CN111951939A - Medical resource optimal allocation method, system, equipment and storage medium - Google Patents
Medical resource optimal allocation method, system, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a medical resource optimal allocation method, a system, equipment and a storage medium, wherein the method comprises the following steps: acquiring outpatient service, hospitalization and operation data; obtaining the daily predicted total amount of the outpatients of the hospital according to the outpatient service data; obtaining the total amount of bed prediction of the hospitalization department according to outpatient service and hospitalization data; obtaining the total medical technical and technical forecast of the outpatient department according to the outpatient data; obtaining the total predicted amount of medical skill of the hospitalization department according to the hospitalization data; obtaining a medical technology prediction total amount according to the medical technology daily prediction total amount of an outpatient department and the medical technology daily prediction total amount of an inpatient department; obtaining the total amount of routine operations in the hospital according to the hospitalization data; obtaining the total amount of daily operation prediction of the hospital according to outpatient data; and comparing the data with actual hospital resources respectively and optimizing hospital medical resources. The method respectively predicts the use conditions of future medical resources by utilizing daily operation data of the hospital, and adjusts and optimizes the medical resource ratio among different systems of the hospital by comparing with an actual value on the premise of keeping the total amount of the medical resources unchanged.
Description
Technical Field
The present invention relates to the field of medical resource management, and in particular, to a method, a system, a device, and a storage medium for optimal allocation of medical resources.
Background
Along with the increasing urban population, medical resources matched with the urban population are increasingly in short supply, most of the urban best-quality medical resources are gathered in the third hospital, and people like going to the third hospital for treatment when having medical needs due to the consideration of self health, so that the medical resources in the third hospital are increasingly in short supply, and the hidden danger of uneven distribution of internal resources of the hospital is further highlighted.
Medical resources of hospitals mainly comprise outpatient service queuing resources, bed resources, medical technology (medical inspection and detection technology) resources, operation resources and the like; how to coordinate the allocation of medical resources through a scientific and reasonable allocation mechanism on the premise of unchanging the total amount of the existing resources is a technical problem which is urgently needed to be solved at present.
Disclosure of Invention
In order to solve at least one of the technical problems in the prior art, an object of the present invention is to provide a method, a system, a device and a storage medium for optimized allocation of medical resources.
According to a first aspect of the embodiments of the present invention, a medical resource optimal allocation method includes the following steps:
acquiring initial data, wherein the initial data comprises outpatient service data, hospitalization data and operation data;
obtaining the daily forecast quantity of the outpatient department according to the outpatient data, and obtaining the daily forecast total quantity of the outpatient department of the hospital according to the daily forecast quantity of the outpatient department;
obtaining the total predicted bed amount of the hospitalization department according to the outpatient service data and the hospitalization data;
obtaining the clinical technical daily prediction quantity of the outpatient department according to the outpatient data, and sequentially summing the clinical technical daily prediction quantity of the outpatient department according to a clinical department list in the outpatient data to obtain the total clinical technical daily prediction quantity of the outpatient department;
obtaining the medical technical daily prediction quantity of the inpatients according to the inpatient data, and sequentially summing the medical technical daily prediction quantity of the inpatients according to an inpatient list in the outpatient data to obtain the total predicted quantity of the inpatient medical technical daily;
obtaining a medical technology prediction total amount according to the outpatient department medical technology daily prediction total amount and the inpatient department medical technology daily prediction total amount;
obtaining the total amount of routine operations of the hospital according to the hospitalization data;
obtaining the total amount of daily operation prediction of the hospital according to the outpatient service data;
and comparing the daily predicted total amount of the outpatient clinic of the hospital, the bed predicted total amount of the inpatient department, the predicted total amount of the medical skill, the conventional operation predicted total amount of the hospital and the daily operation predicted total amount of the hospital with actual resources of the hospital, and optimizing medical resources of the hospital according to a comparison result.
Further, the step of obtaining the daily forecast quantity of the outpatient department according to the outpatient data and obtaining the daily forecast total quantity of the outpatient department of the hospital according to the daily forecast quantity of the outpatient department comprises the following steps:
acquiring the current daily appointment amount of the outpatient department, the daily appointment amount ratio of the outpatient department, the monthly plus number amount of the outpatient department, the daily average plus number amount of the outpatient department and the outpatient burst factor parameter in the outpatient data;
obtaining the monthly plus number fluctuation rate of the outpatient department according to the monthly plus number amount of the outpatient department;
obtaining the daily plus prediction quantity of the outpatient department according to the monthly plus fluctuation rate and the daily average plus quantity of the outpatient department;
obtaining the outpatient department daily prediction quantity according to the outpatient department daily plus sign prediction quantity, the outpatient department present daily reservation quantity, the outpatient department daily reservation quantity ratio and the outpatient department emergency factor parameter;
and respectively calculating the daily forecast quantity of each clinic department, and summing to obtain the daily forecast total quantity of the hospital clinics.
