CN116959686A - Medical information management system and method based on digital integration - Google Patents
Medical information management system and method based on digital integration Download PDFInfo
- Publication number
- CN116959686A CN116959686A CN202310930722.6A CN202310930722A CN116959686A CN 116959686 A CN116959686 A CN 116959686A CN 202310930722 A CN202310930722 A CN 202310930722A CN 116959686 A CN116959686 A CN 116959686A
- Authority
- CN
- China
- Prior art keywords
- target
- hanging
- registration
- registration record
- record
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 19
- 230000010354 integration Effects 0.000 title claims abstract description 15
- 238000003745 diagnosis Methods 0.000 claims abstract description 50
- 238000009960 carding Methods 0.000 claims abstract description 20
- 238000007726 management method Methods 0.000 claims description 32
- 238000004364 calculation method Methods 0.000 claims description 29
- 238000000605 extraction Methods 0.000 claims description 8
- 239000002131 composite material Substances 0.000 claims description 3
- NRNCYVBFPDDJNE-UHFFFAOYSA-N pemoline Chemical compound O1C(N)=NC(=O)C1C1=CC=CC=C1 NRNCYVBFPDDJNE-UHFFFAOYSA-N 0.000 claims description 3
- 230000009471 action Effects 0.000 description 3
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 206010037660 Pyrexia Diseases 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012797 qualification Methods 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 230000008685 targeting Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/02—Reservations, e.g. for tickets, services or events
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Tourism & Hospitality (AREA)
- Development Economics (AREA)
- Human Resources & Organizations (AREA)
- Medical Informatics (AREA)
- Primary Health Care (AREA)
- Public Health (AREA)
- Epidemiology (AREA)
- Biomedical Technology (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- General Health & Medical Sciences (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention relates to the technical field of medical information management, in particular to a medical information management system and method based on digital integration, which comprises the steps of respectively carrying out information carding and extracting on number selection operation carried out by corresponding users in each historical registration record; respectively calculating characteristic indexes of each history registration record; dividing all the historical registration records into a random registration record set and a target registration record set based on the characteristic indexes corresponding to the historical registration records; capturing various operations generated by corresponding users in time sequence in a number selecting operation page for each history registration record in a random number selecting record set and a target number selecting record set respectively, and generating an operation sequence corresponding to each history registration record; and capturing an operation sequence generated in a number selecting operation page in unit time by a user accessing the intelligent guided diagnosis to perform registration operation in real time, and starting intelligent registration recommendation management for the user based on the operation sequence.
Description
Technical Field
The invention relates to the technical field of medical information management, in particular to a medical information management system and method based on digital integration.
Background
The hospital registration is difficult, is always the topic of people's attention, no matter see doctor, examine, buy the medicine, all need register first, and queuing registration in the hospital often needs to consume a lot of time and energy, and see doctor in the hospital and often take place such phenomenon: many people prefer to queue up overnight, and the riding people also need to hang expert numbers, the expert numbers rob hands and the common numbers fall under cold; according to statistics, about 3000 registration persons in a hospital are registered every day, 60% -70% of the hospitals are running specialists, at least half of the hospitals are common diseases, and in fact, even the specialists with high qualification are consulted in a conventional mode like fever, cold and the like;
the phenomenon causes uneven cold and hot registration in the hospital, and of course, a patient who is not lack of a patient only hangs a number at will during the number selection, not the mind of the phenomenon described above, but the phenomenon aggravates uneven hospital registration distribution to a certain extent, and medical resources of the hospital are wasted to a certain extent.
Disclosure of Invention
The invention aims to provide a medical information management system and method based on digital integration, 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 medical information management method based on digital integration comprises the following steps:
step S100: collecting all historical registration records generated by each intelligent diagnosis guiding terminal in a hospital, and respectively carrying out information carding and extraction on the number selecting operation carried out by the corresponding user in each historical registration record;
step S200: based on information obtained by selecting number background carding and extracting number selecting operations of corresponding users in each history registration record, respectively carrying out characteristic index calculation on each history registration record;
step S300: dividing all the historical registration records into a random registration record set and a target registration record set based on the characteristic indexes corresponding to the historical registration records;
step S400: capturing various operations generated by corresponding users in time sequence in a number selecting operation page for each history registration record in a random number selecting record set and a target number selecting record set respectively, and generating an operation sequence corresponding to each history registration record; and capturing an operation sequence generated in a number selecting operation page in unit time by a user accessing the intelligent guided diagnosis to perform registration operation in real time, and starting intelligent registration recommendation management for the user based on the operation sequence.
