CN112489819A - Intelligent medical cloud service system based on big data - Google Patents
Intelligent medical cloud service system based on big data Download PDFInfo
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Abstract
The invention discloses a big data-based intelligent medical cloud service system, which is characterized in that a data acquisition module is used for acquiring inquiry information of a doctor and registration information of a patient, and the inquiry information of the doctor and the registration information of the patient are respectively sent to a treatment analysis module and a registration analysis module; receiving and analyzing inquiry information of a doctor by using a diagnosis analysis module to obtain inquiry analysis information; receiving and analyzing the registration information of the patient by using a registration analysis module to obtain registration analysis information; receiving inquiry analysis information and registration analysis information by using a statistical distribution module, processing and distributing the inquiry analysis information and the registration analysis information to obtain distribution information, and sending the distribution information to a prompt module; prompting the diagnosis of the doctor and the registration of the patient by using a prompting module; the invention is used for solving the problems that the diagnosis condition of a doctor can not be obtained in real time, so that patients can wait in line all the time, and the registration denomination of the doctor can not be dynamically allocated.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a smart medical cloud service system based on the big data.
Background
The intelligent medical treatment utilizes the most advanced Internet of things technology and combines big data to realize the interaction between a patient and medical staff, medical institutions and medical equipment, and the informatization is gradually achieved; by establishing a set of intelligent medical information network platform system, patients can enjoy safe, convenient and high-quality diagnosis and treatment services by using shorter treatment waiting time and paying basic medical expenses.
Patent publication No. CN111159671A discloses a smart medical platform, specifically a smart medical platform which can realize real-time and continuous data communication between patients and medical institutions, further realize the rapid and targeted medical services for scattered patients, effectively improve the service efficiency and fully utilize the existing resources, and is characterized in that the smart medical platform is provided with a background functional unit, and a user management unit, a health data management unit, a service management unit, an equipment management unit, a video management unit and a text information management unit which are respectively connected with the background functional unit, wherein the user management unit is provided with a user list module, and the user management unit accesses the user list module through a front-end registered user to search a non-mobile-end user; the health data management unit is used for managing mobile end user data and providing user early warning setting, map fixing and user data storage services for the mobile end user.
The existing intelligent medical cloud service system has the following defects: the problem that the diagnosis condition of the doctor cannot be acquired in real time so that the patient always waits in line and the problem that the registration denomination of the doctor cannot be dynamically allocated.
Disclosure of Invention
The invention aims to provide a smart medical cloud service system based on big data, and the technical problems to be solved by the invention are as follows:
how to solve the problem that the diagnosis condition of a doctor cannot be acquired in real time in the existing scheme so that a patient can wait in line all the time and the problem that the registration denomination of the doctor cannot be dynamically allocated.
The purpose of the invention can be realized by the following technical scheme: a big data-based intelligent medical cloud service system comprises a data acquisition module, a treatment analysis module, a registration analysis module, a statistical distribution module and a prompt module;
the data acquisition module is used for acquiring inquiry information of doctors and registration information of patients, the inquiry information comprises personal data of the doctors for inquiry of the doctors, inquiry behavior data and inquiry prescription data, and the registration information comprises identity data, registration outpatient service data and registration doctor data of the patients; the inquiry information of the doctor and the registration information of the patient are respectively sent to a diagnosis analysis module and a registration analysis module;
the diagnosis analysis module is used for receiving and analyzing the inquiry information of doctors to obtain the inquiry analysis information and sending the inquiry analysis information to the statistical distribution module, and the specific steps comprise:
the method comprises the following steps: acquiring personal data of doctors, inquiry behavior data and inquiry prescription data in inquiry information;
step two: setting different clinic types to correspond to different clinic preset values, setting different job types to correspond to different job preset values, matching the clinic types in the personal data of the doctor with all the clinic types to obtain the corresponding clinic preset values and marking the corresponding clinic preset values as WM1, matching the job types in the personal data of the doctor with all the job types to obtain the corresponding job preset values and marking the corresponding job preset values as WM 2;
step three: acquiring the time corresponding to the action far away from the computer screen in the inquiry behavior data, marking the time as inquiry starting time TW1, and marking the time corresponding to the action of staring at the computer screen next time as inquiry ending time TW 2;
