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

Research Paper: Process Mining and Synthetic Health Data: Reflections and Lessons Learnt

Analysing the treatment pathways in real-world health data can provide valuable insight for clinicians and decision-makers. However, the procedures for acquiring real-world data for research can be restrictive, time-consuming and risks disclosing identifiable information. Synthetic data might enable representative analysis without direct access to sensitive data. In the first part of our paper, we propose an approach for grading synthetic data for process analysis based on its fidelity to relationships found in real-world data. In the second part, we apply our grading approach by assessing cancer patient pathways in a synthetic healthcare dataset (The Simulacrum provided by the English National Cancer Registration and Analysis Service) using process mining. Visualisations of the patient pathways within the synthetic data appear plausible, showing relationships between events confirmed in the underlying non-synthetic data. Data quality issues are also present within the synthetic data which reflect real-world problems and artefacts from the synthetic dataset’s creation. Process mining of synthetic data in healthcare is an emerging field with novel challenges. We conclude that researchers should be aware of the risks when extrapolating results produced from research on synthetic data to real-world scenarios and assess findings with analysts who are able to view the underlying data

Measuring the impact of COVID-19 on hospital care pathways

Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted

Varieties of interaction: from User Experience to Neuroergonomics:On the occasion of the Human Factors and Ergonomics Society Europe Chapter Annual Meeting in Rome, Italy 2017

