CN111462877A - Medical supervision system based on big data - Google Patents
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Abstract
The invention discloses a medical supervision system based on big data, which comprises a working condition acquisition module, a change collection module, a data processing module, a controller, a comprehensive condition analysis module, a table sorting module, a detailed analysis module and an employee supervision module, wherein the change collection module is used for collecting working conditions of a patient; the working condition acquisition module acquires the daily working condition operation information of each department in the hospital and transmits the daily working condition operation information to the data processing module; after receiving daily working condition operation information of each department in the hospital, the data processing module performs department daily operation analysis operation on the daily working condition operation information to obtain a working condition supervision factor Ri of each department in the hospital in a first time quantum; the invention comprehensively collects and analyzes the medical supervision conditions of each link of a department, calls the conditions of medical care personnel to carry out deep treatment according to the comprehensive supervision conditions of the department, and integrates the overall judgment and the framed supervision of personal factors to carry out omnibearing expression together so as to realize the capability of hierarchical overlapping and point-by-point detailed medical supervision.
Description
Technical Field
The invention relates to the technical field of medical supervision systems, in particular to a medical supervision system based on big data.
Background
The medical supervision system is an application system integrating the fields of medicine, information, management, computers and the like; the medical support is one of necessary technical support means for modern medical operation, so that hospital management is enhanced, personnel configuration is standardized, medical quality is improved, and the like, which is a necessary direction for future medical development.
Most of the existing medical supervision systems only carry out threshold monitoring and data integration on various medical data in a hospital, and lack comprehensive data analysis and deep research processes, so that the comprehensive output result of the medical supervision condition is relatively one-sided and rough, the operation condition of departments in the hospital is difficult to be associated with the change condition of the departments, the conditions of personnel in the departments under the general condition are called to carry out deep processing while the advantages and the disadvantages of the departments are output, and the overall judgment and personal factors are fused to carry out comprehensive expression together, so that the capability of hierarchical overlapping and point-by-point detailed medical supervision is realized;
in order to solve the above-mentioned drawbacks, a technical solution is now provided.
Disclosure of Invention
The invention aims to provide a medical supervision system based on big data, which relates the operation condition of departments with the change condition of the departments, respectively carries out data mark definition and first-order correction formula analysis, and analyzing the data mark definition and the second-order correction formula to obtain the working condition and the variation condition of each department, weighting and comparing the two to obtain supervision intervals at all levels, marking the personnel attitude status of the department in the middle section to be deeply researched by data, grading assignment analysis and weighing formula analysis, and mean value formula analysis and double presetting treatment to obtain personnel factor interval of the department, after all levels of supervision intervals are subjected to color marking and sequential arrangement, medical care personnel in the personnel factor intervals are added to the supervision intervals, and a medical supervision schedule is generated according to the addition and display; the medical supervision conditions of each link of a department are comprehensively collected and analyzed, the conditions of medical workers are called according to the comprehensive supervision conditions of the department for further treatment, and the overall judgment and the framed supervision of personal factors are fused to be comprehensively expressed together, so that the capability of hierarchical, face-to-face and point-to-point detailed medical supervision is realized.
The technical problems to be solved by the invention are as follows:
how to solve the problem that most of the existing medical supervision systems only carry out threshold monitoring and data integration on various medical data in a hospital and lack comprehensive data analysis and deep research processes, so that the comprehensive output result of medical supervision conditions is relatively one-sided and rough, the operation conditions of departments in the hospital are difficult to be related with the change conditions of the departments, the conditions of personnel in the departments under general conditions are called to carry out deep processing while the advantages and the disadvantages of the departments are output, and the overall judgment and personal factors are fused to carry out comprehensive expression together, so that the capability of hierarchical overlapping and point-to-point medical supervision is realized.
