CN115662646A - Construction method and device of medical decision platform, electronic equipment and storage medium - Google Patents
Construction method and device of medical decision platform, electronic equipment and storage medium Download PDFInfo
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Abstract
The present disclosure discloses a construction method and apparatus of a medical decision platform, an electronic device and a storage medium, which relate to the technical field of medical data, and the main technical scheme comprises: firstly, inputting historical diagnosis and treatment data in a medical insurance overall range into a grouping module, analyzing and training the historical diagnosis and treatment data based on a preset training index, and generating a grouping scheme in the medical insurance overall range; secondly, generating a clinical path module and a clinical path service package module based on the historical diagnosis and treatment data; finally, the grouping module, the clinical path module and the clinical path service package module are configured in a construction platform of the medical decision platform; the disease clinical treatment path is determined based on the real medical data in the overall area, the localization of the clinical path module and the clinical path service pack module is realized, and corresponding disease diagnosis and treatment paths are respectively generated according to the real medical data for different genders and ages, so that policy decision related to special crowds is well supported.
Description
Technical Field
The present disclosure relates to the field of medical data processing technologies, and in particular, to a method and an apparatus for constructing a medical decision platform, an electronic device, and a storage medium.
Background
Currently, the choice of treatment regimen for a disease with respect to drugs/consumables is based on the results of randomized controlled trials of drug/consumable response studies.
However, the random control test is relatively limited in population selection, excludes special populations such as the elderly, children, pregnant and lying-in women, and only demonstrates to a certain extent whether the drugs/consumables are available in medicine and clinic, and cannot support policy decisions related to factors such as special populations, treatment cost and treatment expense.
Disclosure of Invention
The disclosure provides a construction method and device of a medical decision platform, electronic equipment and a storage medium.
According to a first aspect of the present disclosure, there is provided a method for constructing a medical decision platform, including:
the historical diagnosis and treatment data in the medical insurance overall planning range are input into the grouping module, the historical diagnosis and treatment data are analyzed and trained on the basis of a preset training index, a grouping scheme in the medical insurance overall planning range is generated, and the grouping scheme comprises: disease diagnosis and surgical procedures;
based on the historical diagnosis and treatment data, learning to obtain a corresponding diagnosis and treatment item set, a medicine set name and a consumable set in the historical diagnosis and treatment data so as to generate the clinical path module;
analyzing treatment details in the historical diagnosis and treatment data to obtain different types of sets and corresponding service packages in each path in the clinical path module so as to generate the clinical path service package module;
configuring the grouping module, the clinical pathway module, and the clinical pathway service package module in a build platform of a medical decision platform.
Optionally, the method further includes:
acquiring diagnostic information to be decided, and determining a disease diagnosis name and/or an operation name corresponding to the diagnostic information to be decided in a grouping module;
in the clinical path module, determining a corresponding diagnosis and treatment project set, a medicine set name and a consumable set according to the disease diagnosis name and/or the operation name; wherein the clinical pathway module comprises an overall treatment regimen for the disease;
in the clinical path service package module, service packages corresponding to the diagnosis and treatment project package, the medicine package name and the consumable package are respectively selected to generate a treatment scheme corresponding to the to-be-decided diagnosis information, wherein the service packages comprise disease treatment names, execution time and treatment times. Optionally, the construction platform of the medical decision platform further includes a diagnosis and treatment cost optimization module, and the method further includes:
configuring the diagnosis and treatment cost optimization module in a construction platform of the medical decision platform;
and carrying out cost measurement and adjustment on the treatment schemes in the clinical path module and the clinical path service package module based on the diagnosis and treatment cost optimization module.
Optionally, the generating the grouping scheme within the medical insurance pool range includes:
and generating fund payment amount corresponding to the grouping on the basis of the historical diagnosis and treatment data.
Optionally, the performing, based on the diagnosis and treatment cost optimization module, cost measurement and adjustment on the treatment schemes in the clinical pathway module and the clinical pathway service package module includes:
adjusting the grouping cost and analyzing the medical cost based on medical expense policies and medical insurance payment policies in the overall area;
and optimizing the treatment schemes in the clinical path module and the clinical path service pack module according to the adjustment result of the group expense and the medical cost analysis result.
