CN114093482A - Mobile diagnosis and treatment oriented medical care patient matching method and system - Google Patents
Mobile diagnosis and treatment oriented medical care patient matching method and system Download PDFInfo
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Abstract
A medical care patient matching method and system for mobile diagnosis and treatment are provided, wherein the matching method comprises the following steps: s1: acquiring basic information of a patient, including identity information and state information, integrating the state information to form a state label, establishing a one-to-many relationship between the identity information and the state label of the patient, and constructing a big data system; s2: acquiring data information of medical personnel, and forming a relational database for storage; s3: carrying out patient care matching according to a preset matching decision, wherein the matching decision at least comprises the following steps: the patient condition classification corresponds to the diagnosis and treatment major of medical staff, the patient condition severity level corresponds to the professional skill level of the medical staff, and a matching result is decided; and a mutual evaluation mechanism of the patient and the medical staff is established, and the matching quality is optimized. The invention also comprises a medical patient matching system facing the mobile diagnosis and treatment. The invention can make the patient-care matching system recommendation result more reasonable, and then improve the quality system of the matching system.
Description
Technical Field
The invention relates to the technical field of medical information, in particular to a medical care patient matching method and system for mobile diagnosis and treatment.
Background
The Internet and nursing mode provides convenient and safe people-benefitting medical nursing services for patients, and the full utilization of nursing resources is realized. In the actual implementation process of the mode, the patient can make an online order at the mobile terminal according to the basic information and the actual state of illness of the patient so as to obtain the service of the medical staff matched with the state of illness classification of the patient. This model has the disadvantage that multiple candidates are available for each different category of medical condition when the patient orders, yet the patient has no objective clear knowledge of the service level of these candidate medical personnel, resulting in a subjective choice. Most medical staff in hospitals have great difference in professional skill level and work experience, which may cause great difference between the condition of a patient and the service level of the selected medical staff, the selected nursing service cannot meet the needs of the patient, or the selected nursing service has redundancy, resulting in waste of service resources.
CN 2020115090241 discloses an online task distribution method, apparatus, electronic device and storage medium, the method includes: acquiring disease information in a task to be distributed, and determining a target department corresponding to the disease information; the disease information is a disease description text corresponding to the target visiting user; determining at least one to-be-selected service receiving user associated with the target department, and determining at least one target service receiving user from the at least one to-be-selected service receiving user; and allocating at least one target consultation user to at least two matching pools according to a preset allocation strategy, and sequentially distributing the tasks to be distributed to the target consultation users in the matching pools based on the consultation traversal duration corresponding to each matching pool so that the target consultation users receive the tasks to be distributed. However, the above solution has the following drawbacks:
(1) this patent determines the type of condition of the patient by entering a textual description into the patient. Common symptoms, such as cold and fever, can be clearly described due to lack of medical expertise of patients, but for other rare symptoms, involving complex body organs and the like, the patients do not even know the name of the organ, and the organ is an important keyword for the symptoms, although the problem is not limited to the organ. Meanwhile, the self-description characters of different patients have subjectivity, and the phrase habits are different, the keyword extraction algorithm of the system is oriented to different patients, the processing method is only an independent template, and the processing efficiency is greatly different for input texts with different structures.
(2) The patent does not evaluate the severity level of the patient's condition, only scores and evaluates medical staff independently when final matching is performed, and the most appropriate medical staff cannot be allocated according to the severity of the patient's condition, so that the best medical level can be screened out, but effective matching cannot be performed, and the reasonable and effective utilization of medical resources is not achieved.
(3) The text data input by the patient is only used for extracting key words to confirm the disease, and the system does not integrate the patient condition data with the patient condition data
The patient identity establishes an incidence relation and is stored in a database; however, patient data is valuable, which is also critical to the ability of medical crosses to achieve application outcome transformation.
(4) The evaluation value of the medical staff is dynamically updated according to the preference of the patient, for example, if the patient prefers to receive orders quickly, the processing speed of the corresponding medical staff in the past order is matched as a relatively important factor, so that the matching can be optimized for different patients. However, the number of times of using the system by different patients is different, namely, the demands of different patients on the system are large and small, and the method obviously increases the burden of the system and wastes system resources.
