CN113421651B - Data intervention and self-adaptive regulation system and method for narrative nursing process - Google Patents
Data intervention and self-adaptive regulation system and method for narrative nursing process Download PDFInfo
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- 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
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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- G—PHYSICS
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
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- G—PHYSICS
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- 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
- G16H40/60—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 operation of medical equipment or devices
Abstract
The invention provides a data intervention and self-adaptive adjustment system and method in a narrative nursing process. The system comprises: the initial data input subsystem is used for inputting basic data information of the patient when the patient makes a first visit; and the data acquisition device is used for recording talking voices of both parties when the nursing staff talk with the target patient. The N data acquisition devices are divided into M groups, and the data acquisition devices in each group are communicated with an edge calculation unit and acquire feedback data from the edge calculation unit; the background centralized regulation and control terminal is used for regulating the grouping state based on the communication states of the N data acquisition devices and the edge computing unit; and the feedback data is a narrative nursing mode obtained by matching the text keywords sent by the data acquisition device with the basic data information from a pre-established narrative nursing database by the edge calculation unit. The invention also provides a corresponding data processing method applied to the narrative nursing process.
Description
Technical Field
The invention belongs to the technical field of clinical nursing, and particularly relates to a data intervention and self-adaptive regulation system and method in a narrative nursing process.
Background
Narrative therapy is a post-modern psychological treatment which has received great attention, and which breaks away from the therapeutic concept of traditionally treating humans as problems, and makes humans more autonomous and motivated by means of "story narration", "problem externalization", "thin to thick", and the like. Through the psychological treatment of narrative, not only the psychology of the principal can be grown, but also the consultant can have the functions of self-stabilization and thinking back again. The narrative therapy is a modern psychological treatment technology which is widely applied at present, has the characteristics of strong operability, remarkable effect and the like, and has higher popularization value.
Narrative care (narrativeruxing) refers to a care practice in which caregivers help patients to realize life and disease story meaning reconstruction by listening and absorbing the stories of the patients, find care points and then perform care intervention on the patients. The narrative nursing can enable the patient to fully express own emotion, complain about the pain and the demand of the heart, establish active psychological defense and be helpful for the medical treatment and disease rehabilitation.
Thomas et al developed a narrative study in 230 African-American hypertension patients, verifying that narrative care promoted patient blood pressure control (HoustonTK, allison JJ, sussman Marc, et al Culturally appropriate storytelling toimprove blood pressure: a randomized trial [ J ]. Annals of InternalMedicine,2011,154 (2): 77-84. DOI: 10.1059/0003-4819-154-2-201101180-00004); cepeta et al applied narrative care to the treatment of cancer patients, and the results showed that the cancer patients under narrative care had a higher happiness index than the control group (CepedaMS, chapmanCR, mirandaN, et al Emotionaldisclosure through patient narrative may improve pain and well-rolling: resultsof a randomized controlled trial in patients with cancer pain [ J ]. J Pain SymptomManage,2008,35 (6): 623-631); cui Wenwei and the like explore the influence of the nursing mode of the narrative medicine on the health education effect of patients with upper gastrointestinal malignant tumor combined hemorrhage, find that the disease knowledge mastery rate, the cognition attitude and the behavior well rate of an experimental group are higher than those of a control group (P < 0.05) (Cui Wenwei, chen Xianyan, bai Qixuan and the like; feng Jindong and the like find that narrative care can help breast cancer postoperative patients to develop more psychological positive treatments (Feng Jindong, li Xiaomin, wang Rui and the like: the intervention effect of narrative therapy on the modes of breast cancer postoperative chemotherapy patients [ J ]. Psychologists, 2015,21 (15): 3-5).
However, there is currently no unified narrative care method step for current clinical practice. Many clinical caregivers can only find limited guidance from the relevant cases learned in limited classroom context teaching when executing the care mode, and the guidance process is unilateral and subjective. How to objectively realize the clinical care process and popularize the clinical narrative care mode by adopting a computer means, the prior art does not give an effective solution.
Disclosure of Invention
In order to solve the technical problems, the invention provides a system and a method for data intervention and self-adaptive adjustment in a narrative nursing process. The system comprises: the initial data input subsystem is used for inputting basic data information of the patient when the patient makes a first visit; and the data acquisition device is used for recording talking voices of both parties when the nursing staff talk with the target patient. The N data acquisition devices are divided into M groups, and the data acquisition devices in each group are communicated with an edge calculation unit and acquire feedback data from the edge calculation unit; the background centralized regulation and control terminal is used for regulating the grouping state based on the communication states of the N data acquisition devices and the edge computing unit; and the feedback data is a narrative nursing mode obtained by matching the text keywords sent by the data acquisition device with the basic data information from a pre-established narrative nursing database by the edge calculation unit. The invention also provides a corresponding data processing method applied to the narrative nursing process.
