CN116759112B - Remote consultation data management system and method based on Internet of things - Google Patents
Remote consultation data management system and method based on Internet of things Download PDFInfo
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Abstract
The invention relates to the technical field of remote consultation data management, in particular to a remote consultation data management system and method based on the Internet of things, comprising the steps of creating a consultation room corresponding to a remote consultation request and starting remote consultation; the method comprises the steps of respectively combing polling consultation information of each history remote consultation record in each target history remote consultation record set, and respectively capturing inquiry information change nodes of doctor ends of each target consultation department; capturing doctor ends of other target consultation departments related to the presence of the consultation at the doctor end of each target consultation department according to the information deviation of the consultation information change nodes in different historical remote consultation records; and performing feature analysis on records of the patient image data retrieval when the doctor end of the target consultation department showing the feature of the consultation association participates in the polling consultation, and performing intelligent retrieval of the patient image data on the doctor end participating in the remote consultation.
Description
Technical Field
The invention relates to the technical field of remote consultation data management, in particular to a remote consultation data management system and method based on the Internet of things.
Background
In the medical field, the remote consultation enables doctors to comprehensively and carefully think, summarize and analyze the illness state of patients without the need of the patients to go in, so that correct diagnosis and scientific and proper treatment schemes are made, the diagnosis accuracy is improved, the time for the patients to see the doctor is saved, and the trouble of long-distance running and queuing of the patients is avoided as the medical industry develops; remote consultation is becoming more important and is already being carried out in many hospitals;
the patient needing to be developed for consultation often has complex illness state and involves the diseases of a plurality of departments, which means that the professional knowledge of each department is involved, so that the consultation is generally required by the plurality of departments, and the diagnosis and treatment flow of some diseases often needs mutual assistance among the plurality of departments, so that the cause can be clearly determined, and the traditional Chinese medicine needs to review more imaging data in the consultation process.
Disclosure of Invention
The invention aims to provide a remote consultation data management system and method based on the Internet of things, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a remote consultation data management method based on the Internet of things comprises the following steps:
step S100: each time the remote consultation management terminal receives a remote consultation request initiated by a patient, locking all target consultation departments which need to respond to the remote consultation request based on the basic identity information, the history medical record information and the illness state description information of the patient; when the doctor receives the remote consultation request representation in the waiting response time, a consultation room corresponding to the remote consultation request is created and remote consultation is started;
step S200: each time a consultation room is remotely created, a polling consultation sequence is formed based on the actual time of each doctor end entering the consultation room from each target consultation department; generating a remote consultation record based on the created consultation room correspondence;
step S300: calling all the history remote consultation records from the remote consultation management terminal, and respectively collecting the history remote consultation records which are completely the same as the target consultation departments which need to respond to the remote consultation requests in all the history remote consultation records to obtain a plurality of target history remote consultation record sets; the method comprises the steps of respectively combing polling consultation information of each history remote consultation record in each target history remote consultation record set, and respectively capturing inquiry information change nodes of doctor ends of each target consultation department;
step S400: capturing doctor ends of other target consultation departments related to the presence of the consultation at the doctor end of each target consultation department according to the information deviation of the consultation information change nodes in different historical remote consultation records;
step S500: the method comprises the steps of respectively calculating characteristic values related to inquiry between doctor ends of every two target consultation departments, performing characteristic analysis on records of patient image data retrieval when doctor ends of the target consultation departments presenting characteristic related inquiry participate in polling consultation, and performing intelligent retrieval of patient image data on doctor ends participating in remote consultation.
Further, step S200 includes: setting a complete query process which occurs at any doctor side and patient side in each polling consultation as a query node in each polling consultation; and extracting characteristic keywords from the query speech of the medical terminal in each query node to obtain a characteristic keyword set A, extracting characteristic keywords from the response information fed back by the patient based on the query speech to obtain a characteristic keyword set B, and constructing a characteristic query information pair A-B corresponding to each query node.