(5) This patent can guarantee that the patient must have medical staff to diagnose it, and in the medical staff side, also must guarantee to have a patient to be accepted and diagnose, nevertheless all has the right of refusing the order to medical personnel, patient, and nursing safety problem is not fully considered to this patent, can't fully give patient, medical personnel to grade simultaneously.
CN2021107340752 discloses an order processing method, system and electronic device, the method includes: acquiring inquiry order information from a client, wherein the inquiry order information comprises symptom information and order channel information; selecting a target doctor set matched with the inquiry order information from a doctor database according to symptom information, order channel information and preset matching rules in the inquiry order information; and sending the inquiry order information to the doctor terminal in the target doctor set. However, the above solution has the following drawbacks:
(1) the patient can acquire the disease by self-defined filling, which is similar to the previous prior patent and is not described herein again.
(2) The patent reads the patient condition information and the patient geographical location information from the order and matches them accordingly. However, the matching method causes incomplete system consideration, causes waste of service resources, and cannot fully and reasonably utilize medical resources.
(3) The evaluation of the patent to medical staff is to utilize historical data to grade doctor service quality, the grade is not comprehensive, and the current data can not be utilized to optimize, so that the grade effect is greatly reduced.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a medical care patient matching method and system for mobile diagnosis and treatment.
The technical scheme of the invention is as follows:
the invention relates to a medical care patient matching method for mobile diagnosis and treatment, which comprises the following steps:
s1: acquiring basic information of a patient, including identity information and state information, integrating the state information to form a state label, establishing a one-to-many relationship between the identity information and the state label of the patient, and constructing a big data system;
s2: acquiring data information of medical personnel, and forming a relational database for storage;
s3: patient care matching is carried out according to a preset matching decision, a medical staff candidate list is obtained according to the matching degree of the patient condition labels and the medical staff data information, the candidate list is arranged according to the grade descending order of the medical staff, the medical staff with the highest grade is selected and recommended to the patient, and if the patient accepts the recommended medical staff, the patient selects a target medical staff to place an order; and if the target medical staff receives the order, the matching is successful, and if the target medical staff does not receive the order, the next target medical staff is continuously searched until the matching is successful.
Further, still include:
s4: after the matching is successful, the medical staff carries out nursing work according to the order; after the work is finished, the medical staff confirms that the nursing work is finished, the nursing system identification considers the finishing state and issues the mutual evaluation notification, the medical staff scores the matching degree of the patient, meanwhile, the patient scores the nursing work finishing condition of the medical staff, the scores are submitted to the nursing system together, the scoring data are fed back to the patient-protecting matching system, and the quality system of the patient-protecting matching system is optimized.
Further, S1 specifically includes: according to the classification of a hospital medical system, a patient determines the classification of the condition and the severity level of the condition by means of image-text guidance; the state information of the patient forms a state label capable of identifying the state information through a keyword extraction algorithm, and the identity information of the patient and the state label are in one-to-many relationship to construct a big data system; and in subsequent nursing orders, the condition labels of the patients are continuously updated, and through data mining, association information is mined among a plurality of condition labels of the patients.
Further, in S2, the medical staff data information includes identity information, department to which the medical staff belongs, medical specialties, and working years.
Further, in S3, the matching factors according to which the matching decision is based include: patient condition classification and healthcare worker clinical expertise, patient condition severity level and healthcare worker occupational skill level, distance between patient and healthcare worker location, age difference of patient and healthcare worker, history matching records of patient and healthcare worker, patient score, and healthcare worker score.
