CN111696654B - Hospital bed intelligent distribution method, system, equipment and storage medium - Google Patents
Hospital bed intelligent distribution method, system, equipment and storage medium Download PDFInfo
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- CN111696654B CN111696654B CN202010387094.8A CN202010387094A CN111696654B CN 111696654 B CN111696654 B CN 111696654B CN 202010387094 A CN202010387094 A CN 202010387094A CN 111696654 B CN111696654 B CN 111696654B
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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
- G16H40/20—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
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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
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
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Abstract
The application discloses a hospital bed intelligent distribution method, a system, equipment and a storage medium, wherein the method comprises the following steps: acquiring an allocation instruction, and acquiring basic information of a first object according to the allocation instruction; screening the distributable second objects according to the basic information and sequencing the second objects to obtain a first sequence; sequentially calculating the matching degree between the second object and the first object according to the first sequence, and sequencing to obtain a second sequence; and distributing the second object to the first object according to the second sequence. The application realizes reasonable allocation and full utilization of hospital bed resources by establishing a bidirectional matching mechanism and allocating according to the real-time conditions of the number of patients and the number of beds.
Description
Technical Field
The application relates to the field of hospital bed management, in particular to an intelligent hospital bed distribution method, system, equipment and storage medium.
Background
Along with gradual advancement of the urban process, the concentration of people living is gradually increased; the medical resource allocation problem generated by the method is highlighted, and the problem is particularly reflected in the field of allocation management of hospital beds. For the allocation of hospital beds, a processing mode is to allocate manually according to real-time patient information and bed information by providing a full-time bed management nurse, and the mode leads the allocation process of medical resources by people in the whole course, so that the problems of unreasonable allocation and the like are very easy to occur, and the doctor-patient relationship is further worsened, so that severe social events are caused; the other mode is to automatically distribute the beds through the system, but in the prior art, the mode of matching the beds by taking the patients as a main body is adopted, and the reasonable distribution of the bed resources is realized to a certain extent by the mode, but the mode of matching the beds by the patients can not ensure the full utilization of the bed resources by only using the unidirectional distribution mechanism, when the number of waiting bed patients is large and the number of available beds is small.
Therefore, how to utilize a bidirectional matching mechanism to realize reasonable allocation and full utilization of hospital bed resources is a technical problem which needs to be solved currently.
Disclosure of Invention
In order to solve at least one of the technical problems in the prior art, the application aims to provide a hospital bed intelligent distribution method, a system, equipment and a storage medium.
According to a first aspect of an embodiment of the present application, a hospital bed intelligent allocation method includes the following steps:
s100, acquiring an allocation instruction, and acquiring basic information of a first object according to the allocation instruction;
s200, screening the distributable second objects according to the basic information and sequencing the second objects to obtain a first sequence;
s300, sequentially calculating the matching degree between the second object and the first object according to the first sequence, and sequencing to obtain a second sequence;
s400, distributing the second object to the first object according to the second sequence;
in steps S100 to S400, when the first subject is a bed, the second subject is a patient; when the first object is a patient, the second object is a bed.
Further, when the first object is a bed and the second object is a patient, the step S200 includes:
screening patients meeting the requirements according to the bed information; the bed information includes at least one of gender information and isolation information;
acquiring and calculating a patient priority score according to patient information of a patient meeting the requirements; the patient information includes at least one of time of admission to hospital information, illness information, system registration time information;
and sequencing the patients according to the patient priority scores and generating the first sequence.
Further, when the first object is a bed and the second object is a patient, the step S300 includes:
sequentially acquiring treatment type information and waiting duration information of a patient according to the first sequence;
obtaining a treatment type weight factor according to the treatment type information, and obtaining a waiting time length weight factor according to the waiting time length information;
calculating the patient matching degree between the patient and the bed according to the patient priority score, the treatment type weight factor and the waiting duration weight factor;
and sequencing the patients according to the patient matching degree and generating the second sequence.
Further, the patient matching is calculated by the following formula:
S＝∑(F*SW)*(DW^([(MIN-(D-A))/7]))
wherein S represents the patient matching degree; f represents patient priority score; SW represents a treatment type weight factor; DW represents a waiting duration weight factor; d represents the current time; a represents a period selection time; MIN represents a preset waiting period.
