CN112542231B - MapReduce and big data-based operation prevention medicine case number management method and system - Google Patents
MapReduce and big data-based operation prevention medicine case number management method and system Download PDFInfo
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- 239000003814 drug Substances 0.000 title claims abstract description 121
- 230000002265 prevention Effects 0.000 title claims description 21
- 238000007726 management method Methods 0.000 title abstract description 13
- 229940124350 antibacterial drug Drugs 0.000 claims abstract description 54
- 230000003449 preventive effect Effects 0.000 claims abstract description 31
- 229940079593 drug Drugs 0.000 claims abstract description 29
- 238000011282 treatment Methods 0.000 claims abstract description 22
- 230000000844 anti-bacterial effect Effects 0.000 claims description 73
- 238000001356 surgical procedure Methods 0.000 claims description 44
- 238000000034 method Methods 0.000 claims description 37
- 230000035876 healing Effects 0.000 claims description 36
- 206010002091 Anaesthesia Diseases 0.000 claims description 35
- 230000037005 anaesthesia Effects 0.000 claims description 35
- 230000003115 biocidal effect Effects 0.000 claims description 32
- 239000003242 anti bacterial agent Substances 0.000 claims description 23
- 238000001647 drug administration Methods 0.000 claims description 6
- 238000011321 prophylaxis Methods 0.000 claims description 3
- 230000000845 anti-microbial effect Effects 0.000 claims 4
- 238000012544 monitoring process Methods 0.000 abstract description 4
- 238000004364 calculation method Methods 0.000 abstract description 2
- 241000894006 Bacteria Species 0.000 abstract 1
- 238000004422 calculation algorithm Methods 0.000 description 7
- 208000015181 infectious disease Diseases 0.000 description 5
- 238000007619 statistical method Methods 0.000 description 3
- 208000035473 Communicable disease Diseases 0.000 description 2
- 206010019909 Hernia Diseases 0.000 description 2
- 208000022362 bacterial infectious disease Diseases 0.000 description 2
- 206010011409 Cross infection Diseases 0.000 description 1
- 206010059866 Drug resistance Diseases 0.000 description 1
- 241000282414 Homo sapiens Species 0.000 description 1
- 208000029836 Inguinal Hernia Diseases 0.000 description 1
- 208000035965 Postoperative Complications Diseases 0.000 description 1
- 244000052616 bacterial pathogen Species 0.000 description 1
- 230000003385 bacteriostatic effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 210000000056 organ Anatomy 0.000 description 1
- 229940043274 prophylactic drug Drugs 0.000 description 1
- 230000000069 prophylactic effect Effects 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 230000009885 systemic effect Effects 0.000 description 1
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Abstract
The invention provides a management method and a system for the number of surgical preventive medicine cases based on MapReduce and big data, which are based on a MapReduce framework, utilize the parallel computing capability of a machine under a distributed system, divide the number of cases of millions and tens of millions of hospitalized persons beyond the limit of the memory and storage of a server into tens of millions and hundreds of millions of small tasks for computing the number of cases of multi-drug resistant bacteria infected by a hospital, simultaneously execute the small tasks on a plurality of machines, and generate a final result by summarizing the intermediate output results of a plurality of small tasks. The invention carries out massive parallel calculation on various calibers, such as millions, tens of millions and billions of hospitalization times, according to the provincial area, according to the hospital grade, according to the hospital bed, according to the comprehensive and special departments, according to public and civil camps and the like, which are unavoidable when the national level and provincial monitoring network is adopted, and can provide effective guidance for the treatment and management of antibacterial drugs of hospitalized patients. Meanwhile, the problem of complex counting and processing of the number of the surgical cases by manual operation is avoided through automatic counting of the number of the surgical cases.
Description
Technical Field
The invention belongs to the technical field of management of surgical preventive medication, and particularly relates to a method and a system for managing the number of surgical preventive medication cases based on MapReduce and big data, in particular to a method and a system for managing the number of surgical preventive application of antibacterial drugs in all hospitalized patients in a certain period of time, which are particularly suitable for the situations that the data volume of the patients to be processed far exceeds the storage (magnetic disk) and the computing capacity (memory and CPU) of a server and task splitting and distribution cannot be performed manually.
Background
The antibacterial drug generally refers to a drug with bactericidal or bacteriostatic activity, and the invention and the application of the antibacterial drug bring convenience for treating a plurality of serious bacterial infectious diseases for human beings, 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 factor leading to infection of the patient. The patient is extremely harmful to the operation, so in the actual diagnosis and treatment process, antibacterial drugs are usually applied to the operation patient in a preventive way, and the operation related infection is avoided. However, the phenomenon of abuse of antibacterial agents often occurs clinically, which results in the occurrence of drug resistance of pathogenic bacteria to antibacterial agents, increasing the difficulty in healing bacterial infectious diseases. Therefore, the use of antibacterial agents should be reasonable since clinical practice, and the abuse of antibacterial agents should be stopped. Therefore, statistics of the number of cases of the operation of applying the antibacterial drug has important significance for the management of the antibacterial drug, and can provide important guidance for the treatment of the postoperative complications. Therefore, how to realize statistics of the number of preventive cases of surgery is a problem to be solved in the art.
The number of preventive medicine cases for operation is relatively easy to calculate in one medical institution, the number of medical institutions such as a common third class A is about fifty thousand per year, and the national or provincial tap hospitals have hundreds of thousands of people. The calculation of the key indexes under the condition of large data of millions, tens of millions, billions and billions of hospitalized patients in provincial areas or nationwide is much more complicated, 2749 of three-level hospitals in China in 2019, 9687 of two-level hospitals and 17487 of the hospitalized patients in public hospitals in 2019, and the original result of one-time statistical analysis is to calculate the time of nearly one year. Therefore, how to develop standardized, normalized and homogenized hospital infection monitoring in hundreds or thousands of hospitals in a region can realize the most urgent problem to be solved in developing a regional informatization monitoring platform for the number of surgical preventive medicine cases in a specified time period under the condition of big data of inpatients.