Further, the decision method for matching decision comprises the following steps:
A. corresponding the patient condition classification to the medical care professional, and corresponding the patient condition severity grade to the medical care professional skill level, and deciding an initial matching result;
B. preferentially screening medical staff which has the closest positioning distance with the patient, the smallest age difference with the patient and has matching records with the patient before according to the initial matching result to obtain a result list;
C. storing the result list in a priority queue, and maintaining the priority queue in a descending order mode according to the historical scores of the medical staff by the patient so as to keep the medical staff with the highest score at the head of the queue;
D. if a plurality of patients with the same type of requirements appear in the matching process at the same time for matching decision, the patients applying for nursing service are placed in a priority queue, and the patients with the highest scores are maintained in the optimal queue in a descending order by medical staff, so that the patients with the highest scores are kept at the head of the queue.
E. If the patient does not accept the medical staff recommended by the system, the maintained priority queue executes dequeuing operation, dequeues the head element, and then continuously recommends the new head element to the patient; if the patient receives the recommended medical personnel, after the order is confirmed, if the medical personnel choose to reject the order, the maintained priority queue executes dequeuing operation, dequeues the queue head element, and then continuously recommends the new medical personnel at the queue head to the patient.
Further, in S4, in the mutual evaluation mechanism between the patient and the medical care personnel, the self-rating of the medical care personnel is updated every predetermined time, and the consideration factors of the care level of the medical care personnel, including the self-fixed job title, the care frequency, the rating of the patient to the medical care personnel, and the time factor of the medical care personnel, are taken into consideration; and acquiring the total score of the medical staff according to the nursing level consideration factors.
Further, the total score of the healthcare worker is obtained by the following formula:
wherein alpha is the scoring score of the patient to the medical care personnel in the mutual evaluation mechanism; beta is the fraction of the number of nursing times of the medical staff; gamma is the fixed job title score of the medical staff; currTime is the current time; epochTime is the time a healthcare worker begins using the care system; demommenderward is the reward score for the number of times recommended in the predetermined time in the near future.
The invention relates to a medical care patient matching system for mobile diagnosis and treatment, which comprises:
the nursing system is used for inputting basic information of a patient, including identity information and state information, integrating the state information to form a state label, establishing a one-to-many relationship between the identity information and the state label of the patient and constructing a big data system; the data information is used for inputting medical staff to form a relational database for storage; the system is also used for sending a scheduling instruction and grading feedback of patients and medical care personnel to the patient and patient matching system and receiving a matching decision result sent by the patient and patient matching system;
the nursing matching system is used for receiving the scheduling of the nursing system to make a matching decision, carrying out nursing matching according to the matching decision and deciding a matching result; establishing a mutual evaluation mechanism of the patient and the medical staff; and send the final result to the care system.
Further, still include:
the patient care matching system is used for storing the decided matching result list in a priority queue, and maintaining the priority queue in a descending order mode through the history scores of the medical care personnel by the patient, so that the medical care personnel with the highest score are kept at the head of the queue and recommended to the patient; and the order accepting information or the order rejecting information of the patient is sent by the nursing system; and if the patient rejection information is received, the patient care matching system carries out dequeuing operation on the maintained priority queue, dequeues the head-of-line element, and then recommends the new head-of-line element to the patient through the care system.
Further, the patient care matching system further comprises: when a plurality of patients with the same type of requirements are subjected to matching decision, the patients applying for nursing service are placed in a priority queue, the priority queue is maintained in a grading descending order arrangement mode of the patients, and the patients with the highest grade are always kept at the head of the queue; after receiving the scheduling of the nursing system and making a matching decision each time, the patient-care matching system distributes the result to the patient at the head of the priority queue, dequeues the patient and continues to receive the next scheduling of the nursing system.
