Applying Process-Oriented Data Science to Dentistry

Background: Healthcare services now often follow evidence-based principles, so technologies such as process and data mining will help inform their drive towards optimal service delivery. Process mining (PM) can help the monitoring and reporting of this service delivery, measure compliance with guidelines, and assess effectiveness. In this research, PM extracts information about clinical activity recorded in dental electronic health records (EHRs) converts this into process-models providing stakeholders with unique insights to the dental treatment process. This thesis addresses a gap in prior research by demonstrating how process analytics can enhance our understanding of these processes and the effects of changes in strategy and policy over time. It also emphasises the importance of a rigorous and documented methodological approach often missing from the published literature. Aim: Apply the emerging technology of PM to an oral health dataset, illustrating the value of the data in the dental repository, and demonstrating how it can be presented in a useful and actionable manner to address public health questions. A subsidiary aim is to present the methodology used in this research in a way that provides useful guidance to future applications of dental PM. Objectives: Review dental and healthcare PM literature establishing state-of-the-art. Evaluate existing PM methods and their applicability to this research’s dataset. Extend existing PM methods achieving the aims of this research. Apply PM methods to the research dataset addressing public health questions. Document and present this research’s methodology. Apply data-mining, PM, and data-visualisation to provide insights into the variable pathways leading to different outcomes. Identify the data needed for PM of a dental EHR. Identify challenges to PM of dental EHR data. Methods: Extend existing PM methods to facilitate PM research in public health by detailing how data extracts from a dental EHR can be effectively managed, prepared, and used for PM. Use existing dental EHR and PM standards to generate a data reference model for effective PM. Develop a data-quality management framework. Results: Comparing the outputs of PM to established care-pathways showed that the dataset facilitated generation of high-level pathways but was less suitable for detailed guidelines. Used PM to identify the care pathway preceding a dental extraction under general anaesthetic and provided unique insights into this and the effects of policy decisions around school dental screenings. Conclusions: Research showed that PM and data-mining techniques can be applied to dental EHR data leading to fresh insights about dental treatment processes. This emerging technology along with established data mining techniques, should provide valuable insights to policy makers such as principal and chief dental officers to inform care pathways and policy decisions

The application of process mining to care pathway analysis in the NHS

Background: Prostate cancer is the most common cancer in men in the UK and the sixth-fastest increasing cancer in males. Within England survival rates are improving, however, these are comparatively poorer than other countries. Currently, information available on outcomes of care is scant and there is an urgent need for techniques to improve healthcare systems and processes. Aims: To provide prostate cancer pathway analysis, by applying concepts of process mining and visualisation and comparing the performance metrics against the standard pathway laid out by national guidelines. Methods: A systematic review was conducted to see how process mining has been used in healthcare. Appropriate datasets for prostate cancer were identified within Imperial College Healthcare NHS Trust London. A process model was constructed by linking and transforming cohort data from six distinct database sources. The cohort dataset was filtered to include patients who had a PSA from 2010-2015, and validated by comparing the medical patient records against a Case-note audit. Process mining techniques were applied to the data to analyse performance and conformance of the prostate cancer pathway metrics to national guideline metrics. These techniques were evaluated with stakeholders to ascertain its impact on user experience. Results: Case note audit revealed 90% match against patients found in medical records. Application of process mining techniques showed massive heterogeneity as compared to the homogenous path laid out by national guidelines. This also gave insight into bottlenecks and deviations in the pathway. Evaluation with stakeholders showed that the visualisation and technology was well accepted, high quality and recommended to be used in healthcare decision making. Conclusion: Process mining is a promising technique used to give insight into complex and flexible healthcare processes. It can map the patient journey at a local level and audit it against explicit standards of good clinical practice, which will enable us to intervene at the individual and system level to improve care.Open Acces

Verslo procesų prognozavimo ir imitavimo taikant sisteminių įvykių žurnalų analizės metodus tyrimas