The purpose of the invention can be realized by the following technical scheme:
a medical supervision system based on big data comprises a working condition acquisition module, a change collection module, a data processing module, a controller, a comprehensive condition analysis module, a table sorting module, a detailed analysis module and an employee supervision module;
the working condition acquisition module is used for acquiring daily working condition operation information of each department in the hospital and transmitting the daily working condition operation information to the data processing module;
after receiving daily working condition operation information of each department in the hospital, the data processing module performs department daily operation analysis operation on the daily working condition operation information to obtain a working condition supervision factor Ri of each department in the hospital in a first time quantum, and transmits the working condition supervision factor Ri to the comprehensive condition analysis module through the controller;
the change collection module is used for collecting daily change floating information of each department in the hospital and transmitting the daily change floating information to the data processing module;
the data processing module carries out department specification change processing operation on the department according to the collected daily change floating information of each department in the hospital to obtain the change supervision factor Pi of each department in the hospital in the second time quantum and transmits the change supervision factor Pi to the comprehensive condition analysis module through the controller;
the comprehensive analysis module respectively gives weight coefficients r and p to each department in the hospital after receiving the working condition supervision factor Ri of each department in the hospital in the first time quantum and the change supervision factor Pi of each department in the hospital in the second time quantum, wherein p is greater than r and r + p is 2.5911, the supervision evaluation index Mi of each department in the hospital in the same time quantum is obtained according to a formula Mi is Ri + r + Pi p, i is 1.. n, and when the supervision evaluation index Mi is greater than the maximum value of a preset range m, is located in the preset range m and is less than the minimum value of the preset range m, each department is respectively placed in a key supervision interval, a medium deep study interval and a normal operation interval, and is transmitted to the form arrangement module together, and the medium deep study interval is transmitted to the detailed analysis module;
the detailed analysis module acquires each department in the moderate deep study interval according to the received moderate deep study interval, and the staff supervision module is used for calling attitude working condition information of each medical worker in the department in the same time quantum and carrying out staff service analysis operation on the attitude working condition information to obtain a working condition service attitude index Gj of each medical worker in the department in the same time quantum, and the detailed analysis module is used for obtaining the working condition service attitude index Gj of each medical worker in the department in the same time quantum according to a formula
Obtaining the average value H of the working condition service attitude index Gj of each medical worker in the department in the same time quantum, and when Gj is more than or equal to a preset value g and is more than or equal to the average value H, placing the medical worker corresponding to Gj and the department in a worker factor interval together, and transmitting the worker factor interval and the worker factor interval to a table sorting module, and performing no treatment under other conditions;
the staff supervision module is used for acquiring attitude working condition information of each medical staff of each department in the hospital and storing the attitude working condition information into an internal folder;
after receiving the key supervision interval, the medium deep study interval, the normal operation interval and the personnel factor interval, the form sorting module parallelly marks all departments corresponding to the key supervision interval by red, also parallelly marks all departments corresponding to the normal operation interval by green into vertical columns, parallelly marks all departments corresponding to the medium deep study interval by yellow into vertical columns, attaches each medical personnel corresponding to the personnel factor interval to the corresponding department in the medium deep study interval, and generates a medical supervision degree table together with the vertical columns marked by colors and sends the medical supervision degree table to a display screen.
Furthermore, the daily working condition operation information of each department in the hospital consists of the nursing density, the equipment operation amount and the medicine usage amount of each department in the hospital; the nursing density quantity represents the total registered number of people of each department in the hospital, divided by the total medical care number, the equipment operation quantity represents the total equipment number of each department in the hospital, divided by the average use time of the equipment, the medicine use quantity represents the total medicine consumption number of each department in the hospital, and the data are obtained through a network monitoring platform, a recorder and the like;
the specific steps of the analysis operation of the daily operation of the department are as follows:
the method comprises the following steps: acquiring daily working condition operation information of each department in the hospital in a first time quantum, and respectively marking a nursing density amount, an equipment operation amount and a medicine use amount corresponding to the daily working condition operation information as Qi, Wi and Ei, wherein i is 1.. n, the Qi, the Wi and the Ei are in one-to-one correspondence with each other, the first time quantum represents the duration of one month, the variable i corresponds to each department in the hospital, and the variable n represents a positive integer greater than 1;
step two: according to the formula
The working condition supervision factors Ri, q, w and e of each department in the hospital in the first time quantum are obtained as first-order supervision correction factors, q is larger than w and larger than e, and q + w + e is 5.6181.
