CN111613292A - Reasonable medication judgment decision system based on big data - Google Patents
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Abstract
The invention discloses a rational medication judgment decision system based on big data, which comprises a medication acquisition module, a medication analysis module, a controller, a signal editing module, a medication rule processing module, a medication rule collection module and a display screen, wherein the medication acquisition module is used for acquiring medication information; the invention combines and compares the patient condition, the illness state and the medication condition of each same department among hospitals together in parallel, makes targeted division on medication supervision conditions corresponding to the departments through the definition of the patient, the illness state and the medication data, the assignment and comparison of a range decision layer, the analysis of a weight decision formula and the cross judgment processing of a point value range, and makes formula combination comparison and feedback in horizontal and vertical directions on the medication holding quantity, the medication input quantity and the medication output quantity of the departments of the hospitals, so as to ensure the rationality of the use of the drugs and the continuity of the operation of the medication quantity through the parallel measurement mode of the medication of the departments and the comprehensive hierarchical data processing.
Description
Technical Field
The invention relates to the technical field of a rational medication judgment decision system, in particular to a rational medication judgment decision system based on big data.
Background
The reasonable medication is a treatment guidance scheme which is based on the medicine theory, safe, effective, economical and appropriate for using the medicine; rational administration emphasizes not only the effectiveness of the drug, but also the economic tolerance of the patient.
Most of the existing rational drug use judgment decision systems only carry out data monitoring and threshold value alarming on the aspect of drug use amount, can not comprehensively carry out deep analysis and judgment on the use condition and the operation condition of the drug, and the patient condition, the illness state and the medication state of each same department among hospitals are difficult to be combined and compared in parallel, the medication supervision states corresponding to the departments are divided in a targeted way through the definition of the patient, the illness state and the medication data, the assignment and comparison of a range decision layer, the weight decision formula analysis and the point value range cross judgment processing, and making horizontal and vertical bidirectional formulas for the medicine-holding quantity, the medicine-input quantity and the medicine-output quantity of the department of the hospital to combine, compare and feed back, the reasonability of the use of the medicine and the continuity of the running of the dosage are ensured by a parallel measurement mode of the department medicine and comprehensive hierarchical data processing;
in order to solve the above-mentioned drawbacks, a technical solution is now provided.
Disclosure of Invention
The invention aims to provide a rational medicine taking judgment decision system based on big data, which is characterized in that the patient conditions, the illness states and the medicine taking conditions of each same department among hospitals are combined and compared together in parallel, the range decision layer is assigned and compared, the weight decision formula analysis and the point value range cross judgment processing are carried out through the definition of the patient, the illness states and the medicine taking data, the range decision layer is assigned and compared, the medicine taking supervision states corresponding to the departments are divided in a targeted mode, the medicine taking reserve, the medicine taking input quantity and the medicine taking output quantity of the departments of the hospitals are combined and compared with a formula in a transverse and longitudinal direction and fed back, and the rationality of the medicine use and the continuity of the medicine quantity operation are ensured through the parallel measurement mode of the department medicine taking and the comprehensive hierarchical data processing.
The technical problems to be solved by the invention are as follows:
how to solve the problem that most of the existing rational medication judgment decision systems only carry out data monitoring and threshold value alarming on the aspect of medication dosage, cannot comprehensively carry out deep analysis and judgment on the use condition and the operation condition of the medication, is difficult to carry out parallel combination and comparison on the condition of a patient, the condition of an illness and the medication condition of each same department among hospitals, carries out targeted division on the medication supervision condition corresponding to the department through the definition of the patient, the condition of the illness and the medication data, assignment comparison of a range decision layer, weight decision formula analysis and point value range cross judgment processing, carries out formula combination comparison and feedback on medication reserve, medication input quantity and medication output quantity of the department of the hospital in a transverse and longitudinal direction, and carries out comprehensive hierarchical data processing through a parallel measurement mode and comprehensive hierarchical data processing of the medication of the department, to ensure the reasonability of the medicine use and the continuity of the medicine quantity operation.