Further, step S300 includes:
step S301: extracting characteristic keywords from basic identity information, history medical record information and illness state description information of a patient in each history remote consultation record in each target history remote consultation record set to obtain a patient characteristic information set P1, and acquiring identity information of each doctor end responding to each history remote consultation record; the identity information comprises title information, job information, tampering field information and department information; giving the doctor ends with the same identity information the same label; respectively collecting the marking labels corresponding to the doctor terminals responding to each history remote consultation record to obtain a marking label set P2; respectively collecting historical remote consultation records with the similarity between the patient characteristic information set P1 and the marking label set P2 being greater than a similarity threshold value to obtain a plurality of characteristic consultation record sets;
the consultation target is often a disease with complicated disease condition and multiple departments, and the goal consultation departments responding to the remote consultation request complete the collection of historical remote consultation records so as to initially divide records with identity on consultation;
step S302: respectively extracting a polling consultation sequence corresponding to each doctor end and corresponding department information in each history remote consultation record; in each characteristic consultation record set, respectively for each target consultation departmentExtracting characteristic query information pairs of all query nodes participated in corresponding historical remote consultation records by a doctor end of each target consultation department according to the generation sequence of the query nodes to obtain a characteristic query information pair sequence { L } of the doctor end corresponding to the corresponding historical remote consultation records of each target consultation department 1 、L 2 、...、L n -a }; wherein L is 1 、L 2 、...、L n Respectively representing characteristic query information pairs corresponding to the 1 st, 2 nd, n th query nodes participated in by the doctor end of each target consultation department in the corresponding history remote consultation records; let every two adjacent feature query information pairs L i And L is equal to i+1 One inquiry information change node p=l formed in the corresponding characteristic inquiry information pair sequence i →L i+1 ；
The above-mentioned completion of the collection of characteristic consultation records based on the critical information of the patient's condition and the critical information of the doctor's expertise involved in the consultation is to further divide the same query information pair that may appear during the consultation, and it is understood that the diagnostic query information generated by the doctor based on the diagnosis and elimination of some diseases to the patient during the query is similar.
Further, step S400 includes:
step S401: respectively collecting characteristic inquiry information pair sequences extracted from each history remote consultation record at the doctor end of each department in each characteristic consultation record set, and respectively comparing deviation of corresponding inquiry information change nodes between every two characteristic inquiry information pair sequences;
step S402: if the doctor side of a target consultation department D extracts characteristic query information pair sequences R from two history remote consultation records respectively 1 And R is 2 Let R be 1 An ith query node L participated by a doctor end of a certain target consultation department D i And the (i+1) th query node L i+1 The j-th inquiry information change node is P (j) =L i →L i+1 Let R be 2 The ith query node L engaged in by D i ' and i+1th query node L i+1 ' formedThe j-th inquiry information changing node is P (j)' =l i ’→L i+1 ' when meeting L i ＝L i ’，L i+1 ≠L i+1 ' at the time, judge R 1 And R is 2 The j-th inquiry information change node is a reference change node;
step S403: at R 1 In the corresponding history remote consultation record, the doctor end of a certain target consultation department D participates in L i+1 Extracting all the query nodes existing before, and collecting characteristic query information corresponding to all the query nodes to obtain a set S1; at R 2 In the corresponding history remote consultation record, the doctor end of a certain target consultation department D participates in L i+1 Extracting all the query nodes existing before, and collecting characteristic query information corresponding to all the query nodes to obtain a set S2;
step S404: and calculating to obtain a set Q=S2U S1-S2U S1, extracting the target consultation departments corresponding to each characteristic query information pair in the set Q, judging each target consultation department except a certain target consultation department and the target consultation departments, and meeting one-time consultation association based on two history remote consultation records.
Further, step S500 includes:
step S501: the method comprises the steps that the times meeting the requirements of inquiry association are displayed in any one feature consultation record set in any two target consultation departments and accumulated to obtain total times X; calculating a characteristic value beta of inquiry association between any two target consultation departments, wherein beta=X/[ m (m-1)/2 ]; wherein m represents the total number of history remote consultation records contained in any one of the feature consultation record sets; if beta between any two target consultation departments is more than a characteristic threshold, judging that the any two target consultation departments present characteristic inquiry association;
step S502: extracting all the history remote consultation records of the doctor end of any two target consultation departments h1 and h2 which present characteristic consultation association, and setting the total number of all the history remote consultation records as M1; respectively identifying the types of the image data which are acquired by the doctor end of the h2 or the doctor end of the h1 when the doctor end of the h1 or the doctor end of the h2 makes a consultation on the patient in each history remote consultation record;
step S503: respectively accumulating the total times N1 of various image data which are called when the doctor end of h1 or the doctor end of h2 makes a consultation on the patient in M1 historical remote consultation records; and determining the priority of automatic retrieval of various image data at the doctor end of the corresponding h2 or the doctor end of the h1 when the doctor end of the h1 or the doctor end of the h2 inquires the patient according to the corresponding total times from large to small.
