CN110750614A - Hospital intelligent service evaluation method, system, equipment and storage medium - Google Patents

Hospital intelligent service evaluation method, system, equipment and storage medium Download PDF

Info

Publication number
CN110750614A
CN110750614A CN201910681173.7A CN201910681173A CN110750614A CN 110750614 A CN110750614 A CN 110750614A CN 201910681173 A CN201910681173 A CN 201910681173A CN 110750614 A CN110750614 A CN 110750614A
Authority
CN
China
Prior art keywords
questionnaire
target
question
patient
grade
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
Application number
CN201910681173.7A
Other languages
Chinese (zh)
Other versions
CN110750614B (en
Inventor
范春
徐一涵
徐安琪
李娅
韩娇娇
王涛
刘宁
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Wei Ning Health Science And Technology Group Ltd By Share Ltd
Original Assignee
Wei Ning Health Science And Technology Group Ltd By Share Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Wei Ning Health Science And Technology Group Ltd By Share Ltd filed Critical Wei Ning Health Science And Technology Group Ltd By Share Ltd
Priority to CN201910681173.7A priority Critical patent/CN110750614B/en
Publication of CN110750614A publication Critical patent/CN110750614A/en
Application granted granted Critical
Publication of CN110750614B publication Critical patent/CN110750614B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/31—Indexing; Data structures therefor; Storage structures
    • G06F16/316—Indexing structures
    • 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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • 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
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/20—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Theoretical Computer Science (AREA)
  • General Business, Economics & Management (AREA)
  • Health & Medical Sciences (AREA)
  • Strategic Management (AREA)
  • Epidemiology (AREA)
  • Primary Health Care (AREA)
  • Development Economics (AREA)
  • Educational Administration (AREA)
  • Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Public Health (AREA)
  • Medical Informatics (AREA)
  • General Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Tourism & Hospitality (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Marketing (AREA)
  • Game Theory and Decision Science (AREA)
  • Biomedical Technology (AREA)
  • Software Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Medical Treatment And Welfare Office Work (AREA)

Abstract

The invention discloses an evaluation method, a system, equipment and a storage medium for intelligent service of a hospital, which are characterized in that the evaluation method comprises the following steps: presetting a patient investigation question bank; presetting a questionnaire grade index table; acquiring the current evaluation grade of a hospital to be evaluated; determining a target questionnaire grade corresponding to a to-be-evaluated hospital, at least one target questionnaire question grade corresponding to the target questionnaire grade and the number of target questionnaire questions corresponding to each target questionnaire question grade according to the current evaluation grade and the questionnaire grade index table, and extracting the current questionnaire questions from a questionnaire library to generate questionnaires; generating a questionnaire result corresponding to the questionnaire; and (4) evaluating the intelligent service of the hospital according to all questionnaire results in a preset time of the hospital to be evaluated. The invention introduces the evaluation of the intelligent service of the hospital participated by the patient, and gives different categories of problems to the patients with different diagnosis and treatment items, so that the final evaluation of the intelligent service of the hospital is more comprehensive, objective and fair.

