CN113889212B - Pain relieving evaluation system for nursing of severe medical department - Google Patents
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Abstract
The invention provides an analgesic evaluation system for nursing of severe medical department, comprising: the data acquisition module is used for acquiring information acquisition instructions, acquiring information data of a patient based on the information acquisition instructions, and uploading the information data to the evaluation module; the evaluation module is used for receiving the information data, analyzing the information data and generating an analgesia evaluation report of the patient based on an analysis result; and the display module is used for generating a report data graph based on the analgesia evaluation report and displaying the report data graph. The analgesic evaluation report of the patient is determined by collecting and evaluating the information data of the patient, so that the accuracy of analgesic evaluation of the patient is improved, and meanwhile, the intuitiveness of the analgesic evaluation result of the patient is improved by displaying the report data graph, so that the high efficiency of the analgesic evaluation system for nursing of the critical medical department is greatly improved.
Description
Technical Field
The invention relates to the technical field of intelligent monitoring and evaluation, in particular to an analgesic evaluation system for nursing of severe medical science.
Background
Pain is currently the most common, most important symptom in the disease and is presented as subjective sensation to humans, and today the assessment of patient analgesia is generally described by the patient's own experiences as an assessment of the patient's degree of analgesia;
however, the evaluation of patient analgesia by description has a large unilateral performance, and meanwhile, in the present technology, the patient analgesia degree can be evaluated by facial recognition, which has a large uncertain factor, so that in order to improve the accuracy, intuitiveness and high efficiency of the analgesia evaluation of the severe medical science, the invention provides a nursing analgesia evaluation system for the severe medical science.
Disclosure of Invention
The invention provides a nursing analgesia evaluation system for severe medical science, which is used for acquiring information data of a patient and evaluating the information data to determine an analgesia evaluation report of the patient, so that the accuracy of the analgesia evaluation of the patient is improved, and meanwhile, the intuitiveness of the analgesia evaluation result of the patient is improved by displaying a report data diagram, so that the high efficiency of the nursing analgesia evaluation system for severe medical science is greatly improved.
A nursing analgesic assessment system for critical medical disciplines, comprising:
the data acquisition module is used for acquiring information acquisition instructions, acquiring information data of a patient based on the information acquisition instructions, and uploading the information data to the evaluation module;
the evaluation module is used for receiving the information data, analyzing the information data and generating an analgesia evaluation report of the patient based on an analysis result;
and the display module is used for generating a report data graph based on the analgesia evaluation report and displaying the report data graph.
Preferably, a nursing analgesia evaluation system for critical medical disciplines, the data acquisition module comprises:
the fingerprint data acquisition unit is used for acquiring fingerprint information of the target worker and identifying the fingerprint information to determine fingerprint data;
the matching unit is used for matching the fingerprint data with target fingerprint data in a preset fingerprint database and judging whether the target worker can acquire a login request currently;
when the fingerprint data are matched with target fingerprint data in a preset fingerprint database, judging that the target worker can acquire a login request;
Otherwise, judging that the target worker can not acquire the login request;
a login request acquisition unit, configured to generate a mapping relationship according to the fingerprint data and target fingerprint data in the preset fingerprint database, and generate a login request based on the mapping relationship;
the instruction generation unit is used for logging in a target system based on the login request, determining an instruction coding information node according to the acquisition requirement of the target worker, determining an instruction generation script based on the coding information node, and generating the information acquisition instruction based on the instruction generation script.
Preferably, a nursing analgesic evaluation system for critical medical disciplines, the evaluation module further comprises:
a basic information acquisition unit configured to acquire a name of the patient, and at the same time, determine an acquisition time of the analgesic evaluation report;
a label creation unit for creating a document label based on the name of the patient and the acquisition time of the analgesic evaluation report;
the filing unit is used for establishing a label for the analgesia evaluation report based on the document label and generating an analgesia document of the patient;
the encryption unit is used for acquiring the hospitalization number of the patient, and simultaneously setting the authority of the analgesic document according to the hospitalization number of the patient and the name of the patient;
And the storage unit is used for acquiring a file storage path and storing the analgesic document with the set authority through the file storage path.
Preferably, a nursing analgesia evaluation system for critical medical disciplines, the data acquisition module comprises:
the instruction reading unit is used for reading the information acquisition instruction, determining the operation logic and the operation field of the information acquisition instruction based on the reading result;
the instruction calculation unit is used for carrying out instruction operation based on the operation logic to determine the instruction content of the information acquisition instruction, determining the operation step of the information acquisition instruction based on the instruction content of the information acquisition instruction and the operation field, and generating a target sub-instruction based on the operation step, wherein the information acquisition instruction comprises: the first target sub-instruction, the second target sub-instruction and the third target sub-instruction;
the instruction execution unit is used for determining first information data of the patient according to the first target sub-instruction, determining second information data of the patient according to the second target sub-instruction, and determining third data information of the patient according to the third target sub-instruction;
An information data encapsulation unit, configured to generate a data information list according to the first information data, the second information data, and the third information data, and at the same time, check the data information list according to a preset data detection rule, and determine whether the data of the data information list is complete;
if the data information list accords with the preset detection rule, judging that the data of the data information list is complete, and meanwhile, packaging the data in the data information list to obtain an information data packet of the patient;
otherwise, the information data of the patient is acquired again according to the information acquisition instruction, and when the data information list accords with the preset detection rule, an information data packet of the patient is acquired.