The purpose of the invention can be realized by the following technical scheme:
a rational medication judgment decision system based on big data comprises a medication acquisition module, a medication analysis module, a controller, a signal editing module, a medication rule processing module, a medication rule collection module and a display screen;
the medication acquisition module is used for acquiring the information of the medication of the state of illness of each same department among hospitals within a period of time and transmitting the information to the medication analysis module;
the medication analysis module carries out medication supervision analysis operation on the medication information according to the received medical condition medication information of each same department among hospitals within a period of time to obtain a non-supervision-required signal, a normal supervision signal and a supervision processing signal corresponding to each same department among the hospitals within a first time level, transmits the non-supervision-required signal, the normal supervision signal and the supervision processing signal to the signal editing module through the controller, and transmits the supervision processing signal to the medication rule processing module through the controller;
the signal editing module edits the 'department medication specification without excessive supervision' text to be sent to a display screen according to each same department corresponding to the received signal without supervision; the signal editing module edits a text that the department has reasonable medicine use and keeps supervision strength according to each same department corresponding to the received normal supervision signal and sends the text to a display screen; the signal editing module edits texts of 'department over-medication and supervision measure increase' according to each same department corresponding to the received supervision processing signal, and the texts are sent to a display screen through the flashing marks;
the medicine rule processing module calls the medicine use reserve, the medicine use input quantity and the medicine use output quantity of each same department among hospitals in the same time level corresponding to the same department from the medicine rule collecting module through each same department in the supervision processing signal, and carries out the medicine use operation monitoring processing operation on the medicine rule processing module, and the specific steps are as follows:
the method comprises the following steps: acquiring the medicine use reserve, the medicine use input quantity and the medicine use output quantity of each same department among hospitals in a first time level corresponding to the supervision processing signal, and respectively marking the medicine use reserve, the medicine use input quantity and the medicine use output quantity of each department of any one of the hospitals as Az, Sz and Dz, wherein z is 1.
Step two: firstly according to the formula of horizontal movement
Obtaining a department drug solid content factor F of any hospital in a first time level corresponding to the supervision processing signal, wherein rho is a rated department drug solid content ratio, and the rho is 1.2151; then according to the column formula
Obtaining a department drug dosage factor G of any hospital in a first time level corresponding to the supervision processing signal, wherein a, s and d are dosage evaluation factors, a is larger than s and larger than d, and a + s + d is 4.2185, namely calculation items of horizontal acting drug dosage holding quantity, drug dosage input quantity and drug dosage output quantity, and the column is a calculation item of each department;
step three: when the department medicine solid quantity factor F of any hospital in the first time level corresponding to the supervision processing signal is larger than a preset value F, and the department medicine dosage factor G of any hospital in the first time level corresponding to the supervision processing signal is larger than a preset value G, the hospital generates a signal with sufficient medicine property dosage;
the medicine rule collection module is used for collecting medicine use reserve, medicine use input quantity and medicine use output quantity of each same department among hospitals, storing the medicine use reserve, the medicine use input quantity and the medicine use output quantity into an internal folder, and obtaining all the data according to modes such as a network medical platform and the like;
the medicine preserving quantity represents the medicine preserving and storing quantity of the same action and effect type, the medicine input quantity represents the medicine input and introduction quantity of the same action and effect type, and the medicine output quantity represents the medicine output and use quantity of the same action and effect type;
and transmitting the enough drug property quantity signal, the missing drug property quantity signal or the steady drug property quantity signal of the hospital in the first time level corresponding to the supervision processing signal to the signal editing module;
the signal editing module edits a text that the dosage of a department is large but the medicine supply is sufficient and the medicine purchasing decision power is reduced according to the received signal that the dosage of the medicine property of the hospital in the first time level corresponding to the supervision processing signal is sufficient, and the text is sent to a display screen; the signal editing module edits a text that the dosage of a department is large and the medicine supply is insufficient and the medicine purchasing decision power is increased according to the medicine dosage missing signal of the hospital within a first time level corresponding to the received supervision processing signal, and the text is sent to a display screen through letter marks; the signal editing module edits a text of 'large dosage for department and stable medicine supply, and keeps purchasing decision power for medicine' according to a steady signal of the dosage of medicine property of the hospital in a first time level corresponding to the received supervision processing signal and sends the text to a display screen.