According to a second aspect of the present disclosure, there is provided a construction apparatus of a medical decision platform, including:
the first generation unit is used for inputting historical diagnosis and treatment data in the medical insurance planning range into the grouping module, analyzing and training the historical diagnosis and treatment data based on a preset training index, and generating a grouping scheme in the medical insurance planning range, wherein the grouping scheme comprises: disease diagnosis and surgical procedures;
the second generation unit is used for learning to obtain a diagnosis and treatment item set, a medicine set name and a consumable set corresponding to the historical diagnosis and treatment data based on the historical diagnosis and treatment data so as to generate the clinical path module;
a third generation unit, configured to analyze treatment details in the historical diagnosis and treatment data to obtain different types of suites and corresponding service packages in each path in the clinical path module, so as to generate the clinical path service package module;
a first configuration unit for configuring the grouping module, the clinical pathway module and the clinical pathway service package module in a construction platform of a medical decision platform.
Optionally, the apparatus further comprises:
the first determining unit is used for acquiring the information to be decided and diagnosed and determining the disease diagnosis name and/or the operation name corresponding to the information to be decided and diagnosed in the grouping module;
the second determining unit is used for determining a corresponding diagnosis and treatment project set, a medicine set name and a consumable set according to the disease diagnosis name and/or the operation name in the clinical path module; wherein the clinical pathway module comprises an overall treatment regimen for the disease;
and a fourth generating unit, configured to select, in a clinical pathway service package module, service packages corresponding to the diagnosis and treatment item suite, the drug suite name, and the consumable suite to support, respectively, so as to generate a treatment scheme corresponding to the to-be-decided diagnosis information, where the service packages include a disease treatment name, an execution time, and a treatment frequency.
Optionally, the construction platform of the medical decision platform further includes a diagnosis and treatment cost optimization module, and the apparatus further includes:
the second configuration unit is used for configuring the diagnosis and treatment cost optimization module in a construction platform of the medical decision platform;
and the adjusting unit is used for carrying out cost measurement and adjustment on the treatment schemes in the clinical path module and the clinical path service package module based on the diagnosis and treatment cost optimizing module.
Optionally, the second generating unit is further configured to generate a fund payment amount corresponding to the group based on the historical diagnosis and treatment data.
Optionally, the adjusting unit includes:
the analysis module is used for adjusting the grouped expenses and analyzing the medical cost based on medical expense policies and medical insurance payment policies in the overall area;
and the optimization module is used for optimizing the treatment schemes in the clinical path module and the clinical path service pack module according to the adjustment result of the grouping expense and the medical cost analysis result.
According to a third aspect of the present disclosure, there is provided an electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect.
According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of the aforementioned first aspect.
According to a fifth aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the method as set forth in the preceding first aspect.
The construction method, the construction device, the electronic equipment and the storage medium of the medical decision platform provided by the disclosure have the main technical scheme that: the historical diagnosis and treatment data in the medical insurance planning range are input into the grouping module, the historical diagnosis and treatment data are analyzed and trained based on a preset training index, a grouping scheme in the medical insurance planning range is generated, and the grouping scheme comprises the following steps: disease diagnosis and surgical procedures; based on the historical diagnosis and treatment data, learning to obtain a diagnosis and treatment item set, a medicine set name and a consumable set corresponding to the historical diagnosis and treatment data so as to generate the clinical path module; analyzing treatment details in the historical diagnosis and treatment data to obtain different types of sets and corresponding service packages in each path in the clinical path module so as to generate the clinical path service package module; configuring the grouping module, the clinical pathway module, and the clinical pathway service package module in a build platform of a medical decision platform; compared with the prior art, the disease clinical treatment path is determined based on the real medical data in the overall area, the localization of the clinical path module and the clinical path service pack module is realized, the corresponding disease diagnosis and treatment paths are respectively generated according to the real medical data for different sexes and ages, and the policy decision related to special crowds is well supported.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present application, nor do they limit the scope of the present application. Other features of the present application will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
fig. 1 is a schematic flow chart of a method for constructing a medical decision platform according to an embodiment of the present disclosure;
fig. 2 is a schematic flow chart illustrating an application method of a medical decision platform according to an embodiment of the present disclosure;
fig. 3 is a decision flow diagram of a medical decision platform provided by an embodiment of the present disclosure;
fig. 4 is a schematic flow chart illustrating a method for optimizing diagnosis and treatment costs according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a device for constructing a medical decision platform according to an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of another medical decision platform construction device provided in the embodiment of the present disclosure;
fig. 7 is a schematic block diagram of an example electronic device 500 provided by embodiments of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of embodiments of the present disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
A method, an apparatus, an electronic device, and a storage medium for constructing a medical decision platform according to an embodiment of the present disclosure are described below with reference to the drawings.