Disclosure of Invention
The invention aims at overcoming the defects of the prior art and provides a method and a system for managing the number of preventive cases of operation. The statistical method for the number of the surgical preventive cases has strong practicability, can accurately count the number of the surgical cases according to the needs of users, and can provide effective guidance for the treatment and management of antibacterial drugs of inpatients. Meanwhile, the problem of complex counting and processing of the number of the surgical cases by manual operation is avoided through automatic counting of the number of the surgical cases.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the method for managing the number of surgical preventive cases based on MapReduce and big data comprises the following steps:
s1, acquiring hospitalization process information A, antibacterial medicine doctor advice records F, operation information G, selected statistical time, operation departments, incision grades, operation classifications, operating doctors, anesthesia modes, operation time, ASA scores, operation names, healing grades, operation positions, NNIS scores, first-time emergency treatment, operation rooms, a patient in the hospital, the purpose of medication, the mode of medication, antibiotic grades and determining authority departments of users according to identity information of the users;
s2, acquiring the time of admission and the time of discharge of a patient based on the hospitalization process information A, and taking the time of admission and the time of discharge of the patient as parameters g.MC2;
s3, dividing the operation information G into operation information G (a) _Y occurring during the current hospitalization period and operation information G (a) _N occurring during the non-current hospitalization period based on the parameter g.MC2;
s4, judging whether an operation record exists in the operation information G (a) _Y, if so, executing the step S5, and if not, outputting the number of the operation prevention cases to be 0;
s5, acquiring operation starting time and 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.open 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 within the authority range and operation information G (c) _N not within 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 an incision level selection list and operation information G (e) _N not in the incision level selection list;
s10, dividing the operation information G (e) _Y into operation information G (f) _Y in a 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 an 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 within a limited operation duration range and operation information G (i) _N not within the limited operation duration range;
S14, dividing the operation information G (i) _Y into operation information G (j) _Y in an ASA score selection list and operation information G (j) _N not in the ASA score 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 grade selection list and operation information G (m) _N not in the healing grade selection list;
s17, dividing the operation information G (m) _Y into operation information G (N) _Y in an operation position selection list and operation information G (N) _N not in the operation position selection list;
s18, dividing the operation information G (N) _Y into operation information G (p) _Y in an NNIS score selection list and operation information G (p) _N not in the NNIS score selection list;
s19, dividing the operation information G (p) _Y into operation information G (q) _Y in a selection emergency selection list and operation information G (q) _N not in the selection emergency selection list;
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 an operation record exists in the operation information G (S) _Y, if so, executing the step S23, and if not, outputting the number of the operation prevention cases to be 0;
s23, dividing the antibacterial medicine order record F into an antibacterial medicine order F (a) _Y with an order start time in the hospitalization period and an antibacterial medicine order F (a) _N with an order start time not in the hospitalization period based on the parameter g.MC2;
s24, dividing the antibacterial medicine orders F (a) _Y into orders F (b) _Y in a medicine purpose selection list and orders F (b) _N not in the medicine purpose selection list;
s25, dividing the antibacterial medicine orders F (b) _Y into orders F (c) _Y in a drug administration mode selection list and orders F (c) _N not in the drug administration mode selection list;
s26, dividing the antibacterial medicine orders F (c) _Y into orders F (d) _Y in an antibiotic grade selection list and orders F (d) _N not in the antibiotic grade selection list;
s27, judging whether an antibacterial medicine doctor advice record exists in the antibacterial medicine doctor advice F (d) _Y, if so, executing the step S28, and if not, outputting the number of preventive cases of operation to be 0;
s28, acquiring the order start time and the order end time of each antibacterial drug order based on the antibacterial drug order F (d) _Y, and constructing a parameter g.THW with a parameter data type of a start-stop time period list;
S29, dividing the operation information G (S) _Y into operation information G (t) _Y for using the antibacterial medicine in the perioperative period and operation information G (t) _N for not using the antibacterial medicine in the perioperative period based on the parameters g.THW and g.QA4. Open;
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 hospital procedure information includes a patient case number, an admission department, an admission time, an discharge department, and an discharge time.
Further, the antibacterial drug order records comprise patient case numbers, order departments, antibacterial drug names, start times, end times, antibiotic grades, administration modes, administration purposes, order doctors and order doctor grades.
Further, the surgical information includes patient case number, surgery department, surgery category, surgeon, anesthesia mode, surgery name, surgery start time, surgery end time, incision, healing grade, ASA, emergency treatment on first stage, surgery location, NNIS score, surgery room, surgery number.
Further, the administration is for prophylaxis.
The invention also provides a management system for the number of preventive cases of operation based on MapReduce and big data, which comprises:
The acquisition module is used for acquiring hospitalization process information A, antibacterial medicine doctor order records F, operation information G, selected statistical time, operation departments, incision grades, operation classifications, operation doctors, anesthesia modes, operation time, ASA scores, operation names, healing grades, operation positions, NNIS scores, first-time emergency treatment, operation rooms, a patient in the hospital, the purpose of medication, the mode of medication, antibiotic grades and determining authority departments of users according to identity information of the users;
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 taking the admission time and the discharge time as parameters g.MC2 together;
a first dividing module for dividing the operation information G into operation information G (a) _y occurring during the present hospitalization period and operation information G (a) _n occurring during the non-present hospitalization period based on the parameter g.mc2;
the first judging module is used for judging whether the operation information G (a) _Y contains an operation record or not, if yes, executing the step S5, and if not, outputting the number of the operation prevention cases to be 0;
the second acquisition module is used for acquiring operation starting time and operation ending time based on the operation information G (a) _Y and jointly used as perioperative parameters g.QA4.open of the operation;
A second dividing module for dividing the surgical information G (a) _y into surgical information G (b) _y within a statistical time range and surgical information G (b) _n not within the statistical time range;
a third dividing module for dividing the surgical information G (b) _y into surgical information G (c) _y within a right range and surgical information G (c) _n not within the right range;
a fourth division module for dividing the surgical information G (c) _y into surgical information G (d) _y within the selected surgical department range and surgical information G (d) _n not within the selected range;
a fifth division module for dividing the surgical information G (d) _y into surgical information G (e) _y in an incision level selection list and surgical information G (e) _n not in an incision level selection list;
a sixth division module for dividing the surgical information G (e) _y into surgical information G (f) _y within a selected surgical classification range and surgical information G (f) _n not within the selected range;
a seventh division module for dividing the operation information G (f) _y into operation information G (G) _y in a surgeon selection list and operation information G (G) _n not in a surgeon selection list;
an eighth dividing module for dividing the operation information G (G) _y into operation information G (h) _y on an anesthesia mode selection list and operation information G (h) _n not on an anesthesia mode selection list;
A ninth division module for dividing 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 for dividing the surgical information G (i) _y into surgical information G (j) _y in the ASA score selection list and surgical information G (j) _n not in the ASA score selection list;
an eleventh dividing module for dividing 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 division module for dividing the surgical information G (k) _y into surgical information G (m) _y in a healing level selection list and surgical information G (m) _n not in a healing level selection list;
a thirteenth division module for dividing the surgical information G (m) _y into surgical information G (N) _y in a surgical position selection list and surgical information G (N) _n not in a surgical position selection list;
a fourteenth division module for dividing the surgical information G (N) _y into surgical information G (p) _y in an NNIS score selection list and surgical information G (p) _n not in an NNIS score selection list;
a fifteenth division module for dividing the operation information G (p) _y into operation information G (q) _y in the optional emergency selection list and operation information G (q) _n not in the optional emergency selection list;
A sixteenth dividing module for 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;
a seventeenth dividing module for dividing the operation information G (r) _y into operation information G(s) _y defining the number of operations and operation information G(s) _n not defining the number of operations;
the second judging module is used for judging whether the operation information G (S) _Y contains an operation record, if yes, executing step S23, and if not, outputting the number of the operation prevention cases to be 0;
an eighteenth dividing module, configured to divide the antibacterial drug order record F into an antibacterial drug order F (a) _y with an order start time in the hospitalization period and an antibacterial drug order F (a) _n with an order start time not in the hospitalization period based on the parameter g.mc2;
a nineteenth dividing module, configured to divide the antibacterial drug order F (a) _y into an order F (b) _y on a medication destination selection list and an order F (b) _n not on a medication destination selection list;
a twentieth dividing module, configured to divide the antibacterial drug order F (b) _y into an order F (c) _y in a administration mode selection list and an order F (c) _n not in the administration mode selection list;
A twenty-first dividing module for dividing the antibacterial drug order F (c) _y into an order F (d) _y on an antibiotic grade selection list and an order F (d) _n not on an antibiotic grade selection list;
the third judging module is used for judging whether the antibacterial medicine orders F (d) _Y have antibacterial medicine orders records, if yes, executing the step S28, and if not, outputting the number of the surgical preventive medicine cases to be 0;
the third acquisition module is used for acquiring the doctor's advice start time and doctor's advice end time of each antibacterial medicine doctor's advice based on the antibacterial medicine doctor's advice F (d) _Y, and constructing a parameter g.THW with the parameter data type of a starting and ending time period list;
a twenty-second dividing module for dividing the operation information G(s) _y into operation information G (t) _y for using the antibacterial agent in the perioperative period and operation information G (t) _n for not using the antibacterial agent in the perioperative period based on the parameter g.thw and the parameter g.qa4. Opotid;
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 hospital procedure information includes a patient case number, an admission department, an admission time, an discharge department, and an discharge time.