Furthermore, the medication information of the disease condition consists of the number of medicines, the price of medicines, the number of people who take medicines, the age of the patient, the income of the patient, the duration of the disease condition and the number of times of repeated disease conditions, the corresponding units are boxes or bottles, yuan, one, year, yuan, year and times respectively, and the data are obtained according to a network medical platform and other modes;
the specific steps of the medication supervision and analysis operation are as follows:
the method comprises the following steps: acquiring condition medication information of each same department among hospitals within a first time level, and calibrating a medication weighing index Qi by dividing the total medication quantity by the total medication number and multiplying the average medication price by the average medication price according to the medication number, medication price and medication number associated with medication in the condition medication information, wherein i is 1.
Acquiring condition medication information of each same department among hospitals within a first time level, and calibrating a patient weighing index Wi, i is 1.. n by dividing the average age of the patient by the average income of the patient according to the patient age and the patient income related to the patient in the condition medication information;
acquiring the medication information of the disease condition of each same department among hospitals within a first time level, and calibrating the disease condition measurement index Ei by multiplying the total disease condition duration by the total disease condition repetition number, wherein the disease condition duration and the disease condition repetition number are related to the disease condition in each department, and the i is 1.
Qi, Wi and Ei are in one-to-one correspondence with each other, the first time level represents the time of one month, the medication weighing index Qi of each same department among hospitals in the first time level, the patient weighing index Wi of each same department among hospitals in the first time level, and the illness state weighing index Ei of each same department among hospitals in the first time level, wherein the variable i is in one-to-one correspondence with each same department, and the variable n represents a positive integer greater than 1;
step two: when the medication weighing index Qi of each same department among hospitals in the first time level is larger than the maximum value of the rated decision medication range M, is positioned in the rated decision medication range M and is smaller than the minimum value of the rated decision medication range M, scalar positive values M1, M2 and M3 are respectively assigned to the medication weighing index Qi, and M1 is larger than M2 and is larger than M3;
when the patient metric indices Wi of each same department among hospitals within the first time class are respectively located at a first patient decision level, a second patient decision level, a third patient decision level and a fourth patient decision level, scalar positive values N1, N2, N3 and N4 are respectively assigned, and N1 is less than N2 and less than N3 and less than N4;
when the disease state measurement indexes Ei of each same department among hospitals in the first time level are respectively positioned in a first disease state decision layer, a second disease state decision layer and a third disease state decision layer, scalar positive values L1, L2 and L3 are respectively given to the disease state measurement indexes Ei, and L1 is smaller than L2 and smaller than L3;
step three: respectively endowing a medication weighing index Qi, a patient weighing index Wi and an illness state weighing index Ei of each same department among hospitals in a first time level with decision weight coefficients q, w and e, wherein q is larger than e and is larger than w, and q + w + e is 5.2152;
obtaining medication supervision factors Ri of each same department among hospitals in a first time level according to a formula Ri Qi q w Ei n, and the i 1
Obtaining the evaluation quantity T of the medication supervision factors Ri of each same department among hospitals within a first time level;
step four: when the evaluation quantity T of the medication supervision factor Ri of each same department among hospitals in the first time level is larger than the maximum value of the preset evaluation range r, generating a supervision-free signal together for each same department corresponding to the medication supervision factor Ri in the preset evaluation range r, generating a normal supervision signal together for each same department corresponding to the medication supervision factor Ri between the maximum value of the preset evaluation range r and the evaluation quantity T, and generating a supervision processing signal together for each same department corresponding to the medication supervision factor Ri exceeding the evaluation quantity T;
when the evaluation quantity T of the medication supervision factor Ri of each same department among hospitals in the first time level is within the preset evaluation range r, the minimum value of the preset evaluation range r and each same department corresponding to the medication supervision factor Ri among the evaluation quantities T generate a no supervision signal together, the maximum value of the preset evaluation range r and each same department corresponding to the medication supervision factor Ri among the evaluation quantities T generate a normal supervision signal together, each same department corresponding to the medication supervision factor Ri outside the preset evaluation range r generates a supervision processing signal together, and the minimum value of the preset evaluation range r of the medication supervision factor Ri of each same department among the hospitals in the first time level is 0, so that the analysis process of the two types of conditions is obtained.