Proceedings of the HFES European Chapter conferenc

Interaction Design Patterns und CSCL-Scripts für Videolernumgebungen

In den letzten Jahren haben Lernvideos im Bereich des informellen und formellen Lernens an Bedeutung gewonnen. Inwieweit Lernende bei der Nutzung von Videos unterstützt werden und Lehrende didaktische Szenarien umsetzen können, hängt jedoch von der eingesetzten Videolernumgebung ab. Es ist Anliegen der vorliegende Arbeit, Prinzipien des User Interface Designs sowie Komponenten und Mechanismen videobasierter Lehr-Lern-Szenarien in Bezug auf Videolernumgebungen zu identifizieren, zu beschreiben und technisch zu realisieren. Das Ziel besteht darin, Gestaltungsprinzipien in Form von Interaction Design Patterns zu erarbeiten und computergestützte videobasierte Lehr-Lern-Szenarien mit Hilfe von CSCL-Scripts durch eine geeignete Spezifikation und Formalisierung zu realisieren. Für die Erarbeitung der Interaction Design Patterns wurden 121 Videolernumgebungen hinsichtlich 50 Kategorien in einer Inhaltsanalyse empirisch untersucht und dokumentiert. Unter Berücksichtigung ähnlicher Patterns aus thematisch verwandten Pattern Languages und den Erfahrungen aus der Implementierung und dem Einsatz von Videolernumgebungen entstanden 45 Interaction Design Patterns für verbreitete Lösungen für wiederkehrende Probleme bei der Gestaltung und Entwicklung von Videolernumgebungen. Diese Patterns wurden auf Pattern Konferenzen diskutiert und im Anschluss evaluiert sowie bei der Konzeption, Entwicklung und Bewertung mehrerer Videolernumgebungen angewendet. Zudem wurde das Software Framework VI-TWO vorgestellt, mit dem sich fast alle Patterns auf einfache Weise in Web-Anwendungen realisieren lassen. Zur Spezifikation videobasierter CSCL-Scripts wurden existierende videobasierte und nicht videobasierte Scripts analysiert. Im Ergebnis unterschieden sich videobasierte CSCL-Scripts von allgemeinen CSCL-Scripts vor allem hinsichtlich der mit dem Video verknüpften oder darin verankerten Aufgaben und Aktivitäten. Videos werden dabei nicht als monolithische Einheiten, sondern als zeitkontinuierliche Medien betrachtet, in denen weitere Informationen zeitgenau verankert und Lernaktivitäten stattfinden können. Außerdem ließen sich drei Typen videobasierter CSCL-Scripts identifizieren: (1) Scripts zur Analyse und Diskussion von Videoinhalten, (2) Peer Annotation Scripts einschließlich dem Peer Assessment und (3) Jigsaw-Scripts, die das problembasierte Lernen mit Hilfe von Videos ermöglichen. Unabhängig davon variiert die Komplexität der Scripts auf drei Stufen: (1) sofern voneinander abgegrenzte zeitliche Phasen von Lernaktivitäten bestehen, (2) wenn darüber hinaus die Teilnehmer innerhalb von Gruppen Aufgaben bearbeiten (intra-group) und (3) falls außerdem Aufgaben auch gruppenübergreifend bearbeitet werden (inter-group). Auf Grundlage der Spezifikation konnte ein Datenmodell und ein Modell für die nutzerseitige Modellierung von Scripts verschiedener Komplexitätsstufen sowie Typen entwickelt und in dem CSCL-System VI-LAB realisiert werden. Diese Arbeit leistet in zweifacher Hinsicht einen Beitrag zur Forschung im Bereich E-Learning. Zum einen beschreiben die Interaction Design Patterns wiederkehrende User Interface Lösungen und stellen somit ein Hilfsmittel für Designer, Software Entwickler und Lehrende bei der Gestaltung und Implementierung von Videolernumgebungen dar. Zum anderen wurden durch die Spezifikation und softwareseitige Umsetzung videobasierter CSCL-Scripts Voraussetzungen geschaffen, die den praktischen Einsatz und die weitere Untersuchung des kollaborativen Lernens mit Videos ermöglichen.:1 Einführung 19 1.1 Motivation 19 1.2 Herausforderungen und Forschungsfragen 20 1.2.1 Interaction Design Patterns 20 1.2.2 Videobasierte CSCL-Scripts 22 1.3 Kapitelübersicht