CN112599228A - Intelligent shift switching method and system for medical institution - Google Patents
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Abstract
The invention provides an intelligent shift switching method and an intelligent shift switching system for a medical institution, wherein the method comprises the following steps: s1: acquiring medical data of each database of a medical institution; s2: performing data analysis processing on the acquired medical data to generate shift data information; s3: storing the shift data information in a preset data center; s4: when a shift information acquisition request is received, acquiring the shift data information to be displayed matched with the shift information acquisition request from the data center, processing the shift data information to be displayed and outputting the processed shift data information to the data display equipment for displaying. The intelligent shift-switching system of the medical institution comprises an acquisition module, a processing module, a storage module, a confirmation module and an output module. According to the technical scheme, the data of the medical institution are collected and integrated, the data are extracted and highlighted, automatic collection and statistics are carried out, the omission of manual recording and statistics is reduced, and the efficiency and accuracy of shift change are greatly accelerated.
Description
Technical Field
The invention relates to the field of medical data processing, in particular to an intelligent shift switching method and system for a medical institution.
Background
Currently, the work shift refers to the work shift that a person on duty (such as a doctor or a nurse) in a hospital will send to the next colleague on duty in a replacement after the work shift is completed, so that the next colleague on duty can pay attention to the work shift, and the work shift can be completed better. Good shift change has special significance for maintaining normal operation order and ensuring good medical working quality and link quality.
However, the current shift change excessively depends on manual work, the shift change is carried out by adopting modes such as dictation or handwriting, and shift change records are difficult to standardize and unify. Moreover, when the personnel shift, the personnel usually work for a long time, which is just a fatigue period, and the data arrangement by means of memory is easy to miss, and the phenomenon of listening wrongly and missing is easy to happen to the personnel who shift.
One important reason for these phenomena is that manual interaction of shift data information has many problems: 1. the system has the advantages that the system is dispersive in data and multiple in data, department early shift data are dispersed in systems such as a nursing system and an electronic medical record for hospitalization, and medical data of patients are multiple every day; 2. the data is not extracted, the key points are not highlighted, the data is not effectively extracted and classified, and the key points are not extracted when the shift is handed over; 3. the information of the shift is classified unevenly and is not intuitive, and listeners are easy to miss.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an intelligent shift switching method and system for a medical institution, which realize automatic data analysis and aggregation, emphatically display shift switching information and improve the shift switching efficiency and accuracy. The specific technical scheme is as follows:
an intelligent shift-switching method for a medical institution, comprising the following steps:
s1: acquiring medical data of each database of a medical institution;
s2: performing data analysis processing on the acquired medical data to generate shift data information;
s3: storing the shift data information in a preset data center;
s4: when a shift information acquisition request is received, acquiring the shift data information to be displayed matched with the shift information acquisition request from the data center, processing the shift data information to be displayed and outputting the processed shift data information to the data display equipment for displaying.
In a particular embodiment, each of the medical institution databases comprises a combination of one or more of: hospital management information system database, electronic case database, nursing system database.
In a specific embodiment, the content of the medical data includes one or a combination of the following: diagnostic data, care data, surgical data.
In a specific embodiment, the type of the medical data includes one or a combination of the following: numerical data, text data, and image data.
In one embodiment, the data analysis process of step S2 is to classify and aggregate the acquired medical data mainly based on the patient.
In a specific embodiment, the data analysis processing of step S2 further includes performing semantic analysis processing on the acquired medical data, and extracting medical feature data, and the specific steps include:
s21, defining top-level concepts, and associating the top-level concepts to form a medical knowledge map pattern diagram; the top layer concept comprises one or a combination of the following: symptoms, diseases, drugs, departments, and examinations;
s22: extracting entities, attributes and attribute values based on the medical knowledge map pattern map of S21, and performing entity alignment, entity type alignment and entity attribute alignment on the extraction result to construct a medical knowledge map;
s23: the medical characteristic data is obtained by identifying the symptom in the acquired medical data based on the medical knowledge map of S23, analyzing the constituent components of the identified symptom, and performing normalization processing.
More specifically:
the concrete operation of the entity alignment is as follows: establishing a synonymy relation between entities;
the entity type alignment operation is to determine the entity type by a method based on voting and data source priority, and specifically includes: taking the result with the highest ticket number as the final entity type; when a plurality of types obtain the highest ticket number, determining the entity type according to the data source with the maximum weight in the highest ticket number;
the entity attribute alignment operation is as follows: and establishing a mapping relation between the extracted entity attributes and the attributes in the pattern graph.