In the following, the specific technical solutions of the present application will be described from three different aspects.
In a first aspect of the invention, a data intervention and adaptive adjustment system for a narrative care process is provided, the system comprising an initial data entry subsystem, N data acquisition devices worn by a plurality of caregivers, M edge computing units located in different patient gathering areas, and a background centralized control terminal.
More specifically, the data acquisition device is provided with a voice input component, a voice-to-text component, an emotion prediction model, an emotion judgment component, a text keyword recognition component and a scoring component.
The initial data input subsystem is used for inputting basic information of a patient when the patient makes a first visit, and preprocessing the basic information;
the N data acquisition devices are divided into M groups, and the data acquisition devices in each group are communicated with one edge calculation unit and acquire feedback data from the edge calculation unit;
the background centralized regulation and control terminal regulates the grouping state based on the communication states of the N data acquisition devices and the edge calculation unit;
the feedback data is a narrative nursing mode obtained by matching the text keywords sent by the data acquisition device with the basic data information from a pre-established narrative nursing database by the edge calculation unit, and the narrative nursing mode comprises a plurality of narrative nursing dialogues.
When the caregiver finishes the conversation with the target patient, determining a scoring value for the feedback data acquired at this time through the scoring component, and sending the scoring value to the edge computing unit, wherein the edge computing unit updates a recommended weight value of the narrative care conversation of the pre-established narrative care database based on the scoring value.
In a second aspect of the present invention, a data intervention and adaptive adjustment method implemented based on the data intervention and adaptive adjustment system of the first aspect is provided.
The method mainly comprises the following steps of S700-S760:
s700: inputting basic information of a patient when the patient makes a first visit, and preprocessing the basic information to obtain a first text keyword;
s710: when a nursing staff carries out conversation with a target patient, carrying out emotion prediction on the two-party conversation in real time;
s720: judging whether the emotion prediction value exceeds a preset standard value, if so, entering the next step; otherwise, returning to step S710;
s730: identifying a second text keyword in the double-side conversation, and sending the second text keyword to a corresponding edge computing unit;
s740: the nursing staff acquires feedback data sent by the edge calculating unit, and returns to execute the step S710 based on the feedback data;
s750: judging whether the conversation is ended or not, if so, prompting a nursing staff to score;
s760: and updating the recommended weight value of the feedback data based on the score.
In the above step, the step S710 performs the emotion prediction using a combination of dictionary-based and machine learning-based methods;
the step S710 further includes: and judging the position of the nursing staff or the target patient, and determining an edge computing unit corresponding to the data acquisition device worn by the nursing staff based on the position.
In the step S750, if the conversation is finished, the local storage space of the data acquisition device worn by the caregiver is cleared.
The method of the second aspect described above may be automated by computer program instructions. Through the idea of modularized programming, a module controller can be formed so as to realize standardized control after being connected with different data acquisition devices. Accordingly, in a third aspect of the present invention there is provided a microcontroller chip comprising a memory and a processor, the memory having stored thereon computer executable program instructions, the program instructions being executed by the processor to perform part or all of the steps of the method of the second aspect described above after the microcontroller chip is connected to a data acquisition device.
According to the technical scheme, based on objective patient data and real-time conversation data, the pre-established narrative nursing database resource is fully utilized, comprehensive and objective clinical narrative nursing intervention is realized, and result verification and experience popularization of a narrative nursing mode are facilitated.
Further advantages of the invention will be further elaborated in the description section of the embodiments in connection with the drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a diagram of a sub-module combination architecture of a data intervention and adaptive adjustment system for a narrative care process according to one embodiment of the invention
FIG. 2 is a schematic diagram of the internal functional components of the data acquisition device in the system of FIG. 1
FIG. 3 is a schematic diagram of data processing for implementing narrative care interventions and adjustments based on the system of FIG. 1
FIG. 4 is a flow chart of a data processing method implemented based on the system of FIG. 1 for use in a narrative care process
FIG. 5 is a schematic diagram of an emotion prediction model used in the above embodiment
Detailed Description
Referring to fig. 1, a data intervention and adaptive regulation system sub-module combination architecture diagram of a narrative care process according to one embodiment of the invention.
In fig. 1, the system includes an initial data entry subsystem, N data acquisition devices worn by multiple caregivers, M edge computing units located in different multiple patient aggregation areas, and a background centralized regulation terminal.
In various embodiments of the invention, the patient collection area may be a clinical hospital ward or a single care area, such as multiple collection areas located in an open care area.
The initial data input subsystem is used for inputting basic information of a patient when the patient makes a first visit, and preprocessing the basic information.