Further, the step of obtaining a total predicted bed amount of the hospitalization department according to the outpatient service data and the hospitalization data comprises:
acquiring the number of outpatient department inpatients and the outpatient quantity of outpatient departments in the outpatient data, and the current bed usage amount, the hospitalization burst factor parameter, the average use days of the beds of the inpatient departments and the daily average number of outpatients of the inpatient departments in the inpatient data;
obtaining the prediction quantity of newly added beds in the hospital department according to the outpatient quantity of the outpatient department and the parameter of the hospitalization burst factor;
obtaining the predicted discharge amount according to the average daily discharge number of the inpatients and the average use days of beds of the inpatients;
obtaining a bed position prediction increment of the hospital department according to the predicted discharge amount and the bed position prediction amount newly added in the hospital department;
and obtaining the total predicted amount of the bed in the hospital department according to the predicted increment of the bed in the hospital department and the current usage amount of the bed in the hospital department.
Further, the step of obtaining the clinical technical daily prediction quantity of the outpatient department according to the outpatient data, and sequentially summing the clinical technical daily prediction quantity of the outpatient department according to a clinical list in the outpatient data to obtain the total clinical technical daily prediction quantity of the outpatient department comprises the following steps:
acquiring the number of outpatient department-to-medical technology historical people, the outpatient department outpatient quantity, the number of outpatient department single medical technology currently used in the month, the number of outpatient department total medical technology currently used in the month and the outpatient department list in the outpatient data;
obtaining a first conversion rate according to the number of outpatient department medical skill history people and the outpatient quantity of the outpatient department;
obtaining the clinical technical daily forecast of the outpatient department according to the first conversion rate, the number of the current month of the individual medical techniques of the outpatient department, the number of the current month of the total medical techniques of the outpatient department and the daily forecast of the outpatient department;
and sequentially summing the clinic medical technology daily prediction quantity of the clinic according to the clinic department list to obtain the clinic medical technology daily prediction total quantity.
Further, the step of obtaining the predicted daily amount of medical skill of the department of hospitalization according to the hospitalization data, and sequentially summing the predicted daily amount of medical skill of the department of hospitalization according to the list of the department of hospitalization in the outpatient service data to obtain the predicted total amount of medical skill of the department of hospitalization sequentially includes:
acquiring the historical medical skill-to-medical skill history number, the historical inpatient number, the single medical skill current month number, the total medical skill current month number and the inpatient department list of the inpatient departments in the inpatient data;
obtaining a second conversion rate according to the historical number of hospitalizations and the historical number of medical skill-to-medical skill history of the hospitalization department;
obtaining the forecast quantity of medical skills of the hospital departments according to the second conversion rate, the number of the single medical skills of the hospital departments at the current month, the number of the total medical skills of the hospital departments at the current month and the forecast total quantity of beds of the hospital departments;
and sequentially summing the medical technical daily prediction amount of the hospitalization departments according to the hospitalization department list to obtain the total medical technical daily prediction amount of the hospitalization departments.
Further, the step of obtaining a total amount of routine hospital surgery prediction based on the hospitalization data comprises:
acquiring historical hospitalization number in the hospitalization data and historical conventional operation number in the operation data;
obtaining a third conversion rate according to the historical conventional operating population and the historical hospitalization population;
and obtaining the total amount predicted by the routine operation of the hospital according to the third conversion rate and the total amount predicted by the hospital bed in the hospital department.
Further, the step of obtaining the total daily operation forecast amount of the hospital according to the outpatient service data comprises:
acquiring the number of historical outpatients in the outpatient service data and the number of historical operations in the operation data during the day;
obtaining a fourth conversion rate according to the number of the historical daytime surgeries and the number of the historical outpatients;
and obtaining the total forecast amount of the daily operations of the hospital according to the fourth conversion rate and the total forecast amount of the outpatient service days of the hospital.
According to a second aspect of the embodiments of the present invention, a medical resource optimal allocation system comprises the following modules:
the initial data module is used for acquiring initial data, wherein the initial data comprises outpatient service data, hospitalization data and operation data;
the hospital outpatient daily total prediction quantity module is used for obtaining the outpatient daily prediction quantity according to the outpatient data and obtaining the hospital outpatient daily total prediction quantity according to the outpatient daily prediction quantity;
the total bed prediction amount module of the hospital department is used for obtaining the total bed prediction amount of the hospital department according to the outpatient service data and the hospitalization data;
the total quantity module for predicting the medical skill day of the outpatient department is used for obtaining the predicted quantity of the medical skill day of the outpatient department according to the outpatient data and summing the predicted quantity of the medical skill day of the outpatient department in sequence according to the outpatient department list in the outpatient data to obtain the total quantity predicted by the medical skill day of the outpatient department;
the total prediction quantity module of medical skill days of the inpatient department is used for obtaining the predicted quantity of medical skill days of the inpatient department according to the inpatient data and summing the predicted quantity of medical skill days of the inpatient department in sequence according to the inpatient department list in the outpatient data to obtain the total predicted quantity of medical skill days of the inpatient department;
the medical technology total prediction module is used for obtaining a medical technology total prediction amount according to the medical technology daily prediction amount of the outpatient department and the medical technology daily prediction amount of the inpatient department;
the hospital routine operation total prediction quantity module is used for obtaining the hospital routine operation total prediction quantity according to the hospitalization data;
the hospital day surgery total prediction quantity module is used for obtaining the hospital day surgery total prediction quantity according to the outpatient service data;
and the resource optimization module is used for comparing the daily predicted total amount of the outpatient clinic of the hospital, the bed predicted total amount of the inpatient department, the medical technology predicted total amount, the conventional operation predicted total amount of the hospital and the daily operation predicted total amount of the hospital with actual resources of the hospital and optimizing medical resources of the hospital according to a comparison result.