Further, step S100 includes: setting the hanging number corresponding to each history registration record as a first target hanging number; capturing a corresponding number selecting operation page of a corresponding user when registering from each historical registration record, and setting other hanging numbers except the first target hanging number in the number selecting operation page as second target hanging numbers; and acquiring the first target hanging number and the corresponding consultation cost information of each second target hanging number, the information of the adequacy field of the corresponding consultation doctor and the consultation time information of the corresponding consultation doctor.
Further, step S200 includes:
step S201: extracting characteristic keywords of the information of the adept field of the corresponding doctor in the first target hanging number and the second target hanging number respectively to obtain characteristic keyword sets corresponding to the first target hanging number and the second target hanging number respectively; when the feature keyword set B of a certain second target hanging number and the feature keyword set A of the first target hanging number meet the following conditionsAnd the card (C) is not less than k, and judging that a certain second target hanging number is the hanging number meeting the first characteristic association relation between the first target hanging number and the second target hanging number; wherein, card (C) represents the number of feature keywords contained in the set C; calculating a first index coefficient beta 1=1/(m+1)/M corresponding to the first target hanging number; wherein m represents the number of the hanging number meeting the first characteristic association relation with the first target hanging number; m represents the total number of hanging diagnosis numbers contained in the number selection operation page;
the judgment mechanism of the first characteristic association relation can know that the hanging number meeting the first characteristic association relation with the first target hanging number has a part of the field with coincidence and good quality with the doctor of the doctor corresponding to the hanging number of the user target registration, namely the similar hanging number of the user target registration, when the number selection operation page has more hanging numbers meeting the first characteristic association relation with the first target hanging number, the more options capable of realizing similar diagnosis effects with doctors with target diagnosis of the user exist, namely, the lower the user destination embodied on the first target hanging number is, the user is more likely to select one of a plurality of doctors with similar fields, and the possibility of hanging the doctor is lower; the smaller the first index coefficient beta 1 is, the smaller the randomness is reflected when the user makes a selection on the first target hanging number, and the stronger the subjectivity is;
step S202: when the feature keyword set B' of the hanging number meeting the first feature association relation with the first target hanging number and the feature keyword set A of the first target hanging number meet the following conditionsMarking the characteristic keywords contained in the set C in the characteristic keyword set A; after marking the feature keyword set A based on feature keyword sets of all the triage numbers meeting the first feature association relation with the first target triage number, calculating a second index coefficient beta 2 = N/N corresponding to the first target triage number, wherein the number N of feature keywords which are not marked is remained in the feature keyword set A; wherein N represents the number of the feature keywords contained in the feature keyword set A; a first characteristic index α1=β1+β2 of the historical registration record corresponding to the first target diagnosis number is calculated.
The smaller the value of n, i.e., the second index coefficient β2, the higher the likelihood that the user will be presented with a doctor's visit in the first target screening number due to the n feature keywords, because the doctor is unique to other doctors in the list of selections, e.g., the doctor is good at treating the direction in which the user wants to visit, the smaller the second index coefficient β2, the less random the user will present when selecting the first target screening number, and the more subjective.