Preferably, a nursing analgesic evaluation system for critical medical disciplines, the evaluation module comprising:
an information data reading unit, configured to read information data of the patient and determine a data identifier of the information data, where the data identifier of the information data includes: the identity information identification of the patient, the case identification of the patient and the current sign data identification of the patient;
The data classification unit is used for respectively generating a first data layer, a second data layer and a third data layer based on the identity information identification of the patient, the case identification of the patient and the current sign data identification of the patient, and simultaneously respectively filling the information data of the patient into the first data layer and the second data layer based on the third data layer according to the information identification of the information data;
the first data layer reading unit is used for reading the identity information data of the patient, determining the age data and the sex data of the patient based on the identity information data of the patient, and simultaneously, matching the age data and the sex data in a preset reference database to determine a first analgesia index of the patient;
the second data layer reading unit is used for reading the case information data of the patient, carrying out structuring processing on the case information data of the patient and determining a structural map of the patient;
the map reading unit is used for reading the structural map, evaluating and determining the health weight of the patient, and simultaneously, performing health evaluation on the patient according to a preset algorithm and the health weight of the patient, and determining a second analgesia index of the patient based on an evaluation result;
A third data layer reading unit, configured to input current sign information data of the patient into a preset convolutional neural network, perform learning training on the current sign information data according to the preset convolutional neural network, output a training result, and determine a third analgesia index of the patient according to the training result;
and an evaluation report generation unit configured to generate an analgesia evaluation report based on the first analgesia index, the second analgesia index, and the third analgesia index.
Preferably, a nursing analgesic evaluation system for critical medical disciplines, the evaluation report generation unit includes:
the reading report unit is used for reading the analgesia evaluation report, scoring the current analgesia degree of the patient according to the reading result and obtaining a scoring result;
a grading unit for determining an analgesic grade of the patient based on the scoring result;
the alarm unit is used for comparing the pain relieving grade of the patient with a preset pain relieving grade and judging whether an alarm is required or not;
when the analgesia grade of the patient is smaller than the preset analgesia grade, judging that no alarm is needed;
When the analgesia level of the patient is equal to the preset analgesia level, judging that an alarm is required, and executing a first alarm operation;
and when the pain relieving level of the patient is greater than the preset pain relieving level, judging that the patient needs to be warned, and executing a second warning operation.
Preferably, a nursing analgesic evaluation system for critical medical disciplines, the display module includes:
a keyword acquisition unit for retrieving keywords of the analgesic evaluation report;
a graph parameter determining unit, configured to determine graph parameter content based on the keyword of the analgesia evaluation report, and set an x axis and a y axis according to the graph parameter content, and at the same time, set minimum scale values of the x axis and the y axis according to the size of the report data value of the analgesia evaluation report;
and the report data graph generating unit is used for drawing the report data value of the analgesia evaluation report in a report data graph based on the graph parameters and the minimum scale values of the x axis and the y axis, and generating and displaying the report data graph.
Preferably, the pain relieving evaluation system for nursing of the critical medical department, the report data graph generating unit further includes:
The report summarizing unit is used for acquiring all analgesic evaluation reports in preset time in a preset report database;
the data summarizing unit is used for integrating report data of all pain relieving evaluation reports within the preset time and generating a data line graph based on an integration result;
and the pain relieving trend acquisition unit is used for reading the data line graph, determining the pain relieving trend of the patient, and estimating the pain relieving grade after the patient based on the pain relieving trend of the patient.