Description

Hospital intelligent service evaluation method, system, equipment and storage medium
Technical Field
The invention belongs to the field of evaluation of intelligent medical treatment, and particularly relates to an evaluation method, system, equipment and storage medium for intelligent service of a hospital.
Background
The intelligent service of the hospital is the important content of intelligent hospital construction, namely, the medical service process is more convenient and efficient by applying the information technology, the medical experience of patients is improved, and the information interconnection and sharing of the patients are enhanced. At present, the 'evaluation standard system (trial implementation) of hospital intelligent service grading' (national medical office letter [ 2019 ] 236) issued by China divides the hospital intelligent service into 0-5 grades so as to guide the scientific and normative development of the construction of intelligent hospitals, promote the intelligent and informatization means of the hospitals, improve the medical treatment and efficiency and improve the intelligent level of the hospital service. In the existing evaluation method for the intelligent service of the hospital, the evaluation group only performs unilateral evidence obtaining on the indexes of the hospital service flow, application function, data display, data sharing and the like aiming at the hospital, the hospital serves the patients, and in the evaluation of the intelligent service taking the patients as the center, the participation of the patients is lacked, so that the evaluation result is not comprehensive, objective and fair.
Disclosure of Invention
The invention aims to overcome the defects that the evaluation result of the intelligent service of the hospital is not comprehensive, objective and fair in the prior art, and provides an evaluation method, system, equipment and storage medium for the intelligent service of the hospital.
The invention solves the technical problems through the following technical scheme:
an evaluation method of intelligent service of a hospital, comprising:
s10, presetting a patient survey question bank, wherein the patient survey question bank stores multi-level questionnaire questions corresponding to the content of the hospital intelligent service;
s20, presetting a questionnaire grade index table, wherein the questionnaire grade index table stores the corresponding relation between review grades and questionnaire grades, and also stores questionnaire subject grades contained in each questionnaire grade and questionnaire subject numbers corresponding to the questionnaire subjects of each grade;
s30, acquiring the current evaluation grade of the hospital to be evaluated;
s40, determining a target questionnaire grade corresponding to the hospital to be evaluated, at least one target questionnaire question grade corresponding to the target questionnaire grade and the number of target questionnaire questions corresponding to each target questionnaire question grade according to the current review grade and the questionnaire grade index table;
s50, extracting current questionnaire questions from the questionnaire database according to the target questionnaire question levels and the target questionnaire question quantity to generate questionnaires;
s60, generating a questionnaire result corresponding to the questionnaire;
and S70, evaluating the intelligent service of the hospital according to all questionnaire results in a preset time of the hospital to be evaluated.
Preferably, the patient question bank includes a plurality of categories of questionnaire questions, and before step S40, the evaluation method further includes:
s31, presetting a patient category library of the hospital to be evaluated, wherein the patient category library stores the corresponding relation between diagnosis and treatment items of patients and patient categories;
the questionnaire level index table further stores a corresponding relationship between the patient category and the questionnaire subject category, and step S40 specifically includes:
s41, determining a target questionnaire grade corresponding to the to-be-evaluated hospital, at least one target questionnaire subject grade corresponding to different patient classes under the target questionnaire grade, at least one target questionnaire subject grade corresponding to each target questionnaire subject grade and the number of target questionnaire subjects corresponding to each target questionnaire subject grade according to the patient classes and the current review grade;
step S50 specifically includes:
s51, extracting multiple groups of current questionnaire questions from the questionnaire database according to the patient categories, the target questionnaire question levels and the target questionnaire question quantity to generate multiple questionnaires, wherein each questionnaire corresponds to each patient category one to one;
step S60 specifically includes:
s61, acquiring the current diagnosis and treatment item of a current patient to be examined and treated by the hospital to be examined and determining the current patient category of the current patient;
s62, extracting a questionnaire corresponding to the current patient according to the current patient category;
and S63, generating a questionnaire result corresponding to the questionnaire of the current patient.
Preferably, after step S10, the evaluation method further includes:
s11, giving weight to each questionnaire question in the patient questionnaire question bank based on an expert scoring method;
step S51 specifically includes:
s511, determining a mapping numerical range corresponding to the sequence number of each questionnaire subject in each target questionnaire subject level according to the weight of each questionnaire subject in each target questionnaire subject level under each target questionnaire subject type for each patient type;
s512, respectively obtaining target numerical values of the number of the questions of the target questionnaire based on a random number function;
s513, matching each target value with the mapping value range, and determining the sequence number of the target questionnaire question corresponding to each target value;
and S514, extracting the current questionnaire questions from the questionnaire question library according to the sequence numbers of all the target questionnaire questions.
Preferably, the step S511 determines the mapping value range by the following formula, which specifically includes:
Lpfor the mapping numerical range of the p-th questionnaire question in the nth target questionnaire question level under the mth target questionnaire question category,
Figure RE-GDA0002190794790000032
total number of questionnaire questions of nth target questionnaire question level under mth target questionnaire question category, WkThe weight of the kth questionnaire topic in the nth target questionnaire topic level under the mth target questionnaire topic category;
in step S512, the target value is determined by the following formula, which specifically includes:
Qi＝Rand(1，X)，i＝[1，I]，
Figure RE-GDA0002190794790000033
Qithe target value is the ith target value, Rand (1, X) is a random number function, X is the upper limit value of the random number function, and I is the number of the target questionnaires under the nth target questionnaire topic class under the mth target questionnaire topic class.
Preferably, after step S11, the evaluation method further includes:
s12, presetting a maximum weight value;
s13, presetting the questionnaire questions with the weight value of the maximum weight value as the questionnaire questions to be selected;
before step S511, the evaluation method further includes:
s5101, presetting a first number of the questionnaire questions required to be selected in the target questionnaire questions under each target questionnaire question class;
s5102, randomly extracting a first number of the requisite questionnaire questions from all the requisite questionnaire questions at each target questionnaire question level under each target questionnaire question category in the questionnaire library as the current questionnaire questions;
s5103, determining a second quantity of the current questionnaire questions to be extracted according to the first quantity;
in step S512, a second number of target values are respectively obtained based on the random number function.
An evaluation system of hospital intelligent service comprises an item bank presetting module, an index table presetting module, a hospital evaluation grade obtaining module, a questionnaire item determining module, an extracting module, a questionnaire result generating module and an evaluation module;
the question bank presetting module is used for presetting a patient survey question bank, and the patient survey question bank stores multi-level questionnaire questions corresponding to the content of the intelligent service of the hospital;
the index table presetting module is used for presetting a questionnaire grade index table, the questionnaire grade index table stores the corresponding relation between review grades and questionnaire grades, and also stores the questionnaire question grades contained in each questionnaire grade and the questionnaire question quantity corresponding to each questionnaire question grade;
the hospital evaluation grade acquisition module is used for acquiring the current evaluation grade of the hospital to be evaluated;
the questionnaire question determining module is used for determining a target questionnaire grade corresponding to the hospital to be evaluated, at least one target questionnaire question grade corresponding to the target questionnaire grade and the number of target questionnaire questions corresponding to each target questionnaire question grade according to the current review grade and the questionnaire grade index table;
the extraction module is used for extracting the current questionnaire questions from the questionnaire question library according to the target questionnaire question levels and the target questionnaire question quantity to generate questionnaires;
the questionnaire result generating module is used for generating questionnaire results corresponding to the questionnaires;
the evaluation module is used for evaluating the hospital intelligent service according to all questionnaire results in a preset time of the hospital to be evaluated.
Preferably, the patient questionnaire question bank comprises questionnaire questions of a plurality of categories, the questionnaire level index table further stores the corresponding relationship between the patient category and the questionnaire question category, and the evaluation system further comprises a patient category bank preset module;
