WO2022142720A1 - 基于用药数据的慢病管理方法、装置与电子设备 - Google Patents

基于用药数据的慢病管理方法、装置与电子设备 Download PDF

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WO2022142720A1
WO2022142720A1 PCT/CN2021/128775 CN2021128775W WO2022142720A1 WO 2022142720 A1 WO2022142720 A1 WO 2022142720A1 CN 2021128775 W CN2021128775 W CN 2021128775W WO 2022142720 A1 WO2022142720 A1 WO 2022142720A1
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medication
patient
data
preset
medical plan
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PCT/CN2021/128775
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English (en)
French (fr)
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赵加坤
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医渡云（北京）技术有限公司
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H70/00—ICT specially adapted for the handling or processing of medical references
    • G16H70/40—ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage

Definitions

  • the present disclosure relates to the field of Internet medical technology, and in particular, to a method, device and electronic device for chronic disease management based on medication data.
  • the purpose of the present disclosure is to provide a chronic disease management method, device and electronic device based on medication data, which are used to at least to a certain extent overcome the physical changes and therapeutic effects of chronic disease patients during the long-term medication process, which can only be judged subjectively by the patients Or the management dilemma of chronic diseases such as delayed treatment timing caused by active investigation by doctors.
  • a chronic disease management method based on medication data including: acquiring a medical plan corresponding to a patient's latest visit and medication data within a preset time period, where the medication data at least includes medication feedback information; determine the patient's adverse drug reaction data according to the medication data and the medical plan; determine the patient's medical plan suggestion information according to the drug adverse reaction data.
  • a chronic disease management device based on medication data, including: a data acquisition module configured to obtain a medical plan corresponding to a patient's latest visit and medication data within a preset time period, where The medication data includes at least medication feedback information; a state judgment module is configured to determine the patient's adverse medication reaction data according to the medication data and the medical plan; a suggestion providing module is configured to determine the patient's medication adverse reaction data according to the medication adverse reaction data. information about the patient's medical plan recommendations.
  • an electronic device comprising: a memory; and a processor coupled to the memory, the processor configured to execute any one of the above based on instructions stored in the memory method described in item.
  • a computer-readable storage medium on which a program is stored, and when the program is executed by a processor, implements the method for chronic disease management based on medication data as described in any one of the above.
  • FIG. 1 is a flowchart of a chronic disease management method based on medication data in an exemplary embodiment of the present disclosure
  • FIG. 2 is a flowchart of a method for obtaining medical regimen and medication data in an exemplary embodiment of the present disclosure
  • FIG. 3 is a flowchart of a method for determining adverse drug reaction data in an exemplary embodiment of the present disclosure
  • FIG. 4 is a flowchart of a method for determining a target group and a status label corresponding to a patient in an exemplary embodiment of the present disclosure
  • FIG. 5 is a flowchart of a method for determining an adverse reaction treatment level in an exemplary embodiment of the present disclosure
  • FIG. 6 is a flowchart of a method of determining a medical protocol recommendation in an exemplary embodiment of the present disclosure
  • FIG. 7 is a block diagram of a chronic disease management device based on medication data in an exemplary embodiment of the present disclosure
  • FIG. 8 is a block diagram of an electronic device in an exemplary embodiment of the present disclosure.
  • Example embodiments will now be described more fully with reference to the accompanying drawings.
  • Example embodiments can be embodied in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
  • the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
  • numerous specific details are provided in order to give a thorough understanding of the embodiments of the present disclosure.
  • those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be employed.
  • well-known solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
  • FIG. 1 is a flowchart of a chronic disease management method based on medication data in an exemplary embodiment of the present disclosure.
  • the chronic disease management method 100 based on medication data may include:
  • Step S1 obtaining the medical plan corresponding to the last visit of the patient and the medication data within a preset time period, the medication data including at least medication feedback information;
  • Step S2 determining the adverse drug reaction data of the patient according to the medication data and the medical plan
  • Step S3 determining medical plan suggestion information of the patient according to the adverse drug reaction data.
  • the embodiments of the present disclosure can automatically obtain the patient's latest medical plan and medication data, automatically determine the patient's adverse drug reaction data, and then automatically provide medical plan suggestion information, which can replace the doctor to monitor the patient's physical changes in a timely manner during the medication process. And provide timely medical advice when patients experience physical changes to avoid delaying treatment opportunities.
  • step S1 the medical plan corresponding to the patient's latest visit and medication data within a preset time period are acquired, where the medication data at least includes medication feedback information.
  • FIG. 2 is a specific implementation of step S1 in the first embodiment of the present disclosure for obtaining a medical plan corresponding to a patient's latest visit and medication data within a preset time period.
  • step S1 may include:
  • Step S11 obtaining the medical plan corresponding to the last visit of the patient, where the medical plan includes the age of the patient, the diagnosis of the disease, the means of treatment, the type of medication, and the frequency of medication;
  • Step S12 acquiring the medication punch-in data and medication feedback information of the patient within a preset time period, where the medication feedback information includes the patient's selection results of multiple target adverse reaction words.
  • step S1 the medical plan and medication data can be obtained through the video medication punch-in information.
  • the patient needs to clock in the medication according to the doctor's order information and give feedback on the adverse reaction information according to the medication situation.
  • the patient's medication information and the patient's adverse reaction information can be collected, combined with the personal visit information provided by the patient when they visit the hospital, and the data can be normalized to form a chronic disease management health plan for this patient. file information.
  • the treatment means in the medical regimen may include, for example, intramuscular injection, drip injection, physical therapy, and the like.
  • some basic information of the patient can also be obtained in step S1, such as gender, living habits (whether smoking, drinking, drinking tea, exercising, etc.), education level (used to assist in determining how to interpret medical advice), etc. Wait.
  • the medication punch-in data is, for example, a medication punch-in video uploaded by a patient through a preset interface, which can be used to calculate the patient's medication continuity and compliance.
  • the medication feedback information can be extracted from the medication punch-in video, or by providing the patient with a check box after each uploading the medication punch-in video, which is determined according to the patient's selection of the check box; further, it can also be determined according to the wearable
  • the device monitors the acquisition of physiological signs of the patient.
  • the medication feedback information can be provided by the patient in various ways, which is not particularly limited in the present disclosure.
  • the medication feedback information may include, for example, the results of the patient's selection of multiple target adverse reaction words.
  • step S12 the medicines taken by the patient may be obtained first according to the patient's medical plan, and then the description of adverse reactions corresponding to each medicine taken by the patient may be obtained.
  • Language processing methods such as text structuring
  • vocabulary normalization processing are used to extract multiple target adverse reaction vocabulary corresponding to the drug taken by the patient, and provide these target adverse reaction vocabulary to the user for selection when the patient punches in each time.
  • the user's selection results of these target adverse reaction words are obtained as part of the medication feedback information.
  • Text structuring refers to the post-structural processing of long text data, which is to extract information and knowledge from various unformatted texts, and then format and store them for information retrieval and knowledge discovery.
  • Data normalization processing (vocabulary normalization) is a basic work of data mining. Different evaluation indicators often have different dimensions and dimension units. This situation will affect the results of data analysis. In order to eliminate the difference between indicators. The dimensional impact of data needs to be standardized to solve the comparability between data indicators. After the original data is standardized, each index is in the same order of magnitude, which is suitable for comprehensive comparative evaluation.
  • the medical plan may be normalized in combination with the diagnosis name, operation name, examination name, drug name, etc. in the medical plan, so as to facilitate the subsequent sorting of medical plans corresponding to the target group. For example, if ultrasound examination is mentioned in the treatment plan, then only words such as ultrasound such as "B-ultrasound” and “echocardiography” need to be retrieved in the follow-up examination, and the two retrieved examination names can be combined into one "Ultrasound" and record.
  • the medication feedback information input by the user through voice or text can also be obtained, the medication feedback information formed in natural language is subjected to text structuring and vocabulary normalization processing, and the user's natural language is converted into standard vocabulary, and then Provides the data basis for the status assessment below. It can be understood that, if the medical plan in the present disclosure is described by a doctor using natural language, text structuring and vocabulary normalization processing are also required, which will not be repeated here.
  • FIG. 3 is a specific implementation manner of determining the adverse drug reaction data of the patient according to the medication data and the medical plan in step S2 in the first embodiment of the present disclosure.
  • step S2 may include:
  • Step S21 according to the preset category information of the patient, the medical plan and the medication data, determine a target group and a plurality of state labels corresponding to the patient;
  • Step S22 obtaining adverse reaction vocabulary according to the medication feedback information in the medication data
  • Step S23 determining an adverse reaction treatment level according to the adverse reaction vocabulary and the medication data