The invention has the beneficial effects that:
(1) the patient is helped to confirm the disease information by using the image-text guiding mode, namely, the image is more convincing than the text and is easier to understand for the patient, and the patient can also supplement the disease information by inputting the text, so that the disease information confirmed by the patient is more accurate, and on the other hand, the patient can also know more about the disease of the patient;
(2) the identity information and the disease information of the patient form an association relation to be stored in the database, so that a foundation can be laid for the subsequent construction of a big data system, when a plurality of data are accumulated, association information is mined among a plurality of disease labels of the patient, a new thought can be continuously provided for diagnosis and treatment work, and the key of application result conversion can be realized by medical cross;
(3) the disease condition classification of the patient corresponds to the diagnosis and treatment specialties of the medical staff, the disease severity level of the patient corresponds to the professional skill level of the medical staff, and the most appropriate medical staff can be distributed according to the disease severity of the patient during final matching execution, so that the best medical level is screened out, and the medical resources are fully utilized to achieve effective matching;
(4) by establishing a mutual evaluation mechanism, medical care personnel and patients have the right to reject orders, so that the nursing safety is greatly improved, the medical care experience comfort of the patients and the medical care personnel is greatly improved, the selection is flexible, and unnecessary troubles are not caused;
(5) the matching decision is made by taking the factors of patient condition classification, diagnosis and treatment specialties of medical staff, patient condition severity grade, professional skill level of the medical staff, distance between the patient and a positioning place of the medical staff, age difference between the patient and the medical staff, historical matching records of the patient and the medical staff, patient score and medical staff score into consideration, so that the whole system is more comprehensive and comprehensive, the waste of service resources is avoided, the medical resources are fully and reasonably utilized, and the matching quality and high quality are improved;
(6) in the mutual evaluation mechanism of the patient and the medical staff, the self score of the medical staff is updated at preset intervals, and the nursing level consideration factors of the medical staff, including the self fixed job title of the medical staff, nursing times, the score of the patient to the medical staff and time factors, can be given different weights according to the influence degree of each factor, and the weight can be dynamically optimized according to the time factors, so that the matching system can be continuously optimized, and the matching quality is greatly improved.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of the present invention.
Detailed Description
The invention will be described in further detail below with reference to the drawings and specific examples.
As shown in fig. 1: a medical care patient matching method facing mobile diagnosis and treatment comprises the following steps:
s101: acquiring basic information of a patient, including identity information and disease state information, integrating the disease state information into a disease state label, establishing a one-to-many relationship between the identity information and the disease state label of the patient, and constructing a big data system.
In particular, basic information of the patient is entered by the care system, integrated into an information tag, and stored in a database. The basic information of the patient comprises identity information capable of uniquely identifying the identity of the patient and condition information of the patient, and the identity information and the condition information are the basis for constructing a large data system.
According to the classification of the hospital medical system, the nursing system helps the patient to determine the self condition classification and the disease severity grade in a picture and text guiding mode. Wherein, the picture and text guide means: in the nursing system, pictures with various common disease classifications are stored and matched with characters, and then the pictures are displayed to a patient through a UI of the nursing system, and are mainly used for determining what disease types and severity of the disease conditions. For example: patient a is faced with a plurality of pictures given by the care system, which highlight the affected part, for example, if the head of fig. 1 shows red, the patient is asked whether the head is headache, and if the waist of fig. 2 shows red, the patient is asked whether the waist is pain. For example, the patient is determined to be headache, the patient clicks to enter the next step for guidance, the UI characters guide the patient to further determine the illness state, and for example, the head is impacted by physics due to cold catching, other factors enter the next step after the determination is finished; if the classification of cold catching is determined in the last step, next step is that a UI displays characters, the number of days for which the headache is expressed continuously, whether the over-temperature is measured (the body temperature is large) and the like, the cold catching degree is determined by inquiring a plurality of factors, and if the patient is guided to be determined to be mild, the patient needs only a simple medication.
The state of illness information of the patient is processed by a keyword extraction algorithm to form a state of illness label capable of identifying the state of illness information. The patient identity information and the condition labels are established in a one-to-many relationship for constructing a big data system. In addition, in subsequent nursing orders, the state of illness labels of the patients can be continuously supplemented and updated, and through data mining, associated information is mined among a plurality of state of illness labels of the patients, so that a new idea is continuously provided for diagnosis and treatment work.
Wherein, the big data system means: for example, a patient has a plurality of order records in a nursing system, the records are data information accumulated by processing the order each time, each order has an information label of the recorded patient, which is equivalent to a one-to-many relationship, and whether different diseases have certain relationships or not can be analyzed by statistics, and through accumulating a larger data volume, association information is mined among a plurality of disease labels of the patient, so that the data function is fully utilized.