the patient category library presetting module is used for presetting a patient category library of the to-be-evaluated hospital, wherein the patient category library stores the corresponding relation between diagnosis and treatment items of patients and patient categories;
the questionnaire question determining module is used for determining a target questionnaire grade corresponding to the hospital to be evaluated according to the patient category and the current evaluation grade, at least one target questionnaire question category corresponding to different patient categories under the target questionnaire grade, at least one target questionnaire question grade corresponding to each target questionnaire question category and the number of target questionnaire questions corresponding to each target questionnaire question grade;
the extraction module is used for extracting a plurality of groups of current questionnaire questions from the questionnaire database according to the patient categories, the target questionnaire question levels and the target questionnaire question quantity to generate a plurality of questionnaires, and each questionnaire corresponds to each patient category one to one;
the questionnaire result generating module comprises a current patient category acquiring unit, a questionnaire generating unit and a questionnaire result generating unit;
the current patient category acquisition unit is used for acquiring a current diagnosis and treatment item of a current patient who is treated by the to-be-evaluated hospital and determining the current patient category of the current patient;
the questionnaire generating unit is used for extracting questionnaires corresponding to the current patients according to the current patient categories;
the questionnaire result generating unit is used for generating questionnaire results corresponding to the questionnaire of the current patient.
Preferably, the evaluation system further comprises a weight assignment module, and the extraction module comprises a mapping numerical value range determination unit, a target numerical value acquisition unit, a questionnaire question sequence number determination unit and a questionnaire question extraction unit;
the weight giving module is used for giving weight to each question in the patient survey question bank based on an expert scoring method;
the mapping numerical range determining unit is used for determining a mapping numerical range corresponding to the sequence number of each questionnaire topic in each target questionnaire topic level according to the weight of each questionnaire topic in each target questionnaire topic level in each target questionnaire topic type in each patient type;
the target value acquisition unit is used for respectively acquiring target values of the number of target questionnaire questions based on a random number function;
the questionnaire topic sequence number determining unit is used for matching each target value with the mapping value range and determining the sequence number of the target questionnaire topic corresponding to each target value;
and the questionnaire topic extraction unit is used for extracting the current questionnaire topic from the investigation question library according to the sequence numbers of all target questionnaire topics.
Preferably, the mapping numerical range determining unit determines the mapping numerical range by using the following formula, specifically including:
Figure RE-GDA0002190794790000061
Lpfor the mapping numerical range of the p-th questionnaire question in the nth target questionnaire question level under the mth target questionnaire question category,
Figure RE-GDA0002190794790000062
total number of questionnaire questions of nth target questionnaire question level under mth target questionnaire question category, WkThe weight of the kth questionnaire topic in the nth target questionnaire topic level under the mth target questionnaire topic category;
the target value obtaining unit determines the target value through the following formula, and specifically includes:
Qi＝Rand(1，X)，i＝[1，I]，
Figure RE-GDA0002190794790000063
Qithe target value is the ith target value, Rand (1, X) is a random number function, X is the upper limit value of the random number function, and I is the number of the target questionnaires under the nth target questionnaire topic class under the mth target questionnaire topic class.
Preferably, the evaluation system further comprises a weight maximum value presetting module and a requisite questionnaire question presetting module, and the extraction module further comprises a first quantity determining unit and a second quantity determining unit;
the weight maximum value presetting module is used for presetting a weight maximum value;
the mandatory questionnaire topic presetting module is used for presetting the questionnaire topic with the weight value of the maximum weight value in the questionnaire topics as the mandatory questionnaire topic;
the first quantity determining unit is used for presetting a first quantity of the questionnaire questions required to be selected in the target questionnaire questions under each target questionnaire question class;
the questionnaire topic extraction unit is further used for randomly extracting a first number of the requisite questionnaire topics from all the requisite questionnaire topics at each target questionnaire topic level under each target questionnaire topic type in the questionnaire library as the current questionnaire topics;
the second quantity determining unit is used for determining a second quantity of the current questionnaire questions to be extracted according to the first quantity;
the target value acquiring unit is used for acquiring a second number of target values respectively based on a random number function.
An electronic device comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the evaluation method of the intelligent hospital service.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method for assessing hospital intelligence services.
The positive progress effects of the invention are as follows: according to the invention, the patient participates in the evaluation of the intelligent service of the hospital, the investigation question bank for the evaluation of the patient is established according to the content of the intelligent service of the hospital, and the differentiated investigation questionnaire is generated based on the diagnosis and treatment items of the patient, so that the patient facing different diagnosis and treatment items gives different categories of problems, the pertinence is stronger, and the final evaluation of the intelligent service of the hospital is more comprehensive, objective and fair.
Drawings
Fig. 1 is a flowchart of an evaluation method of hospital intelligent services according to embodiment 1 of the present invention.
Fig. 2 is a flowchart of another embodiment of the method for evaluating hospital intelligence services according to embodiment 1 of the present invention.
Fig. 3 is a flowchart of step 60 of the method for evaluating hospital intelligent services according to embodiment 1 of the present invention.
Fig. 4 is a flowchart of an evaluation method of hospital intelligence services according to embodiment 2 of the present invention.
Fig. 5 is a flowchart of step S51 in the method for evaluating hospital intelligent services according to embodiment 2 of the present invention.
Fig. 6 is a flowchart of an evaluation method of hospital intelligence services according to embodiment 3 of the present invention.
Fig. 7 is a flowchart of step S51 in the method for evaluating hospital intelligent services according to embodiment 3 of the present invention.
Fig. 8 is a block diagram of an evaluation system for hospital intelligence services according to embodiment 4 of the present invention.
Fig. 9 is a schematic block diagram of a questionnaire result generating module in the evaluation system of the hospital intelligent service according to embodiment 4 of the present invention.
Fig. 10 is a block diagram of an evaluation system for hospital intelligent services according to embodiment 5 of the present invention.
Fig. 11 is a block diagram of an extraction module in the evaluation system of the hospital intelligent service according to embodiment 5 of the present invention.
Fig. 12 is a block diagram of an evaluation system for hospital intelligent services according to embodiment 6 of the present invention.
Fig. 13 is a block diagram of an extraction module in the evaluation system of the hospital intelligent service according to embodiment 6 of the present invention.
Fig. 14 is a schematic structural diagram of an electronic device according to embodiment 7 of the present invention.
Detailed Description
The invention is further illustrated by the following examples, which are not intended to limit the scope of the invention.
Example 1
An evaluation method of hospital intelligent service, as shown in fig. 1, includes:
s10, presetting a patient investigation question bank; the patient survey question bank stores multi-level questionnaire questions corresponding to the content of the intelligent service of the hospital; it should be noted that, in the embodiment, the generation of the patient question bank is based on the "hospital intelligent service grading evaluation standard system" (trial implementation) issued by the existing country, which is hereinafter referred to as "standard", and if the relevant implementation standard changes, the question in the corresponding patient question bank is updated and modified accordingly. Specifically, the system function evaluation contents of levels 1-5 in the standard are converted into the problems facing the patient survey, the evaluation items of which the patients can directly experience the service effect during the treatment are comprehensively covered, and the level 0 is no information service, so a questionnaire is not set. This example is an exemplary presentation of questionnaire questions in a patient questionnaire library, see tables 1-3.
S20, presetting a questionnaire level index table; the questionnaire grade index table stores the corresponding relation between the review grade and the questionnaire grade, and also stores the questionnaire question grade contained in each questionnaire grade and the quantity of questionnaire questions corresponding to the questionnaire questions in each grade;
s30, acquiring the current evaluation grade of the hospital to be evaluated;