Further, the rated decision medication range m is between 400 and 800; below the first patient decision level representation 1/1000, the second patient decision level representation 1/1000-3/1000, the third patient decision level representation 3/1000-9/1000, the fourth patient decision level representation 9/1000 or above; the first disease decision layer is less than 0.5, the second disease decision layer is between 0.5 and 2.0, the third disease decision layer is more than 2.0, and the range and the numerical value of the items are obtained by comprehensive and hierarchical recording contents on the network medical platform.
The invention has the beneficial effects that:
the method comprises the steps of collecting medication information of the disease state of each same department among hospitals, wherein the medication information of the disease state consists of medication quantity, medication price, medication number, patient age, patient income, disease duration and disease repetition times, and carrying out medication supervision analysis operation on the medication information, namely, recalibrating medication data, disease data and patient data in the medication information of the disease state of each same department among the hospitals, carrying out assignment comparison, weight decision formula analysis and point value range cross judgment processing on the medication data, and dividing to obtain various supervision signals corresponding to each same department among the hospitals;
the method comprises the steps of carrying out text editing display on various supervision signals, calling medicine use reserve quantity, medicine use input quantity and medicine use output quantity of each same department among hospitals corresponding to supervision processing signals, carrying out medicine use operation monitoring processing operation on the medicine use reserve quantity, the medicine use input quantity and the medicine use output quantity, wherein the medicine use reserve quantity represents the medicine storage quantity of the same action effect type, the medicine use input quantity represents the medicine input and introduction quantity of the same action effect type, and the medicine use output quantity represents the medicine output and use quantity of the same action effect type; the method comprises the steps of calibrating the medicine use holding amount, the medicine use input amount and the medicine use output amount of each department of the hospital, dividing the medicine use holding amount, the medicine use input amount and the medicine use output amount to obtain various medicine use amount signals of the hospital corresponding to supervision processing signals through combination of comparison and feedback of a horizontal and vertical bidirectional formula, and sending, displaying and marking the medicine use amount signals to output, namely comparing the medicine use amount signals of the parallel departments to the integral medicine use amount analysis of the hospital to comprehensively make deep analysis and judgment on the use condition and the operation condition of the medicine;
and then the patient condition, the illness state and the medication condition of each same department among hospitals are combined and compared together in parallel, the medication supervision conditions corresponding to the departments are divided in a targeted manner through the definition of the patient, the illness state and the medication data, the assignment and comparison of a range decision layer, the analysis of a weight decision formula and the cross judgment processing of a point value range, and the medication reservation quantity, the medication input quantity and the medication output quantity of the departments of the hospitals are combined, compared and fed back in a formula in a transverse and longitudinal direction, so that the rationality of the use of the medicine and the continuity of the operation of the dosage are ensured through the parallel measurement mode of the medication of the departments and the comprehensive hierarchical data processing.
Drawings
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 rational medication judgment decision system based on big data comprises a medication acquisition module, a medication analysis module, a controller, a signal editing module, a medication rule processing module, a medication rule collection module and a display screen;
the medicine collection module collects the medicine information of the state of illness of each same department among hospitals in a period of time and transmits the medicine information to the medicine analysis module, and the medicine information of the state of illness is composed of medicine quantity, medicine price, medicine number, patient age, patient income, duration of the state of illness and the number of times of repeated state of illness;
the medication analysis module carries out medication supervision and analysis operation on the medication information according to the received medical condition medication information of each same department among hospitals within a period of time, and the specific steps are as follows:
the method comprises the following steps: acquiring condition medication information of each same department among hospitals within a first time level, and calibrating a medication weighing index Qi by dividing the total medication quantity by the total medication number and multiplying the average medication price by the average medication price according to the medication number, medication price and medication number associated with medication in the condition medication information, wherein i is 1.
