CN113850410B - Disease type charge control project optimization method, system, corresponding equipment and storage medium - Google Patents
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Abstract
The application discloses a disease category charge control project optimization method, a system, corresponding equipment and a storage medium, wherein the method comprises the following steps: acquiring diagnosis and treatment item sequences of more than two cases of the same disease species; constructing diagnosis and treatment item sets based on the diagnosis and treatment item sequence and calculating the support degree and the support cost of each constructed diagnosis and treatment item set; taking the diagnosis and treatment item set with the support degree larger than a predetermined support degree threshold value as a candidate diagnosis and treatment item set; only reserving the maximum diagnosis and treatment item set in the candidate diagnosis and treatment item sets with inclusion relation with each other for the candidate diagnosis and treatment item sets with the same initial diagnosis and treatment item; sequencing all the maximum diagnosis and treatment item sets according to the support cost; and determining cost control diagnosis and treatment items according to the sequencing result. The method and the device can improve the efficiency and accuracy of determining the diagnosis and treatment items needing focus control cost.
Description
Technical Field
The application relates to the field of electric digital data processing, in particular to a disease category and expense control project optimization method. The application also relates to a disease and expense control project optimization system and a corresponding computer device and computer readable medium.
Background
Clinical pathways have positive implications for normative treatment and cost control. Chinese patent application CN111192644A discloses a construction method of a clinical pathway, which comprises: forming a diagnosis and treatment sequence corresponding to each user according to the diagnosis and treatment time of each diagnosis and treatment item, determining the incidence relation between any two diagnosis and treatment items according to the diagnosis and treatment sequence of each diagnosis and treatment item in the diagnosis and treatment sequence, constructing an initial clinical path according to the incidence relation between each diagnosis and treatment item, determining position connection symbols between each node according to the corresponding incidence relation, and connecting each node, wherein each node comprises node information, and the node information comprises global node weight and adjacent node weight; and carrying out optimization operation on the initial clinical path according to the node information to obtain a target clinical path corresponding to each disease type. Although the patent application mentions some clinical path node optimization ideas, such as node deletion and merging based on node weight, position distance, etc., the patent application does not solve the determination problem of the critical clinical path and has poor disease and pest control effect.
The key clinical path refers to a path formed by key nodes (key diagnosis and treatment items) in the clinical path, and the key nodes refer to diagnosis and treatment item nodes with higher cost in the same disease category. Therefore, in order to improve the control efficiency and control accuracy of diagnosis and treatment costs of disease species, it is necessary to accurately determine the key diagnosis and treatment items so as to control the corresponding operation modes and the use quantity based on the key diagnosis and treatment items, thereby achieving the purpose of efficiently and accurately controlling costs.
Disclosure of Invention
The invention provides a disease category charge control project optimization method, a disease category charge control project optimization system, corresponding equipment and a storage medium, which can improve the efficiency and accuracy of determining a diagnosis and treatment project needing to be subjected to focus charge control.
In a first aspect of the present invention, a method for optimizing a disease category charge control project is provided, including:
acquiring diagnosis and treatment item sequences of more than two cases of the same disease species;
constructing diagnosis and treatment item sets based on the diagnosis and treatment item sequence and calculating the support degree and the support cost of each constructed diagnosis and treatment item set, wherein the support degree is the proportion of the number of cases of the corresponding disease species using diagnosis and treatment items contained in the corresponding diagnosis and treatment item set to the total number of cases of the disease species, and the support cost is the product of the sum of reference costs corresponding to the diagnosis and treatment items contained in the corresponding diagnosis and treatment item set and the corresponding support degree;
taking the diagnosis and treatment item set with the support degree larger than a predetermined support degree threshold value as a candidate diagnosis and treatment item set;
only reserving the maximum diagnosis and treatment item set in the candidate diagnosis and treatment item sets with inclusion relation with each other for the candidate diagnosis and treatment item sets with the same initial diagnosis and treatment item;
sequencing all the maximum diagnosis and treatment item sets according to the support cost;
and determining cost control diagnosis and treatment items according to the sequencing result.