In order to better implement the method, a remote consultation data management system is also provided, and the system comprises: the remote consultation management module, the remote consultation record management module, the polling consultation information carding module, the consultation association identification judgment module and the remote consultation data intelligent management module;
the remote consultation management module is used for locking all target consultation departments which need to respond to the remote consultation request based on the basic identity information, the history medical record information and the illness state description information of the patient whenever the remote consultation management terminal receives a remote consultation request initiated by the patient; when the doctor receives the remote consultation request representation in the waiting response time, a consultation room corresponding to the remote consultation request is created and remote consultation is started;
the remote consultation record management module is used for forming a polling consultation sequence based on the actual time of each doctor end entering the consultation room from each target consultation department every time a consultation room is created remotely; generating a remote consultation record based on the created consultation room correspondence;
the polling consultation information carding module is used for calling all the history remote consultation records from the remote consultation management terminal; the method comprises the steps of respectively combing polling consultation information of each history remote consultation record in each target history remote consultation record set, and respectively capturing inquiry information change nodes of doctor ends of each target consultation department;
the inquiry association identification judgment module is used for capturing doctor ends of other target consultation departments associated with the inquiry of the doctor ends of the target consultation departments according to the information deviation of the inquiry information change nodes existing in different historical remote consultation records of the doctor ends of the target consultation departments;
the remote consultation data intelligent management module is used for respectively carrying out characteristic value calculation of consultation association between doctor ends of every two target consultation departments, carrying out characteristic analysis on records of patient image data retrieval when doctor ends of the target consultation departments presenting characteristic of the consultation association participate in polling consultation, and carrying out intelligent retrieval of patient image data on doctor ends participating in remote consultation.
Further, the polling consultation information combing module comprises a history remote consultation record classification management unit and a consultation information changing node combing unit;
the history remote consultation record classification management unit is used for calling all history remote consultation records from the remote consultation management terminal and respectively combing the polling consultation information of each history remote consultation record in each target history remote consultation record set;
the inquiry information changing node combing unit is used for respectively capturing inquiry information changing nodes for doctor terminals of all target consultation departments.
Further, the remote consultation data intelligent management module comprises a characteristic value calculation management unit and a consultation management unit;
the characteristic value calculation management unit is used for respectively carrying out characteristic value calculation of inquiry association between doctor ends of the target consultation departments;
the consultation management unit is used for carrying out feature analysis on records of patient image data retrieval when doctor ends of target consultation departments showing feature of the consultation association participate in the polling consultation, and carrying out intelligent retrieval on patient image data of doctor ends participating in the remote consultation.