step four: the method comprises the steps of obtaining a time mark TK1 corresponding to an action of staring at a computer screen in the inquiry behavior data and an action of starting to tap a keyboard in the inquiry start drug data, and obtaining a time mark TK2 corresponding to an action of staring at the computer screen in the inquiry behavior data and an action of stopping tapping the keyboard in the inquiry start drug data; counting the time difference between the time corresponding to the action of the doctor away from the computer screen for the first time and the time corresponding to the action of the keyboard for the last time, and marking the time as diagnosis time TZ;
step five: obtaining a question value of a doctor by using a formula;
step six: comparing the asking value with a preset standard asking threshold, if the asking value is not greater than the standard asking threshold, judging that the diagnosis time of the doctor is lower than the standard diagnosis time, and calculating the time difference between the diagnosis time and the standard diagnosis time to obtain the idle time; if the asking value is larger than the standard asking threshold, judging that the diagnosis time of the doctor is higher than the standard diagnosis time, and calculating the time difference between the diagnosis time and the standard diagnosis time to obtain the overtime time;
step seven: combining the inquiry value with the spare time and the overtime time to obtain inquiry analysis information;
the registration analysis module is used for receiving and analyzing registration information of a patient to obtain registration analysis information and sending the registration analysis information to the statistical distribution module;
the statistical distribution module is used for receiving the inquiry analysis information and the registration analysis information, processing and distributing the inquiry analysis information and the registration analysis information to obtain distribution information, and sending the distribution information to the prompt module;
the prompting module is used for prompting the diagnosis of a doctor and the registration of a patient.
Preferably, the registration analysis module is configured to receive registration information of a patient and analyze the registration information to obtain registration analysis information, and the specific steps include:
s21: acquiring identity data, registration outpatient data and registration doctor data of a patient in registration information;
s22: marking the identity card number in the identity data as a registration number Bi, i being 1,2.. n; matching the clinic types in the registered clinic data with all clinic types to obtain corresponding clinic preset values and marking the clinic preset values as GM1, marking the registered time in the registered clinic data as pending time DT1, matching the registered doctor types in the registered doctor data with all position types to obtain corresponding position matching values and marking the position matching values as GM 2;
s23: obtaining the waiting equivalence of the patient by using a formula, wherein the formula is as follows:
Qdd＝β×GM1×GM2×(DT1-Tk-TZk)
wherein Q isddExpressed as the waiting value of the patient, beta is expressed as a preset registration correction factor, Tk is expressed as a preset diagnosis starting time, TZk is expressed as the accumulated time of the actual diagnosis after the diagnosis is started by the doctor, and k is 1 and 2;
s24: analyzing the value to be equivalent, if the value to be equivalent is not more than zero, judging that the patient cannot hang the number of the position type doctor corresponding to the outpatient service type within the diagnosis time period, and generating registration failure data; if the waiting equivalence is larger than zero, judging that the patient can hang the number of the doctor with the position type corresponding to the outpatient service type in the diagnosis time period, and generating successful registration data;
s25: and combining the value to be registered with the registration failure data and the registration success data to obtain registration analysis information.
Preferably, the statistical distribution module is configured to receive the inquiry analysis information and the registration analysis information, perform processing distribution to obtain distribution information, and specifically includes:
s31: acquiring inquiry analysis information and registration analysis information;
s32: obtaining the time difference to be diagnosed by the difference between the vacant time length and the overtime time length in different diagnosis time periods, and analyzing the time difference to be diagnosed;
s33: if the time difference to be diagnosed is less than zero, judging that no redundant time exists in the diagnosis time for registration, and generating a first signal to be diagnosed; if the time difference to be diagnosed is larger than zero and the time difference to be diagnosed is smaller than the preset standard diagnosis time difference, judging that redundant time exists in the diagnosis time but registration cannot be carried out, and generating a second signal to be diagnosed; if the time difference to be diagnosed is larger than zero and the time difference to be diagnosed is larger than the preset standard diagnosis time difference, judging that redundant time exists in the diagnosis time period and registration can be carried out, and generating a third signal to be diagnosed;
s34: stopping generating the registration denominations of the first to-be-diagnosed signal and the second to-be-diagnosed signal corresponding to the doctor in the diagnosis time period by using the first to-be-diagnosed signal and the second to-be-diagnosed signal, and generating the registration denominations of the third to-be-diagnosed signal corresponding to the doctor in the diagnosis time period by using the third to-be-diagnosed signal;
s35: acquiring the outpatient service type to be registered of the patient according to the registration failure data in the registration analysis information, matching the outpatient service type with the outpatient service type of the doctor corresponding to the third signal to be diagnosed, generating distribution success data if the matching results are the same, and generating distribution failure data if the matching results are different;
s36: the first signal to be diagnosed, the second signal to be diagnosed and the third signal to be diagnosed form allocation data to be diagnosed, the allocation success data and the allocation failure data form allocation matching data, and the allocation data to be diagnosed and the allocation matching data are combined to obtain allocation information.