Further, step S200 includes:
step S211: setting a time threshold Tr, and judging that a certain second target hanging number is a hanging number meeting a second characteristic association relation with the first target hanging number when the visit time t of a corresponding doctor in the certain second target hanging number and the visit time te of a corresponding doctor in the first target hanging number meet |te-t|+.Tr, and the visit cost S corresponding to the certain second target hanging number and the visit cost Se corresponding to the first target hanging number meet S=Se;
step S212: calculating a second characteristic index alpha 2= (R+1)/M of the history registration record corresponding to the first target diagnosis number; wherein R represents the number of the hanging number meeting the second characteristic association relation with the first target hanging number; m represents the total number of hanging diagnosis numbers contained in the number selection operation page; calculating a comprehensive characteristic index delta=alpha 1+alpha 2 for the history registration record corresponding to the first target registration number
Further, step S300 includes:
step S301: will correspond to the maximum composite characteristic index delta max Setting the historical registration record of the number as a first target registration record, and setting the corresponding minimum comprehensive characteristic index delta min Setting the historical registration record of the register as a second target registration record;
step S302: when the comprehensive characteristic index g of a certain historical registration record meets the requirement of |g-delta min |<|g-δ max Classifying a certain historical registration record and a second target registration record into one type to obtain a target number selection record set; when the comprehensive characteristic index g of a certain historical registration record meets the requirement of |g-delta min |>|g-δ max Classifying a certain historical registration record and a first target registration record into one type to obtain a random number selection record set;
further, step S400 includes:
step S401: when a user who carries out registration operation on the real-time access intelligent guided diagnosis captures an operation sequence W generated in a number selection operation page in unit time, respectively carrying out similarity comparison on the operation sequence W and the operation sequences corresponding to each history registration record in a random number selection record set and a target number selection record set;
step S402: the method comprises the steps of obtaining the number h1 of historical registration records with similarity larger than a similarity threshold value between a random number selection record set and an operation sequence W, and obtaining the number h2 of historical registration records with similarity larger than the similarity threshold value between a target number selection record set and the operation sequence W; when h1> h2, pushing the optimal number of the hanging diagnosis at the current moment to a user, when the user selects the optimal number of the hanging diagnosis to make a diagnosis, importing registration records generated by the user selecting the optimal number of the hanging diagnosis to make a diagnosis into a random number selection record set, and when the user does not adopt the optimal number of the hanging diagnosis, importing historical registration records generated by the user into a target number selection record set; the optimal hanging number is the hanging number with the shortest predicted waiting time; when h1 is less than or equal to h2, the history registration records generated by a certain user are imported into the target registration record set.
The medical information management system comprises a number selecting operation carding module, a characteristic index calculation module, a history registration record dividing management module and an intelligent registration recommendation management module;
the system comprises a number selecting operation carding module, a number selecting module and a number selecting module, wherein the number selecting operation carding module is used for collecting all historical registration records generated by each intelligent diagnosis guiding terminal in a hospital and respectively carrying out information carding and extraction on number selecting operations carried out by corresponding users in each historical registration record;
the characteristic index calculation module is used for respectively carrying out characteristic index calculation on each historical registration record according to information obtained by carrying out number selection background combing and extraction on the number selection operation of the corresponding user in each historical registration record
The historical registration record division management module is used for dividing all the historical registration records into a random registration record set and a target registration record set according to the characteristic indexes corresponding to the historical registration records;
the intelligent registration recommendation management module is used for capturing various operations generated by corresponding users in time sequence in a registration operation page to each history registration record in the random registration record set and the target registration record set respectively, and generating an operation sequence corresponding to each history registration record; and capturing an operation sequence generated in a number selecting operation page in unit time by a user accessing the intelligent guided diagnosis to perform registration operation in real time, and starting intelligent registration recommendation management for the user based on the operation sequence.
Further, the characteristic index calculation module comprises an index coefficient calculation unit and a comprehensive characteristic index calculation unit;
the index coefficient calculation unit is used for calculating a first index coefficient and a second index coefficient for the hanging diagnosis number corresponding to each history registration record according to the registration operation information of the user corresponding to each history registration record;
and the comprehensive characteristic index calculation unit is used for receiving the data in the index coefficient calculation unit and calculating the comprehensive characteristic index for each history registration record.
Compared with the prior art, the invention has the following beneficial effects: the invention can realize the feature analysis of all the history registration records in the hospital, and can measure whether the user operating to generate the corresponding history registration records has definite targeting or strong randomness when selecting the registration by calculating a comprehensive feature index, if the current user is judged to be in registration, the registration recommendation help is provided for the user with weak registration target requirements by intervention with strong randomness, the waiting time of the user is reduced, the equilibrium regulation and control of the registration in the hospital are realized to a certain extent, and the phenomenon of uneven cold and hot registration in the hospital is regulated and controlled to a certain extent.
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 digital integration-based medical information management method according to the present invention;
fig. 2 is a schematic structural diagram of a medical information management system based on digital integration according to the present invention.