Preferably, a nursing analgesic evaluation system for critical medical disciplines, the evaluation module further comprises:
a detection parameter acquisition unit configured to acquire a time for performing analgesic detection on the patient and a speed for performing analgesic detection on the patient;
a first calculation unit configured to calculate an analgesic evaluation efficiency η for the patient based on a time of analgesic detection for the patient and a speed of analgesic detection for the patient;
wherein λ represents a regulator of analgesic evaluation efficiency for the patient and is valued (0.03,0.04); ζ represents an analgesic assessment factor for the patient and has a value in the range (0.98,0.99); t represents the time for analgesic testing of the patient; t represents a prescribed time for analgesic detection of the patient, and T is not less than T; v represents the speed of analgesic testing of the patient; v represents the same as the The patient performs analgesic detection at a specified speed, and V is more than or equal to V; k represents the amount of information data processed in a unit time; k represents the data amount of the specified information processed in unit time, and K is more than or equal to K; oc (oc) 1 Representing a first weight setting value, oc, associated with an analgesic assessment factor to thereby effect an assessment of analgesia 2 Representing a second weight setting value related to time, speed and information quantity, thereby realizing analgesic evaluation; and 0.5<∝ 1 +∝ 2 <1；
The comparison unit is used for comparing the calculated analgesia evaluation efficiency with preset analgesia evaluation efficiency and judging whether the analgesia evaluation system needs to be optimized or not;
when the analgesia evaluation efficiency is greater than or equal to the preset analgesia evaluation efficiency, judging that the analgesia evaluation system does not need to be optimized;
otherwise, judging that the analgesic evaluation system needs to be optimized;
the second calculation unit is used for calculating an optimization coefficient for optimizing the analgesia evaluation system based on the analgesia evaluation efficiency and the preset analgesia evaluation efficiency;
wherein δ represents an optimization factor for optimizing the analgesic evaluation system; η (eta) 1 Representing the preset analgesia evaluation efficiency; s represents the total information data amount actually processed; μ represents a data processing error rate, and the value range is (0.1, 0.2);
And the optimizing unit is used for optimizing the analgesia evaluation system based on the optimizing coefficient, and calculating the analgesia evaluation efficiency of the patient again until the analgesia evaluation efficiency of the patient reaches the preset analgesia evaluation efficiency, and stopping optimizing the analgesia evaluation system.
Compared with the prior art, the invention has the beneficial effects that:
1. the analgesic evaluation report of the patient is determined by collecting and evaluating the information data of the patient, so that the accuracy of analgesic evaluation of the patient is improved, and meanwhile, the intuitiveness of the analgesic evaluation result of the patient is improved by displaying the report data graph, so that the high efficiency of the analgesic evaluation system for nursing of the critical medical department is greatly improved.
2. By acquiring the identity, case identification and sign table identification of the patient, different data of the patient are stored in the corresponding data layer, and the analgesic condition of the patient under different conditions is ensured by analyzing the data of the different data layers, so that the analgesic condition of the patient is accurately evaluated, and the analgesic evaluation accuracy of the patient is improved.
3. The analgesic evaluation efficiency of the patient is accurately calculated, so that the performance of the analgesic evaluation system can be mastered in real time, and when the analgesic evaluation performance is too low, the evaluation efficiency is optimized through the optimization factor, so that the practicability of the system is greatly improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a block diagram of a care analgesic evaluation system for critical medical disciplines in accordance with an embodiment of the present invention;
FIG. 2 is a block diagram of a data acquisition module in a care analgesia evaluation system for critical medical disciplines in accordance with an embodiment of the present invention;
fig. 3 is a block diagram of an evaluation module in a nursing analgesic evaluation system for critical medical subjects according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Example 1:
the present embodiment provides a nursing analgesic evaluation system for critical medical disciplines, as shown in fig. 1, including:
the data acquisition module is used for acquiring information acquisition instructions, acquiring information data of a patient based on the information acquisition instructions, and uploading the information data to the evaluation module;
the evaluation module is used for receiving the information data, analyzing the information data and generating an analgesia evaluation report of the patient based on an analysis result;
and the display module is used for generating a report data graph based on the analgesia evaluation report and displaying the report data graph.
In this embodiment, the information data of the patient includes: patient identity information data, patient case information data, patient current sign information data;
the identity information data of the patient comprise the age, name, hospitalization number, height, weight and the like of the patient; the case information data of the patient may be: medical history of the patient, allergic history of the patient, and the like; the patient's current sign information data may be: a current blood pressure value, a current blood glucose value, a current heart rate value, etc. of the patient.
In this embodiment, the information acquisition instructions may be instructions for acquiring information data of the patient.
In this embodiment, the pain assessment report may be obtained by analyzing information data of a patient, determining the pain degree of the patient, and the like, and analyzing the pain condition of the patient.
The beneficial effects of the technical scheme are as follows: the analgesic evaluation report of the patient is determined by collecting and evaluating the information data of the patient, so that the accuracy of analgesic evaluation of the patient is improved, and meanwhile, the intuitiveness of the analgesic evaluation result of the patient is improved by displaying the report data graph, so that the high efficiency of the analgesic evaluation system for nursing of the critical medical department is greatly improved.
Example 2:
on the basis of embodiment 1, this embodiment provides a nursing analgesia evaluation system for severe medical department, as shown in fig. 2, the data acquisition module includes:
the fingerprint data acquisition unit is used for acquiring fingerprint information of the target worker and identifying the fingerprint information to determine fingerprint data;
the matching unit is used for matching the fingerprint data with target fingerprint data in a preset fingerprint database and judging whether the target worker can acquire a login request currently;
When the fingerprint data are matched with target fingerprint data in a preset fingerprint database, judging that the target worker can acquire a login request;
otherwise, judging that the target worker can not acquire the login request;
a login request acquisition unit, configured to generate a mapping relationship according to the fingerprint data and target fingerprint data in the preset fingerprint database, and generate a login request based on the mapping relationship;
the instruction generation unit is used for logging in a target system based on the login request, determining an instruction coding information node according to the acquisition requirement of the target worker, determining an instruction generation script based on the coding information node, and generating the information acquisition instruction based on the instruction generation script.
