CN112700878A - Clinical path optimization method based on process mining - Google Patents
Clinical path optimization method based on process mining Download PDFInfo
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Abstract
The invention discloses a clinical path optimization method based on process mining, which comprises the steps of firstly, obtaining a diagnosis and treatment charging bill, preprocessing the diagnosis and treatment charging bill and extracting characteristics, then, clustering and mining the diagnosis and treatment charging bill by utilizing an LDA subject clustering model and a PROM process mining tool to obtain a corresponding process model; then, extracting various items in the diagnosis and treatment charging bill, and preprocessing synonyms to obtain corresponding process logs; secondly, dividing the execution sequence in the process log, and aligning the process model and the process log based on a set alignment principle to obtain a corresponding alignment set; and finally, screening all the alignment sets, completing the optimization of clinical paths and improving the use effect of the clinical paths.
Description
Technical Field
The invention relates to the technical field of data mining, in particular to a clinical path optimization method based on process mining.
Background
The clinical path shows how the diagnosis and treatment work of diseases is carried out step by step, reflects how doctors and nurses work separately and cooperate to carry out the diagnosis and treatment work of diseases together, and also reflects how various medical resources are consumed step by step. The clinical path management can standardize the diagnosis and treatment process of diseases, improve the medical quality, improve the satisfaction degree of patients, and effectively control the consumption of medical resources and the increase of medical expenses.
Although clinical pathway management is receiving wide attention from countries around the world, practical effects are not ideal. The establishment of the clinical pathway is a very complex and time-consuming process, and needs the expert of each department to work with, and the implementation of the clinical pathway in a specific hospital must fully consider the various specific conditions of the implementation hospital, if the clinical pathway is completely established by human study, the efficiency is very slow, and the influence of human factors cannot be ignored, so that the experience of the clinical pathway is very poor.
Disclosure of Invention
The invention aims to provide a clinical path optimization method based on process mining, which improves the use effect of clinical paths.
In order to achieve the above object, the present invention provides a clinical pathway optimization method based on process mining, comprising the following steps:
acquiring a diagnosis and treatment charging bill, and clustering and process mining the diagnosis and treatment charging bill to obtain a corresponding process model;
extracting various items in the diagnosis and treatment charging bill, and preprocessing synonyms to obtain corresponding process logs;
dividing the execution sequence in the process log, and aligning the process model with the process log based on a set alignment principle to obtain a corresponding alignment set;
and screening all the alignment sets to complete the optimization of clinical paths.
The method comprises the following steps of obtaining diagnosis and treatment charging bills, clustering the diagnosis and treatment charging bills and mining the diagnosis and treatment charging bills in a process, and obtaining corresponding process models, wherein the method comprises the following steps:
acquiring a diagnosis and treatment charging bill, and deleting multiple fields set in the diagnosis and treatment charging bill;
and clustering and mining the diagnosis and treatment charging bill by using an LDA subject clustering model and a PROM process mining tool to obtain a corresponding process model.
The method comprises the following steps of clustering and mining the diagnosis and treatment charging bill by using an LDA theme clustering model and a PROM process mining tool to obtain a corresponding process model, wherein the method comprises the following steps:
extracting characteristics of the diagnosis and treatment charging bill, and inputting the extracted activity characteristics into the LDA theme clustering model to obtain a corresponding theme model;
and clustering the topic models by day, and carrying out process mining on the clustering results by utilizing the PROM process mining tool to obtain corresponding process models.
Dividing the execution sequence in the process log, aligning the process model with the process log based on a set alignment principle, and obtaining a corresponding alignment set, including:
dividing an execution sequence in the process log into a mandatory execution sequence and a selective execution sequence;
and aligning the topic model with the selection execution sequence based on a set alignment principle to obtain a corresponding alignment set.
Aligning the topic model with the selection execution sequence based on a set alignment principle to obtain a corresponding alignment set, including:
if the topic model is the same as the selection execution sequence, writing the corresponding topic model and the selection execution sequence into the constructed alignment set;
and if the theme model is different from the selection execution sequence, reducing the alignment range by measuring the cost to obtain the corresponding alignment set.
Wherein, if the topic model is not the same as the selection execution sequence, reducing an alignment range by measuring cost to obtain the corresponding alignment set, including:
if the topic model is not the same as the selection execution sequence, searching a plurality of distance values between the topic model and the selection execution sequence;
and by measuring the cost, arranging the distance values in an ascending order, and writing the first topic model in the ordering and the selection execution sequence into the corresponding alignment set.
The invention relates to a clinical path optimization method based on process mining, which comprises the steps of firstly, obtaining a diagnosis and treatment charging bill, preprocessing the diagnosis and treatment charging bill and extracting characteristics, then, clustering and mining the diagnosis and treatment charging bill by utilizing an LDA subject clustering model and a PROM process mining tool to obtain a corresponding process model; then, extracting various items in the diagnosis and treatment charging bill, and preprocessing synonyms to obtain corresponding process logs; secondly, dividing the execution sequence in the process log, and aligning the process model and the process log based on a set alignment principle to obtain a corresponding alignment set; and finally, screening all the alignment sets, completing the optimization of clinical paths and improving the use effect of the clinical paths.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram illustrating steps of a clinical pathway optimization method based on process mining according to the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
Referring to fig. 1, the present invention provides a method for optimizing a clinical path based on process mining, including:
s101, obtaining a diagnosis and treatment charging bill, and clustering and process mining the diagnosis and treatment charging bill to obtain a corresponding process model.