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 medical information management method based on digital integration comprises the following steps:
step S100: collecting all historical registration records generated by each intelligent diagnosis guiding terminal in a hospital, and respectively carrying out information carding and extraction on the number selecting operation carried out by the corresponding user in each historical registration record;
wherein, step S100 includes: setting the hanging number corresponding to each history registration record as a first target hanging number; capturing a corresponding number selecting operation page of a corresponding user when registering from each historical registration record, and setting other hanging numbers except the first target hanging number in the number selecting operation page as second target hanging numbers; the method comprises the steps of respectively obtaining first target hanging numbers and the visit expense information corresponding to each second target hanging number, the information of the adequacy field of corresponding consultants and the visit time information of the corresponding consultants;
step S200: based on information obtained by selecting number background carding and extracting number selecting operations of corresponding users in each history registration record, respectively carrying out characteristic index calculation on each history registration record;
wherein, step S200 includes:
step S201: extracting characteristic keywords of the information of the adept field of the corresponding doctor in the first target hanging number and the second target hanging number respectively to obtain characteristic keyword sets corresponding to the first target hanging number and the second target hanging number respectively; when the feature keyword set B of a certain second target hanging number and the feature keyword set A of the first target hanging number meet the following conditionsAnd the card (C) is not less than k, and judging that a certain second target hanging number is the hanging number meeting the first characteristic association relation between the first target hanging number and the second target hanging number; wherein, card (C) represents the number of feature keywords contained in the set C; calculating a first index coefficient beta 1=1/(m+1)/M corresponding to the first target hanging number; wherein m represents the number of the hanging number meeting the first characteristic association relation with the first target hanging number; m represents the total number of hanging diagnosis numbers contained in the number selection operation page;
for example, the feature keyword set corresponding to the first target hanging number is { feature keyword a, feature keyword b, feature keyword c, feature keyword d }; the feature keyword set corresponding to a certain second target hanging number is { feature keyword a, feature keyword b, feature keyword c, feature keyword f }; because { feature keyword a, feature keyword b, feature keyword c, feature keyword d } n { feature keyword a, feature keyword b, feature keyword c, feature keyword f } = { feature keyword a, feature keyword b, feature keyword c }, card { feature keyword a, feature keyword b, feature keyword c } = 3 is not less than 2;
in conclusion, judging that a certain second target hanging number is a hanging number meeting a first characteristic association relation with the first target hanging number;
step S202: when the feature keyword set B' of the hanging number meeting the first feature association relation with the first target hanging number and the feature keyword set A of the first target hanging number meet the following conditionsMarking the characteristic keywords contained in the set C in the characteristic keyword set A; after marking the feature keyword set A based on feature keyword sets of all the triage numbers meeting the first feature association relation with the first target triage number, calculating a second index coefficient beta 2 = N/N corresponding to the first target triage number, wherein the number N of feature keywords which are not marked is remained in the feature keyword set A; wherein N represents the number of the feature keywords contained in the feature keyword set A; calculate the corresponding first orderA first characteristic index α1=β1+β2 of the history of the label count;
wherein, step S200 includes:
step S211: setting a time threshold Tr, and judging that a certain second target hanging number is a hanging number meeting a second characteristic association relation with the first target hanging number when the visit time t of a corresponding doctor in the certain second target hanging number and the visit time te of a corresponding doctor in the first target hanging number meet |te-t|+.Tr, and the visit cost S corresponding to the certain second target hanging number and the visit cost Se corresponding to the first target hanging number meet S=Se;
step S212: calculating a second characteristic index alpha 2= (R+1)/M of the history registration record corresponding to the first target diagnosis number; wherein R represents the number of the hanging number meeting the second characteristic association relation with the first target hanging number; m represents the total number of hanging diagnosis numbers contained in the number selection operation page; calculating a comprehensive characteristic index delta=alpha 1+alpha 2 for the history registration record corresponding to the first target diagnosis number;
step S300: dividing all the historical registration records into a random registration record set and a target registration record set based on the characteristic indexes corresponding to the historical registration records;
wherein, step S300 includes:
step S301: will correspond to the maximum composite characteristic index delta max Setting the historical registration record of the number as a first target registration record, and setting the corresponding minimum comprehensive characteristic index delta min Setting the historical registration record of the register as a second target registration record;
step S302: when the comprehensive characteristic index g of a certain historical registration record meets the requirement of |g-delta min |<|g-δ max Classifying a certain historical registration record and a second target registration record into one type to obtain a target number selection record set; when the comprehensive characteristic index g of a certain historical registration record meets the requirement of |g-delta min |>|g-δ max Classifying a certain historical registration record and a first target registration record into one type to obtain a random number selection record set;