Further, the daily change floating information of each department in the hospital consists of the hospitalization change level, the equipment change level and the medicine change level of each department in the hospital; the hospitalization change level represents the newly increased total number of hospitalization in each department in the hospital, divided by the newly increased total number of discharge from the hospital, the equipment change level represents the total length of equipment used by the newly increased total number of hospitalization in each department in the hospital, multiplied by the total number of equipment used by the original total number of hospitalization in each department, the medicine change level represents the average number of medicines purchased by the newly increased total number of hospitalization in each department in the hospital, and all the data are obtained by a network monitoring platform, a recorder and the like;
the specific steps of the departmental norm change processing operation are as follows:
the method comprises the following steps: acquiring daily change floating information of each department in the hospital in a second time quantum, and respectively marking the hospital changing level, the equipment changing level and the medicine changing level corresponding to the daily change floating information as Ti, Yi and Ui, wherein i is 1.. n, the Ti, Yi and Ui are in one-to-one correspondence, the second time quantum and the first time quantum are in the same time quantum, and the Qi, Wi and Ei are in one-to-one correspondence with the Ti, Yi and Ui;
step two: according to the formula
The variable supervision factors Pi, t, y and u for each department in the hospital in the second amount of time are obtained as second-order supervision correction factors, t is greater than y and t + y + u is 4.5218.
Furthermore, the attitude working condition information of each medical worker in the department consists of the total times of non-on-time card punching, the total times of complaint feedback and criticizing report and the total overtime of overtime and attendance of each medical worker in the department, and all the data are obtained by a network monitoring platform, a recorder and the like;
the specific steps of the employee service analysis operation are as follows:
the method comprises the following steps: acquiring attitude working condition information of each medical worker in the department in the same time quantum, and respectively marking the corresponding total times of non-on-time card punching, the total times of complaint feedback and report criticizing and the total overtime and duty time as Sj, Dj and Fj, wherein j is 1.. b, the Sj, the Dj and the Fj are in one-to-one correspondence with each other, a variable j corresponds to each medical worker in the department, and a variable b represents a positive integer greater than 1;
step two: when the total number Sj of untimely card punching times of each medical staff of the department in the same time quantum corresponds to the first card punching level, the second card punching level or the third card punching level, marking positive values Z1, Z2 or Z3 respectively, wherein Z1 is greater than Z2 and is greater than Z3; when the total number Dj of complained feedback and report criticizing times of each medical staff of the department in the same time quantum corresponds to the first class of discipline, the second class of discipline or the third class of discipline, the total number djustment and report criticizing times is respectively endowed with mark positive values X1, X2 or X3, and X1 is larger than X2 and is larger than X3; when the total overtime and duty time Fj of each medical staff of the department in the same time quantum corresponds to the first dedication grade, the second dedication grade or the third dedication grade, the marking is given as a positive value C1, C2 or C3 respectively, and C1 is smaller than C2 and smaller than C3;
step three: according to the formula Gj, Sj, s, d and f, working condition service attitude indexes Gj, s, d and f of each medical worker of the department in the same time quantum are obtained, wherein the working condition service attitude indexes Gj, s, d and f are attitude weighing factors, d is larger than f, and s + d + f is 3.6218.
Furthermore, the first card punching level corresponds to 15 times of non-on-time card punching and is more than 15 times of non-on-time card punching, the second card punching level corresponds to 5 times to 15 times of non-on-time card punching, and the third card punching level corresponds to 5 times of non-on-time card punching and is less than 5 times of non-on-time card punching; the first class of discipline and the complaint feedback and the reporting criticism are corresponding to 5 times or more, the second class of discipline and the complaint feedback and the reporting criticism are corresponding to 2 to 5 times, and the third class of discipline and the complaint feedback and the reporting criticism are corresponding to 2 times or less; the first dedication level and the total time of the overtime and the duty on duty are corresponding to 100 hours or more, the second dedication level and the total time of the overtime and the duty on duty are corresponding to 40 hours to 100 hours, and the third dedication level and the total time of the overtime and the duty on duty are corresponding to 40 hours or less.