Acquiring condition medication information of each same department among hospitals within a first time level, and calibrating a patient weighing index Wi, i is 1.. n by dividing the average age of the patient by the average income of the patient according to the patient age and the patient income related to the patient in the condition medication information;
acquiring the medication information of the disease condition of each same department among hospitals within a first time level, and calibrating the disease condition measurement index Ei by multiplying the total disease condition duration by the total disease condition repetition number, wherein the disease condition duration and the disease condition repetition number are related to the disease condition in each department, and the i is 1.
Qi, Wi and Ei are in one-to-one correspondence with each other, and the first time level represents the duration of one month;
step two: when the medication weighing index Qi of each same department among hospitals in the first time level is larger than the maximum value of the rated decision medication range M, is positioned in the rated decision medication range M and is smaller than the minimum value of the rated decision medication range M, scalar positive values M1, M2 and M3 are respectively assigned to the medication weighing index Qi, wherein M1 is larger than M2 and larger than M3, and the rated decision medication range M represents a time range of 400-800;
when the patient metric indices Wi for each of the same department across hospitals within the first time horizon are at the first, second, third and fourth patient decision levels, respectively, then scalar positive values N1, N2, N3 and N4 are assigned, respectively, with N1 being less than N2 being less than N3 being less than N4, while the first patient decision level represents below 1/1000, the second patient decision level represents between 1/1000 and 3/1000, the third patient decision level represents between 3/1000 and 9/1000, and the fourth patient decision level represents above 9/1000;
when the disease state measurement indexes Ei of each same department among hospitals in the first time level are respectively positioned in a first disease state decision layer, a second disease state decision layer and a third disease state decision layer, scalar positive values L1, L2 and L3 are respectively given to the disease state measurement indexes Ei, L1 is smaller than L2 and smaller than L3, the first disease state decision layer represents that the disease state measurement indexes are less than 0.5, the second disease state decision layer represents that the disease state measurement indexes are between 0.5 and 2.0, and the third disease state decision layer represents that the disease state measurement indexes are more than 2.0;
step three: respectively endowing a medication weighing index Qi, a patient weighing index Wi and an illness state weighing index Ei of each same department among hospitals in a first time level with decision weight coefficients q, w and e, wherein q is larger than e and is larger than w, and q + w + e is 5.2152;
obtaining medication supervision factors Ri of each same department among hospitals in a first time level according to a formula Ri Qi q w Ei n, and the i 1
Obtaining the evaluation quantity T of the medication supervision factors Ri of each same department among hospitals within a first time level;
step four: when the evaluation quantity T of the medication supervision factor Ri of each same department among hospitals in the first time level is larger than the maximum value of the preset evaluation range r, generating a supervision-free signal together for each same department corresponding to the medication supervision factor Ri in the preset evaluation range r, generating a normal supervision signal together for each same department corresponding to the medication supervision factor Ri between the maximum value of the preset evaluation range r and the evaluation quantity T, and generating a supervision processing signal together for each same department corresponding to the medication supervision factor Ri exceeding the evaluation quantity T;
when the evaluation quantity T of the medication supervision factor Ri of each same department among hospitals in the first time level is within the preset evaluation range r, generating a no-supervision-needed signal together with each same department corresponding to the medication supervision factor Ri between the evaluation quantities T and the minimum value of the preset evaluation range r, generating a normal supervision signal together with each same department corresponding to the medication supervision factor Ri between the evaluation quantities T and the maximum value of the preset evaluation range r, generating a supervision processing signal together with each same department corresponding to the medication supervision factor Ri outside the preset evaluation range r, and setting the minimum value of the preset evaluation range r of the medication supervision factor Ri of each same department among hospitals in the first time level to be 0, namely the analysis process of the two types of conditions;
the method comprises the steps of obtaining a non-supervision-required signal, a normal supervision signal and a supervision processing signal corresponding to each same department among hospitals in a first time level, transmitting the non-supervision-required signal, the normal supervision signal and the supervision processing signal to a signal editing module through a controller, and transmitting the supervision processing signal to a medicine rule processing module through the controller;
the signal editing module edits the 'department medication specification without excessive supervision' text to be sent to a display screen according to each same department corresponding to the received signal without supervision; the signal editing module edits a text that the department has reasonable medicine use and keeps supervision strength according to each same department corresponding to the received normal supervision signal and sends the text to a display screen; the signal editing module edits a text of 'department over-medication and supervision measure increase' according to each same department corresponding to the received supervision processing signal, and the text is sent to a display screen through a flashing mark;
the medicine rule processing module calls the medicine use reserve, the medicine use input quantity and the medicine use output quantity of each same department among hospitals in the same time level corresponding to the same department from the medicine rule collecting module through each same department in the supervision processing signal, and carries out the medicine use operation monitoring processing operation on the medicine rule processing module, and the specific steps are as follows:
the method comprises the following steps: acquiring the medicine use reserve, the medicine use input quantity and the medicine use output quantity of each same department among hospitals in a first time level corresponding to the supervision processing signal, and respectively marking the medicine use reserve, the medicine use input quantity and the medicine use output quantity of each department of any one of the hospitals as Az, Sz and Dz, wherein z is 1.