step S400: capturing various operations generated by corresponding users in time sequence in a number selecting operation page for each history registration record in a random number selecting record set and a target number selecting record set respectively, and generating an operation sequence corresponding to each history registration record; capturing an operation sequence generated in a number selection operation page in unit time by a user accessing the intelligent guided diagnosis for registering operation in real time, and starting intelligent registration recommendation management for the user based on the operation sequence;
wherein, step S400 includes:
step S401: when a user who carries out registration operation on the real-time access intelligent guided diagnosis captures an operation sequence W generated in a number selection operation page in unit time, respectively carrying out similarity comparison on the operation sequence W and the operation sequences corresponding to each history registration record in a random number selection record set and a target number selection record set;
step S402: the method comprises the steps of obtaining the number h1 of historical registration records with similarity larger than a similarity threshold value between a random number selection record set and an operation sequence W, and obtaining the number h2 of historical registration records with similarity larger than the similarity threshold value between a target number selection record set and the operation sequence W; when h1> h2, pushing the optimal number of the hanging diagnosis at the current moment to a user, when the user selects the optimal number of the hanging diagnosis to make a diagnosis, importing registration records generated by the user selecting the optimal number of the hanging diagnosis to make a diagnosis into a random number selection record set, and when the user does not adopt the optimal number of the hanging diagnosis, importing historical registration records generated by the user into a target number selection record set; the optimal hanging number is the hanging number with the shortest predicted waiting time; when h1 is less than or equal to h2, the history registration records generated by a certain user are imported into the target registration record set.
The medical information management system comprises a number selecting operation carding module, a characteristic index calculation module, a history registration record dividing management module and an intelligent registration recommendation management module;
the system comprises a number selecting operation carding module, a number selecting module and a number selecting module, wherein the number selecting operation carding module is used for collecting all historical registration records generated by each intelligent diagnosis guiding terminal in a hospital and respectively carrying out information carding and extraction on number selecting operations carried out by corresponding users in each historical registration record;
the characteristic index calculation module is used for carrying out the number selection background carding and extracting the information obtained according to the number selection operation of the corresponding user in each history registration record, and carrying out characteristic index calculation on each history registration record respectively;
the characteristic index calculation module comprises an index coefficient calculation unit and a comprehensive characteristic index calculation unit;
the index coefficient calculation unit is used for calculating a first index coefficient and a second index coefficient for the hanging diagnosis number corresponding to each history registration record according to the registration operation information of the user corresponding to each history registration record;
the comprehensive characteristic index calculation unit is used for receiving the data in the index coefficient calculation unit and calculating the comprehensive characteristic index for each history registration record;
the historical registration record division management module is used for dividing all the historical registration records into a random registration record set and a target registration record set according to the characteristic indexes corresponding to the historical registration records;
the intelligent registration recommendation management module is used for capturing various operations generated by corresponding users in time sequence in a registration operation page to each history registration record in the random registration record set and the target registration record set respectively, and generating an operation sequence corresponding to each history registration record; and capturing an operation sequence generated in a number selecting operation page in unit time by a user accessing the intelligent guided diagnosis to perform registration operation in real time, and starting intelligent registration recommendation management for the user based on the operation sequence.
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 (8)
1. A digital integration-based medical information management method, the method comprising:
step S100: collecting all historical registration records generated by each intelligent diagnosis guiding terminal in a hospital, and respectively carrying out information carding and extraction on the number selecting operation carried out by the corresponding user in each historical registration record;
step S200: based on information obtained by selecting number background carding and extracting number selecting operations of corresponding users in each history registration record, respectively carrying out characteristic index calculation on each history registration record;
step S300: dividing all the historical registration records into a random registration record set and a target registration record set based on the characteristic indexes corresponding to the historical registration records;
step S400: capturing various operations generated by corresponding users in time sequence in a number selecting operation page for each history registration record in a random number selecting record set and a target number selecting record set respectively, and generating an operation sequence corresponding to each history registration record; capturing an operation sequence generated in a number selecting operation page in unit time by a user accessing the intelligent guided diagnosis in real time, and starting intelligent registration recommendation management for the user based on the operation sequence.