The invention has the beneficial effects that:
the invention collects the daily operating condition running information of each department in the hospital, and the daily operating condition running information consists of nursing density, equipment running amount and medicine usage amount; the nursing density quantity represents the total registered number of each department in the hospital, divided by the total medical care number, the equipment operation quantity represents the total equipment number of each department in the hospital, divided by the average use time of the equipment, the medicine use quantity represents the total medicine consumption number of each department in the hospital, and the daily operation analysis operation of the departments is carried out, namely the nursing density quantity, the equipment operation quantity and the medicine use quantity corresponding to the daily working condition operation information of each department in the hospital are defined by data marks and analyzed by a first-order correction formula, so that the working condition supervision factor Ri of each department in the hospital in the first time quantum is obtained;
daily change floating information of each department in the hospital is collected, and the daily change floating information consists of a hospitalization change level, an equipment change level and a medicine change level; the hospital change level represents the newly increased total number of hospitalization in each department in the hospital, divided by the newly increased total number of discharged hospital, the equipment change level represents the total equipment duration used by the newly increased total number of hospitalization in each department in the hospital, multiplied by the total equipment frequency used by the original total number of hospitalization, the medicine change level represents the average medicine quantity purchased by the newly increased total number of hospitalization in each department in the hospital, and the average medicine quantity is subjected to department standard change processing operation, namely, the hospital change level, the equipment change level and the medicine change level corresponding to daily change floating information of each department in the hospital are defined by data marks and analyzed by a second-order correction formula, so that the change supervision factor Pi of each department in the hospital in the second time quantum is obtained;
ri and Pi are weighted and compared with each other in intervals to obtain a key supervision interval, a medium deep study interval and a normal operation interval, attitude working condition information of each medical worker in the department in the same time quantum is called according to each department in the medium deep study interval, staff service analysis is carried out on the attitude working condition information, namely the total times of non-on-time card punching, the total times of complaint feedback and report criticism and the total time of shift and duty are subjected to data marking, graded assignment analysis and weighted formula analysis to obtain the working condition service attitude index Gj of each medical worker in the department in the same time quantum, the working condition service attitude index Gj is subjected to mean value formula analysis and double preset treatment to obtain a personnel factor interval, and all departments in each supervision interval are color-marked and arranged in parallel, all medical workers in the personnel factor interval are added to corresponding departments in the corresponding supervision interval, and a medical supervision schedule is generated according to the medical supervision schedule and sent to a display screen;
then the operation status of the departments is associated with the department change status, the operation status and the change status of each department are obtained through analysis after data mark definition, first-order correction formula analysis and data mark definition and second-order correction formula analysis, the operation status and the change status of each department are obtained through weighting processing and interval comparison, the supervision intervals of each level are obtained, the personnel attitude status of the department in the middle to-be-deeply-researched interval is subjected to data mark, grading assignment analysis and weighing formula analysis, mean value formula analysis and double presetting processing, the personnel factor interval of the department is obtained, the personnel factor interval of each level is added to the supervision interval after the supervision intervals are subjected to color mark and sequence arrangement, and a medical personnel in the personnel factor interval is generated according to the result and sent to be displayed; the medical supervision conditions of each link of a department are comprehensively collected and analyzed, the conditions of medical workers are called according to the comprehensive supervision conditions of the department for further treatment, and the overall judgment and the framed supervision of personal factors are fused to be comprehensively expressed together, so that the capability of hierarchical, face-to-face and point-to-point detailed medical supervision is realized.
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In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a block diagram of the system of the present invention.
Detailed Description
As shown in fig. 1, a medical supervision system based on big data includes a working condition acquisition module, a change collection module, a data processing module, a controller, an overall condition analysis module, a table sorting module, a detailed analysis module, and an employee supervision module;
the working condition acquisition module acquires the daily working condition operation information of each department in the hospital and transmits the daily working condition operation information to the data processing module, and the daily working condition operation information of each department in the hospital consists of the nursing density, the equipment operation amount and the medicine use amount of each department in the hospital; the nursing density quantity represents the total registered number of people of each department in the hospital, divided by the total medical care number, the equipment operation quantity represents the total equipment number of each department in the hospital, divided by the average use time of the equipment, the medicine use quantity represents the total medicine consumption number of each department in the hospital, and the data are obtained through a network monitoring platform, a recorder and the like;