In a second aspect of the present invention, there is provided a disease category charge control project optimization system, including:
the system comprises an acquisition module, a judging module and a judging module, wherein the acquisition module is used for acquiring diagnosis and treatment item sequences of more than two cases of the same disease type;
the construction module is used for constructing diagnosis and treatment item sets based on the diagnosis and treatment item sequence and calculating the support degree and the support cost of each constructed diagnosis and treatment item set, wherein the support degree is the proportion of the number of cases of corresponding disease types using diagnosis and treatment items contained in the corresponding diagnosis and treatment item sets to the total number of cases of the disease types, and the support cost is the product of the sum of reference costs corresponding to the diagnosis and treatment items contained in the corresponding diagnosis and treatment item sets and the corresponding support degree;
the selection module is used for taking the diagnosis and treatment item set with the support degree larger than a predetermined support degree threshold value as a candidate diagnosis and treatment item set;
the screening module is used for only reserving the maximum diagnosis and treatment item set in the candidate diagnosis and treatment item sets with the inclusion relation with each other for the candidate diagnosis and treatment item sets with the same initial diagnosis and treatment items;
the ordering module is used for ordering all the maximum diagnosis and treatment item sets according to the support cost;
and the determining module is used for determining the cost control diagnosis and treatment items according to the sequencing result.
In a third aspect of the invention, a computer device is provided, comprising a processor, a memory and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the method according to the first aspect of the invention or implements the functions of the system according to the second aspect of the invention.
According to a fourth aspect of the present invention, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method according to the first aspect of the present invention or performs the functions of the system according to the second aspect of the present invention.
According to the invention, the diagnosis and treatment item sets are constructed based on the diagnosis and treatment item sequence, the support degree and the support cost of each constructed diagnosis and treatment item set are calculated, the diagnosis and treatment item set with the support degree larger than the support degree threshold value is used as a candidate diagnosis and treatment item set, and then only the maximum diagnosis and treatment item set in the candidate diagnosis and treatment item sets with inclusion relation with each other is reserved for the candidate diagnosis and treatment item set with the same initial diagnosis and treatment item, so that the key path of the clinical path of the disease species can be accurately excavated. By sequencing the support cost corresponding to all the maximum diagnosis and treatment item sets and determining the cost control diagnosis and treatment items according to the sequencing result, the diagnosis and treatment items needing key or priority cost control can be accurately and efficiently determined or found, so that the cost control efficiency and accuracy are improved, and the rationality of the diagnosis and treatment items and cost is ensured.
Other features and advantages of the present invention will become more apparent from the detailed description of the embodiments of the present invention when taken in conjunction with the accompanying drawings.
Drawings
FIG. 1 is a flow chart of one embodiment of a method according to the present invention;
FIG. 2 is a block diagram of one embodiment of a system according to the present invention.
For the sake of clarity, the figures are schematic and simplified drawings, which only show details which are necessary for understanding the invention and other details are omitted.
Detailed Description
Embodiments and examples of the present invention will be described in detail below with reference to the accompanying drawings.
The scope of applicability of the present invention will become apparent from the detailed description given hereinafter. It should be understood, however, that the detailed description and the specific examples, while indicating preferred embodiments of the invention, are given by way of illustration only.
Typically, there is one clinical sequence of clinical items for each case, where the clinical items are usually arranged in chronological order. The clinical items may differ from case to case.
FIG. 1 is a flow chart of a preferred embodiment of the disease charge control project optimization method according to the present invention.
In step S102, a medical treatment item sequence of two or more cases of the same disease type is acquired. As described above, the clinical item sequence is obtained by analyzing the clinical item information of the clinical path of the disease category, and one clinical item sequence exists for each case.
In step S104, data normalization is performed on each acquired medical treatment item sequence to obtain a target medical treatment item sequence.
Because the diagnosis and treatment items of each case may have the problems of inconsistent expression and fine granularity, in order to improve the treatment efficiency and treatment effect, the diagnosis and treatment item sequence can be subjected to standardization treatment. However, data normalization is not an essential step. In the case of relatively high data quality, a data normalization process may not be required. Of course, possible consequences caused by inconsistent expression of the diagnosis and treatment items, finer granularity and the like can also be ignored.