The patient can know the self condition better by integrating and storing the condition information of the patient; meanwhile, the value of medical big data is fully utilized, and medical workers can be helped to comprehensively analyze various medical data of patients by building a huge medical data warehouse, so that a more scientific and reasonable treatment scheme can be formulated.
S102: and acquiring data information of medical personnel to form a relational database for storage.
Specifically, data information of medical personnel, including but not limited to identity information, a department to which the medical personnel belong, medical specialties and working years, is input by the nursing system, the information is recorded, and a multi-establishment relational database is established according to the entity attribute relationship and stored in the nursing system to serve as a matching basis of the patient-nursing matching system. This step is for the standard management of data, and then is convenient for the subsequent matching algorithm to obtain data.
For example: each medical staff is an entity, the entity comprises a plurality of attributes, such as name, age, department and the like, the work ID number of the medical staff is used as a main key of the database to serve for query work, the entity class of the medical staff can be packaged through programming languages such as Java and the like, the entity information of the medical staff is stored into a database MySQL or Oracle, and a relational database is established.
S103: patient care matching is carried out, a medical staff candidate list is obtained according to the patient condition type and the matching degree of the patient, the candidate list is arranged according to the grade descending order of the medical staff, the medical staff with the highest grade is selected and recommended to the patient, if the patient receives the recommended medical staff, the patient selects a target medical staff to place an order, the system sends a notice to the target medical staff, if the target medical staff receives the order, the system sends an order task creation success message notice, if the target medical staff does not receive the order, the next target medical staff is continuously searched until the matching is successful.
Specifically, medical care screening matching is carried out through a patient care matching system, patients with different requirements are matched with medical care personnel in different departments and different professional skill levels, and the most suitable medical care personnel are recommended to the patients.
The specific matching mode comprises the following steps:
A. the matching algorithm is based on the following factors:
in the patient-care matching system, the matching algorithm can make matching decisions according to some factors of patients and medical staff. The decision making basis factors of the matching algorithm mainly comprise:
(1) patient condition classification and medical professional diagnosis and treatment;
(2) patient severity level and healthcare worker occupational skill level;
(3) the distance between the location of the patient and the medical staff;
(4) age difference of patient and medical staff;
(5) matching records are recorded before the patient and the medical staff;
(6) patient scoring and healthcare worker scoring.
The distance between the patient and the medical staff is preferably determined in such a way that the system can only be incorporated when a plurality of hospitals are jointly used.
B. The matching algorithm execution flow comprises the following steps:
in the decision making process, the matching algorithm primarily considers the patient condition classification to correspond to the medical professional of the medical staff, and the patient condition severity level to correspond to the professional skill level of the medical staff. Therefore, the matching system can decide an initial matching result, and the matching algorithm can further optimize the result, namely, medical staff which is close to the patient in positioning distance, has a small age difference value with the patient and has a matching record with the patient before are preferentially screened out.
After the final decision list is obtained, the patient care matching system stores the result list in a priority queue, and the priority queue is maintained in a mode of descending grading of the medical staff, namely the medical staff with the highest grade is always kept at the head of the queue. This has the advantage of facilitating handling of the following order cancellation situations:
on the patient side, if the patient does not accept the medical care personnel recommended by the patient care matching system, the order rejection message is fed back to the patient care matching system through the patient care system, the first queue maintained by the patient care matching system executes dequeuing operation, dequeues the first queue element, and then continuously recommends the new first queue element to the patient through the patient care system.
If the patient receives the medical care personnel recommended by the nursing system, after the order is confirmed, if the medical care personnel choose to reject the order, the order rejection message is fed back to the patient care matching system through the nursing system, the prior queue maintained by the patient care matching system executes dequeuing operation, the queue head element is dequeued, and then the new medical care personnel at the head of the queue are continuously recommended to the patient through the nursing system.