In a specific embodiment, the type of the shift information acquisition request is a nursing shift information acquisition request, a medical shift information acquisition request or a surgical shift information acquisition request.
In a specific embodiment, the step S4 of "processing the shift data information to be displayed" specifically includes: and extracting data matched with the shift information acquisition request, classifying the data according to a preset module, comparing the data with a preset standard, and performing exception marking on the data which does not meet the preset standard.
In one specific embodiment, step S4 highlights the medical characteristic data and the data of the abnormality label when displayed.
The invention also provides an intelligent shift-switching system for a medical institution, which is used for realizing the method and comprises the following steps:
the acquisition module is used for acquiring medical data of each database of the medical institution;
the processing module is used for carrying out data analysis processing on the acquired medical data to generate shift data information;
the storage module is used for storing the shift data information in a preset data center;
the confirmation module is used for acquiring the shift information matched with the shift information acquisition request from the data center when the shift information acquisition request is received;
and the output module is used for processing the acquired shift data information and outputting the processed shift data information to the data display equipment for display.
The invention has at least the following beneficial effects:
(1) according to the technical scheme, data scattered in each management system in a medical institution are collected and integrated, data extraction and highlight display are carried out by combining information handover requirements of the handover, automatic collection statistics are carried out, careless omission of manual recording and statistics is reduced, and efficiency and accuracy of the handover are greatly improved.
(2) Through semantic analysis of the recorded texts of doctors and nurses and combination of medical knowledge maps, automatic classification, labeling and sorting of text information data are achieved, and the text information data are displayed through visual charts, so that various conditions in the medical care process are more visually displayed.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a general flow chart of an intelligent shift-switching method of a medical institution in an embodiment;
FIG. 2 is a flow diagram of the semantic analysis process in an embodiment;
FIG. 3 is an exemplary diagram of a semantic analysis flow in an embodiment;
FIG. 4 is a medical knowledge map pattern diagram in an embodiment.
Detailed Description
The present invention will be further described with reference to the following embodiments. The drawings are only for purposes of illustration and are not to be construed as limiting the patent.
At present, in a medical institution, various information data of medical treatment, nursing and operation are dispersed in each management system, connection is not established among the data, and data statistics and recording are needed to be carried out manually during shift change.
The medical institution intelligent shift switching method provided by the invention comprises three processes of data collection, data analysis and processing and data visual display, so that the intelligent analysis and display of data are realized, the shift switching efficiency is greatly accelerated, and the complete information transfer is ensured.
Examples
As shown in fig. 1, the present embodiment provides an intelligent shift switching method for a medical institution, including:
s1: medical data of each database of the medical institution is acquired.
Wherein each of the healthcare facility databases comprises a combination of one or more of: hospital management information system database, electronic case database, nursing system database.
The Hospital management Information System (HIS) is an Information System that comprehensively manages the flows of people, physical distribution, and financial resources of a Hospital and its various departments by using modern means such as computer software and hardware technology, network communication technology, and collects, stores, processes, extracts, transmits, summarizes, and processes data generated at various stages of medical activities to generate various Information, thereby providing comprehensive and automatic management and various services for the overall operation of the Hospital.
The electronic medical Record (CPR) is a digitized medical Record stored, managed, transmitted and reproduced by electronic devices (computers, health cards, etc.) to replace the handwritten paper medical Record, and the content includes all information of the paper medical Record.
The nursing system is an information system which utilizes information technology, computer technology and network communication technology to collect, store, process, transmit and inquire nursing management and service technical information so as to improve the nursing management quality, is an important subsystem of a hospital information system, and comprises nursing workload, nursing quality control, integral nursing, nurse technical files, nursing teaching and scientific research, nursing article supply, medical advice processing, error analysis, nurse manpower arrangement and other nursing information.
S2: and performing data analysis processing on the acquired medical data to generate shift data information.
Specifically, the content of the medical data includes one or a combination of several of the following: diagnostic data, care data, surgical data. The types of the medical data comprise one or a combination of several of the following: numerical data, text data, and image data.
The data analysis processing in step S2 is specifically to classify and aggregate the acquired medical data mainly based on the patient.
Specifically, the data analysis processing in step S2 further includes performing semantic analysis processing on the acquired medical data, and extracting medical feature data.
