CN112489748A - Cloud computing-based medical system and medical resource allocation method thereof - Google Patents
Cloud computing-based medical system and medical resource allocation method thereof Download PDFInfo
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Abstract
The invention provides a cloud computing-based medical system and a medical resource allocation method thereof, wherein the allocation method comprises the following steps: acquiring registration information of each patient registered within a set time period and medical capability data of a doctor who sits and diagnoses within the set time period; acquiring the complexity of the disease of a patient according to registration information of the patient, and acquiring the medical level of the patient according to the working years, the recovery rate and the recurrence rate of a doctor; calculating the diagnosis time length required by each doctor to diagnose each patient; obtaining an optimal medical resource allocation scheme according to the visit duration of each doctor to each patient in a set time period; and respectively sending the patients to be treated by each doctor, the time and the diagnosis duration of each patient to be treated in the optimal medical resource allocation scheme to corresponding doctor terminals, and respectively sending the doctor to be treated by each patient, the diagnosis time and the diagnosis duration to corresponding user terminals. The technical scheme provided by the invention can solve the problem that medical resources cannot be utilized most efficiently in the prior art.
Description
Technical Field
The invention relates to the technical field of medical systems based on cloud computing, in particular to a medical system based on cloud computing and a medical resource allocation method thereof.
Background
The hospital is a necessary mechanism for ensuring the life health of people, and when people suffer from diseases, particularly serious diseases, the people need to go to the hospital to see a doctor, eliminate the diseases and obtain health.
In a traditional hospital, people need to register at a registration place when going to a doctor, then queue up, and go to a corresponding consulting room to see a doctor when waiting for a called number. The phenomenon that the patients need to wait in the waiting room of the hospital often appears in some important consulting rooms, the patients need to wait in the waiting room of the hospital in the queuing process, the waiting room is frequently occupied by more people and is not beneficial to the rest of the patients, and even some patients need to arrive at the hospital early to rob the numbers in order to hang on the expert numbers, so that the body and mind of the patients are further burdened.
With the development of computer technology and information technology, the frequency of using networks by people is higher and higher, big data is another subversive technical change of an IT industry after cloud computing and the Internet of things, and a big data platform is widely used in the industries such as the Internet, finance and operators. The key of the development of regional medical health informatization is to realize information sharing, flowing and intelligent application by taking a patient as a center. In practice, however, different medical institutions often use different servers, networks, and information systems. Because the difference of each information system is large and the support degree of the standard is different, the information island phenomenon is serious; although some hospitals establish a data exchange platform to share data of the hospital, the data sharing across hospitals and regions is still extremely difficult, and the main reason is the lack of a large-scale resource sharing and computing service system which is extensible, safe, consistent, popular and efficient. Cloud computing is used as a next generation computing mode, a centralized construction mode of a regional medical information sharing platform is constructed by adopting a cloud computing technology, different medical institutions are comprehensively integrated in a system, the system throughput and the resource use efficiency are improved, the service cost can be reduced, and the reliability, the usability and the flexibility are improved, so that a medical information sharing platform with unified standards is formed.
The patient can carry out registration operation through the network platform of the medical system, and when the patient is registered through the network platform of the medical system, the medical system arranges the time for the patient to see a doctor through the registration sequence of the patient. However, patients have different diseases, doctors have different medical levels and specialties, and medical resources cannot be utilized most efficiently according to the way of visiting a doctor at the registration time.