Business process (BP) analysis is one of the core activities in organisations that lead to improvements and achievement of a competitive edge. BP modelling and simulation are one of the most widely applied methods for analysing and improving BPs. The analysis requires to model BP and to apply analysis techniques to the models to answer queries leading to improvements. The input of the analysis process is BP models. The models can be in the form of BP models using industry-accepted BP modelling languages, mathematical models, simulation models and others. The model creation is the most important part of the BP analysis, and it is both time-consuming and costly activity. Nowadays most of the data generated in the organisations are electronic. Therefore, the re-use of such data can improve the results of the analysis. Thus, the main goal of the thesis is to improve BP analysis and simulation by proposing a method to discover a BP model from an event log and automate simulation model generation. The dissertation consists of an introduction, three main chapters and general conclusions. The first chapter discusses BP analysis methods. In addition, the process mining research area is presented, the techniques for automated model discovery, model validation and execution prediction are analysed. The second part of the chapter investigates the area of BP simula-tion. The second chapter of the dissertation presents a novel method which automatically discovers Bayesian Belief Network from an event log and, furthermore, automatically generates BP simulation model. The discovery of the Bayesian Belief Network consists of three steps: the discovery of a directed acyclic graph, generation of conditional probability tables and their combination. The BP simulation model is generated from the discovered directed acyclic graph and uses the belief network inferences during the simulation to infer the execution of the BP and to generate activity data dur-ing the simulation. The third chapter presents the experimental research of the proposed network and discusses the validity of the research and experiments. The experiments use selected logs that exhibit a wide array of behaviour. The experiments are performed in order to test the discovery of the graphs, the inference of the current process instance execution probability, the predic-tion of the future execution of the process instances and the correctness of the simulation. The results of the dissertation were published in 9 scientific publica-tions, 2 of which were in reviewed scientific journals indexed in Clarivate Analytics Science Citation Index

Engineering Agile Big-Data Systems

To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, the precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design.Engineering Agile Big-Data Systems outlines an approach to dealing with these problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals, and, in a number of case studies, shows how the tools and methodology have been used to improve a variety of academic and business systems

Process mining for healthcare: Characteristics and challenges

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.This work is partially supported by ANID FONDECYT 1220202, Dirección de Investigación de la Vicerrectoría de Investigación de la Pontificia Universidad Católica de Chile - PUENTE [Grant No. 026/ 2021]; and Agencia Nacional de Investigación y Desarrollo [Grant Nos. ANID-PFCHA/Doctorado Nacional/2019–21190116, ANID-PFCHA/ Doctorado Nacional/2020–21201411]. With regard to the co-author Hilda Klasky, this manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-accessplan).Peer ReviewedArticle signat per 55 autors/es: Jorge Munoz-Gama (a)* , Niels Martin (b,c)* , Carlos Fernandez-Llatas (d,g)* , Owen A. Johnson (e)* , Marcos Sepúlveda (a)* , Emmanuel Helm (f)* , Victor Galvez-Yanjari (a)* , Eric Rojas (a) , Antonio Martinez-Millana (d) , Davide Aloini (k) , Ilaria Angela Amantea (l,q,r) , Robert Andrews (ab), Michael Arias (z) , Iris Beerepoot (o) , Elisabetta Benevento (k) , Andrea Burattin (ai), Daniel Capurro (j) , Josep Carmona (s) , Marco Comuzzi (w), Benjamin Dalmas (aj,ak), Rene de la Fuente (a) , Chiara Di Francescomarino (h) , Claudio Di Ciccio (i) , Roberto Gatta (ad,ae), Chiara Ghidini (h) , Fernanda Gonzalez-Lopez (a) , Gema Ibanez-Sanchez (d) , Hilda B. Klasky (p) , Angelina Prima Kurniati (al), Xixi Lu (o) , Felix Mannhardt (m), Ronny Mans (af), Mar Marcos (v) , Renata Medeiros de Carvalho (m), Marco Pegoraro (x) , Simon K. Poon (ag), Luise Pufahl (u) , Hajo A. Reijers (m,o) , Simon Remy (y) , Stefanie Rinderle-Ma (ah), Lucia Sacchi (t) , Fernando Seoane (g,am,an), Minseok Song (aa), Alessandro Stefanini (k) , Emilio Sulis (l) , Arthur H. M. ter Hofstede (ab), Pieter J. Toussaint (ac), Vicente Traver (d) , Zoe Valero-Ramon (d) , Inge van de Weerd (o) , Wil M.P. van der Aalst (x) , Rob Vanwersch (m), Mathias Weske (y) , Moe Thandar Wynn (ab), Francesca Zerbato (n) // (a) Pontificia Universidad Catolica de Chile, Chile; (b) Hasselt University, Belgium; (c) Research Foundation Flanders (FWO), Belgium; (d) Universitat Politècnica de València, Spain; (e) University of Leeds, United Kingdom; (f) University of Applied Sciences Upper Austria, Austria; (g) Karolinska Institutet, Sweden; (h) Fondazione Bruno Kessler, Italy; (i) Sapienza University of Rome, Italy; (j) University of Melbourne, Australia; (k) University of Pisa, Italy; (l) University of Turin, Italy; (m) Eindhoven University of Technology, The Netherlands; (n) University of St. Gallen, Switzerland; (o) Utrecht University, The Netherlands; (p) Oak Ridge National Laboratory, United States; (q) University of Bologna, Italy; (r) University of Luxembourg, Luxembourg; (s) Universitat Politècnica de Catalunya, Spain; (t) University of Pavia, Italy; (u) Technische Universitaet Berlin, Germany; (v) Universitat Jaume I, Spain; (w) Ulsan National Institute of Science and Technology (UNIST), Republic of Korea; (x) RWTH Aachen University, Germany; (y) University of Potsdam, Germany; (z) Universidad de Costa Rica, Costa Rica; (aa) Pohang University of Science and Technology, Republic of Korea; (ab) Queensland University of Technology, Australia; (ac) Norwegian University of Science and Technology, Norway; (ad) Universita degli Studi di Brescia, Italy; (ae) Lausanne University Hospital (CHUV), Switzerland; (af) Philips Research, the Netherlands; (ag) The University of Sydney, Australia; (ah) Technical University of Munich, Germany; (ai) Technical University of Denmark, Denmark; (aj) Mines Saint-Etienne, France; (ak) Université Clermont Auvergne, France; (al) Telkom University, Indonesia; (am) Karolinska University Hospital, Sweden; (an) University of Borås, SwedenPostprint (published version