Further, when the first object is a patient and the second object is a bed, the step S200 includes:
screening beds meeting requirements according to patient information; the patient information includes at least one of gender information and isolation information;
acquiring and calculating a bed priority score according to bed information of a bed meeting the requirements; the bed information comprises at least one of the available number of beds and the number of patients waiting for beds in the disease area where the bed is positioned;
and sequencing the beds according to the priority scores of the beds and generating the first sequence.
Further, when the first object is a bed and the second object is a patient, the step S300 includes:
sequentially acquiring the available bed position number and the waiting bed patient number of the disease area where the bed is positioned according to the first sequence;
obtaining a treatment type weight factor according to the treatment type information of the patient;
calculating the bed matching degree between the beds and the patients according to the bed priority scores, the number of available beds, the number of patients waiting for beds and the treatment type weight factors;
and sequencing the beds according to the bed matching degree and generating the second sequence.
Further, the bed matching degree is obtained by calculation according to the following formula:
S＝∑(F*SW)*((A-W)/A)
wherein S represents the matching degree of the bed; f represents the priority grade of the bed; SW represents a treatment type weight factor; a represents the number of available beds; w represents the number of patients waiting for bed.
According to a second aspect of an embodiment of the present application, a hospital bed intelligent distribution system comprises the following modules:
the instruction acquisition module is used for acquiring an allocation instruction and acquiring basic information of the first object according to the allocation instruction; the first object comprises at least one of a patient, a bed;
the first sequence acquisition module is used for screening the distributable second objects according to the basic information and sequencing the second objects to obtain a first sequence; the second object comprises at least one of a patient, a bed;
the second sequence acquisition module is used for sequentially calculating the matching degree between the second object and the first object according to the first sequence and sequencing the matching degree to obtain a second sequence;
an allocation module for allocating the second object to the first object according to the second sequence;
wherein when the first subject is a bed, the second subject is a patient; when the first object is a patient, the second object is a bed.
According to a third aspect of an embodiment of the application, an apparatus comprises:
at least one processor;
at least one memory for storing at least one program;
the at least one program, when executed by the at least one processor, causes the at least one processor to implement the method as described in the first aspect.
According to a fourth aspect of embodiments of the present application, a computer readable storage medium has stored therein a processor executable program for implementing the method of the first aspect when executed by a processor.
The beneficial effects of the application are as follows: by establishing a bidirectional matching mechanism, the hospital bed resources are distributed according to the real-time conditions of the number of patients and the number of beds, so that the reasonable distribution and full utilization of the hospital bed resources are realized.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description is made with reference to the accompanying drawings of the embodiments of the present application or the related technical solutions in the prior art, and it should be understood that the drawings in the following description are only for convenience and clarity of describing some embodiments in the technical solutions of the present application, and other drawings may be obtained according to these drawings without the need of inventive labor for those skilled in the art.
FIG. 1 is a flow chart of a hospital bed intelligent allocation method provided by an embodiment of the application;
FIG. 2 is a flow chart of an implementation provided by an embodiment of the present application;
FIG. 3 is a block diagram of an embodiment of the present application;
fig. 4 is a device connection diagram provided in an embodiment of the present application.
Detailed Description
The conception, specific structure, and technical effects produced by the present application will be clearly and completely described below with reference to the embodiments and the drawings to fully understand the objects, aspects, and effects of the present application.
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
The terms "first," "second," "third," and "fourth" and the like in the description and in the claims and drawings are used for distinguishing between different objects and not necessarily for describing a particular sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The embodiment of the application provides an intelligent hospital bed allocation method, which can be applied to a terminal, a server, or software running in the terminal or the server, such as an application program with an image color constancy processing function, and the like. The terminal may be, but is not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, etc. The server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, basic cloud computing services such as big data and artificial intelligent platforms. Referring to fig. 1, the method includes the following steps S100 to S400:
s100, acquiring an allocation instruction, and acquiring basic information of a first object according to the allocation instruction;
s200, screening the distributable second objects according to the basic information and sequencing the second objects to obtain a first sequence; before screening the assignable second objects according to the basic information, firstly checking whether the first object has a specific second object, if so, directly assigning the specific second object to the first object; if not, screening and acquiring the distributable second objects and sequencing;
alternatively, when the first object is a bed and the second object is a patient, step S200 may be implemented by:
s2011, screening patients meeting requirements according to bed information; the bed information includes at least one of sex information and isolation information;
s2012, obtaining and calculating a patient priority score according to patient information of the patient meeting the requirement; the patient information includes at least one of a period admission time information, a illness state information, a system registration time information;
and S2013, sequencing the patients according to the patient priority scores and generating a first sequence.