C. The patient care matching system processes concurrent tasks:
in the follow-up mechanism, the care system will assign a score to the patient through the mutual evaluation mechanism. The aim is to make the patients with higher scores preferentially obtain the nursing service of the medical staff recommended by the system. This can be embodied when the patient matching system processes multiple matching requests simultaneously. That is, at the same time, the matching algorithm may make matching decisions for a plurality of patients with the same type of needs, and the patient care matching system may place the patients who apply for the care services in a priority queue, and maintain the priority queue in a way of descending order of the scores of the patients, that is, always keep the highest-score patient at the head of the queue. After receiving the scheduling of the nursing system and making a matching decision each time, the patient-care matching system distributes the result to the patient at the head of the priority queue, dequeues the patient and continues to receive the next scheduling of the nursing system.
The evaluation of the patient to the medical staff means to grade the completion of the medical staff's nursing work. The score of the healthcare worker may be used as a factor in a prior decision of the matching algorithm, i.e., the more highly scored healthcare worker is more likely to be recommended to the patient by the matching algorithm through the decision.
The scoring of the patient by the medical staff means that the medical staff scores the degree of cooperation of the patient in the nursing process. The patient's score will determine whether the patient is preferentially served by the matching algorithm, i.e., the higher the score, the more priority the patient will be to obtain care resources.
In the embodiment, the patient care matching system is combined with the mutual evaluation mechanism, so that on one hand, the nursing work risk management is enhanced, the safety of patients and medical care personnel can be protected, and the reasonable utilization of nursing resources is realized. For a patient with a lower score, this means that the patient was in the previous care service and presented with difficulty in coordination and poor attitude. The patient is protected by the mechanism of the matching system, so that the patient has lower priority to obtain the nursing service of the medical staff recommended by the matching system, and the medical staff can take the score of the patient into consideration according to the requirement of the medical staff, so as to determine whether to receive the order. For the medical staff with lower grade, the medical staff is not high in the professional skill level in the past nursing work, and the nursing work is not well completed. The patient protection matching system mechanism ensures that the recommended priority of the medical staff is lower in the matching process; meanwhile, the functions and advantages of the data are fully utilized, a big data system is formed, and a foundation is provided for effective information mining.
S104: after the matching is successful, the medical staff carries out nursing work according to the order; after the work is finished, the medical staff confirms that the nursing work is finished, the nursing system identification considers the finishing state and issues the mutual evaluation notification, the medical staff scores the matching degree of the patient, meanwhile, the patient scores the nursing work finishing condition of the medical staff, the scores are submitted to the nursing system together, the scoring data are fed back to the patient-protecting matching system, and the quality system of the patient-protecting matching system is optimized.
Specifically, the data from the cross-rating phase will continually optimize the process, optimizing the matching quality system, essentially by updating the score of the healthcare worker himself, which essentially represents the level of care of the healthcare worker.
The level of care of a healthcare worker is primarily related to several factors:
(1) medical staff self fixes the job title;
(2) the number of nursing times;
(3) patient scoring of medical personnel;
(4) time.
The medical staff can represent that the medical care level is higher if the medical staff is a higher-level job title.
The number of care orders, as the number of care orders the healthcare worker takes, should gradually increase in their own care level.
The patient's score to medical personnel, and in the mechanism of commenting each other, the patient can represent this medical personnel's care level to a certain extent to medical personnel's score.
In time, as the working years of the medical staff increase, the degree of the nursing level of the medical staff is governed by the fixed job title of the medical staff is gradually reduced, namely, the influence of the fixed job title of the medical staff on the nursing level is reduced, and the influence of the nursing times and the evaluation of the mutual evaluation mechanism on the nursing level is increased.
Thus, different factors should have different effects on the healthcare worker's own score, and the effects of certain factors may change dynamically over time.