Preferably, the prompting module is used for prompting the diagnosis of the doctor and the registration of the patient, and the specific steps include:
s41: acquiring actions far away from a computer screen in the inquiry behavior data and generating a first prompt signal, generating inquiry starting information of a doctor by using the first prompt signal, prompting a waiting patient by using a prompter, acquiring actions of staring at the computer screen next time and generating a second prompt signal, generating inquiry finishing information of the doctor by using the second prompt signal, and prompting the waiting patient by using the prompter;
s42: acquiring the action of starting to knock a keyboard in the inquiry prescription data and generating a third prompt signal, generating the information that a doctor starts to prescribe a medicine by using the third prompt signal and prompting the waiting patient by using a prompter; acquiring action of staring at a computer screen in the inquiry behavior data and action of stopping knocking a keyboard in the inquiry prescription data to generate a fourth prompt signal, generating medicine starting information of a doctor by using the fourth prompt signal and prompting a waiting patient by using a prompter;
s43: acquiring the typing speed of a doctor for typing a keyboard and the sliding speed of a mouse, acquiring the pull-down position of a medicine according to the first letter of the medicine, and utilizing a formula
Acquiring the expected time for a doctor to prescribe a medicine;
wherein δ is represented as a preset typing correction factor, χ is represented as a preset sliding correction factor, v1 is represented as a typing rate of a keyboard, v2 is represented as a sliding rate of a mouse, s1 is represented as a total number of types, and h is represented as a sliding number of the mouse;
s44: and carrying out diagnosis countdown prompting on the waiting patients by a prompting device by utilizing the expected time for opening the medicines.
Preferably, the asked value of the doctor is obtained by using a formula, wherein the formula is as follows:
wherein Q iswkExpressed as physician's asking value,. mu.as preset asking drug correction factor, a1, a2 as different scale factors, and a1>a2。
The invention has the beneficial effects that:
according to various aspects disclosed by the invention, the data acquisition module is utilized to acquire inquiry information of a doctor and registration information of a patient, the inquiry information comprises personal data of the doctor who inquires the doctor, inquiry behavior data and inquiry prescription data, and the registration information comprises identity data, registration outpatient data and registration doctor data of the patient; the inquiry information of the doctor and the registration information of the patient are respectively sent to the treatment analysis module and the registration analysis module, and the inquiry information of the doctor and the registration information of the patient are analyzed and processed, so that data support can be provided for the diagnosis condition of the doctor and the registration allocation condition of the patient, the transparency of the diagnosis information of the doctor and the dynamic allocation of the registration denominations can be effectively improved, and the waiting experience and the registration experience of the patient are improved;
the inquiry analysis module is used for receiving and analyzing inquiry information of a doctor to obtain inquiry analysis information, the inquiry analysis information is sent to the statistical distribution module, inquiry values of the doctor are obtained by analyzing and calculating the inquiry information, and inquiry conditions of the doctor can be obtained in real time and prompt in time by analyzing the inquiry values;
the registration analysis module is used for receiving and analyzing registration information of a patient to obtain registration analysis information, and the registration analysis information is sent to the statistical distribution module; the registration information is analyzed and calculated to obtain the waiting equivalence of the patient, so that the allocation condition of the registration denominations can be dynamically obtained, and the registration efficiency of the patient is improved;
receiving inquiry analysis information and registration analysis information by using a statistical distribution module, processing and distributing the inquiry analysis information and the registration analysis information to obtain distribution information, and sending the distribution information to a prompt module; the prompting module is used for prompting the diagnosis of the doctor and the registration of the patient to acquire the expected time for the prescription of the doctor, and the prompt for the countdown of the diagnosis of the waiting patient is realized by the prompt device according to the expected time for the prescription, so that the defects that the waiting effect of the patient is poor and the registration denomination cannot be acquired in time due to the fact that the diagnosis condition of the doctor cannot be disclosed in time can be overcome.