In an embodiment, the sequence of medical items may be normalized by similarity mapping and/or classification merging. The similarity mapping is to map the irregular diagnosis and treatment item expression to the national or industrial diagnosis and treatment item standard version to perform standard and uniform processing, wherein any appropriate natural language processing technology, technology based on full text search and deep semantic matching and the like can be used for improving the matching effect of the diagnosis and treatment items. The classification and combination is to combine lower diagnosis and treatment items with relatively fine granularity into a large upper category so as to reduce the sample size of the diagnosis and treatment items. The categorical consolidation of the clinical items may be performed using any suitable clinical knowledge base, including empirical rules and knowledge mapping techniques, as well as text classification techniques.
Through data standardization processing, the diagnosis and treatment items of each case are expressed into a target diagnosis and treatment item sequence with diagnosis and treatment items sequenced according to time sequence: t1, T2, \8230andTi, wherein T represents medical treatment items, i represents the number of medical treatment items related to a corresponding case, and the time of the previous medical treatment item is earlier than the time of the next medical treatment item.
As an example, one disease category including 5 cases and 4 diagnosis and treatment items T1 to T4 is taken as an example here and below. The obtained target diagnosis and treatment item sequence is shown in the following table:
in step S106, a frequent k diagnosis and treatment item set is constructed from k =1 based on the target diagnosis and treatment item sequence, the time sequence of the diagnosis and treatment items is maintained, a frequent k +1 diagnosis and treatment item set is constructed based on the frequent k diagnosis and treatment item set until a candidate diagnosis and treatment item set cannot be found any more, and the support degree and the support cost of each constructed diagnosis and treatment item set are calculated at the same time. Each constructed medical item set includes a corresponding number of chronologically arranged medical items. The support degree is the proportion of the number of cases of the corresponding disease category using the diagnosis and treatment items contained in the corresponding diagnosis and treatment item set to the total number of cases of the disease category, and the support cost is the product of the sum of the costs corresponding to the diagnosis and treatment items contained in the corresponding diagnosis and treatment item set and the corresponding support degree.
Specifically, the support degree is a ratio of the number of cases used by one or more associated clinical items at the same time to the total number of cases. For example, if the number of cases used by the related medical items a and B is 800, and the total number of cases of the disease category is 10000, the support degree is 800/10000=8% =0.08, that is, 8% of cases use two medical items a and B simultaneously.
The support fee may be a sum of the support degree and the standard fee of each diagnosis and treatment item in the diagnosis and treatment item set, and is represented by the following formula.
The cost of the same medical treatment item for each case may vary depending on the number of medicines, consumables, and the like used. The group statistical analysis can be carried out through the diagnosis and treatment item cost data of a plurality of cases with different diseases, a proper diagnosis and treatment item quantiles are taken as a reference, such as the quartile, and a value range is defined, such as the upper fluctuation and the lower fluctuation by 10%, so that the cost of the corresponding diagnosis and treatment items is accurately reflected, and the quantity and the cost are conveniently controlled in real time. For example, there are 100 cases using the diagnosis and treatment item a, and the costs of the diagnosis and treatment items a are ranked from low to high, and the number ranked in the upper quartile (i.e., 75% position) is used as the reference cost of the diagnosis and treatment item. Of course, any other suitable manner of determining the baseline cost of the medical item may be used.