Further, the antibacterial drug order records comprise patient case numbers, order departments, antibacterial drug names, start times, end times, antibiotic grades, administration modes, administration purposes, order doctors and order doctor grades.
Further, the surgical information includes patient case number, surgery department, surgery category, surgeon, anesthesia mode, surgery name, surgery start time, surgery end time, incision, healing grade, ASA, emergency treatment on first stage, surgery location, NNIS score, surgery room, surgery number.
Further, the administration is for prophylaxis.
The invention details the specific implementation mode of the management of the number of preventive treatment cases of operation, utilizes hospitalization process information A, antibacterial medicine doctor order record F, operation information G, selected statistical time, operation department, incision grade, operation classification, operation doctor, anesthesia mode, operation duration, ASA score, operation name, healing grade, operation position, NNIS score, emergency treatment in a first period, operation room, the number of times of operation of the patient in the hospital, medicine purpose, medicine administration mode, antibiotic grade and authority department of determining the user according to the identity information of the user, and automatically generates the number of preventive treatment cases of preventive treatment application of antibacterial medicine in the whole hospital of hospitalized patient in a determined period. The statistical method for the number of the surgical preventive cases has strong practicability, can accurately count the number of the surgical cases according to the needs of users, and can provide effective guidance for the treatment and management of antibacterial drugs of inpatients. Meanwhile, the problem of complex counting and processing of the number of the surgical cases by manual operation is avoided through automatic counting of the number of the surgical cases.
Drawings
Fig. 1 is a schematic flow chart of the algorithm logic operation of step S1 to step S3 in the present disclosure.
Fig. 2 is a schematic flow chart of the algorithm logic operation from step S4 to step S9 in the present disclosure.
Fig. 3 is a schematic flow chart of the algorithm logic operation from step S10 to step S13 in the present disclosure.
Fig. 4 is a schematic flow chart of the algorithm logic operation from step S14 to step S17 in the present disclosure.
Fig. 5 is a schematic flow chart of the algorithm logic operation from step S18 to step S21 in the present disclosure.
Fig. 6 is a schematic diagram of an algorithm logic operation flow of step S12 to step S25 in the present disclosure.
Fig. 7 is a schematic flow chart of the algorithm logic operation from step S16 to step S30 in the present disclosure.
Detailed Description
The invention is further described below with reference to the drawings and specific examples, which are not intended to be limiting.
In the following examples, the X (y) type is described: x represents a data set with a certain type; y represents a sequence number, and is used for distinguishing data sets of the same type of data before and after in different logic units; x (y) represents the data set under different logical units for a certain type of data; y represents a compliance; n represents an unconformity;
example 1
As shown in fig. 1-7, the present embodiment provides a method for managing the number of cases for surgical prevention based on MapReduce and big data, including the following steps:
S1, acquiring hospitalization process information A, antibacterial medicine doctor advice records F, operation information G, selected statistical time, operation departments, incision grades, operation classifications, operating doctors, anesthesia modes, operation time, ASA scores, operation names, healing grades, operation positions, NNIS scores, first-time emergency treatment, operation rooms, a patient in the hospital, the purpose of medication, the mode of medication, antibiotic grades and determining authority departments of users according to identity information of the users; the number of cases of preventive surgery refers to the number of cases of preventive surgery in a patient hospitalized throughout a defined period of time. Systemic prophylactic antibacterial drug application from date of admission to date of discharge for hospitalized patients for the purpose of surgical treatment is considered perioperative prophylactic drug application. The operation of applying the antibacterial agent should satisfy: 1. the patient has operation records within the statistical time range; 2. patients use antibacterial drugs prophylactically during surgical peri-surgery; 3. meeting the option requirements of users. Therefore, the invention acquires hospitalization process information A, antibacterial medicine doctor advice record F, operation information G and the like to screen the operation of the application antibacterial medicine. The inpatient process information is used for integrally recording inpatient processes, and specifically comprises a patient case number, an admission department, an admission time, an discharge department and an discharge time. The antibacterial medicine doctor advice record is used for recording antibacterial medicine doctor advice information issued by doctors to each patient, and specifically comprises patient medical records, an ordering department, an antibacterial medicine name, a starting time, an ending time, an antibiotic grade, a medicine administration mode, a medicine purpose, an ordering doctor and an ordering doctor grade. The operation information is used for recording specific conditions of the operation performed by the patient, including patient case number, operation department, operation category, operation doctor, anesthesia mode, operation name, operation starting time, operation ending time, incision, healing grade, ASA, emergency treatment, operation position, NNIS score, operation room and operation times. The hospital procedure information A and the antibacterial medicine order record F and the operation information G are information acquired or recorded by hospital workers in work.
In addition, the invention selects the statistical time and the department, and manages the number of preventive cases of operation in the appointed time period and the appointed department. The hospital data has corresponding privacy, so that the user is required to acquire corresponding data authority for the statistics and management of the hospital data in the invention. 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 operating user and manages the number of the preventive cases of the operation on the data in the authority department.
The invention also sets corresponding surgery department, incision grade, surgery classification, doctor, anesthesia mode, surgery duration, ASA score, surgery name, healing grade, surgery position, NNIS score, emergency treatment in the first stage, surgery department, the patient's current hospital admission operation, medicine purpose, medicine administration mode and antibiotic grade, and realizes accurate statistics and monitoring of the medicine case number for surgery prevention.