s40, determining a target questionnaire grade corresponding to the hospital to be evaluated, at least one target questionnaire question grade corresponding to the target questionnaire grade and the number of target questionnaire questions corresponding to each target questionnaire question grade according to the current review grade and the questionnaire grade index table;
s50, extracting current questionnaire questions from the questionnaire database according to the target questionnaire question levels and the target questionnaire question quantity to generate questionnaires;
s60, generating a questionnaire result corresponding to the questionnaire;
and S70, evaluating the intelligent service of the hospital according to all questionnaire results in a preset time of the hospital to be evaluated.
It should be noted that, in the embodiment, only the evaluation of the patient participating in the hospital intelligent service is described, and in the actual evaluation, the questionnaire result and the indexes of the hospital service flow, the application function, the data display, the data sharing and the like obtained by performing evidence obtaining aiming at the hospital in the conventional manner can be further comprehensively considered, so that the evaluation of the hospital intelligent service is realized.
Figure BDA0002144096550000101
Figure BDA0002144096550000111
Figure BDA0002144096550000131
Figure BDA0002144096550000141
Figure BDA0002144096550000151
In this embodiment, the patient question bank includes questionnaire questions in a plurality of categories, and before step S40, as shown in fig. 2, the evaluation method further includes:
s31, presetting a patient category library of the hospital to be evaluated, wherein the patient category library stores the corresponding relation between diagnosis and treatment items of patients and patient categories;
the questionnaire level index table further stores a corresponding relationship between the patient category and the questionnaire subject category, and step S40 specifically includes:
s41, determining a target questionnaire grade corresponding to the to-be-evaluated hospital, at least one target questionnaire subject grade corresponding to different patient classes under the target questionnaire grade, at least one target questionnaire subject grade corresponding to each target questionnaire subject grade and the number of target questionnaire subjects corresponding to each target questionnaire subject grade according to the patient classes and the current review grade;
further, step S50 specifically includes:
s51, extracting multiple groups of current questionnaire questions from the questionnaire database according to the patient categories, the target questionnaire question levels and the target questionnaire question quantity to generate multiple questionnaires, wherein each questionnaire corresponds to each patient category one to one;
further, as shown in fig. 3, step S60 specifically includes:
s61, acquiring the current diagnosis and treatment item of a current patient to be examined and treated by the hospital to be examined and determining the current patient category of the current patient;
s62, extracting a questionnaire corresponding to the current patient according to the current patient category;
and S63, generating a questionnaire result corresponding to the questionnaire of the current patient.
Specifically, based on the patient hospitalizing scene, the questionnaire subjects can be classified into three categories, namely a basic category, a signed patient category and a continuous medical service category, wherein the basic category subjects face all patients, the signed patient category subjects face patients signed with the hospital for home medical service, and the continuous medical service category subjects face patients requiring multiple medical services such as follow-up visit and follow-up diagnosis.
Referring to tables 4 and 5, the present embodiment exemplarily shows the composition table of the patient questionnaire database and the index table of questionnaire grades, wherein the questionnaire grades include, but are not limited to, grades 1 to 5, and the questionnaire subject grades include, but are not limited to, grades 1 to 5;
TABLE 4 patient survey question bank composition table
Figure BDA0002144096550000171
TABLE 5 questionnaire level index table composition table
Figure BDA0002144096550000172
It should be noted that, the survey group obtains the corresponding questionnaire grades according to the review grades reported by the hospital and extracts and generates the number of questionnaire questions that is the same as the number of patient categories, such as: if the questionnaire rating is obtained as 4 based on the hospital review rating, then the corresponding needs to be extracted from the patient questionnaire library: generating questionnaires of the patient according to 5 basic class 3-level questions, 13 basic class 4-level questions, 1 signed patient class 3-level questions, 1 signed patient class 4-level questions, 1 continuous medical service class 3-level questions and 1 continuous medical service class 4-level questions, and further generating different questionnaires for different classes of patients, wherein if the questionnaires of the basic class of patients are 5 basic class 3-level questions and 13 basic class 4-level questions, the questionnaires of the signed patient class are as follows: the questionnaires of the continuous medical service type comprise 5 basic type 3-level questions, 13 basic type 4-level questions, 1 signed patient type 3-level questions and 1 signed patient type 4-level questions, and the questionnaires of the continuous medical service type comprise 5 basic type 3-level questions, 13 basic type 4-level questions, 1 continuous medical service type 3-level questions and 1 continuous medical service type 4-level questions. The hospital can issue questionnaires in an online mode and an offline mode to acquire information. On-line mode is directed at the patient who has registered hospital cell-phone APP, and the day that its diagnosis is ended pushes the questionnaire. The offline mode is oriented to all patients, a plurality of questionnaire filling self-service machines are placed in hospital outpatient halls and discharge places, and the questionnaire can be filled after the patients swipe identity cards/medical insurance cards for identity authentication.
In the embodiment, the patient is introduced to participate in the evaluation of the hospital intelligent service, the investigation question bank for the patient to evaluate is established according to the content of the hospital intelligent service, and the differentiated investigation questionnaire is generated based on the diagnosis and treatment items of the patient, so that the patient facing different diagnosis and treatment items gives different types of problems, the pertinence is stronger, and the final evaluation of the hospital intelligent service is more comprehensive, objective and fair.
Example 2
The evaluation method of the hospital intelligent service in this embodiment is further improved on the basis of embodiment 1, and in order to further ensure that the questionnaire survey result performed on the patient is more objective, as shown in fig. 4, after step S10, the evaluation method further includes:
s11, giving weight to each questionnaire question in the patient questionnaire question bank based on an expert scoring method;
further, referring to fig. 5, step S51 specifically includes:
s511, determining a mapping numerical range corresponding to the sequence number of each questionnaire subject in each target questionnaire subject level according to the weight of each questionnaire subject in each target questionnaire subject level under each target questionnaire subject type for each patient type;
s512, respectively obtaining target numerical values of the number of the questions of the target questionnaire based on a random number function;
s513, matching each target value with the mapping value range, and determining the sequence number of the target questionnaire question corresponding to each target value;
and S514, extracting the current questionnaire questions from the questionnaire question library according to the sequence numbers of all the target questionnaire questions.
In step S511, the mapping numerical range is determined by the following formula, which specifically includes:
Lpfor the mapping numerical range of the p-th questionnaire question in the nth target questionnaire question level under the mth target questionnaire question category,
Figure BDA0002144096550000182
total number of questionnaire questions of nth target questionnaire question level under mth target questionnaire question category, WkThe weight of the kth questionnaire topic in the nth target questionnaire topic level under the mth target questionnaire topic category;
in step S512, the target value is determined by the following formula, which specifically includes:
Figure BDA0002144096550000183
Qithe target value is the ith target value, Rand (1, X) is a random number function, X is the upper limit value of the random number function, and I is the number of the target questionnaires under the nth target questionnaire topic class under the mth target questionnaire topic class.
It should be noted that questionnaire questions of different target questionnaire question levels under different target questionnaire question categories are extracted independently, in this embodiment, the current questionnaire question is generated by a weight extraction method, the algorithm is objective and reasonable, wherein weights can be given by expert groups according to the evaluation key points, and the operation is flexible.
Example 3
The evaluation method of the hospital intelligent service in this embodiment is further improved based on embodiment 2, as shown in fig. 6, after step S11, the evaluation method further includes:
s12, presetting a maximum weight value;
s13, presetting the questionnaire questions with the weight value of the maximum weight value as the questionnaire questions to be selected;
further, referring to fig. 7, in step S51 and before step S511, the evaluation method further includes:
s5101, presetting a first number of the questionnaire questions required to be selected in the target questionnaire questions under each target questionnaire question class;
s5102, randomly extracting a first number of the requisite questionnaire questions from all the requisite questionnaire questions at each target questionnaire question level under each target questionnaire question category in the questionnaire library as the current questionnaire questions;