Specifically, according to actual research needs, a corresponding diagnosis and treatment charging bill is obtained, then the diagnosis and treatment charging bill is preprocessed, and the preprocessing process is to delete various fields set in the diagnosis and treatment charging bill: wherein, the various fields set comprise input original hospital charging data, at least including fields of patient number, charging item name, charging item category, total usage amount and date (indicating which patient uses which medical services in which day); the method specifically comprises the following steps:
1) processing the data of the charging items, deleting data irrelevant to disease diagnosis and treatment, and deleting expressions irrelevant to the name of the charging items; the specific treatment process comprises the following steps:
1-1) deleting data records which are not related to disease diagnosis and treatment, such as the charging records of which the charging item categories are bed fees, heating fees, other fees and various self fees;
1-2) deleting the detailed explanation of the name of the charging project, such as 'nasogastric tube placing (feeding, medicine injection, duodenal perfusion according to 2 yuan/time charging)', wherein the detailed description in brackets describes the use scene and the charging basis, and the rejection of the content in the brackets does not cause misunderstanding of the name of the project;
1-3) deleting the characters of 'import' and 'domestic' in the name of the charging item;
1-4) unifying different descriptions of the same charging item, such as 12-channel dynamic electrocardiogram and twelve-channel electrocardiographic examination, which are unified into 12-channel dynamic electrocardiogram;
1-5) deleting the one-time word in the name of the charging item;
1-6) deleting characters of 'injection' and 'capsule' in the name of the charging item;
1-7) deleting the word of 'bedside' in the name of the charging item.
In order to reduce the calculation amount of subsequent calculation, after the fields are deleted, the charging diagnosis and treatment bill is subjected to standardized conversion:
2-1) the total amount of the same patient used in the same charging item on the same day is added;
2-2) normalizing the total usage amount of different charging items of the same patient on the same day
Are normalized to the interval [0,100 ];
adjusting the output format of the data after the standardized conversion, and the specific process is as follows:
3-1) assigning a unique number to all the toll items;
3-2) arranging the charging data processed in the step (1-2) into a following output format of patient number @ date, wherein the charging item number, the charging item number and the charging item number indicate specific charging items of a certain patient on a certain day, and the number of times of repetition of the same charging item number is the number of the charging items normalized on the day.
Extracting characteristics of the diagnosis and treatment charging bill, inputting the extracted activity characteristics into the LDA theme clustering model to obtain a corresponding theme model, wherein the corresponding theme model can reflect which themes are served by diagnosis and treatment on the same day of diagnosis and treatment; and reflects which diagnosis and treatment items need to be taken after the diagnosis and treatment subject is determined.
Then, clustering the topic models by day, comparing the probabilities corresponding to the topic types with a set threshold, if the probabilities are larger than the threshold, giving the topic models to the responded diagnosis and treatment documents, then arranging the probabilities corresponding to all the topic models in the diagnosis and treatment documents in a descending order, connecting the probabilities by using a dash "-", obtaining corresponding composite topics, finally sequencing the composite topics according to document dates to obtain corresponding diagnosis and treatment topic sequences, and finishing clustering the topic models by day.
And carrying out process mining on the clustering result by utilizing the PROM process mining tool to obtain a corresponding process model, wherein the process mining comprises the following steps:
the diagnosis and treatment subject sequence is arranged into a corresponding log file, and data mining is carried out on the log file to obtain a clinical path. The method specifically comprises the following steps:
1) arranging the obtained patient diagnosis and treatment subject sequence into a corresponding log file according to the PROM process digging tool requirement;
2) directly using a heuristic process mining algorithm in PROM to mine an input patient diagnosis and treatment subject sequence log file to obtain a clinical path, namely a process model, of a corresponding disease.
S102, extracting various items in the diagnosis and treatment charging bill, and preprocessing synonyms to obtain corresponding process logs.
Specifically, according to the diagnosis and treatment charging bill, a plurality of items in the diagnosis and treatment charging bill are extracted, for example: the patient number, the item name, the unit price, the quantity, the date and the like, and induction, replacement and deletion are carried out on the systematic synonyms of the item class, and the method specifically comprises the following steps:
measuring semantic similarity between different projects based on an Intrasic IC-based method;
processing according to the similarity of the charging items, outputting all pairs of charging items with the similarity value not less than 0.8 to a must-links file, and outputting the charging item number in the format of 'MERGE _ charging item number'; wherein the number of the charging item is the number obtained in S101, and the induction of the synonym is completed.
Synonyms with similarity values between 0.5 and 0.8 are replaced with a standardized vocabulary.
And deleting synonyms with the same similarity. The required process log is obtained.