According to a third aspect of embodiments of the present invention, an apparatus, comprises:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement a method as described in the first aspect.
According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium has stored therein a processor-executable program which, when executed by a processor, is configured to implement the method of the first aspect.
The invention has the beneficial effects that: the daily operation data of the hospital is used as a prediction basis to respectively predict outpatient service, bed, medical skill and operation resources of the hospital, and the medical resource ratio among different systems of the hospital is adjusted and optimized on the premise that the total amount of the existing medical resources of the hospital is not changed by comparing the predicted value with the actual value.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description is made on the drawings of the embodiments of the present invention 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 invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a method provided by an embodiment of the present invention;
FIG. 2 is a block diagram of a module connection provided by an embodiment of the present invention;
fig. 3 is a connection diagram of a device provided by an embodiment of the present invention.
Detailed Description
The conception, the specific structure and the technical effects of the present invention will be clearly and completely described in conjunction with the embodiments and the accompanying drawings to fully understand the objects, the schemes and the effects of the present invention.
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," "third," and "fourth," etc. in the description and claims of the invention and in the accompanying drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively 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 can be included in at least one embodiment of the invention. The appearances of the phrase 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. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
To facilitate understanding of the present disclosure by those skilled in the art, some terms appearing herein are to be interpreted accordingly, as follows:
day surgery: the operation in the same day is also called day operation, non-hospitalization operation, homework operation in the same day and the like, and has the advantages of reducing the hospitalization time of the patient, shortening the operation waiting time of the patient, reducing the hospitalization days and lightening the economic burden.
Burst factor parameter: the correction parameters generated by the emergency or epidemic situation with long duration are actively set by human intervention, such as the increase of the fever clinic amount, the respiration clinic amount and the hospitalization amount caused by the new coronary pneumonia, and the reduction of other clinic amounts.
Medical skill: also called as auxiliary diagnosis and treatment, which refers to a medical technology that uses special diagnosis and treatment technologies and equipment to cooperate with clinical departments to diagnose and treat diseases; generally, a hospital is provided with a special medical department, and the department is also called a non-clinical department because the hospital is not provided with a hospital bed and does not receive patients, and is a technical support system in the hospital system.
First conversion rate: refers to the proportion of persons who need a corresponding medical service to diagnose or check the condition of a disease among those who are consulted through an outpatient department.
Second conversion rate: the ratio of the patients who need to be treated in hospital in the hospital department to be diagnosed or examined by the corresponding medical service.
Third conversion rate: refers to the proportion of people who need to undergo conventional surgical treatment among those who are hospitalized in the hospitalized department.
Fourth conversion rate: refers to the proportion of people who need to receive daytime surgical treatment, among those who are interviewed via an outpatient department.