In the embodiment, following the above example, assuming that the reference costs of the clinical items T1-T4 are 20, 50, 30, and 100, respectively, the sequence of the clinical items in the horizontal format shown in the above table may be first converted into a case set in the vertical format based on the clinical items, so as to obtain 1 frequent clinical item set, and calculate the support degree and the support cost of each frequent 1 clinical item set, as shown in the following table:
frequent 1 diagnosis and treatment item set
| Diagnosis and treatment item set | Case collection | Degree of support | Supporting fees |
| T1 | 1,2,3,5 | 0.8 | 16 |
| T2 | 1,2,4,5 | 0.8 | 40 |
| T3 | 1,3,4,5 | 0.8 | 24 |
| T4 | 2,3,4,5 | 0.8 | 80 |
Then, constructing a frequent 2 diagnosis and treatment item set by the frequent 1 diagnosis and treatment item set, and solving an intersection of case sets of the corresponding frequent 1 diagnosis and treatment item set to determine the case sets and the number of cases corresponding to the diagnosis and treatment items contained in the frequent 2 diagnosis and treatment item set, and calculating the support degree and the support cost of each frequent 2 diagnosis and treatment item set. And then constructing a frequent 3 diagnosis and treatment item set by the frequent 2 diagnosis and treatment item sets, solving an intersection of case sets of the corresponding frequent 2 diagnosis and treatment item sets, calculating the support degree and the support cost of each frequent 3 diagnosis and treatment item set, and so on until a candidate diagnosis and treatment item set cannot be found. Following the above example, the constructed frequent 2, 3, 4 diagnosis and treatment item sets and their corresponding support degrees and support costs are respectively shown in the following list:
frequent 2-item diagnosis and treatment item set
| Diagnosis and treatment item set | Case intersection | Degree of support | Supporting fees |
| T1,T2 | 1,2,5 | 0.6 | 42 |
| T1,T3 | 1,3,5 | 0.6 | 30 |
| T1,T4 | 2,3,5 | 0.6 | 72 |
| T2,T3 | 1,4,5 | 0.6 | 48 |
| T2,T4 | 2,4,5 | 0.6 | 90 |
| T3,T4 | 3,4,5 | 0.6 | 78 |
Frequent 3 diagnosis and treatment item set
| Diagnosis and treatment item set | Case intersection | Degree of support | Supporting fees |
| T1,T2,T3 | 1,5 | 0.4 | 40 |
| T1,T2,T4 | 2,5 | 0.4 | 68 |
| T1,T3,T4 | 3,5 | 0.4 | 60 |
| T2,T3,T4 | 4,5 | 0.4 | 72 |
Frequent 4-item diagnosis and treatment item set
| Diagnosis and treatment item set | Case intersection | Degree of support | Supporting fees |
| T1,T2,T3,T4 | 5 | 0.2 | 40 |
It should be noted that, when constructing the frequent k +1 diagnosis and treatment item set based on the frequent k diagnosis and treatment item sets, the frequent k diagnosis and treatment item sets with the support degree smaller than the predetermined support degree threshold value may be removed first, and then the frequent k +1 diagnosis and treatment item sets may be constructed based on the remaining frequent k diagnosis and treatment item sets, so as to improve the processing efficiency.
In step S108, the constructed diagnosis and treatment item set with the support degree greater than the support degree threshold is used as a candidate diagnosis and treatment item set, and a diagnosis and treatment item set with low support degree is filtered. The threshold of support can be determined according to specific applications, such as specific conditions of a hospital, etc., to which the present invention is applied. For the above example, if the support threshold is determined to be 0.4, the obtained candidate clinical item sets are shown in the following table:
in step S110, for candidate clinical item sets having the same initial clinical item, only the largest clinical item set among the candidate clinical item sets having a inclusion relationship with each other (considering the time sequence of the clinical items) is retained, and the clinical items included in the largest clinical item set form a critical clinical path of the corresponding disease category. For example, if the candidate diagnosis and treatment item set starting with the diagnosis and treatment item T1 in the above table includes the "T1" set and the "T1, T2" set, only the maximum diagnosis and treatment item set "T1, T2, T3" is retained, and so on, all the obtained maximum diagnosis and treatment item sets are shown in the following table:
in step S112, all the maximum diagnosis and treatment item sets are sorted according to the support fee.