  • Step S24 when the adverse reaction treatment level reaches a first preset level, update at least one status label among the plurality of status labels corresponding to the patient according to the medication data.
  • step S21 firstly, group management of patients may be performed based on the medical fee plan and medication data after the normalization of the vocabulary, so as to provide a data basis for providing medical plan suggestions in the future.
  • FIG. 4 is a specific implementation manner of determining the target group corresponding to the patient and a plurality of status labels according to the preset category information of the patient, the medical plan and the medication data in step S21 in the first embodiment of the present disclosure . 4, in one embodiment, step S21 may include:
  • Step S211 according to the medical plan and the medication data, determine the attribute values of multiple first preset attributes and the attribute values of multiple second preset attributes of the patient, the multiple first preset attributes At least include the type of disease, the severity of the disease and the medication compliance, and the second preset attribute at least includes the type of medication, the frequency of medication, and the response to the medication;
  • Step S212 adding the patient to the target group, and each patient in the target group has the same attribute value of the plurality of first preset attributes
  • Step S213 Determine the plurality of state labels corresponding to the patient according to the attribute value of the second preset attribute.
  • the patients may be grouped into user groups according to the relevant information of different patients.
  • K-means clustering algorithm and decision tree algorithm can be used to divide patients with the same basic conditions into the same group according to the current patient's disease type, disease severity and medication compliance, etc., and according to the patient's medication data , medication frequency data, and patient follow-up cycle data to label patients with corresponding labels to distinguish different patients in the same group.
  • patients in the same group are highly similar; due to different specific medical regimens, patients in the same group are under different conditions Often faced with different specific medical options. Therefore, in the embodiment of the present disclosure, patients are managed in groups, and corresponding labels are applied, and in the subsequent process, specific medical plans corresponding to patients with different labels in the same group are collected to form a medical plan data set, and the basic medical plan can be obtained. It provides strong data support for the subsequent medical plan recommendations for patients with changing conditions.
  • the preset category information of the patient may include, for example, basic information provided by the patient, such as age, gender, education level, and living habits;
  • the medical scheme has an attribute that has an important influence;
  • the second preset attribute may further include, for example, drug allergy history, follow-up period, and other attributes that have less influence on the medical scheme or only affect the details of the medical scheme.
  • Those skilled in the art can set grouping standards and labels according to actual conditions, which are not particularly limited in the present disclosure.
  • the adverse reaction refers to the occurrence of harmful reactions irrelevant to the purpose of treatment in the course of the patient taking the medicine according to the normal usage and dosage to prevent, diagnose or treat the disease.
  • the specific conditions of occurrence are the use of normal doses and normal usage, and the content excludes reactions caused by drug abuse, excessive misuse, non-prescribed use of drugs, and quality problems.
  • the preset adverse reaction vocabulary can be found in the vocabulary normalization result of the medication feedback information.
  • the preset adverse reaction words can be, for example, vomiting, fever, nausea, headache, rash, etc., which can be obtained from medical books, and will not be repeated here in this disclosure.
  • FIG. 5 is a specific implementation manner of determining an adverse reaction treatment level according to the adverse reaction vocabulary and the medication data in step S23 in the first embodiment of the present disclosure.
  • step S23 may include:
  • Step S231 acquiring multiple target adverse reaction words corresponding to the medicines taken by the patient;
  • Step S232 when the proportion of the multiple target adverse reaction words included in the adverse reaction vocabulary is greater than a first preset ratio, determine that the adverse reaction treatment level is the first preset level;
  • Step S233 when the ratio of the adverse reaction vocabulary including the multiple target adverse reaction vocabulary is greater than a second preset ratio and less than or equal to the first preset ratio, determine that the adverse reaction treatment level is the second preset level;
  • Step S234 when the ratio of the adverse reaction vocabulary including the multiple target adverse reaction vocabulary is less than or equal to the second preset ratio, determine that the adverse reaction treatment level is a third preset level.
  • a plurality of target adverse reaction vocabularies may be obtained based on the medicines currently taken by the patient (see the description of step S12). Since the instructions for each drug will list the possible adverse reactions of this drug, when a patient has an adverse reaction during taking the drug, it is necessary to judge the possibility that the adverse reaction is caused by the drug.
  • the treatment level of the adverse reaction in different states is set.
  • the adverse reaction of the patient includes the adverse reaction of more than the first preset proportion corresponding to the medicine he is taking, it means that the patient has a serious adverse reaction due to taking the medicine, and the medication regimen may need to be adjusted at this time. Therefore, this situation is set as the first preset level. If the adverse reactions of the patient are only a part of the adverse reactions corresponding to the drugs they are taking (between the second preset ratio and the first preset ratio), it means that these adverse reactions are not very serious, but the patient needs to be observed. Therefore, this situation is set as the second preset level.
  • the second preset ratio in the above steps is smaller than the first preset ratio.
  • the first preset ratio may be, for example, 60%
  • the second preset ratio may be, for example, 30%, which is not particularly limited in the present disclosure.
  • the adverse reaction treatment level is set to three. In other embodiments of the present disclosure, more and more adverse reaction treatment levels may also be set, and the present disclosure is not limited thereto.
  • the calculation can be performed based on the patient's current relevant information, and the current patient's current information can be calculated.
  • the basic situation is evaluated to provide data support for subsequent automatic response measures.
  • step S3 the relationship between the patient's medical plan and the treatment situation reflected by the medication data and the subsequent patient medication and diagnosis and treatment recommendation information can be saved through the data table, for example, the patient's medication check-in status within a period of time is stable, there is no adverse reaction, and Judging that the patient's condition is in good condition based on the patient's follow-up data, a reasonable recommended follow-up time and related medication adjustments can be made for the patient based on the current treatment situation.
  • the medical plan suggestion information can be formed through the medication template and the follow-up suggestion template.
  • the priority of template usage can be set to select different templates in different situations.
  • FIG. 6 is a specific implementation manner of determining the medical plan suggestion information of the patient according to the adverse drug reaction data in step S3 in the first embodiment of the present disclosure.
  • step S3 may include:
  • Step S31 obtaining a medical plan data set composed of medical plans of all patients in the target group corresponding to the patient, and each medical plan in the medical plan data set includes a state label that records the corresponding patient;
  • Step S32 in the medical plan data set corresponding to the target group, search for a target medical plan whose matching degree of the state label corresponding to the patient exceeds a preset value;
  • Step S33 pushing the target medical plan as the medical plan suggestion information to the doctor corresponding to the patient.
  • the embodiment of the present disclosure can realize the medical scheme through grouping and labeling. Share with precision.
  • the patient's label can be updated every time the patient's medication feedback information is obtained, so as to match the appropriate medical plan suggestion information according to the label, and timely intervene in the patient's medical status.
  • the embodiment shown in FIG. 6 can be applied when the adverse reaction treatment level is level 1 and the medical plan needs to be adjusted, or when it is judged that the patient's health status has improved according to the patient's recent medical plan and medication data, the label corresponding to the patient can be adjusted. This in turn adjusts the information on the medical plan recommendations for the patient.
  • Information on medical plan recommendations can be provided to patients in addition to physicians.
  • the above-mentioned doctors and patients may, for example, refer to doctor accounts and patient accounts registered on the medical platform implementing the method provided by the present disclosure, and provide medical plan suggestion information to doctors or patients by means of in-site messages, pop-up windows, and the like.
  • a doctor's manual judgment is required in this case.
  • a medication concern suggestion may be pushed to the doctor corresponding to the patient.
  • the proportion of automatically pushing medical plan recommendation information can be increased, and the situations that require doctor's attention and manual intervention can be reduced.
  • the doctor can also push medication attention suggestions to remind the doctor to pay attention to the adverse reaction description.
  • Patient A can check in every day according to the medication check-in rules, but in today's check-in, the user feeds back the adverse reaction information a1, a2, and a3.
  • the adverse reaction monitoring rules are triggered, and the adverse reaction vocabulary is extracted and matched to determine the current
  • the adverse reaction words only accounted for 40% of the multiple target adverse reaction words corresponding to the drugs taken by the patients, and the adverse reaction treatment level belonged to the second preset level.
  • the doctor of patient A pushes medication attention suggestions, and at the same time monitors the doctor's plan changes and the current patient's medication in the following week.
  • the doctor of patient A pushes the medical plan suggestion information including the target medical plan and the patient's medication data. If the doctor conducts diagnosis and treatment for the patient based on the information suggested by the medical plan, the diagnosis and treatment data (medical plan) will be continuously monitored to ensure the chronic disease management effect of the patient.
  • the embodiments of the present disclosure can automatically monitor and track the medical status of the patient and evaluate the curative effect, and based on the Internet technology, timely and accurately remind the doctor to give timely feedback on the medication and adverse reactions of the patient, so as to improve the management efficiency of the doctor, monitor and track the medication status of the patient , which can accurately and efficiently monitor the discharge of patients, save labor costs for hospitals, save treatment costs for patients, and effectively solve the situation that patients with chronic diseases have adverse reactions during medication but delay treatment.