S2, acquiring the time of admission and the time of discharge of a patient based on the hospitalization process information A, and taking the time of admission and the time of discharge of the patient as parameters g.MC2; the invention firstly acquires the hospitalization process information A of a patient, and further acquires relevant information of the admission time and discharge time fields in the hospitalization process information A, and the hospitalization process information A and the discharge time field are taken as parameters g.MC2 together. This step is to select the time of admission and discharge of the patient's hospitalization as a parameter that can be cited.
S3, dividing the operation information G into operation information G (a) _Y occurring during the current hospitalization period and operation information G (a) _N occurring during the non-current hospitalization period based on the parameter g.MC2; in order to know the operation record information of the error time which does not occur in the hospitalization period, the invention firstly screens the operation information G and selects the operation information G (a) _Y which is performed in the patient discharge time range, namely the operation information G (a) _Y which occurs in the hospitalization period. Specifically, the present invention filters out the operation information G (a) _n that the operation time does not occur during the present hospitalization period based on the comparison of the "operation start time", "operation end time" fields and the in-and-out time parameter g.mc2 in the operation information, and obtains the operation information G (a) _y that is performed in the patient in-and-out time range.
S4, judging whether the operation information G (a) _Y contains an operation record, if yes, executing the step S5, and if not, outputting the number of the operation prevention cases to be 0. Specifically, the invention judges according to the operation information G (a) _Y, if the patient still has records after the steps, the operation is continued downwards, if the patient does not have records, the operation is ended, and the result 0 is output.
S5, acquiring operation starting time and 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.open of the operation; the invention determines the parameter of the perioperative period based on the operation information G (a) _Y, and prepares for acquiring intersection operation information in the perioperative period and doctor's advice time range subsequently. The perioperative phase is a whole process surrounding the operation, starting from the patient's decision to receive the surgical treatment, to the surgical treatment until substantial recovery, including a period of time before, during and after the operation, in particular from the time the surgical treatment is determined until the treatment associated with this operation is substantially completed. In the present invention, the perioperative time is determined as the day from the day before the operation start time to the 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 invention firstly screens the operation information G (a) _Y occurring in the hospitalization period based on the statistical time, specifically, the invention acquires an operation starting time field in the operation information G (a) _Y occurring in the hospitalization period, judges whether the operation starting time in the operation record occurring in the hospitalization period belongs to the range of the statistical time period or not, if yes, adds the operation record into the operation information G (b) _Y in the statistical time period, otherwise, adds the operation record into the 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 within the authority range and operation information G (c) _N not within the authority range; because the rights of each user are different, the invention screens the operation information G (b) _Y based on the rights department, so that the data operated by the user is suitable for the corresponding rights. The field of 'operating department' in the operation information is compared with the authority department, and whether the field of 'operating department' belongs to the range of the authority department is judged. The operation information G (c) _y is operation information in departments that are within the authority range managed by the user, and the operation information G (c) _n is operation information in departments that are not within 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 operation department range, wherein in the invention, the operation patient number is monitored based on the specific operation department, and a user can manage the operation patient number aiming at the specific operation department. The "surgery department" field in the surgery information is compared with the selected surgery department, and whether the "surgery department" field belongs to the selected surgery department range is judged.
S9, dividing the operation information G (d) _Y into operation information G (e) _Y in an incision level selection list and operation information G (e) _N not in the incision level selection list; the invention can manage the times of the surgical patient cases according to the specific incision grades so as to determine the surgical conditions of incisions of each grade. As described above, the user selects the incision level, and the selected incision level constitutes the incision level selection list. Therefore, the invention screens the operation information G (d) _Y based on the incision grade selected by the user, so that the counted and screened data are suitable for the incision grade selected by the user independently, the user can select the corresponding data according to the needs, and the times of the operation patient in the incision grade selection list are counted.
S10, dividing the operation information G (e) _Y into operation information G (f) _Y in a selected operation classification range and operation information G (f) _N not in the selected range; the surgical classification is a set of operations with rules, such as the classification of surgery as hernia surgery, which includes inguinal hernia repair, laparoscopic hernia repair, high ligation, etc. The user can manage the times of the surgical patient cases according to specific surgical classifications, so that the invention screens the surgical information G (e) _Y based on the selected surgical classifications, so that the counted and screened data are suitable for the surgical classifications selected by the user, the user can select corresponding data according to the needs, and the times of the patient cases of the specific surgical classifications are counted.
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 an operation doctor selection list; the invention can manage the times of the operation patient cases aiming at specific surgeons so as to determine the occurrence of the infection of the operation part executed by the appointed doctor. Therefore, as described above, the present invention selects the surgeon, and the selected surgeon composes the surgeon selection list, and the present invention screens the surgeon 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 needed, and count the number of patient cases in which the specific surgeon performs the surgery.
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 times of the operation patient according to the specific anesthesia mode so as to determine the infection occurrence condition of the operation part with the designated anesthesia mode. Therefore, as described above, the anesthesia mode is selected, the selected anesthesia modes form an anesthesia mode selection list, and the operation information G (G) _Y is screened based on the selected anesthesia mode list, so that the statistical and screened data are adapted to the anesthesia modes selected by the user independently, the user can select corresponding data according to the needs, and the number of times of the patient operating in the specific anesthesia mode is counted.
S13, dividing 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; the user can manage the times of the surgical patient cases 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 time length, so that the counted and screened data are suitable for the operation time length selected by the user independently, the user can select the corresponding data according to the needs, and the times of the patient operated under the specific operation time length are counted.
S14, dividing the operation information G (i) _Y into operation information G (j) _Y in an ASA score selection list and operation information G (j) _N not in the ASA score selection list; the user can manage the times of the operation patient according to the specific ASA scores so as to determine the operation conditions of different ASA scores. Therefore, the invention screens the operation information G (i) _Y based on the selected ASA scores, and as described above, the invention selects ASA scores, and the selected ASA scores form an ASA score selection list, so that the data counted and screened are adapted to ASA scores selected by a user independently, the user can select corresponding data according to the needs, and the times of the ASA scores corresponding to the operation patient are 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 surgical names, and the selected surgical names constitute a surgical name selection list. The user can manage the times of the surgical patient cases according to the specific surgical names, so that the invention screens the surgical information G (j) _Y based on the selected surgical names, so that the counted and screened data are suitable for the surgical names selected by the user independently, the user can select the corresponding data according to the needs, and the times of the surgical patient cases with the specific surgical names are counted. The "operation name" field in the operation information is compared with the selected operation name, and whether the "operation name" field is within the selected operation name range is judged.
