CN112542231A

CN112542231A - MapReduce and big data-based management method and system for number of cases for surgical prophylaxis

Info

Publication number: CN112542231A
Application number: CN202011311773.3A
Authority: CN
Inventors: 林�建; 霍瑞; 陈春平
Original assignee: Hangzhou Xinglin Information Technology Co ltd
Current assignee: Hangzhou Xinglin Information Technology Co ltd
Priority date: 2020-11-20
Filing date: 2020-11-20
Publication date: 2021-03-23
Anticipated expiration: 2040-11-20
Also published as: CN112542231B

Abstract

The invention provides a management method and a management system for the number of cases for operation prevention based on MapReduce and big data, which are based on a MapReduce framework and utilize the parallel computing capability of machines under a distributed system to divide the number of cases of millions and millions of inpatients which exceed the memory and storage limit of a server into tens of millions and hundreds of millions of small tasks for calculating the number of cases of hospital infection with multi-drug-resistant bacteria, execute the small tasks on a plurality of machines simultaneously, and generate a final result by summarizing intermediate output results of the small tasks. The invention carries out massive parallel calculation on the large data which can not be avoided when the national level and the provincial level monitor networks and comprises million, million and hundred million levels of inpatients according to provincial and municipal areas, hospital levels, hospital beds, synthesis and specialization, official and civil calibers and the like, and can provide effective guidance for the treatment and management of antibacterial drugs of inpatients. Meanwhile, the problem that the manual operation case counting is complex is solved by automatically counting the operation cases.

Description

MapReduce and big data-based management method and system for number of cases for surgical prophylaxis

Technical Field

The invention belongs to the technical field of management of operation preventive medications, and particularly relates to a management method and a management system for the number of cases of operation preventive medications based on MapReduce and big data, in particular to a management method and a management system for the number of cases of operations preventive medications applied to operation in hospital-wide inpatients within a certain period, which are particularly suitable for a scene that the data volume of patients to be processed far exceeds the storage (magnetic disc) and the computing capacity (memory and CPU) of a server and the tasks cannot be split and distributed manually.

Background

The invention and application of the antibacterial drug bring convenience for human beings to treat a plurality of serious bacterial infectious diseases, and effectively reduce the death rate of various infectious diseases. The application of the antibacterial agent needs to be reasonably selected according to different infectious diseases. Surgery on hospitalized patients is a common cause of infection in patients. The harm caused by the surgical infection of the patient is very large, so that in the actual diagnosis and treatment process, the antibacterial drug is usually applied to the surgical patient in a preventive manner, and the surgical related infection is avoided. However, the phenomenon of abuse of antibacterial drugs often occurs in clinic, so that pathogenic bacteria generate drug resistance to the antibacterial drugs, and the curing difficulty of bacterial infectious diseases is increased. Therefore, the antibacterial drugs should be used reasonably from the beginning of clinical practice, and abuse of the antibacterial drugs is firmly stopped. Therefore, the statistics of the times of the operation cases applying the antibacterial drugs has important significance for the management of the antibacterial drugs, and can provide important guidance for the treatment of the subsequent complications of the operation. Therefore, how to realize the statistics of the number of the medical cases for operation prevention becomes an urgent problem to be solved in the field.

The number of the medical cases for operation prevention is relatively easy to calculate in a medical institution, the number of the ordinary medical institutions such as the third-class A and the like is about fifty thousand per year, and the state or provincial-class tap hospitals have hundreds of thousands of people. The calculation of the key indexes is complex under the condition of large data of millions, billions and billions of inpatients of hundreds and thousands of medical institutions in provincial regions or nationwide ranges, 2749 third-level hospitals in the China in 2019, 9687 second-level hospitals in 2019 and 17487 thousands of inpatients in public hospitals, and the initial result of one-time statistical analysis needs to be calculated in the last year. Therefore, how to carry out standardized, normalized and homogeneous nosocomial infection monitoring in hundreds of hospitals and thousands of hospitals in one area and realize the number of the cases for operation prophylaxis in a specified time period under the condition of big data of inpatients becomes the most urgent problem to be solved for developing the regional information monitoring platform.

Disclosure of Invention

The invention aims to provide a method and a system for managing the number of cases for operation prevention, aiming at the defects of the prior art. The counted number of the operation prevention medicine cases is high in practicability, the number of the operation cases can be accurately counted according to the needs of users, and effective guidance can be provided for treatment and management of antibacterial medicines of inpatients. Meanwhile, the problem that the manual operation case counting is complex is solved by automatically counting the operation cases.

In order to achieve the purpose, the invention adopts the following technical scheme:

the management method of the number of the cases for operation prevention based on MapReduce and big data comprises the following steps:

s1, obtaining hospitalization process information A, an antibacterial medicine order record F, operation information G, selected statistical time, an operating department, an incision grade, operation classification, an operating doctor, an anesthesia mode, operation duration, ASA (acrylonitrile styrene acrylate) score, an operation name, a healing grade, an operation position, an NNIS (NNIS) score, phase-selective emergency treatment, an operating room, the operation of the patient in the hospital for the first time, a medicine application purpose, a medicine application mode, an antibiotic grade and determining a permission department of the user according to identity information of the user;

s2, acquiring the admission time and the discharge time of the patient based on the hospitalization process information A, and taking the admission time and the discharge time as the parameter g.MC2;

s3, dividing the operation information G into operation information G (a) Y occurring during the present hospitalization and operation information G (a) N occurring during the non-present hospitalization based on the parameter g.mc2;

s4, judging whether the operation information G (a) Y has operation records, if yes, executing a step S5, and if not, outputting the number of the operation preventive medications to be 0;

s5, acquiring the operation starting time and the operation ending time based on the operation information G (a) _ Y, and taking the operation starting time and the operation ending time as perioperative parameters g.QA4. optimal of the operation;