  • the present disclosure further provides a chronic disease management device based on medication data, which can be used to execute the above method embodiments.
  • FIG. 7 is a block diagram of a chronic disease management device based on medication data in an exemplary embodiment of the present disclosure.
  • the chronic disease management device 700 based on medication data may include:
  • the data collection module 71 is configured to obtain the medical plan corresponding to the last visit of the patient and the medication data within a preset time period, the medication data at least including medication feedback information;
  • the state judgment module 72 is configured to determine the adverse drug reaction data of the patient according to the medication data and the medical plan;
  • a suggestion providing module 73 is configured to determine medical plan suggestion information of the patient according to the adverse drug reaction data.
  • the state judging module 72 is configured to: determine the target group and the target group corresponding to the patient according to the preset category information of the patient, the medical plan and the medication data. Multiple status labels; obtain adverse reaction vocabulary according to the medication feedback information in the medication data; determine the adverse reaction treatment level according to the adverse reaction vocabulary and the medication data; When the level is set, at least one state label among the plurality of state labels corresponding to the patient is updated according to the medication data.
  • the suggestion providing module 73 is configured to: acquire a medical plan data set consisting of medical plans of all patients in the target group corresponding to the patient, the medical plan data Collecting each medical plan includes recording the state label of the corresponding patient; in the medical plan data set corresponding to the target group, search for a target medical plan whose matching degree of the state label corresponding to the patient exceeds a preset value ; Push the target medical plan as the medical plan suggestion information to the doctor corresponding to the patient.
  • the suggestion providing module 73 is configured to: when the adverse reaction treatment level reaches a second preset level, push a medication attention suggestion to the doctor corresponding to the patient.
  • the state determination module 72 is configured to: acquire multiple target adverse reaction words corresponding to the medicines taken by the patient; and include the multiple targets in the adverse reaction words When the proportion of adverse reaction words is greater than the first preset proportion, the adverse reaction treatment level is determined to be the first preset level; when the proportion of the adverse reaction words including the multiple target adverse reaction words is greater than the first preset level.
  • the adverse reaction treatment level is determined to be the second preset level; when the adverse reaction vocabulary includes the proportion of the multiple target adverse reaction vocabulary , when it is less than or equal to the second preset ratio, determine that the adverse reaction treatment level is a third preset level; wherein, the second preset ratio is smaller than the first preset ratio.
  • the state judgment module 72 is configured to: determine attribute values of a plurality of first preset attributes and a plurality of second attributes of the patient according to the medical plan and the medication data Attribute values of preset attributes, the plurality of first preset attributes at least include disease type, disease severity, and medication compliance, and the second preset attribute at least include medication type, medication frequency, and medication response;
  • the patient joins the target group, and each patient in the target group has the same attribute value of the plurality of first preset attributes; determining the corresponding patient according to the attribute value of the second preset attribute of the plurality of status labels.
  • the data collection module 71 is configured to: acquire a medical plan corresponding to the patient's latest visit, where the medical plan includes the patient's age, diagnosed disease, treatment method, medication type, medication frequency; obtain the medication punch-in data and medication feedback information of the patient within a preset time period, and the medication feedback information includes the patient's selection results of multiple target adverse reaction words.
  • modules or units of the apparatus for action performance are mentioned in the above detailed description, this division is not mandatory. Indeed, according to embodiments of the present disclosure, the features and functions of two or more modules or units described above may be embodied in one module or unit. Conversely, the features and functions of one module or unit described above may be further divided into multiple modules or units to be embodied.
  • an electronic device capable of implementing the above method is also provided.
  • aspects of the present disclosure may be implemented as a system, method or program product. Therefore, various aspects of the present disclosure can be embodied in the following forms: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, which may be collectively referred to herein as implementations "circuit", “module” or "system”.
  • FIG. 8 An electronic device 800 according to this embodiment of the present disclosure is described below with reference to FIG. 8 .
  • the electronic device 800 shown in FIG. 8 is only an example, and should not impose any limitation on the function and scope of use of the embodiments of the present disclosure.
  • electronic device 800 takes the form of a general-purpose computing device.
  • the components of the electronic device 800 may include, but are not limited to, the above-mentioned at least one processing unit 810 , the above-mentioned at least one storage unit 820 , and a bus 830 connecting different system components (including the storage unit 820 and the processing unit 810 ).
  • the storage unit stores program codes, and the program codes can be executed by the processing unit 810, so that the processing unit 810 executes various exemplary methods according to the present disclosure described in the above-mentioned “Exemplary Methods” section of this specification. Implementation steps.
  • the processing unit 810 may perform the steps shown in the above embodiments.
  • the storage unit 820 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 8201 and/or a cache storage unit 8202 , and may further include a read only storage unit (ROM) 8203 .
  • RAM random access storage unit
  • ROM read only storage unit
  • the storage unit 820 may also include a program/utility 8204 having a set (at least one) of program modules 8205, such program modules 8205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, An implementation of a network environment may be included in each or some combination of these examples.
  • the bus 830 may be representative of one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local area using any of a variety of bus structures bus.
  • the electronic device 800 may also communicate with one or more external devices 900 (eg, keyboards, pointing devices, Bluetooth devices, etc.), with one or more devices that enable a user to interact with the electronic device 800, and/or with Any device (eg, router, modem, etc.) that enables the electronic device 800 to communicate with one or more other computing devices. Such communication may take place through input/output (I/O) interface 850 . Also, the electronic device 800 may communicate with one or more networks (eg, a local area network (LAN), a wide area network (WAN), and/or a public network such as the Internet) through a network adapter 860 . As shown, network adapter 860 communicates with other modules of electronic device 800 via bus 830 . It should be understood that, although not shown, other hardware and/or software modules may be used in conjunction with electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives and data backup storage systems.
  • the exemplary embodiments described herein may be implemented by software, or may be implemented by software combined with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure may be embodied in the form of software products, and the software products may be stored in a non-volatile storage medium (which may be CD-ROM, U disk, mobile hard disk, etc.) or on the network , including several instructions to cause a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to an embodiment of the present disclosure.
  • a computing device which may be a personal computer, a server, a terminal device, or a network device, etc.
  • a computer-readable storage medium on which a program product capable of implementing the above-described method of the present specification is stored.
  • various aspects of the present disclosure may also be implemented in the form of a program product comprising program code for causing the program product to run on a terminal device when the program product is run on a terminal device.
  • the terminal device performs the steps according to various exemplary embodiments of the present disclosure described in the above-mentioned "Example Method" section of this specification.
  • a program product for implementing the above method according to an embodiment of the present disclosure may employ a portable compact disc read only memory (CD-ROM) and include program codes, and may be executed on a terminal device such as a personal computer.
  • CD-ROM portable compact disc read only memory
  • the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
  • the program product may employ any combination of one or more readable media.
  • the readable medium may be a readable signal medium or a readable storage medium.
  • the readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or a combination of any of the above. More specific examples (non-exhaustive list) of readable storage media include: electrical connections with one or more wires, portable disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.
  • a computer readable signal medium may include a propagated data signal in baseband or as part of a carrier wave with readable program code embodied thereon. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • a readable signal medium can also be any readable medium, other than a readable storage medium, that can transmit, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
  • Program code embodied on a readable medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
  • Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages—such as Java, C++, etc., as well as conventional procedural Programming Language - such as the "C" language or similar programming language.
  • the program code may execute entirely on the user computing device, partly on the user device, as a stand-alone software package, partly on the user computing device and partly on a remote computing device, or entirely on the remote computing device or server execute on.
  • the remote computing device may be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (eg, using an Internet service provider business via an Internet connection).
  • LAN local area network
  • WAN wide area network
  • the embodiments of the present disclosure can automatically obtain the patient's latest medical plan and medication data, automatically obtain the patient's adverse drug reaction data based on the medication data, and then automatically provide medical plan suggestion information, which can replace the doctor's health care for chronically ill patients during the long-term medication process.
  • Manage state changes and treatment effects provide timely medical advice when patients experience physical changes, avoid delaying treatment opportunities, and alleviate chronic disease patients’ physical changes and treatment effects during long-term medication. This leads to the management dilemma of chronic diseases such as delayed treatment timing.