Disclosure of Invention
The invention aims to provide a cloud computing-based medical system and a medical resource allocation method thereof, and aims to solve the problem that medical resources cannot be utilized most efficiently in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
a medical system based on cloud computing structurally comprises a doctor terminal, a cloud server and a user terminal, wherein the user terminal is used for sending registration information to the cloud server, and the doctor terminal is used for inputting the sitting and examining time of a doctor; the cloud server is used for executing a medical resource allocation method, and the method comprises the following steps:
the method comprises the following steps: acquiring registration information of each patient registered within a set time period and medical capability data of a doctor who sits and diagnoses within the set time period;
the registration information of the patient comprises the disease symptoms, the disease severity and the disease duration of the patient, and the medical capability data of the doctor comprises the working years, the recovery rate, the recurrence rate and the average time length for diagnosing each disease symptom of the doctor;
step two: acquiring the complexity of the disease of a patient according to registration information of the patient, and acquiring the medical level of the patient according to the working years, the recovery rate and the recurrence rate of a doctor;
step three: calculating the diagnosis time required by each doctor to diagnose each patient:
obtaining a first time length used by a doctor for diagnosing the patient according to the complexity of the disease condition of the patient and the medical level of the doctor, obtaining a second time length used by the doctor for diagnosing the patient according to the average time length used by the doctor for diagnosing the disease condition of the patient, and calculating the diagnosis time length required by the doctor for diagnosing the patient according to the first time length and the second time length;
step four: obtaining an optimal medical resource allocation scheme according to the visit duration of each doctor to each patient in a set time period; the optimal medical resource allocation scheme is a medical resource allocation scheme with the largest number of patients capable of being treated within a set time period;
step five: and respectively sending the patients to be treated by each doctor and the diagnosis time and the diagnosis duration of each patient to be treated in the optimal medical resource allocation scheme to corresponding doctor terminals, and respectively sending the doctor to be treated by each patient and the diagnosis time and the diagnosis duration to corresponding user terminals.
Further, the method for acquiring the complexity of the disease condition of the patient according to the registration information of the patient in the step two comprises the following steps:
classifying the diseases, and scoring according to the treatment difficulty of each disease to obtain scores of various diseases;
obtaining the disease symptoms of the patients, obtaining the corresponding classification of the disease symptoms, taking the score corresponding to the disease symptoms as the disease symptom score of the patients, and setting the score as L1；
Let the severity of the disease be L2If the duration of the disease is h, the patient has a complicated disease condition
G1＝αL1+βL2+γln h
Wherein α, β and γ are each a patient condition score L1Disease severity L2And the length of illness h.
Further, the method for acquiring the medical level according to the working years, the recovery rate and the recurrence rate of the doctor in the step two comprises the following steps:
set the working years of doctors as L0The recovery rate of the patient is P1The recurrence rate is P2The medical level of the doctor is
G2＝aln L0+bP1-cP2
Wherein a, b and c are respectively the working years L of the doctor0Treatment of the patient's recovery rate P1Recurrence Rate P2The weight of (c).
Further, let the first duration be H1The second duration is H2The required diagnosis time for the doctor to diagnose the patient is
H＝mH1+nH2
Wherein m and n are each a first duration H1And a second duration H2And m + n is 1.
Further, the method for obtaining the optimal medical resource allocation scheme in the fourth step comprises:
randomly sequencing the registered patients within a set time period for a set number of times;
the number of patients that can be treated in each ranking is calculated as follows:
according to the sequence, allocating the patients to the doctors with the shortest diagnosis time in turn;
when a doctor is assigned a full member, no patient is assigned to the doctor;
when all patients are allocated completely or all doctors are allocated fully, judging that the allocation is completed, and obtaining the medical resource allocation scheme under the sequencing;
when the assignment is complete, calculating the total number of patients diagnosed under the ordering;
after the number of patients to be diagnosed in each sequence is obtained, taking the sequence with the largest number of patients to be diagnosed as the optimal sequence, and taking the medical resource allocation scheme of the sequence as the optimal medical resource allocation scheme;
the doctor is assigned with full staff, and the sum of the diagnosis time lengths required by the patient assigned to the doctor is not less than the length of the set time period.
A medical system medical resource allocation method based on cloud computing comprises the following steps:
the method comprises the following steps: acquiring registration information of each patient registered within a set time period and medical capability data of a doctor who sits and diagnoses within the set time period;
the registration information of the patient comprises the disease symptoms, the disease severity and the disease duration of the patient, and the medical capability data of the doctor comprises the working years, the recovery rate, the recurrence rate and the average time length for diagnosing each disease symptom of the doctor;
step two: acquiring the complexity of the disease of a patient according to registration information of the patient, and acquiring the medical level of the patient according to the working years, the recovery rate and the recurrence rate of a doctor;
step three: calculating the diagnosis time required by each doctor to diagnose each patient:
obtaining a first time length used by a doctor for diagnosing the patient according to the complexity of the disease condition of the patient and the medical level of the doctor, obtaining a second time length used by the doctor for diagnosing the patient according to the average time length used by the doctor for diagnosing the disease condition of the patient, and calculating the diagnosis time length required by the doctor for diagnosing the patient according to the first time length and the second time length;
step four: obtaining an optimal medical resource allocation scheme according to the visit duration of each doctor to each patient in a set time period; the optimal medical resource allocation scheme is a medical resource allocation scheme with the largest number of patients capable of being treated within a set time period;
step five: and respectively sending the patients to be treated by each doctor and the diagnosis time and the diagnosis duration of each patient to be treated in the optimal medical resource allocation scheme to corresponding doctor terminals, and respectively sending the doctor to be treated by each patient and the diagnosis time and the diagnosis duration to corresponding user terminals.