Alternatively, when the first object is a patient and the second object is a bed, step S200 may be implemented by:
s2021, screening beds meeting requirements according to patient information; the patient information includes at least one of gender information and isolation information;
s2022, acquiring and calculating a bed priority score according to bed information of the beds meeting the requirements; the bed information comprises at least one of the number of available beds and the number of patients waiting for beds in the disease area where the bed is positioned;
s2023, sequencing the beds according to the priority scores of the beds and generating a first sequence.
S300, sequentially calculating the matching degree between the second object and the first object according to the first sequence, and sequencing to obtain a second sequence;
alternatively, when the first object is a bed and the second object is a patient, step S300 may be implemented by:
s3011, sequentially acquiring treatment type information and waiting time information of a patient according to a first sequence;
s3012, obtaining a treatment type weight factor according to treatment type information, and obtaining a waiting time length weight factor according to waiting time length information;
s3013, calculating the matching degree between the patient and the bed through the patient priority score, the treatment type weight factor and the waiting time length weight factor;
s3014, sorting the patients according to the matching degree of the patients and generating a second sequence.
Alternatively, when the first object is a patient and the second object is a bed, step S300 may be implemented by:
s3021, sequentially acquiring the number of available beds and the number of patients waiting for beds in a disease area where the beds are located according to a first sequence;
s3022, obtaining a treatment type weight factor according to treatment type information of the patient;
s3023, calculating the matching degree between the bed and the patient through the bed priority grade, the available bed number, the waiting bed patient number and the treatment type weight factors;
s3024, sorting the beds according to the matching degree and generating a second sequence.
S400, distributing the second object to the first object according to the second sequence.
In steps S100 to S400, when the first subject is a bed, the second subject is a patient; when the first object is a patient, the second object is a bed.
The patient matching degree is calculated mainly by the formula (1):
S＝∑(F*SW)*(DW^([(MIN-(D-A))/7])) (1)
wherein S is a patient' S fitness score; f is the priority score of the patient; SW is a patient's treatment-type weighting factor, for example: in some embodiments, we set SW in the disease area to 1, SW in the family room to 0.9, SW in the unconfined department to 0.8; DW is a waiting time length weight factor; d is the current time; a is the period selection time; (D-a) is the patient's waiting time; MIN is a preset waiting period, for example: in some embodiments, we set DW to 0.9, MIN to-13, and calculate for one interval a week, then when: -13 to-8, the result is 1; -7 to-1, resulting in 1 x 0.9;0 to 6, the result is 1 x 0.9; in the time of 7 to 13 hours, the catalyst, the result is 1 x 0.9.
The bed matching degree is calculated mainly through the formula (2):
S＝∑(F*SW)*((A-W)/A)) (2)
wherein S is the matching degree of the bed; f is the priority grade of the bed; SW is a patient's treatment-type weighting factor, for example: in some embodiments, we set SW in the disease area to 1, SW in the family room to 0.9, SW in the unconfined department to 0.8; a is the number of available beds; w is the number of patients waiting for bed.
Referring to fig. 2, there is shown an execution flow chart provided according to an embodiment of the present application, starting allocation; basic information of a bed to be allocated or a patient to be admitted is checked;
if the bed is to be matched, checking whether the bed is reserved by a patient, if so, returning patient information by the system and distributing the patient information; if not, screening patients acceptable to the bed; calculating a patient priority score and ordering; calculating the matching degree of the patients and arranging the patients; selecting the most suitable patient according to the sorting result; and ending the distribution.