s6, dividing the operation information G (a) Y into operation information G (b) Y in a statistical time range and operation information G (b) N not in the statistical time range;

s7, dividing the operation information G (b) Y into operation information G (c) Y in the authority range and operation information G (c) N not in the authority range;

s8, dividing the operation information G (c) Y into operation information G (d) Y in the selected operation department range and operation information G (d) N not in the selected range;

s9, dividing the operation information G (d) _ Y into operation information G (e) _ Y in the incision grade selection list and operation information G (e) _ N not in the incision grade selection list;

s10, dividing the operation information G (e) Y into operation information G (f) Y in the selected operation classification range and operation information G (f) N not in the selected range;

s11, dividing the operation information G (f) Y into operation information G (g) Y in an operation doctor selection list and operation information G (g) N not in the operation doctor selection list;

s12, dividing the operation information G (g) _ Y into operation information G (h) _ Y in an anesthesia mode selection list and operation information G (h) _ N not in the anesthesia mode selection list;

s13, dividing the operation information G (h) Y into operation information G (i) Y in a limited operation duration range and operation information G (i) N not in the limited operation duration range;

s14, dividing the operation information G (i) _ Y into operation information G (j) _ Y in an ASA scoring selection list and operation information G (j) _ N not in the ASA scoring selection list;

s15, dividing the operation information G (j) Y into operation information G (k) Y in an operation name selection list and operation information G (k) N not in the operation name selection list;

s16, dividing the operation information G (k) _ Y into operation information G (m) _ Y in a healing level selection list and operation information G (m) _ N not in the healing level selection list;

s17, dividing the operation information G (m) Y into operation information G (N) Y on an operation position selection list and operation information G (N) N not on the operation position selection list;

s18, dividing the operation information G (N) _ Y into operation information G (p) _ Y on an NNIS scoring selection list and operation information G (p) _ N not on the NNIS scoring selection list;

s19, dividing the operation information G (p) _ Y into operation information G (q) _ Y of an emergency selection list in the selected period and operation information G (q) _ N of an emergency selection list not in the selected period;

s20, dividing the operation information G (q) _ Y into operation information G (r) _ Y in an operation room selection list and operation information G (r) _ N not in the operation room selection list;

s21, dividing the operation information G (r) _ Y into operation information G (S) _ Y with limited operation times and operation information G (S) _ N without limited operation times;

s22, judging whether the operation information G (S) _ Y has operation records, if yes, executing a step S23, and if not, outputting the number of the operation preventive medications to be 0;

s23, based on the parameter g.mc2, dividing the antibacterial medication order record F into an antibacterial medication order F (a) -Y with an order start time during the current hospitalization period and an antibacterial medication order F (a) -N with an order start time not during the current hospitalization period;

s24, dividing the antibacterial medicine order F (a) Y into an order F (b) Y for a selection list of medicine taking purposes and an order F (b) N for a selection list of medicine taking purposes;

s25, dividing the antibacterial medicine medical orders F (b) Y into medical orders F (c) Y in a drug administration mode selection list and medical orders F (c) N not in the drug administration mode selection list;

s26, dividing the antibacterial medicine medical order F (c) _ Y into an order F (d) _ Y in an antibiotic level selection list and an order F (d) _ N not in the antibiotic level selection list;

s27, judging whether an antibacterial medicine order record exists in the antibacterial medicine order F (d) Y, if so, executing a step S28, and if not, outputting that the number of the operation preventive medication cases is 0;

s28, acquiring the order starting time and the order ending time of each antibacterial medicine order based on the antibacterial medicine orders F (d) Y, and constructing a parameter g.THW with a parameter data type of a starting-stopping time period list;

s29, dividing the operation information G (S) _ Y into operation information G (t) _ Y using antibacterial drugs in the perioperative period and operation information G (t) _ N not using antibacterial drugs in the perioperative period based on the parameter g.THW and the parameter g.QA4. optimal;

s30, counting data according to the operation information G (t) _ Y, and outputting 0 if the operation information G (t) _ Y is empty; if not, outputting the corresponding number.

Further, the hospitalization process information comprises a patient case number, an admission department, admission time, a discharge department and discharge time.

Further, the antibacterial medicine order record comprises a patient case number, an order department, an antibacterial medicine name, a starting time, an ending time, an antibiotic grade, a drug administration mode, a drug administration purpose, an order doctor and an order doctor grade.

Further, the operation information comprises the patient case number, the operating department, the operation category, the operating doctor, the anesthesia mode, the operation name, the operation starting time, the operation ending time, the incision, the healing grade, ASA, phase selection emergency treatment, the operation position, NNIS score, the operating room and the operation times.

Further, the administration is for the purpose of prophylaxis.

The invention also provides a management system of the number of cases for operation prevention based on MapReduce and big data, which comprises the following steps:

the acquisition module is used for acquiring hospitalization process information A, an antibacterial medicine advice record F, operation information G, selected statistical time, an operating department, incision grades, operation classification, an operating doctor, an anesthesia mode, operation duration, ASA (acrylonitrile styrene acrylate) grade, an operation name, a healing grade, an operation position, NNIS (NNIS) grade, phase-selective emergency treatment, an operating room, the operation of the patient in the hospital for the first time, a medicine application purpose, a medicine application mode and an antibiotic grade and determining an authority department of the user according to identity information of the user;

the first acquisition module is used for acquiring the admission time and the discharge time of the patient based on the hospitalization process information A, and the admission time and the discharge time are jointly used as parameters g.MC2;

a first dividing module, configured to divide the surgical information G into surgical information G (a) Y occurring during a current hospitalization period and surgical information G (a) N occurring during a non-current hospitalization period based on the parameter g.mc2;

a first determining module, configured to determine whether a surgical record exists in the surgical information g (a) _ Y, if yes, execute step S5, and if not, output a number of cases for surgical prophylaxis equal to 0;