s5103, determining a second quantity of the current questionnaire questions to be extracted according to the first quantity;
step S512 specifically includes: respectively acquiring a second number of target values based on a random number function;
in further steps S513 and S514, a current questionnaire topic is extracted based on the second number of target values.
In the actual evaluation, in consideration of the importance of part of the questionnaire questions, a certain number of necessary questions need to be extracted and put into the questionnaire questions when the questionnaire questions are generated.
The above process of extracting the questions of the current questionnaire is further described by taking a specific example:
assuming that the hospital review level is obtained and it is further determined that the questionnaire level corresponding to the current hospital is level 4, then it is necessary to extract from the patient questionnaire library: and generating questionnaire questions of the hospital by using 5 basic class 3-level questions, 13 basic class 4-level questions, 1 signed patient class 3-level questions, 1 signed patient class 4-level questions, 1 continuous medical service class 3-level questions and 1 continuous medical service class 4-level questions. Take the example of extracting 13 basic class 4 topics:
referring to table 1, the question bank shares 23 basic class level 4 questions, from which 13 are extracted. The specific numbering of the 23 questions is: a ═ 1.4.1, 1.4.4, 1.4.5, 1.4.6, 4.4.1, 4.4.2, 4.4.3, 4.4.4, 4.4.5, 5.4.1, 5.4.2, 6.4.1, 6.4.1, 7.4.2, 7.4.3, 9.4.1, 9.4.3, 12.4.1, 12.4.2, 13.4.1, 13.4.2, 14.4.1, 14.4.2 (the first number is the item category number, the second number is the questionnaire question level, and the third number is the content number in that category).
(one) weight value setting for 23 basic class 4-level questions
The weight value is defined as an integer between 1 and 10, and the specific weight value W is as follows: a < W > - {1.4.1<5>, 1.4.4<10>, 1.4.5<8>, 1.4.6<6>, 4.4.1<10>, 4.4.2<9>, 4.4.3<3>, 4.4.4<6>, 4.4.5<1>, 5.4.1<7>, 5.4.2<4>, 6.4.1<4>, 6.4.2<10>, 7.4.2<5>, 7.4.3<6>, 9.4.1<9>, 9.4.3<10>, 12.4.1<2>, 12.4.2<4>, 13.4.1<10>, 13.4.2<3>, 14.4.1<10>, 14.4.2<7 >.
(II) problem of extracting necessary questionnaire
The total number of questions with the weight of 10 being the maximum is 6, and a first number of 4 questionnaire questions are randomly extracted from the 6 necessary questionnaire questions, assuming that random extraction results in 1.4.4, 4.4.1, 9.4.3, 14.4.1.
(III) problem of extracting remaining questionnaire
The remaining 9 questions were randomly drawn from the 17 other questionnaire topics with reference weights.
1) Mapping the weights corresponding to the 17 questions to corresponding mapping numerical value ranges respectively;
L1.4.1＝[1，5]；L1.4.5＝[6，13]，L1.4.6＝[14，19]，L4.4.2＝[20，28]， L4.4.3＝[29，31]，L4.4.4＝[32，37]，L4.4.5＝[38]，L5.4.1＝[39，45]，L5.4.2＝[46，49]，L6.4.1＝[50，53]，L7.4.2＝[54，58]，L7.4.3＝[59，64]，L9.4.1＝[65，73]， L12.4.1＝[74，75]，L12.4.2＝[76，79]，L13.4.2＝[80，82]，L14.4.2＝[83，89]。
2) obtaining 9 target values based on a random number function;
the upper limit of the random number function is set, which is the sum of the weights of the above 17 questions, that is, 89. Assuming that Q1 is randomly obtained and 47, it falls to L5.4.2 ═ 46, 49, i.e. the successful extraction problem 5.4.2, and the above algorithm is performed 9 times in succession, 9 problems can be obtained, assuming 1.4.5, 4.4.3, 4.4.4, 5.4.2, 6.4.1, 7.4.3, 9.4.1, 12.4.1, 13.4.2.
Further, the 5 basic class 3-level questions are extracted according to the weight by the method, and the assumed results are 1.3.4, 4.3.2, 6.3.1, 9.3.1 and 14.3.2; the contracted patient class and the continuous medical service class extract 1-pass 3-level questions and 1-pass 4-level questions, respectively, and assume that 10.3.2, 10.4.1, 11.3.2 and 15.4.2 are obtained.
The questionnaire questions that are ultimately matched to the current hospital are as follows:
1.4.4，4.4.1，9.4.3，14.4.1，1.4.5，4.4.3，4.4.4，5.4.2，6.4.1，7.4.3，9.4.1，12.4.1，13.4.2，1.3.4，4.3.2，6.3.1，9.3.1，14.3.2，10.3.2，10.4.1，11.3.2， 15.4.2。
it should be noted that, for questionnaire topics of different categories, extraction may be performed in a weighted manner, or may be performed randomly according to actual needs.
Example 4
An evaluation system of hospital intelligent service is shown in fig. 8, and comprises a question bank presetting module 1, an index table presetting module 2, a hospital evaluation grade obtaining module 3, a questionnaire question determining module 4, an extraction module 5, a questionnaire result generating module 6 and an evaluation module 7;
the question bank presetting module 1 is used for presetting a patient survey question bank, and multilevel questionnaire questions corresponding to the content of the intelligent service of the hospital are stored in the patient survey question bank; it should be noted that, in the embodiment, the generation of the patient question bank is based on the "hospital intelligent service grading evaluation standard system" (trial implementation) issued by the existing country, hereinafter referred to as "standard", and if the relevant implementation standard changes, the question in the corresponding patient question bank will be updated and modified accordingly. Specifically, the system function evaluation contents of levels 1 to 5 in the standard are converted into the problems facing the patient survey, the evaluation items of which the patient can directly experience the service effect during the treatment are comprehensively covered, and the level 0 is no information service, so a questionnaire is not set.
The index table presetting module 2 is used for presetting a questionnaire grade index table, wherein the questionnaire grade index table stores the corresponding relation between review grades and questionnaire grades, and also stores questionnaire question grades contained in each questionnaire grade and the questionnaire question quantity corresponding to each questionnaire question grade;
the hospital evaluation grade acquisition module 3 is used for acquiring the current evaluation grade of the hospital to be evaluated;
the questionnaire question determining module 4 is used for determining a target questionnaire grade corresponding to the hospital to be evaluated, at least one target questionnaire question grade corresponding to the target questionnaire grade and the number of target questionnaire questions corresponding to each target questionnaire question grade according to the current review grade and the questionnaire grade index table;
the extraction module 5 is used for extracting the current questionnaire questions from the questionnaire question library according to the target questionnaire question levels and the target questionnaire question quantity to generate questionnaires;
the questionnaire result generating module 6 is used for generating questionnaire results corresponding to the questionnaires;
the evaluation module 7 is used for evaluating the hospital intelligent service according to all questionnaire results in a preset time of the hospital to be evaluated.
It should be noted that, in the embodiment, only the evaluation of the patient participating in the hospital intelligent service is described, and in the actual evaluation, the questionnaire result and the indexes of the hospital service flow, the application function, the data display, the data sharing and the like obtained by performing evidence obtaining aiming at the hospital in the conventional manner can be further comprehensively considered, so that the evaluation of the hospital intelligent service is realized.
In this embodiment, the patient questionnaire question bank includes questionnaire questions of a plurality of categories, the questionnaire hierarchical index table further stores a correspondence between the patient category and the questionnaire question category, see fig. 8, and the evaluation system further includes a patient category bank presetting module 8;
the patient category library presetting module 8 is used for presetting a patient category library of the hospital to be evaluated, wherein the patient category library stores the corresponding relation between diagnosis and treatment items of patients and patient categories;
the questionnaire topic determining module 4 is configured to determine, according to the patient category and the current review level, a target questionnaire grade corresponding to the hospital to be evaluated, at least one target questionnaire topic category corresponding to different patient categories under the target questionnaire grade, at least one target questionnaire topic grade corresponding to each target questionnaire topic category, and a target questionnaire topic number corresponding to each target questionnaire topic grade;
the extraction module 5 is configured to extract multiple groups of current questionnaire questions from the questionnaire library according to the patient categories, the target questionnaire question levels, and the target questionnaire question numbers to generate multiple questionnaires, where each questionnaire corresponds to each patient category one to one;
further, as shown in fig. 9, the questionnaire result generating module 6 includes a current patient category obtaining unit 61, a questionnaire generating unit 62, and a questionnaire result generating unit 63;
the current patient category obtaining unit 61 is configured to obtain a current diagnosis and treatment item of a current patient for the examination of the examination hospital to be tested and determine a current patient category of the current patient;
the questionnaire generating unit 62 is configured to extract a questionnaire corresponding to the current patient according to the current patient category;
the questionnaire result generating unit 63 is configured to generate a questionnaire result corresponding to the questionnaire of the current patient.
Specifically, based on the patient hospitalizing scene, the questionnaire subjects can be classified into three categories, namely a basic category, a signed patient category and a continuous medical service category, wherein the basic category subjects face all patients, the signed patient category subjects face patients signed with the hospital for home medical service, and the continuous medical service category subjects face patients requiring multiple medical services such as follow-up visit and follow-up diagnosis.