2. The method for managing medical information based on digital integration according to claim 1, wherein the step S100 comprises: setting the hanging number corresponding to each history registration record as a first target hanging number; capturing a corresponding number selecting operation page of the corresponding user when registering from each historical registration record, and setting other number hanging diagnosis numbers except the first target number hanging diagnosis number in the number selecting operation page as a second target number hanging diagnosis number; and acquiring the first target hanging number and the visit expense information corresponding to each second target hanging number, the information of the adequacy field of the corresponding doctor and the information of the visit time of the corresponding doctor.
3. The method for managing medical information based on digital integration according to claim 2, wherein the step S200 includes:
step S201: extracting characteristic keywords of the information of the areas of the specificities of the corresponding doctors in the first target hanging number and the second target hanging numbers respectively to obtain characteristic keyword sets corresponding to the first target hanging number and the second target hanging numbers respectively; when the feature keyword set B of a certain second target hanging number and the feature keyword set A of the first target hanging number meet the following conditionsAnd the card (C) is not less than k, and judging that the certain second target hanging number is the hanging number meeting the first characteristic association relation with the first target hanging number; wherein, card (C) represents the number of feature keywords contained in the set C; calculating a first index coefficient beta 1=1/(m+1)/M corresponding to the first target hanging number; wherein m represents the number of the hanging number meeting the first characteristic association relation with the first target hanging number; m represents the total number of hanging diagnosis numbers contained in the number selection operation page;
step S202: when a feature keyword set B' of a hanging number meeting a first feature association relation with the first target hanging number and a feature keyword set A of the first target hanging number meet the following conditionsMarking the characteristic keywords contained in the set C in the characteristic keyword set A; after marking the feature keyword set A based on feature keyword sets of all the triage numbers meeting a first feature association relation with the first target triage number, calculating a second index coefficient beta 2 = N/N corresponding to the first target triage number, wherein the number N of feature keywords which are not marked is remained in the feature keyword set A; wherein N represents the number of the feature keywords contained in the feature keyword set A; and calculating a first characteristic index alpha 1 = beta 1+ beta 2 of the historical registration record corresponding to the first target registration number.
4. A method of managing medical information based on digital integration according to claim 3, wherein said step S200 comprises:
step S211: setting a time threshold Tr, and judging that a certain second target hanging number is a hanging number meeting a second characteristic association relation with a first target hanging number when the visit time t of a corresponding doctor in the certain second target hanging number and the visit time te of a corresponding doctor in the first target hanging number meet |te-t|+.Tr, and the visit cost S corresponding to the certain second target hanging number and the visit cost Se corresponding to the first target hanging number meet S=Se;
step S212: calculating a second characteristic index alpha of a historical registration record corresponding to the first target registration number
2= (r+1)/M; wherein R represents the number of the hanging number meeting the second characteristic association relation with the first target hanging number; m represents the total number of hanging diagnosis numbers contained in the number selection operation page; and calculating the comprehensive characteristic index delta=alpha 1+alpha 2 of the historical registration record corresponding to the first target consultation number.
5. The method for managing medical information based on digital integration according to claim 4, wherein the step S300 comprises:
step S301: will correspond to the maximum composite characteristic index delta max History of (2)The registration record is set as a first target registration record, and the corresponding minimum comprehensive characteristic index delta min Setting the historical registration record of the register as a second target registration record;
step S302: when the comprehensive characteristic index g of a certain historical registration record meets the requirement of |g-delta min |<|g-δ max Classifying the certain historical registration record and the second target registration record into one type to obtain a target registration record set; when the comprehensive characteristic index g of a certain historical registration record meets the requirement of |g-delta min |>|g-δ max And classifying the history registration record and the first target registration record into one type to obtain a random number selection record set.
6. The method for managing medical information based on digital integration according to claim 5, wherein the step S400 includes:
step S401: when a user who carries out registration operation on the real-time access intelligent guided diagnosis captures an operation sequence W generated in a number selection operation page in unit time, respectively carrying out similarity comparison on the operation sequence W and the operation sequences corresponding to each history registration record in a random number selection record set and a target number selection record set;
step S402: the method comprises the steps of obtaining the number h1 of historical registration records with similarity larger than a similarity threshold value between a random number selection record set and an operation sequence W, and obtaining the number h2 of historical registration records with similarity larger than the similarity threshold value between a target number selection record set and the operation sequence W; when h1> h2, pushing the optimal number of the hanging diagnosis at the current moment to the user, when the user selects the optimal number of the hanging diagnosis to visit, importing registration records generated by the user selecting the optimal number of the hanging diagnosis to visit into a random number selection record set, and when the user does not adopt the optimal number of the hanging diagnosis, importing historical registration records generated by the user into a target number selection record set; the optimal hanging number is the hanging number with the shortest predicted waiting time; and when h1 is less than or equal to h2, importing the history registration record generated by the certain user into a target registration record set.