the second acquisition module is used for acquiring the operation starting time and the operation ending time based on the operation information G (a) _ Y, and the operation starting time and the operation ending time are jointly used as perioperative parameters g.QA4. optimal of the operation;

the second dividing module is used for dividing the operation information G (a) _ Y into operation information G (b) _ Y in a statistical time range and operation information G (b) _ N not in the statistical time range;

the third dividing module is used for dividing the operation information G (b) _ Y into operation information G (c) _ Y in the authority range and operation information G (c) _ N not in the authority range;

the fourth dividing module is used for dividing the operation information G (c) _ Y into operation information G (d) _ Y in the range of the selected operation department and operation information G (d) _ N not in the range of the selection;

a fifth dividing module, configured to divide the operation information g (d) _ Y into operation information g (e) _ Y in the incision level selection list and operation information g (e) _ N not in the incision level selection list;

the sixth dividing module is used for dividing the operation information G (e) _ Y into operation information G (f) _ Y in the selected operation classification range and operation information G (f) _ N not in the selected range;

a seventh dividing module, configured to divide the operation information g (f) _ Y into operation information g (g) _ Y in the operating surgeon selection list and operation information g (g) _ N not in the operating surgeon selection list;

an eighth dividing module, configured to divide the operation information g (g) _ Y into operation information g (h) _ Y in the anesthesia mode selection list and operation information g (h) _ N not in the anesthesia mode selection list;

a ninth dividing module, configured to divide the operation information g (h) _ Y into operation information g (i) _ Y within a limited operation duration range and operation information g (i) _ N not within the limited operation duration range;

a tenth dividing module, configured to divide the surgery information g (i) _ Y into surgery information g (j) _ Y in the ASA scoring selection list and surgery information g (j) _ N not in the ASA scoring selection list;

an eleventh dividing module, configured to divide the operation information g (j) _ Y into operation information g (k) _ Y in the operation name selection list and operation information g (k) _ N not in the operation name selection list;

a twelfth dividing module, configured to divide the operation information g (k) _ Y into operation information g (m) _ Y in a healing level selection list and operation information g (m) _ N not in the healing level selection list;

a thirteenth dividing module, configured to divide the operation information g (m) _ Y into operation information g (N) _ Y in the operation position selection list and operation information g (N) _ N not in the operation position selection list;

a fourteenth dividing module, configured to divide the surgical information g (N) _ Y into surgical information g (p) _ Y in the NNIS score selection list and surgical information g (p) _ N not in the NNIS score selection list;

a fifteenth dividing module, configured to divide the surgical information g (p) _ Y into surgical information g (q) _ Y in the elective emergency selection list and surgical information g (q) _ N not in the elective emergency selection list;

a sixteenth dividing module, configured to divide the operation information g (q) _ Y into operation information g (r) _ Y in the operating room selection list and operation information g (r) _ N not in the operating room selection list;

a seventeenth dividing module, configured to divide the operation information g (r) _ Y into operation information g(s) _ Y for a limited number of operations and operation information g(s) _ N for no limited number of operations;

a second determining module, configured to determine whether a surgical record exists in the surgical information g (S) _ Y, if yes, execute step S23, and if not, output a number of cases for surgical prophylaxis equal to 0;

an eighteenth dividing module, configured to divide the antibacterial medical order record F into an antibacterial medical order F (a) _ Y with an order start time in the current hospitalization period and an antibacterial medical order F (a) _ N with an order start time not in the current hospitalization period based on the parameter g.mc2;

a nineteenth division module for dividing the antibacterial medication order f (a) _ Y into an order f (b) _ Y for a selection list of medication purposes and an order f (b) _ N for a selection list of medication purposes;

a twentieth division module for dividing the antibacterial medicine order f (b) _ Y into an order f (c) _ Y on the administration mode selection list and an order f (c) _ N not on the administration mode selection list;

a twenty-first partitioning module for partitioning the antibacterial medication order F (c) _ Y into an order F (d) _ Y at an antibiotic level selection list and an order F (d) _ N not at an antibiotic level selection list;

a third determining module, configured to determine whether an antibacterial medical order record exists in the antibacterial medical order f (d) _ Y, if yes, execute step S28, and if no, output a number of cases for surgery preventive medication as 0;

the third acquisition module is used for acquiring the order starting time and the order ending time of each antibacterial medicine order based on the antibacterial medicine orders F (d) Y and constructing a parameter data type as a parameter g.THW of a start-stop time period list;

a twenty-second dividing module, configured to divide the operation information g(s) _ Y into operation information g (t) _ Y about the use of the antibacterial agent in the perioperative period and operation information g (t) _ N about the non-use of the antibacterial agent in the perioperative period, based on the parameter g.thw and the parameter g.qa4. optimal;

the output module is used for counting data according to the operation information G (t) _ Y, and outputting 0 if the operation information G (t) _ Y is empty; if not, outputting the corresponding number.

Further, the administration is for the purpose of prophylaxis.

The invention discloses a specific implementation mode of managing the number of medical cases for operation prevention, which is characterized in that the number of the medical cases for operation preventive application of the antibacterial drugs in all hospitalized patients in a determined period is automatically generated by utilizing hospitalization process information A, an antibacterial drug order record F, operation information G, selected statistical time, an operating department, an incision grade, operation classification, an operating doctor, an anesthesia mode, operation duration, ASA (acetyl styrene acrylate) score, an operation name, a healing grade, an operation position, NNIS score, phase-selective emergency treatment, an operating room, the operation of the patient for the first time of hospital admission, drug use purpose, drug administration mode and antibiotic grade, and determining the authority department of the user according to the identity information of the user. The counted number of the operation prevention medicine cases is high in practicability, the number of the operation cases can be accurately counted according to the needs of users, and effective guidance can be provided for treatment and management of antibacterial medicines of inpatients. Meanwhile, the problem that the manual operation case counting is complex is solved by automatically counting the operation cases.