Further, the method for acquiring the complexity of the disease condition of the patient according to the registration information of the patient in the step two comprises the following steps:
classifying the diseases, and scoring according to the treatment difficulty of each disease to obtain scores of various diseases;
obtaining the disease symptoms of the patients, obtaining the corresponding classification of the disease symptoms, taking the score corresponding to the disease symptoms as the disease symptom score of the patients, and setting the score as L1；
Let the severity of the disease be L2If the duration of the disease is h, the patient has a complicated disease condition
G1＝αL1+βL2+γln h
Wherein α, β and γ are each a patient condition score L1Disease severity L2And the length of illness h.
Further, the method for acquiring the medical level according to the working years, the recovery rate and the recurrence rate of the doctor in the step two comprises the following steps:
set the working years of doctors as L0The recovery rate of the patient is P1The recurrence rate is P2The medical level of the doctor is
G2＝aln L0+bP1-cP2
Wherein a, b and c are respectively the working years L of the doctor0Treatment of the patient's recovery rate P1Recurrence Rate P2The weight of (c).
Further, let the first duration be H1The second duration is H2The required diagnosis time for the doctor to diagnose the patient is
H＝mH1+nH2
Wherein m and n are each a first duration H1And a second duration H2And m + n is 1.
Further, the method for obtaining the optimal medical resource allocation scheme in the fourth step comprises:
randomly sequencing the registered patients within a set time period for a set number of times;
the number of patients that can be treated in each ranking is calculated as follows:
according to the sequence, allocating the patients to the doctors with the shortest diagnosis time in turn;
when a doctor is assigned a full member, no patient is assigned to the doctor;
when all patients are allocated completely or all doctors are allocated fully, judging that the allocation is completed, and obtaining the medical resource allocation scheme under the sequencing;
when the assignment is complete, calculating the total number of patients diagnosed under the ordering;
after the number of patients to be diagnosed in each sequence is obtained, taking the sequence with the largest number of patients to be diagnosed as the optimal sequence, and taking the medical resource allocation scheme of the sequence as the optimal medical resource allocation scheme;
the doctor is assigned with full staff, and the sum of the diagnosis time lengths required by the patient assigned to the doctor is not less than the length of the set time period.
The invention has the beneficial effects that:
according to the technical scheme provided by the invention, after the patient is registered, the medical system can determine the patient treatment time according to the patient condition and the medical level of a doctor, and the patient only needs to go to the hospital at the patient treatment time, does not need to go to the hospital first for registration and then queue for calling, and even does not need to go to the hospital in advance for queuing for registration. In addition, the technical scheme provided by the invention can optimally allocate medical resources according to the illness state of the patient and the medical level of the doctor, so that the most patients can see a doctor within a set time period, and the medical resources are utilized most efficiently. Therefore, the technical scheme provided by the invention can solve the problem that the medical resources cannot be utilized most efficiently in the prior art.
Drawings
Fig. 1 is a schematic structural diagram of a cloud computing-based medical system in an embodiment of the system of the invention;
fig. 2 is a flowchart of a medical resource allocation method of a cloud computing-based medical system in an embodiment of the system of the present invention.
Detailed Description
The embodiment of the system is as follows:
the embodiment provides a medical system based on cloud computing, the structure of which is shown in fig. 1, and the medical system comprises a cloud server, a user terminal and a doctor terminal, wherein the user terminal and the doctor terminal are both in communication connection with the cloud server.
The user terminal is provided with a registration module and a registration result query module, the registration module is used for inputting registration information of the patient and sending the registration information to the cloud server, the registration result query module is used for querying registration results, and the registration results are the time of seeing a doctor, the time of seeing a doctor and the doctor of seeing a doctor; the doctor terminal is provided with a sitting diagnosis time setting module and a sitting diagnosis task query module, the sitting diagnosis time setting module is used for inputting the sitting diagnosis time for a doctor to input and sending the sitting diagnosis time to the cloud server, and the sitting diagnosis task query module is used for querying patients needing to be subjected to a doctor during the sitting diagnosis period and the time and duration of each patient to be subjected to the doctor. The cloud server is used for obtaining an optimal medical resource allocation scheme through a medical resource allocation method according to the registration information of the patient and the sitting and examining time of the doctor, and sending the optimal medical resource allocation scheme to the corresponding user terminal and the doctor terminal respectively.
