Decomposed process discovery and conformance checking

Decomposed process discovery and decomposed conformance checking are the corresponding variants of the two monolithic fundamental problems in process mining (van der Aalst 2011): automated process discovery, which considers the problem of discovering a process model from an event log (Leemans 2009), and conformance checking, which addresses the problem of analyzing the adequacy of a process model with respect to observed behavior (Munoz-Gama 2009), respectively. The term decomposed in the two definitions is mainly describing the way the two problems are tackled operationally, to face their computational complexity by splitting the initial problem into smaller problems, that can be solved individually and often more efficiently.Postprint (author's final draft

Challenges and opportunities of applying natural language processing in business process management

The Business Process Management (BPM) field focuses in the coordination of labor so that organizational processes are smoothly executed in a way that products and services are properly delivered. At the same time, NLP has reached a maturity level that enables its widespread application in many contexts, thanks to publicly available frameworks. In this position paper, we show how NLP has potential in raising the benefits of BPM practices at different levels. Instead of being exhaustive, we show selected key challenges were a successful application of NLP techniques would facilitate the automation of particular tasks that nowadays require a significant effort to accomplish. Finally, we report on applications that consider both the process perspective and its enhancement through NLP.Peer ReviewedPostprint (published version

Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol

Introduction In the UK, primary care is seen as the optimal context for delivering care to an ageing population with a growing number of long-term conditions. However, if it is to meet these demands effectively and efficiently, a more precise understanding of existing care processes is required to ensure their configuration is based on robust evidence. This need to understand and optimise organisational performance is not unique to healthcare, and in industries such as telecommunications or finance, a methodology known as ‘process mining’ has become an established and successful method to identify how an organisation can best deploy resources to meet the needs of its clients and customers. Here and for the first time in the UK, we will apply it to primary care settings to gain a greater understanding of how patients with two of the most common chronic conditions are managed. Methods and analysis The study will be conducted in three phases; first, we will apply process mining algorithms to the data held on the clinical management system of four practices of varying characteristics in the West Midlands to determine how each interacts with patients with hypertension or type 2 diabetes. Second, we will use traditional process mapping exercises at each practice to manually produce maps of care processes for the selected condition. Third, with the aid of staff and patients at each practice, we will compare and contrast the process models produced by process mining with the process maps produced via manual techniques, review differences and similarities between them and the relative importance of each. The first pilot study will be on hypertension and the second for patients diagnosed with type 2 diabetes

Question-Driven Methodology for Analyzing Emergency Room Processes Using Process Mining

[EN] In order to improve the efficiency and effectiveness of Emergency Rooms (ER), it is important to provide answers to frequently-posed questions regarding all relevant processes executed therein. Process mining provides different techniques and tools that help to obtain insights into the analyzed processes and help to answer these questions. However, ER experts require certain guidelines in order to carry out process mining effectively. This article proposes a number of solutions, including a classification of the frequently-posed questions about ER processes, a data reference model to guide the extraction of data from the information systems that support these processes and a question-driven methodology specific for ER. The applicability of the latter is illustrated by means of a case study of an ER service in Chile, in which ER experts were able to obtain a better understanding of how they were dealing with episodes related to specific pathologies, triage severity and patient discharge destinations.This project was partially funded by Fondecyt Grants 1150365 and 11130577 from the Chilean National Commission on Scientific and Technological Research (CONICYT), the Ph.D. Scholarship Program of CONICYT Chile (CONICYT-Doctorado Nacional/2014-63140180), the Ph.D. Scholarship Program of CONICIT Costa Rica and by Universidad de Costa Rica Professor Fellowships.Rojas, E.; Sepúlveda, M.; Munoz-Gama, J.; Capurro, D.; Traver Salcedo, V.; Fernández Llatas, C. (2017). Question-Driven Methodology for Analyzing Emergency Room Processes Using Process Mining. Applied Sciences. 7(3):1-29. https://doi.org/10.3390/app7030302S12973Welch, S. J., Asplin, B. R., Stone-Griffith, S., Davidson, S. J., Augustine, J., & Schuur, J. (2011). Emergency Department Operational Metrics, Measures and Definitions: Results of the Second Performance Measures and Benchmarking Summit. Annals of Emergency Medicine, 58(1), 33-40. doi:10.1016/j.annemergmed.2010.08.040Jansen-Vullers, M., & Reijers, H. (2005). Business Process Redesign in Healthcare: Towards a Structured Approach. INFOR: Information Systems and Operational Research, 43(4), 321-339. doi:10.1080/03155986.2005.11732733Grol, R., & Grimshaw, J. (1999). Evidence-Based Implementation of Evidence-Based Medicine. The Joint Commission Journal on Quality Improvement, 25(10), 503-513. doi:10.1016/s1070-3241(16)30464-3Fernández-Llatas, C., Meneu, T., Traver, V., & Benedi, J.-M. (2013). Applying Evidence-Based Medicine in Telehealth: An Interactive Pattern Recognition Approximation. International Journal of Environmental Research and Public Health, 10(11), 5671-5682. doi:10.3390/ijerph10115671Radnor, Z. J., Holweg, M., & Waring, J. (2012). Lean in healthcare: The unfilled promise? Social Science & Medicine, 74(3), 364-371. doi:10.1016/j.socscimed.2011.02.011Rojas, E., Munoz-Gama, J., Sepúlveda, M., & Capurro, D. (2016). Process mining in healthcare: A literature review. Journal of Biomedical Informatics, 61, 224-236. doi:10.1016/j.jbi.2016.04.007Neumuth, T., Jannin, P., Schlomberg, J., Meixensberger, J., Wiedemann, P., & Burgert, O. (2010). Analysis of surgical intervention populations using generic surgical process models. International Journal of Computer Assisted Radiology and Surgery, 6(1), 59-71. doi:10.1007/s11548-010-0475-yFernandez-Llatas, C., Lizondo, A., Monton, E., Benedi, J.-M., & Traver, V. (2015). Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems. Sensors, 15(12), 29821-29840. doi:10.3390/s151229769Rebuge, Á., & Ferreira, D. R. (2012). Business process analysis in healthcare environments: A methodology based on process mining. Information Systems, 37(2), 99-116. doi:10.1016/j.is.2011.01.003Partington, A., Wynn, M., Suriadi, S., Ouyang, C., & Karnon, J. (2015). Process Mining for Clinical Processes. ACM Transactions on Management Information Systems, 5(4), 1-18. doi:10.1145/2629446Basole, R. C., Braunstein, M. L., Kumar, V., Park, H., Kahng, M., Chau, D. H. (Polo), … Thompson, M. (2015). Understanding variations in pediatric asthma care processes in the emergency department using visual analytics. Journal of the American Medical Informatics Association, 22(2), 318-323. doi:10.1093/jamia/ocu016Suriadi, S., Andrews, R., ter Hofstede, A. H. M., & Wynn, M. T. (2017). Event log imperfection patterns for process mining: Towards a systematic approach to cleaning event logs. Information Systems, 64, 132-150. doi:10.1016/j.is.2016.07.011De Medeiros, A. K. A., Weijters, A. J. M. M., & van der Aalst, W. M. P. (2007). Genetic process mining: an experimental evaluation. Data Mining and Knowledge Discovery, 14(2), 245-304. doi:10.1007/s10618-006-0061-7Aalst, W. M. P. van der. (2005). Business alignment: using process mining as a tool for Delta analysis and conformance testing. Requirements Engineering, 10(3), 198-211. doi:10.1007/s00766-005-0001-xVan der Aalst, W., Adriansyah, A., & van Dongen, B. (2012). Replaying history on process models for conformance checking and performance analysis. WIREs Data Mining and Knowledge Discovery, 2(2), 182-192. doi:10.1002/widm.1045Song, M., & van der Aalst, W. M. P. (2008). Towards comprehensive support for organizational mining. Decision Support Systems, 46(1), 300-317. doi:10.1016/j.dss.2008.07.00

