CN114974538A - Ward nursing early warning management system based on big data - Google Patents
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Abstract
The invention discloses a large data-based ward nursing early warning management system which comprises a ward basic information acquisition module, a patient basic nursing monitoring analysis module, a patient physiological index nursing monitoring analysis module, a patient rest posture nursing monitoring analysis module, a patient rest environment nursing monitoring analysis module, a ward nursing early warning analysis module, an early warning display terminal and a data storage module. The physiological index measurement standard index corresponding to the patient is obtained through comprehensive analysis, deviation and error caused by artificial memory are effectively avoided, certain subjectivity is eliminated, the defect that events such as untimely measurement and the like occur due to the negligence of work of medical staff is overcome, the abnormal response efficiency of the physiological index of the patient is greatly improved, the nursing quality and the nursing effect of the patient are improved, meanwhile, the trust problem of the patient to the medical staff is effectively solved, and the satisfaction and the trust of the patient to hospitals and the medical staff are enhanced.
Description
Technical Field
The invention relates to the technical field of ward nursing early warning, in particular to a ward nursing early warning management system based on big data.
Background
With the steady improvement of medical level and the increasing requirement of more and more patients on the nursing quality, the nursing quality is reduced due to objective reasons such as insufficient equipment of medical staff, and the like, so the importance of early warning management on ward nursing is obvious.
In the current development, the ward nursing early warning still adopts the manual ward patrol or adopts the mode that the patient rings the alarm to nurse the ward, obviously, the current ward nursing early warning still has the following defects:
1. when carrying out early warning monitoring to patient's basic nursing at present, all regularly change and disinfect to patient's bed and clothing usually, do not carry out the change of unscheduled according to patient's bed and the wholeness degree of clothing, lead to the patient to appear cross infection's possibility increase in the ward is rested, not only reduced patient's basic nursing's validity, still make patient's rest and support the comfort not high, can't satisfy patient's basic demand.
2. When early warning monitoring is carried out to patient's physiological index nursing at present, often carry out patient's physiological index through the mode of medical personnel's artificial memory and measure, there is very big subjectivity, lead to measuring untimely the emergence of waiting the incident because of the carelessness that medical personnel worked easily, can't improve the unusual response efficiency of patient's physiological index, make patient's nursing quality and nursing effect reduce, still influenced patient's trust to medical personnel simultaneously, make the patient reduce the satisfaction of hospital.
3. The recuperation state of patient has directly influenced patient's recuperation effect, only aims at patient's basic nursing and patient's physiological index aspect when carrying out early warning management to ward nursing at present, does not carry out the analysis to this, has caused certain influence to patient's the recovery condition, and then can't ensure patient's health recovery rate, has prolonged patient's recovery cycle for patient's recuperation effect is not good.
Disclosure of Invention
In order to overcome the defects in the background art, the embodiment of the invention provides a ward nursing early warning management system based on big data, which can effectively solve the problems related to the background art.
The purpose of the invention can be realized by the following technical scheme:
a big data-based ward care early warning management system comprises:
the basic ward information acquisition module is used for counting the number of the wards, sequentially numbering the wards into 1,2, a.
The patient basic nursing monitoring and analyzing module is used for monitoring the basic nursing of each patient in each ward through the camera and analyzing the basic nursing standard-reaching coefficients to obtain the basic nursing standard-reaching coefficients corresponding to each patient in each ward;
the patient physiological index nursing monitoring and analyzing module is used for monitoring the measurement times of physiological indexes corresponding to all patients in all wards, the interval duration of the measurement of the physiological indexes and the measurement values of the measurement of the physiological indexes in all times in a set time period and analyzing to obtain a physiological index nursing standard-reaching coefficient corresponding to all the patients in all the wards, wherein the patient physiological index nursing monitoring and analyzing module comprises a physiological index measurement standard analyzing unit, a physiological index conformity analyzing unit and a physiological index nursing standard-reaching analyzing unit;
the patient nursing posture monitoring and analyzing module is used for monitoring the nursing postures corresponding to the patients in the wards through the camera and analyzing the nursing posture standard-reaching coefficients to obtain the nursing posture standard-reaching coefficients corresponding to the patients in the wards;
the patient rest environment nursing monitoring and analyzing module is used for monitoring the rest environment of each ward to obtain rest environment information of each ward, and analyzing the rest environment information to obtain a rest environment nursing standard-reaching coefficient corresponding to each ward;
the ward nursing early warning analysis module is used for analyzing the basic nursing standard-reaching coefficient, the physiological index nursing standard-reaching coefficient, the rest posture nursing standard-reaching coefficient and the rest environment nursing standard-reaching coefficient corresponding to each patient in each ward to obtain an early warning set;
the early warning display terminal is used for carrying out corresponding display based on the early warning set;
and the data storage module is used for storing the standard measurement interval duration, the standard measurement times and the qualified measurement value of the physiological indexes corresponding to the patients in the wards, and storing the recommended rest posture image corresponding to the patients in the wards.
Preferably, the analysis obtains a standard-reaching basic care coefficient corresponding to each patient in each ward, and the specific analysis steps are as follows:
extracting monitoring images of basic nursing corresponding to each patient in each ward, acquiring bed images and clothes images corresponding to each patient in each ward, counting the number of stains on the beds of each patient in each ward and the number of stains on the clothes, acquiring the stain area of each stain and the stain area of each stain, and acquiring the use duration of the beds and the wear duration of the clothes corresponding to each patient in each ward from a background;
comprehensively analyzing the number of the stains on the bed corresponding to each patient in each ward, the stain area corresponding to each stain and the using time of the bed to obtain the bed standard index corresponding to each patient in each ward, and recording the bed standard index as
i denotes the ward number, i 1,2,.. and n, j denotes the patient number, and j 1, 2.. and m;
comprehensively analyzing the number of the dirty points on the corresponding clothes and trousers of each patient in each ward, the corresponding stain area of each stain point and the wearing time of the clothes and trousers to obtain the clothes and trousers standard index corresponding to each patient in each ward, and recording the standard index as the standard index
Comprehensively analyzing the bed standard index and the clothes standard index corresponding to each patient in each ward to obtain the basic nursing standard-reaching coefficient corresponding to each patient in each ward, wherein the specific calculation formula is
Expressed as the corresponding basic nursing standard-reaching coefficient, beta, of the jth patient in the ith ward 1 、β 2 Respectively expressed as weight factors corresponding to the set bed specification index and the set clothes specification index.