The flow of the medical resource allocation method executed by the cloud server in this embodiment is shown in fig. 2, and includes the following steps:
the method comprises the following steps: registration information of each patient registered within a set time period and medical capability data of a doctor who sits for a diagnosis within the set time period are acquired.
Registration information of the patient includes the patient's condition, the severity of the condition and the length of the condition, and medical competence data of the physician includes the physician's years of employment, the rate of recovery, the rate of recurrence and the average length of time used for diagnosing each condition.
Step two: the disease complexity of the patient is obtained according to the registration information of the patient, and the medical level of the patient is obtained according to the working years, the recovery rate and the recurrence rate of the doctor.
Step three: calculating the diagnosis time required by each doctor to diagnose each patient:
obtaining a first time length used by a doctor for diagnosing the patient according to the complexity of the disease condition of the patient and the medical level of the doctor, obtaining a second time length used by the doctor for diagnosing the patient according to the average time length used by the doctor for diagnosing the disease condition of the patient, and calculating the diagnosis time length required by the doctor for diagnosing the patient according to the first time length and the second time length.
Step four: obtaining an optimal medical resource allocation scheme according to the visit duration of each doctor to each patient in a set time period; the optimal medical resource allocation scheme is the medical resource allocation scheme with the largest number of patients that can be treated in a set time period.
Step five: and respectively sending the patients to be treated by each doctor and the diagnosis time and the diagnosis duration of each patient to be treated in the optimal medical resource allocation scheme to corresponding doctor terminals, and respectively sending the doctor to be treated by each patient and the diagnosis time and the diagnosis duration to corresponding user terminals.
In step two of this embodiment, the method for obtaining the complexity of the disease condition of the patient according to the disease condition, the severity of the disease condition and the duration of the disease condition comprises:
classifying each disease symptom, and scoring each disease symptom according to the diagnosis difficulty of each disease symptom, wherein the score is higher when the diagnosis difficulty is higher, so that the score of each disease symptom is obtained;
then obtaining the corresponding classification of the disease symptoms of the patient after obtaining the disease symptoms of the patient, and taking the score corresponding to the disease symptoms as the disease symptom score of the patient, wherein the score is L1；
Let the severity of the disease be L2If the duration of the disease is h, the patient has a complicated disease condition
G1＝αL1+βL2+γln h
Wherein α, β and γ are each a patient condition score L1Disease severity L2And the length of illness h.
The method for acquiring the medical level of a doctor according to the working years, the recovery rate and the recurrence rate of the doctor comprises the following steps:
set the working years of doctors as L0The recovery rate of the patient is P1The recurrence rate is P2The medical level of the doctor is
G2＝aln L0+bP1-cP2
Wherein a, b and c are respectively the working years L of the doctor0And the recovery rate P of the patient1Recurrence rate P2The weight of (c).
In step three, the method for acquiring the first duration is as follows:
firstly, acquiring historical data, counting the average time for doctors of each medical level grade to diagnose patients with each disease complexity degree, and storing the average time;
then, after the disease complexity of the patient and the medical level of the doctor are obtained, the average time length of the patient of which the doctor at the medical level diagnoses the disease complexity is found from the stored data as the first time length.
When the second time length is acquired, firstly, the average time length required by a doctor for diagnosing various diseases is acquired, then the types of the diseases of the patient are acquired, and the average time length required by the doctor for treating the types of the diseases is taken as the second time length for the doctor to diagnose the patient.
In step three, let the first duration be H1The second duration is H2The diagnosis time for the doctor to take a consultation of the patient is
H＝mH1+nH2
Wherein m and n are each a first duration H1And a second duration H2And m + n is 1.
In step four, the method for obtaining the optimal medical resource allocation scheme comprises the following steps:
(1) and acquiring doctors for sitting diagnosis in a set time period, and randomly sequencing the registered patients in the set time period for a set number of times.
