponding nursing scoring form for each medical record to be supervised based on different label types.

[0159] Step 302: The nursing quality control specialist uses the nursing supervision module to score each item of information in N medical records to be supervised based on the nursing scoring form.

[0160] Step 303: The nursing review specialist uses the clinical supervision module to review the scoring results of each item of information in N medical records to be supervised. If the review is passed, proceed to Step 305; if the review fails, proceed to Step 304.

[0161] Step 304: The nursing supervision module sends the medical records to be supervised that fail the review and the scoring results of each item of information to a pre-specified third-party department within the hospital, and the third-party department conducts a review. If the review is passed, go to Step 305; otherwise, the nursing supervision module records the number of times the nursing quality control specialist fails the review. When the number of times of failing the review reaches Threshold 1, the nursing quality control specialist will be retrained. If the number of times of failing the review reaches Threshold 2, the nursing quality control qualification of the nursing quality control specialist will be deprived. Threshold 1 is less than Threshold 2.

[0162] Step 305: Summarize all the scoring results of all the medical records to be supervised that pass the review as the final nursing supervision result.

[0163] However, according to experience, when conducting nursing supervision, experienced nursing staff usually make correct evaluations of each item of information in the medical records to be supervised. Therefore, the subsequent review steps can be omitted.

[0164] Then in combination with Figure 7 , in an embodiment of the present invention, another preferred implementation manner is that the nursing supervision module generates the clinical supervision result of the medical record to be supervised by the following steps:

[0165] Step 3-1: The nursing supervision module obtains the label of the medical record to be supervised, and generates a corresponding nursing scoring form for each medical record to be supervised based on different label types.

[0166] Step 3-2: The nursing quality control specialist uses the nursing supervision module to score each item of information of N medical records to be supervised based on the nursing scoring form.

[0167] Step 3-3: Summarize all the scoring results of all the medical records to be supervised as the final nursing supervision result.

[0168] In the above Step 302 and Step 3-2, assuming that there are a total of J nursing quality control specialists in the hospital, a preferred implementation manner of distributing N medical records to be supervised is the same as the method of distributing N medical records to be supervised to K clinical quality control specialists described in the above Step 202, so as to achieve the purpose of evenly distributing N medical records to be supervised as much as possible, which will not be elaborated here.

[0169] In the above Step 303, assuming that there are a total of H nursing review specialists in the hospital, a preferred implementation manner of distributing N medical records to be supervised is the same as the method of distributing N medical records to be supervised to K clinical quality control specialists described in the above Step 202, so as to achieve the purpose of evenly distributing N medical records to be supervised as much as possible, which will not be elaborated here.

[0170] V) Result output module

[0171] It is used to visually output the clinical supervision results obtained by the clinical supervision module and the nursing supervision results obtained by the nursing supervision module, and can export summary tables or detailed tables (as Figure 7 shown), or can also statistically process the results as shown in Figure 8 and then visually display them. Figure 8 The pie chart in it shows the proportion of all medical records selected for quality control supervision in the current month for the [discharge method]. The bar chart on the right shows the calculation of the score difference between the scores of the clinical quality control specialist and the clinical review specialist on the front page of the medical record, and accumulatively summarizes them according to the [discharge department] to generate a report to observe the difference in the score results, and focuses on re-review or the addition of a third party for review. The bar chart on the left shows the calculation of the score difference between the scores of the clinical quality control specialist and the clinical review specialist for the medical history, and accumulatively summarizes them according to the [discharge department] to generate a report to observe the difference in the score results, and focuses on re-review or the addition of a third party for review.

[0172] Those skilled in the art can understand that the features recited in the various embodiments and / or claims of the present disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly recited in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features recited in the various embodiments and / or claims of the present disclosure can be combined and combined in various ways. All such combinations and / or combinations fall within the scope of the present disclosure.

[0173] The above describes the embodiments disclosed in the present invention. However, these embodiments are only for illustrative purposes and not for limiting the scope of the present disclosure. Although the various embodiments are described separately above, this does not mean that the measures in each embodiment cannot be used advantageously in combination. The scope of the present disclosure is defined by the appended claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art can make various substitutions and modifications, and all such substitutions and modifications should fall within the scope of the present disclosure.