after receiving daily working condition operation information of each department in the hospital, the data processing module performs department daily operation analysis operation on the received daily working condition operation information, and the method comprises the following specific steps of:
the method comprises the following steps: acquiring daily working condition operation information of each department in the hospital in a first time quantum, and respectively marking a nursing density amount, an equipment operation amount and a medicine use amount corresponding to the daily working condition operation information as Qi, Wi and Ei, wherein i is 1.. n, the Qi, the Wi and the Ei are in one-to-one correspondence with each other, the first time quantum represents the duration of one month, the variable i corresponds to each department in the hospital, and the variable n represents a positive integer greater than 1;
step two: according to the formula
Obtaining working condition supervision factor values Ri, q, w and e of departments in the hospital in the first time quantum, wherein the working condition supervision factor values Ri, q, w and e are first-order supervision correction factors, q is greater than w and greater than e, and q + w + e is 5.6181;
working condition supervision factor Ri of each department in the hospital in the first time quantum is obtained and is transmitted to the comprehensive condition analysis module through the controller;
the change collection module collects daily change floating information of each department in the hospital and transmits the daily change floating information to the data processing module, and the daily change floating information of each department in the hospital consists of hospitalization change level, equipment change level and medicine change level of each department in the hospital; the hospitalization change level represents the newly increased total number of hospitalization in each department in the hospital, divided by the newly increased total number of discharge from the hospital, the equipment change level represents the total length of equipment used by the newly increased total number of hospitalization in each department in the hospital, multiplied by the total number of equipment used by the original total number of hospitalization in each department, the medicine change level represents the average number of medicines purchased by the newly increased total number of hospitalization in each department in the hospital, and all the data are obtained by a network monitoring platform, a recorder and the like;
the data processing module carries out department standard change processing operation on the department according to the collected daily change floating information of each department in the hospital, and the specific steps are as follows:
the method comprises the following steps: acquiring daily change floating information of each department in the hospital in a second time quantum, and respectively marking the hospital changing level, the equipment changing level and the medicine changing level corresponding to the daily change floating information as Ti, Yi and Ui, wherein i is 1.. n, the Ti, Yi and Ui are in one-to-one correspondence, the second time quantum and the first time quantum are in the same time quantum, and the Qi, Wi and Ei are in one-to-one correspondence with the Ti, Yi and Ui;
step two: according to the formula
Obtaining the variable supervision factor Pi of each department in the hospital in the second time quantum, wherein t, y and u are second-order supervision correction factors, t is greater than y, and t + y + u is 4.5218;
obtaining the change supervision factor Pi of each department in the hospital in the second time quantum, and transmitting the change supervision factor Pi to the comprehensive condition analysis module through the controller;
the comprehensive analysis module receives the working condition supervision factor Ri of each department in the hospital in the first time quantum and the change supervision factor Pi of each department in the hospital in the second time quantum, then respectively gives the working condition supervision factor Ri and the change supervision factor Pi of each department in the hospital in the first time quantum, wherein p is larger than r and r + p is 2.5911, and obtains the supervision evaluation index Mi of each department in the hospital in the same time quantum according to the formula Mi is Ri + r + Pi p, i is 1.. n, when the supervision evaluation index Mi is larger than the maximum value of the preset range m, is located in the preset range m and is smaller than the minimum value of the preset range m, each department is respectively placed in a key supervision interval, a medium deep study interval and a normal operation interval, and is transmitted to the form analysis module together, and the medium deep study interval is transmitted to the detailed analysis module;
the detailed analysis module acquires each department in the moderate deep study interval according to the received moderate deep study interval, and transfers attitude working condition information of each medical worker in the department in the same time quantum from the staff supervision module, wherein the attitude working condition information of each medical worker in the department consists of the total times of non-on-time card punching, the total times of complaint feedback and report criticizing and the total time of overtime and duty on duty of each medical worker in the department, and the data are acquired by a network monitoring platform, a recorder and the like and are subjected to staff service analysis operation, and the detailed analysis module comprises the following specific steps:
the method comprises the following steps: acquiring attitude working condition information of each medical worker in the department in the same time quantum, and respectively marking the corresponding total times of non-on-time card punching, the total times of complaint feedback and report criticizing and the total overtime and duty time as Sj, Dj and Fj, wherein j is 1.. b, the Sj, the Dj and the Fj are in one-to-one correspondence with each other, a variable j corresponds to each medical worker in the department, and a variable b represents a positive integer greater than 1;