It should be noted that, the survey group obtains the corresponding questionnaire grades according to the review grades reported by the hospital and extracts and generates the number of questionnaire questions that is the same as the number of patient categories, such as: if the questionnaire rating is obtained as 4 based on the hospital review rating, then the corresponding needs to be extracted from the patient questionnaire library: generating questionnaires of the patient according to 5 basic class 3-level questions, 13 basic class 4-level questions, 1 signed patient class 3-level questions, 1 signed patient class 4-level questions, 1 continuous medical service class 3-level questions and 1 continuous medical service class 4-level questions, and further generating different questionnaires for different classes of patients, wherein if the questionnaires of the basic class of patients are 5 basic class 3-level questions and 13 basic class 4-level questions, the questionnaires of the signed patient class are as follows: the questionnaires of the continuous medical service type comprise 5 basic type 3-level questions, 13 basic type 4-level questions, 1 signed patient type 3-level questions and 1 signed patient type 4-level questions, and the questionnaires of the continuous medical service type comprise 5 basic type 3-level questions, 13 basic type 4-level questions, 1 continuous medical service type 3-level questions and 1 continuous medical service type 4-level questions. The hospital can issue questionnaires in an online mode and an offline mode to acquire information. On-line mode is directed at the patient who has registered hospital cell-phone APP, and the day that its diagnosis is ended pushes the questionnaire. The offline mode is oriented to all patients, a plurality of questionnaire filling self-service machines are placed in hospital outpatient halls and discharge places, and the questionnaire can be filled after the patients swipe identity cards/medical insurance cards for identity authentication.
In the embodiment, the patient is introduced to participate in the evaluation of the hospital intelligent service, the investigation question bank for the patient to evaluate is established according to the content of the hospital intelligent service, and the differentiated investigation questionnaire is generated based on the diagnosis and treatment items of the patient, so that the patient facing different diagnosis and treatment items gives different types of problems, the pertinence is stronger, and the final evaluation of the hospital intelligent service is more comprehensive, objective and fair.
Example 5
The evaluation of the hospital intelligent service in this embodiment is the same as that in embodiment 4, as shown in fig. 10 and 11, the evaluation system further includes a weight assignment module 9, and the extraction module 5 includes a mapping numerical range determination unit 51, a target numerical value acquisition unit 52, a questionnaire topic serial number determination unit 53, and a questionnaire topic extraction unit 54;
the weight giving module 9 is used for giving a weight to each question in the patient survey question bank based on an expert scoring method;
the mapping numerical range determining unit 51 is configured to determine, according to the weight of each questionnaire topic in each target questionnaire topic level under each target questionnaire topic type, a mapping numerical range corresponding to the sequence number of each questionnaire topic in each target questionnaire topic level;
the target value obtaining unit 52 is configured to obtain target values of the number of questions of the target questionnaire based on a random number function;
the questionnaire topic sequence number determining unit 53 is configured to match each target value with the mapping value range, and determine a sequence number of a target questionnaire topic corresponding to each target value;
the questionnaire topic extraction unit 54 is configured to extract the current questionnaire topic from the questionnaire library according to the sequence numbers of all target questionnaire topics.
In this embodiment, the mapping numerical range determining unit 51 determines the mapping numerical range by using the following formula, which specifically includes:
Figure BDA0002144096550000241
Lpfor the mapping numerical range of the p-th questionnaire question in the nth target questionnaire question level under the mth target questionnaire question category,
Figure BDA0002144096550000242
total number of questionnaire questions of nth target questionnaire question level under mth target questionnaire question category, WkThe weight of the kth questionnaire topic in the nth target questionnaire topic level under the mth target questionnaire topic category;
the target value obtaining unit 52 determines the target value by the following formula, which specifically includes:
Figure BDA0002144096550000251
Qithe target value is the ith target value, Rand (1, X) is a random number function, X is the upper limit value of the random number function, and I is the number of the target questionnaires under the nth target questionnaire topic class under the mth target questionnaire topic class.
It should be noted that questionnaire questions of different target questionnaire question levels under different target questionnaire question categories are extracted independently, in this embodiment, the current questionnaire question is generated by a weight extraction method, the algorithm is objective and reasonable, wherein weights can be given by expert groups according to the evaluation key points, and the operation is flexible.
Example 6
The evaluation method of the intelligent hospital service in this embodiment is further improved on the basis of embodiment 5, as shown in fig. 12, the evaluation system further includes a weight maximum value presetting module 10 and a requisite questionnaire topic presetting module 11, as shown in fig. 13, the extraction module 5 further includes a first quantity determining unit 55 and a second quantity determining unit 56;
the weight maximum value presetting module 10 is used for presetting a weight maximum value;
the optional questionnaire topic presetting module 11 is used for presetting the questionnaire topic with the weight value of the maximum weight value in the questionnaire topics as an optional questionnaire topic;
the first quantity determining unit 55 is configured to preset a first quantity of questionnaire questions to be selected in the target questionnaire questions at each target questionnaire question level in each target questionnaire question category;
the questionnaire topic extraction unit 54 is further configured to randomly extract a first number of indispensable questionnaire topics from all indispensable questionnaire topics at each target questionnaire topic level in each target questionnaire topic category in the questionnaire library as the current questionnaire topic;
the second quantity determining unit 56 is configured to determine a second quantity of current questionnaire questions to be extracted according to the first quantity;
the target value obtaining unit 52 is configured to obtain a second number of target values respectively based on a random number function.
It should be noted that, in the actual evaluation, in consideration of the importance of part of the questionnaires, a certain number of necessary questions need to be extracted and put into the questionnaire questions when the questionnaire questions are generated, and in addition, the questionnaire questions of different categories can be extracted in a weighted manner, or can be extracted randomly according to the actual needs.
Example 7
An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method for assessing hospital intelligence services according to any one of embodiments 1-3.
Fig. 14 is a schematic structural diagram of an electronic device provided in this embodiment. FIG. 14 illustrates a block diagram of an exemplary electronic device 90 suitable for use in implementing embodiments of the present invention. The electronic device 90 shown in fig. 14 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 14, the electronic device 90 may be embodied in the form of a general purpose computing device, which may be, for example, a server device. The components of the electronic device 90 may include, but are not limited to: at least one processor 91, at least one memory 92, and a bus 93 that connects the various system components (including the memory 92 and the processor 91).
The bus 93 includes a data bus, an address bus, and a control bus.
Memory 92 may include volatile memory, such as Random Access Memory (RAM)921 and/or cache memory 922, and may further include Read Only Memory (ROM) 923.
Memory 92 may also include a program tool 925 having a set (at least one) of program modules 924, such program modules 924 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, and in some combination, may include an implementation of a network environment.
The processor 91 executes various functional applications and data processing by running a computer program stored in the memory 92.
The electronic device 90 may also communicate with one or more external devices 94 (e.g., keyboard, pointing device, etc.). Such communication may be through an input/output (I/O) interface 95. Also, the electronic device 90 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via a network adapter 96. The network adapter 96 communicates with the other modules of the electronic device 90 via the bus 93. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 90, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, among others.
It should be noted that although in the above detailed description several units/modules or sub-units/modules of the electronic device are mentioned, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the units/modules described above may be embodied in one unit/module, according to embodiments of the present application. Conversely, the features and functions of one unit/module described above may be further divided into embodiments by a plurality of units/modules.
Example 8
A computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method for assessing hospital intelligence services as described in any one of embodiments 1-3.
More specific examples, among others, that the readable storage medium may employ may include, but are not limited to: a portable disk, a hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible embodiment, the invention can also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps of implementing the method for assessing a hospital intelligence service according to any one of embodiments 1 to 3, when said program product is run on said terminal device.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of illustration only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.