Step two: firstly according to the formula of horizontal movement
Obtaining a department drug solid content factor F of any hospital in a first time level corresponding to the supervision processing signal, wherein rho is a rated department drug solid content ratio, and the rho is 1.2151; then according to the column formula
Obtaining a department drug dosage factor G of any hospital in a first time level corresponding to the supervision processing signal, wherein a, s and d are dosage evaluation factors, a is larger than s and larger than d, and a + s + d is 4.2185, namely calculation items of horizontal acting drug dosage holding quantity, drug dosage input quantity and drug dosage output quantity, and the column is a calculation item of each department;
step three: when the department medicine solid quantity factor F of any hospital in the first time level corresponding to the supervision processing signal is larger than a preset value F, and the department medicine dosage factor G of any hospital in the first time level corresponding to the supervision processing signal is larger than a preset value G, the hospital generates a signal with sufficient medicine property dosage;
the medicine rule collecting module collects the medicine use holding amount, the medicine use input amount and the medicine use output amount of each same department among hospitals and stores the medicine use holding amount, the medicine use input amount and the medicine use output amount into an internal folder;
the medicine preserving quantity represents the medicine preserving and storing quantity of the same action and effect type, the medicine input quantity represents the medicine input and introduction quantity of the same action and effect type, and the medicine output quantity represents the medicine output and use quantity of the same action and effect type;
and transmitting the enough drug property quantity signal, the missing drug property quantity signal or the steady drug property quantity signal of the hospital in the first time level corresponding to the supervision processing signal to the signal editing module;
the signal editing module edits a text that the dosage of a department is large but the medicine supply is sufficient and the medicine purchasing decision power is reduced according to the received signal that the dosage of the medicine property of the hospital in the first time level corresponding to the supervision processing signal is sufficient, and the text is sent to a display screen; the signal editing module edits a text that the medicine consumption of a department is large and the medicine supply is insufficient and the medicine purchasing decision power is increased according to the medicine property consumption missing signal of the hospital in a first time level corresponding to the received supervision processing signal, and the text is sent to a display screen through letter marks; the signal editing module edits a text of 'large dosage for department and stable medicine supply, and keeps purchasing decision power for medicine' according to the steady signal of the medicine property dosage of the hospital in the first time level corresponding to the received supervision processing signal and sends the text to the display screen.
The invention combines and compares the patient condition, the illness state and the medication condition of each same department among hospitals together in parallel, makes targeted division on medication supervision conditions corresponding to the departments through the definition of the patient, the illness state and the medication data, the assignment and comparison of a range decision layer, the analysis of a weight decision formula and the cross judgment processing of a point value range, and makes formula combination comparison and feedback in horizontal and vertical directions on the medication holding quantity, the medication input quantity and the medication output quantity of the departments of the hospitals, so as to ensure the rationality of the use of the drugs and the continuity of the operation of the medication quantity through the parallel measurement mode of the medication of the departments and the comprehensive hierarchical data processing.