(2) The number of patients that can be treated in each ranking is calculated as follows:
according to the ranking order, allocating the patients to the doctors with the shortest diagnosis time in turn;
according to the sequence, starting from the first patient, acquiring the diagnosis time required by each doctor for diagnosing the patient, and distributing the patient to the doctor with the shortest diagnosis time; in the same way, the patients after being sorted are respectively distributed to the doctors with the shortest diagnosis time;
when a doctor is allocated with a full member, the doctor is not allocated with a patient any more, and the patient is allocated to other doctors with the shortest required diagnosis time;
the medical resources are distributed in the set time period, so the sitting diagnosis time of the doctor is limited by the set time period; let N be the number of patients who have been assigned to one of the doctors who diagnosed the patient with t being the diagnosis time for the patient with iiThe diagnosis time for the doctor to diagnose the N patients is
Wherein N is an integer greater than 1 and i is an integer.
If T is larger than the length of the set time period, the doctor is judged to be allocated with full members, if a doctor with the shortest diagnosis time of the subsequent patient is the doctor, the doctor is not allocated to the doctor, and the doctor with the shortest diagnosis time among other doctors not full of the doctor is allocated.
When all doctors are allocated with full members or all patients are allocated to the doctors, judging that the sequencing allocation is finished, and obtaining a medical resource allocation scheme under the sequencing;
the obtained medical resource allocation scheme comprises the patients to be treated by each doctor, and the diagnosis time and the diagnosis duration of each patient to be treated.
When the assignment is completed, the sum of the number of all doctor patients in the order is calculated as the number of patients in the order.
And setting the total M as doctors, wherein the number of patients assigned to the jth doctor is as follows:
wherein M is an integer greater than 1 and j is an integer.
(3) And taking the sequence with the maximum number of the patients receiving the consultation as the optimal sequence, and taking the medical allocation resources of the sequence as the optimal medical allocation scheme.
In the optimal medical resource allocation scheme, one doctor needs to take a visit to X patients, and the time for starting to take the visit to the patients is T0Then, the visit time for the p-th patient is:
wherein T isqThe length of diagnosis required to visit the q patient.
In this embodiment, the medical system further comprises a customer service terminal, the user terminal is provided with a consultation module, and the consultation module is used for the user to input question information; the user terminal sends the question information input by the user to the customer service terminal, the customer service terminal is used by customer service personnel, and the answer of the question is input on the customer service terminal after the question information of the user is received; the customer service terminal sends the answer of the question to the cloud server, and the cloud server sends the answer of the question to the user terminal, so that information interaction between the customer service terminal and the user terminal is realized, and manual guidance is provided for the user.
In this embodiment, the user terminal is further provided with a doctor information query module, and the doctor terminal is further provided with a patient information query module; the patient sends information to the cloud server through the doctor information query module, and queries information of a doctor who receives a doctor, such as the working years of the doctor, the professional specialties and the like; the doctor sends information to the cloud server through the patient query module, queries registration information of a patient to be examined, and presciens the state of an illness of the patient in advance.
The method comprises the following steps:
the present embodiment provides a medical resource allocation method for a medical system based on cloud computing, which is the same as the medical resource allocation method for a medical system based on cloud computing in the above system embodiment, and the method has been described in detail in the above system embodiment, and will not be described here.
The embodiments of the present invention disclosed above are intended merely to help clarify the technical solutions of the present invention, and it is not intended to describe all the details of the invention nor to limit the invention to the specific embodiments described. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (10)
1. A medical system based on cloud computing structurally comprises a doctor terminal, a cloud server and a user terminal, wherein the user terminal is used for sending registration information to the cloud server, and the doctor terminal is used for inputting the sitting and examining time of a doctor; the cloud server is used for executing a medical resource allocation method, and the method comprises the following steps:
the method comprises the following steps: acquiring registration information of each patient registered within a set time period and medical capability data of a doctor who sits and diagnoses within the set time period;
the registration information of the patient comprises the disease symptoms, the disease severity and the disease duration of the patient, and the medical capability data of the doctor comprises the working years, the recovery rate, the recurrence rate and the average time length for diagnosing each disease symptom of the doctor;
step two: acquiring the complexity of the disease of a patient according to registration information of the patient, and acquiring the medical level of the patient according to the working years, the recovery rate and the recurrence rate of a doctor;
step three: calculating the diagnosis time required by each doctor to diagnose each patient:
obtaining a first time length used by a doctor for diagnosing the patient according to the complexity of the disease condition of the patient and the medical level of the doctor, obtaining a second time length used by the doctor for diagnosing the patient according to the average time length used by the doctor for diagnosing the disease condition of the patient, and calculating the diagnosis time length required by the doctor for diagnosing the patient according to the first time length and the second time length;
step four: obtaining an optimal medical resource allocation scheme according to the visit duration of each doctor to each patient in a set time period; the optimal medical resource allocation scheme is a medical resource allocation scheme with the largest number of patients capable of being treated within a set time period;
step five: and respectively sending the patients to be treated by each doctor and the diagnosis time and the diagnosis duration of each patient to be treated in the optimal medical resource allocation scheme to corresponding doctor terminals, and respectively sending the doctor to be treated by each patient and the diagnosis time and the diagnosis duration to corresponding user terminals.