In this embodiment, the fingerprint information may be entered in advance by the target worker for obtaining the system login request.
In this embodiment, the targeted worker may be a doctor or nurse of the critical medical department.
In this embodiment, the login request may be used to login to a target system, and the target system may be a nursing analgesia assessment system of a critical medical discipline.
In this embodiment, the mapping relationship may be based on a correspondence relationship between fingerprint data and target fingerprint data.
In this embodiment, the acquisition requirement of the target worker may be data that the target worker wants to acquire, such as information data of the patient at the present time or information data of the patient at a previous time.
The beneficial effects of the technical scheme are as follows: the fingerprint data is confirmed, so that the safety of information data of a patient is protected, and the safety performance of the system is improved.
Example 3:
on the basis of embodiment 1, this embodiment provides a nursing analgesic evaluation system for critical medical department, as shown in fig. 3, the evaluation module further includes:
a basic information acquisition unit configured to acquire a name of the patient, and at the same time, determine an acquisition time of the analgesic evaluation report;
a label creation unit for creating a document label based on the name of the patient and the acquisition time of the analgesic evaluation report;
the filing unit is used for establishing a label for the analgesia evaluation report based on the document label and generating an analgesia document of the patient;
the encryption unit is used for acquiring the hospitalization number of the patient, and simultaneously setting the authority of the analgesic document according to the hospitalization number of the patient and the name of the patient;
And the storage unit is used for acquiring a file storage path and storing the analgesic document with the set authority through the file storage path.
In this embodiment, the document label may be established by the name of the patient and the time of acquisition of the pain assessment report, for classifying the pain management document.
The beneficial effects of the technical scheme are as follows: the document is established for the analgesic evaluation report, and the permission setting is added, so that the safety of the document is protected, and the integrity of data is improved by storing the analgesic document, so that the high efficiency of the analgesic evaluation system for nursing of the critical medical science is improved.
Example 4:
on the basis of embodiment 1, this embodiment provides a nursing analgesia evaluation system for severe medical department, the data acquisition module includes:
the instruction reading unit is used for reading the information acquisition instruction, determining the operation logic and the operation field of the information acquisition instruction based on the reading result;
the instruction calculation unit is used for carrying out instruction operation based on the operation logic to determine the instruction content of the information acquisition instruction, determining the operation step of the information acquisition instruction based on the instruction content of the information acquisition instruction and the operation field, and generating a target sub-instruction based on the operation step, wherein the information acquisition instruction comprises: the first target sub-instruction, the second target sub-instruction and the third target sub-instruction;
The instruction execution unit is used for determining first information data of the patient according to the first target sub-instruction, determining second information data of the patient according to the second target sub-instruction, and determining third data information of the patient according to the third target sub-instruction;
an information data encapsulation unit, configured to generate a data information list according to the first information data, the second information data, and the third information data, and at the same time, check the data information list according to a preset data detection rule, and determine whether the data of the data information list is complete;
if the data information list accords with the preset detection rule, judging that the data of the data information list is complete, and meanwhile, packaging the data in the data information list to obtain an information data packet of the patient;
otherwise, the information data of the patient is acquired again according to the information acquisition instruction, and when the data information list accords with the preset detection rule, an information data packet of the patient is acquired.
In this embodiment, the operation logic of the information acquisition instruction may be, for example, and operation logic, or operation logic, exclusive or operation logic, or the like.
In this embodiment, the first target sub-instruction may be an instruction to acquire first information data, the second target sub-instruction may be an instruction to acquire second information data, and the third target sub-instruction may be an instruction to acquire third information data.
In this embodiment, the first information data may be identity information data of the patient, the second information data may be case information data of the patient, and the third information data may be current sign information data.
In this embodiment, the preset data detection rule may be a standard for detecting whether the data in the data information list is complete, for example, it is: the preset data detection rule is that the total number of data is 100, and when the number of data in the detected data information list is 99, the data of the data information list is judged to be incomplete.
The beneficial effects of the technical scheme are as follows: by generating the corresponding information acquisition instruction, the accurate and effective acquisition of the pain data of the patient is realized, the acquired data is checked after the data acquisition, the accuracy of the data acquisition is improved, and the accuracy of pain relieving evaluation of the patient is improved.