If the patient is to be matched, checking whether the patient has bed reservation, if so, returning bed information by the system and distributing; if not, screening a disease area which the patient can enter; calculating the priority scores of the available beds in the disease area and sequencing; acquiring available bed information in a disease area, and calculating the matching degree of beds and arranging the beds; selecting the most applicable bed according to the sequencing result; and ending the distribution.
The bidirectional matching mechanism can be switched to use or used simultaneously according to the real-time environment of a hospital, generally, when a large number of patients are to be admitted at present, the patients are selected by taking the bed as a main body, so that the patients which most need medical resources are ensured to be admitted preferentially, and the principle of reasonable allocation of social resources is met; when the number of hospital beds is large at present, the patient is taken as a main body to select the beds, so that the patient can be hospitalized in the environment most suitable for the illness state of the patient, and the patient is facilitated to recover the physical health.
Referring to fig. 3, the application also provides an intelligent hospital bed distribution system, which comprises the following modules:
the instruction acquisition module 301 is configured to acquire an allocation instruction, and obtain basic information of the first object according to the allocation instruction; the first object comprises at least one of a patient, a bed;
the first sequence acquisition module 302 is connected with the instruction acquisition module 301 to realize interaction, and is used for screening the assignable second objects according to the basic information and sequencing the second objects to obtain a first sequence; the second object comprises at least one of a patient, a bed;
the second sequence acquisition module 303 is connected with the first sequence acquisition module 302 to realize interaction, and is used for sequentially calculating the matching degree between the second object and the first object according to the first sequence and sequencing to obtain a second sequence;
the allocation module 304 is connected to the second sequence obtaining module 303 to implement interaction, and is configured to allocate the second object to the first object according to the second sequence.
Wherein when the first subject is a bed, the second subject is a patient; when the first object is a patient, the second object is a bed.
Referring to fig. 4, the present application also provides an apparatus comprising:
at least one processor 401;
at least one memory 402 for storing at least one program;
the at least one program, when executed by the at least one processor 401, causes the at least one processor 401 to implement the method as shown in fig. 1.
The content of the method embodiment shown in fig. 1 is applicable to the embodiment of the present system, and the functions specifically implemented by the embodiment of the present apparatus are the same as those of the method embodiment shown in fig. 1, and the beneficial effects achieved by the method embodiment shown in fig. 1 are the same as those achieved by the method embodiment shown in fig. 1.
The present application also provides a computer readable storage medium in which a processor executable program is stored, which when executed by a processor is adapted to carry out the method as shown in fig. 1.
The content of the method embodiment shown in fig. 1 is applicable to the storage medium embodiment, and the functions implemented by the storage medium embodiment are the same as those of the method embodiment shown in fig. 1, and the advantages achieved by the method embodiment shown in fig. 1 are the same as those achieved by the method embodiment shown in fig. 1.
It is to be understood that all or some of the steps, systems, and methods disclosed above may be implemented in software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.
The embodiments of the present application have been described in detail with reference to the accompanying drawings, but the present application is not limited to the above embodiments, and various changes can be made within the knowledge of one of ordinary skill in the art without departing from the spirit of the present application.
Claims (10)
1. The intelligent hospital bed allocation method is characterized by comprising the following steps of:
s100, acquiring an allocation instruction, and acquiring basic information of a first object according to the allocation instruction;
s200, screening the distributable second objects according to the basic information and sequencing the second objects to obtain a first sequence;
s300, sequentially calculating the matching degree between the second object and the first object according to the first sequence, and sequencing to obtain a second sequence;
s400, distributing the second object to the first object according to the second sequence;
in steps S100 to S400, when the first object is a bed, the second object is a patient; when the first object is a patient, the second object is a bed.
2. The hospital bed intelligent allocation method according to claim 1, wherein when the first object is a bed and the second object is a patient, the step S200 comprises:
screening patients meeting the requirements according to the bed information; the bed information includes at least one of gender information and isolation information;
acquiring and calculating a patient priority score according to patient information of a patient meeting the requirements; the patient information includes at least one of time of admission to hospital information, illness information, system registration time information;
and sequencing the patients according to the patient priority scores and generating the first sequence.