To achieve a complete characterization of the healthcare worker's care level, the healthcare worker score can be dynamically updated. In order to improve the system performance, under the global mode of the whole system, a part of medical staff with the most recommended times of the patient matching system in the latest period of time can be recorded, the part of medical staff is stored in a cache database, a timing task is further set, and grading updating is carried out at fixed time intervals. In this process, an assignment formula for scoring the medical staff is formulated, taking into account the 4 factors considered above. The method specifically comprises the following steps:
first, an appropriate base score is set for each factor:
setting the scoring score of a patient to medical staff in a mutual evaluation mechanism as alpha, the scoring of the nursing times of the medical staff as beta and the scoring of the self-fixed job title of the medical staff as gamma; the current time is curTime, the time when a certain medical staff starts to use the nursing system is epochTime, the reward score of the latest recommended times is recammenderWard, and the total score is as follows:
the number of times of recent recommendation, in the matching mechanism, if a medical staff is recommended more times by the matching algorithm in the recent period of time, because the score of the medical staff is higher, the factor should have a larger weight in the process of updating the score, namely, the score is more influenced.
The scoring of the medical staff by the patient with the basic score of alpha is the most intuitive and real embodiment of the nursing level of the medical staff, the influence of the patient on the latest scoring of the medical staff is required to be larger, the weight of the patient is set to be 10, and the influence of the patient is marked to be 10.
For the number of nursing times of the medical staff, the basic score is beta, its weight is set to 5, and its influence is identified as 5.
For a healthcare worker's fixed job title, the base score is γ, which should be given a different level of base score according to the high-low level of the job title. Furthermore, over time, the caregivers' own job title impact should be reduced. A time factor is therefore introduced to limit the influence of this factor on the score. In the formula, currTime-epochTime finally takes days, namely, the fixed job title influence of a medical staff begins to be reduced after the medical staff uses the nursing system to perform nursing work for about one year.
It should be noted that the above weight range of this embodiment is 1-10, 1-10 represents the grading of the influence, the influence is classified into 3 grades, the maximum influence is 10, the influence is generally 5, and the smaller influence is 1.
Through continuous optimization of the nursing level representation mode of the medical staff, the score of the medical staff can reflect the real nursing level of the medical staff more comprehensively, so that the recommending result of the patient and patient protection matching system tends to be more reasonable, and the quality system of the matching system is improved.
Claims (10)
1. A medical patient matching method for mobile diagnosis and treatment is characterized by comprising the following steps:
s1: acquiring basic information of a patient, including identity information and state information, integrating the state information into a state label, establishing a one-to-many relationship between the identity information and the state label of the patient, and constructing a big data system;
s2: acquiring data information of medical personnel, and forming a relational database for storage;
s3: patient care matching is carried out according to a preset matching decision, a medical staff candidate list is obtained according to the matching degree of the patient condition label and the medical staff data information, the candidate list is arranged according to the grades of the medical staff, the medical staff with the highest grade is selected and recommended to the patient, and if the patient receives the recommended medical staff, the patient selects a target medical staff to place an order; and if the target medical staff accepts the order, the matching is successful, and if the target medical staff does not accept the order, the next target medical staff is continuously searched until the matching is successful.
2. The mobile medical treatment-oriented medical patient matching method according to claim 1, further comprising:
s4: after the matching is successful, the medical staff carries out nursing work according to the order; after the work is finished, the medical staff confirms that the nursing work is finished, the nursing system identification considers the finishing state and issues the mutual evaluation notification, the medical staff scores the matching degree of the patient, meanwhile, the patient scores the nursing work finishing condition of the medical staff, the scores are submitted to the nursing system together, the scoring data are fed back to the patient-protecting matching system, and the quality system of the patient-protecting matching system is optimized.
3. The mobile medical treatment oriented medical patient matching method according to claim 1, wherein the step S1 specifically includes: according to the classification of a hospital medical system, a patient determines the classification of the condition and the severity level of the condition by means of image-text guidance; the state information of the patient forms a state label capable of identifying the state information through a keyword extraction algorithm, and the identity information of the patient and the state label are in one-to-many relationship to construct a big data system; and in subsequent nursing orders, the condition labels of the patients are continuously updated, and association information is mined among a plurality of condition labels of the patients through data mining.