Preferably, the physiological indicators include heart rate, blood pressure, body temperature and respiratory rate.
Preferably, the physiological index measurement specification analysis unit is configured to calculate a physiological index measurement specification index corresponding to each patient in each ward, and a specific calculation process of the physiological index measurement specification index is as follows:
extracting interval duration corresponding to each measurement of heart rate, blood pressure, body temperature and respiratory frequency from interval duration corresponding to each measurement of physiological index of each patient in each ward, respectively screening out longest measurement interval duration corresponding to heart rate, longest measurement interval duration corresponding to blood pressure, longest measurement interval duration corresponding to body temperature and longest measurement interval duration corresponding to respiratory frequency from the interval durations, and respectively recording the longest measurement interval durations as the longest measurement interval durations
And
extracting the standard measurement interval duration of the corresponding physiological index of each patient in each ward from the data storage module, positioning the heart rate standard measurement interval duration, the blood pressure standard measurement interval duration, the body temperature standard measurement interval duration and the respiratory frequency standard measurement interval duration, and respectively recording as X' ij 、Y′ ij 、T′ ij And H' ij ；
According to the formula
Calculating the coincidence index of the corresponding measurement interval of each patient in each ward,
expressed as coincidence index of the corresponding measurement interval of the jth patient in the ith ward, e expressed as natural constant, c 1 、c 2 、c 3 、c 4 Respectively representing the weight factors corresponding to preset heart rate measurement interval duration, blood pressure measurement interval duration, body temperature measurement interval duration and respiratory rate measurement interval duration;
from within each ward within a set period of timePositioning the heart rate measurement times, the blood pressure measurement times, the body temperature measurement times and the respiratory frequency measurement times in the measurement times of the physiological indexes corresponding to the patients, comparing the heart rate measurement times, the blood pressure measurement times, the body temperature measurement times and the respiratory frequency measurement times with the standard measurement times of the physiological indexes corresponding to the patients in the wards stored in the data storage module, further calculating the coincidence index of the corresponding measurement times of the patients in the wards, and recording the coincidence index as the coincidence index
Substituting the coincidence indexes of the corresponding measurement intervals and the coincidence indexes of the measurement times of each patient in each ward into a formula
In the method, the physiological index measurement standard index corresponding to each patient in each ward is calculated,
expressed as a physiological index measurement specification index, beta, corresponding to the ith patient in the ith ward 3 、β 4 And respectively representing the coincidence indexes of the preset measurement interval and the weight factors corresponding to the coincidence indexes of the measurement times.
Preferably, the physiological index compliance analysis unit is configured to calculate a physiological index compliance index corresponding to each patient in each ward, and the specific calculation steps are as follows:
screening out maximum heart rate, minimum heart rate, maximum blood pressure, minimum blood pressure, maximum body temperature, minimum body temperature, maximum respiratory frequency and minimum respiratory frequency from the measured values of physiological indexes corresponding to all patients in each ward;
extracting qualified heart rate, qualified blood pressure, qualified body temperature and qualified respiratory rate corresponding to each patient in each ward from qualified measurement values of physiological indexes corresponding to each patient in each ward;
the maximum heart rate, the minimum heart rate, the maximum blood pressure, the minimum blood pressure, the maximum body temperature, the minimum body temperature, the maximum respiratory frequency and the minimum respiratory frequency corresponding to each patient in each ward are compared with the corresponding qualified heart rate, qualified blood pressure, qualified body temperature and qualified body temperatureThe respiratory frequencies are compared to obtain the physiological index conformity index corresponding to each patient in each ward, and the physiological index conformity index is recorded as
Preferably, the physiological index nursing standard-reaching analysis unit is used for calculating a physiological index nursing standard-reaching coefficient corresponding to each patient in each ward, and the specific calculation formula is
Expressed as the nursing standard-reaching index, tau, of the physiological index corresponding to the jth patient in the ith ward 1 、τ 2 Respectively expressed as a set physiological index measurement standard index and a weight factor corresponding to the physiological index conformity index.
Preferably, the analysis obtains a nursing standard-reaching coefficient of the rest posture corresponding to each patient in each ward, and the specific analysis steps are as follows:
extracting monitoring images of the corresponding rest postures of the patients in the wards, recording the monitoring images as actual rest posture images, overlapping and comparing the actual rest posture images corresponding to the patients in the wards with the recommended rest posture images corresponding to the patients in the wards stored in the data storage module to obtain a coincidence area, and recording the coincidence area as a coincidence area
Extracting the actual resting posture contour central point position and the recommended resting posture contour central point position corresponding to each patient in each ward from the actual resting posture image and the recommended resting posture image corresponding to each patient in each ward respectively, further obtaining the distance between the actual resting posture contour central point position and the recommended resting posture contour central point position corresponding to each patient in each ward, taking the distance as the moving distance, and recording the distance as the moving distance
According to the formula
Calculating the nursing standard-reaching coefficient of the recuperation posture corresponding to each patient in each ward,
is expressed as a nursing standard reaching coefficient, M ', of the jth patient in the ith ward' ij 、L′ ij Respectively expressed as the profile area of the suggested rest posture, the allowed moving distance and the x corresponding to the jth patient in the ith ward 1 、χ 2 The values are expressed as coefficient factors corresponding to the set overlapping area and the set moving distance, respectively.