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Abstract

一种基于用药数据的慢病管理方法、装置与电子设备。基于用药数据的慢病管理方法包括：获取患者最近一次就诊对应的医疗方案以及预设时间段内的用药数据，所述用药数据至少包括用药反馈信息；根据所述用药数据及所述医疗方案确定所述患者的用药不良反应数据；根据所述用药不良反应数据确定所述患者的医疗方案建议信息。该方法可以自动对患者在离院后服药过程中的身体变化进行判断，及时自动提供医疗方案建议信息。

Description

基于用药数据的慢病管理方法、装置与电子设备
交叉引用
本公开要求于2020年12月31日提交的申请号为202011633320.2、名称为“基于用药数据的慢病管理方法、装置与电子设备”的中国专利申请的优先权，该中国专利申请的全部内容通过引用全部并入本文。
技术领域
本公开涉及互联网医疗技术领域，具体而言，涉及一种基于用药数据的慢病管理方法、装置与电子设备。
背景技术
慢病患者常常无法处于医生的监控下，是否按时服药、服药过程中是否有身体变化、有身体变化时是否需要复诊常常均由患者本人根据主观思维来决定。由于患者本人通常不具有专业的医疗知识，常导致贻误治疗时机、降低疗效、增加治疗成本，严重的甚至引起生命危险。
现有的处理方法通常为医生定期翻阅所属患者的病历，定期回访，但是这种严重依赖于主观行动的处置方法，在医生繁忙的情况下常常无法实现。对于慢病患者而言，由于治疗时间较长，无法要求医生随时对其身体状态进行监控，如果患者在长期服药过程中产生身体状态变化，医生无法及时干预，常导致慢病的治疗无法实现预期效果。
需要说明的是，在上述背景技术部分公开的信息仅用于加强对本公开的背景的理解，因此可以包括不构成对本领域普通技术人员已知的现有技术的信息。
发明内容
本公开的目的在于提供一种基于用药数据的慢病管理方法、装置与电子设备，用于至少在一定程度上克服慢病患者在长期服药过程中的身体变化和治疗效果仅能由患者主观判断或医生主动调查而导致的贻误治疗时机等慢病管理困境。
根据本公开实施例的第一方面，提供一种基于用药数据的慢病管理方法，包括：获取患者最近一次就诊对应的医疗方案以及预设时间段内的用药数据，所述用药数据至少包括用药反馈信息；根据所述用药数据及所述医疗方案确定所述患者的用药不良反应数据；根据所述用药不良反应数据确定所述患者的医疗方案建议信息。
根据本公开实施例的第二方面，提供一种基于用药数据的慢病管理装置，包括：数据采集模块，设置为获取患者最近一次就诊对应的医疗方案以及预设时间段内的用药数据，所述用药数据至少包括用药反馈信息；状态判断模块，设置为根据所述用药数据及所述医疗方案确定所述患者的用药不良反应数据；建议提供模块，设置为根据所述用药不良反应 数据确定所述患者的医疗方案建议信息。
根据本公开的第三方面，提供一种电子设备，包括：存储器；以及耦合到所述存储器的处理器，所述处理器被配置为基于存储在所述存储器中的指令，执行如上述任意一项所述的方法。
根据本公开的第四方面，提供一种计算机可读存储介质，其上存储有程序，该程序被处理器执行时实现如上述任意一项所述的基于用药数据的慢病管理方法。
应当理解的是，以上的一般描述和后文的细节描述仅是示例性和解释性的，并不能限制本公开。
附图说明
此处的附图被并入说明书中并构成本说明书的一部分，示出了符合本公开的实施例，并与说明书一起用于解释本公开的原理。显而易见地，下面描述中的附图仅仅是本公开的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本公开示例性实施例中基于用药数据的慢病管理方法的流程图；
图2是本公开示例性实施例中获取医疗方案和用药数据的方法的流程图；
图3是本公开示例性实施例中确定用药不良反应数据的方法的流程图；
图4是本公开示例性实施例中确定患者对应的目标群组和状态标签的方法的流程图；
图5是本公开示例性实施例中确定不良反应处置等级的方法的流程图；
图6是本公开示例性实施例中确定医疗方案建议的方法的流程图；
图7是本公开示例性实施例中一种基于用药数据的慢病管理装置的方框图；
图8是本公开示例性实施例中一种电子设备的方框图。
具体实施方式
现在将参考附图更全面地描述示例实施方式。然而，示例实施方式能够以多种形式实施，且不应被理解为限于在此阐述的范例；相反，提供这些实施方式使得本公开将更加全面和完整，并将示例实施方式的构思全面地传达给本领域的技术人员。所描述的特征、结构或特性可以以任何合适的方式结合在一个或更多实施方式中。在下面的描述中，提供许多具体细节从而给出对本公开的实施方式的充分理解。然而，本领域技术人员将意识到，可以实践本公开的技术方案而省略所述特定细节中的一个或更多，或者可以采用其它的方法、组元、装置、步骤等。在其它情况下，不详细示出或描述公知技术方案以避免喧宾夺主而使得本公开的各方面变得模糊。
此外，附图仅为本公开的示意性图解，图中相同的附图标记表示相同或类似的部分，因而将省略对它们的重复描述。附图中所示的一些方框图是功能实体，不一定必须与物理或逻辑上独立的实体相对应。可以采用软件形式来实现这些功能实体，或在一个或多个硬 件模块或集成电路中实现这些功能实体，或在不同网络和/或处理器装置和/或微控制器装置中实现这些功能实体。
下面结合附图对本公开示例实施方式进行详细说明。
图1是本公开示例性实施例中基于用药数据的慢病管理方法的流程图。
参考图1，基于用药数据的慢病管理方法100可以包括：
步骤S1，获取患者最近一次就诊对应的医疗方案以及预设时间段内的用药数据，所述用药数据至少包括用药反馈信息；
步骤S2，根据所述用药数据及所述医疗方案确定所述患者的用药不良反应数据；
步骤S3，根据所述用药不良反应数据确定所述患者的医疗方案建议信息。
本公开实施例通过自动获取患者最近的医疗方案和用药数据，自动判断患者的用药不良反应数据，进而自动提供医疗方案建议信息，可以代替医生对患者在服药过程中的身体变化情况进行及时监控，并在患者发生身体变化时及时提供医疗建议，避免贻误治疗时机。
下面，对基于用药数据的慢病管理方法100的各步骤进行详细说明。
在步骤S1，获取患者最近一次就诊对应的医疗方案以及预设时间段内的用药数据，所述用药数据至少包括用药反馈信息。
图2是本公开第一实施例中步骤S1获取患者最近一次就诊对应的医疗方案以及预设时间段内的用药数据的具体实施方式。参考图2，在一个实施例中，步骤S1可以包括：
步骤S11，获取所述患者最近一次就诊对应的医疗方案，所述医疗方案包括患者年龄、诊断疾病、治疗手段、用药种类、用药频次；
步骤S12，获取所述患者在预设时间段内的用药打卡数据以及用药反馈信息，所述用药反馈信息包括患者对多个目标不良反应词汇的选择结果。
步骤S1可以通过视频服药打卡信息获取医疗方案和用药数据。在患者管理时，患者需要根据医生的医嘱信息进行服药打卡并根据服药情况进行不良反应信息反馈。在患者进行服药打卡时，可以收集患者的用药信息、患者的不良反应信息，结合患者在医院就诊时提供的个人就诊信息，对此信息进行数据归一化，形成针对此患者的慢病管理健康档案信息。
在本公开实施例中，医疗方案中的治疗手段例如可以包括肌肉注射、点滴注射、物理治疗等。此外，除了获取医疗方案，还可以在步骤S1获取一些患者的基础信息，例如性别、生活习惯(是否吸烟、饮酒、喝茶、运动等)、教育程度(用于辅助确定医疗建议解释方式)等等。
用药打卡数据例如为患者通过预设接口上传的用药打卡视频，从而可以用于计算患者的用药连续情况和依从性。