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Methods and tools for analysis and management of risks and regulatory compliance in the healthcare sector: the Hospital at Home – HaH

Changing or creating a new organization means creating a new process. Each process involves many risks that need to be identified and managed. The main risks considered here are procedural risks and legal risks. The former are related to the risks of errors that may occur during processes, while the latter are related to the compliance of processes with regulations. Therefore, managing the risks implies proposing changes to the processes that allow the desired result: an optimized process. In order to manage a company and optimize it in the best possible way, not only should the organizational aspect, risk management and legal compliance be taken into account, but it is important that they are all analyzed simultaneously with the aim of finding the right balance that satisfies them all. This is exactly the aim of this thesis, to provide methods and tools to balance these three characteristics, and to enable this type of optimization, ICT support is used. This work is not intended to be a computer science or law thesis but an interdisciplinary thesis. Most of the work done so far is vertical and in a specific domain. The particularity and aim of this thesis is not so much to carry out an in-depth analysis of a particular aspect, but rather to combine several important aspects, normally analyzed separately, which however have an impact on each other and influence each other. In order to carry out this kind of interdisciplinary analysis, the knowledge base of both areas was involved and the combination and collaboration of different experts in the various fields was necessary. Although the methodology described is generic and can be applied to all sectors, a particular use case was chosen to show its application. The case study considered is a new type of healthcare service that allows patients in acute disease to be hospitalized to their home. This provide the possibility to perform experiments using real hospital database

Methods and tools for analysis and management of risks and regulatory compliance in the healthcare sector: the hospital at home – HaH

Changing or creating an organisation means creating a new process. Each process involves many risks that need to be identified and managed. The main risks considered here are procedural and legal risks. The former are related to the risks of errors that may occur during processes, while the latter are related to the compliance of processes with regulations. Managing the risks implies proposing changes to the processes that allow the desired result: an optimised process. In order to manage a company and optimise it in the best possible way, not only should the organisational aspect, risk management and legal compliance be taken into account, but it is important that they are all analysed simultaneously with the aim of finding the right balance that satisfies them all. This is the aim of this thesis, to provide methods and tools to balance these three characteristics, and to enable this type of optimisation, ICT support is used. This work isn’t a thesis in computer science or law, but rather an interdisciplinary thesis. Most of the work done so far is vertical and in a specific domain. The particularity and aim of this thesis is not to carry out an in-depth analysis of a particular aspect, but rather to combine several important aspects, normally analysed separately, which however have an impact and influence each other. In order to carry out this kind of interdisciplinary analysis, the knowledge base of both areas was involved and the combination and collaboration of different experts in the various fields was necessary. Although the methodology described is generic and can be applied to all sectors, the case study considered is a new type of healthcare service that allows patients in acute disease to be hospitalised to their home. This provide the possibility to perform experiments using real hospital database