Preferably, the analysis obtains a nursing standard-reaching coefficient of the rest environment corresponding to each ward, and the specific analysis steps are as follows:
extracting dust concentration, noise value, temperature and humidity from rest environment information in each ward and recording as f i 、g i 、k i And p i ；
According to the formula
Calculating to obtain the nursing standard-reaching coefficient psi of the rest environment corresponding to each ward i Expressed as nursing standard-reaching coefficients of the rest environment corresponding to the ith ward, f ', g', k 'and p' are respectively expressed as set allowable dust concentration, allowable noise value, allowable temperature and allowable humidity, v 1 、v 2 、v 3 、v 4 And respectively expressed as coefficient factors corresponding to the set dust concentration, noise value, temperature and humidity.
Preferably, the early warning set includes an early warning ward set and an early warning patient set, and the specific acquisition process is as follows:
comparing the basic nursing standard-reaching coefficient corresponding to each patient in each ward with a set basic nursing standard-reaching coefficient threshold, if the basic nursing standard-reaching coefficient corresponding to a certain patient in a certain ward is smaller than the basic nursing standard-reaching coefficient threshold, marking the ward as an early-warning ward, and marking the patient as an early-warning patient;
comparing the physiological index nursing standard-reaching coefficient corresponding to each patient in each ward with a set physiological index nursing standard-reaching coefficient threshold, if the physiological index nursing standard-reaching coefficient corresponding to a certain patient in a certain ward is smaller than the physiological index nursing standard-reaching coefficient threshold, marking the ward as an early warning ward, and marking the patient as an early warning patient;
comparing the rest posture nursing standard-reaching coefficient corresponding to each patient in each ward with a set rest posture nursing standard-reaching coefficient threshold, if the rest posture nursing standard-reaching coefficient corresponding to a certain patient in a certain ward is smaller than the rest posture nursing standard-reaching coefficient threshold, marking the ward as an early-warning ward, and marking the patient as an early-warning patient;
comparing the rest environment nursing standard-reaching coefficient corresponding to each ward with a set rest environment nursing standard-reaching coefficient threshold, and if the rest environment nursing standard-reaching coefficient corresponding to a certain ward is smaller than the rest environment nursing standard-reaching coefficient threshold, marking the ward as an early-warning ward;
and counting to obtain the number of early warning wards and the number of early warning patients, further respectively constructing an early warning ward set and an early warning patient set, and combining the early warning ward set and the early warning patient set to generate an early warning set.
Compared with the prior art, the embodiment of the invention has at least the following advantages or beneficial effects:
(1) according to the invention, the camera is used for acquiring images of the bed and clothes of the patient, the bed standard index and the clothes standard index are obtained through analysis, the standard-reaching coefficient of basic nursing corresponding to the patient is obtained comprehensively, and then the patient with unqualified standard-reaching coefficient of basic nursing is replaced and disinfected in time, so that the possibility of cross infection of the patient is avoided, the quality of basic nursing of a ward is improved, the basic requirements of the patient are met to the maximum extent, and the problem of low comfort of recuperation of the patient is effectively solved.
(2) The invention comprehensively analyzes the coincidence index of the corresponding measurement interval of the patient and the coincidence index of the measurement times to obtain the physiological index measurement standard index corresponding to the patient, effectively avoids deviation and error caused by artificial memory, eliminates certain subjectivity, overcomes the defect of untimely measurement and other events caused by the carelessness of medical personnel, greatly improves the response efficiency of abnormal physiological indexes of the patient, improves the nursing quality and the nursing effect of the patient, effectively solves the problem of trust of the patient to the medical personnel, and enhances the satisfaction and the trust of the patient to hospitals and medical personnel.
(3) According to the invention, the camera is used for carrying out image acquisition on the rest posture of the patient, and the standard index of the rest posture nursing standard corresponding to the patient is obtained through analysis, so that whether the rest posture of the patient is consistent with the standard rest posture corresponding to the state of an illness of the patient can be intuitively known, the correctness of the rest posture of the patient is effectively ensured, the recovery period of the patient is shortened, the body recovery rate of the patient is effectively ensured, the effectiveness and the reliability of the rest posture nursing of the patient are greatly improved, and the ward nursing early warning is more convincing.
(4) According to the invention, the rest environment in the ward is monitored, and the rest environment nursing standard-reaching index corresponding to the ward is obtained through analysis, so that a good environment is provided for the rest of the patient, the negative influence of the change of the rest environment on the state of illness of the patient is avoided, a reliable reference basis is provided for the medical care personnel to reasonably adjust the ward environment, the requirement of the patient on recovery is met, the adjustment rate of the abnormal environment of the ward is ensured, the comfort of the ward environment is maintained, and the requirement of the patient on the ward environment is met.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a schematic diagram of the system module connection according to the present invention.
FIG. 2 is a schematic diagram of the connection of a patient physiological index nursing monitoring analysis module according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the invention provides a large data-based ward nursing early warning management system, which comprises a ward basic information acquisition module, a patient basic nursing monitoring analysis module, a patient physiological index nursing monitoring analysis module, a patient recuperation posture nursing monitoring analysis module, a patient recuperation environment nursing monitoring analysis module, a ward nursing early warning analysis module, an early warning display terminal and a data storage module.