用药反馈信息既可以通过用药打卡视频进行提取，也可以通过在患者每次上传用药打卡视频后，为患者提供复选框，根据患者对复选框的选取确定；更进一步，还可以根据可穿戴设备监控患者的生理体征获取。用药反馈信息可以由患者通过多种方式提供，本公开 对此不作特殊限制。
在本公开实施例中，用药反馈信息例如可以包括患者对多个目标不良反应词汇的选择结果。
本公开实施例重点关注与患者自身情况相关的不良反应，因此，在步骤S12，可以首先根据患者的医疗方案获取患者服用的药物，进而获取患者服用的每种药物对应的不良反应描述，通过自然语言处理手段(例如文本结构化)和词汇归一化处理，提取出与患者服用药物对应的多个目标不良反应词汇，在患者每次打卡时将这些目标不良反应词汇提供给用户供其选择，最后获取用户对这些目标不良反应词汇的选择结果作为本次用药反馈信息的一部分。
文本结构化是指对长文本数据进行后结构化处理就是对各种非格式化的文本进行信息和知识提取，然后进行格式化存储，从而进行信息检索和知识发现。数据归一化处理(词汇归一化)是数据挖掘的一项基础工作，不同评价指标往往具有不同的量纲和量纲单位，这样的情况会影响到数据分析的结果，为了消除指标之间的量纲影响，需要进行数据标准化处理，以解决数据指标之间的可比性。原始数据经过数据标准化处理后，各指标处于同一数量级，适合进行综合对比评价。
此外，在步骤S1，还可以结合医疗方案中的诊断名称、手术名称、检查名称、药品名称等内容对医疗方案进行归一化处理，以便于后续整理目标群组对应的医疗方案。例如，治疗方案中提及超声检查，则后续检查中只需要检索到超声一类如“B超”、“超声心动图”等词汇，即可将这两种被检索到的检查名称归一为“超声”并记录。
在一个实施例中，还可以获取用户通过语音或文字输入的用药反馈信息，对自然语言形成的用药反馈信息进行文本结构化和词汇归一化处理，将用户的自然语言转换成标准词汇，进而为下面的状态评估提供数据基础。可以理解的是，本公开中的医疗方案如果是医生使用自然语言叙述的，也需要进行文本结构化和词汇归一化处理，于此不再赘述。
图3是本公开第一实施例中步骤S2根据所述用药数据及所述医疗方案确定所述患者的用药不良反应数据的具体实施方式。参考图3，在一个实施例中，步骤S2可以包括：
步骤S21，根据所述患者的预设种类信息、所述医疗方案及所述用药数据，确定所述患者对应的目标群组和多个状态标签；
步骤S22，根据所述用药数据中的所述用药反馈信息获取不良反应词汇；
步骤S23，根据所述不良反应词汇以及所述用药数据确定不良反应处置等级；
步骤S24，在所述不良反应处置等级达到第一预设等级时，根据所述用药数据更新所述患者对应的所述多个状态标签中的至少一个状态标签。
在步骤S21，首先可以基于上述词汇归一化后的医疗费方案和用药数据对患者进行分群管理，为后续提供医疗方案建议提供数据基础。
图4是本公开第一实施例中步骤S21根据所述患者的预设种类信息、所述医疗方案及所述用药数据，确定所述患者对应的目标群组和多个状态标签的具体实施方式。参考图 4，在一个实施例中，步骤S21可以包括：
步骤S211，根据所述医疗方案和所述用药数据，确定所述患者的多个第一预设属性的属性值和多个第二预设属性的属性值，所述多个第一预设属性至少包括疾病种类、疾病严重等级和服药依从性，所述第二预设属性至少包括用药种类、用药频率、药物反应；
步骤S212，将所述患者加入所述目标群组，所述目标群组中的每名患者的所述多个第一预设属性的属性值均相同；
步骤S213，根据所述第二预设属性的属性值确定所述患者对应的所述多个状态标签。
在步骤S211，可以通过不同患者的相关信息对患者进行用户分群。例如，可以通过K-means聚类算法和决策树算法等，根据当前患者的疾病种类、疾病严重等级和服药依从性等将基本情况相同的患者分为同一个群组，并根据患者的用药数据、用药频次数据、患者的复诊周期数据为患者打上对应的标签，以在同一个群组中区分不同患者的情况。
由于基本情况相同(疾病相同、疾病严重程度相同、服药依从性相同)，同一群组中的患者的医疗方案相似性较高；由于具体的医疗方案不同，同一群组中的患者在不同情况下通常面临不同的具体医疗方案。因此，本公开实施例对患者进行分群组管理、打上对应的标签，并且在后续流程中收集同一群组中不同标签的患者对应的具体医疗方案以形成医疗方案数据集，可以获取该种基本情况下不同情况的患者适合的医疗方案，继而为后续为情况变化的患者提供医疗方案建议提供有力的数据支持。
在步骤S211，患者的预设种类信息例如可以包括年龄、性别、教育程度、生活习惯等患者提供的基本信息；第一预设属性例如还可以包括患者教育程度、年龄、生活习惯等更多对医疗方案具有重要影响的属性；第二预设属性例如还可以包括药物过敏史、复诊周期等更多对医疗方案具有较小影响或仅影响医疗方案的细节的属性。本领域技术人员可以根据实际情况设置分群标准和标签，本公开对此不作特殊限制。
在步骤S22，不良反应是指患者在服药过程中按正常用法、用量应用药物预防、诊断或治疗疾病过程中，发生与治疗目的无关的有害反应。其特定的发生条件是按正常剂量与正常用法用药，在内容上排除了因药物滥用、超量误用、不按规定方法使用药品及质量问题等情况所引起的反应。
在本公开实施例中，可以在用药反馈信息的词汇归一化结果中，找到预设的不良反应词汇。预设的不良反应词汇例如可以为呕吐、发烧、恶心、头疼、皮疹等等，可以通过医典得到，本公开于此不再赘述。
图5是本公开第一实施例中步骤S23根据所述不良反应词汇以及所述用药数据确定不良反应处置等级的具体实施方式。参考图5，在一个实施例中，步骤S23可以包括：
步骤S231，获取与所述患者服用的药品对应的多个目标不良反应词汇；
步骤S232，在所述不良反应词汇中包含所述多个目标不良反应词汇的比例，大于第一预设比例时，确定所述不良反应处置等级为所述第一预设等级；
步骤S233，在所述不良反应词汇包含所述多个目标不良反应词汇的比例，大于第二 预设比例且小于或等于所述第一预设比例时，确定所述不良反应处置等级为第二预设等级；
步骤S234，在所述不良反应词汇包含所述所述多个目标不良反应词汇的比例，小于或等于所述第二预设比例时，确定所述不良反应处置等级为第三预设等级。
首先，可以基于患者当前所服用的药物获取多个目标不良反应词汇(参见步骤S12的说明)。由于每种药物的说明书中均会列出服用此药可能产生的不良反应，因此当患者在服药过程中产生了不良反应时，需要判断该不良反应是由药物引起的可能性。
本公开实施例中，基于不良反应词汇占多个目标不良反应词汇的比例来确定患者的不良反应属于哪种状态，进而设置不同状态下不良反应的处置等级。
在图5所示实施例，如果患者的不良反应包括了其服用的药品对应的第一预设比例以上的不良反应，说明患者因为服药产生了严重的不良反应，此时可能需要调整用药方案，因此将此种情况设定为第一预设等级。如果患者的不良反应仅为其所服用药物对应的不良反应中的一部分(第二预设比例到第一预设比例之间)，说明这些不良反应不算很严重，但是需要对患者进行观察，因此将此种情况设定为第二预设等级。如果患者的不良反应仅为其服用的药品对应的不良反应中的很小一部分(低于第二预设比例)，说明患者的不良反应可能并非由药物引起，将此种情况设定为第三预设等级。以上步骤中的第二预设比例小于第一预设比例，第一预设比例例如可以为60％，第二预设比例例如可以为30％，本公开对此不作特殊限制。
在图5所示实施例中，将不良反应处置等级设置为三级，在本公开的其他实施例中，也可以设置更多更多不良反应处置等级，本公开不以此为限。
通过以上过程，可以在患者出现不良反应(通过用药数据得到)或者在诊疗过程中出现关键节点事件(通过最近一次就诊的医疗方案得到)时，基于患者当前的相关信息进行计算，对患者当前的基本情况进行评价，从而为后续自动提供应对措施提供数据支持。
在步骤S3，可以通过数据表保存患者的医疗方案及用药数据所体现的治疗情况与后续的患者用药及诊疗的推荐信息的关联关系，比如患者在一段时间内用药打卡情况稳定、无不良反应并且基于患者的复诊数据判断患者病情状况良好，则可以为患者基于当前的治疗情况合理的推荐复诊时间和相关的用药调整。此外，还可以设置不同情况下的用药方案以及不同情况下的复诊建议，比如基于患者的服药打卡及近期的检查检验数据，判断当前患者需要加强治疗，则为医生进行推荐：患者当前用药进行某种治疗，并且需要调整复诊频次。可以通过用药模板、复诊建议模板来形成医疗方案建议信息。在一个实施例中，可以设置模板使用的优先级以在不同情况下选用不同的模板。
图6是本公开第一实施例中步骤S3根据所述用药不良反应数据确定所述患者的医疗方案建议信息的具体实施方式。参考图6，在一个实施例中，步骤S3可以包括：
步骤S31，获取所述患者对应的所述目标群组中由全部患者的医疗方案构成的医疗方案数据集，所述医疗方案数据集中每个医疗方案包括记录其对应的患者的状态标签；
步骤S32，在所述目标群组对应的医疗方案数据集中，查找与所述患者对应的所述状态标签的匹配程度超过预设值的目标医疗方案；
步骤S33，将所述目标医疗方案作为所述医疗方案建议信息推送给所述患者对应的医生。
由于患者的具体情况千差万别，例如患者患有多种疾病，如果仅依靠医典形成医疗方案建议信息，则会出现误判风险，与患者的实际情况匹配度较低的情况，如果每次都找医生实时判断，则会极大降低医疗效率。
因此，本公开实施例中通过获取基本情况相同(同一群组)的患者对应的医疗方案，可以得到该种基本情况下每种具体情况对应的合理的医疗方案，由于这些医疗方案均获取自每名患者的实际医疗方案，且具有患者具体情况对应的标签，因此能够更好地与相同基本情况、相同具体情况的其他患者形成有效的匹配。
由于对于不同疾病对不良反应的要求不同，因此对于不良反应的预警也不同，因此相比仅基于不良反应和医典的匹配程度推荐医疗方案，本公开实施例通过分组和标签可以实现医疗方案的精准共享。
可以在每次获取到患者的用药反馈信息时更新患者的标签，从而根据标签匹配合适的医疗方案建议信息，对患者的医疗状态进行及时干预。
图6所示实施例可以应用在不良反应处置等级为一级时，需要调整医疗方案时，或者，根据患者最近的医疗方案和用药数据判断患者健康状态有好转时，可以调整患者对应的标签，进而调整对患者的医疗方案建议信息。
医疗方案建议信息除了提供给医生，还可以提供给患者。以上所述的医生和患者例如可以指在实施本公开提供的方法的医疗平台上注册的医生账户和患者账户，通过站内信、弹窗等方式对医生或患者提供医疗方案建议信息。
在一些实施例中，可能比较难以很确定地判断患者的状态，此时需要医生进行人工判断。例如，在一个实施例中，可以在所述不良反应处置等级达到第二预设等级时，对所述患者对应的医生推送用药关注建议。通过合理设置各类词汇和比例，可以提高自动推送医疗方案建议信息的情况的比例，减少需要医生关注和人工干预的情况。此外，如果患者出现了目标不良反应以外的不良反应，在用药反馈数据中输入了不良反应描述，也可以对医生推送用药关注建议，提醒医生关注该不良反应描述。
以下是本公开一个实施例中一个应用场景的介绍。
患者A能够每天都按照服药打卡规则进行服药打卡，但是在今日打卡中用户反馈了不良反应信息a1、a2、a3,此时触发不良反应监控规则，对不良反应词汇进行提取和匹配，确定当前的不良反应词在患者所服用药品对应的多个目标不良反应词汇中仅占40％，不良反应处置等级属于第二预设等级。此时，对患者A的医生推送用药关注建议，同时监控医生的方案变更情况以及当前患者在后续一周的用药情况，若在后续一周的用药中，患者继续出现a1、a2、a3的不良反应或同等级及高等级的不良反应，则在对患者A的医生 推送用药关注建议的同时，更改患者对应标签得到当前标签组，从而根据患者所在的目标群组中当前标签组对应的目标医疗方案，对患者A的医生推送包括该目标医疗方案和患者用药数据的医疗方案建议信息。若医生基于医疗方案建议信息为患者进行诊疗，则持续监控诊疗数据(医疗方案)，保证患者的慢病管理效果。
本公开实施例通过自动对患者的医疗状态进行监控追踪以及疗效评估，基于互联网技术，及时准确地提醒医生对患者的用药和不良反应进行及时反馈，可以提高医生的管理效率监控追踪患者的用药情况，可以准确、高效实现对患者的离院监控，为医院节省人力成本，为患者节省治疗成本，有效解决慢病患者在服药中出现不良反应但贻误治疗的情况。
对应于上述方法实施例，本公开还提供一种基于用药数据的慢病管理装置，可以用于执行上述方法实施例。
图7是本公开示例性实施例中一种基于用药数据的慢病管理装置的方框图。
参考图7，基于用药数据的慢病管理装置700可以包括：
数据采集模块71，被配置为获取患者最近一次就诊对应的医疗方案以及预设时间段内的用药数据，所述用药数据至少包括用药反馈信息；
状态判断模块72，被配置为根据所述用药数据及所述医疗方案确定所述患者的用药不良反应数据；
建议提供模块73，被配置为根据所述用药不良反应数据确定所述患者的医疗方案建议信息。
在本公开的一种示例性实施例中，状态判断模块72被配置为：根据所述患者的预设种类信息、所述医疗方案及所述用药数据，确定所述患者对应的目标群组和多个状态标签；根据所述用药数据中的所述用药反馈信息获取不良反应词汇；根据所述不良反应词汇以及所述用药数据确定不良反应处置等级；在所述不良反应处置等级达到第一预设等级时，根据所述用药数据更新所述患者对应的所述多个状态标签中的至少一个状态标签。
在本公开的一种示例性实施例中，建议提供模块73被配置为：获取所述患者对应的所述目标群组中由全部患者的医疗方案构成的医疗方案数据集，所述医疗方案数据集中每个医疗方案包括记录其对应的患者的状态标签；在所述目标群组对应的医疗方案数据集中，查找与所述患者对应的所述状态标签的匹配程度超过预设值的目标医疗方案；将所述目标医疗方案作为所述医疗方案建议信息推送给所述患者对应的医生。
在本公开的一种示例性实施例中，建议提供模块73被配置为：在所述不良反应处置等级达到第二预设等级时，对所述患者对应的医生推送用药关注建议。
在本公开的一种示例性实施例中，状态判断模块72被配置为：获取与所述患者服用的药品对应的多个目标不良反应词汇；在所述不良反应词汇中包含所述多个目标不良反应词汇的比例，大于第一预设比例时，确定所述不良反应处置等级为所述第一预设等级；在所述不良反应词汇包含所述多个目标不良反应词汇的比例，大于第二预设比例且小于或等于所述第一预设比例时，确定所述不良反应处置等级为第二预设等级；在所述不良反应词 汇包含所述所述多个目标不良反应词汇的比例，小于或等于所述第二预设比例时，确定所述不良反应处置等级为第三预设等级；其中，所述第二预设比例小于所述第一预设比例。
在本公开的一种示例性实施例中，状态判断模块72被配置为：根据所述医疗方案和所述用药数据确定所述患者的多个第一预设属性的属性值和多个第二预设属性的属性值，所述多个第一预设属性至少包括疾病种类、疾病严重等级和服药依从性，所述第二预设属性至少包括用药种类、用药频率、药物反应；将所述患者加入所述目标群组，所述目标群组中的每名患者的所述多个第一预设属性的属性值均相同；根据所述第二预设属性的属性值确定所述患者对应的所述多个状态标签。