The embodiment of the invention provides a medical resource optimal allocation method, which can be applied to a terminal, a server, 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, and the like. 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 basic cloud computing services such as cloud service, a cloud database, cloud computing, a cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN, and a big data and artificial intelligence platform. Referring to fig. 1, the method includes the following steps S100 to S900:
s100, acquiring initial data, wherein the initial data comprises outpatient service data, hospitalization data and operation data;
s200, obtaining the daily forecast quantity of the outpatient department according to the outpatient data, and obtaining the daily forecast total quantity of the outpatient department of the hospital according to the daily forecast quantity of the outpatient department;
alternatively, step S200 may be implemented by:
s201, acquiring the current daily appointment amount of the outpatient department, the daily appointment amount ratio of the outpatient department, the monthly plus number amount of the outpatient department, the daily average plus number amount of the outpatient department and the outpatient emergency factor parameter in outpatient data;
s202, obtaining the monthly plus number fluctuation rate of the outpatient department according to the monthly plus number of the outpatient department; the monthly plus sign fluctuation rate of the outpatient department is mainly obtained by taking the plus sign amount of two adjacent months of the outpatient department as a calculation parameter, calculating the ratio of the plus sign amount of the previous month to the plus sign amount of the next month, and subtracting 1 from the ratio;
s203, obtaining the daily plus prediction quantity of the outpatient department according to the monthly plus fluctuation rate and the daily average plus quantity of the outpatient department; specifically, the calculation is performed by the following formula:
a＝b*(1-c)
wherein, a represents the outpatient number-adding prediction quantity, b represents the daily average number-adding quantity of the outpatient department, and c represents the monthly number-adding fluctuation rate of the outpatient department;
s204, obtaining the outpatient department daily prediction quantity according to the outpatient department daily plus sign prediction quantity, the outpatient department present daily reservation quantity, the outpatient department daily reservation quantity ratio and the outpatient department emergency factor parameter; the calculation of the daily predicted quantity of the outpatient department is divided into two cases according to the limit of whether the daily predicted quantity of the outpatient department reaches the maximum value or not;
when the outpatient service appointment amount on the day is larger than or equal to the maximum value, the method is calculated by the following formula:
d＝(e+a)*(1+f)
wherein d represents the daily forecast of the outpatient department, e represents the appointment forecast of the current day, a represents the outpatient plus sign forecast, and f represents the outpatient burst factor parameter;
when the outpatient service appointment amount on the day is smaller than the maximum value, the method is calculated by the following formula:
wherein d represents the daily predicted quantity of the outpatient department, e represents the scheduled quantity of the outpatient department on the same day, a represents the number-adding predicted quantity of the outpatient department, f represents the parameter of the outpatient burst factor, and g represents the daily scheduled quantity ratio of the outpatient department;
s205, respectively calculating the daily forecast of each clinic department, and finally summing the daily forecasts of each department to obtain the daily forecast total amount of the hospital clinics;
s300, obtaining the total prediction amount of the bed in the hospital department according to the outpatient service data and the hospitalization data;
alternatively, step S300 may be implemented by:
s301, acquiring the number of inpatients, the number of outpatients, the current bed usage, the sudden factor parameter, the average use days and the daily number of departures in the inpatients in the outpatient data;
s302, obtaining a newly added bed prediction quantity of an inpatient department according to the outpatient quantity of the outpatient department and the inpatient burst factor parameter; specifically, the calculation is performed by the following formula:
h＝i*(1+j)
wherein h represents the prediction quantity of newly added beds in the hospitalization department, i represents the outpatient quantity of the outpatient department, and j represents the parameter of the hospitalization burst factor;
s303, obtaining the expected discharge amount according to the average daily discharge number of the inpatient department and the average use days of beds in the inpatient department; the expected discharge amount is obtained by multiplying the average number of persons discharged from the hospital by the average use days of beds in the hospital;
s304, obtaining a bed position prediction increment of the hospital department according to the predicted discharge amount and the bed position prediction amount newly added in the hospital department; the prediction increment of the bed positions of the hospitalization departments is obtained by subtracting the predicted discharge amount from the prediction amount of newly added bed positions of the hospitalization departments;
s305, obtaining the total prediction quantity of the bed in the hospital department according to the prediction increment of the bed in the hospital department and the current bed usage amount in the hospital department; the total predicted bed amount of the hospitalization department is obtained by summing the current bed usage amount of the hospitalization department with the bed prediction increment of the hospitalization department;
generally, when the total predicted bed amount of the hospital department is larger than the total bed amount of the hospital department, the bed resource prediction is in shortage, and the larger the difference between the two numbers is, the larger the resource shortage degree is;
when the total predicted bed amount of the hospitalization departments is smaller than the total bed amount of the hospitalization departments, bed resources are predicted to be remained, and the larger the difference between the two numbers is, the larger the resource idling degree is;
s400, obtaining the clinical technical daily prediction quantity of the outpatient department according to the outpatient data, and summing the clinical technical daily prediction quantity of the outpatient department in sequence according to a clinical department list in the outpatient data to obtain the total clinical technical daily prediction quantity of the outpatient department;