Compared with the prior art, the invention has the following beneficial effects: according to the invention, the inquiry information influence generated in the inquiry process between departments in the remote consultation record is regularly mined, inquiry information deviation conditions generated based on different inquiry sequences are captured, and the diagnosis influence between two inquiry departments is defined; the consultation often involves multiple subjects, the illness state of the patient often is complex, and the diseases of a plurality of departments are involved, so that the imaging data often is more, the intelligent and automatic in the image data calling process are realized as far as possible through capturing the inquiry departments with diagnosis influence, the phenomena of inconvenience in calling and low calling efficiency due to the complicated imaging data in the consultation process are reduced, the related imaging data are displayed as timely and clearly as possible during the consultation of multiple subjects, the diagnosis and treatment effect is improved, and the consultation efficiency is improved.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of a remote consultation data management method based on the Internet of things;
fig. 2 is a schematic structural diagram of a remote consultation data management system based on the internet of things.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions: a remote consultation data management method based on the Internet of things comprises the following steps:
step S100: each time the remote consultation management terminal receives a remote consultation request initiated by a patient, locking all target consultation departments which need to respond to the remote consultation request based on the basic identity information, the history medical record information and the illness state description information of the patient; when the doctor receives the remote consultation request representation in the waiting response time, a consultation room corresponding to the remote consultation request is created and remote consultation is started;
step S200: each time a consultation room is remotely created, a polling consultation sequence is formed based on the actual time of each doctor end entering the consultation room from each target consultation department; generating a remote consultation record based on the created consultation room correspondence;
wherein, step S200 includes: setting a complete query process which occurs at any doctor side and patient side in each polling consultation as a query node in each polling consultation; extracting characteristic keywords from the query speech of the medical terminal of each query node to obtain a characteristic keyword set A, extracting characteristic keywords from the response information fed back by the patient based on the query speech to obtain a characteristic keyword set B, and constructing a characteristic query information pair A-B corresponding to each query node;
step S300: calling all the history remote consultation records from the remote consultation management terminal, and respectively collecting the history remote consultation records which are completely the same as the target consultation departments which need to respond to the remote consultation requests in all the history remote consultation records to obtain a plurality of target history remote consultation record sets; the method comprises the steps of respectively combing polling consultation information of each history remote consultation record in each target history remote consultation record set, and respectively capturing inquiry information change nodes of doctor ends of each target consultation department;
wherein, step S300 includes:
step S301: extracting characteristic keywords from basic identity information, history medical record information and illness state description information of a patient in each history remote consultation record in each target history remote consultation record set to obtain a patient characteristic information set P1, and acquiring identity information of each doctor end responding to each history remote consultation record; the identity information comprises title information, job information, tampering field information and department information; giving the doctor ends with the same identity information the same label; respectively collecting the marking labels corresponding to the doctor terminals responding to each history remote consultation record to obtain a marking label set P2; respectively collecting historical remote consultation records with the similarity between the patient characteristic information set P1 and the marking label set P2 being greater than a similarity threshold value to obtain a plurality of characteristic consultation record sets;
step S302: respectively extracting a polling consultation sequence corresponding to each doctor end and corresponding department information in each history remote consultation record; in each characteristic consultation record set, extracting characteristic query information pairs of all query nodes participated in corresponding historical remote consultation records by doctor terminals of each target consultation department respectively, and arranging according to the generation sequence of the query nodes to obtain a characteristic query information pair sequence { L } of corresponding historical remote consultation records by doctor terminals of each target consultation department 1 、L 2 、...、L n -a }; wherein L is 1 、L 2 、...、L n Respectively representing characteristic query information pairs corresponding to the 1 st, 2 nd, n th query nodes participated in by the doctor end of each target consultation department in the corresponding history remote consultation records; let every two adjacent feature query information pairs L i And L is equal to i+1 One inquiry information change node p=l formed in the corresponding characteristic inquiry information pair sequence i →L i+1 ；
Step S400: capturing doctor ends of other target consultation departments related to the presence of the consultation at the doctor end of each target consultation department according to the information deviation of the consultation information change nodes in different historical remote consultation records;
wherein, step S400 includes:
step S401: respectively collecting characteristic inquiry information pair sequences extracted from each history remote consultation record at the doctor end of each department in each characteristic consultation record set, and respectively comparing deviation of corresponding inquiry information change nodes between every two characteristic inquiry information pair sequences;
