A data-driven framework of typical treatment process extraction and evaluation

BACKGROUND A clinical pathway (CP) defines a standardized care process for a well-defined patient group that aims to improve patient outcomes and promote patient safety. However, the construction of a new pathway from scratch is a time-consuming task for medical staff because it involves many factors, including objects, multidisciplinary collaboration, sequential design, and outcome measurements. Recently, the rapid development of hospital information systems has allowed the storage of large volumes of electronic medical records (EMRs), and this information constitutes an abundant data resource for building CPs using data-mining methods. METHODS We provide an automatic method for extracting typical treatment processes from EMRs that consists of four key steps. First, a novel similarity method is proposed to measure the similarity of two treatment records. Then, we perform an affinity propagation (AP) clustering algorithm to cluster doctor order set sequences (DOSSs). Next, a framework is proposed to extract a high-level description of each treatment cluster. Finally, we evaluate the extracted typical treatment processes by matching the treatment cluster with external information, such as the treatment efficacy, length of stay, and treatment cost. RESULTS By experiments on EMRs of 8287 cerebral infarction patients, it is concluded that our proposed method can effectively extract typical treatment processes from treatment records, and also has great potential to improve treatment outcome by personalizing the treatment process for patients with different conditions. CONCLUSION The extracted typical treatment processes are intuitive and can provide managerial guidance for CP redesign and optimization. In addition, our work can assist clinicians in clearly understanding their routine treatment processes and recommend optimal treatment pathways for patients.

[1]  Abdul V. Roudsari,et al.  Computerization of workflows, guidelines, and care pathways: a review of implementation challenges for process-oriented health information systems , 2011, J. Am. Medical Informatics Assoc..

[2]  Riccardo Bellazzi,et al.  Temporal electronic phenotyping by mining careflows of breast cancer patients , 2017, J. Biomed. Informatics.

[3]  Jimeng Sun,et al.  Constraint based temporal event sequence mining for Glioblastoma survival prediction , 2016, J. Biomed. Informatics.

[4]  Huilong Duan,et al.  Discovery of clinical pathway patterns from event logs using probabilistic topic models , 2014, J. Biomed. Informatics.

[5]  Huilong Duan,et al.  Summarizing clinical pathways from event logs , 2013, J. Biomed. Informatics.

[6]  Fei Wang,et al.  Mining and exploring care pathways from electronic medical records with visual analytics , 2015, J. Biomed. Informatics.

[7]  Alessandro Laio,et al.  Clustering by fast search and find of density peaks , 2014, Science.

[8]  Fei Wang,et al.  Supervised patient similarity measure of heterogeneous patient records , 2012, SKDD.

[9]  Germain Forestier,et al.  Classification of surgical processes using dynamic time warping , 2012, J. Biomed. Informatics.

[10]  Huilong Duan,et al.  On mining latent treatment patterns from electronic medical records , 2015, Data Mining and Knowledge Discovery.

[11]  Yan Zhuang,et al.  [Complex network analysis on dynamic change regularity of combining use of Chinese and western medicine in 27,678 cases with ischemic stroke in acute phase]. , 2015, Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica.

[12]  Chonghui Guo,et al.  Incremental Affinity Propagation Clustering Based on Message Passing , 2014, IEEE Transactions on Knowledge and Data Engineering.

[13]  Huilong Duan,et al.  Similarity Measure Between Patient Traces for Clinical Pathway Analysis: Problem, Method, and Applications , 2014, IEEE Journal of Biomedical and Health Informatics.

[14]  Wei Wei,et al.  Textual analysis and visualization of research trends in data mining for electronic health records , 2017 .

[15]  Wil M. P. van der Aalst,et al.  Process Mining , 2016, Springer Berlin Heidelberg.

[16]  Diogo R. Ferreira,et al.  Business process analysis in healthcare environments: A methodology based on process mining , 2012, Inf. Syst..

[17]  Vipin Kumar,et al.  Mining Electronic Health Records: A Survey , 2017, ArXiv.

[18]  Mit Critical Data.  Secondary Analysis of Electronic Health Records , 2016 .

[19]  Jorge Munoz-Gama,et al.  Process mining in healthcare: A literature review , 2016, J. Biomed. Informatics.

[20]  Hui Xiong,et al.  Fast affinity propagation clustering based on incomplete similarity matrix , 2017, Knowledge and Information Systems.

[21]  Wil M. P. van der Aalst,et al.  Process Mining Techniques: an Application to Stroke Care , 2008, MIE.

[22]  Wil M. P. van der Aalst,et al.  Process Mining - Discovery, Conformance and Enhancement of Business Processes , 2011 .

[23]  Zhuowen Tu,et al.  Similarity network fusion for aggregating data types on a genomic scale , 2014, Nature Methods.

[24]  Wil M. P. van der Aalst,et al.  Process Mining in Healthcare , 2020, Interactive Process Mining in Healthcare.

[25]  J. Cheah,et al.  Development and implementation of a clinical pathway programme in an acute care general hospital in Singapore. , 2000, International journal for quality in health care : journal of the International Society for Quality in Health Care.

[26]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[27]  Geoff Hall,et al.  Process mining routinely collected electronic health records to define real-life clinical pathways during chemotherapy , 2017, Int. J. Medical Informatics.

[28]  S. Brunak,et al.  Mining electronic health records: towards better research applications and clinical care , 2012, Nature Reviews Genetics.

[29]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[30]  Sooyoung Yoo,et al.  Assessment of hospital processes using a process mining technique: Outpatient process analysis at a tertiary hospital , 2016, Int. J. Medical Informatics.