7. A medical information management system for executing a digital integrated medical information management method according to any one of claims 1 to 6, wherein the system comprises a number selecting operation carding module, a characteristic index calculating module, a history registration record dividing management module and an intelligent registration recommendation management module;
the number selecting operation carding module is used for collecting all the history registration records generated by each intelligent diagnosis guiding terminal in the hospital, and respectively carrying out information carding and extraction on the number selecting operation carried out by the corresponding user in each history registration record;
the characteristic index calculation module is used for carrying out characteristic index calculation on each historical registration record according to information obtained by carrying out number selection background carding and extraction on the number selection operation of the corresponding user in each historical registration record;
the history registration record division management module is used for dividing all history registration records into a random registration record set and a target registration record set according to the characteristic indexes corresponding to the history registration records;
the intelligent registration recommendation management module is used for capturing various operations generated by corresponding users in time sequence in a registration operation page to generate an operation sequence corresponding to each history registration record for each history registration record in the random registration record set and the target registration record set respectively; capturing an operation sequence generated in a number selecting operation page in unit time by a user accessing the intelligent guided diagnosis in real time, and starting intelligent registration recommendation management for the user based on the operation sequence.
8. The medical information management system according to claim 7, wherein the characteristic index calculation module includes an index coefficient calculation unit, a comprehensive characteristic index calculation unit;
the index coefficient calculation unit is used for calculating a first index coefficient and a second index coefficient for the hanging diagnosis number corresponding to each history registration record according to the registration operation information of the user corresponding to each history registration record;
the comprehensive characteristic index calculation unit is used for receiving the data in the index coefficient calculation unit and calculating the comprehensive characteristic index for each history registration record.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310930722.6A CN116959686B (en) | 2023-07-27 | 2023-07-27 | Medical information management system and method based on digital integration |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310930722.6A CN116959686B (en) | 2023-07-27 | 2023-07-27 | Medical information management system and method based on digital integration |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116959686A true CN116959686A (en) | 2023-10-27 |
| CN116959686B CN116959686B (en) | 2024-02-13 |
Family
ID=88454383
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310930722.6A Active CN116959686B (en) | 2023-07-27 | 2023-07-27 | Medical information management system and method based on digital integration |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116959686B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111415760A (en) * | 2020-03-26 | 2020-07-14 | 潘佳亮 | Doctor recommendation method, system, computer equipment and storage medium |
| CN112164451A (en) * | 2020-09-18 | 2021-01-01 | 中国建设银行股份有限公司 | Intelligent diagnosis guiding and registering method, device, equipment and storage medium |
| WO2021139146A1 (en) * | 2020-01-09 | 2021-07-15 | 平安国际智慧城市科技股份有限公司 | Information recommendation method, device, computer-readable storage medium, and apparatus |
| CN113836388A (en) * | 2020-06-08 | 2021-12-24 | 北京达佳互联信息技术有限公司 | Information recommendation method and device, server and storage medium |
| CN114141377A (en) * | 2021-04-29 | 2022-03-04 | 深圳市康比特信息技术有限公司 | Method for establishing diagnosis rule base, method and equipment for checking diagnosis information |
| CN114723181A (en) * | 2022-06-07 | 2022-07-08 | 常州云燕医疗科技有限公司 | Digital integrated operating room signal transmission system and method based on block chain |
| CN115238173A (en) * | 2022-06-30 | 2022-10-25 | 山东省玖玖医养健康产业有限公司 | Behavior analysis and medical service pushing method, equipment and medium based on big data |