Claims

1. A medical record online supervision and remote collaboration management system based on random sampling, where each patient has a unique treatment number, and all patient information corresponding to the same treatment number is stored in various different information subsystems within the hospital. It is characterized in that The system includes: A data random sampling module, which is used to randomly obtain the treatment numbers of N patients as the treatment numbers to be supervised, where N≥1, and obtain all patient information within a certain time length corresponding to each treatment number to be supervised from different information subsystems in the hospital. Among them, it is ensured that at least one treatment number to be supervised is drawn from each ward area in the hospital; A medical record generation module, which is used to generate N medical records to be supervised based on the treatment numbers of N patients to be supervised obtained by the data random sampling module and all patient information of each sub-information system in the hospital; A clinical supervision module, which is used to generate a front page of medical record scoring form and a medical history scoring form. After obtaining the scores of the front page information of the inpatient medical record and other information except the front page information of the inpatient medical record in N medical records to be supervised based on the front page of medical record scoring form and the medical history scoring form respectively, a clinical supervision result is generated based on the scoring results; A nursing supervision module, which is used to generate a nursing scoring form. After obtaining the scores of various information in N medical records to be supervised based on the nursing scoring form, a nursing supervision result is generated based on the scoring results; A result output module, which is used to visually output the clinical supervision result obtained by the clinical supervision module and the nursing supervision result obtained by the nursing supervision module.

2. The online medical record supervision and remote collaboration management system based on random sampling according to claim 1, wherein, The data random sampling module uses the following method to achieve the extraction of N treatment numbers to be supervised and the acquisition of corresponding patient information: Step 101: Obtain the treatment numbers of all inpatients in the hospital; Step 102: Divide all treatment numbers into N sets according to the value of the "discharge ward area" field corresponding to each treatment number obtained from the inpatient sub-information system in the hospital, where N is the number of ward areas in the hospital and N≥1; Step 103: Traverse all treatment numbers in each set in turn, extract at least one treatment number of a patient to be supervised from each set, and obtain all patient information within a certain time length corresponding to each treatment number to be supervised.

3. The online medical record supervision and remote collaboration management system based on random sampling according to claim 2, characterized in that In step 103, when traversing the current set: Step 1031: Obtain the current treatment number; Step 1032: Obtain the total cost information corresponding to the current treatment number from the inpatient sub-information system in the hospital. If the total cost is not greater than the preset cost threshold, go to step 1033, otherwise, go to step 1034; Step 1033: Obtain the discharge method information corresponding to the current treatment number from the inpatient sub-information system in the hospital. If the discharge method information is "death" or "discharge against medical advice", go to step 1034, otherwise, take the next treatment number in the current set as the current treatment number, and return to step 1031. After traversing all treatment numbers in the current set, go to step 1035; Step 1034: Take the current treatment number as the treatment number to be extracted, take the next treatment number in the current set as the current treatment number, and return to step 1031. After traversing all treatment numbers in the current set, go to step 1036; Step 1035: Randomly extract M treatment numbers from the current set as the treatment numbers to be extracted, where M≥1, and go to step 1036; Step 1036: Obtain the hospitalization time information corresponding to all the treatment numbers to be extracted from the in-hospital inpatient information system. After excluding the treatment numbers to be extracted with a hospitalization time not greater than a pre-set hospitalization time threshold, randomly select at least one treatment number to be extracted from the remaining treatment numbers to be extracted as the treatment number to be supervised, and proceed to Step 1037; Step 1037: Obtain all the patient information within a certain time period from the in-hospital inpatient information system and each specialty information system, which corresponds to the current treatment number to be supervised.

4. The online medical record supervision and remote collaboration management system based on random sampling according to claim 1, wherein The medical record generation module includes: A medical record generation unit, which is used to generate N medical records to be supervised based on the treatment numbers to be supervised of N patients obtained by the data random sampling module and all the patient information of each sub-information system in the hospital; A verification unit, which is used to verify the integrity of the N medical records to be supervised generated by the medical record generation unit. If the verification fails, the medical record generation unit shall regenerate the medical records to be supervised to replace the medical records to be supervised that fail the verification. Among them, the generated medical records to be supervised also need to be verified by the verification unit until all N medical records to be supervised pass the verification. A labeling unit, which is used to label the N medical records to be supervised with different labels.

5. The online supervision and remote collaboration management system for medical records based on random sampling according to claim 1, wherein The medical record to be supervised at least includes the information of the following fields: The information on the front page of the inpatient medical record, which further includes patient information, diagnosis and treatment information, hospitalization information, and expense information; admission record; progress record; preoperative discussion record; operation record; postoperative progress record; discharge record; death record; Death medical record discussion record; consultation record; pathological data; auxiliary examination report form; medical imaging examination data; doctor's order form; operation consent form; anesthesia consent form; anesthesia preoperative visit record; surgical safety verification record; surgical inventory record; anesthesia record; anesthesia postoperative visit record; blood transfusion treatment informed consent form; special examination (special treatment) consent form; critical illness (severe illness) notice; temperature sheet; nursing record for critically ill (critical) patients; expense settlement list.