Process mining for healthcare: Characteristics and challenges

[EN] Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.This work is partially supported by ANID FONDECYT 1220202, Direccion de Investigacion de la Vicerrectoria de Investigacion de la Pontificia Universidad Catolica de Chile-PUENTE [Grant No. 026/2021] ; and Agencia Nacional de Investigacion y Desarrollo [Grant Nos. ANID-PFCHA/Doctorado Nacional/2019-21190116, ANID-PFCHA/Doctorado Nacional/2020-21201411] . With regard to the co-author Hilda Klasky, this manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE) . The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan)Munoz Gama, J.; Martin, N.; Fernández Llatas, C.; Johnson, OA.; Sepúlveda, M.; Helm, E.; Galvez-Yanjari, V.... (2022). Process mining for healthcare: Characteristics and challenges. Journal of Biomedical Informatics. 127:1-15. https://doi.org/10.1016/j.jbi.2022.10399411512

Data and Process Mining Applications on a Multi-Cell Factory Automation Testbed

This paper presents applications of both data mining and process mining in a factory automation testbed. It mainly concentrates on the Manufacturing Execution System (MES) level of production hierarchy. Unexpected failures might lead to vast losses on investment or irrecoverable damages. Predictive maintenance techniques, active/passive, have shown high potential of preventing such detriments. Condition monitoring of target pieces of equipment beside defined thresholds forms basis of the prediction. However, monitored parameters must be independent of environment changes, e.g. vibration of transportation equipments such as conveyor systems is variable to workload. This work aims to propose and demonstrate an approach to identify incipient faults of the transportation systems in discrete manufacturing settings. The method correlates energy consumption of the described devices with the workloads. At runtime, machine learning is used to classify the input energy data into two pattern descriptions. Consecutive mismatches between the output of the classifier and the workloads observed in real time indicate possibility of incipient failure at device level. Currently, as a result of high interaction between information systems and operational processes, and due to increase in the number of embedded heterogeneous resources, information systems generate unstructured and massive amount of events. Organizations have shown difficulties to deal with such an unstructured and huge amount of data. Process mining as a new research area has shown strong capabilities to overcome such problems. It applies both process modelling and data mining techniques to extract knowledge from data by discovering models from the event logs. Although process mining is recognised mostly as a business-oriented technique and recognised as a complementary of Business Process Management (BPM) systems, in this paper, capabilities of process mining are exploited on a factory automation testbed. Multiple perspectives of process mining is employed on the event logs produced by deploying Service Oriented Architecture through Web Services in a real multi-robot factory automation industrial testbed, originally used for assembly of mobile phones