Clinical Pathway Generation from Hospital Information System

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Procedia Computer Science 00 (2018) 000–000 Procedia Computer Science (2018) 000–000 Procedia Computer Science 13900 (2018) 545–553

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The International Academy of Information Technology and Quantitative Management, the Peter The International Academy Kiewit of Information Quantitative Management, the Peter Institute,Technology University and of Nebraska Kiewit Institute, University of Nebraska

Clinical Pathway Generation from Hospital Information System Clinical Pathway Generation from Hospital Information System Shusaku Tsumotoaa , Tomohiro Kimurabb , Haruko Iwatabb , Shoji Hiranoaa Shusaku Tsumoto , Tomohiro Kimura , Haruko Iwata , Shoji Hirano

a Department

of Medical Informatics, Faculty of Medicine, Shimane University, 89-1 Enya-cho, Izumo 693-8501 Japan MedicalDivision, Informatics, Faculty of Medicine, Shimane University, Enya-cho, Izumo 693-8501 Japan MedicalofServices Faculty of Medicine, Shimane University, 89-189-1 Enya-cho, Izumo 693-8501 Japan b Medical Services Division, Faculty of Medicine, Shimane University, 89-1 Enya-cho, Izumo 693-8501 Japan

a Department b

Abstract Abstract This paper proposes the following three-fold clinical care generation method. First, the system extracts subgrouping from clinical This proposes the following three-fold clinical care generation method. theclustering. system extracts from clinical casespaper with the same Diagnostic Procedure Combination code (DPC) by mixtureFirst, model Then, subgrouping it constructs classification cases with Diagnostic code (DPC) Finally, by mixture model clustering.byThen, constructs classification models of the eachsame subgroup by theProcedure analysis Combination of discharge summaries. cases are classified usingit the classification model models of each subgroup by the analysis discharge summaries. Finally,method cases are the classification model and a clinical pathway is generated for eachofnew subgroup.. The proposed wasclassified evaluatedby on using the datasets extracted hospital and a clinical pathway is generated for each new subgroup.. The proposed method was evaluated on the datasets extracted hospital information system, whose results show that plausible clinical pathways were obtained, compared with previously introduced information methods. system, whose results show that plausible clinical pathways were obtained, compared with previously introduced methods. c 2018  2018 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. © c 2018  The Authors. by Elsevier B.V. This is open access article the license This is an an open accessPublished article under under the CC CC BY-NC-ND BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer review under responsibility of the scientific committee of The International Academy of Information Technology and QuanPeer review under responsibility of the scientific committee of The International Academy of Information Technology and Peer review under responsibility of the scientific committee The International Academy of Information Technology and Quantitative Management, the Peter Kiewit Institute, University ofofNebraska. Quantitative Management, the Peter Kiewit Institute, University of Nebraska. titative Management, the Peter Kiewit Institute, University of Nebraska. Keywords: Clinical pathway; Hierarchical clustering; EM clustering; Hospital information system Keywords: Clinical pathway; Hierarchical clustering; EM clustering; Hospital information system

1. Introduction 1. Introduction Iwata and Tsumoto have proposed induction of clinical pathway from executed nursing actions for each disease Iwata Tsumoto have proposed induction of the clinical nursing actions for Combination each disease based on and clustering methods[1, 2]. In those studies, casespathway assignedfrom to theexecuted same Diagnostic Procedure based on clustering methods[1, 2]. In those studies, the cases assigned to the same Diagnostic Procedure Combination code (DPC) were used, where the effectiveness of the method was confirmed in domains such as opthalmology. code (DPC) were of used, the effectiveness ofthe thegenerated method was confirmed in domains such asThe opthalmology. However, in cases brainwhere infarction or lung cancer, pathways were more complicated. main reason However, in cases of brain infarction or lung cancer, the generated pathways were more complicated. The main reason is that although a single DPC code is assigned, there may be some varieties in clinical courses for such diseases. The is that although a single DPC code is assigned, there may be some varieties in clinical courses for such diseases. The generated pathways can be viewed as superposition of such different types of disease progression. generated pathways can be viewed as superposition of such different types of disease progression. In this paper, such clinical cases are decomposed into several subcatergories of clinical courses by using EM In this paper, such models clinical [3]. cases are decomposed into several subcatergories clinical by using clustering or mixture Then, by using discharge summaries, classifiersoffor these courses subcategories willEM be clustering or mixture models [3]. Then, by using discharge summaries, classifiers for these subcategories will be generated. Finally, for each subcategory, clinical pathway generation is applied. generated. Finally, for each subcategory, clinical pathway generation is applied. E-mail address: [email protected] E-mail address: [email protected]

c 2018 The Authors. Published by Elsevier B.V. 1877-0509  1877-0509 © The Authors. Published by c 2018 1877-0509  2018 Thearticle Authors. Published by Elsevier Elsevier B.V. B.V. This is an open access under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is an anopen openaccess accessarticle articleunder underthethe CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer review under responsibility of scientific committee of The International Academy of Information Technology and Quantitative ManagePeer review under responsibility of the scientific committee of The International Academy of Information Technology and Quantitative Peer review under responsibility of the scientific committee of The International Academy of Information Technology and Quantitative Management, the Peter Kiewit Institute, University of Nebraska. Management, the Peter Kiewit Institute, University of Nebraska. ment, the Peter Kiewit Institute, University of Nebraska. 10.1016/j.procs.2018.10.233

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The proposed method was evaluated on data extracted from hospital information system ,whose results show that the method induces more simple clinical pathways. The paper is organized as follows. Section 2 gives research background, where an example of a clinical pathway is shown. Section 3 introduces EM algorithm where original data are decomposed into subcategories. Then by using discharge summaries, classifiers for subcategories will be obtained. Then, shows the revised version of clinical pathway generation. Section 4 illustrates how the process works by using datasets of cerebral infarction extracted from hospital information system. Finally, Section 5 concludes this paper. 2. Background 2.1. Hospital Information System 2.1.1. Electronic Patient Records and Medical Payment Hosptial information system (HIS) [4] plays an import role in clinical services in large scale hospital. It store almost all histories of clinical actions, including physical examinations, laboratory examinations, radiological examations and applied therapies, The core part of hosptial information, for doctors and nurses are called electronic patient records. Such storage of data can be viewed as big data: Tsumoto et al. [5] applies temporal data mining techniques to datasets extracted from HIS. Furthermore, HIS automatically calculated all medical payments by using histories of clinical actions, since a point of medical payment is assigned to each clinical actions by government. Since each patient needs complicated calculus for medical payment, HIS is indispenable to efficient medical payment service. 2.1.2. Diagnosis Procedure Combination (DPC) From 2003, Diagnosis Procedure Combination (DPC) introduces large-scale hospitals in order for the government to cut medical expenditure. DPC is assigned to a disease for which the major parts of medical resourses were used during hospitalization of a patient. For each date of hospital stay, a payment point is assigned for each DPC code. Thus, medical payment by DPC depends on the length of hospital stay and diagnosis and medical procedures, and differs from the existing payment system, which depends on a piece rate of each medical actions. The daily payment differs for among almost 2,500 groups of DPC defined by the International Statistical Classification of Diseases and Related Health Problems (ICD-10), and procedures, such as operations (K and J codes of the existing payment system). DPC codes are used to describe each hospitalization in hospital information system from the viewpoint of medical payment. Thus, the assigned code may differ from diseases of medical interest. This discrepancy between medical payment and diseases may cause a problem in data analysis as shown below. From granular computing of view, we can say that each interest or focus has different information granularity and that if our interest is diffrent from interest when data is stored, some preprocessing is needed to generate datasets for our interest. 2.2. Clinical Pathway A clinical pathway for a disease describes a schedule of medical care, which is optimized during the hospitalization [6, 7]. It is very important for efficient clinical process management, but usually its construction is manually acquired from doctors or nurses, according to their experiences. Let us give an example. Figure 1 shows a clinical pathway on cataracta used in Shimane University hospital. The hospitalization period consists of three periods: preoperation, operation and post-operation periods. The preoperation date is denoted by -1 day, and operation date is by 0 day. The pathway will be executed as follows. For the preoperation date, body temperature (BT), pulse rate (PR) and blood pressure (BP) are checked and preoperation instruction will be given. For operation date, BT, PR and BP are checked, and the symptoms of nausea, vomitting and eye pain are inspected. Then, during postoperation period, in addition to nursing orders for the operation date, coaching will be applied. Finally, if the status of a patient is stable, the patient will be discharged five days after the operation.



Shusaku Tsumoto et al. / Procedia Computer Science 139 (2018) 545–553 S. Tsumoto et al. / Procedia Computer Science 00 (2018) 000–000 Presurger y

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Fig. 1. Clinical Pathway for Cataracta

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Fig. 2. Clinical Pathway for Cataracta Obtained from Data

Hospital Information System (HIS) Construction or Revision of Clinical Pathway

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Fig. 3. Construction of Clinical Pathway (Clustering+Feature Selection)

Fig. 4. Construction of Clinical Pathway

2.3. Clinical Pathway Construction This subsection summarizes methods for clinical pathway construction [8, 1, 2, 9]. Tsumoto and Iwata firstly introduces combination of agglomerative hierachical clustering and feature selection method for its construction [8, 1](Figure 3.) Clustering is applied to data on executed nursing orders where rows and columns give nursing orders and date of hospitalization. Then, grouping of nursing actions is extracted. Such groups can be used as classification labels, and information gain is calculated for each attribute. Then, attribute will be grouped by the values of the gains. Since attributes are each date of hospitalization, such grouping of attributes corresponds to clinical schedule (Figure 2.) It is notable that the above second step (calculation of information gain) is actually a kind of grouping. Thus, clustering for attributes (attribute clustering) can be applied for this purpose. Tsumoto et al. introduces combination of sample clusting and attribute clustering, called dual clustering [2] and obtains the same performance as the former approach [1]. Tsumoto et al. generalize the approach [9] with combination of data decomposition and dual clustering. The method consists of the following five steps (Figure 4): first, histories of nursing orders are extracted from hospital information system. Second, orders are classified into several groups by using clustering on the principal components (sample clustering). Third, feature selection method is applied and the dataset is decomposed into subtables. The second and the third process will be repeated until the clustering results are converged. Figure 2 shows the pathway generated by the above construction algorithm shown in Figure 4 The induced results show that coaching and wash, whose chronological characteristics are similar to the orders indispensable to the treatment of glaucoma, were not included in the existing pathway. Furthermore, coaching and wash should be treated as postoperation follow-up and routine process, respectively. The results show that the method is able to construct a clinical pathway for this disease. Furthermore, the temporal intervals suggested the optimal and maximum length of stay.

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3. EM clustering and Revised Pathway Construction 3.1. Assumption for Mixture Normal Model Since DPC is mainly assigned to a code, based on medical treatment, several different clinical cases may belong to the same code due to the same medical treatment. Thus, the users should take care of the subgroups of disease categories in the same DPC coded cases and decompose clinical cases into homogenous cases. If we can assume that the different disease progression may reflect on the corresponding temporal evolution, the distribution of length stay can be represented as mixture of several diseases. When each distribution of subcategories follows normal distribution, mixture nomral model, or EM clustering can be applied for decomposition of the whole datasets. 3.2. Mixture Normal Model Let θ and x denote a parameter and a datum. Then, the probability of observation of x is represented as a linear combination of normal distribution: p(x, θ) =

M 

πm φ(x, µm , Σm ),

(1)

m=1

where M, µm , and Σm denote the number of clusters and mean and covariance matrix for cluster m. 3.3. Clustering From Bayes theorem, the posterior probability that cluster m is observed is: p(m|x, θ) =

p(x, m; θ) πm φ(x; µm , Σm ) , = M p(x; θ) m=1 πm φ(x; µm , Σm )

where φ(x; µm , Σm ) denotes an element for likelihood function. Since we assume normal distribution, φ(x; µm , Σm ) = 

1 2πσ2m

exp(−

(x − µm )2 ) 2σ2m

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M n  

πm φ(xi ; µm , Σm ) =

i=1 m=1

M n   i=1

1 (xi − µm )2 πm  exp(− ) 2σ2m 2πσ2m m=1

EM algorithm[10] estimates a set of paramers {µm , Σm } by using maximum likelihood estimation. 3.4. Number of Clusters Partition-based clustering, such as K-means, needs to assume the number of clusters, which is one of the problem of the method, compared with hiearchical clustering. However, in case of mixture normal model, since the parameters of normal distribution are means and variance, we can count the number of parameters for each number of clusters, where AIC can be applied to estimate the fitting. From Equation ( 1), one cluster has three parameters: a coefficient of linear combination, mean and variance. Since the total sum of the coefficients is equal to 1, the total number of parameters are 3k − 1 where k is the number of clusters. Then, AIC is defined as: AIC = −2 ∗ (log likelihood) + 2 ∗ (3k − 1).



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Hospital Information System Summary counts of Executed Nursing Orders for each date

Summary counts of Executed Nursing Orders for each date

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EM clustering w.r.t length of stay for #Clusters=k (Mixture Normal Model)

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Fig. 5. Revised Clinical Pathway Construction

AIC Estimation

  

Morphological Analysis

Ranking of Keyword by Correspondence Analysis

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Combination Pathway

Fig. 6. Total Process for Clinical Pathway Generation

3.5. Decomposition of Clinical Cases Since EM clustering assigns a probability belonging to a cluster m to each case, dataset may not be decomposed into subclasses. Thus, we apply the following criteria for data decomposition. 1. Assume the number of clusters k and apply EM algorithm for mixture normal model to the data of length of stays in the clinical cases assigned to the same DPC code. 2. Calculate the probability for each classs for each date. 3. If the two clusters have the same probability values at the time point, this point is set to the cut-off point, Otherwise, the mean of the value before and after will be used as a cut-off point. 4. By using the cut-off points, decompose datasets to subcategories. 3.6. Revised Pathway Construction Figure 5 shows revised version of clinical pathway construction. First, summary counts of executed nursing orders are calculated to the cases with one assigned DPC code. Then, for the cluster number k =0 to 10, each loglikelihood is calculated and AIC value is estimated.Then the best cluster number is selected, according to the AIC value. For the fixed cluster number k, dual clustering is applied for each subcluster and pathway will be constructed for each cluster. 3.7. Clinical Meaning for Each Cluster obtained by EM clustereing The meaning of each cluster is not explicitly described in the data, we must depend on the linked data. Here, discharge summaries were used to captuer the meaning of clusters. After two clusters were obtained, discharge summaries were extracted from the hospital information system and morphological analysis (RMeCab) [11] was used to create the term matrix. Then, corresponding analysis was applied to the matrix and tf-idf of each keyword for each category were calculated [12]. 3.8. Total Process for Clinical Pathway Generation Figure 6 depicts total process for clinical pathway generation. From hospital information system, given DPC, the system extracts summary counts of Executed Nursing Orders for each date and discharge summaries. To the summary counts, it applies EM clustering and decompose original samples into subclusters. Then, clinical pathway generation is applied to each subcluster. On the other hand, classifiers for subclusters obtained by EM clusterng are constructed. Finally, classfiiers and clinical pathways are combined.

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4. Example Let us illustrate the above pathway generation by using datasets extracted from HIS in Shimane university Hospital. The data is composed of 80 cases coded by DPC (cerebral infarction: 010060x099030x) whose patients were admitted to the university hospital in 2015. 4.1. EM clustering Let us consider the fitting for the data on patients suffered from cerebral infarction admitted to Shimane University Hospital in fiscal year of 2015. Table 1 shows the values of AIC, where k = 2 is the best value. Table 1. Number of Clusters, Loglikelihood and AIC

#Clusters 2 3 4 5 6 7 8 9

Log likelihood -296.1122 -293.6723 -293.6574 -291.5038 -288.7785 -287.4351 -288.7281 -286.2126

AIC 600.2244 601.3446 607.3148 609.0076 609.5573 612.8702 621.4562 622.4252

4.2. Pathway Generation for Cerebral Infarction The pathway construction method shown in Figure 5 was applied to data on cerebral infarction as follows. 1. Dual clustering was applied to summary counts of cerbral infarction (DPC: 010060x099030x) extracted from hospital information system, and nursing orders were partitioned into 3 to 5 groups. Concerning the length of stay, three clusters were obtained. Figure 7 show the pathway obtained by this raw data 1 . 2. EM clustering was applied and AIC results shows that two clusters were best fitting. Let group1 and group 2 denote shorter and longer stay cases. 3. Cut-off point was estimated and each case was assigned to either cluster. 4. For each subgroup, dual clustering was applied and clinical pathway for each was induced. These pathway generation can be applied recursively, but in this case, the algorithm was terminated only by one pass. The derived pathways were shown in Figure 9 and 10. It is easy to see that the pathway induced from all the data is derived as a combination of one induced from group 1 (shorter stay) and the other one from group 2 (longer stay). 4.3. Classification Model Finally, let us consider the classifiers of two subgroups of brain infarction. Table 2 and 3 show the top-10 frequent keywords for short stay group and longer stay group. The lists characterizes the nature of each group: Longer stay group may include keywords related with motor paralysis, where a patient needs rehabilitation and may transfer to the region hospital for chronic care. Classifiers, such as decision tree, random forest or deep learners can be constructed by using the above keyword lists. 1

Usually, since a surgical operation is not applied to cerebral infarction, presurgery period (such as Day −1) will not be included.



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Table 2. Frequent Keywords for Short Stay Group

Keyword Fracture Home ADL Pons Numbness X-ray Clinic Mouth Corner Swallowing Pathological

tf-idf 0.01105774 0.009951966 0.0084776 0.0084776 0.008109009 0.008109009 0.008109009 0.008109009 0.008109009 0.007740418

Table 3. Frequent Keywords for Longer Stay Group

Keyword Transfer Commune Izumo HemiCapsule Babinski Intenal Capsule Civil HDL Tube

tf-idf 0.012553504 0.01033818 0.009230518 0.009230518 0.009230518 0.008861297 0.008861297 0.008122856 0.007753635 0.007753635

4.4. Evaluation of Classifier Evaluation process is based on repeated 2-fold cross validation[13]. First, a given dataset is randomly split into training examples and test samples half in half. Then, training examples is used for construction of a classifier, and the derived classifier is evaluated by remaining test samples. The above procedures were repeated for 100 times in this

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experiment, and the averaged accuracy was calculated. Fig. 8 plots the evaluation results. The vertical axis denotes the averaged accuracy of a classifier, and the horizontal axis denotes the number of selected keywords. 5. Conclusion This paper proposes the following two-fold clinical care generation method. First, the system proposes how clinical cases with the same DPC code are characterized by mixture model clustering, and construct classification model by the analysis of discharge summaries. Then, cases are classified by using the classification model and a clinical pathway is generated for each new class. The proposed method was evaluated on the datasets extracted hospital information system, whose results show that plausible clinical pathways were obtained, compared with previously introduced methods. Acknowledgements This research is supported by Grant-in-Aid for Scientific Research (B) 15H2750 and 18H03289 from Japan Society for the Promotion of Science(JSPS). References [1] H. Iwata, S. Hirano, S. Tsumoto, Maintenance and discovery of domain knowledge for nursing care using data in hospital information system, Fundam. Inform. 137 (2) (2015) 237–252. doi:10.3233/FI-2015-1177. URL http://dx.doi.org/10.3233/FI-2015-1177 [2] Y. Tsumoto, H. Iwata, S. Hirano, S. Tsumoto, Construction of clinical pathway using dual clustering, Neuroscience and Biomedical Engineering 3. [3] G. J. McLachlan, D. Peel, Finite Mixture Models, Wiley, New York, 2000. [4] G. Melton, C. J. McDonald, P. C. Tang, G. Hripcsak, Elecronic Health Records, 4th Edition, Springer, New York, 2014, Ch. 16.



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