Fig. 1 is a schematic flow chart of a method for constructing a medical decision platform according to an embodiment of the present disclosure.
As shown in fig. 1, the method comprises the following steps:
Because medical insurance policies in different regions have differences and medical insurance reimbursement standards of different hospitals in the same region are different, a decision platform needs to be constructed according to historical diagnosis and treatment data in the current medical insurance planning range; the diagnosis and treatment data comprises sex, age, diseases, complications and related treatment data of the patient; the preset training index includes: indexes such as Diagnosis Related Groups (DRG)/Big Data-based group number of disease categories (DIP), case combination Index (Case Mix Index, CMI), time consumption Index, cost consumption Index and total medical cost; the grouping scheme also comprises a disease code and a disease name, and the disease name also comprises a disease diagnosis code and a surgical operation code.
The historical diagnosis and treatment data are historical real diagnosis and treatment data, and the diagnosis and treatment data in two years in the current medical insurance overall planning area can be selected for analysis when the historical diagnosis and treatment data are selected in consideration of updating of medical technologies and research and development of new drugs.
And 102, learning to obtain a diagnosis and treatment item set, a medicine set name and a consumable set corresponding to the historical diagnosis and treatment data based on the historical diagnosis and treatment data so as to generate the clinical path module.
Based on the diagnosis and treatment data of the same disease, sex and age in the historical diagnosis and treatment data, learning to extract an optimal treatment path and determining the current optimal treatment path, wherein the optimal treatment path comprises a treatment item set, a medicine set and a consumable set, a treatment item set code, a medicine set code and a consumable set code.
The clinical path service package module comprises a diagnosis and treatment item code, a diagnosis and treatment item name, a diagnosis and treatment medical advice execution time, a diagnosis and treatment medical advice execution frequency, a hospital code of a diagnosis and treatment item and a medical insurance code mapping relation table; the method comprises the following steps of (1) mapping a drug code, a drug name, a drug order execution time, a drug order execution frequency, a hospital code of a drug and a medical insurance code to a relational table; the method comprises the following steps of (1) identifying a consumable item code, a consumable item name, a consumable medical advice execution time, consumable medical advice execution times, a hospital code of the consumable item and a medical insurance code mapping comparison relation table, wherein the code is used for confirming a corresponding item from a corresponding set name; the hospital code and medical insurance code mapping comparison relation table of the diagnosis and treatment items, the hospital code and medical insurance code mapping comparison relation table of the medicines and the hospital code and medical insurance code mapping comparison relation table of the consumables are used for corresponding the medical insurance codes and the hospital codes and unifying different codes of the same article.
The medical decision platform provides services based on the constructed grouping module, the clinical path module and the clinical path service package module, and provides a diagnosis and treatment scheme for patients.