step two: when the total number Sj of untimely card punching times of each medical staff of the department in the same time quantum corresponds to the first card punching level, the second card punching level or the third card punching level, marking positive values Z1, Z2 or Z3 respectively, wherein Z1 is greater than Z2 and is greater than Z3; when the total number Dj of complained feedback and report criticizing times of each medical staff of the department in the same time quantum corresponds to the first class of discipline, the second class of discipline or the third class of discipline, the total number djustment and report criticizing times is respectively endowed with mark positive values X1, X2 or X3, and X1 is larger than X2 and is larger than X3; when the total overtime and duty time Fj of each medical staff of the department in the same time quantum corresponds to the first dedication grade, the second dedication grade or the third dedication grade, the marking is given as a positive value C1, C2 or C3 respectively, and C1 is smaller than C2 and smaller than C3;
step three: obtaining working condition service attitude indexes Gj of each medical worker in the department in the same time quantum according to a formula Gj, Sj, s, d and f, wherein s is larger than f, and s + d + f is 3.6218;
wherein, the first punch card level corresponds to 15 times of non-on-time punching and more than the first punch card level, the second punch card level corresponds to 5 times to 15 times of non-on-time punching, and the third punch card level corresponds to 5 times of non-on-time punching and less than the second punch card level; the first class of discipline and the feedback of complaint and the reporting criticism are corresponding to 5 times or more, the second class of discipline and the feedback of complaint and the reporting criticism are corresponding to 2 times to 5 times, and the third class of discipline and the feedback of complaint and the reporting criticism are corresponding to 2 times or less; the first dedication grade and the total time of overtaking and duty on duty is 100 hours or more, the second dedication grade and the total time of overtaking and duty on duty is 40 hours to 100 hours, and the third dedication grade and the total time of overtaking and duty on duty is 40 hours or less;
obtaining the working condition service attitude index Gj of each medical worker in the department in the same time quantum according to a formula
Obtaining the average value H of the working condition service attitude index Gj of each medical worker in the department in the same time quantum, and when Gj is more than or equal to a preset value g and is more than or equal to the average value H, placing the medical worker corresponding to Gj and the department in a worker factor interval together, and transmitting the worker factor interval and the worker factor interval to a table sorting module, and performing no treatment under other conditions;
the staff supervision module collects attitude working condition information of each medical staff of each department in the hospital and stores the attitude working condition information into an internal folder;
after receiving the key supervision interval, the middle to-be-deeply-studied interval, the normal operation interval and the personnel factor interval, the form sorting module parallelly marks all departments corresponding to the key supervision interval by red, also parallelly marks all departments corresponding to the normal operation interval by green into vertical columns, parallelly marks all departments corresponding to the middle to-be-deeply-studied interval by yellow into vertical columns, attaches each medical personnel corresponding to the personnel factor interval to the corresponding department in the middle to-be-deeply-studied interval, and generates a medical supervision degree table together with the vertical columns after color marking and sends the medical supervision degree table to a display screen.
The invention relates to a method for analyzing the personnel attitude of departments, which comprises the steps of associating department operation conditions with department variation conditions, analyzing the department operation conditions through a data mark definition and a first-order correction formula analysis, analyzing the data mark definition and the second-order correction formula analysis to obtain the working conditions and the variation conditions of all departments, performing weighting processing and interval comparison on the working conditions and the variation conditions together to obtain supervision intervals of all levels, performing data mark, hierarchical assignment analysis and scaling formula analysis, mean value formula analysis and double presetting processing on the personnel attitude conditions of the departments in a medium to-be-deeply-studied interval to obtain personnel factor intervals of the departments, adding medical personnel in the personnel factor intervals into the supervision intervals after the supervision intervals of all levels are subjected to color mark and sequential arrangement, and generating a medical supervision degree table for sending and displaying; the medical supervision conditions of each link of a department are comprehensively collected and analyzed, the conditions of medical workers are called according to the comprehensive supervision conditions of the department for further treatment, and the overall judgment and the framed supervision of personal factors are fused to be comprehensively expressed together, so that the capability of hierarchical, face-to-face and point-to-point detailed medical supervision is realized.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (5)
1. A medical supervision system based on big data is characterized by comprising a working condition acquisition module, a change collection module, a data processing module, a controller, a comprehensive condition analysis module, a table sorting module, a detailed analysis module and an employee supervision module;
the working condition acquisition module is used for acquiring daily working condition operation information of each department in the hospital and transmitting the daily working condition operation information to the data processing module;
after receiving daily working condition operation information of each department in the hospital, the data processing module performs department daily operation analysis operation on the daily working condition operation information to obtain a working condition supervision factor Ri of each department in the hospital in a first time quantum, and transmits the working condition supervision factor Ri to the comprehensive condition analysis module through the controller;
the change collection module is used for collecting daily change floating information of each department in the hospital and transmitting the daily change floating information to the data processing module;