und Forschungsmethoden 25 1.3.1 Kapitelübersicht 25 1.3.2 Forschungsmethoden je Kapitel 27 2 Lernen mit Videos 29 2.1 Terminologie, Einsatzfelder und Potentiale von Lernvideos 30 2.1.1 Begriffsbestimmung 30 2.1.2 Einsatzfelder und Szenarien für das Lernen mit Videos 32 2.1.3 Potentiale des Medium Video 34 2.2 Videos im Kontext kognitiver Lerntheorien 36 2.2.1 Theorie der kognitiven Last 36 2.2.2 Kognitive Theorie des Lernens mit Multimedia 38 2.3 Interaktivität audiovisueller Lernmedien 44 2.4 Lernformen 48 2.4.1 Rezeptives Lernen 49 2.4.2 Selbstgesteuertes Lernen 50 2.4.3 Kollaboratives Lernen 52 2.5 Zusammenfassung 56 3 Videolernumgebungen und Hypervideos 59 3.1 Terminologie und Modelle 60 3.1.1 Videolernumgebung 60 3.1.2 Terminologie: Video, Hypervideo und interaktives Video 62 3.1.3 Spezifikationen für Hypervideo-Dokumente 65 3.1.4 Modelle des zeitlichen Layouts 66 3.2 Human Video Interface 69 3.2.1 Gestaltungsraum von Hypervideos 70 3.2.2 Usability-Herausforderungen von Human Video Interfaces 74 3.3 Technische Herausforderungen 76 3.3.1 Download und Cache-Management / Echte Nicht-Linearität 77 3.3.2 Synchronisierung von Video und Annotationen 78 3.3.3 Adressierung und Abruf von Medienfragmenten 78 3.3.4 Deklarative Ansätze der Repräsentation von Multimedia 80 3.4 Produktion und Integration in Lernumgebungen 81 3.4.1 Produktion: Vorgehensweisen und Paradigmen 82 3.4.2 Integration in Lernumgebungen und Zusammenspiel mit Diensten im WWW 85 3.5 Zusammenfassung 87 4 Interaction Design Patterns für Videolernumgebungen 91 4.1 Einführung in Design Patterns 92 4.1.1 Design Patterns 95 4.1.2 Mustersprache 101 4.1.3 Verwandte Ansätze im Interaction Design 104 4.1.4 Verwandte Pattern Languages 106 4.2 Systematische Elaboration von Design Patterns 109 4.2.1 Stand der Forschung bzgldem Pattern Mining 110 4.2.2 Inhaltsanalyse von Videolernumgebungen 112 4.2.3 Analyse und Integration ähnlicher Muster bzwMustersprachen 128 4.2.4 Verfassen sowie Revision und Evaluation der Muster 130 4.2.5 Konstruktion der Pattern Language 135 4.3 Pattern Language für Videolernumgebungen 140 4.3.1 Struktur der Pattern Language 140 4.3.2 Angrenzende Mustersprachen 144 4.3.3 Repräsentation in einer Wissensbasis 145 4.3.4 Anwendungs- und Einsatzszenarien 148 4.3.5 Exemplarische Interaction Design Patterns 151 4.4 Zusammenfassung 168 5 Videobasierte CSCL-Scripts 171 5.1 Einführung 172 5.1.1 Hintergrund zu Scripts und CSCL-Scripts 172 5.1.2 Definition videobasierter CSCL-Scripts 175 5.1.3 Mehrwert und Potentiale 177 5.1.4 Typisierung videobasierter CSCL-Scripts 178 5.2 Spezifikation videobasierter CSCL-Scripts 184 5.2.1 Script-Komponenten 185 5.2.2 Script-Mechanismen 194 5.3 Modellierung von CSCL-Scripts 197 5.3.1 Komplexitätslevel 200 5.3.2 Verwandte Systeme und Ansätze zur Modellierung von Scripts 201 5.3.3 Konzept für eine formale Repräsentation 206 5.3.4 Konzept zur Modellierung im User Interface 209 5.4 Zusammenfassung 212 6 Realisierung von Patterns und Scripts 215 6.1 VI-TWO: JavaScript Framework für interaktive Videos 216 6.1.1 Anforderungen 217 6.1.2 Verwandte Arbeiten 219 6.1.3 Architektur von VI-TWO 222 6.1.4 Videoplayer 224 6.1.5 Videoannotationen 225 6.1.6 Makrointeraktivität in Kollektionen von Videos 229 6.1.7 Autorenwerkzeuge 232 6.2 VI-LAB: CSCL-System für videobasierte CSCL-Scripts 235 6.2.1 Anforderungen 236 6.2.2 Architektur von VI-LAB 238 6.2.3 Modellierung videobasierter CSCL-Scripts 241 6.2.4 Monitoring 244 6.3 Anwendungsbeispiele für VI-TWO und VI-LAB 246 6.3.1 Vi-Wiki 246 6.3.2 IWRM education 247 6.3.3 VI-LAB (Version 1) auf Basis von Wordpress 247 6.3.4 VI-LAB (Version 2) auf Basis von node.js 248 6.3.5 Theresienstadt explained 249 6.4 Zusammenfassung 252 7 Schlussbetrachtung 255 7.1 Beitrag der Arbeit zur Forschung 255 7.2 Kritische Würdigung 256 7.3 Ausblick 25