Example 5:
on the basis of embodiment 1, this embodiment provides a nursing analgesic evaluation system for intensive care medicine, the evaluation module includes:
An information data reading unit, configured to read information data of the patient and determine a data identifier of the information data, where the data identifier of the information data includes: the identity information identification of the patient, the case identification of the patient and the current sign data identification of the patient;
the data classification unit is used for respectively generating a first data layer, a second data layer and a third data layer based on the identity information identification of the patient, the case identification of the patient and the current sign data identification of the patient, and simultaneously respectively filling the information data of the patient into the first data layer and the second data layer based on the third data layer according to the information identification of the information data;
the first data layer reading unit is used for reading the identity information data of the patient, determining the age data and the sex data of the patient based on the identity information data of the patient, and simultaneously, matching the age data and the sex data in a preset reference database to determine a first analgesia index of the patient;
the second data layer reading unit is used for reading the case information data of the patient, carrying out structuring processing on the case information data of the patient and determining a structural map of the patient;
The map reading unit is used for reading the structural map, evaluating and determining the health weight of the patient, and simultaneously, performing health evaluation on the patient according to a preset algorithm and the health weight of the patient, and determining a second analgesia index of the patient based on an evaluation result;
a third data layer reading unit, configured to input current sign information data of the patient into a preset convolutional neural network, perform learning training on the current sign information data according to the preset convolutional neural network, output a training result, and determine a third analgesia index of the patient according to the training result;
and an evaluation report generation unit configured to generate an analgesia evaluation report based on the first analgesia index, the second analgesia index, and the third analgesia index.
In this embodiment, the data identifier is a tag used to tag the patient information data type.
In this embodiment, the first data layer is for recording patient identification information, the second data layer is for recording patient case data, and the third data layer is for recording patient sign data.
In this embodiment, the preset reference database is set in advance and is used to store the reference data of the analgesic level.
In this embodiment, the first analgesic index is a specific reference value used to evaluate the current degree of analgesia of the user.
In this embodiment, the structuring may be converting the case information of the patient into a corresponding data structure, facilitating the generation of a corresponding case map.
In this embodiment, the health weight may be used to measure the health of the patient under different etiologies, for example, the patient has more current etiology, such as hypertension, hyperglycemia, etc., and the health weight of the patient is lower.
In this embodiment, the preset algorithm is set in advance, for example, may be a bayesian algorithm.
In this embodiment, the second analgesia index is the calculation of analgesia in different cases of the patient based on historical cases of the patient.
In this embodiment, the predetermined convolutional neural network is set in advance for training the patient's vital sign data.
In this embodiment, the third pain index is used to indicate the extent to which the patient is affected by the patient's current physical sign information.
The beneficial effects of the technical scheme are as follows: by acquiring the identity, case identification and sign table identification of the patient, different data of the patient are stored in the corresponding data layer, and the analgesic condition of the patient under different conditions is ensured by analyzing the data of the different data layers, so that the analgesic condition of the patient is accurately evaluated, and the analgesic evaluation accuracy of the patient is improved.
Example 6:
on the basis of embodiment 5, this embodiment provides a nursing analgesic evaluation system for intensive care medicine, the evaluation report generating unit including:
the reading report unit is used for reading the analgesia evaluation report, scoring the current analgesia degree of the patient according to the reading result and obtaining a scoring result;
a grading unit for determining an analgesic grade of the patient based on the scoring result;
the alarm unit is used for comparing the pain relieving grade of the patient with a preset pain relieving grade and judging whether an alarm is required or not;
when the analgesia grade of the patient is smaller than the preset analgesia grade, judging that no alarm is needed;
when the analgesia level of the patient is equal to the preset analgesia level, judging that an alarm is required, and executing a first alarm operation;
and when the pain relieving level of the patient is greater than the preset pain relieving level, judging that the patient needs to be warned, and executing a second warning operation.
In this embodiment, the preset analgesia level is set in advance, for example, when the analgesia level reaches level 4, an alarm is given.
The beneficial effects of the technical scheme are as follows: by dividing the analgesic grades of the patients, corresponding alarm operation is carried out when the patients are at different analgesic grades, so that a user can take corresponding measures in time, and the pain relieving nursing efficiency is improved.
Example 7:
on the basis of embodiment 1, this embodiment provides a nursing analgesia evaluation system for severe medical department, the display module includes:
a keyword acquisition unit for retrieving keywords of the analgesic evaluation report;
a graph parameter determining unit, configured to determine graph parameter content based on the keyword of the analgesia evaluation report, and set an x axis and a y axis according to the graph parameter content, and at the same time, set minimum scale values of the x axis and the y axis according to the size of the report data value of the analgesia evaluation report;
and the report data graph generating unit is used for drawing the report data value of the analgesia evaluation report in a report data graph based on the graph parameters and the minimum scale values of the x axis and the y axis, and generating and displaying the report data graph.
In this embodiment, the key word may be a certain data segment or word in the analgesia evaluation report that can indicate the patient's analgesia condition.
In this embodiment, the graphic parameter content may be content to be presented on the drawing.
The beneficial effects of the technical scheme are as follows: the method has the advantages that the corresponding chart form is generated by the analgesic data of the patient, so that the intuitiveness of the analgesic evaluation result of the patient is improved, and the high efficiency of the analgesic evaluation system for nursing of the critical medical department is greatly improved.
Example 8:
on the basis of embodiment 7, this embodiment provides a nursing analgesia evaluation system for serious medical department, the report data map generating unit further includes:
the report summarizing unit is used for acquiring all analgesic evaluation reports in preset time in a preset report database;
the data summarizing unit is used for integrating report data of all pain relieving evaluation reports within the preset time and generating a data line graph based on an integration result;
and the pain relieving trend acquisition unit is used for reading the data line graph, determining the pain relieving trend of the patient, and estimating the pain relieving grade after the patient based on the pain relieving trend of the patient.
In this embodiment, the preset report database is used to store all current analgesia report forms for the patient.
In this embodiment, the preset time is set in advance, and may be, for example, three days or the like.
The beneficial effects of the technical scheme are as follows: by summarizing all the current analgesic reports of the patient and preparing a corresponding data line graph, the patient or medical staff can know the analgesic level of the patient for a certain period of time, so that measures can be taken in advance conveniently, and the high efficiency of the analgesic evaluation system for nursing of severe medical department is improved greatly.
Example 9:
on the basis of embodiment 1, there is provided an analgesic evaluation system for nursing of a critical medical department in this embodiment, the evaluation module further includes:
a detection parameter acquisition unit configured to acquire a time for performing analgesic detection on the patient and a speed for performing analgesic detection on the patient;
a first calculation unit configured to calculate an analgesic evaluation efficiency η for the patient based on a time of analgesic detection for the patient and a speed of analgesic detection for the patient;
wherein λ represents a regulator of analgesic evaluation efficiency for the patient and is valued (0.03,0.04); ζ represents an analgesic assessment factor for the patient and has a value in the range (0.98,0.99); t represents the time for analgesic testing of the patient; t represents a prescribed time for analgesic detection of the patient, and T is not less than T; v represents the speed of analgesic testing of the patient; v represents a prescribed rate of analgesia detection of the patient, and V is greater than or equal to V; k represents the amount of information data processed in a unit time; k represents the data amount of the specified information processed in unit time, and K is more than or equal to K; oc (oc) 1 Representing a first weight setting value, oc, associated with an analgesic assessment factor to thereby effect an assessment of analgesia 2 Representing a second weight setting value related to time, speed and information quantity, thereby realizing analgesic evaluation; and 0.5<∝ 1 +∝ 2 <1；
The comparison unit is used for comparing the calculated analgesia evaluation efficiency with preset analgesia evaluation efficiency and judging whether the analgesia evaluation system needs to be optimized or not;
when the analgesia evaluation efficiency is greater than or equal to the preset analgesia evaluation efficiency, judging that the analgesia evaluation system does not need to be optimized;
otherwise, judging that the analgesic evaluation system needs to be optimized;
the second calculation unit is used for calculating an optimization coefficient for optimizing the analgesia evaluation system based on the analgesia evaluation efficiency and the preset analgesia evaluation efficiency;
wherein δ represents an optimization factor for optimizing the analgesic evaluation system; η (eta) 1 Representing the preset analgesia evaluation efficiency; s represents the total information data amount actually processed; μ represents a data processing error rate, and the value range is (0.1, 0.2);
and the optimizing unit is used for optimizing the analgesia evaluation system based on the optimizing coefficient, and calculating the analgesia evaluation efficiency of the patient again until the analgesia evaluation efficiency of the patient reaches the preset analgesia evaluation efficiency, and stopping optimizing the analgesia evaluation system.
In this embodiment, the analgesic evaluation efficiency may be a parameter used to measure whether the analgesic evaluation system needs to be optimized based on the time of analgesic detection for the patient and the efficiency of the analgesic evaluation obtained by the speed calculation of analgesic detection for the patient.
In this embodiment, the preset analgesia evaluation efficiency may be set in advance for determining whether the current analgesia evaluation efficiency meets the expected standard.
In this embodiment, the optimization factor may be a dimensionless factor that is used to represent the degree of optimization of the analgesic assessment system.