alternatively, step S400 may be implemented by:
s401, acquiring the number of outpatient department-to-medical technology historical people, the outpatient department outpatient quantity, the number of outpatient department single medical technology current months, the number of outpatient department total medical technology current months and an outpatient department list in outpatient data;
s402, obtaining a first conversion rate according to the number of outpatient department to medical skill history people and the outpatient quantity of the outpatient department; the first conversion rate is the ratio of the number of outpatient service department to medical skill history people to the outpatient service quantity of the outpatient service department;
s403, obtaining the clinical technical daily forecast of the outpatient department according to the first conversion rate, the number of the outpatient department in a single medical technology currently in the month, the number of the outpatient department in a total medical technology currently in the month and the outpatient department daily forecast; the daily prediction quantity of the medical skills of the outpatient department is obtained by calculating the ratio of the number of the current month of the individual medical skills of the outpatient department to the number of the current month of the total medical skills of the outpatient department, and then multiplying the number of the current month of the total medical skills of the outpatient department by the first conversion rate in sequence;
s404, sequentially summing the clinical technical daily prediction of the clinic according to the clinic list to obtain the clinical technical daily prediction total amount;
s500, obtaining the medical technical daily prediction quantity of the inpatients according to the inpatients data, and sequentially summing the medical technical daily prediction quantity of the inpatients according to an inpatients list in the outpatients data to obtain the total predicted quantity of the inpatients medical technical daily;
alternatively, step S500 may be implemented by:
s501, acquiring the number of historical inpatient department to medical skill history, the number of historical inpatients, the number of single medical skill current months, the number of total medical skill current months and an inpatient department list in the inpatient data;
s502, obtaining a second conversion rate according to the number of historical hospitalizations and the number of historical medical skill-to-medical skill conversion of hospitalization departments; the second conversion rate is the ratio of the number of the resident in the hospital to the number of the medical skill history;
s503, obtaining the forecast of the medical skill day of the hospital department according to the second conversion rate, the number of the current month of the individual medical skill of the hospital department, the total number of the current month of the total medical skill of the hospital department and the forecast total amount of the bed position of the hospital department; the hospital department medical skill daily prediction quantity is obtained by calculating the ratio of the number of the current month of individual medical skill of the hospital department to the number of the current month of the total medical skill of the hospital department, and then multiplying the ratio by the total prediction quantity of the bed of the hospital department and the second conversion rate in sequence;
s504, sequentially summing the medical technical daily prediction quantity of the hospitalization departments according to the hospitalization department list to obtain the total medical technical daily prediction quantity of the hospitalization departments;
s600, according to the total predicted amount of medical skills of outpatient departments and the total predicted amount of medical skills of inpatient departments, obtaining a total predicted amount of medical skills;
when the total medical skill prediction amount is larger than the scheduling amount of the medical skill reservation subsystem, the shortage of medical skill resources is indicated;
when the total medical skill prediction amount is smaller than the scheduling amount of the medical skill reservation subsystem, indicating that the medical skill resources are idle;
s700, obtaining the total amount predicted by the routine operation of the hospital according to hospitalization data;
alternatively, step S700 may be implemented by:
s701, acquiring historical hospitalization number in hospitalization data and historical conventional operation number in operation data;
s702, obtaining a third conversion rate according to the historical conventional number of operators and the historical number of hospitalizations; the third conversion rate is the ratio of the number of historical routine surgeries to the number of historical hospitalizations;
s703, obtaining the total amount predicted by the routine operation of the hospital according to the third conversion rate and the total amount predicted by the bed of the hospital department; the total amount of the routine operation forecast of the hospital is obtained by multiplying the total amount of the bed forecast of the hospital department by the third conversion rate;
s800, obtaining the daily operation prediction total amount of the hospital according to outpatient service data;
alternatively, step S800 may be implemented by the following steps;
s801, acquiring the number of historical outpatients in outpatient service data and the number of historical operations in operation data during the day;
s802, obtaining a fourth conversion rate according to the number of the operation persons in the historical daytime and the number of the outpatient services in the historical time; the fourth conversion rate is the ratio of the number of the operation persons in the historical daytime to the number of the outpatient clinics in the historical time;
s803, obtaining the total operation forecast amount in the hospital day according to the fourth conversion rate and the total forecast amount in the hospital outpatient service day; the total amount of the daily operation forecast of the hospital is obtained by multiplying the total amount of the daily forecast of the outpatient clinic of the hospital by the fourth conversion rate;
s900, comparing the total daily prediction amount of the outpatient service of the hospital, the total prediction amount of the bed of the inpatient department, the total prediction amount of medical skill, the total prediction amount of the routine operation of the hospital and the total prediction amount of the day operation of the hospital with actual resources of the hospital, and optimizing medical resources of the hospital according to the comparison result.