Drawings
The invention will be further described with reference to the accompanying drawings.
Fig. 1 is a schematic block diagram of a big data-based intelligent medical cloud service system 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.
Example 1
Referring to fig. 1, the invention relates to a smart medical cloud service system based on big data, which comprises a data acquisition module, a treatment analysis module, a registration analysis module, a statistical distribution module and a prompt module;
the data acquisition module is used for acquiring inquiry information of doctors and registration information of patients, the inquiry information comprises personal data of the doctors for inquiry of the doctors, inquiry behavior data and inquiry prescription data, and the registration information comprises identity data, registration outpatient service data and registration doctor data of the patients; the inquiry information of the doctor and the registration information of the patient are respectively sent to a diagnosis analysis module and a registration analysis module;
the diagnosis analysis module is used for receiving and analyzing the inquiry information of doctors to obtain the inquiry analysis information and sending the inquiry analysis information to the statistical distribution module, and the specific steps comprise:
the method comprises the following steps: acquiring personal data of doctors, inquiry behavior data and inquiry prescription data in inquiry information;
step two: setting different clinic types to correspond to different clinic preset values, setting different job types to correspond to different job preset values, matching the clinic types in the personal data of the doctor with all the clinic types to obtain the corresponding clinic preset values and marking the corresponding clinic preset values as WM1, matching the job types in the personal data of the doctor with all the job types to obtain the corresponding job preset values and marking the corresponding job preset values as WM 2;
step three: acquiring the time corresponding to the action far away from the computer screen in the inquiry behavior data, marking the time as inquiry starting time TW1, and marking the time corresponding to the action of staring at the computer screen next time as inquiry ending time TW 2;
step four: the method comprises the steps of obtaining a time mark TK1 corresponding to an action of staring at a computer screen in the inquiry behavior data and an action of starting to tap a keyboard in the inquiry start drug data, and obtaining a time mark TK2 corresponding to an action of staring at the computer screen in the inquiry behavior data and an action of stopping tapping the keyboard in the inquiry start drug data; counting the time difference between the time corresponding to the action of the doctor away from the computer screen for the first time and the time corresponding to the action of the keyboard for the last time, and marking the time as diagnosis time TZ;
step five: obtaining a question value of a doctor by using a formula; the formula is:
wherein Q iswkExpressed as physician's asking value,. mu.as preset asking drug correction factor, a1, a2 as different scale factors, and a1>a2；
Step six: comparing the asking value with a preset standard asking threshold, if the asking value is not greater than the standard asking threshold, judging that the diagnosis time of the doctor is lower than the standard diagnosis time, and calculating the time difference between the diagnosis time and the standard diagnosis time to obtain the idle time; if the asking value is larger than the standard asking threshold, judging that the diagnosis time of the doctor is higher than the standard diagnosis time, and calculating the time difference between the diagnosis time and the standard diagnosis time to obtain the overtime time;
step seven: combining the inquiry value with the spare time and the overtime time to obtain inquiry analysis information;
the registration analysis module is used for receiving and analyzing registration information of a patient to obtain registration analysis information and sending the registration analysis information to the statistical distribution module;
the registration analysis module is used for receiving registration information of a patient and analyzing the registration information to obtain registration analysis information, and the specific steps comprise:
acquiring identity data, registration outpatient data and registration doctor data of a patient in registration information;
marking the identity card number in the identity data as a registration number Bi, i being 1,2.. n; matching the clinic types in the registered clinic data with all clinic types to obtain corresponding clinic preset values and marking the clinic preset values as GM1, marking the registered time in the registered clinic data as pending time DT1, matching the registered doctor types in the registered doctor data with all position types to obtain corresponding position matching values and marking the position matching values as GM 2;
obtaining the waiting equivalence of the patient by using a formula, wherein the formula is as follows:
Qdd＝β×GM1×GM2×(DT1-Tk-TZk)
wherein Q isddExpressed as the waiting value of the patient, beta is expressed as a preset registration correction factor, Tk is expressed as a preset diagnosis starting time, TZk is expressed as the accumulated time of the actual diagnosis after the diagnosis is started by the doctor, and k is 1 and 2;
analyzing the value to be equivalent, if the value to be equivalent is not more than zero, judging that the patient cannot hang the number of the position type doctor corresponding to the outpatient service type within the diagnosis time period, and generating registration failure data; if the waiting equivalence is larger than zero, judging that the patient can hang the number of the doctor with the position type corresponding to the outpatient service type in the diagnosis time period, and generating successful registration data;
and combining the value to be registered with the registration failure data and the registration success data to obtain registration analysis information.