Claims (12)

1. An evaluation method for intelligent services of a hospital, characterized in that the evaluation method comprises:
s10, presetting a patient survey question bank, wherein the patient survey question bank stores multi-level questionnaire questions corresponding to the content of the hospital intelligent service;
s20, presetting a questionnaire grade index table, wherein the questionnaire grade index table stores the corresponding relation between review grades and questionnaire grades, and also stores questionnaire subject grades contained in each questionnaire grade and questionnaire subject numbers corresponding to the questionnaire subjects of each grade;
s30, acquiring the current evaluation grade of the hospital to be evaluated;
s40, determining a target questionnaire grade corresponding to the hospital to be evaluated, at least one target questionnaire question grade corresponding to the target questionnaire grade and the number of target questionnaire questions corresponding to each target questionnaire question grade according to the current review grade and the questionnaire grade index table;
s50, extracting current questionnaire questions from the questionnaire database according to the target questionnaire question levels and the target questionnaire question quantity to generate questionnaires;
s60, generating a questionnaire result corresponding to the questionnaire;
and S70, evaluating the hospital intelligent service according to all questionnaire results in a preset time of the hospital to be evaluated.
2. The method of evaluating hospital intelligent services according to claim 1, wherein said patient questionnaire questions database includes questionnaire questions of a plurality of categories, and before step S40, the method further comprises:
s31, presetting a patient category library of the hospital to be evaluated, wherein the patient category library stores the corresponding relation between diagnosis and treatment items of patients and patient categories;
the questionnaire level index table further stores a corresponding relationship between the patient category and the questionnaire subject category, and step S40 specifically includes:
s41, determining a target questionnaire grade corresponding to the hospital to be evaluated, at least one target questionnaire subject grade corresponding to different patient classes under the target questionnaire grade, at least one target questionnaire subject grade corresponding to each target questionnaire subject grade and the number of target questionnaire subjects corresponding to each target questionnaire subject grade according to the patient classes and the current review grade;
step S50 specifically includes:
s51, extracting multiple groups of current questionnaire questions from the questionnaire database according to the patient categories, the target questionnaire question levels and the target questionnaire question quantity to generate multiple questionnaires, wherein each questionnaire corresponds to each patient category one to one;
step S60 specifically includes:
s61, acquiring the current diagnosis and treatment item of a current patient to be examined and treated by the hospital to be examined and determining the current patient category of the current patient;
s62, extracting a questionnaire corresponding to the current patient according to the current patient category;
and S63, generating a questionnaire result corresponding to the questionnaire of the current patient.
3. The method for evaluating hospital intelligent services according to claim 2, wherein after step S10, said method further comprises:
s11, giving weight to each questionnaire question in the patient questionnaire question bank based on an expert scoring method;
step S51 specifically includes:
s511, determining a mapping numerical range corresponding to the sequence number of each questionnaire topic in each target questionnaire topic level according to the weight of each questionnaire topic in each target questionnaire topic level under each target questionnaire topic type for each patient type;
s512, respectively obtaining target numerical values of the number of the questions of the target questionnaire based on a random number function;
s513, matching each target value with the mapping value range, and determining the sequence number of the target questionnaire question corresponding to each target value;
and S514, extracting the current questionnaire questions from the questionnaire question library according to the sequence numbers of all the target questionnaire questions.
4. The method for evaluating hospital intelligent services according to claim 3, wherein said mapping range of values is determined in step S511 by the following formula, which includes:
Figure FDA0002144096540000021
Lpfor the mapping numerical range of the p-th questionnaire topic in the nth target questionnaire topic level under the mth target questionnaire topic category,
Figure FDA0002144096540000022
total number of questionnaire questions, W, for nth target questionnaire question class under mth target questionnaire question categorykThe weight of the kth questionnaire topic in the nth target questionnaire topic level under the mth target questionnaire topic category;
in step S512, the target value is determined by the following formula, which specifically includes:
Figure FDA0002144096540000031
Qithe target value is the ith target value, Rand (1, X) is a random number function, X is the upper limit value of the random number function, and I is the number of the target questionnaire questions under the nth target questionnaire question class under the mth target questionnaire question class.
5. The method of assessing hospital intelligence services of claim 3 wherein following step S11, said method further comprises:
s12, presetting a maximum weight value;
s13, presetting the questionnaire questions with the weight value of the maximum weight value as the questionnaire questions to be selected;
before step S511, the evaluation method further includes:
s5101, presetting a first number of the questionnaire questions required to be selected in the target questionnaire questions under each target questionnaire question class;
s5102, randomly extracting a first number of the requisite questionnaire questions from all the requisite questionnaire questions at each target questionnaire question level under each target questionnaire question category in the questionnaire library as the current questionnaire questions;
s5103, determining a second quantity of the current questionnaire questions to be extracted according to the first quantity;
in step S512, a second number of target values are respectively obtained based on the random number function.
6. The system for testing and evaluating the intelligent service of the hospital is characterized by comprising a question bank presetting module, an index table presetting module, a hospital evaluation grade obtaining module, a questionnaire question determining module, an extracting module, a questionnaire result generating module and a testing and evaluating module;
the question bank presetting module is used for presetting a patient survey question bank, and the patient survey question bank stores multi-level questionnaire questions corresponding to the content of the intelligent service of the hospital;
the index table presetting module is used for presetting a questionnaire grade index table, the questionnaire grade index table stores the corresponding relation between review grades and questionnaire grades, and also stores the questionnaire question grades contained in each questionnaire grade and the questionnaire question quantity corresponding to each questionnaire question grade;
the hospital evaluation grade acquisition module is used for acquiring the current evaluation grade of the hospital to be evaluated;
the questionnaire question determining module is used for determining a target questionnaire grade corresponding to the hospital to be evaluated, at least one target questionnaire question grade corresponding to the target questionnaire grade and the number of target questionnaire questions corresponding to each target questionnaire question grade according to the current review grade and the questionnaire grade index table;
the extraction module is used for extracting the current questionnaire questions from the questionnaire question library according to the target questionnaire question levels and the target questionnaire question quantity to generate questionnaires;
the questionnaire result generating module is used for generating questionnaire results corresponding to the questionnaires;
the evaluation module is used for evaluating the hospital intelligent service according to all questionnaire results in a preset time of the hospital to be evaluated.
7. The system of claim 6, wherein the patient database comprises questionnaire questions of a plurality of categories, the questionnaire rating index table further stores the corresponding relationship between the patient category and the questionnaire question category, and the system further comprises a patient category database presetting module;
the patient category library presetting module is used for presetting a patient category library of the to-be-evaluated hospital, wherein the patient category library stores the corresponding relation between diagnosis and treatment items of patients and patient categories;
the questionnaire question determining module is used for determining a target questionnaire grade corresponding to the hospital to be evaluated according to the patient category and the current review grade, at least one target questionnaire question category corresponding to different patient categories under the target questionnaire grade, at least one target questionnaire question grade corresponding to each target questionnaire question category and the number of target questionnaire questions corresponding to each target questionnaire question grade;
the extraction module is used for extracting a plurality of groups of current questionnaire questions from the questionnaire database according to the patient categories, the target questionnaire question levels and the target questionnaire question quantity to generate a plurality of questionnaires, and each questionnaire corresponds to each patient category one to one;
the questionnaire result generating module comprises a current patient category acquiring unit, a questionnaire generating unit and a questionnaire result generating unit;
the current patient category acquisition unit is used for acquiring a current diagnosis and treatment item of a current patient who is treated by the to-be-evaluated hospital and determining the current patient category of the current patient;
the questionnaire generating unit is used for extracting questionnaires corresponding to the current patients according to the current patient categories;
the questionnaire result generating unit is used for generating questionnaire results corresponding to the questionnaire of the current patient.
8. The system of claim 7, further comprising a weight assignment module, wherein the extraction module comprises a mapping value range determination unit, a target value acquisition unit, a questionnaire question sequence number determination unit, and a questionnaire question extraction unit;
the weight giving module is used for giving weight to each questionnaire question in the patient questionnaire question bank based on an expert scoring method;
the mapping numerical range determining unit is used for determining a mapping numerical range corresponding to the sequence number of each questionnaire topic in each target questionnaire topic level according to the weight of each questionnaire topic in each target questionnaire topic level in each target questionnaire topic type in each patient type;
the target value acquisition unit is used for respectively acquiring target values of the number of the questions of the target questionnaire based on a random number function;
the questionnaire topic sequence number determining unit is used for matching each target value with the mapping value range and determining the sequence number of the target questionnaire topic corresponding to each target value;
the questionnaire topic extraction unit is used for extracting the current questionnaire topic from the questionnaire library according to the sequence numbers of all target questionnaire topics.
9. The system of claim 8, wherein the mapping value range determining unit determines the mapping value range according to the following formula, and comprises:
Figure FDA0002144096540000051
Lpfor the mapping numerical range of the p-th questionnaire topic in the nth target questionnaire topic level under the mth target questionnaire topic category,
Figure FDA0002144096540000052
total number of questionnaire questions, W, for nth target questionnaire question class under mth target questionnaire question categorykThe weight of the kth questionnaire topic in the nth target questionnaire topic level under the mth target questionnaire topic category;
the target value obtaining unit determines the target value through the following formula, and specifically includes:
Qithe target value is the ith target value, Rand (1, X) is a random number function, X is the upper limit value of the random number function, and I is the number of the target questionnaire questions under the nth target questionnaire question class under the mth target questionnaire question class.
10. The system of claim 8, further comprising a weight maximum value presetting module and a requisite questionnaire subject presetting module, wherein the extraction module further comprises a first quantity determining unit and a second quantity determining unit;
the weight maximum value presetting module is used for presetting a weight maximum value;
the required questionnaire topic presetting module is used for presetting the questionnaire topic with the weight value of the maximum weight value in the questionnaire topics as the required questionnaire topic;
the first quantity determining unit is used for presetting a first quantity of the questionnaire questions required to be selected in the target questionnaire questions under each target questionnaire question category and each target questionnaire question level;
the questionnaire topic extraction unit is further used for randomly extracting a first number of the necessary questionnaire topics from all the necessary questionnaire topics under each target questionnaire topic level under each target questionnaire topic type in the questionnaire library as the current questionnaire topic;
the second quantity determining unit is used for determining a second quantity of the current questionnaire questions to be extracted according to the first quantity;
the target value acquiring unit is used for acquiring a second number of target values respectively based on a random number function.
11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of assessing hospital intelligence services of any one of claims 1 to 5 when executing the computer program.
12. A computer-readable storage medium, on which a computer program is stored, characterized in that said program, when executed by a processor, implements the steps of the method for assessing hospital intelligence services according to any one of claims 1 to 5.
CN201910681173.7A 2019-07-25 2019-07-25 Hospital intelligent service evaluation method, system, equipment and storage medium Active CN110750614B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910681173.7A CN110750614B (en) 2019-07-25 2019-07-25 Hospital intelligent service evaluation method, system, equipment and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910681173.7A CN110750614B (en) 2019-07-25 2019-07-25 Hospital intelligent service evaluation method, system, equipment and storage medium