The clinical data taking the electronic medical record as the core records the disease, diagnosis and treatment information of patients, which is a main data source of clinical scientific research, however, the current hospital electronic medical record system records five cases of complaint, various medical documents and the like, doctors use natural texts, and the recorded data of the five cases of complaint, various medical documents and the like can not be directly used for analyzing and processing the data basically. The critical medical science research information platform needs to perform structured processing on symptoms, allergy history, vaccination conditions, infectious diseases, smoking, drinking conditions and the like, so that data can be researched and used by the critical medical science research information platform.
The method for converting the natural text language of the electronic medical record into the structured data can establish a standardized medical health knowledge map containing diseases, symptoms and the like, then associate the structured text with a knowledge base through text mining and entity linking means, and semantically analyze a flow chart as shown in fig. 3.
The method comprises the following specific steps:
s21, defining top-level concepts, and associating the top-level concepts to form a medical knowledge map pattern diagram; the top layer concept comprises one or a combination of the following: symptoms, diseases, drugs, departments, and examinations; as shown in fig. 4, specifically:
each top-level concept can be subdivided according to specific technologies, for example, the concept of 'symptom' is subdivided into two sub-concepts of 'Chinese medicine symptom' and 'western medicine symptom', and the concept of 'medicine' is subdivided into two sub-concepts of 'traditional Chinese medicine' and 'western medicine'.
The top-level concepts are associated through attributes, such as attributes of 'symptom-related diseases', 'disease-related departments', and the like. In the pattern diagram, each concept gives examples, which form a scenario in clinical practice: a patient with head headache simultaneously suffers from sneezing, aversion to cold and other symptoms, and needs to go to the internal medicine for diagnosis and carry out blood examination, temperature measurement and other related examinations. The patient is finally diagnosed as 'summer cold' with 'tonsillitis', and is advised to take western medicine 'aspirin' and traditional Chinese medicine 'Xiaochaihu'.
S22: extracting entities, attributes and attribute values based on the medical knowledge map pattern map of S21, and performing entity alignment, entity type alignment and entity attribute alignment on the extraction result to construct a medical knowledge map; the method comprises the following steps of knowledge fusion, specifically:
entity alignment refers to establishing a synonymous relationship between entities, and the specific operation refers to adding the synonymous relationship extracted from the medical health website and the Chinese encyclopedia website into the medical knowledge map.
The entity type alignment solves the problem of data conflict of one entity corresponding to a plurality of mutually exclusive types. And determining the entity type by adopting a method based on voting and the priority of the data source. The overall thought is as follows: the result with the highest number of votes is used as the final type of the entity; and when a plurality of types are present to obtain the highest ticket number, determining a final result according to the data source with the maximum weight in the highest ticket number.
Entity attribute alignment mainly establishes a mapping relation from the extracted entity attributes to attributes in the pattern diagram. For the medical health website, the same type of entities contain the same entity attributes, and the mapping rule of the entity attributes to the pattern graph is manually established. For example, the 3 attributes of "joint pain" extracted from the "information box" are a symptom part, a related department, and a related disease, and are mapped to the symptom-related part, the symptom-related department, and the symptom-related disease in the pattern diagram, respectively.
S23: the medical characteristic data is obtained by identifying the symptom in the acquired medical data based on the medical knowledge map of S23, analyzing the constituent components of the identified symptom, and performing normalization processing. Specifically, the method comprises the following steps:
first, symptoms in the text need to be identified, and the identification method is referred to the extraction method of the medical entity.
The identified symptoms then need to be subjected to a compositional analysis. The Chinese symptom can be divided into the following 16 components: atomic symptoms, conjunctions, negatives, existential words, degree words, developmental words, enabling words, disabling words, action words, situation qualifiers, orientation words, part words, core words, sensory words, characteristic words, modifiers, as shown in the following table:
the Chinese symptom is subjected to composition analysis, similar to Chinese word segmentation and part-of-speech tagging, the Chinese symptom can be regarded as a sequence tagging task, and the method is realized by using a Conditional Random Field (CRF) or a long-term memory (LSTM) network + CRF and the like. After the constituent components of each symptom are obtained, normalization treatment can be carried out on the constituent components, such as atomic symptoms of pain, pain and pain, which are unified into pain; for the terms of degree and negatives, "none" can be quantified as 0, "slight" as 0.2, "some" as 0.4, "clear" as 0.6, "broad" as 0.8, and "extreme" as 1.