the data processing module carries out department specification change processing operation on the department according to the collected daily change floating information of each department in the hospital to obtain the change supervision factor Pi of each department in the hospital in the second time quantum and transmits the change supervision factor Pi to the comprehensive condition analysis module through the controller;
the comprehensive analysis module respectively gives weight coefficients r and p to each department in the hospital after receiving the working condition supervision factor Ri of each department in the hospital in the first time quantum and the change supervision factor Pi of each department in the hospital in the second time quantum, wherein p is greater than r and r + p is 2.5911, the supervision evaluation index Mi of each department in the hospital in the same time quantum is obtained according to a formula Mi is Ri + r + Pi p, i is 1.. n, and when the supervision evaluation index Mi is greater than the maximum value of a preset range m, is located in the preset range m and is less than the minimum value of the preset range m, each department is respectively placed in a key supervision interval, a medium deep study interval and a normal operation interval, and is transmitted to the form arrangement module together, and the medium deep study interval is transmitted to the detailed analysis module;
the detailed analysis module acquires each department in the moderate deep study interval according to the received moderate deep study interval, and the staff supervision module is used for calling attitude working condition information of each medical worker in the department in the same time quantum and carrying out staff service analysis operation on the attitude working condition information to obtain a working condition service attitude index Gj of each medical worker in the department in the same time quantum, and the detailed analysis module is used for obtaining the working condition service attitude index Gj of each medical worker in the department in the same time quantum according to a formula
Obtaining the average value H of the working condition service attitude index Gj of each medical worker in the department in the same time quantum, and when Gj is larger than or equal to a preset value g and is larger than or equal to the average value H, placing the medical worker corresponding to Gj and the department in a worker factor interval together and transmitting the worker factor interval and the worker factor interval to a table sorting module;
the staff supervision module is used for acquiring attitude working condition information of each medical staff of each department in the hospital and storing the attitude working condition information into an internal folder;
after receiving the key supervision interval, the medium deep study interval, the normal operation interval and the personnel factor interval, the form sorting module parallelly marks all departments corresponding to the key supervision interval by red, also parallelly marks all departments corresponding to the normal operation interval by green into vertical columns, parallelly marks all departments corresponding to the medium deep study interval by yellow into vertical columns, attaches each medical personnel corresponding to the personnel factor interval to the corresponding department in the medium deep study interval, and generates a medical supervision degree table together with the vertical columns marked by colors and sends the medical supervision degree table to a display screen.
2. The big data-based medical supervision system according to claim 1, wherein the daily operation information of each department in the hospital is composed of nursing density, equipment operation and medicine usage of each department in the hospital; the nursing density quantity represents the total registered number of people of each department in the hospital, divided by the total medical care number, the equipment operation quantity represents the total equipment number of each department in the hospital, divided by the average use time of the equipment, and the medicine use quantity represents the total medicine consumption quantity of each department in the hospital;
the specific steps of the analysis operation of the daily operation of the department are as follows:
the method comprises the following steps: acquiring daily working condition operation information of each department in a hospital in a first time quantum, and respectively marking a nursing density, an equipment operation amount and a medicine use amount corresponding to the daily working condition operation information as Qi, Wi and Ei, wherein i is 1.. n, the Qi, the Wi and the Ei are in one-to-one correspondence with each other, and the first time quantum represents the duration of one month;
step two: according to the formula
The working condition supervision factors Ri, q, w and e of each department in the hospital in the first time quantum are obtained as first-order supervision correction factors, q is larger than w and larger than e, and q + w + e is 5.6181.
3. The big-data-based medical supervision system according to claim 1, wherein the daily change floating information of each department in the hospital consists of hospitalization change level, equipment change level and medicine change level of each department in the hospital; the hospitalization change level represents the newly increased total number of hospitalization in each department in the hospital, divided by the newly increased total number of discharge from the hospital, the equipment change level represents the total equipment duration used by the newly increased total number of hospitalization in each department in the hospital, multiplied by the total equipment frequency used by the original total number of hospitalization in the department, and the medicine change level represents the average medicine quantity purchased by the newly increased total number of hospitalization in each department in the hospital;
the specific steps of the departmental norm change processing operation are as follows:
the method comprises the following steps: acquiring daily change floating information of each department in the hospital in a second time quantum, and respectively marking the hospital changing level, the equipment changing level and the medicine changing level corresponding to the daily change floating information as Ti, Yi and Ui, wherein i is 1.. n, the Ti, Yi and Ui are in one-to-one correspondence, and the second time quantum and the first time quantum are in the same time quantum;
step two: according to the formula
The variable supervision factors Pi, t, y and u for each department in the hospital in the second amount of time are obtained as second-order supervision correction factors, t is greater than y and t + y + u is 4.5218.