The statistical distribution module is used for receiving inquiry analysis information and registration analysis information and performing processing distribution to obtain distribution information, and the specific steps comprise:
acquiring inquiry analysis information and registration analysis information;
obtaining the time difference to be diagnosed by the difference between the vacant time length and the overtime time length in different diagnosis time periods, and analyzing the time difference to be diagnosed;
if the time difference to be diagnosed is less than zero, judging that no redundant time exists in the diagnosis time for registration, and generating a first signal to be diagnosed; if the time difference to be diagnosed is larger than zero and the time difference to be diagnosed is smaller than the preset standard diagnosis time difference, judging that redundant time exists in the diagnosis time but registration cannot be carried out, and generating a second signal to be diagnosed; if the time difference to be diagnosed is larger than zero and the time difference to be diagnosed is larger than the preset standard diagnosis time difference, judging that redundant time exists in the diagnosis time period and registration can be carried out, and generating a third signal to be diagnosed;
stopping generating the registration denominations of the first to-be-diagnosed signal and the second to-be-diagnosed signal corresponding to the doctor in the diagnosis time period by using the first to-be-diagnosed signal and the second to-be-diagnosed signal, and generating the registration denominations of the third to-be-diagnosed signal corresponding to the doctor in the diagnosis time period by using the third to-be-diagnosed signal;
acquiring the outpatient service type to be registered of the patient according to the registration failure data in the registration analysis information, matching the outpatient service type with the outpatient service type of the doctor corresponding to the third signal to be diagnosed, generating distribution success data if the matching results are the same, and generating distribution failure data if the matching results are different;
the first signal to be diagnosed, the second signal to be diagnosed and the third signal to be diagnosed form allocation data to be diagnosed, the allocation success data and the allocation failure data form allocation matching data, and the allocation data to be diagnosed and the allocation matching data are combined to obtain allocation information.
The prompting module is used for prompting the diagnosis of a doctor and the registration of a patient, and comprises the following specific steps:
acquiring actions far away from a computer screen in the inquiry behavior data and generating a first prompt signal, generating inquiry starting information of a doctor by using the first prompt signal, prompting a waiting patient by using a prompter, acquiring actions of staring at the computer screen next time and generating a second prompt signal, generating inquiry finishing information of the doctor by using the second prompt signal, and prompting the waiting patient by using the prompter;
acquiring the action of starting to knock a keyboard in the inquiry prescription data and generating a third prompt signal, generating the information that a doctor starts to prescribe a medicine by using the third prompt signal and prompting the waiting patient by using a prompter; acquiring action of staring at a computer screen in the inquiry behavior data and action of stopping knocking a keyboard in the inquiry prescription data to generate a fourth prompt signal, generating medicine starting information of a doctor by using the fourth prompt signal and prompting a waiting patient by using a prompter;
acquiring the typing speed of a doctor for typing a keyboard and the sliding speed of a mouse, acquiring the pull-down position of a medicine according to the first letter of the medicine, and utilizing a formula
Acquiring the expected time for a doctor to prescribe a medicine;
wherein δ is represented as a preset typing correction factor, χ is represented as a preset sliding correction factor, v1 is represented as a typing rate of a keyboard, v2 is represented as a sliding rate of a mouse, s1 is represented as a total number of types, and h is represented as a sliding number of the mouse;
the diagnosis countdown prompt is carried out on the waiting patients through the prompter by utilizing the expected time for opening the medicines;
the above formulas are obtained by collecting a large amount of data and performing software simulation, and the coefficients in the formulas are set by those skilled in the art according to actual conditions.