The construction method of the medical decision platform provided by the present disclosure comprises the following main technical scheme: the historical diagnosis and treatment data in the medical insurance planning range are input into the grouping module, the historical diagnosis and treatment data are analyzed and trained based on a preset training index, a grouping scheme in the medical insurance planning range is generated, and the grouping scheme comprises the following steps: disease diagnosis and surgical procedures; based on the historical diagnosis and treatment data, learning to obtain a diagnosis and treatment item set, a medicine set name and a consumable set corresponding to the historical diagnosis and treatment data so as to generate the clinical path module; analyzing treatment details in the historical diagnosis and treatment data to obtain different types of sets and corresponding service packages in each path in the clinical path module so as to generate the clinical path service package module; configuring the grouping module, the clinical pathway module, and the clinical pathway service package module in a build platform of a medical decision platform; compared with the prior art, the disease clinical treatment path is determined based on the real medical data in the overall area, the localization of the clinical path module and the clinical path service pack module is realized, the corresponding disease diagnosis and treatment paths are respectively generated according to the real medical data for different sexes and ages, and the policy decision related to special crowds is well supported.
As an extension of the embodiment of the above application, when generating the grouping scheme within the medical insurance orchestration range, the method further includes: and generating fund payment amount corresponding to the grouping based on the historical diagnosis and treatment data.
After the construction of the medical decision platform is completed in the application embodiment, a disease treatment scheme of the best clinical practice research can be decided based on the medical decision platform through the basic information and the diagnosis information of the patient; as shown in fig. 2, fig. 2 is a schematic flowchart of an application method of a medical decision platform according to an embodiment of the present disclosure, including:
Referring to fig. 3, fig. 3 is a decision flow chart of a medical decision platform according to an embodiment of the present application; the to-be-decided diagnosis information includes basic information and diagnosis information of the patient, the basic information includes information such as age and sex of the patient, the diagnosis information includes disease information of the patient, whether the patient has complications or not, whether the patient has undergone an operation or not, and the like.
Please continue to refer to fig. 3; confirming the path code according to the disease diagnosis name and/or the operation name, confirming the current clinical path name according to the path code, confirming the diagnosis and treatment item set code, the medicine set code and the consumable set code in the clinical path based on the age, the sex and the like of the patient, and further confirming the corresponding diagnosis and treatment item set, the medicine set and the consumable set.
Identifying diagnosis and treatment item codes, medicine codes and consumable part codes according to the identified diagnosis and treatment item set, medicine set and consumable part set, and further identifying treatment details such as treatment item names, execution time, execution times, medicine names, execution time, execution times, consumable part names, execution time, execution times and the like; the information is acquired, and meanwhile, a comparison table of the diagnosis and treatment item hospital and the medical insurance code, a comparison relation of the medicine hospital and the medical insurance code and a comparison relation of the consumable hospital and the medical insurance code are acquired, so that the medical expense of the patient can be conveniently reimbursed after the patient completes treatment.
With the development of science and technology, medical technology and medicines are also continuously brought forward, so that the clinical path in the clinical path module needs to be optimized, and the cost of the clinical path needs to be measured and calculated again; referring to fig. 4, fig. 4 is a schematic flowchart illustrating a method for optimizing diagnosis and treatment costs according to an embodiment of the present disclosure, including:
And 302, carrying out cost measurement and adjustment on the treatment schemes in the clinical path module and the clinical path service package module based on the diagnosis and treatment cost optimization module.
Summarizing, analyzing and measuring newly generated real medical data according to a preset time threshold value according to a clinical path module and a clinical path service pack module, detecting whether a new technology, a new medicine or a new application of the existing technology and the medicine exists in the newly generated medical data, if so, detecting an application effect (treatment days, cost and the like), and if the generated effect is superior to a clinical path in a medical decision platform, optimizing the clinical path in the medical decision platform; after the clinical path in the medical decision platform is optimized, adjusting the grouped cost and analyzing the medical cost according to a medical expense policy and a medical insurance payment policy in the general planning area; and optimizing the treatment schemes in the clinical path module and the clinical path service pack module according to the adjustment result of the grouping expense and the medical cost analysis result.
According to the method and the device, the real curative effect and the actual payment condition of the medicine/consumable can be analyzed according to real world medical data and diagnosis and treatment data, the adjustment of treatment schemes such as medicine/consumable use and the adjustment of related fund payment amount and proportion can be carried out more efficiently by combining with DRG/DIP groups.