In fig. 1, the system further comprises a narrative care database. The narrative care database is pre-established and comprises a plurality of narrative care modes, each narrative care mode comprises a plurality of narrative care dialogs, and each narrative care dialog is provided with a recommended weight value.
The N data acquisition devices are divided into M groups, and the data acquisition devices in each group are communicated with one edge calculation unit and acquire feedback data from the edge calculation unit;
the background centralized regulation and control terminal regulates the grouping state based on the communication states of the N data acquisition devices and the edge calculation unit;
the feedback data is a narrative nursing mode obtained by matching the text keywords sent by the data acquisition device with the basic data information from a pre-established narrative nursing database by the edge calculation unit, and the narrative nursing mode comprises a plurality of narrative nursing dialogues.
More specifically, assuming that there are different first packets and second packets, if a data communication delay between a first data acquisition device in a first packet and the edge calculation unit exceeds a predetermined time value, the first data acquisition device is moved from the first packet to the second packet, and the number of first data acquisition devices in the packet is not greater than the first packet.
Thus, the feedback data can be ensured to respond in time, and the clinical real-time requirement is met.
See fig. 2, based on fig. 1.
In fig. 2, the data acquisition device includes a voice input component, a voice-to-text component, an emotion prediction model, an emotion judgment component, a text keyword recognition component, and a scoring component.
As a specific embodiment, the data acquisition device is a portable wearable device.
When the nursing staff wears the data acquisition device to carry out conversation with a target patient, the voice input assembly inputs conversation voices of the two parties in real time;
the speech-to-text component converts the conversational speech into text paragraphs, and the emotion prediction model predicts emotion change values of the target patient based on the text paragraphs;
the emotion judging component judges whether the emotion change value exceeds a preset standard value
When the emotion change value exceeds a preset standard value, the text keyword recognition component recognizes a second text keyword in the text paragraph and sends the second text keyword to an edge calculation unit corresponding to the group where the data acquisition device is located.
More specifically, the above process of the present invention can be seen in fig. 3.
In fig. 3, two data processing flows are shown in parallel.
In a first data processing flow, when the nursing staff wears the data acquisition device to talk with a target patient, the voice input component inputs talking voices of both parties in real time; the voice-to-text component converts the talking voice into a text paragraph and predicts an emotion change value of the target patient based on the text paragraph; when the emotion change value exceeds a preset standard value, the text keyword recognition component recognizes a second text keyword in the text paragraph and sends the second text keyword to an edge calculation unit corresponding to the group where the data acquisition device is located.
In the second data processing flow, the initial data input subsystem is used for inputting basic data information of a patient when the patient makes a first visit, and preprocessing the basic data information;
specifically, the preprocessing includes extracting a first text keyword related to a narrative nursing mode from the basic information, and sending the first text keyword to a local storage unit of the edge computing unit after associating the first text keyword with the identification information of the patient.
Although not shown, in the above embodiment, the data acquisition device further includes a scoring component;
when the caregiver ends the conversation with the target patient, determining a scoring value for the feedback data acquired this time by the scoring component and sending the scoring value to the edge computing unit, which updates a recommended weight value of the narrative care session of the pre-established narrative care database based on the scoring value.
Based on the embodiments of fig. 1-3, fig. 4 presents a corresponding data processing method applied to the narrative care process.
In fig. 4, a method of data intervention and adaptation of a narrative care process is disclosed, said method comprising the steps of:
s700: inputting basic information of a patient when the patient makes a first visit, and preprocessing the basic information to obtain a first text keyword;
s710: when a nursing staff carries out conversation with a target patient, carrying out emotion prediction on the two-party conversation in real time;
s720: judging whether the emotion prediction value exceeds a preset standard value, if so, entering the next step; otherwise, returning to step S710;
s730: identifying a second text keyword in the double-side conversation, and sending the second text keyword to a corresponding edge computing unit;
s740: the nursing staff acquires feedback data sent by the edge calculating unit, and returns to execute the step S710 based on the feedback data;
s750: judging whether the conversation is ended or not, if so, prompting a nursing staff to score;
s760: and updating the recommended weight value of the feedback data based on the score.
As a further preferred aspect, the step S710 further includes: and judging the position of the nursing staff or the target patient, and determining an edge computing unit corresponding to the data acquisition device worn by the nursing staff based on the position.
And, in the step S750, if the conversation is finished, the local storage space of the data acquisition device worn by the nursing staff is cleared.
Further, referring to fig. 5, the step S710 performs the emotion prediction using a combination of dictionary-based and machine learning-based methods.
The emotion prediction referred to in the above embodiments of the present invention, also called text emotion analysis, is a piece of content involved in natural language processing and text mining. In short, we judge the emotion bias of a text and comments through an algorithm, so that the subjective emotion of the original person expressing the text is quickly known.