step S402: if the doctor side of a target consultation department D extracts feature queries from two history remote consultation records respectivelyInformation pair sequence R 1 And R is 2 Let R be 1 An ith query node L participated by a doctor end of a certain target consultation department D i And the (i+1) th query node L i+1 The j-th inquiry information change node is P (j) =L i →L i+1 Let R be 2 The ith query node L engaged in by D i ' and i+1th query node L i+1 The j-th inquiry information change node of the 'structure is P (j)' =l i ’→L i+1 ' when meeting L i ＝L i ’，L i+1 ≠L i+1 ' at the time, judge R 1 And R is 2 The j-th inquiry information change node is a reference change node;
step S403: at R 1 In the corresponding history remote consultation record, the doctor end of a certain target consultation department D participates in L i+1 Extracting all the query nodes existing before, and collecting characteristic query information corresponding to all the query nodes to obtain a set S1; at R 2 In the corresponding history remote consultation record, the doctor end of a certain target consultation department D participates in L i+1 Extracting all the query nodes existing before, and collecting characteristic query information corresponding to all the query nodes to obtain a set S2;
for example, in some two history remote consultation records L1, L2, the target consultation department includes A, B, C; the doctor terminal a is a doctor from the target consultation department A, the doctor terminal B is a doctor from the target consultation department B, and the doctor terminal C is a doctor from the target consultation department C;
performing a round-robin consultation between the patients and the polling consultation sequences a, b and c; 3 rounds of round diagnosis are known, and 3 complete inquiry times are known to be completed between doctors and patients from each target consultation department in each history remote consultation record; each inquiry corresponds to a characteristic inquiry information pair;
in the first round of diagnosis in L1, the characteristic query information pairs corresponding to a, b and c are a1, b1 and c1 respectively; in the second round of diagnosis, the characteristic query information pairs corresponding to a, b and c are a2, b2 and c2 respectively; in the third round of diagnosis, the characteristic query information pairs corresponding to a, b and c are a3, b3 and c3 respectively;
to sum up, in L1, the characteristic query information pair sequence of the 3 rounds of consultation is:
a1→b1→c1→a2→b2→c2→a3→b3→c3；
in the first round of diagnosis in L2, the characteristic query information pairs corresponding to a, b and c are a1', b1' and c1'; in the second round of diagnosis, the characteristic query information pairs corresponding to a, b and c are a2', b2' and c2'; in the third round of diagnosis, the characteristic query information pairs corresponding to a, b and c are a3', b3' and c3';
to sum up, in L2, the characteristic query information pair sequence of the 3 rounds of consultation is:
a1’→b1’→c1’→a2’→b2’→c2’→a3’→b3’→c3’
to sum up, the characteristic query information pair sequence of a corresponding to L1 is { a1, a2, a3}; b corresponds to L1 and has a characteristic query information pair sequence { b1, b2, b3}; c corresponds to L1 and has a feature query information pair sequence { c1, c2, c3};
a corresponds to L2 and has a characteristic query information pair sequence { a1', a2', a3' }; b corresponds to the characteristic query information pair sequence of L2 as { b1', b2', b3' }; c corresponds to the characteristic query information pair sequence of L1 as { c1', c2', c3' };
in summary, the inquiry information change node a2'→a3' composed of a2', a3' corresponds to the inquiry information change node a2→a3 composed of a2, a 3;
if a3' noteq.a3, in L1, aggregating all the characteristic query information pairs of the query nodes generated by a before participating in a3, including a1, b1, c1, a2, b2, c2; in L2, aggregating all characteristic query information pairs of query nodes generated by a before participating in a3', including a1', b1', c1', a2', b2', c2';
step S404: calculating to obtain a set Q=S2U S1-S2U S1, extracting each characteristic query information pair in the set Q to correspond to a target consultation department, judging each other target consultation department except a certain target consultation department and the target consultation department, and meeting one-time consultation association based on two history remote consultation records;
step S500: calculating characteristic values related to inquiry between doctor ends of every two target consultation departments, performing characteristic analysis on records of patient image data retrieval when doctor ends of the target consultation departments presenting characteristic related to inquiry participate in polling consultation, and performing intelligent retrieval of patient image data on doctor ends participating in remote consultation;
wherein, step S500 includes:
step S501: the method comprises the steps that the times meeting the requirements of inquiry association are displayed in any one feature consultation record set in any two target consultation departments and accumulated to obtain total times X; calculating a characteristic value beta of inquiry association between any two target consultation departments, wherein beta=X/[ m (m-1)/2 ]; wherein m represents the total number of history remote consultation records contained in any one of the feature consultation record sets; if beta between any two target consultation departments is more than a characteristic threshold, judging that the any two target consultation departments present characteristic inquiry association;
step S502: extracting all the history remote consultation records of the doctor end of any two target consultation departments h1 and h2 which present characteristic consultation association, and setting the total number of all the history remote consultation records as M1; respectively identifying the types of the image data which are acquired by the doctor end of the h2 or the doctor end of the h1 when the doctor end of the h1 or the doctor end of the h2 makes a consultation on the patient in each history remote consultation record;
step S503: respectively accumulating the total times N1 of various image data which are called when the doctor end of h1 or the doctor end of h2 makes a consultation on the patient in M1 historical remote consultation records; and determining the priority of automatic retrieval of various image data at the doctor end of the corresponding h2 or the doctor end of the h1 when the doctor end of the h1 or the doctor end of the h2 inquires the patient according to the corresponding total times from large to small.