The working principle of the invention is as follows:
acquiring inquiry information of a doctor and registration information of a patient by using a data acquisition module, wherein the inquiry information comprises personal data of the doctor who makes an inquiry, inquiry behavior data and inquiry prescription data, and the registration information comprises identity data, registration outpatient data and registration doctor data of the patient; the inquiry information of the doctor and the registration information of the patient are respectively sent to the treatment analysis module and the registration analysis module, and the inquiry information of the doctor and the registration information of the patient are analyzed and processed, so that data support can be provided for the diagnosis condition of the doctor and the registration allocation condition of the patient, the transparency of the diagnosis information of the doctor and the dynamic allocation of the registration denominations can be effectively improved, and the waiting experience and the registration experience of the patient are improved;
receiving and analyzing the inquiry information of the doctor by using the inquiry analysis module to obtain inquiry analysis information, sending the inquiry analysis information to the statistical distribution module, analyzing and calculating the inquiry information by using a formula QddAcquiring the value to be equivalent of a patient (DT1-Tk-TZk), analyzing the question value, comparing the question value with a preset standard question threshold value, judging that the diagnosis time of a doctor is lower than the standard diagnosis time if the question value is not greater than the standard question threshold value, and calculating the time difference between the diagnosis time and the standard diagnosis time to obtain the idle time; if the asking value is greater than the standard asking threshold, judging that the diagnosis time of the doctor is greater than the standard diagnosis time, calculating the time difference between the diagnosis time and the standard diagnosis time to obtain the overtime time, acquiring the asking condition of the doctor in real time according to the spare time and the overtime time, and timely performing registration denomination distribution;
the registration analysis module is used for receiving and analyzing registration information of a patient to obtain registration analysis information, and the registration analysis information is sent to the statistical distribution module; by analyzing and calculating registration information, formula Q is utilizedddAcquiring a waiting equivalence value of the patient (beta multiplied by GM1 multiplied by GM2 x) (DT1-Tk-TZk), analyzing the waiting equivalence value, judging that the patient cannot hang the number of the position type doctor corresponding to the clinic type in the diagnosis time period if the waiting equivalence value is not larger than zero, and generating registration failure data; if the waiting equivalence is larger than zero, the fact that the patient can hang the number of the doctor with the position type corresponding to the outpatient service type in the diagnosis time period is judged, registration success data are generated, the allocation condition of the registration denominations can be dynamically obtained, and the registration efficiency of the patient is improved;
the statistical distribution module is used for receiving inquiry analysis information and registration analysis information, processing and distributing the inquiry analysis information and the registration analysis information to obtain distribution information, and sending the distribution information to the prompt moduleA block; the prompt module is used for prompting the diagnosis of the doctor and the registration of the patient, and the formula is used
The expected time for the doctor to open the medicine is acquired, and the prompt for the diagnosis countdown of the waiting patient is carried out by the prompter according to the expected time for the medicine to open the medicine, so that the defects that the waiting effect of the patient is poor and the registration name cannot be acquired in time due to the fact that the diagnosis condition of the doctor cannot be disclosed in time can be overcome.
In the embodiments provided by the present invention, it should be understood that the disclosed system and method can be implemented in other ways. For example, the above-described embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the method of the embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware form, and can also be realized in a form of hardware and a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is to be understood that the word "comprising" does not exclude other modules or steps, and the singular does not exclude the plural. A plurality of modules or means recited in the system claims may also be implemented by one module or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above examples are only intended to illustrate the technical process of the present invention and not to limit the same, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical process of the present invention without departing from the spirit and scope of the technical process of the present invention.