在本公开的一种示例性实施例中，数据采集模块71被配置为：获取所述患者最近一次就诊对应的医疗方案，所述医疗方案包括患者年龄、诊断疾病、治疗手段、用药种类、用药频次；获取所述患者在预设时间段内的用药打卡数据以及用药反馈信息，所述用药反馈信息包括患者对多个目标不良反应词汇的选择结果。
由于装置700的各功能已在其对应的方法实施例中予以详细说明，本公开于此不再赘述。
应当注意，尽管在上文详细描述中提及了用于动作执行的设备的若干模块或者单元，但是这种划分并非强制性的。实际上，根据本公开的实施方式，上文描述的两个或更多模块或者单元的特征和功能可以在一个模块或者单元中具体化。反之，上文描述的一个模块或者单元的特征和功能可以进一步划分为由多个模块或者单元来具体化。
在本公开的示例性实施例中，还提供了一种能够实现上述方法的电子设备。
所属技术领域的技术人员能够理解，本公开的各个方面可以实现为系统、方法或程序产品。因此，本公开的各个方面可以具体实现为以下形式，即：完全的硬件实施方式、完全的软件实施方式(包括固件、微代码等)，或硬件和软件方面结合的实施方式，这里可以统称为“电路”、“模块”或“系统”。
下面参照图8来描述根据本公开的这种实施方式的电子设备800。图8显示的电子设备800仅仅是一个示例，不应对本公开实施例的功能和使用范围带来任何限制。
如图8所示，电子设备800以通用计算设备的形式表现。电子设备800的组件可以包括但不限于：上述至少一个处理单元810、上述至少一个存储单元820、连接不同系统组件(包括存储单元820和处理单元810)的总线830。
其中，所述存储单元存储有程序代码，所述程序代码可以被所述处理单元810执行，使得所述处理单元810执行本说明书上述“示例性方法”部分中描述的根据本公开各种示例性实施方式的步骤。例如，所述处理单元810可以执行如上实施例所示的步骤。
存储单元820可以包括易失性存储单元形式的可读介质，例如随机存取存储单元(RAM)8201和/或高速缓存存储单元8202，还可以进一步包括只读存储单元(ROM)8203。
存储单元820还可以包括具有一组(至少一个)程序模块8205的程序/实用工具8204， 这样的程序模块8205包括但不限于：操作系统、一个或者多个应用程序、其它程序模块以及程序数据，这些示例中的每一个或某种组合中可能包括网络环境的实现。
总线830可以为表示几类总线结构中的一种或多种，包括存储单元总线或者存储单元控制器、外围总线、图形加速端口、处理单元或者使用多种总线结构中的任意总线结构的局域总线。
电子设备800也可以与一个或多个外部设备900(例如键盘、指向设备、蓝牙设备等)通信，还可与一个或者多个使得用户能与该电子设备800交互的设备通信，和/或与使得该电子设备800能与一个或多个其它计算设备进行通信的任何设备(例如路由器、调制解调器等等)通信。这种通信可以通过输入/输出(I/O)接口850进行。并且，电子设备800还可以通过网络适配器860与一个或者多个网络(例如局域网(LAN)，广域网(WAN)和/或公共网络，例如因特网)通信。如图所示，网络适配器860通过总线830与电子设备800的其它模块通信。应当明白，尽管图中未示出，可以结合电子设备800使用其它硬件和/或软件模块，包括但不限于：微代码、设备驱动器、冗余处理单元、外部磁盘驱动阵列、RAID系统、磁带驱动器以及数据备份存储系统等。
通过以上的实施方式的描述，本领域的技术人员易于理解，这里描述的示例实施方式可以通过软件实现，也可以通过软件结合必要的硬件的方式来实现。因此，根据本公开实施方式的技术方案可以以软件产品的形式体现出来，该软件产品可以存储在一个非易失性存储介质(可以是CD-ROM，U盘，移动硬盘等)中或网络上，包括若干指令以使得一台计算设备(可以是个人计算机、服务器、终端装置、或者网络设备等)执行根据本公开实施方式的方法。
在本公开的示例性实施例中，还提供了一种计算机可读存储介质，其上存储有能够实现本说明书上述方法的程序产品。在一些可能的实施方式中，本公开的各个方面还可以实现为一种程序产品的形式，其包括程序代码，当所述程序产品在终端设备上运行时，所述程序代码用于使所述终端设备执行本说明书上述“示例性方法”部分中描述的根据本公开各种示例性实施方式的步骤。
根据本公开的实施方式的用于实现上述方法的程序产品可以采用便携式紧凑盘只读存储器(CD-ROM)并包括程序代码，并可以在终端设备，例如个人电脑上运行。然而，本公开的程序产品不限于此，在本文件中，可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。
所述程序产品可以采用一个或多个可读介质的任意组合。可读介质可以是可读信号介质或者可读存储介质。可读存储介质例如可以为但不限于电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。可读存储介质的更具体的例子(非穷举的列表)包括：具有一个或多个导线的电连接、便携式盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。
计算机可读信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了可读程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意合适的组合。可读信号介质还可以是可读存储介质以外的任何可读介质，该可读介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。
可读介质上包含的程序代码可以用任何适当的介质传输，包括但不限于无线、有线、光缆、RF等等，或者上述的任意合适的组合。
可以以一种或多种程序设计语言的任意组合来编写用于执行本公开操作的程序代码，所述程序设计语言包括面向对象的程序设计语言—诸如Java、C++等，还包括常规的过程式程序设计语言—诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算设备上执行、部分地在用户设备上执行、作为一个独立的软件包执行、部分在用户计算设备上部分在远程计算设备上执行、或者完全在远程计算设备或服务器上执行。在涉及远程计算设备的情形中，远程计算设备可以通过任意种类的网络，包括局域网(LAN)或广域网(WAN)，连接到用户计算设备，或者，可以连接到外部计算设备(例如利用因特网服务提供商来通过因特网连接)。
此外，上述附图仅是根据本公开示例性实施例的方法所包括的处理的示意性说明，而不是限制目的。易于理解，上述附图所示的处理并不表明或限制这些处理的时间顺序。另外，也易于理解，这些处理可以是例如在多个模块中同步或异步执行的。
本领域技术人员在考虑说明书及实践这里公开的发明后，将容易想到本公开的其它实施方案。本申请旨在涵盖本公开的任何变型、用途或者适应性变化，这些变型、用途或者适应性变化遵循本公开的一般性原理并包括本公开未公开的本技术领域中的公知常识或惯用技术手段。说明书和实施例仅被视为示例性的，本公开的真正范围和构思由权利要求指出。
工业实用性
本公开实施例通过自动获取患者最近的医疗方案和用药数据，自动基于用药数据获取患者的用药不良反应数据，进而自动提供医疗方案建议信息，可以代替医生对慢病患者在长期服药过程中的身体状态变化以及治疗效果进行管理，在患者发生身体变化时及时提供医疗建议，避免贻误治疗时机，缓解慢病患者在长期服药过程中的身体变化和治疗效果仅能由患者主观判断或医生主动调查而导致的贻误治疗时机等慢病管理困境。