Referring to fig. 2, the invention further provides a medical resource optimal allocation system, which comprises the following modules:
an initial data module 201, configured to acquire initial data, where the initial data includes outpatient service data, hospitalization data, and surgical data;
the hospital outpatient day total prediction quantity module 202 is connected with the initial data module 201 to realize interaction and is used for obtaining outpatient day predicted quantity according to outpatient data and obtaining hospital outpatient day total prediction quantity according to the outpatient day predicted quantity;
the total bed prediction amount module 203 of the hospital department is connected with the initial data module 201 to realize interaction and is used for obtaining the total bed prediction amount of the hospital department according to the outpatient service data and the hospitalization data;
the total quantity of clinical technical daily prediction module 204 of the outpatient department is connected with the initial data module 201 to realize interaction and is used for obtaining the daily prediction quantity of the clinical technical of the outpatient department according to the outpatient data and sequentially summing the daily prediction quantity of the clinical technical of the outpatient department according to a list of the outpatient department in the outpatient data to obtain the total quantity of the daily prediction of the clinical technical of the outpatient department;
the total medical technical day prediction amount module 205 of the inpatient department is connected with the initial data module 201 to realize interaction and is used for obtaining the total medical technical day prediction amount of the inpatient department according to the inpatient data and sequentially summing the total medical technical day prediction amount of the inpatient department according to the inpatient department list in the outpatient data;
the medical technology total prediction module 206 is respectively connected with the clinic medical technology daily total prediction module 204 and the hospital medical technology daily total prediction module 205 to realize interaction, and is used for obtaining the medical technology total prediction according to the clinic medical technology daily total prediction and the hospital medical technology daily total prediction;
the hospital routine operation total prediction quantity module 207 is connected with the initial data module 201 to realize interaction and is used for obtaining the hospital routine operation total prediction quantity according to the hospitalization data;
the hospital day surgery total prediction amount module 208 is connected with the initial data module 201 to realize interaction and is used for obtaining the hospital day surgery total prediction amount according to outpatient service data;
and the resource optimization module 209 is respectively connected with the hospital outpatient day total prediction module 202, the hospital department bed total prediction module 203, the medical skill total prediction module 206, the hospital routine operation total prediction module 207 and the hospital day operation total prediction module 208 to realize interaction, and is used for comparing the hospital outpatient day total prediction, the hospital department bed total prediction, the medical skill total prediction, the hospital routine operation total prediction and the hospital day operation total prediction with actual hospital resources and optimizing hospital medical resources according to comparison results.
Referring to fig. 3, the present invention also provides an apparatus comprising:
at least one processor 301;
at least one memory 302 for storing at least one program;
when the at least one program is executed by the at least one processor 301, the at least one processor 301 is caused to implement the method as shown in fig. 1.
The contents in the method embodiment shown in fig. 1 are all applicable to the apparatus embodiment, the functions specifically implemented by the apparatus embodiment are the same as those in the method embodiment shown in fig. 1, and the obtained beneficial effects are also the same as those in the method embodiment shown in fig. 1.
The present invention also provides a computer readable storage medium in which a processor-executable program is stored, which, when executed by a processor, is adapted to implement the method as shown in fig. 1.
The contents in the method embodiment shown in fig. 1 are all applicable to the present storage medium embodiment, the functions implemented by the present storage medium embodiment are the same as those in the method embodiment shown in fig. 1, and the advantageous effects achieved by the present storage medium embodiment are also the same as those achieved by the method embodiment shown in fig. 1.
It will be understood that all or some of the steps, systems of methods disclosed above may be implemented as 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 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 is well known to those of ordinary skill 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 accessed by a computer. In addition, 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 as known to those skilled in the art.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.
Claims (10)
1. A medical resource optimal allocation method is characterized by comprising the following steps:
acquiring initial data, wherein the initial data comprises outpatient service data, hospitalization data and operation data;
obtaining the daily forecast quantity of the outpatient department according to the outpatient data, and obtaining the daily forecast total quantity of the outpatient department of the hospital according to the daily forecast quantity of the outpatient department;
obtaining the total predicted bed amount of the hospitalization department according to the outpatient service data and the hospitalization data;
obtaining the clinical technical daily prediction quantity of the outpatient department according to the outpatient data, and sequentially summing the clinical technical daily prediction quantity of the outpatient department according to a clinical department list in the outpatient data to obtain the total clinical technical daily prediction quantity of the outpatient department;
obtaining the medical technical daily prediction quantity of the inpatients according to the inpatient data, and sequentially summing the medical technical daily prediction quantity of the inpatients according to an inpatient list in the outpatient data to obtain the total predicted quantity of the inpatient medical technical daily;
obtaining a medical technology prediction total amount according to the outpatient department medical technology daily prediction total amount and the inpatient department medical technology daily prediction total amount;
obtaining the total amount of routine operations of the hospital according to the hospitalization data;
obtaining the total amount of daily operation prediction of the hospital according to the outpatient service data;
and comparing the daily predicted total amount of the outpatient clinic of the hospital, the bed predicted total amount of the inpatient department, the predicted total amount of the medical skill, the conventional operation predicted total amount of the hospital and the daily operation predicted total amount of the hospital with actual resources of the hospital, and optimizing medical resources of the hospital according to a comparison result.
2. The method according to claim 1, wherein the step of obtaining the predicted daily amount of the outpatient department according to the outpatient data and the predicted daily amount of the hospital outpatient department according to the predicted daily amount of the outpatient department comprises:
acquiring the current daily appointment amount of the outpatient department, the daily appointment amount ratio of the outpatient department, the monthly plus number amount of the outpatient department, the daily average plus number amount of the outpatient department and the outpatient burst factor parameter in the outpatient data;
obtaining the monthly plus number fluctuation rate of the outpatient department according to the monthly plus number amount of the outpatient department;
obtaining the daily plus prediction quantity of the outpatient department according to the monthly plus fluctuation rate and the daily average plus quantity of the outpatient department;
obtaining the outpatient department daily prediction quantity according to the outpatient department daily plus sign prediction quantity, the outpatient department present daily reservation quantity, the outpatient department daily reservation quantity ratio and the outpatient department emergency factor parameter;
and respectively calculating the daily forecast quantity of each clinic department, and summing to obtain the daily forecast total quantity of the hospital clinics.