-
2023
- 2023-07-27 CN CN202310930722.6A patent/CN116959686B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021139146A1 (en) * | 2020-01-09 | 2021-07-15 | 平安国际智慧城市科技股份有限公司 | Information recommendation method, device, computer-readable storage medium, and apparatus |
| CN111415760A (en) * | 2020-03-26 | 2020-07-14 | 潘佳亮 | Doctor recommendation method, system, computer equipment and storage medium |
| CN113836388A (en) * | 2020-06-08 | 2021-12-24 | 北京达佳互联信息技术有限公司 | Information recommendation method and device, server and storage medium |
| CN112164451A (en) * | 2020-09-18 | 2021-01-01 | 中国建设银行股份有限公司 | Intelligent diagnosis guiding and registering method, device, equipment and storage medium |
| CN114141377A (en) * | 2021-04-29 | 2022-03-04 | 深圳市康比特信息技术有限公司 | Method for establishing diagnosis rule base, method and equipment for checking diagnosis information |
| CN114723181A (en) * | 2022-06-07 | 2022-07-08 | 常州云燕医疗科技有限公司 | Digital integrated operating room signal transmission system and method based on block chain |
| CN115238173A (en) * | 2022-06-30 | 2022-10-25 | 山东省玖玖医养健康产业有限公司 | Behavior analysis and medical service pushing method, equipment and medium based on big data |
Non-Patent Citations (4)
| Title |
|---|
| XIN GAO等: "Accuracy Analysis of Triage Recommendation Based on CNN, RNN and RCNN Models", 《2021 IEEE ASIA-PACIFIC CONFERENCE ON IMAGE PROCESSING, ELECTRONICS AND COMPUTERS (IPEC)》, pages 1323 - 1327 * |
| 何萍: "区域医疗专家预约云服务系统的建模与优化研究", 《中国博士学位论文全文数据库 医药卫生科技辑》, no. 03, pages 054 - 2 * |
| 张本: "基于深度学习的导诊系统的设计与实现", 《中国优秀硕士学位论文全文数据库 医药卫生科技辑》, no. 05, pages 080 - 45 * |
| 杨光华等: "实现互联网医疗信息资源精准搜索的策略初探", 《中国卫生信息管理杂志》, vol. 14, no. 06, pages 826 - 831 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116959686B (en) | 2024-02-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US5822731A (en) | Adjusting a hidden Markov model tagger for sentence fragments | |
| US5717914A (en) | Method for categorizing documents into subjects using relevance normalization for documents retrieved from an information retrieval system in response to a query | |
| WO2020213843A1 (en) | User-customized medical information provision system and operating method therefor | |
| US5873076A (en) | Architecture for processing search queries, retrieving documents identified thereby, and method for using same | |
| US9361428B2 (en) | System and method for electronically managing medical data files | |
| US7774377B2 (en) | Range definition method and system | |
| US5659742A (en) | Method for storing multi-media information in an information retrieval system | |
| US5664109A (en) | Method for extracting pre-defined data items from medical service records generated by health care providers | |
| US5737734A (en) | Query word relevance adjustment in a search of an information retrieval system | |
| US5721902A (en) | Restricted expansion of query terms using part of speech tagging | |
| US5742816A (en) | Method and apparatus for identifying textual documents and multi-mediafiles corresponding to a search topic | |
| US6385611B1 (en) | System and method for database retrieval, indexing and statistical analysis | |
| US7478049B2 (en) | Text generation and searching method and system | |
| US6208988B1 (en) | Method for identifying themes associated with a search query using metadata and for organizing documents responsive to the search query in accordance with the themes | |
| WO1996041288A1 (en) | Apparatus and method for centralized storage of heterogeneous medical records in managed health care organization | |
| CN101271489A (en) | Medicine case information management system | |
| Shapira et al. | Experimentation with an information filtering system that combines cognitive and sociological filtering integrated with user stereotypes | |
| CN112509656A (en) | Grade evaluation method and device based on medical institution, computer equipment and medium | |
| CN116959686B (en) | Medical information management system and method based on digital integration | |
| CN116934060A (en) | Hospital call intelligent queuing method and system | |
| CN117497149A (en) | Hospital management method and system based on artificial intelligence | |
| WO1997010542A1 (en) | Royalty management in an information retrieval system | |
| Vijayarajeswari et al. | An enhanced symptom clustering with profile based prescription suggestion in biomedical application | |
| WO1996041275A1 (en) | Apparatus and method for centralized storage of heterogeneous medical records in managed health care organization | |
| CN116759112B (en) | Remote consultation data management system and method based on Internet of things |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |