To the emergency room and back again: Circular healthcare pathways for acute functional neurological disorders

Background and objectives Studies of Functional Neurological Disorders (FND) are usually outpatient-based. To inform service development, we aimed to describe patient pathways through healthcare events, and factors affecting risk of emergency department (ED) reattendance, for people presenting acutely with FND. Methods Acute neurology/stroke teams at a UK city hospital were contacted regularly over 8 months to log FND referrals. Electronic documentation was then reviewed for hospital healthcare events over the preceding 8 years. Patient pathways through healthcare events over time were mapped, and mixed effects logistic regression was performed for risk of ED reattendance within 1 year. Results In 8 months, 212 patients presented acutely with an initial referral suggesting FND. 20% had subsequent alternative diagnoses, but 162 patients were classified from documentation review as possible (17%), probable (28%) or definite (55%) FND. In the preceding 8 years, these 162 patients had 563 ED attendances and 1693 inpatient nights with functional symptoms, but only 26% were referred for psychological therapy, only 66% had a documented diagnosis, and care pathways looped around ED. Three better practice pathway steps were each associated with lower risk of subsequent ED reattendance: documented FND diagnosis (OR = 0.32, p = 0.004), referral to clinical psychology (OR = 0.35, p = 0.04) and outpatient neurology follow-up (OR = 0.25, p < 0.001). Conclusion People that present acutely to a UK city hospital with FND tend to follow looping pathways through hospital healthcare events, centred around ED, with low rates of documented diagnosis and referral for psychological therapy. When better practice occurs, it is associated with lower risk of ED reattendance

To the emergency room and back again : circular healthcare pathways for acute functional neurological disorders

Background and objectives: Studies of Functional Neurological Disorders (FND) are usually outpatient-based. To inform service development, we aimed to describe patient pathways through healthcare events, and factors affecting risk of emergency department (ED) reattendance, for people presenting acutely with FND. Methods: Acute neurology/stroke teams at a UK city hospital were contacted regularly over 8 months to log FND referrals. Electronic documentation was then reviewed for hospital healthcare events over the preceding 8 years. Patient pathways through healthcare events over time were mapped, and mixed effects logistic regression was performed for risk of ED reattendance within 1 year. Results: In 8 months, 212 patients presented acutely with an initial referral suggesting FND. 20% had subsequent alternative diagnoses, but 162 patients were classified from documentation review as possible (17%), probable (28%) or definite (55%) FND. In the preceding 8 years, these 162 patients had 563 ED attendances and 1693 inpatient nights with functional symptoms, but only 26% were referred for psychological therapy, only 66% had a documented diagnosis, and care pathways looped around ED. Three better practice pathway steps were each associated with lower risk of subsequent ED reattendance: documented FND diagnosis (OR = 0.32, p = 0.004), referral to clinical psychology (OR = 0.35, p = 0.04) and outpatient neurology follow-up (OR = 0.25, p < 0.001). Conclusion: People that present acutely to a UK city hospital with FND tend to follow looping pathways through hospital healthcare events, centred around ED, with low rates of documented diagnosis and referral for psychological therapy. When better practice occurs, it is associated with lower risk of ED reattendance

General System Theory and the Use of Process Mining to Improve Care Pathways.

General System Theory was proposed in the post-war period as a unifying framework for interdisciplinary science based on the idea that systems have a set of similar properties and characteristics regardless of discipline. General System Theory laid the foundations for talking about things in terms of systems, many of its terms are now embedded in everyday language and it underpins a broad range of systems approaches and systems thinking. This chapter will describe the key elements of the original General System Theory (GST) including control, feedback, emergence, holism and the notion of a hierarchy of systems within systems. It will review the origin, content and foundational role of systems theory in biology, medicine, computer science, organizational theory and its central contribution to health informatics. In recent years, healthcare organizations have been encouraged to see themselves within the context of learning health systems (LHS) and to use emerging big data analytics techniques such as process mining to develop better, integrated and personalized pathways of care for patients. We use GST to reflect on these emerging approaches through a discussion and case study on recent work in urgent and emergency care. Our aim is to trace the influence of GST through emerging LHS ideas and use the framework of GST to reflect on the opportunities and limitations of our process mining approach. In particular, we will reflect on how GST can explain successes and failure in the application of process mining to care pathways and the challenges and opportunities ahead

Analisis Kesiapan Penerapan Process Mining pada Sistem Manajemen Pembelajaran Universitas Telkom

Sistem manajemen pembelajaran (Learning Management System/ LMS) berbasis komputer telah banyak digunakan untuk mengelola pembelajaran dalam institusi pendidikan, termasuk universitas. LMS merekam dan mengelola akses pengguna secara otomatis dalam bentuk event log. Data dalam event log tersebut dapat dianalisis untuk mengenali pola penggunaan LMS sebagai pertimbangan pengembangan LMS. Salah satu metode yang dapat diadopsi adalah process mining, yaitu menganalisis data event log berbasis proses. Analisis data berbasis proses ini bertujuan untuk memodelkan proses yang terjadi dan terekam dalam LMS, mengecek kesesuaian pelaksanaan proses dengan prosedur, dan mengusulkan pengembangan proses di masa mendatang. Makalah ini mengeksplorasi kesiapan data penggunaan LMS di Universitas Telkom sebagai subjek penelitian untuk dianalisis dengan pendekatan process mining. Sepanjang pengetahuan kami, belum ada penelitian sebelumnya yang melakukan analisis data berbasis proses pada LMS ini. Kontribusi penelitian ini adalah eksplorasi peluang untuk menganalisis proses pembelajaran dan pengembangan metode pembelajaran berbasis LMS. Analisis kesiapan LMS dilakukan berdasarkan daftar pengecekan komponen yang dibutuhkan dalam process mining. Makalah ini mengikuti tahap-tahap utama dalam Process Mining Process Methodology (PM2). Studi kasus yang dieksplorasi adalah proses pembelajaran pada satu mata kuliah dalam satu semester berdasarkan event log yang diekstrak dari LMS. Hasil penelitian ini menunjukkan bahwa analisis data dalam LMS ini dapat digunakan untuk menganalisis performansi pembelajaran di Universitas Telkom dari kelompok pengguna yang berbeda-beda dan dapat dikembangkan untuk menganalisis data pada studi kasus yang lebih besar. Studi kelayakan ini diakhiri dengan diskusi tentang kelayakan LMS untuk dianalisis dengan process mining, evaluasi oleh tim ahli LMS, dan usulan pengembangan LMS di masa mendatang.  AbstractComputer-based Learning Management Systems (LMS) are commonly used in educational institutions, including universities. An LMS records and manages user access logs in an event log. Data in an event log can be analysed to understand patterns in the LMS usage to support recommendations for improvements. One promising method is process mining, which is a process-based data analytics working on event logs. Process mining aims to discover process models as recorded in the LMS, conformance checking of process execution to the defined procedure, and suggest improvements. This paper explores the feasibility of Telkom University LMS usage data to be analysed using process mining. To the best of our knowledge, there was no previous research doing process-based data analytics on this LMS. This paper contributes to explore opportunities to analyse learning processes and enhance LMS-based learning methods. The feasibility study is based on a data component checklist for process mining. This paper is written following the main stages on the Process Mining Project Methodology (PM2). We explore a case study of the learning process of a course in a semester, based on an event log extracted from the LMS. The results show that data analytics on this LMS can be used to analyse learning process performance in Telkom University, based on different user roles. This feasibility study is concluded with a discussion on the feasibility of the LMS to be analysed using process mining, an evaluation by the representative of the LMS expert team, and a recommendation for improvements

Performance Analysis of Emergency Room Episodes Through Process Mining

The performance analysis of Emergency Room episodes is aimed at providing decision makers with knowledge that allows them to decrease waiting times, reduce patient congestion, and improve the quality of care provided. In this case study, Process Mining is used to determine which activities, sub-processes, interactions, and characteristics of episodes explain why some episodes have a longer duration. The employed method and the results obtained are described in detail to serve as a guide for future performance analysis in this domain. It was discovered that the main cause of the increment in the episode duration is the occurrence of a loop between the Examination and Treatment sub-processes. It was also found out that as the episode severity increases, the number of repetitions of the Examination&#8211;Treatment loop increases as well. Moreover, the episodes in which this loop is more common are those that lead to Hospitalization as discharge destination. These findings might help to reduce the occurrence of this loop, in turn lowering the episode duration and, consequently, providing faster attention to more patients

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

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

Analysing the Impact of Changes in User Interface of e-Health Record Systems on Clinical Pathways using Process Mining

The provision of care in a hospital includes a series of activities that are often recorded in the electronic health record (EHR) systems. Analysing the data in these EHRs has the potential to support the understanding of care processes and exploring the opportunities for process improvement. One of the emerging data analytics approaches for such analyses is process mining, and one critical challenge in working with EHR data is that processes might change over time. This thesis uses a process mining approach to detect process change over time and analyse the impact of those changes on the EHR data. The overall aim is to summarise the attributable change in the data due to the process so that clinicians can better analyse the data. Three datasets were used in this study to understand the variability of the EHR systems. The first dataset is a publicly available EHR data that was used for developing the methods and supporting the reproducibility of the research. The second dataset is a de-identified subset of the database of cancer patients from the Leeds Cancer Centre. The second dataset was used in the experiments to improve on the results of a previous study using the same dataset. The third dataset was the full Leeds Cancer Centre EHR database after more comprehensive ethics was approved. In the third dataset, experiments were done to analyse the impact of a known system change on clinical pathways and to explore process change over time without a known system change. All three datasets were analysed using process mining. Process mining was shown to be useful for analysing clinical pathways and exploring process changes over time. It can be used to visualise the process before and after a known change. When the system change is unknown, process mining can be used to explore the process execution over time and identify the potential period where the system was changed. This thesis explores some aspects of the complex interrelatedness of process and user interface (UI) of the EHR system