Corresponding to the construction method of the medical decision platform, the invention also provides a construction device of the medical decision platform. Since the device embodiment of the present invention corresponds to the method embodiment described above, details that are not disclosed in the device embodiment may refer to the method embodiment described above, and are not described again in the present invention.
Fig. 5 is a schematic structural diagram of a device for constructing a medical decision platform according to an embodiment of the present disclosure, as shown in fig. 5, including:
the first generation unit 41 is configured to input historical diagnosis and treatment data within the medical insurance orchestration range into the grouping module, perform analysis training on the historical diagnosis and treatment data based on a preset training index, and generate a grouping scheme within the medical insurance orchestration range, where the grouping scheme includes: disease diagnosis and surgical procedures;
a second generating unit 42, configured to learn, based on the historical diagnosis and treatment data, a diagnosis and treatment item suite, a drug suite name, and a consumable suite corresponding to the historical diagnosis and treatment data, so as to generate the clinical path module;
a third generating unit 43, configured to analyze the treatment details in the historical clinical data to obtain different types of suites and corresponding service packages in each path in the clinical path module, so as to generate the clinical path service package module;
a first configuration unit 44 configured to configure the grouping module, the clinical pathway module, and the clinical pathway service package module in a construction platform of a medical decision platform.
The construction device of the medical decision platform provided by the disclosure comprises the following main technical schemes: the historical diagnosis and treatment data in the medical insurance planning range are input into the grouping module, the historical diagnosis and treatment data are analyzed and trained based on a preset training index, a grouping scheme in the medical insurance planning range is generated, and the grouping scheme comprises the following steps: disease diagnosis and surgical procedures; based on the historical diagnosis and treatment data, learning to obtain a diagnosis and treatment item set, a medicine set name and a consumable set corresponding to the historical diagnosis and treatment data so as to generate the clinical path module; analyzing treatment details in the historical diagnosis and treatment data to obtain different types of sets and corresponding service packages in each path in the clinical path module so as to generate the clinical path service package module; configuring the grouping module, the clinical pathway module, and the clinical pathway service package module in a build platform of a medical decision platform; compared with the related art, the disease clinical treatment path is determined based on the real medical data in the overall area, the localization of the clinical path module and the clinical path service pack module is realized, the corresponding disease diagnosis and treatment paths are respectively generated according to the real medical data for different genders and ages, and policy decisions related to special crowds are well supported.
Further, in a possible implementation manner of this embodiment, as shown in fig. 6, the apparatus further includes:
the first determining unit 45 is configured to obtain to-be-decided diagnosis information, and determine a disease diagnosis name and/or an operation name corresponding to the to-be-decided diagnosis information in the grouping module;
a second determining unit 46, configured to determine, in the clinical pathway module, a corresponding diagnosis and treatment item suite, a corresponding medicine suite name, and a corresponding consumable suite according to the disease diagnosis name and/or the operation name; wherein the clinical pathway module comprises an overall treatment regimen for the disease;
a fourth generating unit 47, configured to select, in a clinical pathway service package module, service packages corresponding to the diagnosis and treatment item suite, the name of the drug suite, and the consumable suite, respectively, so as to generate a treatment scheme corresponding to the to-be-decided diagnosis information, where the service packages include a disease treatment name, an execution time, and a treatment frequency.
Further, in a possible implementation manner of this embodiment, as shown in fig. 6, the construction platform of the medical decision platform further includes a diagnosis and treatment cost optimization module, and the apparatus further includes:
a second configuration unit 48, configured to configure the diagnosis and treatment cost optimization module in a construction platform of the medical decision platform;
and the adjusting unit 49 is configured to perform cost measurement and adjustment on the treatment schemes in the clinical pathway module and the clinical pathway service package module based on the diagnosis and treatment cost optimization module.
Further, in a possible implementation manner of this embodiment, the second generating unit 42 is further configured to generate a fund payment amount corresponding to a group based on the historical diagnosis and treatment data.