S16, dividing the operation information G (k) _Y into operation information G (m) _Y in a healing grade selection list and operation information G (m) _N not in the healing grade selection list; the user can manage the times of the surgical patient cases according to specific healing grades so as to determine the surgical conditions of different healing grades. As described above, the present invention selects healing levels, and the selected healing levels constitute a healing level selection list. Therefore, the invention screens the operation information G (k) _Y based on the selected healing grade, so that the counted and screened data are suitable for the healing grade selected by the user independently, the user can select the corresponding data according to the needs, and the times of the patient operation with the specific healing grade are counted.
S17, dividing the operation information G (m) _Y into operation information G (N) _Y in an operation position selection list and operation information G (N) _N not in the operation position selection list; the user can manage the times of the surgical patient cases according to the specific surgical position so as to determine the surgical conditions of different surgical positions. The surgical site is divided into superficial incision, deep incision and organ cavity gap. 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 are suitable for the operation position selected by the user independently, the user can select the corresponding data according to the requirement, and the times of the patient in the operation of the specific operation position are counted.
S18, dividing the operation information G (N) _Y into operation information G (p) _Y in an NNIS score selection list and operation information G (p) _N not in the NNIS score selection list; the invention can manage the times of the operation patient cases aiming at different NNIS scores so as to determine the operation conditions of different NNIS scores. As described above, the present invention selects the NNIS scores, and the selected NNIS scores form an NNIS score selection list. Therefore, the invention screens the operation information G (n) _Y based on the selected NNIS score, so that the counted and screened data are matched with the NNIS score selected by the user independently, the user can select the corresponding data according to the needs, and the times of the patient in the specific NNIS scoring operation are counted.
S19, dividing the operation information G (p) _Y into operation information G (q) _Y in a selection emergency selection list and operation information G (q) _N not in the selection emergency selection list; the invention can manage the times of the operation patient according to different operation types (the period selection emergency), so as to determine the operation condition of the period selection emergency. As described above, the present invention selects the term-selecting emergency, and the selected term-selecting emergency constitutes a term-selecting emergency selection list. Therefore, the invention screens the operation information G (p) _Y based on the selected period-selecting emergency, so that the statistics and screened data are suitable for the period-selecting emergency selected by the user, the user can select the corresponding data according to the needs, and the patient number of the specific period-selecting emergency operation is 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 operation patient cases aiming at the specific operating rooms so as to determine the operation conditions of different operating rooms. As described above, the present invention selects operating rooms, 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 operating room, so that the statistical and screened data are suitable for the operating room selected by the user independently, the user can select the corresponding data according to the needs, and the number of times of the patient operating in the specific operating 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 in the operation for the specific patient in the first operation of the hospital, 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 patient in the first operation of the hospital, so that the counted and screened data are suitable for the operation times selected by the user independently, the user can select the corresponding data according to the needs, and the times of the patient in the operation with the specific operation times are counted.
S22, judging whether the operation information G (S) _Y has an operation record, if yes, executing the step S23, and if not, outputting the number of the operation prevention cases to be 0. Specifically, the invention judges according to the operation information G(s) _Y, if the patient still has records after the steps, the operation is continued downwards, if the patient does not have records, the operation is ended, and the result 0 is output.
S23, dividing the antibacterial medicine order record F into an antibacterial medicine order F (a) _Y with an order start time in the hospitalization period and an antibacterial medicine order F (a) _N with an order start time not in the hospitalization period based on the parameter g.MC2; aiming at the situation that a user wants to know whether to take the preventive medicine for operation during hospitalization, the invention screens obviously wrong data according to the parameter g.MC2. Specifically, the present invention filters out the antibacterial drug order F (a) _n that the "start time" is not during patient hospitalization based on the comparison of the "start time" field in the antibacterial drug order record with the time of entry and exit parameter g.mc2, resulting in one antibacterial drug order F (a) _y that the "start time" is used during patient hospitalization.
S24, dividing the antibacterial medicine orders F (a) _Y into orders F (b) _Y in a medicine purpose selection list and orders F (b) _N not in the medicine purpose selection list; the invention monitors the number of cases of the operation prevention, so that the antibacterial medicine orders with the purpose of prevention and the purpose of non-prevention are selected and do not belong to the management range. As described above, the present invention selects the drug purpose, and the selected drug purpose constitutes a drug purpose selection list. Therefore, the present invention screens the antibacterial drug order F (a) _y based on the "purpose of medication" field in the antibacterial drug order. When the 'purpose of medication' field belongs to the content in the selection list of the purpose of medication, the field belongs to the antibacterial medicine orders F (b) _Y, otherwise, the field belongs to the antibacterial medicine orders F (b) _N.
S25, dividing the antibacterial medicine orders F (b) _Y into orders F (c) _Y in a drug administration mode selection list and orders F (c) _N not in the drug administration mode selection list; as described above, the present invention selects a mode of administration, and the selected mode of administration constitutes a mode of administration selection list. The invention screens the antibacterial drug orders F (b) _Y based on the "mode of administration" field in the antibacterial drug orders. When the 'administration mode' field belongs to the content in the administration mode selection list, the field belongs to the antibacterial medicine orders F (c) _Y, and otherwise, the field belongs to the antibacterial medicine orders F (c) _N.
S26, dividing the antibacterial medicine orders F (c) _Y into orders F (d) _Y in an antibiotic grade selection list and orders F (d) _N not in the antibiotic grade 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 drug order F (c) _y based on the "antibiotic grade" field in the antibacterial drug order. When the "antibiotic grade" field belongs to the content in the antibiotic grade selection list, then belongs to the antibacterial order F (d) _y, otherwise belongs to the antibacterial order F (d) _n.
And S27, judging whether an antibacterial medicine order record exists in the antibacterial medicine orders F (d) _Y, if so, executing the step S28, and if not, outputting the number of preventive cases of operation to be 0. Specifically, the invention judges according to the antibacterial medicine doctor's advice F (d) _Y, if the patient still has records after the steps, the operation is continued downwards, if the patient does not have records, the operation is ended, and the result 0 is output.
S28, acquiring the order start time and the order end time of each antibacterial drug order based on the antibacterial drug order F (d) _Y, and constructing a parameter g.THW with a parameter data type of a start-stop time period list; the invention determines the parameter g.THW of the list of start-stop time periods of each antibacterial drug order based on the antibacterial drug order F (d) _Y. The parameter g.thw is a parameter list consisting of order start time, order end time. Specifically, the start time and end time fields in the antibacterial drug order F (d) _y are acquired, and for each order, a corresponding parameter g.thw is generated as [ start time, end time ].
S29, dividing the operation information G (S) _Y into operation information G (t) _Y for using the antibacterial medicine in the perioperative period and operation information G (t) _N for not using the antibacterial medicine in the perioperative period based on the parameters g.THW and g.QA4. Open; specifically, the invention compares the parameter g.THW and the parameter g.QA4. Open, judges whether the parameter g.THW and the parameter g.QA4. Open are crossed, if so, the invention belongs to the operation information G (t) _Y of using the antibacterial medicament in the perioperative period, otherwise, the invention belongs to the operation information G (t) _N of not using the antibacterial medicament in the perioperative period, and thus, the operation records which are not intersected in the perioperative period and the doctor's advice start-stop period 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. The obtained operation information G (t) _Y is the operation record of the preventive application of the antibacterial medicine in the whole hospital hospitalized patients in the determined period. If the record in the operation information G (t) _Y is empty, 0 is output, and if the record is not empty, the corresponding record number is output as the operation prevention medicine case number. When a specific surgical record needs to be output, G (t) _y is output.