In order to better implement the method, a remote consultation data management system is also provided, and the system comprises: the remote consultation management module, the remote consultation record management module, the polling consultation information carding module, the consultation association identification judgment module and the remote consultation data intelligent management module;
the remote consultation management module is used for locking all target consultation departments which need to respond to the remote consultation request based on the basic identity information, the history medical record information and the illness state description information of the patient whenever the remote consultation management terminal receives a remote consultation request initiated by the patient; when the doctor receives the remote consultation request representation in the waiting response time, a consultation room corresponding to the remote consultation request is created and remote consultation is started;
the remote consultation record management module is used for forming a polling consultation sequence based on the actual time of each doctor end entering the consultation room from each target consultation department every time a consultation room is created remotely; generating a remote consultation record based on the created consultation room correspondence;
the polling consultation information carding module is used for calling all the history remote consultation records from the remote consultation management terminal; the method comprises the steps of respectively combing polling consultation information of each history remote consultation record in each target history remote consultation record set, and respectively capturing inquiry information change nodes of doctor ends of each target consultation department;
the polling consultation information combing module comprises a history remote consultation record classification management unit and a consultation information changing node combing unit;
the history remote consultation record classification management unit is used for calling all history remote consultation records from the remote consultation management terminal and respectively combing the polling consultation information of each history remote consultation record in each target history remote consultation record set;
the inquiry information changing node combing unit is used for respectively capturing inquiry information changing nodes for doctor terminals of all target consultation departments;
the inquiry association identification judgment module is used for capturing doctor ends of other target consultation departments associated with the inquiry of the doctor ends of the target consultation departments according to the information deviation of the inquiry information change nodes existing in different historical remote consultation records of the doctor ends of the target consultation departments;
the remote consultation data intelligent management module is used for respectively carrying out characteristic value calculation of consultation association between doctor ends of every two target consultation departments, carrying out characteristic analysis on records of patient image data retrieval when the doctor ends of the target consultation departments presenting the characteristic consultation association participate in polling consultation, and carrying out intelligent retrieval on the patient image data of the doctor ends participating in remote consultation;
the remote consultation data intelligent management module comprises a characteristic value calculation management unit and a consultation management unit;
the characteristic value calculation management unit is used for respectively carrying out characteristic value calculation of inquiry association between doctor ends of the target consultation departments;
the consultation management unit is used for carrying out feature analysis on records of patient image data retrieval when doctor ends of target consultation departments showing feature of the consultation association participate in the polling consultation, and carrying out intelligent retrieval on patient image data of doctor ends participating in the remote consultation.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. The remote consultation data management method based on the Internet of things is characterized by comprising the following steps of:
step S100: each time a remote consultation management terminal receives a remote consultation request initiated by a patient, locking all target consultation departments which need to respond to the remote consultation request based on basic identity information, history medical record information and illness state description information of the patient; when the doctor is in the response waiting time, each target consultation department indicates acceptance of the remote consultation request, creates a consultation room corresponding to the remote consultation request and starts remote consultation;
step S200: each time a consultation room is remotely created, a polling consultation sequence is formed based on the actual time of each doctor end entering the consultation room from each target consultation department; generating a remote consultation record based on the created consultation room correspondence;
step S300: calling all the history remote consultation records from the remote consultation management terminal, and respectively collecting the history remote consultation records which are completely the same as the target consultation departments which need to respond to the remote consultation requests in all the history remote consultation records to obtain a plurality of target history remote consultation record sets; the method comprises the steps of respectively combing polling consultation information of each history remote consultation record in each target history remote consultation record set, and respectively capturing inquiry information change nodes of doctor ends of each target consultation department;