The beneficial effects of the technical scheme are as follows: the analgesic evaluation efficiency of the patient is accurately calculated, so that the performance of the analgesic evaluation system can be mastered in real time, and when the analgesic evaluation performance is too low, the evaluation efficiency is optimized through the optimization factor, so that the practicability of the system is greatly improved.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (8)
1. A nursing analgesic assessment system for critical medical disciplines, comprising:
the data acquisition module is used for acquiring information acquisition instructions, acquiring information data of a patient based on the information acquisition instructions, and uploading the information data to the evaluation module;
the evaluation module is used for receiving the information data, analyzing the information data and generating an analgesia evaluation report of the patient based on an analysis result;
the display module is used for generating a report data diagram based on the analgesia evaluation report and displaying the report data diagram;
wherein the evaluation module further comprises:
a detection parameter acquisition unit configured to acquire a time for performing analgesic detection on the patient and a speed for performing analgesic detection on the patient;
a first calculation unit configured to calculate an analgesic evaluation efficiency η for the patient based on a time of analgesic detection for the patient and a speed of analgesic detection for the patient;
wherein λ represents a regulator of analgesic evaluation efficiency for the patient and is valued (0.03,0.04); ζ represents an analgesic assessment factor for the patient and has a value in the range (0.98,0.99); t represents the time for analgesic testing of the patient; t represents a prescribed time for analgesic detection of the patient, and T is not less than T; v represents the speed of analgesic testing of the patient; v represents a prescribed rate of analgesia detection of the patient, and V is greater than or equal to V; k represents the amount of information data processed in a unit time; k is represented in The data quantity of the regulated information processed in unit time is more than or equal to K; oc (oc) 1 Representing a first weight setting value, oc, associated with an analgesic assessment factor to thereby effect an assessment of analgesia 2 Representing a second weight setting value related to time, speed and information quantity, thereby realizing analgesic evaluation; and 0.5<∝ 1 +∝ 2 <1；
The comparison unit is used for comparing the calculated analgesia evaluation efficiency with preset analgesia evaluation efficiency and judging whether the analgesia evaluation system needs to be optimized or not;
when the analgesia evaluation efficiency is greater than or equal to the preset analgesia evaluation efficiency, judging that the analgesia evaluation system does not need to be optimized;
otherwise, judging that the analgesic evaluation system needs to be optimized;
the second calculation unit is used for calculating an optimization coefficient for optimizing the analgesia evaluation system based on the analgesia evaluation efficiency and the preset analgesia evaluation efficiency;
wherein δ represents an optimization factor for optimizing the analgesic evaluation system; η (eta) 1 Representing the preset analgesia evaluation efficiency; s represents the total information data amount actually processed; μ represents a data processing error rate, and the value range is (0.1, 0.2);
and the optimizing unit is used for optimizing the analgesia evaluation system based on the optimizing coefficient, and calculating the analgesia evaluation efficiency of the patient again until the analgesia evaluation efficiency of the patient reaches the preset analgesia evaluation efficiency, and stopping optimizing the analgesia evaluation system.
2. The nursing analgesic evaluation system for critical medical disciplines as set forth in claim 1 wherein said data acquisition module comprises:
the fingerprint data acquisition unit is used for acquiring fingerprint information of the target worker and identifying the fingerprint information to determine fingerprint data;
the matching unit is used for matching the fingerprint data with target fingerprint data in a preset fingerprint database and judging whether the target worker can acquire a login request currently;
when the fingerprint data are matched with target fingerprint data in a preset fingerprint database, judging that the target worker can acquire a login request;
otherwise, judging that the target worker can not acquire the login request;
a login request acquisition unit, configured to generate a mapping relationship according to the fingerprint data and target fingerprint data in the preset fingerprint database, and generate a login request based on the mapping relationship;
the instruction generation unit is used for logging in a target system based on the login request, determining an instruction coding information node according to the acquisition requirement of the target worker, determining an instruction generation script based on the coding information node, and generating the information acquisition instruction based on the instruction generation script.
3. The nursing analgesic assessment system for critical care medicine as claimed in claim 1 wherein the assessment module further comprises:
a basic information acquisition unit configured to acquire a name of the patient, and at the same time, determine an acquisition time of the analgesic evaluation report;
a label creation unit for creating a document label based on the name of the patient and the acquisition time of the analgesic evaluation report;
the filing unit is used for establishing a label for the analgesia evaluation report based on the document label and generating an analgesia document of the patient;
the encryption unit is used for acquiring the hospitalization number of the patient, and simultaneously setting the authority of the analgesic document according to the hospitalization number of the patient and the name of the patient;
and the storage unit is used for acquiring a file storage path and storing the analgesic document with the set authority through the file storage path.
4. The nursing analgesic evaluation system for critical medical disciplines as set forth in claim 1 wherein said data acquisition module comprises:
the instruction reading unit is used for reading the information acquisition instruction, determining the operation logic and the operation field of the information acquisition instruction based on the reading result;
The instruction calculation unit is used for carrying out instruction operation based on the operation logic to determine the instruction content of the information acquisition instruction, determining the operation step of the information acquisition instruction based on the instruction content of the information acquisition instruction and the operation field, and generating a target sub-instruction based on the operation step, wherein the information acquisition instruction comprises: the first target sub-instruction, the second target sub-instruction and the third target sub-instruction;
the instruction execution unit is used for determining first information data of the patient according to the first target sub-instruction, determining second information data of the patient according to the second target sub-instruction, and determining third information data of the patient according to the third target sub-instruction;
an information data encapsulation unit, configured to generate a data information list according to the first information data, the second information data, and the third information data, and at the same time, check the data information list according to a preset data detection rule, and determine whether the data of the data information list is complete;
if the data information list accords with the preset detection rule, judging that the data of the data information list is complete, and meanwhile, packaging the data in the data information list to obtain an information data packet of the patient;
Otherwise, the information data of the patient is acquired again according to the information acquisition instruction, and when the data information list accords with the preset detection rule, an information data packet of the patient is acquired.