Drawings

FIG. 1 is a schematic diagram illustrating the logic operation of the algorithm from step S1 to step S3 according to the present disclosure.

FIG. 2 is a schematic diagram illustrating the logic operation of the algorithm from step S4 to step S9 according to the present disclosure.

FIG. 3 is a schematic diagram illustrating the logic operation of the algorithm from step S10 to step S13 according to the present disclosure.

FIG. 4 is a schematic diagram illustrating the logic operation of the algorithm from step S14 to step S17 according to the present disclosure.

FIG. 5 is a schematic diagram illustrating the logic operation of the algorithm from step S18 to step S21 according to the present disclosure.

FIG. 6 is a schematic diagram illustrating the logic operation of the algorithm from step S12 to step S25 according to the present disclosure.

FIG. 7 is a schematic diagram illustrating the logic operation of the algorithm from step S16 to step S30 according to the present disclosure.

Detailed Description

The invention is further described with reference to the following drawings and specific examples, which are not intended to be limiting.

In the following examples, X (y) type specification: x represents a data set of a certain type; y represents a serial number used for distinguishing a front data set and a rear data set of the same type of data in different logic units; x (y) represents a data set under different logical units for a certain type of data; y represents a coincidence condition; n represents nonconforming;

example one

As shown in fig. 1 to 7, the present embodiment provides a method for managing the number of cases for operation prophylaxis based on MapReduce and big data, including the following steps:

s1, obtaining hospitalization process information A, an antibacterial medicine order record F, operation information G, selected statistical time, an operating department, an incision grade, operation classification, an operating doctor, an anesthesia mode, operation duration, ASA (acrylonitrile styrene acrylate) score, an operation name, a healing grade, an operation position, an NNIS (NNIS) score, phase-selective emergency treatment, an operating room, the operation of the patient in the hospital for the first time, a medicine application purpose, a medicine application mode, an antibiotic grade and determining a permission department of the user according to identity information of the user; the number of the operation preventive doses is the number of the operation doses of the antibacterial agent applied in the operation preventive manner in the hospitalized patients in the whole hospital in a certain period. The application of systemic preventive antibacterial drugs from the time of admission to the time of discharge of hospitalized patients who are admitted to the hospital for the purpose of surgical treatment is regarded as the application of perioperative preventive drugs. The operation using the antibacterial drug should satisfy the following requirements: 1. the patient has a surgical record within the statistical time range; 2. patients use antibacterial drugs prophylactically during perioperative periods of surgery; 3. and the option requirements of the user are met. Therefore, the invention obtains the hospitalization process information a, the antibacterial medical advice record F, the operation information G, and the like to screen the operation to which the antibacterial drug is applied. The hospitalization process information is used for integrally recording the hospitalization process of the patient, and specifically comprises the patient case number, the hospital admission department, the hospital admission time, the hospital discharge department and the hospital discharge time. The antibacterial medicine order record is used for recording antibacterial medicine order information which is prescribed by a doctor to each patient, and specifically comprises a patient case number, an order department, an antibacterial medicine name, a starting time, an ending time, an antibiotic grade, a drug administration mode, a drug administration purpose, an order doctor and an order doctor grade. The operation information is used for recording the specific conditions of the operation performed by the patient, including the patient case number, the operating department, the operation category, the operator, the anesthesia mode, the operation name, the operation starting time, the operation ending time, the incision, the healing grade, the ASA, the phase-selective emergency call, the operation position, the NNIS score, the operating room and the operation times. The hospitalization process information A, the antibacterial medical advice records F and the operation information G are information collected or input by hospital workers in work.

In addition, the invention selects the statistical time and department, and manages the specified time period and the number of the cases for operation prevention in the specified department. The hospital data has corresponding privacy, so that the statistics and management of the hospital data in the invention require a user to acquire corresponding data authority. The data authority of the user is associated with the corresponding identity information, so that the invention determines the authority department of the user according to the identity information of the operation user and manages the number of the medical cases for operation prevention on the data in the authority department.

The invention also sets corresponding operating departments, incision grades, operation classifications, operating doctors, anesthesia modes, operation duration, ASA scores, operation names, healing grades, operation positions, NNIS scores, phase-selective emergency treatment, operating rooms, operations of patients in the hospital for the first time, medicine purposes, medicine administration modes and antibiotic grades, thereby realizing the accurate statistics and monitoring of the number of the medicine cases for operation prevention.

S2, acquiring the admission time and the discharge time of the patient based on the hospitalization process information A, and taking the admission time and the discharge time as the parameter g.MC2; the method comprises the steps of firstly obtaining the hospitalization process information A of a patient, and further obtaining the relevant information of the fields of the admission time and the discharge time, wherein the relevant information is jointly used as the parameter g.MC2. This step is to select the time of admission and discharge of the patient as a quoted parameter.

S3, dividing the operation information G into operation information G (a) Y occurring during the present hospitalization and operation information G (a) N occurring during the non-present hospitalization based on the parameter g.mc2; in order to solve the problem of operation record information of wrong time which does not occur in the period of the current hospitalization, the invention firstly screens the operation information G, selects the operation information G (a) _ Y which is performed in the time range of the patient's admission and discharge, namely the operation information G (a) _ Y which occurs in the period of the current hospitalization. Specifically, the invention filters out the operation information G (a) and N which does not occur during the current hospitalization period based on the comparison between the field of ' operation start time ' and ' operation end time ' in the operation information and the parameter g.MC2 of the hospitalization and discharge time, and obtains the operation information G (a) and Y which are performed in the time range of the patient's hospitalization and discharge.