Claims (10)

  1. 一种基于用药数据的慢病管理方法，包括：
    获取患者最近一次就诊对应的医疗方案以及预设时间段内的用药数据，所述用药数据至少包括用药反馈信息；
    根据所述用药数据及所述医疗方案确定所述患者的用药不良反应数据；
    根据所述用药不良反应数据确定所述患者的医疗方案建议信息。
  2. 如权利要求1所述的基于用药数据的慢病管理方法，其中，所述根据所述用药数据及所述医疗方案确定所述患者的用药不良反应数据包括：
    根据所述患者的预设种类信息、所述医疗方案及所述用药数据确定所述患者对应的目标群组和多个状态标签；
    根据所述用药数据中的所述用药反馈信息获取不良反应词汇；
    根据所述不良反应词汇以及所述用药数据确定不良反应处置等级；
    在所述不良反应处置等级达到第一预设等级时，根据所述用药数据更新所述患者对应的所述多个状态标签中的至少一个状态标签。
  3. 如权利要求2所述的基于用药数据的慢病管理方法，其中，所述根据所述用药不良反应数据确定所述患者的医疗方案建议信息包括：
    获取所述患者对应的所述目标群组中由全部患者的医疗方案构成的医疗方案数据集，所述医疗方案数据集中每个医疗方案包括记录其对应的患者的状态标签；
    在所述目标群组对应的医疗方案数据集中，查找与所述患者对应的所述状态标签的匹配程度超过预设值的目标医疗方案；
    将所述目标医疗方案作为所述医疗方案建议信息推送给所述患者对应的医生。
  4. 如权利要求2所述的基于用药数据的慢病管理方法，其中，所述根据所述用药不良反应数据确定所述患者的医疗方案建议信息包括：
    在所述不良反应处置等级达到第二预设等级时，对所述患者对应的医生推送用药关注建议。
  5. 如权利要求2所述的基于用药数据的慢病管理方法，其中，所述根据所述不良反应词汇以及所述用药数据确定不良反应处置等级包括：
    获取与所述患者服用的药品对应的多个目标不良反应词汇；
    在所述不良反应词汇中包含所述多个目标不良反应词汇的比例，大于第一预设比例时，确定所述不良反应处置等级为所述第一预设等级；
    在所述不良反应词汇包含所述多个目标不良反应词汇的比例，大于第二预设比例且小于或等于所述第一预设比例时，确定所述不良反应处置等级为第二预设等级；
    在所述不良反应词汇包含所述所述多个目标不良反应词汇的比例，小于或等于所述第二预设比例，确定所述不良反应处置等级为第三预设等级；
    其中，所述第二预设比例小于所述第一预设比例。
  6. 如权利要求2所述的基于用药数据的慢病管理方法，其中，所述根据所述医疗方案和所述用药数据确定所述患者对应的目标群组和状态标签包括：
    根据所述医疗方案和所述用药数据确定所述患者的多个第一预设属性的属性值和多个第二预设属性的属性值，所述多个第一预设属性至少包括疾病种类、疾病严重等级和服药依从性，所述第二预设属性至少包括用药种类、用药频率、药物反应；
    将所述患者加入所述目标群组，所述目标群组中的每名患者的所述多个第一预设属性的属性值均相同；
    根据所述第二预设属性的属性值确定所述患者对应的所述多个状态标签。
  7. 如权利要求1所述的基于用药数据的慢病管理方法，其中，所述获取患者最近一次就诊对应的医疗方案以及预设时间段内的用药数据包括：
    获取所述患者最近一次就诊对应的医疗方案，所述医疗方案包括患者年龄、诊断疾病、治疗手段、用药种类、用药频次；
    获取所述患者在预设时间段内的用药打卡数据以及用药反馈信息，所述用药反馈信息包括患者对多个目标不良反应词汇的选择结果。
  8. 一种基于用药数据的慢病管理装置，包括：
    数据采集模块，被配置为获取患者最近一次就诊对应的医疗方案以及预设时间段内的用药数据，所述用药数据至少包括用药反馈信息；
    状态判断模块，被配置为根据所述用药数据及所述医疗方案确定所述患者的用药不良反应数据；
    建议提供模块，被配置为根据所述用药不良反应数据确定所述患者的医疗方案建议信息。
  9. 一种电子设备，包括：
    存储器；以及
    耦合到所述存储器的处理器，所述处理器被配置为基于存储在所述存储器中的指令，执行如权利要求1-7任一项所述的基于用药数据的慢病管理方法。
  10. 一种计算机可读存储介质，其上存储有程序，该程序被处理器执行时实现如权利要求1-7任一项所述的基于用药数据的慢病管理方法。
PCT/CN2021/128775 2020-12-31 2021-11-04 基于用药数据的慢病管理方法、装置与电子设备 WO2022142720A1 (zh)

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115274061A (zh) * 2022-09-26 2022-11-01 广州美术学院 一种安抚患者心理的交互方法、装置、设备和储存介质
CN115831312A (zh) * 2022-11-24 2023-03-21 上海市精神卫生中心（上海市心理咨询培训中心） 一种用药异常识别方法及系统
CN116805525A (zh) * 2023-08-21 2023-09-26 营动智能技术(山东)有限公司 一种慢性病信息化管理方法、系统及存储介质
CN117504062A (zh) * 2023-11-11 2024-02-06 深圳医和家智慧医疗科技有限公司 一种自动计量定量给药的智能化雾化用药器及给药方法

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112786140A (zh) * 2020-12-31 2021-05-11 医渡云（北京）技术有限公司 基于用药数据的慢病管理方法、装置与电子设备
CN113936767A (zh) * 2021-09-30 2022-01-14 逸群海豚信息科技(上海)有限公司 一种哮喘患者的健康管理方法
CN118173235A (zh) * 2024-05-14 2024-06-11 恺恩泰(南京)科技有限公司 一种慢病的医生端管理系统

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108648792A (zh) * 2018-05-04 2018-10-12 河北省人民医院 用药信息管理系统、方法及终端设备
CN109390043A (zh) * 2017-10-20 2019-02-26 上海市同仁医院 临床药学监护管理系统及其监护管理方法
CN110689939A (zh) * 2019-08-27 2020-01-14 天津开心生活科技有限公司 一种用药序列的推荐方法、装置、可读介质及电子设备
CN112786140A (zh) * 2020-12-31 2021-05-11 医渡云（北京）技术有限公司 基于用药数据的慢病管理方法、装置与电子设备

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US12057206B2 (en) * 2019-05-31 2024-08-06 International Business Machines Corporation Personalized medication non-adherence evaluation

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109390043A (zh) * 2017-10-20 2019-02-26 上海市同仁医院 临床药学监护管理系统及其监护管理方法
CN108648792A (zh) * 2018-05-04 2018-10-12 河北省人民医院 用药信息管理系统、方法及终端设备
CN110689939A (zh) * 2019-08-27 2020-01-14 天津开心生活科技有限公司 一种用药序列的推荐方法、装置、可读介质及电子设备
CN112786140A (zh) * 2020-12-31 2021-05-11 医渡云（北京）技术有限公司 基于用药数据的慢病管理方法、装置与电子设备

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115274061A (zh) * 2022-09-26 2022-11-01 广州美术学院 一种安抚患者心理的交互方法、装置、设备和储存介质
CN115274061B (zh) * 2022-09-26 2023-01-06 广州美术学院 一种安抚患者心理的交互方法、装置、设备和储存介质
CN115831312A (zh) * 2022-11-24 2023-03-21 上海市精神卫生中心（上海市心理咨询培训中心） 一种用药异常识别方法及系统
CN116805525A (zh) * 2023-08-21 2023-09-26 营动智能技术(山东)有限公司 一种慢性病信息化管理方法、系统及存储介质
CN116805525B (zh) * 2023-08-21 2023-11-07 营动智能技术(山东)有限公司 一种慢性病信息化管理方法、系统及存储介质
CN117504062A (zh) * 2023-11-11 2024-02-06 深圳医和家智慧医疗科技有限公司 一种自动计量定量给药的智能化雾化用药器及给药方法

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