The disclosure is further illustrated with reference to the following specific examples:
type data of the participation operation:
the type data of the participation operation includes:
hospitalization information A, antibacterial medicine doctor order record F and operation information G
Hospitalization procedure information a:
antibacterial order record F:
surgical information G:
the statistical time is 2019-01-06:00:00 to 2019-01-20:23:59:59
Rights department: all departments
The user selects the surgery department: full selection
The user selects the surgical site of infection: not selected, i.e. not restricted
The user selects the surgical name: not selected, i.e. not restricted
The user selects a surgical classification: not selected, i.e. not restricted
The user selects the surgeon: not selected, i.e. not restricted
The user selects the anesthesia mode: not selected, i.e. not restricted
The user selects the duration of the operation: not selected, i.e. not restricted
User selection of ASA score: not selected, i.e. not restricted
User selection of healing grade: not selected, i.e. not restricted
The user selects the surgical site: not selected, i.e. not restricted
User selection of NNIS ratings: not selected, i.e. not restricted
User selection of NNIS ratings: not selected, i.e. not restricted
The user selects a period selection emergency call: not selected, i.e. not restricted
The user selects the operating room: not selected, i.e. not restricted
The user selects the number of surgeries: not selected, i.e. not restricted
Data change for each step
A first step of:
input: patient hospitalization information a output:
MC2, which has a value of [ 2019-01-01:00:12, 2019-01-12:03:00:12 ]
And a second step of:
input: surgical information G and time of discharge into/out of hospital g.mc2[ 2019-01-01:00:12, 2019-01-1203:00:12] output:
G(a)_Y：
G(a)_N：
and a third step of:
input: surgical record G (a) _y output:
true, mark continue to execute downwards
Fourth step:
input: surgical record G (a) _y output:
perioperative parameters g.QA4.open, which values are [ 2019-01-06:08:00:00, 2019-01-0808:30:00], [ 2019-01-08:00:00, 2019-01-10:08:30:00 ]
Fifth step:
input: surgical record G (a) _Y and statistical time [2019-01-06 00:00:00,2019-01-2023:59:59] output:
G(b)_Y：
G(b)_N：
sixth step:
input: surgical record G (b) _y and user-selected rights department output:
G(c)_Y：
G(c)_N：
seventh step:
input: surgical record G (c) _y and user selected surgical department (unselected) output:
G(d)_Y：
G(d)_N：
eighth step:
input: surgical record G (d) _y and user selected incision level (unselected) output:
G(e)_Y：
G(e)_N：
ninth step:
input: surgical records G (e) _y and user selected surgical class (unselected) output:
G(f)_Y：
G(f)_N：
Tenth step:
input: surgical record G (f) _y and user selected surgeon (unselected) output:
G(g)_Y：
G(g)_N：
eleventh step:
input: surgical record G (G) _y and user selected anesthesia mode (unselected) output:
G(h)_Y：
G(h)_N：
twelfth step:
input: surgical record G (h) _y and user selected surgical duration (unselected) output:
G(i)_Y：
G(i)_N：
thirteenth step:
input: surgical record G (i) _y and user selected ASA score (unselected) output:
G(j)_Y：
G(j)_N：
fourteenth step:
input: surgical record G (j) _y and user-selected surgical name (unselected) output:
G(k)_Y：
G(k)_N：
fifteenth step:
input: surgical record G (k) _y and user selected healing level (unselected) output:
G(m)_Y：
G(m)_N：
sixteenth step:
input: surgical record G (m) _y and user selected surgical position (unselected) output:
G(n)_Y：
G(n)_N：
seventeenth step:
input: surgical record G (n) _y and user selected NNIS score (unselected) output:
G(p)_Y：
G(p)_N：
eighteenth step:
input: surgical record G (p) _y and user selected optional emergency (unselected) output:
G(q)_Y：
G(q)_N：
nineteenth step:
input: surgical records G (q) _Y and user selected operating room (not selected)
And (3) outputting:
G(r)_Y：
G(r)_N：
twentieth step:
input: surgical record G (r) _y and user selected patient current admission number-of-times surgical (unselected) output:
G(s)_Y：
G(s)_N：
Twenty-first step:
input: surgical record G(s) _Y
And (3) outputting:
true (meaning continue to operate downward)
Twenty-second step:
input: antibacterial drug order record F and time of discharge of hospital g.MC2
And (3) outputting:
F(a)_Y:
F(a)_N：
twenty-third step:
input: antibacterial drug order F (a) _Y and user-selected drug purpose (not selected)
And (3) outputting:
F(b)_N：
twenty-fourth step:
input: antibacterial drug order F (b) _Y and user-selected mode of administration (not selected)
And (3) outputting:
F(c)_Y:
F(c)_N：
twenty-fifth step:
input: antibacterial order F (c) _Y and user-selected antibiotic grade (not selected)
And (3) outputting:
F(d)_Y:
F(d)_N：
twenty-sixth step:
input: antibacterial medicine order F (d) _Y
And (3) outputting:
true (continue down operation)
Twenty-seventh step:
input: antibacterial medicine doctor's advice F (d) _Y and time of discharge from hospital g.MC2
And (3) outputting:
the parameter g.THW of the list of start and stop time periods has a value of [ 2019-01-03:08:00, 2019-01-0608:30:00], [2019-01-02 08:00:00, 2019-01-02:08:30:00 ].