S103, dividing the execution sequence in the process log, and aligning the process model and the process log based on a set alignment principle to obtain a corresponding alignment set.
Specifically, the multiple item classes in the process log are combined to obtain a corresponding execution sequence, and the execution sequence is divided into a mandatory execution sequence and a selective execution sequence, where the mandatory execution sequence is an indispensable execution sequence, and is directly divided into corresponding alignment sets without passing through an alignment rule, and the set alignment rule is to divide corresponding data into corresponding alignment sets according to different alignment conditions, specifically:
if the topic model is the same as the selected execution sequence and is directly aligned, writing the corresponding topic model and the selected execution sequence into the constructed alignment set;
if the topic model is not the same as the selected execution sequence, reducing an alignment range by measuring cost to obtain the corresponding alignment set, specifically: searching a plurality of distance values between the topic model and the selection execution sequence; and arranging the plurality of distance values according to an ascending order by measuring the cost, writing the topic model which is the first distance value in the order and the selected execution sequence into the corresponding alignment set, and reducing the alignment range by continuously positioning deviation to obtain the optimal alignment set.
S104, screening all the alignment sets to complete the optimization of clinical paths.
Specifically, all the alignment sets are counted and summarized, and are arranged according to an ascending order, and then the first alignment set in the ordering is used as a required diagnosis and treatment path, that is, the activity in the smallest alignment set in the alignment sets is used as the best diagnosis and treatment path, so that the diagnosis and treatment path is optimized, the intervention of human factors is reduced, the speed is increased, the occurrence of errors is reduced, and the use experience of clinical paths is improved.
The invention relates to a clinical path optimization method based on process mining, which comprises the steps of firstly, obtaining a diagnosis and treatment charging bill, preprocessing the diagnosis and treatment charging bill and extracting characteristics, then, clustering and mining the diagnosis and treatment charging bill by utilizing an LDA subject clustering model and a PROM process mining tool to obtain a corresponding process model; then, extracting various items in the diagnosis and treatment charging bill, and preprocessing synonyms to obtain corresponding process logs; secondly, dividing the execution sequence in the process log, and aligning the process model and the process log based on a set alignment principle to obtain a corresponding alignment set; and finally, screening all the alignment sets, completing the optimization of clinical paths and improving the use effect of the clinical paths.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (6)
1. A clinical path optimization method based on process mining is characterized by comprising the following steps:
acquiring a diagnosis and treatment charging bill, and clustering and process mining the diagnosis and treatment charging bill to obtain a corresponding process model;
extracting various items in the diagnosis and treatment charging bill, and preprocessing synonyms to obtain corresponding process logs;
dividing the execution sequence in the process log, and aligning the process model with the process log based on a set alignment principle to obtain a corresponding alignment set;
and screening all the alignment sets to complete the optimization of clinical paths.
2. The process mining-based clinical path optimization method of claim 1, wherein obtaining a billing bill for encounter, clustering the billing bill for encounter and performing process mining to obtain a corresponding process model, comprises:
acquiring a diagnosis and treatment charging bill, and deleting multiple fields set in the diagnosis and treatment charging bill;
and clustering and mining the diagnosis and treatment charging bill by using an LDA subject clustering model and a PROM process mining tool to obtain a corresponding process model.
3. The clinical path optimization method based on process mining as claimed in claim 2, wherein the clustering and mining of the diagnosis and treatment billing bills by using LDA topic clustering model and PROM process mining tool to obtain corresponding process model comprises:
extracting characteristics of the diagnosis and treatment charging bill, and inputting the extracted activity characteristics into the LDA theme clustering model to obtain a corresponding theme model;
and clustering the topic models by day, and carrying out process mining on the clustering results by utilizing the PROM process mining tool to obtain corresponding process models.
4. The clinical path optimization method based on process mining according to claim 1, wherein the dividing of the execution sequence in the process log and the aligning of the process model with the process log based on a set alignment rule to obtain a corresponding alignment set comprises:
dividing an execution sequence in the process log into a mandatory execution sequence and a selective execution sequence;
and aligning the topic model with the selection execution sequence based on a set alignment principle to obtain a corresponding alignment set.
5. The process mining-based clinical path optimization method of claim 4, wherein aligning the topic model with the selection execution sequence based on a set alignment rule to obtain a corresponding alignment set comprises:
if the topic model is the same as the selection execution sequence, writing the corresponding topic model and the selection execution sequence into the constructed alignment set;
and if the theme model is different from the selection execution sequence, reducing the alignment range by measuring the cost to obtain the corresponding alignment set.
6. The process mining-based clinical pathway optimization method of claim 5, wherein if the topic model is not identical to the selection execution sequence, reducing an alignment range by measuring a cost to obtain the corresponding alignment set comprises:
if the topic model is not the same as the selection execution sequence, searching a plurality of distance values between the topic model and the selection execution sequence;
and by measuring the cost, arranging the distance values in an ascending order, and writing the first topic model in the ordering and the selection execution sequence into the corresponding alignment set.
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| CN113161001A (en) * | 2021-05-12 | 2021-07-23 | 东北大学 | Process path mining method based on improved LDA |
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