step S400: capturing doctor ends of other target consultation departments associated with the consultation of the doctor ends of the target consultation departments according to the information deviation of the consultation information change nodes existing in different historical remote consultation records of the doctor ends of the target consultation departments;
step S500: calculating characteristic values related to inquiry between doctor ends of every two target consultation departments, performing characteristic analysis on records of patient image data retrieval when doctor ends of the target consultation departments presenting characteristic related to inquiry participate in polling consultation, and performing intelligent retrieval of patient image data on doctor ends participating in remote consultation;
the step S400 includes:
step S401: respectively collecting characteristic inquiry information pair sequences extracted from each history remote consultation record at the doctor end of each department in each characteristic consultation record set, and respectively comparing deviation of corresponding inquiry information change nodes between every two characteristic inquiry information pair sequences;
step S402: if the doctor side of a target consultation department D extracts characteristic query information pair sequences R from two history remote consultation records respectively 1 And R is 2 Let R be 1 An i-th query node L participated by a doctor end of the certain target consultation department D i And the (i+1) th query node L i+1 The j-th inquiry information change node is P (j) =L i →L i+1 Let R be 2 The ith query node L engaged in by D i ' and i+1th query node L i+1 The j-th inquiry information change node of the 'structure is P (j)' =l i ’→L i+1 ' when meeting L i ＝L i ’，L i+1 ≠L i+1 ' at the time, judge R 1 And R is 2 The j-th inquiry information change node is a reference change node;
step S403: at R 1 In the corresponding history remote consultation record, the doctor end of the certain target consultation department D participates in L i+1 Extracting all the query nodes existing before, and collecting characteristic query information corresponding to all the query nodes to obtain a set S1; at R 2 In the corresponding history remote consultation record, the doctor end of the certain target consultation department D participates in L i+1 Extracting all the query nodes existing before, and collecting characteristic query information corresponding to all the query nodes to obtain a set S2;
step S404: calculating to obtain a set Q=S2U S1-S2U S1, extracting each characteristic query information pair in the set Q to correspond to a target consultation department, judging each other target consultation department except the certain target consultation department and the target consultation department, and meeting one-time consultation association based on the two history remote consultation records;
the step S500 includes:
step S501: the method comprises the steps that the times meeting the requirements of inquiry association are displayed in any one feature consultation record set in any two target consultation departments and accumulated to obtain total times X; calculating a characteristic value beta of the inquiry association between any two target consultation departments, wherein beta=X/[ m (m-1)/2 ]; wherein m represents the total number of history remote consultation records contained in any one of the feature consultation record sets; if the beta between any two target consultation departments is more than a characteristic threshold, judging that the any two target consultation departments present characteristic inquiry association;
step S502: extracting all the history remote consultation records of the doctor end of any two target consultation departments h1 and h2 which present characteristic consultation association, and setting the total number of all the history remote consultation records as M1; respectively identifying the types of the image data which are acquired by the doctor end of the h2 or the doctor end of the h1 when the doctor end of the h1 or the doctor end of the h2 makes a consultation on the patient in each history remote consultation record;
step S503: respectively accumulating the total times N1 of various image data which are called when the doctor end of h1 or the doctor end of h2 makes a consultation on the patient in M1 historical remote consultation records; and determining the priority of automatic retrieval of various image data at the doctor end of the corresponding h2 or the doctor end of the h1 when the doctor end of the h1 or the doctor end of the h2 inquires the patient according to the corresponding total times from large to small.
2. The method for remote consultation data management based on the internet of things according to claim 1, wherein the step S200 includes: setting a complete query process which occurs at any doctor side and patient side in each poll consultation as a query node in each poll consultation; and extracting characteristic keywords from the query speech of the medical terminal in each query node to obtain a characteristic keyword set A, extracting characteristic keywords from the answer information fed back by the patient based on the query speech to obtain a characteristic keyword set B, and constructing a characteristic query information pair A-B corresponding to each query node.