4. The mobile medical treatment-oriented medical care matching method according to claim 1, wherein in S2, the medical care data information includes identity information, department to which the medical care data information belongs, medical expertise, and working life.
5. The mobile medical treatment-oriented medical care matching method according to claim 1, wherein in S3, the matching factors according to which the matching decision is based include: patient condition classification and healthcare worker clinical expertise, patient condition severity level and healthcare worker occupational skill level, distance between patient and healthcare worker location, age difference of patient and healthcare worker, history matching records of patient and healthcare worker, patient score, and healthcare worker score.
6. The mobile medical treatment-oriented medical patient matching method according to claim 4, wherein the decision-making method for matching decision-making comprises the following steps:
A. corresponding the patient condition classification to the medical care professional, and corresponding the patient condition severity grade to the medical care professional skill level, and deciding an initial matching result;
B. preferentially screening medical staff which has the closest positioning distance with the patient, the smallest age difference with the patient and has matching records with the patient before according to the initial matching result to obtain a result list;
C. storing the result list in a priority queue, and maintaining the priority queue in a descending order mode according to the historical scores of the medical staff by the patient so as to keep the medical staff with the highest score at the head of the queue;
D. if a plurality of patients with the same type of requirements appear in the matching process at the same time for matching decision, the patients applying for nursing service are placed in a priority queue, and the medical staff maintain the optimal queue according to the historical scores of the patients in a descending order mode, so that the patient with the highest score is kept at the head of the queue;
E. if the patient does not accept the medical staff recommended by the system, the maintained priority queue executes dequeuing operation, dequeues the head element, and then continuously recommends the new head element to the patient; if the patient receives the recommended medical personnel, after the order is confirmed, if the medical personnel choose to reject the order, the maintained priority queue executes dequeuing operation, dequeues the queue head element, and then continuously recommends the new medical personnel at the queue head to the patient.
7. The mobile medical treatment-oriented medical patient matching method according to claim 2, wherein in S4, in a mutual evaluation mechanism between the patient and the medical staff, the self-rating of the medical staff is updated every predetermined time, and the care level consideration factors of the medical staff are taken into account, including the self-fixed job title of the medical staff, the nursing times, the rating of the patient to the medical staff, and the time factor; and acquiring the total score of the medical staff according to the nursing level consideration factors.
8. The mobile medical treatment-oriented medical patient matching method according to claim 7, wherein the total score of the medical staff is obtained by the following formula:
wherein alpha is the scoring score of the patient to the medical care personnel in the mutual evaluation mechanism; beta is the fraction of the number of nursing times of the medical staff; gamma is the fixed job title score of the medical staff; currTime is the current time; epochTime is the time a healthcare worker begins using the care system; demommenderward is the reward score for the number of times recommended in the predetermined time in the near future.
9. A medical patient matching system for mobile diagnosis and treatment, comprising:
the nursing system is used for inputting basic information of a patient, including identity information and state information, integrating the state information to form a state label, establishing a one-to-many relationship between the identity information and the state label of the patient and constructing a big data system; the data information is used for inputting medical staff to form a relational database for storage; the system is also used for sending a scheduling instruction and grading feedback of patients and medical care personnel to the patient and patient matching system and receiving a matching decision result sent by the patient and patient matching system;
the nursing matching system is used for receiving the scheduling of the nursing system to make a matching decision, carrying out nursing matching according to the matching decision and deciding a matching result; establishing a mutual evaluation mechanism of the patient and the medical staff; and send the final result to the care system.
10. The mobile phone facing healthcare patient matching system according to claim 9, further comprising:
the patient care matching system is used for storing the decided matching result list in a priority queue, and maintaining the priority queue in a descending order mode through the history scores of the medical care personnel by the patient, so that the medical care personnel with the highest score are kept at the head of the queue and recommended to the patient; and the order accepting information or the order rejecting information of the patient is sent by the nursing system; and if the patient rejection information is received, the patient care matching system carries out dequeuing operation on the maintained priority queue, dequeues the head-of-line element, and then recommends the new head-of-line element to the patient through the care system.
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