2. The cloud-computing-based medical system according to claim 1, wherein the method for acquiring the disease complexity of the patient according to the registration information in the second step is as follows:
classifying the diseases, and scoring according to the treatment difficulty of each disease to obtain scores of various diseases;
obtaining the disease symptoms of the patients, obtaining the corresponding classification of the disease symptoms, taking the score corresponding to the disease symptoms as the disease symptom score of the patients, and setting the score as L1；
Let the severity of the disease be L2If the duration of the disease is h, the patient has a complicated disease condition
G1＝αL1+βL2+γlnh
Wherein α, β and γ are each a patient condition score L1Disease severity L2And the length of illness h.
3. The cloud-computing-based medical system according to claim 1, wherein the method for acquiring the medical level of the doctor according to the working years, the rehabilitation rate and the recurrence rate of the doctor in the second step is as follows:
set the working years of doctors as L0The recovery rate of the patient is P1The recurrence rate is P2The medical level of the doctor is
G2＝alnL0+bP1-cP2
Wherein a, b and c are respectively the working years L of the doctor0Treatment of the patient's recovery rate P1Recurrence Rate P2The weight of (c).
4.The cloud-computing-based medical system of claim 1, wherein the first duration is H1The second duration is H2The required diagnosis time for the doctor to diagnose the patient is
H＝mH1+nH2
Wherein m and n are each a first duration H1And a second duration H2And m + n is 1.
5. The cloud-computing-based medical system according to claim 1, wherein the method for obtaining the optimal medical resource allocation plan in the fourth step is as follows:
randomly sequencing the registered patients within a set time period for a set number of times;
the number of patients that can be treated in each ranking is calculated as follows:
according to the sequence, allocating the patients to the doctors with the shortest diagnosis time in turn;
when a doctor is assigned a full member, no patient is assigned to the doctor;
when all patients are allocated completely or all doctors are allocated fully, judging that the allocation is completed, and obtaining the medical resource allocation scheme under the sequencing;
when the assignment is complete, calculating the total number of patients diagnosed under the ordering;
after the number of patients to be diagnosed in each sequence is obtained, taking the sequence with the largest number of patients to be diagnosed as the optimal sequence, and taking the medical resource allocation scheme of the sequence as the optimal medical resource allocation scheme;
the doctor is assigned with full staff, and the sum of the diagnosis time lengths required by the patient assigned to the doctor is not less than the length of the set time period.
6. A medical system medical resource allocation method based on cloud computing is characterized by comprising the following steps:
the method comprises the following steps: acquiring registration information of each patient registered within a set time period and medical capability data of a doctor who sits and diagnoses within the set time period;
the registration information of the patient comprises the disease symptoms, the disease severity and the disease duration of the patient, and the medical capability data of the doctor comprises the working years, the recovery rate, the recurrence rate and the average time length for diagnosing each disease symptom of the doctor;
step two: acquiring the complexity of the disease of a patient according to registration information of the patient, and acquiring the medical level of the patient according to the working years, the recovery rate and the recurrence rate of a doctor;
step three: calculating the diagnosis time required by each doctor to diagnose each patient:
obtaining a first time length used by a doctor for diagnosing the patient according to the complexity of the disease condition of the patient and the medical level of the doctor, obtaining a second time length used by the doctor for diagnosing the patient according to the average time length used by the doctor for diagnosing the disease condition of the patient, and calculating the diagnosis time length required by the doctor for diagnosing the patient according to the first time length and the second time length;
step four: obtaining an optimal medical resource allocation scheme according to the visit duration of each doctor to each patient in a set time period; the optimal medical resource allocation scheme is a medical resource allocation scheme with the largest number of patients capable of being treated within a set time period;
step five: and respectively sending the patients to be treated by each doctor and the diagnosis time and the diagnosis duration of each patient to be treated in the optimal medical resource allocation scheme to corresponding doctor terminals, and respectively sending the doctor to be treated by each patient and the diagnosis time and the diagnosis duration to corresponding user terminals.