3. The method for remote consultation data management based on the internet of things according to claim 1, wherein the step S300 includes:
step S301: extracting characteristic keywords from basic identity information, history medical record information and illness state description information of a patient in each history remote consultation record in each target history remote consultation record set to obtain a patient characteristic information set P1, and acquiring identity information of each doctor end responding to each history remote consultation record; the identity information comprises title information, job information, adept field information and department information; giving the doctor ends with the same identity information the same label; respectively collecting the marking labels corresponding to the doctor terminals responding to each history remote consultation record to obtain a marking label set P2; respectively collecting historical remote consultation records with the similarity between the patient characteristic information set P1 and the marking label set P2 being greater than a similarity threshold value to obtain a plurality of characteristic consultation record sets;
step S302: respectively extracting a polling consultation sequence corresponding to each doctor end and corresponding department information in each history remote consultation record; in each characteristic consultation record set, extracting characteristic query information pairs of all query nodes participated in by doctor terminals of each target consultation department in corresponding history remote consultation records respectively, and arranging according to the generation sequence of the query nodes to obtain a characteristic query information pair sequence { L ] of doctor terminals of each target consultation department corresponding to the corresponding history remote consultation records 1 、L 2 、...、L n -a }; wherein L is 1 、L 2 、...、L n Respectively representing the 1 st, 2 nd, the doctor end of each target consultation department participates in the corresponding history remote consultation record,.., characteristic query information pairs corresponding to n query nodes; let every two adjacent feature query information pairs L i And L is equal to i+1 One inquiry information change node p=l formed in the corresponding characteristic inquiry information pair sequence i →L i+1 。
4. A remote consultation data management system for executing a remote consultation data management method based on the internet of things according to any of claims 1-3, characterised in that the system includes: the remote consultation management module, the remote consultation record management module, the polling consultation information carding module, the consultation association identification judgment module and the remote consultation data intelligent management module;
the remote consultation management module is used for locking all target consultation departments which need to respond to the remote consultation request based on the basic identity information, the history medical record information and the illness state description information of the patient every time the remote consultation management terminal receives a remote consultation request initiated by the patient; when the doctor is in the response waiting time, each target consultation department indicates acceptance of the remote consultation request, creates a consultation room corresponding to the remote consultation request and starts remote consultation;
the remote consultation record management module is used for forming a polling consultation sequence based on the actual time of each doctor end entering the consultation room from each target consultation department every time a consultation room is created remotely; generating a remote consultation record based on the created consultation room correspondence;
the polling consultation information carding module is used for calling all the history remote consultation records from the remote consultation management terminal; the method comprises the steps of respectively combing polling consultation information of each history remote consultation record in each target history remote consultation record set, and respectively capturing inquiry information change nodes of doctor ends of each target consultation department;
the inquiry association identification judgment module is used for capturing doctor terminals of other target consultation departments associated with the inquiry of the doctor terminals of the target consultation departments according to the information deviation of inquiry information change nodes existing in different historical remote consultation records;
the remote consultation data intelligent management module is used for respectively carrying out characteristic value calculation of consultation association between doctor ends of every two target consultation departments, carrying out characteristic analysis on records of patient image data retrieval when doctor ends of the target consultation departments presenting characteristic of the consultation association participate in polling consultation, and carrying out intelligent retrieval of patient image data on doctor ends participating in remote consultation.
5. The remote consultation data management system according to claim 4, wherein the polling consultation information combing module comprises a history remote consultation record classification management unit and a consultation information changing node combing unit;
the history remote consultation record classification management unit is used for calling all history remote consultation records from the remote consultation management terminal and combing the polling consultation information of each history remote consultation record in each target history remote consultation record set;
the inquiry information changing node combing unit is used for respectively capturing inquiry information changing nodes for doctor terminals of all target consultation departments.
6. The remote consultation data management system according to claim 4, wherein the remote consultation data intelligent management module comprises a characteristic value calculation management unit and a consultation management unit;
the characteristic value calculation management unit is used for respectively carrying out characteristic value calculation of inquiry association between doctor ends of the two-to-two target consultation departments;
the consultation management unit is used for carrying out feature analysis on records of patient image data retrieval when doctor ends of target consultation departments showing feature of the consultation association participate in polling consultation, and carrying out intelligent retrieval of patient image data on doctor ends participating in remote consultation.
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| CN115274141A (en) * | 2022-09-26 | 2022-11-01 | 北京小成素问信息技术有限公司 | Remote medical consultation method and system based on polling |
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| KR101039001B1 (en) * | 2010-03-04 | 2011-06-07 | 가톨릭대학교 산학협력단 | System for medical treatment with consultation and its method |
| CN111276260A (en) * | 2020-01-16 | 2020-06-12 | 创业慧康科技股份有限公司 | Cloud-based cross-medical joint multidisciplinary joint consultation system |
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| CN115274141A (en) * | 2022-09-26 | 2022-11-01 | 北京小成素问信息技术有限公司 | Remote medical consultation method and system based on polling |
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