6. The online supervision and remote collaboration management system for medical records based on random sampling as claimed in claim 4, wherein, The labels include "operation", "non-operation", "observation", "death", and "discharge without doctor's order".

7. The online supervision and remote collaboration management system for medical records based on random sampling according to claim 4, characterized in that, The clinical supervision module, based on the label of each medical record to be supervised, generates a medical record front page scoring form and a medical history scoring form corresponding to the label type for each medical record to be supervised.

8. The online medical record supervision and remote collaboration management system based on random sampling according to claim 4, characterized in that, The clinical supervision module adopts the following steps to generate the clinical supervision results of the medical records to be supervised: Step 201: The clinical supervision module obtains the label of the medical record to be supervised, and based on different label types, generates a corresponding medical record front page scoring form and a medical history scoring form for each medical record to be supervised; Step 202: The clinical quality control specialist uses the clinical supervision module to score the information on the front page of the inpatient medical record and other information except the information on the front page of the inpatient medical record of the N medical records to be supervised based on the medical record front page scoring form and the medical history scoring form respectively; Step 203: The clinical review specialist uses the clinical supervision module to review the scoring results of the information on the front page of the inpatient medical record and the scoring results of other information except the information on the front page of the inpatient medical record of the N medical records to be supervised: If the review passes, proceed to Step 205; if the review fails, proceed to Step 204; Step 204: The clinical supervision module sends the medical records to be supervised that fail the review and the scoring results of the front page information of the inpatient medical record or the scoring results of other information except the front page information of the inpatient medical record to a pre-designated third-party department within the hospital for review. If the review is passed, proceed to Step 205; otherwise, the clinical supervision module records the number of times the clinical quality control specialist fails the review. When the number of times of failing the review reaches Threshold 1, the clinical quality control specialist will be retrained. If the number of times of failing the review reaches Threshold 2, the clinical quality control qualification of the clinical quality control specialist will be revoked. Threshold 1 is less than Threshold 2. Step 205: Aggregate all the scoring results of all the medical records to be supervised that pass the review as the final clinical supervision result.

9. The online medical record supervision and remote collaboration management system based on random sampling according to claim 4, characterized in that, Based on the label of each medical record to be supervised, the nursing supervision module generates a nursing scoring form corresponding to the label type for each medical record to be supervised.

10. The online supervision and remote collaboration management system for medical records based on random sampling according to claim 4, wherein, The nursing supervision module uses the following steps to generate the clinical supervision result of the medical record to be supervised: Step 301: The nursing supervision module obtains the label of the medical record to be supervised and generates a corresponding nursing scoring form for each medical record to be supervised based on different label types. Step 302: The nursing quality control specialist uses the nursing supervision module to score each piece of information in N medical records to be supervised based on the nursing scoring form. Step 303: The nursing review specialist uses the clinical supervision module to review the scoring results of each piece of information in N medical records to be supervised. If the review is passed, proceed to Step 305; if the review fails, proceed to Step 304. Step 304: The nursing supervision module sends the medical records to be supervised that fail the review and the scoring results of each piece of information to a pre-designated third-party department within the hospital for review. If the review is passed, proceed to Step 305; otherwise, the nursing supervision module records the number of times the nursing quality control specialist fails the review. When the number of times of failing the review reaches Threshold 1, the nursing quality control specialist will be retrained. If the number of times of failing the review reaches Threshold 2, the nursing quality control qualification of the nursing quality control specialist will be revoked. Threshold 1 is less than Threshold 2. Step 305: Aggregate all the scoring results of all the medical records to be supervised that pass the review as the final nursing supervision result. Or the nursing supervision module uses the following steps to generate the clinical supervision result of the medical record to be supervised: Step 3-1: The nursing supervision module obtains the label of the medical record to be supervised and generates a corresponding nursing scoring form for each medical record to be supervised based on different label types. Step 3-2: The nursing quality control specialist uses the nursing supervision module to score each piece of information in N medical records to be supervised based on the nursing scoring form. Step 3-3: Aggregate all the scoring results of all the medical records to be supervised as the final nursing supervision result.

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