In addition, the extracted symptoms can be softly linked with symptom entities in the knowledge base according to the separated symptom composition components, so that the standardization of the symptoms can be realized.
S3: storing the shift data information in a preset data center;
s4: when a shift information acquisition request is received, acquiring the shift data information to be displayed matched with the shift information acquisition request from the data center, processing the shift data information to be displayed and outputting the processed shift data information to the data display equipment for displaying.
Specifically, the type of the shift information acquisition request is a nursing shift information acquisition request, a medical shift information acquisition request or an operation shift information acquisition request.
Specifically, the shift data information to be displayed is matched according to the type of the shift information acquisition request.
Specifically, the step S4 of "processing the shift data information to be displayed" includes: and extracting data matched with the shift information acquisition request, classifying the data according to a preset module, comparing the data with a preset standard, and performing exception marking on the data which does not meet the preset standard.
Specifically, when the display is performed in step S4, the medical characteristic data and the data of the abnormality label are highlighted.
For example, when the shift information acquisition request is a nursing shift information acquisition request, the displayed data includes data corresponding to the patients for whom nurses are responsible, such as the number of patients, the symptoms of the corresponding patients, and the comparison of each patient with various parameters of the previous day. The displayed data of the patient only displays abnormal data so as to reduce the display of the data and focus more on the abnormal data; the items of the data display for a particular patient may invoke the data display according to items selected by the patient's attending physician or experienced expert based on his or her own experience, and not all of the data need be displayed.
If the shift information acquisition request is a medical shift information acquisition request, the displayed data includes a unique part of the data in addition to a part of the characteristics of nursing shift, specifically, curves including the treatment process of the patient in charge and corresponding parameters of the body are all displayed according to a time line, for example, time point 1, what medicine is injected, how the parameters of the body are changed, and the like, and corresponding abnormal conditions. For example, a piece of natural text information in the diagnosis and treatment information of the patient recorded by the doctor on duty is converted into four categories of symptoms, signs, examinations and measures, and is displayed on the right side according to the time events in a classified manner, so that abnormal information (abnormal information is highlighted in color) is displayed very intuitively, and the person on duty is given more attention to the abnormal information.
If the shift information acquisition request is a surgical shift information acquisition request, the displayed data includes various data in the surgical treatment process, including text data, image data, and the like, and abnormal data therein is displayed, and the displayed data can be selected according to the experience of the surgeon.
The invention also provides an intelligent shift-switching system for a medical institution, which is used for realizing the method and comprises the following steps:
the acquisition module is used for acquiring medical data of each database of the medical institution;
the processing module is used for carrying out data analysis processing on the acquired medical data to generate shift data information;
the storage module is used for storing the shift data information in a preset data center;
the confirmation module is used for acquiring the shift information matched with the shift information acquisition request from the data center when the shift information acquisition request is received;
and the output module is used for processing the acquired shift data information and outputting the processed shift data information to the data display equipment for display.
The invention has at least the following beneficial effects:
(3) according to the technical scheme, data scattered in each management system in a medical institution are collected and integrated, data extraction and highlight display are carried out by combining information handover requirements of the handover, automatic collection statistics are carried out, careless omission of manual recording and statistics is reduced, and efficiency and accuracy of the handover are greatly improved.
(4) Through semantic analysis of the recorded texts of doctors and nurses and combination of medical knowledge maps, automatic classification, labeling and sorting of text information data are achieved, and the text information data are displayed through visual charts, so that various conditions in the medical care process are more visually displayed.
The above disclosure is only a few specific implementation scenarios of the present invention, however, the present invention is not limited thereto, and any variations that can be made by those skilled in the art are intended to fall within the scope of the present invention.
Claims (10)
1. An intelligent shift switching method for a medical institution is characterized by comprising the following steps:
s1: acquiring medical data of each database of a medical institution;
s2: performing data analysis processing on the acquired medical data to generate shift data information;
s3: storing the shift data information in a preset data center;
s4: when a shift information acquisition request is received, acquiring the shift data information to be displayed matched with the shift information acquisition request from the data center, processing the shift data information to be displayed and outputting the processed shift data information to the data display equipment for displaying.
2. The intelligent shift-handing method for medical institutions according to claim 1, wherein each database of the medical institutions comprises a combination of one or more of the following: hospital management information system database, electronic case database, nursing system database.
3. The intelligent shift-handing method for medical institution of claim 1, wherein the content of the medical data comprises one or more of the following: diagnostic data, care data, surgical data; the types of the medical data comprise one or a combination of several of the following: numerical data, text data, and image data.