4. The medical supervision system based on big data according to claim 1, wherein the attitude and working condition information of each medical staff in the department is composed of the total number of times of non-on-time card punching, the total number of times of complaint feedback and report criticism and the total length of overtime and duty;
the specific steps of the employee service analysis operation are as follows:
the method comprises the following steps: acquiring attitude working condition information of each medical worker in the department in the same time quantum, and respectively marking the corresponding total times of non-on-time card punching, the total times of complaint feedback and report criticizing and the total overtime and duty duration as Sj, Dj and Fj, wherein j is 1.
Step two: when the total number Sj of untimely card punching times of each medical staff of the department in the same time quantum corresponds to the first card punching level, the second card punching level or the third card punching level, marking positive values Z1, Z2 or Z3 respectively, wherein Z1 is greater than Z2 and is greater than Z3; when the total number Dj of complained feedback and report criticizing times of each medical staff of the department in the same time quantum corresponds to the first class of discipline, the second class of discipline or the third class of discipline, the total number djustment and report criticizing times is respectively endowed with mark positive values X1, X2 or X3, and X1 is larger than X2 and is larger than X3; when the total overtime and duty time Fj of each medical staff of the department in the same time quantum corresponds to the first dedication grade, the second dedication grade or the third dedication grade, the marking is given as a positive value C1, C2 or C3 respectively, and C1 is smaller than C2 and smaller than C3;
step three: according to the formula Gj, Sj, s, d and f, working condition service attitude indexes Gj, s, d and f of each medical worker of the department in the same time quantum are obtained, wherein the working condition service attitude indexes Gj, s, d and f are attitude weighing factors, d is larger than f, and s + d + f is 3.6218.
5. The big data-based medical supervision system according to claim 4, wherein the first card punching level corresponds to 15 times of non-on-time punching and above, the second card punching level corresponds to 5 times to 15 times of non-on-time punching, and the third card punching level corresponds to 5 times of non-on-time punching and below; the first class of discipline and the complaint feedback and the reporting criticism are corresponding to 5 times or more, the second class of discipline and the complaint feedback and the reporting criticism are corresponding to 2 to 5 times, and the third class of discipline and the complaint feedback and the reporting criticism are corresponding to 2 times or less; the first dedication level and the total time of the overtime and the duty on duty are corresponding to 100 hours or more, the second dedication level and the total time of the overtime and the duty on duty are corresponding to 40 hours to 100 hours, and the third dedication level and the total time of the overtime and the duty on duty are corresponding to 40 hours or less.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112232843A (en) * | 2020-11-11 | 2021-01-15 | 北京三维天地科技股份有限公司 | Drug supervision system and method based on big data technology |
| CN112582056A (en) * | 2020-12-21 | 2021-03-30 | 曙光星云信息技术（北京）有限公司 | Regional medical information management platform based on big data technology |
| CN115309965A (en) * | 2022-10-12 | 2022-11-08 | 东营市第二人民医院 | Hospital document file online management system based on big data |
| CN117038028A (en) * | 2023-07-06 | 2023-11-10 | 长沙云享医康科技有限公司 | Hospital medical quality supervision and management system and method |
-
2020
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112232843A (en) * | 2020-11-11 | 2021-01-15 | 北京三维天地科技股份有限公司 | Drug supervision system and method based on big data technology |
| CN112582056A (en) * | 2020-12-21 | 2021-03-30 | 曙光星云信息技术（北京）有限公司 | Regional medical information management platform based on big data technology |
| CN115309965A (en) * | 2022-10-12 | 2022-11-08 | 东营市第二人民医院 | Hospital document file online management system based on big data |
| CN115309965B (en) * | 2022-10-12 | 2023-02-03 | 东营市第二人民医院 | Hospital document file online management system based on big data |
| CN117038028A (en) * | 2023-07-06 | 2023-11-10 | 长沙云享医康科技有限公司 | Hospital medical quality supervision and management system and method |
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