Twenty eighth step:
input: surgical record G(s) _y, start-stop time parameter g.thw and perioperative parameter g.qa4.open output:
G(t)_Y：
G(t)_N：
twenty-ninth step:
input: surgical record G (t) _y output: output result value of 0
Example two
The embodiment provides a management system for the number of surgical preventive cases based on MapReduce and big data, which comprises:
the acquisition module is used for acquiring hospitalization process information A, antibacterial medicine doctor order records F, operation information G, selected statistical time, operation departments, incision grades, operation classifications, operation doctors, anesthesia modes, operation time, ASA scores, operation names, healing grades, operation positions, NNIS scores, first-time emergency treatment, operation rooms, a patient in the hospital, the purpose of medication, the mode of medication, antibiotic grades and determining authority departments of users according to identity information of the users;
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 taking the admission time and the discharge time as parameters g.MC2 together;
a first dividing module for dividing the operation information G into operation information G (a) _y occurring during the present hospitalization period and operation information G (a) _n occurring during the non-present hospitalization period based on the parameter g.mc2;
the first judging module is used for judging whether the operation information G (a) _Y contains an operation record or not, if yes, executing the step S5, and if not, outputting the number of the operation prevention cases to be 0;
The second acquisition module is used for acquiring operation starting time and operation ending time based on the operation information G (a) _Y and jointly used as perioperative parameters g.QA4.open of the operation;
a second dividing module for dividing the surgical information G (a) _y into surgical information G (b) _y within a statistical time range and surgical information G (b) _n not within the statistical time range;
a third dividing module for dividing the surgical information G (b) _y into surgical information G (c) _y within a right range and surgical information G (c) _n not within the right range;
a fourth division module for dividing the surgical information G (c) _y into surgical information G (d) _y within the selected surgical department range and surgical information G (d) _n not within the selected range;
a fifth division module for dividing the surgical information G (d) _y into surgical information G (e) _y in an incision level selection list and surgical information G (e) _n not in an incision level selection list;
a sixth division module for dividing the surgical information G (e) _y into surgical information G (f) _y within a selected surgical classification range and surgical information G (f) _n not within the selected range;
a seventh division module for dividing the operation information G (f) _y into operation information G (G) _y in a surgeon selection list and operation information G (G) _n not in a surgeon selection list;
An eighth dividing module for dividing the operation information G (G) _y into operation information G (h) _y on an anesthesia mode selection list and operation information G (h) _n not on an anesthesia mode selection list;
a ninth division module for dividing 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 for dividing the surgical information G (i) _y into surgical information G (j) _y in the ASA score selection list and surgical information G (j) _n not in the ASA score selection list;
an eleventh dividing module for dividing 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 division module for dividing the surgical information G (k) _y into surgical information G (m) _y in a healing level selection list and surgical information G (m) _n not in a healing level selection list;
a thirteenth division module for dividing the surgical information G (m) _y into surgical information G (N) _y in a surgical position selection list and surgical information G (N) _n not in a surgical position selection list;
a fourteenth division module for dividing the surgical information G (N) _y into surgical information G (p) _y in an NNIS score selection list and surgical information G (p) _n not in an NNIS score selection list;
A fifteenth division module for dividing the operation information G (p) _y into operation information G (q) _y in the optional emergency selection list and operation information G (q) _n not in the optional emergency selection list;
a sixteenth dividing module for 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;
a seventeenth dividing module for dividing the operation information G (r) _y into operation information G(s) _y defining the number of operations and operation information G(s) _n not defining the number of operations;
the second judging module is used for judging whether the operation information G (S) _Y contains an operation record, if yes, executing step S23, and if not, outputting the number of the operation prevention cases to be 0;
an eighteenth dividing module, configured to divide the antibacterial drug order record F into an antibacterial drug order F (a) _y with an order start time in the hospitalization period and an antibacterial drug order F (a) _n with an order start time not in the hospitalization period based on the parameter g.mc2;
a nineteenth dividing module, configured to divide the antibacterial drug order F (a) _y into an order F (b) _y on a medication destination selection list and an order F (b) _n not on a medication destination selection list;
A twentieth dividing module, configured to divide the antibacterial drug order F (b) _y into an order F (c) _y in a administration mode selection list and an order F (c) _n not in the administration mode selection list;
a twenty-first dividing module for dividing the antibacterial drug order F (c) _y into an order F (d) _y on an antibiotic grade selection list and an order F (d) _n not on an antibiotic grade selection list;
the third judging module is used for judging whether the antibacterial medicine orders F (d) _Y have antibacterial medicine orders records, if yes, executing the step S28, and if not, outputting the number of the surgical preventive medicine cases to be 0;
the third acquisition module is used for acquiring the doctor's advice start time and doctor's advice end time of each antibacterial medicine doctor's advice based on the antibacterial medicine doctor's advice F (d) _Y, and constructing a parameter g.THW with the parameter data type of a starting and ending time period list;
a twenty-second dividing module for dividing the operation information G(s) _y into operation information G (t) _y for using the antibacterial agent in the perioperative period and operation information G (t) _n for not using the antibacterial agent in the perioperative period based on the parameter g.thw and the parameter g.qa4. Opotid;
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.
Claims (10)
1. The method for managing the number of surgical preventive cases based on MapReduce and big data is characterized by comprising the following steps:
s1, acquiring hospitalization process information A, antibacterial medicine doctor advice records F, operation information G, selected statistical time, operation departments, incision grades, operation classifications, operating doctors, anesthesia modes, operation time, ASA scores, operation names, healing grades, operation positions, NNIS scores, first-time emergency treatment, operation rooms, a patient in the hospital, the purpose of medication, the mode of medication, antibiotic grades and determining authority departments of users according to identity information of the users;
s2, acquiring the time of admission and the time of discharge of a patient based on the hospitalization process information A, and taking the time of admission and the time of discharge of the patient as parameters g.MC2;
s3, dividing the operation information G into operation information G (a) _Y occurring during the current hospitalization period and operation information G (a) _N occurring during the non-current hospitalization period based on the parameter g.MC2;
s4, judging whether an operation record exists in the operation information G (a) _Y, if so, executing the step S5, and if not, outputting the number of the operation prevention cases to be 0;
s5, acquiring operation starting time and 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.open 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 within the authority range and operation information G (c) _N not within 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 an incision level selection list and operation information G (e) _N not in the incision level selection list;
s10, dividing the operation information G (e) _Y into operation information G (f) _Y in a 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 an 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 within a limited operation duration range and operation information G (i) _N not within the limited operation duration range;
S14, dividing the operation information G (i) _Y into operation information G (j) _Y in an ASA score selection list and operation information G (j) _N not in the ASA score 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 grade selection list and operation information G (m) _N not in the healing grade selection list;
s17, dividing the operation information G (m) _Y into operation information G (N) _Y in an operation position selection list and operation information G (N) _N not in the operation position selection list;
s18, dividing the operation information G (N) _Y into operation information G (p) _Y in an NNIS score selection list and operation information G (p) _N not in the NNIS score selection list;
s19, dividing the operation information G (p) _Y into operation information G (q) _Y in a selection emergency selection list and operation information G (q) _N not in the selection emergency selection list;
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 an operation record exists in the operation information G (S) _Y, if so, executing the step S23, and if not, outputting the number of the operation prevention cases to be 0;
s23, dividing the antibacterial medicine order record F into an antibacterial medicine order F (a) _Y with an order start time in the hospitalization period and an antibacterial medicine order F (a) _N with an order start time not in the hospitalization period based on the parameter g.MC2;
s24, dividing the antibacterial medicine orders F (a) _Y into orders F (b) _Y in a medicine purpose selection list and orders F (b) _N not in the medicine purpose selection list;
s25, dividing the antibacterial medicine orders F (b) _Y into orders F (c) _Y in a drug administration mode selection list and orders F (c) _N not in the drug administration mode selection list;
s26, dividing the antibacterial medicine orders F (c) _Y into orders F (d) _Y in an antibiotic grade selection list and orders F (d) _N not in the antibiotic grade selection list;
s27, judging whether an antibacterial medicine doctor advice record exists in the antibacterial medicine doctor advice F (d) _Y, if so, executing the step S28, and if not, outputting the number of preventive cases of operation to be 0;
s28, acquiring the order start time and the order end time of each antibacterial drug order based on the antibacterial drug order F (d) _Y, and constructing a parameter g.THW with a parameter data type of a start-stop time period list;
S29, dividing the operation information G (S) _Y into operation information G (t) _Y for using the antibacterial medicine in the perioperative period and operation information G (t) _N for not using the antibacterial medicine in the perioperative period based on the parameters g.THW and g.QA4. Open;
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.