4. The intelligent shift-handing method for medical institution as claimed in claim 1, wherein the data analysis process of step S2 is specifically to classify and aggregate the acquired medical data mainly for patients.
5. The intelligent shift-switching method for medical institutions according to claim 4, wherein the data analysis processing of step S2 further comprises performing semantic analysis processing on the acquired medical data and extracting medical feature data, and the specific steps include:
s21, defining top-level concepts, and associating the top-level concepts to form a medical knowledge map pattern diagram; the top layer concept comprises one or a combination of the following: symptoms, diseases, drugs, departments, and examinations;
s22: extracting entities, attributes and attribute values based on the medical knowledge map pattern map of S21, and performing entity alignment, entity type alignment and entity attribute alignment on the extraction result to construct a medical knowledge map;
s23: the medical characteristic data is obtained by identifying the symptom in the acquired medical data based on the medical knowledge map of S23, analyzing the constituent components of the identified symptom, and performing normalization processing.
6. The intelligent shift-handing method for medical institutions according to claim 5, wherein the specific operations of the entity alignment are as follows: establishing a synonymy relation between entities;
the entity type alignment operation is to determine the entity type by a method based on voting and data source priority, and specifically includes: taking the result with the highest ticket number as the final entity type; when a plurality of types obtain the highest ticket number, determining the entity type according to the data source with the maximum weight in the highest ticket number;
the entity attribute alignment operation is as follows: and establishing a mapping relation between the extracted entity attributes and the attributes in the pattern graph.
7. The medical institution intelligent shift-changing method as claimed in claim 6, wherein the type of the shift-changing information acquisition request is a nursing shift-changing information acquisition request, a medical shift-changing information acquisition request or a surgical shift-changing information acquisition request.
8. The intelligent shift handing method for medical institutions according to claim 1 or 7, wherein the step S4 of processing the shift handing data information to be displayed specifically comprises the following steps: and extracting data matched with the shift information acquisition request, classifying the data according to a preset module, comparing the data with a preset standard, and performing exception marking on the data which does not meet the preset standard.
9. The intelligent shift handing method for medical institution of claim 8, wherein in the step of displaying at step S4, the medical characteristic data and the abnormal labeled data are highlighted.
10. An intelligent shift-changing system for medical institution, which is used for implementing the intelligent shift-changing method for medical institution according to any one of claims 1-8, and which comprises:
the acquisition module is used for acquiring medical data of each database of the medical institution;
the processing module is used for carrying out data analysis processing on the acquired medical data to generate shift data information;
the storage module is used for storing the shift data information in a preset data center;
the confirmation module is used for acquiring the shift information matched with the shift information acquisition request from the data center when the shift information acquisition request is received;
and the output module is used for processing the acquired shift data information and outputting the processed shift data information to the data display equipment for display.
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| CN115295136A (en) * | 2022-10-08 | 2022-11-04 | 西安巴斯光年软件科技有限公司 | Intelligent nursing shift-switching method and system based on human-computer interaction |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202084037U (en) * | 2011-01-30 | 2011-12-21 | 广西医科大学第一附属医院 | Multimedia information management system supporting displaying for patient in projecting way |
| CN105809599A (en) * | 2016-03-21 | 2016-07-27 | 中国人民解放军总医院 | Emergency medical information system |
| CN111145889A (en) * | 2020-01-20 | 2020-05-12 | 南京鼓楼医院 | Intelligent nursing shift-switching system based on clinical decision support system |
-
2020
- 2020-12-16 CN CN202011492938.1A patent/CN112599228A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202084037U (en) * | 2011-01-30 | 2011-12-21 | 广西医科大学第一附属医院 | Multimedia information management system supporting displaying for patient in projecting way |
| CN105809599A (en) * | 2016-03-21 | 2016-07-27 | 中国人民解放军总医院 | Emergency medical information system |
| CN111145889A (en) * | 2020-01-20 | 2020-05-12 | 南京鼓楼医院 | Intelligent nursing shift-switching system based on clinical decision support system |
Non-Patent Citations (1)
| Title |
|---|
| 唐子惠: "《医学人工智能导论》", 上海科学技术出版社 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115295136A (en) * | 2022-10-08 | 2022-11-04 | 西安巴斯光年软件科技有限公司 | Intelligent nursing shift-switching method and system based on human-computer interaction |
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