The ward basic information acquisition module is respectively connected with the patient basic nursing monitoring analysis module, the patient physiological index nursing monitoring analysis module, the patient recuperation posture nursing monitoring analysis module and the patient recuperation environment nursing monitoring analysis module, the ward nursing early warning analysis module is respectively connected with the patient basic nursing monitoring analysis module, the patient physiological index nursing monitoring analysis module, the patient recuperation posture nursing monitoring analysis module, the patient recuperation environment nursing monitoring analysis module and the early warning display terminal, and the data storage module is respectively connected with the patient physiological index nursing monitoring analysis module and the patient recuperation posture nursing monitoring analysis module.
The basic ward information acquisition module is used for counting the number of the wards, sequentially numbering the wards into 1,2, a.
And the patient basic nursing monitoring and analyzing module is used for monitoring the basic nursing of each patient in each ward and analyzing to obtain the corresponding basic nursing standard-reaching coefficient of each patient in each ward.
Preferably, the analysis obtains a standard-reaching basic care coefficient corresponding to each patient in each ward, and the specific analysis steps are as follows:
extracting monitoring images of basic nursing corresponding to each patient in each ward, acquiring bed images and clothes images corresponding to each patient in each ward, counting the number of stains on the beds of each patient in each ward and the number of stains on the clothes, acquiring the stain area of each stain and the stain area of each stain, and acquiring the use duration of the beds and the wear duration of the clothes corresponding to each patient in each ward from a background;
comprehensively analyzing the number of the stains on the bed corresponding to each patient in each ward, the stain area corresponding to each stain and the using time of the bed to obtain the bed standard index corresponding to each patient in each ward, and recording the bed standard index as
i denotes the ward number, i 1,2,.. and n, j denotes the patient number, and j 1, 2.. and m;
it is to be noted that, according to the formula
Calculating to obtain the bed standard index, z, corresponding to each patient in each ward ij Expressed as the number of spots on the bed corresponding to the jth patient in the ith ward, z 0 Expressed as a set number of allowed spots,
expressed as the stain area of the jth patient corresponding to the w-th stain in the ith ward, w is expressed as the number of the stain, w is 1,2, the.
Is expressed as the using time length of the bed corresponding to the jth patient in the ith ward, y' is expressed as the using time length of a preset reference bed, and alpha 1 、α 2 、α 3 Are respectively represented asAnd setting the number of the stains, the area of the stains and the corresponding influence factors of the using time of the bed.
Comprehensively analyzing the number of the dirty points on the corresponding clothes and trousers of each patient in each ward, the corresponding stain area of each stain point and the wearing time of the clothes and trousers to obtain the clothes and trousers standard index corresponding to each patient in each ward, and recording the standard index as the standard index
It is to be noted that, according to the formula
Calculating to obtain standard index of clothes and trousers corresponding to each patient in each ward, d ij Expressed as the number of spots on the corresponding clothes and trousers of the jth patient in the ith ward,
expressed as the spot area of the jth patient in the ith ward corresponding to the b-th spot, b is expressed as the number of the spot, b is 1,2,.. d,
expressed as the wearing time of the corresponding clothes and trousers of the jth patient in the ith ward, d 0 Expressed as the number of spots allowed to stand in the preset, S 'is expressed as the area of the spots allowed to stand in the preset, u' is expressed as the preset reference wear length of time, alpha 4 、α 5 、α 6 Respectively expressed as the corresponding influence factors of the number of the set stain points, the stain area and the wearing time of the clothes and trousers.
Comprehensively analyzing the bed standard index and the clothes standard index corresponding to each patient in each ward to obtain the basic nursing standard-reaching coefficient corresponding to each patient in each ward, wherein the specific calculation formula is
Is shown as the firstThe standard-reaching coefficient, beta, of basic nursing corresponding to the jth patient in the i wards 1 、β 2 Respectively expressed as weight factors corresponding to the set bed specification index and the clothes specification index.
The invention acquires images of a bed and clothes of a patient through the camera, obtains a bed standard index and a clothes standard index through analysis, comprehensively obtains a basic nursing standard-reaching coefficient corresponding to the patient, and then timely replaces and sterilizes the patient with the unqualified basic nursing standard-reaching coefficient, thereby avoiding the possibility of cross infection of the patient, improving the quality of basic nursing of a ward, meeting the basic requirements of the patient to the utmost extent, and effectively solving the problem of low comfortable sensation of recuperation of the patient.
Referring to fig. 2, the patient physiological index nursing monitoring and analyzing module is configured to monitor the number of times of measurement of a physiological index corresponding to each patient in each ward, the interval duration of each measurement of the physiological index, and a measurement value of each measurement of the physiological index in a set time period, and obtain a nursing standard-reaching coefficient of the physiological index corresponding to each patient in each ward through analysis.
Preferably, the physiological indicators include heart rate, blood pressure, body temperature and respiratory rate.
Preferably, the physiological index measurement specification analysis unit is configured to calculate a physiological index measurement specification index corresponding to each patient in each ward, and a specific calculation process of the physiological index measurement specification index is as follows:
extracting interval duration corresponding to each measurement of heart rate, blood pressure, body temperature and respiratory frequency from interval duration corresponding to each measurement of physiological index of each patient in each ward, respectively screening out longest measurement interval duration corresponding to heart rate, longest measurement interval duration corresponding to blood pressure, longest measurement interval duration corresponding to body temperature and longest measurement interval duration corresponding to respiratory frequency from the interval durations, and respectively recording the longest measurement interval durations as the longest measurement interval durations
And
extracting the standard measurement interval duration of the corresponding physiological index of each patient in each ward from the data storage module, positioning the heart rate standard measurement interval duration, the blood pressure standard measurement interval duration, the body temperature standard measurement interval duration and the respiratory frequency standard measurement interval duration, and respectively recording as X' ij 、Y′ ij 、T′ ij And H' ij ；
According to the formula
Calculating the coincidence index of the corresponding measurement interval of each patient in each ward,
expressed as coincidence index of the corresponding measurement interval of the jth patient in the ith ward, e expressed as natural constant, c 1 、c 2 、c 3 、c 4 Respectively representing the weight factors corresponding to preset heart rate measurement interval duration, blood pressure measurement interval duration, body temperature measurement interval duration and respiratory rate measurement interval duration;
positioning the heart rate measurement times, the blood pressure measurement times, the body temperature measurement times and the respiratory frequency measurement times from the measurement times of the physiological indexes corresponding to the patients in the wards in a set time period, comparing the heart rate measurement times, the blood pressure measurement times, the body temperature measurement times and the respiratory frequency measurement times with the standard measurement times of the physiological indexes corresponding to the patients in the wards stored in the data storage module, further calculating the coincidence index of the corresponding measurement times of the patients in the wards, and recording the coincidence index as the coincidence index
It is to be noted that, according to the formula
Calculating the coincidence index of the corresponding measurement times of each patient in each ward,
respectively expressed as the heart rate measurement times, the blood pressure measurement times, the body temperature measurement times and the respiratory frequency measurement times, X ″, corresponding to the jth patient in the ith ward ij 、Y″ ij 、T″ ij 、H″ ij Respectively expressed as heart rate standard measurement times, blood pressure standard measurement times, body temperature standard measurement times, respiratory frequency standard measurement times and c corresponding to jth patient in ith ward 5 、c 6 、c 7 、c 8 And the weight factors are respectively expressed as the weight factors corresponding to the set heart rate measurement times, blood pressure measurement times, body temperature measurement times and respiratory frequency measurement times.
Substituting the coincidence indexes of the corresponding measurement intervals and the coincidence indexes of the measurement times of each patient in each ward into a formula
In the method, the physiological index measurement standard index corresponding to each patient in each ward is calculated,
expressed as a physiological index measurement specification index, beta, corresponding to the ith patient in the ith ward 3 、β 4 And respectively representing the coincidence indexes of the preset measurement interval and the weight factors corresponding to the coincidence indexes of the measurement times.
It should be noted that the invention comprehensively analyzes the coincidence index of the measurement interval corresponding to the patient and the coincidence index of the measurement times to obtain the measurement specification index of the physiological index corresponding to the patient, effectively avoids deviation and error caused by artificial memory, eliminates certain subjectivity, overcomes the defect of untimely measurement and other events caused by the carelessness of medical personnel, greatly improves the response efficiency of abnormal physiological index of the patient, improves the nursing quality and nursing effect of the patient, effectively solves the problem of trust of the patient to the medical personnel, and enhances the satisfaction and trust of the patient to hospitals and medical personnel.
Preferably, the physiological index compliance analysis unit is configured to calculate a physiological index compliance index corresponding to each patient in each ward, and the specific calculation steps are as follows:
screening out maximum heart rate, minimum heart rate, maximum blood pressure, minimum blood pressure, maximum body temperature, minimum body temperature, maximum respiratory frequency and minimum respiratory frequency from the measured values of physiological indexes corresponding to all patients in each ward;
extracting qualified heart rate, qualified blood pressure, qualified body temperature and qualified respiratory frequency corresponding to each patient in each ward from qualified measurement values of physiological indexes corresponding to each patient in each ward;
comparing the maximum heart rate, the minimum heart rate, the maximum blood pressure, the minimum blood pressure, the maximum body temperature, the minimum body temperature, the maximum respiratory frequency and the minimum respiratory frequency corresponding to each patient in each ward with the qualified heart rate, the qualified blood pressure, the qualified body temperature and the qualified respiratory frequency corresponding to the patient to obtain the physiological index coincidence index corresponding to each patient in each ward, and recording the physiological index coincidence index as the physiological index coincidence index
It should be noted that the physiological index coincidence index specific calculation formula corresponding to each patient in each ward is
Respectively expressed as the heart rate uniformity coefficient, the blood pressure uniformity coefficient, the body temperature uniformity coefficient and the respiratory frequency uniformity coefficient, r, corresponding to the jth patient in the ith ward 1 、r 2 、r 3 、r 4 Respectively expressed as the influence factors corresponding to the set heart rate uniformity coefficient, blood pressure uniformity coefficient, body temperature uniformity coefficient and respiratory rate uniformity coefficient.
Wherein the content of the first and second substances,
respectively expressed as the maximum heart rate and the minimum heart rate, E 'corresponding to the jth patient in the ith ward' ij Expressed as the qualified heart rate for the jth patient in the ith ward,
expressed as the permissible measured heart rate difference, q, for the jth patient in the set ith ward 1 、q 2 The parameters are respectively expressed as influence factors corresponding to the set maximum heart rate and the set minimum heart rate.
Preferably, the physiological index nursing standard-reaching analysis unit is used for calculating a physiological index nursing standard-reaching coefficient corresponding to each patient in each ward, and the specific calculation formula is
Expressed as a nursing standard-reaching index, tau, corresponding to the jth patient in the ith ward 1 、τ 2 Respectively expressed as a set physiological index measurement standard index and a weight factor corresponding to the physiological index conformity index.
And the patient rest posture nursing monitoring and analyzing module is used for monitoring the rest postures corresponding to the patients in the wards through the camera and analyzing the rest postures to obtain the rest posture nursing standard-reaching coefficient corresponding to the patients in the wards.
Preferably, the analysis obtains a nursing standard-reaching coefficient of the rest posture corresponding to each patient in each ward, and the specific analysis steps are as follows:
extracting monitoring images of the corresponding rest postures of the patients in the wards, recording the monitoring images as actual rest posture images, overlapping and comparing the actual rest posture images corresponding to the patients in the wards with the recommended rest posture images corresponding to the patients in the wards stored in the data storage module to obtain a coincidence area, and recording the coincidence area as a coincidence area
Extracting the actual resting posture contour central point position and the recommended resting posture contour central point position corresponding to each patient in each ward from the actual resting posture image and the recommended resting posture image corresponding to each patient in each ward respectively, further obtaining the distance between the actual resting posture contour central point position and the recommended resting posture contour central point position corresponding to each patient in each ward, taking the distance as the moving distance, and recording the distance as the moving distance
According to the formula
Calculating the nursing standard-reaching coefficient of the rest posture of each patient in each ward,
is expressed as a nursing standard reaching coefficient, M ', of the jth patient in the ith ward' ij 、L′ ij Respectively expressed as the profile area of the suggested rest posture, the allowed moving distance and the x corresponding to the jth patient in the ith ward 1 、χ 2 The values are expressed as coefficient factors corresponding to the set overlapping area and the set moving distance, respectively.
It should be noted that the invention acquires images of the rest posture of the patient through the camera, obtains the standard nursing posture index of the patient through analysis, and can intuitively know whether the rest posture of the patient is consistent with the standard rest posture corresponding to the patient condition, thereby not only effectively ensuring the correctness of the rest posture of the patient and being beneficial to the recovery of the patient condition, but also shortening the recovery period of the patient, effectively ensuring the body recovery rate of the patient, greatly improving the effectiveness and reliability of the nursing of the rest posture of the patient and ensuring the convincing early warning of the ward.
And the patient rest environment nursing monitoring and analyzing module is used for monitoring the rest environment of each ward to obtain the rest environment information of each ward, and analyzing to obtain the rest environment nursing standard-reaching coefficient corresponding to each ward.
It should be noted that, the specific monitoring process of the rest environment is as follows:
detecting the dust concentration in each ward through a dust concentration tester to obtain the corresponding dust concentration of each ward;
detecting the noise of each ward through a noise sensor to obtain the noise corresponding to each ward;
and respectively detecting the temperature and the humidity of each ward through a temperature sensor and a humidity sensor to obtain the temperature and the humidity corresponding to each ward.
Preferably, the analysis obtains a nursing standard-reaching coefficient of the rest environment corresponding to each ward, and the specific analysis steps are as follows:
extracting dust concentration, noise value, temperature and humidity from rest environment information in each ward and recording as f i 、g i 、k i And p i ；
According to the formula
Calculating to obtain the nursing standard-reaching coefficient psi of the rest environment corresponding to each ward i Expressed as nursing standard-reaching coefficients of the rest environment corresponding to the ith ward, f ', g', k 'and p' are respectively expressed as set allowable dust concentration, allowable noise value, allowable temperature and allowable humidity, v 1 、v 2 、v 3 、v 4 And respectively expressed as coefficient factors corresponding to the set dust concentration, noise value, temperature and humidity.
It is to be noted that the nursing environment standard-reaching index corresponding to the ward is obtained by monitoring the nursing environment in the ward and analyzing, so that a good environment is provided for the patient to nurse, the negative influence of the change of the nursing environment on the patient's condition is avoided, a reliable reference basis is provided for medical staff to reasonably adjust the ward environment, the patient rehabilitation requirement is met, the adjustment rate of the abnormal environment of the ward is ensured, the comfort of the ward environment is maintained, and the patient requirement on the ward environment is met.
And the ward nursing early warning analysis module is used for analyzing the basic nursing standard-reaching coefficient, the physiological index nursing standard-reaching coefficient, the rest posture nursing standard-reaching coefficient and the rest environment nursing standard-reaching coefficient corresponding to each patient in each ward to obtain an early warning set.
Preferably, the early warning set includes an early warning ward set and an early warning patient set, and the specific acquisition process is as follows:
comparing the basic nursing standard-reaching coefficient corresponding to each patient in each ward with a set basic nursing standard-reaching coefficient threshold, if the basic nursing standard-reaching coefficient corresponding to a certain patient in a certain ward is smaller than the basic nursing standard-reaching coefficient threshold, marking the ward as an early-warning ward, and marking the patient as an early-warning patient;
comparing the physiological index nursing standard-reaching coefficient corresponding to each patient in each ward with a set physiological index nursing standard-reaching coefficient threshold, if the physiological index nursing standard-reaching coefficient corresponding to a certain patient in a certain ward is smaller than the physiological index nursing standard-reaching coefficient threshold, marking the ward as an early warning ward, and marking the patient as an early warning patient;
comparing the rest posture nursing standard-reaching coefficient corresponding to each patient in each ward with a set rest posture nursing standard-reaching coefficient threshold, recording the ward as an early warning ward if the rest posture nursing standard-reaching coefficient corresponding to a certain patient in a certain ward is smaller than the rest posture nursing standard-reaching coefficient threshold, and recording the patient as an early warning patient;
comparing the rest environment nursing standard-reaching coefficient corresponding to each ward with a set rest environment nursing standard-reaching coefficient threshold, and if the rest environment nursing standard-reaching coefficient corresponding to a certain ward is smaller than the rest environment nursing standard-reaching coefficient threshold, marking the ward as an early-warning ward;
and counting to obtain the number of early warning wards and the number of early warning patients, further respectively constructing an early warning ward set and an early warning patient set, and combining the early warning ward set and the early warning patient set to generate an early warning set.
And the early warning display terminal is used for carrying out corresponding display based on the early warning set.
And the data storage module is used for storing the standard measurement interval duration, the standard measurement times and the qualified measurement value of the physiological indexes corresponding to the patients in the wards, and storing the recommended rest posture image corresponding to the patients in the wards.
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 (9)
1. The utility model provides a ward nursing early warning management system based on big data which characterized in that includes:
the basic ward information acquisition module is used for counting the number of the wards, sequentially numbering the wards into 1,2, a.
The patient basic nursing monitoring and analyzing module is used for monitoring the basic nursing of each patient in each ward through the camera and analyzing the basic nursing standard-reaching coefficients to obtain the basic nursing standard-reaching coefficients corresponding to each patient in each ward;
the patient physiological index nursing monitoring and analyzing module is used for monitoring the measurement times of the physiological indexes corresponding to the patients in the wards, the interval duration of the physiological indexes in each measurement and the measurement values of the physiological indexes in each measurement in a set time period and analyzing to obtain the physiological index nursing standard-reaching coefficient corresponding to the patients in the wards, wherein the patient physiological index nursing monitoring and analyzing module comprises a physiological index measurement standard analyzing unit, a physiological index conformity analyzing unit and a physiological index nursing standard-reaching analyzing unit;
the patient nursing posture monitoring and analyzing module is used for monitoring the nursing postures corresponding to the patients in the wards through the camera and analyzing the nursing posture standard-reaching coefficients to obtain the nursing posture standard-reaching coefficients corresponding to the patients in the wards;
the patient rest environment nursing monitoring and analyzing module is used for monitoring the rest environment of each ward to obtain rest environment information of each ward, and analyzing the rest environment information to obtain a rest environment nursing standard-reaching coefficient corresponding to each ward;
the ward nursing early warning analysis module is used for analyzing the basic nursing standard-reaching coefficient, the physiological index nursing standard-reaching coefficient, the rest posture nursing standard-reaching coefficient and the rest environment nursing standard-reaching coefficient corresponding to each patient in each ward to obtain an early warning set;
the early warning display terminal is used for carrying out corresponding display based on the early warning set;
and the data storage module is used for storing the standard measurement interval duration, the standard measurement times and the qualified measurement value of the physiological indexes corresponding to the patients in the wards, and storing the recommended rest posture image corresponding to the patients in the wards.
2. The big-data-based ward care early warning management system according to claim 1, wherein: the analysis obtains the basic nursing standard-reaching coefficient corresponding to each patient in each ward, and the specific analysis steps are as follows:
extracting monitoring images of basic nursing corresponding to each patient in each ward, acquiring bed images and clothes images corresponding to each patient in each ward, counting the number of stains on the beds of each patient in each ward and the number of stains on the clothes, acquiring the stain area of each stain and the stain area of each stain, and acquiring the use duration of the beds and the wear duration of the clothes corresponding to each patient in each ward from a background;
for each wardComprehensively analyzing the number of the stains on the bed corresponding to each patient, the area of the stains corresponding to each stain and the service time of the bed to obtain the standard index of the bed corresponding to each patient in each ward, and recording the standard index as the standard index
i denotes the ward number, i 1,2,.. and n, j denotes the patient number, and j 1, 2.. and m;
comprehensively analyzing the number of the dirty points on the corresponding clothes and trousers of each patient in each ward, the corresponding stain area of each stain point and the wearing time of the clothes and trousers to obtain the clothes and trousers standard index corresponding to each patient in each ward, and recording the standard index as the standard index
Comprehensively analyzing the bed standard index and the clothes standard index corresponding to each patient in each ward to obtain the basic nursing standard-reaching coefficient corresponding to each patient in each ward, wherein the specific calculation formula is
Expressed as the corresponding basic nursing standard-reaching coefficient, beta, of the jth patient in the ith ward 1 、β 2 Respectively expressed as weight factors corresponding to the set bed specification index and the clothes specification index.
3. The big-data-based ward care early warning management system as claimed in claim 1, wherein: the physiological indicators include heart rate, blood pressure, body temperature, and respiratory rate.
4. The big-data-based ward care early warning management system according to claim 3, wherein: the physiological index measurement specification analysis unit is used for calculating a physiological index measurement specification index corresponding to each patient in each ward, and the specific calculation process is as follows:
extracting interval duration corresponding to each measurement of heart rate, blood pressure, body temperature and respiratory frequency from interval duration corresponding to each measurement of physiological index of each patient in each ward, respectively screening out longest measurement interval duration corresponding to heart rate, longest measurement interval duration corresponding to blood pressure, longest measurement interval duration corresponding to body temperature and longest measurement interval duration corresponding to respiratory frequency from the interval durations, and respectively recording the longest measurement interval durations as the longest measurement interval durations
And
extracting the standard measurement interval duration of the corresponding physiological index of each patient in each ward from the data storage module, positioning the heart rate standard measurement interval duration, the blood pressure standard measurement interval duration, the body temperature standard measurement interval duration and the respiratory frequency standard measurement interval duration, and respectively recording as X' ij 、Y′ ij 、T′ ij And H' ij ；
According to the formula
Calculating the coincidence index of the corresponding measurement interval of each patient in each ward,
expressed as coincidence index of the corresponding measurement interval of the jth patient in the ith ward, e expressed as natural constant, c 1 、c 2 、c 3 、c 4 Respectively representing the weight factors corresponding to preset heart rate measurement interval duration, blood pressure measurement interval duration, body temperature measurement interval duration and respiratory rate measurement interval duration;
positioning the heart rate measurement times, the blood pressure measurement times, the body temperature measurement times and the respiratory frequency measurement times from the measurement times of the corresponding physiological indexes of each patient in each ward in a set time periodAnd comparing the times with the standard measurement times of the physiological indexes corresponding to the patients in the wards stored in the data storage module, and further calculating the coincidence index of the corresponding measurement times of the patients in the wards, and recording the coincidence index as the coincidence index
Substituting the coincidence indexes of the corresponding measurement intervals and the coincidence indexes of the measurement times of each patient in each ward into a formula
In the method, the physiological index measurement standard index corresponding to each patient in each ward is calculated,
expressed as a physiological index measurement specification index, beta, corresponding to the ith patient in the ith ward 3 、β 4 And respectively expressed as a coincidence index of a preset measurement interval and a weight factor corresponding to the coincidence index of the measurement times.
5. The big-data-based ward care early warning management system as claimed in claim 4, wherein: the physiological index conformity analysis unit is used for calculating a physiological index conformity index corresponding to each patient in each ward, and the specific calculation steps are as follows:
screening out maximum heart rate, minimum heart rate, maximum blood pressure, minimum blood pressure, maximum body temperature, minimum body temperature, maximum respiratory frequency and minimum respiratory frequency from the measured values of physiological indexes corresponding to all patients in each ward;
extracting qualified heart rate, qualified blood pressure, qualified body temperature and qualified respiratory frequency corresponding to each patient in each ward from qualified measurement values of physiological indexes corresponding to each patient in each ward;
the maximum heart rate, the minimum heart rate, the maximum blood pressure, the minimum blood pressure, the maximum body temperature, the minimum body temperature, the maximum respiratory frequency and the minimum respiratory frequency corresponding to each patient in each ward and the corresponding qualified heart rateComparing the qualified blood pressure, the qualified body temperature and the qualified respiratory frequency to obtain physiological index conformity indexes corresponding to the patients in the wards, and recording the physiological index conformity indexes as
6. The big-data-based ward care early warning management system according to claim 5, wherein: the physiological index nursing standard-reaching analysis unit is used for calculating a physiological index nursing standard-reaching coefficient corresponding to each patient in each ward, and the specific calculation formula is
Expressed as the nursing standard-reaching index, tau, of the physiological index corresponding to the jth patient in the ith ward 1 、τ 2 Respectively expressed as a set physiological index measurement standard index and a weight factor corresponding to the physiological index conformity index.
7. The big-data-based ward care early warning management system according to claim 1, wherein: the analysis obtains the nursing standard-reaching coefficient of the rest posture corresponding to each patient in each ward, and the specific analysis steps are as follows:
extracting monitoring images of the corresponding rest postures of the patients in the wards, recording the monitoring images as actual rest posture images, overlapping and comparing the actual rest posture images corresponding to the patients in the wards with the recommended rest posture images corresponding to the patients in the wards stored in the data storage module to obtain a coincidence area, and recording the coincidence area as a coincidence area
Extracting each patient in each ward from the corresponding actual rest posture image and the corresponding recommended rest posture image of each patient in each ward respectivelyThe position of the center point of the actual recuperation posture profile corresponding to the patient and the position of the center point of the recommended recuperation posture profile are obtained, the distance between the position of the center point of the actual recuperation posture profile corresponding to each patient in each ward and the position of the center point of the recommended recuperation posture profile is further obtained and used as the moving distance and recorded as the moving distance
According to the formula
Calculating the nursing standard-reaching coefficient of the recuperation posture corresponding to each patient in each ward,
is expressed as a nursing standard reaching coefficient, M ', of the jth patient in the ith ward' ij 、L′ ij Respectively expressed as the profile area of the suggested rest posture, the allowed moving distance and the x corresponding to the jth patient in the ith ward 1 、χ 2 The values are expressed as coefficient factors corresponding to the set overlapping area and the set moving distance, respectively.
8. The big-data-based ward care early warning management system according to claim 1, wherein: the analysis obtains the nursing standard-reaching coefficient of the rest environment corresponding to each ward, and the specific analysis steps are as follows:
extracting dust concentration, noise value, temperature and humidity from rest environment information in each ward and recording as f i 、g i 、k i And p i ；
According to the formula
Calculating to obtain the nursing standard-reaching coefficient psi of the rest environment corresponding to each ward i Expressed as nursing standard-reaching coefficients of the rest environment corresponding to the ith ward, and f ', g', k 'and p' are respectively expressed as set allowable dustConcentration, allowable noise value, allowable temperature, allowable humidity, v 1 、v 2 、v 3 、v 4 And respectively expressed as coefficient factors corresponding to the set dust concentration, noise value, temperature and humidity.
9. The big-data-based ward care early warning management system according to claim 1, wherein: the early warning set comprises an early warning ward set and an early warning patient set, and the specific acquisition process comprises the following steps:
comparing the basic nursing standard-reaching coefficient corresponding to each patient in each ward with a set basic nursing standard-reaching coefficient threshold, if the basic nursing standard-reaching coefficient corresponding to a certain patient in a certain ward is smaller than the basic nursing standard-reaching coefficient threshold, marking the ward as an early-warning ward, and marking the patient as an early-warning patient;
comparing the physiological index nursing standard-reaching coefficient corresponding to each patient in each ward with a set physiological index nursing standard-reaching coefficient threshold, if the physiological index nursing standard-reaching coefficient corresponding to a certain patient in a certain ward is smaller than the physiological index nursing standard-reaching coefficient threshold, marking the ward as an early warning ward, and marking the patient as an early warning patient;
comparing the rest posture nursing standard-reaching coefficient corresponding to each patient in each ward with a set rest posture nursing standard-reaching coefficient threshold, if the rest posture nursing standard-reaching coefficient corresponding to a certain patient in a certain ward is smaller than the rest posture nursing standard-reaching coefficient threshold, marking the ward as an early-warning ward, and marking the patient as an early-warning patient;
comparing the rest environment nursing standard-reaching coefficient corresponding to each ward with a set rest environment nursing standard-reaching coefficient threshold, and if the rest environment nursing standard-reaching coefficient corresponding to a certain ward is smaller than the rest environment nursing standard-reaching coefficient threshold, marking the ward as an early-warning ward;
and counting to obtain the number of early warning wards and the number of early warning patients, further respectively constructing an early warning ward set and an early warning patient set, and combining the early warning ward set and the early warning patient set to generate an early warning set.
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