Further, in a possible implementation manner of this embodiment, as shown in fig. 6, the adjusting unit 49 includes:
an analysis module 491 for adjusting the grouped fees and analyzing the medical costs based on the medical fee policy and the medical insurance payment policy in the general planning area;
and an optimizing module 492 for optimizing the treatment plans in the clinical pathway module and the clinical pathway service package module according to the adjustment result of the grouping cost and the medical cost analysis result.
It should be noted that the foregoing explanations of the method embodiments also apply to the apparatus of this embodiment, and the principle is the same, and this embodiment is not limited.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
FIG. 7 shows a schematic block diagram of an example electronic device 500 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 7, the apparatus 500 includes a computing unit 501 which can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 502 or a computer program loaded from a storage unit 508 into a RAM (Random Access Memory) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The calculation unit 501, the ROM 502, and the RAM 503 are connected to each other by a bus 504. An I/O (Input/Output) interface 505 is also connected to the bus 504.
A number of components in the device 500 are connected to the I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, or the like; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508, such as a magnetic disk, optical disk, or the like; and a communication unit 509 such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The computing unit 501 may be a variety of general and/or special purpose processing components with processing and computing capabilities. Some examples of the computing Unit 501 include, but are not limited to, a CPU (Central Processing Unit), a GPU (graphics Processing Unit), various dedicated AI (Artificial Intelligence) computing chips, various computing Units running machine learning model algorithms, a DSP (Digital Signal Processor), and any suitable Processor, controller, microcontroller, and the like. The computing unit 501 performs the various methods and processes described above, such as the construction of a medical decision platform. For example, in some embodiments, the method of construction of the medical decision platform may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 500 via the ROM 502 and/or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured by any other suitable means (e.g., by means of firmware) to perform the aforementioned method of construction of a medical decision platform.
Various implementations of the systems and techniques described here above may be realized in digital electronic circuitry, integrated circuitry, FPGAs (Field Programmable Gate arrays), ASICs (Application-Specific Integrated circuits), ASSPs (Application Specific Standard products), SOCs (System On Chip, system On a Chip), CPLDs (Complex Programmable Logic devices), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, causes the functions/acts specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a RAM, a ROM, an EPROM (Electrically Programmable Read-Only-Memory) or flash Memory, an optical fiber, a CD-ROM (Compact Disc Read-Only-Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a Display device (e.g., a CRT (Cathode Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), internet, and blockchain Network.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server can be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.
It should be noted that artificial intelligence is a subject for studying a computer to simulate some human thinking process and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and has both hardware-level and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing, and the like; the artificial intelligence software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, machine learning/deep learning, a big data processing technology, a knowledge map technology and the like.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (13)
1. A method for constructing a medical decision platform, comprising:
the historical diagnosis and treatment data in the medical insurance overall planning range are input into the grouping module, the historical diagnosis and treatment data are analyzed and trained on the basis of a preset training index, a grouping scheme in the medical insurance overall planning range is generated, and the grouping scheme comprises: disease diagnosis and surgical procedures;
based on the historical diagnosis and treatment data, learning to obtain a diagnosis and treatment item set, a medicine set name and a consumable set corresponding to the historical diagnosis and treatment data so as to generate the clinical path module;
analyzing treatment details in the historical diagnosis and treatment data to obtain different types of sets and corresponding service packages in each path in the clinical path module so as to generate the clinical path service package module;
configuring the grouping module, the clinical pathway module, and the clinical pathway service bundle module in a build platform of a medical decision platform.
2. The method of claim 1, further comprising:
acquiring diagnostic information to be decided, and determining a disease diagnosis name and/or an operation name corresponding to the diagnostic information to be decided in a grouping module;
in a clinical path module, determining a corresponding diagnosis and treatment project set, a medicine set name and a consumable set according to the disease diagnosis name and/or the operation name; wherein the clinical pathway module comprises an overall treatment regimen for the disease;
in the clinical path service package module, service packages corresponding to the diagnosis and treatment project package, the medicine package name and the consumable package are respectively selected to generate a treatment scheme corresponding to the to-be-decided diagnosis information, wherein the service packages comprise disease treatment names, execution time and treatment times.
3. The method of claim 1, wherein the construction platform of the medical decision platform further comprises a diagnosis cost optimization module, and the method further comprises:
configuring the diagnosis and treatment cost optimization module in a construction platform of the medical decision platform;
and carrying out cost measurement and adjustment on the treatment schemes in the clinical path module and the clinical path service package module based on the diagnosis and treatment cost optimization module.
4. The method of claim 3, wherein the generating the grouping scheme within the medical insurance orchestration scope comprises:
and generating fund payment amount corresponding to the grouping based on the historical diagnosis and treatment data.
5. The method of claim 4, wherein the cost calculating and adjusting treatment options in the clinical pathway module and the clinical pathway service package module based on the treatment cost optimization module comprises:
adjusting the grouped expenses and analyzing the medical cost based on a medical expense policy and a medical insurance payment policy in the overall area;
and optimizing the treatment schemes in the clinical path module and the clinical path service pack module according to the adjustment result of the grouping expense and the medical cost analysis result.
6. An apparatus for constructing a medical decision platform, comprising:
the first generation unit is used for inputting historical diagnosis and treatment data in the medical insurance overall planning range into the grouping module, analyzing and training the historical diagnosis and treatment data based on a preset training index, and generating a grouping scheme in the medical insurance overall planning range, wherein the grouping scheme comprises: disease diagnosis and surgical procedures;
the second generation unit is used for learning to obtain a diagnosis and treatment item set, a medicine set name and a consumable set corresponding to the historical diagnosis and treatment data based on the historical diagnosis and treatment data so as to generate the clinical path module;
a third generation unit, configured to analyze treatment details in the historical diagnosis and treatment data to obtain different types of suites and corresponding service packages in each path in the clinical path module, so as to generate the clinical path service package module;
a first configuration unit for configuring the grouping module, the clinical pathway module, and the clinical pathway service package module in a build platform of a medical decision platform.
7. The apparatus of claim 6, further comprising:
the first determining unit is used for acquiring the information to be decided and diagnosed and determining the disease diagnosis name and/or the operation name corresponding to the information to be decided and diagnosed in the grouping module;
the second determining unit is used for determining a corresponding diagnosis and treatment project set, a corresponding medicine set name and a corresponding consumable set according to the disease diagnosis name and/or the operation name in the clinical path module; wherein the clinical pathway module comprises an overall treatment regimen for the disease;
and a fourth generating unit, configured to select, in a clinical pathway service package module, service packages corresponding to the diagnosis and treatment item suite, the drug suite name, and the consumable suite to support, respectively, so as to generate a treatment scheme corresponding to the to-be-decided diagnosis information, where the service packages include a disease treatment name, an execution time, and a treatment frequency.
8. The apparatus of claim 6, wherein the construction platform of the medical decision platform further comprises a diagnosis cost optimization module, and the apparatus further comprises:
the second configuration unit is used for configuring the diagnosis and treatment cost optimization module in a construction platform of the medical decision platform;
and the adjusting unit is used for carrying out cost measurement and adjustment on the treatment schemes in the clinical path module and the clinical path service package module based on the diagnosis and treatment cost optimizing module.
9. The apparatus of claim 8, wherein the second generating unit is further configured to generate a fund payment amount corresponding to the group based on the historical clinical data.
10. The apparatus of claim 9, wherein the adjusting unit comprises:
the analysis module is used for adjusting the grouped expenses and analyzing the medical cost based on medical expense policies and medical insurance payment policies in the overall area;
and the optimization module is used for optimizing the treatment schemes in the clinical path module and the clinical path service pack module according to the adjustment result of the grouping expense and the medical cost analysis result.
11. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.
12. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method according to any one of claims 1-5.
13. A computer program product, characterized in that it comprises a computer program which, when being executed by a processor, carries out the method according to any one of claims 1-5.
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