In the embodiment, for all the maximum diagnosis and treatment item sets, starting from n =1, the maximum diagnosis and treatment item sets with the same top n-1 diagnosis and treatment items are sequentially sorted according to the support cost of the corresponding diagnosis and treatment item set only containing the top n items respectively until all the sorting is completed. For example, since the first diagnosis item is not before the diagnosis item in the maximum diagnosis item set, it is assumed that the diagnosis items before the first diagnosis item are the same. Therefore, all the largest clinical item sets are sorted by the support fee corresponding to the clinical item set including only the first clinical item. Then, for a group of the largest diagnosis and treatment item sets with the same first diagnosis and treatment items, ordering the largest diagnosis and treatment item sets with the same first diagnosis and treatment items according to the support cost corresponding to the diagnosis and treatment item sets only containing the first two items, and for a group of the largest diagnosis and treatment item sets with the same first two items, ordering the largest diagnosis and treatment item sets only containing the first three items according to the support cost corresponding to the diagnosis and treatment item sets only containing the first three items, and so on. In the above example, the first medical items of each maximum medical item set are T1, T2, T3, and T4, respectively, and the support costs of the medical item sets including only T1, T2, T3, and T4 are 16, 40, 24, and 80, respectively, so that the first medical item is ranked as T4, T2, T3, and T1, the maximum medical item set starting with T4 is ranked first, the order starting with T2 is ranked first, and so on. Then, for example, for the maximum diagnosis item sets T2, T4 and T2, T3, T4, the support fees corresponding to the diagnosis item sets respectively including only T2, T4 and T2, T3 are sorted, and so on. For the above example, the ranking results are shown in the following table:
| diagnosis and treatment item set | Degree of support | Supporting expenses |
| T4 | 0.8 | 80 |
| T2,T4 | 0.6 | 90 |
| T2,T3,T4 | 0.4 | 72 |
| T3,T4 | 0.6 | 78 |
| T1,T4 | 0.6 | 72 |
| T1,T2,T4 | 0.4 | 68 |
| T1,T2,T3 | 0.4 | 40 |
| T1,T3,T4 | 0.4 | 60 |
In step S114, the fee-controlled clinical item is determined according to the ranking result.
In an embodiment, based on a set of maximum diagnosis and treatment item sets having the same starting diagnosis and treatment item, one or more diagnosis and treatment items with the highest cost are determined as cost control diagnosis and treatment items of corresponding key clinical paths for diagnosis and treatment items of the same diagnosis and treatment item hierarchy. For example, for the key clinical path which is the largest diagnosis and treatment item set starting from the diagnosis and treatment item T1, the second hierarchy of the diagnosis and treatment items respectively relates to diagnosis and treatment items T4, T2 and T3, and by comparing which diagnosis and treatment item or items among the diagnosis and treatment items is/are higher in cost, where the cost of T4 is higher, T4 can be determined as the cost control diagnosis and treatment item of the key clinical path starting from T1, and by controlling the operation mode of the diagnosis and treatment item or the usage amount of consumable chemicals, the control of the cost of the disease can be realized more accurately and efficiently.
FIG. 2 is a block diagram of a preferred embodiment of a disease charge control project optimization system according to the present invention, comprising:
the acquisition module 202 is used for acquiring diagnosis and treatment item sequences of more than two cases of the same disease type;
a constructing module 204, configured to construct a diagnosis and treatment item set based on the diagnosis and treatment item sequence, and calculate a support degree and a support cost of each constructed diagnosis and treatment item set, where the support degree is a ratio of the number of cases using diagnosis and treatment items included in the corresponding diagnosis and treatment item set to the total number of cases of the corresponding disease type, and the support cost is a product of a sum of reference costs corresponding to the diagnosis and treatment items included in the corresponding diagnosis and treatment item set and the corresponding support degree;
a selecting module 206, configured to use the diagnosis and treatment item set with the support degree greater than a predetermined support degree threshold as a candidate diagnosis and treatment item set;
the screening module 208 is configured to, for candidate diagnosis and treatment item sets having the same initial diagnosis and treatment item, only reserve a largest diagnosis and treatment item set in the candidate diagnosis and treatment item sets having an inclusion relationship with each other;
a sorting module 210, configured to sort all the maximum diagnosis and treatment item sets according to the support cost;
and the determining module 212 is used for determining the charge control diagnosis and treatment items according to the sequencing result.
In another embodiment, the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method embodiment or other corresponding method embodiments described in conjunction with fig. 1 or implements the functions of the system embodiment or other corresponding system embodiments described in conjunction with fig. 2, and is not described herein again.
In another embodiment, the present invention provides a computer device, including a processor, a memory, and a computer program stored in the memory and capable of running on the processor, where the processor implements the steps of the method embodiment or other corresponding method embodiments described in conjunction with fig. 1 or implements the functions of the system embodiment or other corresponding system embodiments described in conjunction with fig. 2 when executing the computer program, and details of the steps are not repeated herein.