2. The method of claim 1, wherein the hospital procedure information comprises patient case number, admission department, admission time, discharge department, discharge time.
3. The method of claim 1, wherein the antimicrobial order records include patient case number, order department, antimicrobial name, start time, end time, antibiotic grade, mode of administration, purpose of administration, order doctor grade.
4. The method of claim 1, wherein the surgical information includes patient case number, surgical department, surgical category, surgeon, anesthesia mode, surgical name, surgical start time, surgical end time, incision, healing grade, ASA, emergency treatment on choice, surgical location, NNIS score, operating room, number of surgeries.
5. A method of managing according to claim 3, characterized in that said administration is for preventive purposes.
6. The utility model provides a management system of operation prevention medicine case number based on MapReduce and big data which characterized in that includes:
the acquisition module is used for acquiring hospitalization process information A, antibacterial medicine doctor order records F, operation information G, selected statistical time, operation departments, incision grades, operation classifications, operation doctors, anesthesia modes, operation time, ASA scores, operation names, healing grades, operation positions, NNIS scores, first-time emergency treatment, operation rooms, a patient in the hospital, the purpose of medication, the mode of medication, antibiotic grades and determining authority departments of users according to identity information of the users;
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 taking the admission time and the discharge time as parameters g.MC2 together;
a first dividing module for dividing the operation information G into operation information G (a) _y occurring during the present hospitalization period and operation information G (a) _n occurring during the non-present hospitalization period based on the parameter g.mc2;
the first judging module is used for judging whether the operation information G (a) _Y contains an operation record or not, if yes, executing the step S5, and if not, outputting the number of the operation prevention cases to be 0;
The second acquisition module is used for acquiring operation starting time and operation ending time based on the operation information G (a) _Y and jointly used as perioperative parameters g.QA4.open of the operation;
a second dividing module for dividing the surgical information G (a) _y into surgical information G (b) _y within a statistical time range and surgical information G (b) _n not within the statistical time range;
a third dividing module for dividing the surgical information G (b) _y into surgical information G (c) _y within a right range and surgical information G (c) _n not within the right range;
a fourth division module for dividing the surgical information G (c) _y into surgical information G (d) _y within the selected surgical department range and surgical information G (d) _n not within the selected range;
a fifth division module for dividing the surgical information G (d) _y into surgical information G (e) _y in an incision level selection list and surgical information G (e) _n not in an incision level selection list;
a sixth division module for dividing the surgical information G (e) _y into surgical information G (f) _y within a selected surgical classification range and surgical information G (f) _n not within the selected range;
a seventh division module for dividing the operation information G (f) _y into operation information G (G) _y in a surgeon selection list and operation information G (G) _n not in a surgeon selection list;
An eighth dividing module for dividing the operation information G (G) _y into operation information G (h) _y on an anesthesia mode selection list and operation information G (h) _n not on an anesthesia mode selection list;
a ninth division module for dividing 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 for dividing the surgical information G (i) _y into surgical information G (j) _y in the ASA score selection list and surgical information G (j) _n not in the ASA score selection list;
an eleventh dividing module for dividing 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 division module for dividing the surgical information G (k) _y into surgical information G (m) _y in a healing level selection list and surgical information G (m) _n not in a healing level selection list;
a thirteenth division module for dividing the surgical information G (m) _y into surgical information G (N) _y in a surgical position selection list and surgical information G (N) _n not in a surgical position selection list;
a fourteenth division module for dividing the surgical information G (N) _y into surgical information G (p) _y in an NNIS score selection list and surgical information G (p) _n not in an NNIS score selection list;
A fifteenth division module for dividing the operation information G (p) _y into operation information G (q) _y in the optional emergency selection list and operation information G (q) _n not in the optional emergency selection list;
a sixteenth dividing module for 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;
a seventeenth dividing module for dividing the operation information G (r) _y into operation information G(s) _y defining the number of operations and operation information G(s) _n not defining the number of operations;
the second judging module is used for judging whether the operation information G (S) _Y contains an operation record, if yes, executing step S23, and if not, outputting the number of the operation prevention cases to be 0;
an eighteenth dividing module, configured to divide the antibacterial drug order record F into an antibacterial drug order F (a) _y with an order start time in the hospitalization period and an antibacterial drug order F (a) _n with an order start time not in the hospitalization period based on the parameter g.mc2;
a nineteenth dividing module, configured to divide the antibacterial drug order F (a) _y into an order F (b) _y on a medication destination selection list and an order F (b) _n not on a medication destination selection list;
A twentieth dividing module, configured to divide the antibacterial drug order F (b) _y into an order F (c) _y in a administration mode selection list and an order F (c) _n not in the administration mode selection list;
a twenty-first dividing module for dividing the antibacterial drug order F (c) _y into an order F (d) _y on an antibiotic grade selection list and an order F (d) _n not on an antibiotic grade selection list;
the third judging module is used for judging whether the antibacterial medicine orders F (d) _Y have antibacterial medicine orders records, if yes, executing the step S28, and if not, outputting the number of the surgical preventive medicine cases to be 0;
the third acquisition module is used for acquiring the doctor's advice start time and doctor's advice end time of each antibacterial medicine doctor's advice based on the antibacterial medicine doctor's advice F (d) _Y, and constructing a parameter g.THW with the parameter data type of a starting and ending time period list;
a twenty-second dividing module for dividing the operation information G(s) _y into operation information G (t) _y for using the antibacterial agent in the perioperative period and operation information G (t) _n for not using the antibacterial agent in the perioperative period based on the parameter g.thw and the parameter g.qa4. Opotid;
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.
7. The management system of claim 6, wherein the hospital procedure information includes patient case number, admission department, admission time, discharge department, discharge time.
8. The management system of claim 6, wherein the antimicrobial order records include patient case number, order department, antimicrobial name, start time, end time, antibiotic grade, mode of administration, purpose of administration, order doctor grade.
9. The management system of claim 6, wherein the surgical information includes patient case number, surgical department, surgical category, surgeon, anesthesia modality, surgical name, surgical start time, surgical end time, incision, healing level, ASA, emergency treatment on choice, surgical location, NNIS score, operating room, number of surgeries.
10. The management system of claim 8, wherein the administration is for prophylaxis.
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