S4, judging whether the operation information G (a) Y has operation records, if yes, executing step S5, and if not, outputting the number of the operation preventive medications to be 0. Specifically, the invention judges according to the operation information G (a) _ Y, if the patient has records after the steps, the operation is continued downwards, if the patient has no records, the operation is ended, and the result 0 is output.

S5, acquiring the operation starting time and the operation ending time based on the operation information G (a) _ Y, and taking the operation starting time and the operation ending time as perioperative parameters g.QA4. optimal of the operation; the perioperative parameters are determined based on the operative information G (a) Y, and preparation is made for obtaining the intersection operative information in the perioperative period and the medical advice time range subsequently. Perioperative is the entire process around the operation, starting from the patient's decision to receive the surgical treatment, and proceeding to the surgical treatment until the basic recovery, including a period of time before, during and after the operation, and in particular from the time the surgical treatment is determined until the treatment associated with the operation is substantially completed. In the present invention, the perioperative time is determined from one day before the operation start time to one day after the operation end time.

S6, dividing the operation information G (a) Y into operation information G (b) Y in a statistical time range and operation information G (b) N not in the statistical time range; the method comprises the steps of firstly screening operation information G (a) _ Y occurring in the period of hospitalization on the basis of statistical time, specifically, acquiring a field of 'operation starting time' in the operation information G (a) _ Y occurring in the period of hospitalization, judging whether the 'operation starting time' in an operation record occurring in the period of hospitalization currently belongs to the range of statistical time period, if so, adding the operation record into the operation information G (b) _ Y in the statistical time period, and otherwise, adding the operation record into the operation information G (b) _ N which is not in the statistical time range.

S7, dividing the operation information G (b) Y into operation information G (c) Y in the authority range and operation information G (c) N not in the authority range; because the authority of each user is different, the invention screens the operation information G (b) _ Y based on the authority department, so that the data operated by the user is adaptive to the corresponding authority. And comparing the 'operating department' field in the operation information with the authority department, and judging whether the 'operating department' field belongs to the scope of the authority department. The operation information g (c) _ Y is operation information in departments that belong to the authority range managed by the user, and the operation information g (c) _ N is operation information in departments that do not belong to the authority range managed by the user.

S8, dividing the operation information G (c) _ Y into operation information G (d) _ Y in the selected operation department range and operation information G (d) _ N not in the selected range, in the invention, the operation patient case frequency is monitored based on the specific operation department, and the user can manage the operation patient case frequency aiming at the specific operation department. And comparing the 'operating room' field in the operation information with the selected operating room, and judging whether the 'operating room' field belongs to the range of the selected operating room.

S9, dividing the operation information G (d) _ Y into operation information G (e) _ Y in the incision grade selection list and operation information G (e) _ N not in the incision grade selection list; according to the invention, a user can manage the number of times of the surgical patient cases aiming at a specific incision grade so as to determine the surgical condition of the incision of each grade. As described above, the user selects the incision grade, and the selected incision grade constitutes the incision grade selection list. Therefore, the invention screens the operation information G (d) _ Y based on the incision grade selected by the user, so that the statistical and screened data are adaptive to the incision grade selected by the user, the user can select the corresponding data according to the requirement, and the times of the patient cases of the operation in the incision grade selection list are counted.

S10, dividing the operation information G (e) Y into operation information G (f) Y in the selected operation classification range and operation information G (f) N not in the selected range; the operation classification is a set of operations with certain rules, for example, the operation classification is hernia operation, and the classification includes inguinal hernia recovery, laparoscopic hernia repair, hernia high ligation, and the like. Therefore, the invention screens the operation information G (e) Y based on the selected operation classification, so that the statistical and screened data is adaptive to the operation classification selected by the user independently, and the user can select the corresponding data as required to count the patient case frequency of the specific operation classification.

S11, dividing the operation information G (f) Y into operation information G (g) Y in an operation doctor selection list and operation information G (g) N not in the operation doctor selection list; the invention can manage the number of times of operating patients aiming at specific operating doctors so as to determine the infection occurrence condition of the operating part executed by the designated doctor. Therefore, as described above, the present invention selects the surgeon, the selected surgeons constitute the surgeon selection list, and the present invention screens the surgical information g (f) _ Y based on the selected surgeon selection list, so that the counted and screened data is adapted to the surgeon selected by the user, and the user can select the corresponding data as required to count the number of times of patients performing surgery by the specific surgeon.

S12, dividing the operation information G (g) _ Y into operation information G (h) _ Y in an anesthesia mode selection list and operation information G (h) _ N not in the anesthesia mode selection list; the invention can manage the number of times of operating patient cases aiming at a specific anesthesia mode so as to determine the occurrence condition of the operation part infection of a designated anesthesia mode. Therefore, as described above, the present invention selects the anesthesia modes, the selected anesthesia modes form an anesthesia mode selection list, and the present invention screens the operation information g (g) _ Y based on the selected anesthesia mode list, so that the counted and screened data is adapted to the anesthesia mode selected by the user, and the user can select the corresponding data as required, and count the number of patient cases operated in a specific anesthesia mode.

S13, dividing the operation information G (h) Y into operation information G (i) Y in a limited operation duration range and operation information G (i) N not in the limited operation duration range; the user can manage the number of times of the surgical patient according to specific surgical time so as to determine the surgical conditions of different surgical time. Therefore, the invention screens the operation information G (h) Y based on the selected operation duration, so that the statistical and screened data are adaptive to the operation duration selected by the user independently, the user can select the corresponding data according to the requirement, and the times of the patient cases of the operation under the specific operation duration are counted.