3. The method of claim 1, wherein the step of obtaining a total predicted amount of bed positions of the hospital department according to the outpatient service data and the hospitalization data comprises:
acquiring the number of outpatient department inpatients and the outpatient quantity of outpatient departments in the outpatient data, and the current bed usage amount, the hospitalization burst factor parameter, the average use days of the beds of the inpatient departments and the daily average number of outpatients of the inpatient departments in the inpatient data;
obtaining the prediction quantity of newly added beds in the hospital department according to the outpatient quantity of the outpatient department and the parameter of the hospitalization burst factor;
obtaining the predicted discharge amount according to the average daily discharge number of the inpatients and the average use days of beds of the inpatients;
obtaining a bed position prediction increment of the hospital department according to the predicted discharge amount and the bed position prediction amount newly added in the hospital department;
and obtaining the total predicted amount of the bed in the hospital department according to the predicted increment of the bed in the hospital department and the current usage amount of the bed in the hospital department.
4. The method for optimized distribution of medical resources according to claim 1, wherein the step of obtaining the clinical technical daily prediction of the outpatient department according to the outpatient data, and sequentially summing the clinical technical daily prediction of the outpatient department according to the outpatient department list in the outpatient data to obtain the total predicted clinical technical daily amount of the outpatient department comprises:
acquiring the number of outpatient department-to-medical technology historical people, the outpatient department outpatient quantity, the number of outpatient department single medical technology currently used in the month, the number of outpatient department total medical technology currently used in the month and the outpatient department list in the outpatient data;
obtaining a first conversion rate according to the number of outpatient department medical skill history people and the outpatient quantity of the outpatient department;
obtaining the clinical technical daily forecast of the outpatient department according to the first conversion rate, the number of the current month of the individual medical techniques of the outpatient department, the number of the current month of the total medical techniques of the outpatient department and the daily forecast of the outpatient department;
and sequentially summing the clinic medical technology daily prediction quantity of the clinic according to the clinic department list to obtain the clinic medical technology daily prediction total quantity.
5. The method according to claim 1, wherein the step of obtaining predicted medical technical days of the inpatients according to the hospitalization data and sequentially summing the predicted medical technical days of the inpatients according to a list of the inpatients in the outpatient data to obtain a predicted total medical technical days of the inpatients comprises:
acquiring the historical medical skill-to-medical skill history number, the historical inpatient number, the single medical skill current month number, the total medical skill current month number and the inpatient department list of the inpatient departments in the inpatient data;
obtaining a second conversion rate according to the historical number of hospitalizations and the historical number of medical skill-to-medical skill history of the hospitalization department;
obtaining the forecast quantity of medical skills of the hospital departments according to the second conversion rate, the number of the single medical skills of the hospital departments at the current month, the number of the total medical skills of the hospital departments at the current month and the forecast total quantity of beds of the hospital departments;
and sequentially summing the medical technical daily prediction amount of the hospitalization departments according to the hospitalization department list to obtain the total medical technical daily prediction amount of the hospitalization departments.
6. The method for optimized distribution of medical resources according to claim 1, wherein the step of obtaining a total amount of routine hospital surgery prediction from the hospitalization data comprises:
acquiring historical hospitalization number in the hospitalization data and historical conventional operation number in the operation data;
obtaining a third conversion rate according to the historical conventional operating population and the historical hospitalization population;
and obtaining the total amount predicted by the routine operation of the hospital according to the third conversion rate and the total amount predicted by the hospital bed in the hospital department.
7. The method of claim 1, wherein the step of deriving a predicted total daily surgical volume for a hospital from the outpatient data comprises:
acquiring the number of historical outpatients in the outpatient service data and the number of historical operations in the operation data during the day;
obtaining a fourth conversion rate according to the number of the historical daytime surgeries and the number of the historical outpatients;
and obtaining the total forecast amount of the daily operations of the hospital according to the fourth conversion rate and the total forecast amount of the outpatient service days of the hospital.