Claims (5)
1. A big data-based intelligent medical cloud service system is characterized by comprising a data acquisition module, a treatment analysis module, a registration analysis module, a statistical distribution module and a prompt module;
the data acquisition module is used for acquiring inquiry information of doctors and registration information of patients, the inquiry information comprises personal data of the doctors for inquiry of the doctors, inquiry behavior data and inquiry prescription data, and the registration information comprises identity data, registration outpatient service data and registration doctor data of the patients; the inquiry information of the doctor and the registration information of the patient are respectively sent to a diagnosis analysis module and a registration analysis module;
the diagnosis analysis module is used for receiving and analyzing the inquiry information of doctors to obtain the inquiry analysis information and sending the inquiry analysis information to the statistical distribution module, and the specific steps comprise:
the method comprises the following steps: acquiring personal data of doctors, inquiry behavior data and inquiry prescription data in inquiry information;
step two: setting different clinic types to correspond to different clinic preset values, setting different job types to correspond to different job preset values, matching the clinic types in the personal data of the doctor with all the clinic types to obtain the corresponding clinic preset values and marking the corresponding clinic preset values as WM1, matching the job types in the personal data of the doctor with all the job types to obtain the corresponding job preset values and marking the corresponding job preset values as WM 2;
step three: acquiring the time corresponding to the action far away from the computer screen in the inquiry behavior data, marking the time as inquiry starting time TW1, and marking the time corresponding to the action of staring at the computer screen next time as inquiry ending time TW 2;
step four: the method comprises the steps of obtaining a time mark TK1 corresponding to an action of staring at a computer screen in the inquiry behavior data and an action of starting to tap a keyboard in the inquiry start drug data, and obtaining a time mark TK2 corresponding to an action of staring at the computer screen in the inquiry behavior data and an action of stopping tapping the keyboard in the inquiry start drug data; counting the time difference between the time corresponding to the action of the doctor away from the computer screen for the first time and the time corresponding to the action of the keyboard for the last time, and marking the time as diagnosis time TZ;
step five: obtaining a question value of a doctor by using a formula;
step six: comparing the asking value with a preset standard asking threshold, if the asking value is not greater than the standard asking threshold, judging that the diagnosis time of the doctor is lower than the standard diagnosis time, and calculating the time difference between the diagnosis time and the standard diagnosis time to obtain the idle time; if the asking value is larger than the standard asking threshold, judging that the diagnosis time of the doctor is higher than the standard diagnosis time, and calculating the time difference between the diagnosis time and the standard diagnosis time to obtain the overtime time;
step seven: combining the inquiry value with the spare time and the overtime time to obtain inquiry analysis information;
the registration analysis module is used for receiving and analyzing registration information of a patient to obtain registration analysis information and sending the registration analysis information to the statistical distribution module;
the statistical distribution module is used for receiving the inquiry analysis information and the registration analysis information, processing and distributing the inquiry analysis information and the registration analysis information to obtain distribution information, and sending the distribution information to the prompt module;
the prompting module is used for prompting the diagnosis of a doctor and the registration of a patient.
2. The big data based intelligent medical cloud service system as claimed in claim 1, wherein the registration analysis module is configured to receive and analyze registration information of a patient to obtain registration analysis information, and the specific steps include:
s21: acquiring identity data, registration outpatient data and registration doctor data of a patient in registration information;
s22: marking the identity card number in the identity data as a registration number Bi, i being 1,2.. n; matching the clinic types in the registered clinic data with all clinic types to obtain corresponding clinic preset values and marking the clinic preset values as GM1, marking the registered time in the registered clinic data as pending time DT1, matching the registered doctor types in the registered doctor data with all position types to obtain corresponding position matching values and marking the position matching values as GM 2;
s23: obtaining the waiting equivalence of the patient by using a formula, wherein the formula is as follows:
Qdd＝β×GM1×GM2×(DT1-Tk-TZk)
wherein Q isddExpressed as the waiting value of the patient, beta is expressed as a preset registration correction factor, Tk is expressed as a preset diagnosis starting time, TZk is expressed as the accumulated time of the actual diagnosis after the diagnosis is started by the doctor, and k is 1 and 2;
s24: analyzing the value to be equivalent, if the value to be equivalent is not more than zero, judging that the patient cannot hang the number of the position type doctor corresponding to the outpatient service type within the diagnosis time period, and generating registration failure data; if the waiting equivalence is larger than zero, judging that the patient can hang the number of the doctor with the position type corresponding to the outpatient service type in the diagnosis time period, and generating successful registration data;
s25: and combining the value to be registered with the registration failure data and the registration success data to obtain registration analysis information.