5. The nursing analgesic assessment system for critical care medicine as claimed in claim 1 wherein the assessment module comprises:
an information data reading unit, configured to read information data of the patient and determine a data identifier of the information data, where the data identifier of the information data includes: the identity information identification of the patient, the case identification of the patient and the current sign data identification of the patient;
the data classification unit is used for respectively generating a first data layer, a second data layer and a third data layer based on the identity information identification of the patient, the case identification of the patient and the current sign data identification of the patient, and simultaneously respectively filling the information data of the patient into the first data layer and the second data layer based on the third data layer according to the information identification of the information data;
the first data layer reading unit is used for reading the identity information data of the patient, determining the age data and the sex data of the patient based on the identity information data of the patient, and simultaneously, matching the age data and the sex data in a preset reference database to determine a first analgesia index of the patient;
The second data layer reading unit is used for reading the case information data of the patient, carrying out structuring processing on the case information data of the patient and determining a structural map of the patient;
the map reading unit is used for reading the structural map, evaluating and determining the health weight of the patient, and simultaneously, performing health evaluation on the patient according to a preset algorithm and the health weight of the patient, and determining a second analgesia index of the patient based on an evaluation result;
a third data layer reading unit, configured to input current sign information data of the patient into a preset convolutional neural network, perform learning training on the current sign information data according to the preset convolutional neural network, output a training result, and determine a third analgesia index of the patient according to the training result;
and an evaluation report generation unit configured to generate an analgesia evaluation report based on the first analgesia index, the second analgesia index, and the third analgesia index.
6. The nursing analgesic evaluation system for serious medical science as claimed in claim 5 wherein the evaluation report generating unit comprises:
The reading report unit is used for reading the analgesia evaluation report, scoring the current analgesia degree of the patient according to the reading result and obtaining a scoring result;
a grading unit for determining an analgesic grade of the patient based on the scoring result;
the alarm unit is used for comparing the pain relieving grade of the patient with a preset pain relieving grade and judging whether an alarm is required or not;
when the analgesia grade of the patient is smaller than the preset analgesia grade, judging that no alarm is needed;
when the analgesia level of the patient is equal to the preset analgesia level, judging that an alarm is required, and executing a first alarm operation;
and when the pain relieving level of the patient is greater than the preset pain relieving level, judging that the patient needs to be warned, and executing a second warning operation.
7. The pain assessment system for nursing care for critical medical disciplines as set forth in claim 1, wherein the display module includes:
a keyword acquisition unit for retrieving keywords of the analgesic evaluation report;
a graph parameter determining unit, configured to determine graph parameter content based on the keyword of the analgesia evaluation report, and set an x axis and a y axis according to the graph parameter content, and at the same time, set minimum scale values of the x axis and the y axis according to the size of the report data value of the analgesia evaluation report;
And the report data graph generating unit is used for drawing the report data value of the analgesia evaluation report in a report data graph based on the graph parameters and the minimum scale values of the x axis and the y axis, and generating and displaying the report data graph.
8. The nursing analgesic evaluation system for serious medical science of claim 7 wherein the report data map generating unit further comprises:
the report summarizing unit is used for acquiring all analgesic evaluation reports in preset time in a preset report database;
the data summarizing unit is used for integrating report data of all pain relieving evaluation reports within the preset time and generating a data line graph based on an integration result;
and the pain relieving trend acquisition unit is used for reading the data line graph, determining the pain relieving trend of the patient, and estimating the pain relieving grade after the patient based on the pain relieving trend of the patient.
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| CN111341403A (en) * | 2020-02-25 | 2020-06-26 | 泰康保险集团股份有限公司 | Patient state monitoring method, device, equipment and computer-readable storage medium |
| CN111803756A (en) * | 2020-06-12 | 2020-10-23 | 江苏爱朋医疗科技股份有限公司 | Intelligent self-control analgesia system |
| TW202133800A (en) * | 2019-08-30 | 2021-09-16 | 橙曦科技股份有限公司 | Real-time pain detection and pain management system |
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| US20110137134A1 (en) * | 2007-01-17 | 2011-06-09 | Thomas Hemmerling | Method and system for administering an anaesthetic |
| TW201019257A (en) * | 2008-11-13 | 2010-05-16 | Univ Nat Taiwan | Patient pain-status monitoring device and method |
| US9579457B2 (en) * | 2013-03-15 | 2017-02-28 | Flint Hills Scientific, L.L.C. | Method, apparatus and system for automatic treatment of pain |
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| TW202133800A (en) * | 2019-08-30 | 2021-09-16 | 橙曦科技股份有限公司 | Real-time pain detection and pain management system |
| CN114786564A (en) * | 2019-08-30 | 2022-07-22 | 橙曦科技股份有限公司 | Real-time pain detection and pain management system |
| CN111341403A (en) * | 2020-02-25 | 2020-06-26 | 泰康保险集团股份有限公司 | Patient state monitoring method, device, equipment and computer-readable storage medium |
| CN111803756A (en) * | 2020-06-12 | 2020-10-23 | 江苏爱朋医疗科技股份有限公司 | Intelligent self-control analgesia system |
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