S14, dividing the operation information G (i) _ Y into operation information G (j) _ Y in an ASA scoring selection list and operation information G (j) _ N not in the ASA scoring selection list; according to the invention, a user can manage the number of times of the surgery patient cases according to the specific ASA scores so as to determine the surgery conditions with different ASA scores. Therefore, the operation information G (i) _ Y is screened based on the selected ASA scores, the ASA scores are selected by the method, the selected ASA scores form an ASA score selection list, statistical and screening data are adaptive to the ASA scores selected by the user independently, the user can select corresponding data according to needs, and the number of times of the patient cases of the operation corresponding to the ASA scores is counted.

S15, dividing the operation information G (j) Y into operation information G (k) Y in an operation name selection list and operation information G (k) N not in the operation name selection list; as described above, the present invention selects the operation names, and the selected operation names constitute an operation name selection list. The user can manage the times of the patient cases of the operation aiming at the specific operation name, therefore, the invention screens the operation information G (j) _ Y based on the selected operation name, so that the counted and screened data is adaptive to the operation name selected by the user independently, and the user can select the corresponding data according to the requirement and count the times of the patient cases with the specific operation name. And comparing the 'operation name' field in the operation information with the selected operation name, and judging whether the 'operation name' field belongs to the range of the selected operation name.

S16, dividing the operation information G (k) _ Y into operation information G (m) _ Y in a healing level selection list and operation information G (m) _ N not in the healing level selection list; the user of the invention can manage the times of the surgical patient cases aiming at the specific healing grade so as to determine the surgical conditions of different healing grades. As described above, the present invention selects a healing grade, and the selected healing grades constitute a healing grade selection list. Therefore, the invention screens the operation information G (k) Y based on the selected healing grade, so that the statistical and screened data are adaptive to the healing grade selected by the user, the user can select the corresponding data as required, and the patient case frequency of the operation with the specific healing grade is counted.

S17, dividing the operation information G (m) Y into operation information G (N) Y on an operation position selection list and operation information G (N) N not on the operation position selection list; the invention can manage the number of times of the surgical patient to the specific surgical position so as to determine the surgical conditions of different surgical positions. The operation positions are divided into superficial incision, deep incision and organ lacuna. As described above, the present invention selects surgical sites, and the selected surgical sites constitute a surgical site selection list. Therefore, the invention screens the operation information G (m) Y based on the selected operation position, so that the counted and screened data is adaptive to the operation position selected by the user, the user can select the corresponding data according to the requirement, and the patient case frequency in the operation of the specific operation position is counted.

S18, dividing the operation information G (N) _ Y into operation information G (p) _ Y on an NNIS scoring selection list and operation information G (p) _ N not on the NNIS scoring selection list; according to the invention, the user can manage the number of the surgical patient cases according to different NNIS scores so as to determine the surgical conditions of different NNIS scores. As described above, the present invention selects NNIS scores, and the selected NNIS scores comprise a NNIS score selection list. Therefore, the operation information G (n) Y is screened based on the selected NNIS scores, so that the statistical and screened data are adaptive to the NNIS scores selected by the user independently, the user can select corresponding data according to needs, and the number of patient cases of a specific NNIS score operation is counted.

S19, dividing the operation information G (p) _ Y into operation information G (q) _ Y of an emergency selection list in the selected period and operation information G (q) _ N of an emergency selection list not in the selected period; the invention can manage the times of the surgical patient cases aiming at different surgical types (phase selection emergency treatment) so as to determine the surgical situation of the phase selection emergency treatment. As described above, the present invention selects a phase selection emergency, and the selected phase selection emergency constitutes a phase selection list of phase selection emergency. Therefore, the invention screens the operation information G (p) Y based on the selected phase selection emergency treatment, so that the statistical and screening data are adaptive to the phase selection emergency treatment selected by the user, the user can select the corresponding data according to the requirement, and the times of the patient cases of the specific phase selection emergency treatment operation are counted.

S20, dividing the operation information G (q) _ Y into operation information G (r) _ Y in an operation room selection list and operation information G (r) _ N not in the operation room selection list; the invention can manage the times of the surgical patient cases aiming at the specific operating room so as to determine the surgical conditions of different operating rooms. As described above, the present invention selects an operating room, and the selected operating rooms constitute an operating room selection list. Therefore, the invention screens the operation information G (q) Y based on the selected operation room, so that the statistical and screened data are adaptive to the operation room selected by the user, the user can select the corresponding data according to the requirement, and the number of times of patients operating in the specific operation room is counted.

S21, dividing the operation information G (r) _ Y into operation information G (S) _ Y with limited operation times and operation information G (S) _ N without limited operation times; the user can manage the times of the patient cases of the operation aiming at the specific operation of the patient which is admitted to the hospital for the first time so as to determine the operation conditions of different operation times. Therefore, the invention screens the operation information G (r) _ Y based on the selected operation of the patient in the hospital for the time, so that the counted and screened data is adaptive to the operation times selected by the user independently, the user can select the corresponding data according to the requirement, and the patient example times in the operation with the specific operation times are counted.

S22, judging whether the operation information G (S) _ Y has operation records, if yes, executing step S23, and if not, outputting the number of the operation preventive medications to be 0. Specifically, the invention judges according to the operation information G(s) _ Y, if the patient has records after the steps, the operation is continued to be carried out downwards, if the patient has no records, the operation is ended, and the result 0 is output.

S23, based on the parameter g.mc2, dividing the antibacterial medication order record F into an antibacterial medication order F (a) -Y with an order start time during the current hospitalization period and an antibacterial medication order F (a) -N with an order start time not during the current hospitalization period; the invention screens the apparently erroneous data according to the parameter g.mc2 for cases where the user wants to know whether or not to take surgical precautionary medication during the hospital stay. Specifically, the antibacterial medicine orders F (a) and N which are not used during the hospitalization period of the patient are filtered out based on comparison between a 'start time' field in the antibacterial medicine order record and the hospitalization and discharge time parameter g.MC2, and an antibacterial medicine order F (a) and Y which is used during the hospitalization period of the patient is obtained.

