Process-oriented Iterative Multiple Alignment for Medical Process Mining

Adapted from biological sequence alignment, trace alignment is a process mining technique used to visualize and analyze workflow data. Any analysis done with this method, however, is affected by the alignment quality. The best existing trace alignment techniques use progressive guide-trees to heuristically approximate the optimal alignment in O(N2L2) time. These algorithms are heavily dependent on the selected guide-tree metric, often return sum-of-pairs-score-reducing errors that interfere with interpretation, and are computationally intensive for large datasets. To alleviate these issues, we propose process-oriented iterative multiple alignment (PIMA), which contains specialized optimizations to better handle workflow data. We demonstrate that PIMA is a flexible framework capable of achieving better sum-of-pairs score than existing trace alignment algorithms in only O(NL2) time. We applied PIMA to analyzing medical workflow data, showing how iterative alignment can better represent the data and facilitate the extraction of insights from data visualization.Comment: accepted at ICDMW 201

Evaluation of Trace Alignment Quality and its Application in Medical Process Mining

Trace alignment algorithms have been used in process mining for discovering the consensus treatment procedures and process deviations. Different alignment algorithms, however, may produce very different results. No widely-adopted method exists for evaluating the results of trace alignment. Existing reference-free evaluation methods cannot adequately and comprehensively assess the alignment quality. We analyzed and compared the existing evaluation methods, identifying their limitations, and introduced improvements in two reference-free evaluation methods. Our approach assesses the alignment result globally instead of locally, and therefore helps the algorithm to optimize overall alignment quality. We also introduced a novel metric to measure the alignment complexity, which can be used as a constraint on alignment algorithm optimization. We tested our evaluation methods on a trauma resuscitation dataset and provided the medical explanation of the activities and patterns identified as deviations using our proposed evaluation methods.Comment: 10 pages, 6 figures and 5 table

Individual Behavior Modeling with Sensors Using Process Mining

[EN] Understanding human behavior can assist in the adoption of satisfactory health interventions and improved care. One of the main problems relies on the definition of human behaviors, as human activities depend on multiple variables and are of dynamic nature. Although smart homes have advanced in the latest years and contributed to unobtrusive human behavior tracking, artificial intelligence has not coped yet with the problem of variability and dynamism of these behaviors. Process mining is an emerging discipline capable of adapting to the nature of high-variate data and extract knowledge to define behavior patterns. In this study, we analyze data from 25 in-house residents acquired with indoor location sensors by means of process mining clustering techniques, which allows obtaining workflows of the human behavior inside the house. Data are clustered by adjusting two variables: the similarity index and the Euclidean distance between workflows. Thereafter, two main models are created: (1) a workflow view to analyze the characteristics of the discovered clusters and the information they reveal about human behavior and (2) a calendar view, in which common behaviors are rendered in the way of a calendar allowing to detect relevant patterns depending on the day of the week and the season of the year. Three representative patients who performed three different behaviors: stable, unstable, and complex behaviors according to the proposed approach are investigated. This approach provides human behavior details in the manner of a workflow model, discovering user paths, frequent transitions between rooms, and the time the user was in each room, in addition to showing the results into the calendar view increases readability and visual attraction of human behaviors, allowing to us detect patterns happening on special days.This research was funded by ITACA SABIEN and partially supported by CONICYT REDI 170136.Dogan, O.; Martinez-Millana, A.; Rojas, E.; Sepulveda, M.; Munoz Gama, J.; Traver Salcedo, V.; Fernández Llatas, C. (2019). Individual Behavior Modeling with Sensors Using Process Mining. Electronics. 8(7):1-17. https://doi.org/10.3390/electronics8070766S11787Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645-1660. doi:10.1016/j.future.2013.01.010Guo, B., Zhang, D., Wang, Z., Yu, Z., & Zhou, X. (2013). 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Process Mining of Medication Revisions in Electronic Health Records

Objective: The objective of this work is to develop process mining techniques for analysing Electronic Health Record (EHR) events in order to uncover factors contributing to the event, and understanding deviations in the process. We have outlined a method for combining data mining with expert review to model the EHR process and develop automated algorithms that can be used to detect potential deviations for a defined process. Introduction: To analyse EHR events meaningfully, process mining can be applied to distil structured process description from a set of real executions. Process mining can be applied for 1) Discovery, 2) Conformance, and 3) Enhancement of processes. This can be used for improving efficiency and safety in the process. Extending process mining to EHR system use, user activity can be analysed to model EHR use behaviour and detect deviations from expected use. Understanding these behaviours could be used to optimize systems through redesign. Here, we explore the application of process mining of medication revisions in an EHR. Methods: We first apply exploratory data analysis (EDA) of medication revisions (i.e. instances of altering a previous medication order) in EHR data to understand the occurrence of revision in the data. Data was retrieved from 6 U.S. ambulatory clinics, and 35,833 medication revision events were analysed. To add domain knowledge to the EDA, physicians manually reviewed a subset of events (n=100) to identify probable cause for these revisions. From the resulting causes, a categorization scheme was developed and fault trees were constructed to model the medication revision process. Additionally, from access pattern of EHR elements used in the expert review, an algorithm for automated detection of revisions was developed. Sensitivity and specificity were calculated for the algorithm used to categorize an order as a revision event. Results: Revisions were classified into 5 categories - Cancel, Discontinue, Duplicate, Update, and Wrong Medication. 55% of the revisions were used as system workarounds to discontinue/update medications. The process model indicated that system issues were most prevalent, including problems in data entry and item selection. An automated algorithm was developed to categorize a medication order as a revision event. Given prevalence of 1.1%, the algorithm performed with 66% sensitivity, 85% accuracy and PPV of 4.8%. Discussion: EHR medication events were process mined by applying both data mining and domain knowledge. For the majority of cases, medication revisions are used as system workarounds. The fault tree analysis also suggests a common cause of these alterations is system issues. Although our automated methods showed lower sensitivity because of these workarounds, they were able to classify successfully those medication revisions that did reflect errors such as Wrong Medication or Duplicate Medications. Conclusion: Process mining was applied to medication revisions and was shown to detect revision events in the data. The process model uncovers factors responsible for revision events and can be used for improving EHR use. The detection algorithm can be useful in real-time monitoring. The vision is to develop monitoring tool for EHR similar to flight recorders and antivirus software