3. The big data based intelligent medical cloud service system as claimed in claim 1, wherein the statistical distribution module is configured to receive inquiry analysis information and registration analysis information, perform processing distribution, and obtain distribution information, and the specific steps include:
s31: acquiring inquiry analysis information and registration analysis information;
s32: obtaining the time difference to be diagnosed by the difference between the vacant time length and the overtime time length in different diagnosis time periods, and analyzing the time difference to be diagnosed;
s33: if the time difference to be diagnosed is less than zero, judging that no redundant time exists in the diagnosis time for registration, and generating a first signal to be diagnosed; if the time difference to be diagnosed is larger than zero and the time difference to be diagnosed is smaller than the preset standard diagnosis time difference, judging that redundant time exists in the diagnosis time but registration cannot be carried out, and generating a second signal to be diagnosed; if the time difference to be diagnosed is larger than zero and the time difference to be diagnosed is larger than the preset standard diagnosis time difference, judging that redundant time exists in the diagnosis time period and registration can be carried out, and generating a third signal to be diagnosed;
s34: stopping generating the registration denominations of the first to-be-diagnosed signal and the second to-be-diagnosed signal corresponding to the doctor in the diagnosis time period by using the first to-be-diagnosed signal and the second to-be-diagnosed signal, and generating the registration denominations of the third to-be-diagnosed signal corresponding to the doctor in the diagnosis time period by using the third to-be-diagnosed signal;
s35: acquiring the outpatient service type to be registered of the patient according to the registration failure data in the registration analysis information, matching the outpatient service type with the outpatient service type of the doctor corresponding to the third signal to be diagnosed, generating distribution success data if the matching results are the same, and generating distribution failure data if the matching results are different;
s36: the first signal to be diagnosed, the second signal to be diagnosed and the third signal to be diagnosed form allocation data to be diagnosed, the allocation success data and the allocation failure data form allocation matching data, and the allocation data to be diagnosed and the allocation matching data are combined to obtain allocation information.
4. The big data based intelligent medical cloud service system as claimed in claim 1, wherein the prompting module is used for prompting diagnosis of a doctor and registration of a patient, and the specific steps include:
s41: acquiring actions far away from a computer screen in the inquiry behavior data and generating a first prompt signal, generating inquiry starting information of a doctor by using the first prompt signal, prompting a waiting patient by using a prompter, acquiring actions of staring at the computer screen next time and generating a second prompt signal, generating inquiry finishing information of the doctor by using the second prompt signal, and prompting the waiting patient by using the prompter;
s42: acquiring the action of starting to knock a keyboard in the inquiry prescription data and generating a third prompt signal, generating the information that a doctor starts to prescribe a medicine by using the third prompt signal and prompting the waiting patient by using a prompter; acquiring action of staring at a computer screen in the inquiry behavior data and action of stopping knocking a keyboard in the inquiry prescription data to generate a fourth prompt signal, generating medicine starting information of a doctor by using the fourth prompt signal and prompting a waiting patient by using a prompter;
s43: acquiring the typing speed of a doctor for typing a keyboard and the sliding speed of a mouse, acquiring the pull-down position of a medicine according to the first letter of the medicine, and utilizing a formula
Acquiring the expected time for a doctor to prescribe a medicine;
wherein δ is represented as a preset typing correction factor, χ is represented as a preset sliding correction factor, v1 is represented as a typing rate of a keyboard, v2 is represented as a sliding rate of a mouse, s1 is represented as a total number of types, and h is represented as a sliding number of the mouse;
s44: and carrying out diagnosis countdown prompting on the waiting patients by a prompting device by utilizing the expected time for opening the medicines.
5. The big data based intelligent medical cloud service system as claimed in claim 1, wherein the question value of the doctor is obtained by using a formula:
wherein the content of the first and second substances,Qwkexpressed as physician's asking value,. mu.as preset asking drug correction factor, a1, a2 as different scale factors, and a1>a2。
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| CN113764081A (en) * | 2021-03-26 | 2021-12-07 | 北京京东拓先科技有限公司 | Method, device and equipment for determining inquiry number and computer readable storage medium |
| CN114373535A (en) * | 2022-01-13 | 2022-04-19 | 刘威 | Novel doctor-patient mechanism system based on Internet |
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| CN113764081A (en) * | 2021-03-26 | 2021-12-07 | 北京京东拓先科技有限公司 | Method, device and equipment for determining inquiry number and computer readable storage medium |
| CN114373535A (en) * | 2022-01-13 | 2022-04-19 | 刘威 | Novel doctor-patient mechanism system based on Internet |
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