In particular, to clinical practice, the emotion that may occur is: happiness, excitement, agitation, violence, tension, confusion, etc.
The dictionary-based text emotion analysis principle mentioned in the present embodiment can be briefly summarized as follows:
first, there is a dictionary that is manually marked. Each of the dictionaries corresponds to either the negative or positive label.
Of course, to be more suitable for the aforementioned clinical applications, the labels herein should be embodied, for example, further subdivided into happy, exciting, violent, strenuous, puzzled.
After the dictionary is provided, text emotion analysis can be started, and text matching can be performed, which is not repeated.
However, the simple dictionary comparison method is not high in accuracy, and therefore, a machine learning method is also required to be combined.
Fig. 5 shows a basic schematic of Word2Vec applied to and learned by a bag-of-Word neural network model.
Word2Vec, so the term means converting a sentence into a vector, i.e., a Word vector. Word2Vec was originally developed by Google in 2013 and is a Word vector conversion model consisting of shallow neural networks.
Word2Vec is typically input as a massive corpus and output as a vector space. Word2Vec is characterized in that each Word in the corpus corresponds to a vector in vector space, and the words having context are mapped to distances in vector space that are closer.
The main structure of Word2Vec is a combination of CBOW (Continuous Bag-of-Words Model) Model and Skip-gram (Continuous Skip-gram) Model. In brief, both are probabilities of a word appearing that are intended to be obtained by context.
The CBOW model predicts the current word by the context of one word (each N words). The Skip-gram, in contrast, predicts its context with one word, resulting in many samples of the current word context and thus available for larger datasets.
According to the technical scheme, based on objective patient data and real-time conversation data, the pre-established narrative nursing database resource is fully utilized, comprehensive and objective clinical narrative nursing intervention is realized, and result verification and experience popularization of a narrative nursing mode are facilitated.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (3)
1. A data intervention and self-adaptive adjustment system for a narrative nursing process, the system comprises an initial data input subsystem, N data acquisition devices worn by a plurality of nursing staff, M edge calculation units positioned in different patient gathering areas and a background centralized regulation terminal;
the method is characterized in that:
the initial data input subsystem is used for inputting basic information of a patient when the patient makes a first visit, and preprocessing the basic information;
the data acquisition device is portable wearable equipment, and the portable wearable equipment is provided with a voice input component, a voice-to-text component and a text keyword recognition component;
the N data acquisition devices are divided into M groups, and the data acquisition devices in each group are communicated with one edge calculation unit and acquire feedback data from the edge calculation unit;
the background centralized regulation and control terminal regulates the grouping state based on the communication states of the N data acquisition devices and the edge calculation unit;
the feedback data is a narrative nursing mode obtained by matching the text keywords sent by the data acquisition device with the basic data information from a pre-established narrative nursing database by the edge calculation unit, and the narrative nursing mode comprises a plurality of narrative nursing dialogues;
the initial data input subsystem preprocesses the basic information, and specifically comprises the following steps:
extracting a first text keyword related to a narrative nursing mode from the basic information, and sending the first text keyword to a local storage unit of the edge calculation unit after being associated with the identification information of the patient;
when the nursing staff wears the data acquisition device to carry out conversation with a target patient, the voice input assembly inputs conversation voices of the two parties in real time;
the voice-to-text component converts the talking voice into a text paragraph and predicts an emotion change value of the target patient based on the text paragraph;
when the emotion change value exceeds a preset standard value, the text keyword recognition component recognizes a second text keyword in the text paragraph and sends the second text keyword to an edge calculation unit corresponding to a group where the data acquisition device is located;
the data acquisition device further comprises a scoring component;
when the caregiver ends the conversation with the target patient, determining a scoring value for the feedback data acquired this time by the scoring component and sending the scoring value to the edge computing unit, which updates a recommended weight value of the narrative care session of the pre-established narrative care database based on the scoring value.
2. A data intervention and adaptive adjustment system for a narrative care process as set forth in claim 1, wherein:
the background centralized regulation and control terminal adjusts the grouping state based on the communication states of the N data acquisition devices and the M edge computing units, and specifically comprises the following steps:
and if the data communication delay between the first data acquisition device in the first group and the edge calculation unit exceeds a preset time value, moving the first data acquisition device from the first group to a second group, wherein the number of the first data acquisition devices in the group is not larger than that of the first group.
3. A data intervention and adaptive adjustment system for a narrative care process according to any one of claims 1-2, wherein:
the pre-established narrative care database comprises a plurality of narrative care modes, each narrative care mode comprises a plurality of narrative care dialogues, and each narrative care dialogue is provided with a recommended weight value.
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| US20190050774A1 (en) * | 2017-08-08 | 2019-02-14 | General Electric Company | Methods and apparatus to enhance emotional intelligence using digital technology |
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