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 (3)
1. A rational medication judgment decision system based on big data is characterized by comprising a medication acquisition module, a medication analysis module, a controller, a signal editing module, a medication rule processing module, a medication rule collection module and a display screen;
the medication acquisition module is used for acquiring the information of the medication of the state of illness of each same department among hospitals within a period of time and transmitting the information to the medication analysis module;
the medication analysis module carries out medication supervision analysis operation on the medication information according to the received medical condition medication information of each same department among hospitals within a period of time to obtain a non-supervision-required signal, a normal supervision signal and a supervision processing signal corresponding to each same department among the hospitals within a first time level, transmits the non-supervision-required signal, the normal supervision signal and the supervision processing signal to the signal editing module through the controller, and transmits the supervision processing signal to the medication rule processing module through the controller;
the signal editing module edits the 'department medication specification without excessive supervision' text to be sent to a display screen according to each same department corresponding to the received signal without supervision; the signal editing module edits a text that the department has reasonable medicine use and keeps supervision strength according to each same department corresponding to the received normal supervision signal and sends the text to a display screen; the signal editing module edits texts of 'department over-medication and supervision measure increase' according to each same department corresponding to the received supervision processing signal, and the texts are sent to a display screen through the flashing marks;
the medicine rule processing module calls the medicine use reserve, the medicine use input quantity and the medicine use output quantity of each same department among hospitals in the same time level corresponding to the same department from the medicine rule collecting module through each same department in the supervision processing signal, and carries out the medicine use operation monitoring processing operation on the medicine rule processing module, and the specific steps are as follows:
the method comprises the following steps: acquiring the medicine use reserve, the medicine use input quantity and the medicine use output quantity of each same department among hospitals in a first time level corresponding to the supervision processing signal, and respectively marking the medicine use reserve, the medicine use input quantity and the medicine use output quantity of each department of any one of the hospitals as Az, Sz and Dz, wherein z is 1.
Step two: firstly according to the formula of horizontal movement
Obtaining a department drug solid content factor F of any hospital in a first time level corresponding to the supervision processing signal, wherein rho is a rated department drug solid content ratio, and the rho is 1.2151; then according to the column formula
Obtaining a medicine dosage factor G of any department of hospital in a first time level corresponding to the supervision processing signal, wherein a, s and d are dosage evaluation factors, a is larger than s and larger than d, and a + s + d is 4.2185;
step three: when the department medicine solid quantity factor F of any hospital in the first time level corresponding to the supervision processing signal is larger than a preset value F, and the department medicine dosage factor G of any hospital in the first time level corresponding to the supervision processing signal is larger than a preset value G, the hospital generates a signal with sufficient medicine property dosage;
the medicine rule collecting module is used for collecting the medicine use holding quantity, the medicine use input quantity and the medicine use output quantity of each same department among hospitals and storing the medicine use holding quantity, the medicine use input quantity and the medicine use output quantity into the internal folder;
the medicine preserving quantity represents the medicine preserving and storing quantity of the same action and effect type, the medicine input quantity represents the medicine input and introduction quantity of the same action and effect type, and the medicine output quantity represents the medicine output and use quantity of the same action and effect type;
and transmitting the enough drug property quantity signal, the missing drug property quantity signal or the steady drug property quantity signal of the hospital in the first time level corresponding to the supervision processing signal to the signal editing module;
the signal editing module edits a text that the dosage of a department is large but the medicine supply is sufficient and the medicine purchasing decision power is reduced according to the received signal that the dosage of the medicine property of the hospital in the first time level corresponding to the supervision processing signal is sufficient, and the text is sent to a display screen; the signal editing module edits a text that the dosage of a department is large and the medicine supply is insufficient and the medicine purchasing decision power is increased according to the medicine dosage missing signal of the hospital within a first time level corresponding to the received supervision processing signal, and the text is sent to a display screen through letter marks; the signal editing module edits a text of 'large dosage for department and stable medicine supply, and keeps purchasing decision power for medicine' according to a steady signal of the dosage of medicine property of the hospital in a first time level corresponding to the received supervision processing signal and sends the text to a display screen.