3. The hospital bed intelligent allocation method according to claim 2, wherein when the first object is a bed and the second object is a patient, the step S300 comprises:
sequentially acquiring treatment type information and waiting duration information of a patient according to the first sequence;
obtaining a treatment type weight factor according to the treatment type information, and obtaining a waiting time length weight factor according to the waiting time length information;
calculating the patient matching degree between the patient and the bed according to the patient priority score, the treatment type weight factor and the waiting duration weight factor;
and sequencing the patients according to the patient matching degree and generating the second sequence.
4. A hospital bed intelligent allocation method according to claim 3, wherein the patient matching is calculated by the following formula:
S=∑(F*SW)*(DW^([(MIN-(D-A))/7]))
wherein S represents the patient matching degree; f represents patient priority score; SW represents a treatment type weight factor; DW represents a waiting duration weight factor; d represents the current time; a represents a period selection time; MIN represents a preset waiting period.
5. The hospital bed intelligent allocation method according to claim 1, wherein when the first object is a patient and the second object is a bed, the step S200 comprises:
screening beds meeting requirements according to patient information; the patient information includes at least one of gender information and isolation information;
acquiring and calculating a bed priority score according to bed information of a bed meeting the requirements; the bed information comprises at least one of the available number of beds and the number of patients waiting for beds in the disease area where the bed is positioned;
and sequencing the beds according to the priority scores of the beds and generating the first sequence.
6. The hospital bed intelligent allocation method according to claim 5, wherein when the first object is a patient and the second object is a bed, the step S300 comprises:
sequentially acquiring the available bed position number and the waiting bed patient number of the disease area where the bed is positioned according to the first sequence;
obtaining a treatment type weight factor according to the treatment type information of the patient;
calculating the bed matching degree between the beds and the patients according to the bed priority scores, the number of available beds, the number of patients waiting for beds and the treatment type weight factors;
and sequencing the beds according to the bed matching degree and generating the second sequence.
7. The intelligent hospital bed allocation method according to claim 6, wherein the bed matching degree is calculated by the following formula:
S = ∑(F*SW)*((A-W)/A)
wherein S represents the matching degree of the bed; f represents the priority grade of the bed; SW represents a treatment type weight factor; a represents the number of available beds; w represents the number of patients waiting for bed.
8. An intelligent hospital bed distribution system is characterized by comprising the following modules:
the instruction acquisition module is used for acquiring an allocation instruction and acquiring basic information of the first object according to the allocation instruction; the first object comprises at least one of a patient, a bed;
the first sequence acquisition module is used for screening the distributable second objects according to the basic information and sequencing the second objects to obtain a first sequence; the second object comprises at least one of a patient, a bed;
the second sequence acquisition module is used for sequentially calculating the matching degree between the second object and the first object according to the first sequence and sequencing the matching degree to obtain a second sequence;
an allocation module for allocating the second object to the first object according to the second sequence;
wherein when the first subject is a bed, the second subject is a patient; when the first object is a patient, the second object is a bed.
9. An intelligent hospital bed distribution device, comprising:
at least one processor;
at least one memory for storing at least one program;
the at least one program, when executed by the at least one processor, causes the at least one processor to implement the method of any of claims 1-7.
10. A computer readable storage medium, in which a processor executable program is stored, characterized in that the processor executable program is for implementing the method according to any of claims 1-7 when being executed by a processor.
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| JP7553108B2 (en) | 2021-04-28 | 2024-09-18 | 株式会社ケアコム | Bed transfer information system |
| CN113870987A (en) * | 2021-10-20 | 2021-12-31 | 南京润海科星物联网智能科技有限公司 | Use method of medical intelligent bed cabinet |
| CN114283932B (en) * | 2022-03-03 | 2022-06-10 | 四川大学华西医院 | Medical resource management method, device, electronic equipment and storage medium |
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Denomination of invention: A method, system, equipment, and storage medium for intelligent allocation of hospital beds Effective date of registration: 20231206 Granted publication date: 20230919 Pledgee: Bank of China Limited by Share Ltd. Guangzhou Haizhu branch Pledgor: Guangdong Baihui Technology Co.,Ltd. Registration number: Y2023980069624 |
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