The various embodiments described herein, or certain features, structures, or characteristics thereof, may be combined as suitable in one or more embodiments of the invention. Additionally, in some cases, the order of steps depicted in the flowcharts and/or in the pipelined process may be modified, as appropriate, and need not be performed exactly in the order depicted. In addition, various aspects of the invention may be implemented using software, hardware, firmware, or a combination thereof, and/or other computer implemented modules or devices that perform the described functions. Software implementations of the present invention may comprise executable code stored in a computer readable medium and executed by one or more processors. The computer-readable medium may include a computer hard drive, ROM, RAM, flash memory, portable computer storage media such as CD-ROM, DVD-ROM, flash drives, and/or other devices with a Universal Serial Bus (USB) interface, and/or any other suitable tangible or non-transitory computer-readable medium or computer memory on which executable code may be stored and executed by a processor. The present invention may be used in conjunction with any suitable operating system.
As used herein, the singular forms "a", "an" and "the" include plural references (i.e., have the meaning "at least one"), unless the context clearly dictates otherwise. It will be further understood that the terms "has," "includes" and/or "including," when used in this specification, specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
The foregoing describes some preferred embodiments of the present invention, but it should be emphasized that the invention is not limited to these embodiments, but can be implemented in other ways within the scope of the inventive subject matter. Various modifications and alterations of this invention will become apparent to those skilled in the art without departing from the spirit and scope of this invention.
Claims (9)
1. A disease category charge control project optimization method is characterized by comprising the following steps:
acquiring a diagnosis and treatment item sequence of more than two cases of the same disease category, wherein the diagnosis and treatment item sequence is sorted according to the time of diagnosis and treatment items;
constructing diagnosis and treatment item sets based on the diagnosis and treatment item sequence and calculating the support degree and the support cost of each constructed diagnosis and treatment item set, wherein the support degree is the proportion of the number of cases of the corresponding disease species using diagnosis and treatment items contained in the corresponding diagnosis and treatment item set to the total number of cases of the disease species, and the support cost is the product of the sum of reference costs corresponding to the diagnosis and treatment items contained in the corresponding diagnosis and treatment item set and the corresponding support degree;
taking the diagnosis and treatment item set with the support degree larger than a predetermined support degree threshold value as a candidate diagnosis and treatment item set;
for candidate diagnosis and treatment item sets with the same initial diagnosis and treatment items, only reserving the maximum diagnosis and treatment item set in the candidate diagnosis and treatment item sets which have inclusion relations with each other and have consistent diagnosis and treatment item sequencing;
sequencing all the maximum diagnosis and treatment item sets according to the support cost;
determining cost control diagnosis and treatment items according to the sequencing result;
wherein said constructing a set of clinical items based on said sequence of clinical items comprises:
converting each diagnosis and treatment item sequence into case sets taking diagnosis and treatment items as a reference to construct frequent 1 diagnosis and treatment item sets, wherein each case set is a set of cases containing a certain diagnosis and treatment item;
starting from k =1, constructing a frequent k +1 diagnosis and treatment item set based on the frequent k diagnosis and treatment item sets until a candidate diagnosis and treatment item set cannot be found, and calculating the intersection of case sets corresponding to the diagnosis and treatment items contained in the frequent k diagnosis and treatment item sets to determine the case set corresponding to the diagnosis and treatment items contained in the frequent k +1 diagnosis and treatment item set.
2. The method of claim 1, further comprising, prior to constructing the set of clinical items:
and carrying out data standardization on the diagnosis and treatment item sequence.
3. The method of claim 2, wherein the data normalization comprises:
mapping the diagnosis and treatment items to national or industrial diagnosis and treatment item standard versions; and/or
And combining more than two lower diagnosis and treatment items into upper diagnosis and treatment items.
4. The method of claim 1, wherein said ranking all of the largest sets of medical items according to support costs comprises:
and sequentially sequencing the maximum diagnosis and treatment item sets with the same first n-1 diagnosis and treatment items from n =1 according to the support cost of the diagnosis and treatment item sets only containing the first n items until all sequencing is finished.
5. The method of claim 4, wherein determining cost-controlled clinical items according to the ranking comprises:
based on a set of ordered maximum diagnosis and treatment item sets with the same initial diagnosis and treatment item, one or more diagnosis and treatment items with the highest cost are determined as cost control diagnosis and treatment items for the diagnosis and treatment items of the same diagnosis and treatment item hierarchy.
6. The method of claim 1, wherein the cost associated with the clinical item is determined based on a quartile cost of the clinical item for all cases using the clinical item.