Publications (2)

Publication Number Publication Date
CN110750614A true CN110750614A (en) 2020-02-04
CN110750614B CN110750614B (en) 2020-12-22

Family

ID=69275846

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910681173.7A Active CN110750614B (en) 2019-07-25 2019-07-25 Hospital intelligent service evaluation method, system, equipment and storage medium

Country Status (1)

Country Link
CN (1) CN110750614B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113674032A (en) * 2021-08-30 2021-11-19 广州快决测信息科技有限公司 Net recommendation value questionnaire model building system and method
CN113723836A (en) * 2021-09-02 2021-11-30 卫宁健康科技集团股份有限公司 Endowment management method and system, computer equipment and storage medium

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102541655A (en) * 2010-12-31 2012-07-04 ***股份有限公司 Method and device for implementing dynamic load balancing
US20150220839A1 (en) * 2012-09-07 2015-08-06 Hugh Macnaught Comparison of user experience with experience of larger group
CN109784654A (en) * 2018-12-17 2019-05-21 平安国际融资租赁有限公司 Task creating method, device, computer equipment and storage medium

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102541655A (en) * 2010-12-31 2012-07-04 ***股份有限公司 Method and device for implementing dynamic load balancing
US20150220839A1 (en) * 2012-09-07 2015-08-06 Hugh Macnaught Comparison of user experience with experience of larger group
CN109784654A (en) * 2018-12-17 2019-05-21 平安国际融资租赁有限公司 Task creating method, device, computer equipment and storage medium

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113674032A (en) * 2021-08-30 2021-11-19 广州快决测信息科技有限公司 Net recommendation value questionnaire model building system and method
CN113723836A (en) * 2021-09-02 2021-11-30 卫宁健康科技集团股份有限公司 Endowment management method and system, computer equipment and storage medium

Also Published As

Publication number Publication date
CN110750614B (en) 2020-12-22

Similar Documents

Publication Publication Date Title
Shao et al. Validity of tests under covariate-adaptive biased coin randomization and generalized linear models
CN110750614B (en) Hospital intelligent service evaluation method, system, equipment and storage medium
CN114187988A (en) Data processing method, device, system and storage medium
CN103810663A (en) Demographic data cleaning method based on face recognition
CN114372060A (en) Data storage method, device, equipment and storage medium
Ragab et al. Design and implementation of a hybrid recommender system for predicting college admission
CN106599291A (en) Method and device for grouping data
Gasa et al. Supervisory support for Ethiopian doctoral students enrolled in an open and distance learning institution
CN109191343B (en) Internet-based clinical teaching management method
US20200342961A1 (en) Cohort selection for agile processes
CN116403693A (en) Method, device, equipment and storage medium for dispatching questionnaire
CN113516205B (en) Employee stability classification method based on artificial intelligence and related equipment
Kalhor et al. Diversity dilemmas: uncovering gender and nationality biases in graduate admissions across top North American computer science programs
CN113707246A (en) Questionnaire data processing method, device, equipment and storage medium
CN114694816A (en) Resource sharing processing method, device, equipment and storage medium
CN113269179A (en) Data processing method, device, equipment and storage medium
Burnham et al. Quality improvement methods to strengthen hemodialysis in Northwest Syria
WO2020188043A1 (en) Method and system to deliver time-driven activity-based-costing in a healthcare setting in an efficient and scalable manner
CN113760777B (en) Application program pressure test method, device, equipment and storage medium
WO2016099576A1 (en) Method and system for determining a site performance index
CN114020687B (en) User retention analysis method, device, equipment and storage medium
CN110688371B (en) Data adjustment method, device, electronic equipment and storage medium
CN118136285A (en) Interaction method, device, equipment and medium for multi-attribute digital people of hospital system
CN112669135B (en) Data acquisition method and device, computer equipment and computer readable storage medium
Giannaris et al. Informatics framework to identify consistent diagnostic techniques

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