S24, dividing the antibacterial medicine order F (a) Y into an order F (b) Y for a selection list of medicine taking purposes and an order F (b) N for a selection list of medicine taking purposes; the present invention monitors the number of cases for surgical prophylaxis, and therefore, antibacterial medication orders selected for preventive purposes and non-preventive purposes are not under the scope of management. As described above, the present invention selects the medication purpose, and the selected medication purpose constitutes a medication purpose selection list. Therefore, the present invention screens the antibacterial medication order f (a) _ Y based on the "medication purpose" field in the antibacterial medication order. The "destination" field belongs to the contents of the destination selection list, then to the antibacterial medication order f (b) _ Y, otherwise to the antibacterial medication order f (b) _ N.

S25, dividing the antibacterial medicine medical orders F (b) Y into medical orders F (c) Y in a drug administration mode selection list and medical orders F (c) N not in the drug administration mode selection list; as described above, the present invention selects a medication method, and the selected medication method constitutes a medication method selection list. The invention screens the antibacterial medical advice F (b) Y based on the 'administration mode' field in the antibacterial medical advice. The "medication method" field belongs to the content in the medication method selection list, then to the antibacterial medical order f (c) _ Y, otherwise to the antibacterial medical order f (c) _ N.

S26, dividing the antibacterial medicine medical order F (c) _ Y into an order F (d) _ Y in an antibiotic level selection list and an order F (d) _ N not in the antibiotic level selection list; as described above, the present invention selects antibiotic grades, and the selected antibiotic grades constitute an antibiotic grade selection list. The invention screens the antibacterial medication orders f (c) Y based on the "antibiotic grade" field in the antibacterial medication orders. The "antibiotic rank" field belongs to the content in the antibiotic rank selection list, then to the antibacterial medication order f (d) _ Y, otherwise to the antibacterial medication order f (d) _ N.

S27, judging whether an antibacterial medicine order record exists in the antibacterial medicine order F (d) Y, if so, executing a step S28, and if not, outputting that the number of the operation preventive medication cases is 0. Specifically, the judgment is carried out according to the antibacterial medicine order F (d) _ Y, if the patient has records after the steps, the operation is continued to be carried out, if the patient does not have records, the operation is ended, and a result 0 is output.

S28, acquiring the order starting time and the order ending time of each antibacterial medicine order based on the antibacterial medicine orders F (d) Y, and constructing a parameter g.THW with a parameter data type of a starting-stopping time period list; the invention determines the parameter g.thw of the start-stop time period list for each antibacterial medication order based on the antibacterial medication order f (d) _ Y. Thw is a parameter list consisting of the order start time and the order end time. Specifically, the start time and end time fields in the antibacterial medical orders f (d) _ Y are obtained, and for each medical order, the corresponding parameter g.thw is generated as [ start time and end time ].

S29, dividing the operation information G (S) _ Y into operation information G (t) _ Y using antibacterial drugs in the perioperative period and operation information G (t) _ N not using antibacterial drugs in the perioperative period based on the parameter g.THW and the parameter g.QA4. optimal; specifically, the parameter g.THW and the parameter g.QA4. optimal are compared, whether the parameter g.THW and the parameter g.QA4. optimal are crossed or not is judged, if the parameter g.THW and the parameter g.QA4. optimal are crossed, the operation information G (t) is Y for using the antibacterial drugs in the perioperative period, otherwise, the operation information G (t) is N for not using the antibacterial drugs in the perioperative period, and the operation records of which the perioperative period and the advice start-stop period are not crossed are filtered.

S30, counting data according to the operation information G (t) _ Y, and outputting 0 if the operation information G (t) _ Y is empty; if not, outputting the corresponding number. After the above processing, the obtained operation information g (t) _ Y is the operation record of the operation preventive application antibacterial drugs in the whole hospital inpatients in the determined time period. If the record in the operation information G (t) _ Y is empty, 0 is output, and if not, the corresponding number of records is output as the number of the operation preventive medications. And when a specific operation record needs to be output, G (t) _ Y is output.

The disclosure is further illustrated below with reference to specific examples:

type data participating in the operation:

the type data participating in the operation includes:

hospitalization information A, antibacterial medical advice record F and operation information G

Hospitalization procedure information a:

antibacterial medication order record F:

operation information G:

the statistical time is 2019-01-0600: 00:00 to 2019-01-2023:59

The authority department: all departments

The user selects the operating department: all selection

The user selects the surgical infection site: not selected, i.e. not restricted

The user selects the name of the operation: not selected, i.e. not restricted

The user selects the surgical category: not selected, i.e. not restricted

The user selects the surgeon: not selected, i.e. not restricted

The user selects an anesthesia mode: not selected, i.e. not restricted

Selecting the operation duration by the user: not selected, i.e. not restricted

The user selects the ASA score: not selected, i.e. not restricted

User selection of healing level: not selected, i.e. not restricted

User selection of surgical site: not selected, i.e. not restricted

User selection of NNIS ranking: not selected, i.e. not restricted

The user selects the period selection emergency treatment: not selected, i.e. not restricted

The user selects the operating room: not selected, i.e. not restricted

The user selects the operation times: not selected, i.e. not restricted

Data change of each step

The first step is as follows:

inputting: and (3) outputting hospitalization information A of the patient:

MC2 with a value of [ 2019-01-0100: 00:12,2019-01-1203:00:12]

The second step is as follows:

inputting: outputting operation information G and the time of admission and discharge g.MC2[ 2019-01-0100: 00:12,2019-01-1203:00:12 ]:

G(a)_Y：

G(a)_N：

the third step:

inputting: surgical record g (a) _ Y output:

true, identify continued downward execution

The fourth step:

inputting: surgical record g (a) _ Y output:

perioperative period parameter g.QA4.operid, with values of [ 2019-01-0608: 00:00,2019-01-0808:30:00], [ 2019-01-0808: 00:00,2019-01-1008: 30:00]