2. The big data-based rational medication judgment decision system according to claim 1, wherein the medication information of the disease state is composed of medication amount, medication price, medication number, patient age, patient income, duration of the disease state and the number of times of repetition of the disease state;
the specific steps of the medication supervision and analysis operation are as follows:
the method comprises the following steps: acquiring condition medication information of each same department among hospitals within a first time level, and calibrating a medication weighing index Qi by dividing the total medication quantity by the total medication number and multiplying the average medication price by the average medication price according to the medication number, medication price and medication number associated with medication in the condition medication information, wherein i is 1.
Acquiring condition medication information of each same department among hospitals within a first time level, and calibrating a patient weighing index Wi, i is 1.. n by dividing the average age of the patient by the average income of the patient according to the patient age and the patient income related to the patient in the condition medication information;
acquiring the medication information of the disease condition of each same department among hospitals within a first time level, and calibrating the disease condition measurement index Ei by multiplying the total disease condition duration by the total disease condition repetition number, wherein the disease condition duration and the disease condition repetition number are related to the disease condition in each department, and the i is 1.
Qi, Wi and Ei are in one-to-one correspondence with each other, and the first time level represents the duration of one month;
step two: when the medication weighing index Qi of each same department among hospitals in the first time level is larger than the maximum value of the rated decision medication range M, is positioned in the rated decision medication range M and is smaller than the minimum value of the rated decision medication range M, scalar positive values M1, M2 and M3 are respectively assigned to the medication weighing index Qi, and M1 is larger than M2 and is larger than M3;
when the patient metric indices Wi of each same department among hospitals within the first time class are respectively located at a first patient decision level, a second patient decision level, a third patient decision level and a fourth patient decision level, scalar positive values N1, N2, N3 and N4 are respectively assigned, and N1 is less than N2 and less than N3 and less than N4;
when the disease state measurement indexes Ei of each same department among hospitals in the first time level are respectively positioned in a first disease state decision layer, a second disease state decision layer and a third disease state decision layer, scalar positive values L1, L2 and L3 are respectively given to the disease state measurement indexes Ei, and L1 is smaller than L2 and smaller than L3;
step three: respectively endowing a medication weighing index Qi, a patient weighing index Wi and an illness state weighing index Ei of each same department among hospitals in a first time level with decision weight coefficients q, w and e, wherein q is larger than e and is larger than w, and q + w + e is 5.2152;
obtaining medication supervision factors Ri of each same department among hospitals in a first time level according to a formula Ri Qi q w Ei n, and the i 1
Obtaining the evaluation quantity T of the medication supervision factors Ri of each same department among hospitals within a first time level;
step four: when the evaluation quantity T of the medication supervision factor Ri of each same department among hospitals in the first time level is larger than the maximum value of the preset evaluation range r, generating a supervision-free signal together for each same department corresponding to the medication supervision factor Ri in the preset evaluation range r, generating a normal supervision signal together for each same department corresponding to the medication supervision factor Ri between the maximum value of the preset evaluation range r and the evaluation quantity T, and generating a supervision processing signal together for each same department corresponding to the medication supervision factor Ri exceeding the evaluation quantity T;
when the evaluation quantity T of the medication supervision factors Ri of each same department among hospitals in the first time level is within the preset evaluation range r, the minimum value of the preset evaluation range r and each same department corresponding to the medication supervision factors Ri among the evaluation quantities T generate a non-supervision-required signal together, the maximum value of the preset evaluation range r and each same department corresponding to the medication supervision factors Ri among the evaluation quantities T generate a normal supervision signal together, and each same department corresponding to the medication supervision factors Ri outside the preset evaluation range r generates a supervision processing signal together.
3. The big-data-based rational medication judgment decision system according to claim 2, wherein the rated decision medication range m represents between 400 and 800; below the first patient decision level representation 1/1000, the second patient decision level representation 1/1000-3/1000, the third patient decision level representation 3/1000-9/1000, the fourth patient decision level representation 9/1000 or above; the first disease decision layer represents less than 0.5, the second disease decision layer represents 0.5 to 2.0, and the third disease decision layer represents more than 2.0.
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