7. The method for allocating medical resources of a cloud-computing-based medical system according to claim 6, wherein the method for acquiring the complexity of the disease condition of the patient according to the registration information of the patient in the second step comprises:
classifying the diseases, and scoring according to the treatment difficulty of each disease to obtain scores of various diseases; obtaining the disease symptoms of the patients, obtaining the corresponding classification of the disease symptoms, taking the score corresponding to the disease symptoms as the disease symptom score of the patients, and setting the score as L1；
Let the severity of the disease be L2If the duration of the disease is h, the patient has a complicated disease condition
G1＝αL1+βL2+γlnh
Wherein α, β and γ are each a patient condition score L1Disease severity L2And the length of illness h.
8. The method for allocating medical resources of a cloud-based computing medical system according to claim 6, wherein in the second step, the method for obtaining the medical level according to the working years, the recovery rate and the recurrence rate of the doctor comprises:
set the working years of doctors as L0The recovery rate of the patient is P1The recurrence rate is P2The medical level of the doctor is
G2＝alnL0+bP1-cP2
Wherein a, b and c are respectively the working years L of the doctor0Treatment of the patient's recovery rate P1Recurrence Rate P2The weight of (c).
9. The cloud-computing-based medical system medical resource allocation method according to claim 6, wherein the first duration is H1The second duration is H2The required diagnosis time for the doctor to diagnose the patient is
H＝mH1+nH2
Wherein m and n are each a first duration H1And a second duration H2And m + n is 1.
10. The medical system medical resource allocation method based on cloud computing according to claim 6, wherein the method for obtaining the optimal medical resource allocation scheme in the fourth step is as follows:
randomly sequencing the registered patients within a set time period for a set number of times;
the number of patients that can be treated in each ranking is calculated as follows:
according to the sequence, allocating the patients to the doctors with the shortest diagnosis time in turn;
when a doctor is assigned a full member, no patient is assigned to the doctor;
when all patients are allocated completely or all doctors are allocated fully, judging that the allocation is completed, and obtaining the medical resource allocation scheme under the sequencing;
when the assignment is complete, calculating the total number of patients diagnosed under the ordering;
after the number of patients to be diagnosed in each sequence is obtained, taking the sequence with the largest number of patients to be diagnosed as the optimal sequence, and taking the medical resource allocation scheme of the sequence as the optimal medical resource allocation scheme;
the doctor is assigned with full staff, and the sum of the diagnosis time lengths required by the patient assigned to the doctor is not less than the length of the set time period.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113506602A (en) * | 2021-07-29 | 2021-10-15 | 深圳万海思数字医疗有限公司 | Remote medical platform doctor scheduling method and device |
| CN114283932A (en) * | 2022-03-03 | 2022-04-05 | 四川大学华西医院 | Medical resource management method, device, electronic equipment and storage medium |
| CN116631638A (en) * | 2023-05-11 | 2023-08-22 | 上海麦色医疗科技有限公司 | Medical data multichannel search system based on artificial intelligence |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113506602A (en) * | 2021-07-29 | 2021-10-15 | 深圳万海思数字医疗有限公司 | Remote medical platform doctor scheduling method and device |
| CN113506602B (en) * | 2021-07-29 | 2023-09-01 | 深圳万海思数字医疗有限公司 | Remote medical platform doctor scheduling method and device |
| CN114283932A (en) * | 2022-03-03 | 2022-04-05 | 四川大学华西医院 | Medical resource management method, device, electronic equipment and storage medium |
| CN114283932B (en) * | 2022-03-03 | 2022-06-10 | 四川大学华西医院 | Medical resource management method, device, electronic equipment and storage medium |
| CN116631638A (en) * | 2023-05-11 | 2023-08-22 | 上海麦色医疗科技有限公司 | Medical data multichannel search system based on artificial intelligence |
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