The fifth step:

inputting: operation record G (a) Y and statistical time [ 2019-01-0600: 00:00,2019-01-2023:59:59] output:

G(b)_Y：

G(b)_N：

a sixth step:

inputting: operation record G (b) _ Y and the authority department output selected by the user:

G(c)_Y：

G(c)_N：

a seventh step of:

inputting: surgical record g (c) _ Y and user selected surgical department (unselected) output:

G(d)_Y：

G(d)_N：

an eighth step:

inputting: surgical record g (d) _ Y and user selected incision grade (unselected) output:

G(e)_Y：

G(e)_N：

a ninth step:

inputting: surgical record g (e) _ Y and user selected surgical category (unselected) output:

G(f)_Y：

G(f)_N：

a tenth step:

inputting: surgical record g (f) _ Y and user-selected surgeon (unselected) output:

G(g)_Y：

G(g)_N：

an eleventh step:

inputting: surgical record g (g) _ Y and user selected anesthesia modality (unselected) output:

G(h)_Y：

G(h)_N：

a twelfth step:

inputting: operation record G (h) _ Y and the operation duration selected by the user (unselected) output:

G(i)_Y：

G(i)_N：

a thirteenth step of:

inputting: surgical record g (i) _ Y and user selected ASA score (unselected) output:

G(j)_Y：

G(j)_N：

a fourteenth step of:

inputting: surgical record g (j) _ Y and the user-selected surgical name (unselected) output:

G(k)_Y：

G(k)_N：

a fifteenth step:

inputting: surgical record g (k) _ Y and user selected healing grade (unselected) output:

G(m)_Y：

G(m)_N：

sixteenth step:

inputting: surgical record g (m) _ Y and user selected surgical site (unselected) output:

G(n)_Y：

G(n)_N：

seventeenth step:

inputting: surgical record g (n) _ Y and user selected NNIS score (unselected) output:

G(p)_Y：

G(p)_N：

an eighteenth step:

inputting: surgical record g (p) _ Y and user-selected phase-selected emergency (unselected) output:

G(q)_Y：

G(q)_N：

a nineteenth step:

inputting: operative record G (q) _ Y and the operating room selected by the user (unselected)

And (3) outputting:

G(r)_Y：

G(r)_N：

the twentieth step:

inputting: operation record G (r) _ Y and the output of the patient's admission to the hospital for the current operation (unselected) selected by the user:

G(s)_Y：

G(s)_N：

a twenty-first step:

inputting: operation record G(s) _ Y

And (3) outputting:

true (meaning continue downward operation)

A twenty-second step:

inputting: antibacterial medication order record F and time to hospital admission g.mc2

And (3) outputting:

F(a)_Y:

F(a)_N：

the twenty-third step:

inputting: antibacterial medical order F (a) Y and user-selected medication purpose (unselected)

And (3) outputting:

F(b)_N：

a twenty-fourth step:

inputting: antibacterial medication order F (b) Y and user selected mode of administration (unselected)

And (3) outputting:

F(c)_Y:

F(c)_N：

a twenty-fifth step:

inputting: antibacterial medication order F (c) _ Y and user selected antibiotic rating (unselected)

And (3) outputting:

F(d)_Y:

F(d)_N：

a twenty-sixth step:

inputting: antibacterial medical advice F (d) Y

And (3) outputting:

true (continue downward operation)

A twenty-seventh step:

inputting: antibacterial medication order F (d) Y and time to discharge g.MC2

And (3) outputting:

THW, the values of which are [ 2019-01-0308: 00:00,2019-01-0608:30:00], [ 2019-01-0208: 00:00,2019-01-0208: 30:00 ].

A twenty-eighth step:

inputting: surgical record g(s) _ Y, start-stop time parameter g.thw and perioperative parameter g.qa4. optimal output:

G(t)_Y：

G(t)_N：

a twenty-ninth step:

inputting: surgical record g (t) _ Y output: the output result value is 0

Example two

The embodiment provides a management system for the number of cases for operation prevention based on MapReduce and big data, which includes:

Claims

1. The management method of the number of the cases for operation prevention based on MapReduce and big data is characterized by comprising the following steps:

2. The management method according to claim 1, wherein the hospitalization procedure information includes patient case number, hospital admission department, hospital admission time, hospital discharge department, and hospital discharge time.

3. The method of claim 1, wherein the antimicrobial medication order record includes a patient case number, an order department, an antimicrobial medication name, a start time, an end time, an antibiotic rating, a mode of administration, a purpose for medication, an ordering physician rating.

4. The method of claim 1, wherein the surgical information includes patient case number, operating room, category of surgery, surgeon, anesthesia procedure, name of surgery, time of surgery start, time of surgery end, incision, level of healing, ASA, phase selection emergency, location of surgery, NNIS score, operating room, number of surgeries.

5. The method of claim 3, wherein the administration is for prophylaxis.

6. MapReduce and big data-based management system for number of cases for surgical prophylaxis, which is characterized by comprising:

7. The management system of claim 6, wherein the hospitalization procedure information includes patient case number, hospital admission department, hospital admission time, hospital discharge department, and hospital discharge time.

8. The management system of claim 6, wherein the antimicrobial medication order record comprises a patient case number, an order department, an antimicrobial medication name, a start time, an end time, an antibiotic rating, a mode of administration, a purpose for medication, an ordering physician rating.

9. The management system of claim 6, wherein the surgical information includes patient case number, operating room, category of surgery, surgeon, anesthesia modality, name of surgery, time of surgery start, time of surgery end, incision, level of healing, ASA, phase selection emergency, location of surgery, NNIS score, operating room, number of surgeries.

10. The management system of claim 8, wherein the administration is for prophylaxis.