8. A medical resource optimal allocation system is characterized by comprising the following modules:
the initial data module is used for acquiring initial data, wherein the initial data comprises outpatient service data, hospitalization data and operation data;
the hospital outpatient daily total prediction quantity module is used for obtaining the outpatient daily prediction quantity according to the outpatient data and obtaining the hospital outpatient daily total prediction quantity according to the outpatient daily prediction quantity;
the total bed prediction amount module of the hospital department is used for obtaining the total bed prediction amount of the hospital department according to the outpatient service data and the hospitalization data;
the total quantity module for predicting the medical skill day of the outpatient department is used for obtaining the predicted quantity of the medical skill day of the outpatient department according to the outpatient data and summing the predicted quantity of the medical skill day of the outpatient department in sequence according to the outpatient department list in the outpatient data to obtain the total quantity predicted by the medical skill day of the outpatient department;
the total prediction quantity module of medical skill days of the inpatient department is used for obtaining the predicted quantity of medical skill days of the inpatient department according to the inpatient data and summing the predicted quantity of medical skill days of the inpatient department in sequence according to the inpatient department list in the outpatient data to obtain the total predicted quantity of medical skill days of the inpatient department;
the medical technology total prediction module is used for obtaining a medical technology total prediction amount according to the medical technology daily prediction amount of the outpatient department and the medical technology daily prediction amount of the inpatient department;
the hospital routine operation total prediction quantity module is used for obtaining the hospital routine operation total prediction quantity according to the hospitalization data;
the hospital day surgery total prediction quantity module is used for obtaining the hospital day surgery total prediction quantity according to the outpatient service data;
and the resource optimization module is used for comparing the daily predicted total amount of the outpatient clinic of the hospital, the bed predicted total amount of the inpatient department, the medical technology predicted total amount, the conventional operation predicted total amount of the hospital and the daily operation predicted total amount of the hospital with actual resources of the hospital and optimizing medical resources of the hospital according to a comparison result.
9. An apparatus, comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method of any one of claims 1-7.
10. A computer-readable storage medium, in which a program executable by a processor is stored, characterized in that the program executable by the processor is adapted to implement the method according to any one of claims 1-7 when executed by the processor.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112542238A (en) * | 2020-12-24 | 2021-03-23 | 南方科技大学 | Hospital outpatient planning method, device, equipment and storage medium |
| CN113298374A (en) * | 2021-05-21 | 2021-08-24 | 王志斌 | Resource management system based on big data and intelligent medical treatment |
| CN113488158A (en) * | 2021-08-03 | 2021-10-08 | 浙江一山智慧医疗研究有限公司 | Department position distribution and allocation method and system suitable for hospital departments |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106920009A (en) * | 2017-03-03 | 2017-07-04 | 北京北青厚泽数据科技有限公司 | The Forecasting Methodology of hospital services amount |
| CN107180271A (en) * | 2017-04-27 | 2017-09-19 | 广州慧扬健康科技有限公司 | The forecasting system of number of hospitalized based on least square method |
| CN108630319A (en) * | 2017-03-21 | 2018-10-09 | ***通信有限公司研究院 | A kind of prediction technique and device based on medical data modeling |
| CN109817317A (en) * | 2019-03-13 | 2019-05-28 | 北京睿勤永尚建设顾问有限公司 | A kind of method of determining operating room quantity and automatic system using this method |
| CN110246573A (en) * | 2019-05-15 | 2019-09-17 | 赛尔科创(深圳)科技有限公司 | The automatic method of hospital's functional planning and areal calculation, device and terminal device |
-
2020
- 2020-07-10 CN CN202010661598.4A patent/CN111951939A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106920009A (en) * | 2017-03-03 | 2017-07-04 | 北京北青厚泽数据科技有限公司 | The Forecasting Methodology of hospital services amount |
| CN108630319A (en) * | 2017-03-21 | 2018-10-09 | ***通信有限公司研究院 | A kind of prediction technique and device based on medical data modeling |
| CN107180271A (en) * | 2017-04-27 | 2017-09-19 | 广州慧扬健康科技有限公司 | The forecasting system of number of hospitalized based on least square method |
| CN109817317A (en) * | 2019-03-13 | 2019-05-28 | 北京睿勤永尚建设顾问有限公司 | A kind of method of determining operating room quantity and automatic system using this method |
| CN110246573A (en) * | 2019-05-15 | 2019-09-17 | 赛尔科创(深圳)科技有限公司 | The automatic method of hospital's functional planning and areal calculation, device and terminal device |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112542238A (en) * | 2020-12-24 | 2021-03-23 | 南方科技大学 | Hospital outpatient planning method, device, equipment and storage medium |
| CN112542238B (en) * | 2020-12-24 | 2022-04-29 | 南方科技大学 | Hospital outpatient planning method, device, equipment and storage medium |
| WO2022134650A1 (en) * | 2020-12-24 | 2022-06-30 | 南方科技大学 | Hospital outpatient planning method and apparatus, and device and storage medium |
| CN113298374A (en) * | 2021-05-21 | 2021-08-24 | 王志斌 | Resource management system based on big data and intelligent medical treatment |
| CN113488158A (en) * | 2021-08-03 | 2021-10-08 | 浙江一山智慧医疗研究有限公司 | Department position distribution and allocation method and system suitable for hospital departments |
| CN113488158B (en) * | 2021-08-03 | 2022-02-11 | 浙江一山智慧医疗研究有限公司 | Department position distribution and allocation method and system suitable for hospital departments |
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