7. An illness and expense control project optimization system, which is characterized in that the system comprises:
the system comprises an acquisition module, a diagnosis and treatment module and a treatment module, wherein the acquisition module is used for acquiring diagnosis and treatment item sequences of more than two cases of the same disease type, and the diagnosis and treatment item sequences are sequenced according to the time of diagnosis and treatment items;
the construction module is used for constructing diagnosis and treatment item sets based on the diagnosis and treatment item sequence and calculating the support degree and the support cost of each constructed diagnosis and treatment item set, wherein the support degree is the proportion of the number of cases of corresponding disease types using diagnosis and treatment items contained in the corresponding diagnosis and treatment item sets to the total number of cases of the disease types, and the support cost is the product of the sum of reference costs corresponding to the diagnosis and treatment items contained in the corresponding diagnosis and treatment item sets and the corresponding support degree;
the selection module is used for taking the diagnosis and treatment item set with the support degree larger than a predetermined support degree threshold value as a candidate diagnosis and treatment item set;
the screening module is used for only reserving the maximum diagnosis and treatment item set which has a containing relationship with each other and has consistent diagnosis and treatment item sequencing for the candidate diagnosis and treatment item sets with the same initial diagnosis and treatment items;
the ordering module is used for ordering all the maximum diagnosis and treatment item sets according to the support cost;
the determining module is used for determining the cost control diagnosis and treatment items according to the sequencing result;
wherein the construction module is to:
converting each diagnosis and treatment item sequence into a case set taking diagnosis and treatment items as a reference to construct 1 frequent diagnosis and treatment item set, wherein each case set is a set of cases containing a certain diagnosis and treatment item;
starting from k =1, constructing a frequent k +1 diagnosis and treatment item set based on the frequent k diagnosis and treatment item sets until a candidate diagnosis and treatment item set cannot be found, and calculating the intersection of case sets corresponding to the diagnosis and treatment items contained in the frequent k diagnosis and treatment item set to determine the case set corresponding to the diagnosis and treatment items contained in the frequent k +1 diagnosis and treatment item set.
8. A computer device comprising a processor, a memory and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method according to any of claims 1-6 when executing the computer program.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN202110883950.3A CN113850410B (en) | 2021-08-03 | 2021-08-03 | Disease type charge control project optimization method, system, corresponding equipment and storage medium |
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| CN105005709A (en) * | 2015-08-19 | 2015-10-28 | 赵蒙海 | Process mining method based on single-disease treatment process |
| CN106033424A (en) * | 2015-03-11 | 2016-10-19 | 哈尔滨工业大学深圳研究生院 | A data mining method and device |
| CN110085323A (en) * | 2019-04-19 | 2019-08-02 | 北京工业大学 | A kind of diagnosis and treatment path discovery method based on electronic health record |
| CN111428198A (en) * | 2020-03-23 | 2020-07-17 | 平安医疗健康管理股份有限公司 | Method, device, equipment and storage medium for determining abnormal medical list |
| CN112652405A (en) * | 2020-12-24 | 2021-04-13 | 平安科技（深圳）有限公司 | Method, device and equipment for mining clinical path and storage medium |
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| US6324533B1 (en) * | 1998-05-29 | 2001-11-27 | International Business Machines Corporation | Integrated database and data-mining system |
| JP5505134B2 (en) * | 2010-06-30 | 2014-05-28 | 富士ゼロックス株式会社 | Program and medical assistance device |
| CN112397171A (en) * | 2020-12-04 | 2021-02-23 | 上海蓬海涞讯数据技术有限公司 | DRG-based method, device, processor and storage medium for realizing monitoring of critical path for diagnosis and treatment items and materials |
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| CN106033424A (en) * | 2015-03-11 | 2016-10-19 | 哈尔滨工业大学深圳研究生院 | A data mining method and device |
| CN105005709A (en) * | 2015-08-19 | 2015-10-28 | 赵蒙海 | Process mining method based on single-disease treatment process |
| CN110085323A (en) * | 2019-04-19 | 2019-08-02 | 北京工业大学 | A kind of diagnosis and treatment path discovery method based on electronic health record |
| CN111428198A (en) * | 2020-03-23 | 2020-07-17 | 平安医疗健康管理股份有限公司 | Method, device, equipment and storage medium for determining abnormal medical list |
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