A microservice architecture for the processing of large geospatial data in the Cloud

With the growing number of devices that can collect spatiotemporal information, as well as the improving quality of sensors, the geospatial data volume increases constantly. Before the raw collected data can be used, it has to be processed. Currently, expert users are still relying on desktop-based Geographic Information Systems to perform processing workflows. However, the volume of geospatial data and the complexity of processing algorithms exceeds the capacities of their workstations. There is a paradigm shift from desktop solutions towards the Cloud, which offers virtually unlimited storage space and computational power, but developers of processing algorithms often have no background in computer science and hence no expertise in Cloud Computing. Our research hypothesis is that a microservice architecture and Domain-Specific Languages can be used to orchestrate existing geospatial processing algorithms, and to compose and execute geospatial workflows in a Cloud environment for efficient application development and enhanced stakeholder experience. We present a software architecture that contains extension points for processing algorithms (or microservices), a workflow management component for distributed service orchestration, and a workflow editor based on a Domain-Specific Language. The main aim is to provide both users and developers with the means to leverage the possibilities of the Cloud, without requiring them to have a deep knowledge of distributed computing. In order to conduct our research, we follow the Design Science Research Methodology. We perform an analysis of the problem domain and collect requirements as well as quality attributes for our architecture. To meet our research objectives, we design the architecture and develop approaches to workflow management and workflow modelling. We demonstrate the utility of our solution by applying it to two real-world use cases and evaluate the quality of our architecture based on defined scenarios. Finally, we critically discuss our results. Our contributions to the scientific community can be classified into three pillars. We present a scalable and modifiable microservice architecture for geospatial processing that supports distributed development and has a high availability. Further, we present novel approaches to service integration and orchestration in the Cloud as well as rule-based and dynamic workflow management without a priori design-time knowledge. For the workflow modelling we create a Domain-Specific Language that is based on a novel language design method

An Integrative Model of Aggression: The Role of Cognitions in Responses to Stressors in Forensic and Non-Forensic Populations

This PhD aimed to further the understanding of aggression through the integration of research findings with theoretical models. As a result, a Stratified Integrated Model of Behavioural Aggression (SIMBA) that specifies and stratifies the roles of stress, cognitive structures and information processing was proposed. This may help guide therapeutic interventions aimed at the reduction of aggressive behaviour and inform risk assessment. A systematic literature review of 77 papers was conducted to assess the relationship between stress systems’ activity and aggression. The results showed that this relationship is likely present and can be both positive and negative. Thematic analysis of these papers identified six themes: 1) the impact of testosterone on the relationship between activity of stress response systems and aggression is undetermined; 2) the presence of sex differences in the relationship between stress response and aggression depends on the stress system and type of aggression; 3) specific disorders do not influence the relationship between stress and aggression; 4) experience of victimisation does not have a clear influence on the relationship between stress systems’ activity and aggression; 5) the relationship between stress response markers and aggression differs among those with high stress exposure; and 6) history of aggression affects the relationship between stress response markers and anger-based aggression. These results highlighted that the stress-aggression relationship is present, but is likely to be indirect. However, the extraneous variables consistently affecting this relationship were not identified. Addressing this issue, study one recruited 20 male students and 11 patients from a high secure hospital to establish the effects of aggression supportive cognitions and stress on aggressive behaviour. To assess aggression after a stress-evoking task, the Taylor Aggression Paradigm was used. It was predicted that while the presence of aggressive Implicit Theories (ITs) would be positively associated with aggressive behaviour towards a stranger, the association of stress would differ between the samples. This was partially supported, as only one specific IT (“I am the law”) was associated with aggression. Furthermore, only elevated skin conductance, but not changes in the heart rate, during the stress task was positively associated with aggression, and only among patients. Study two involved 100 participants (49 men, 48 women, three not disclosed) with an average age of 29. It aimed to investigate the relationship between history of aggressive behaviour, affective states, and neutral and emotional information processing. Event Related Potentials (ERPs) during a Go/No-Go task were utilised to capture cognitive resources allocation, with a “supervisor – employee” laboratory paradigm used to assess aggression. Contrary to expectations, results showed that trait aggressiveness was only related to aggressive behaviour at higher levels of inhibitory processing. The hypothesis that artificially provoked changes in negative and positive affect would be related to aggressive behaviour was also not supported. However, as expected, feeling hostile was associated with short-lived aggressive behaviour, but only for those who had low response inhibition. Moreover, partially supporting expectations, a history of aggressive behaviour moderated the relationship between change in negative affect and aggressive behaviour. The last hypothesis, proposing emotional processing to be a mediator between response inhibition and aggressive behaviour, was also not confirmed. Study three included 462 participants, of whom 300 were adults aged 26 or older (151 men, 149 women), and 162 representing transitional aged youth, aged between 18 and 25 (21 men, 141 women). This study aimed to identify direct and indirect effects exerted by aggression supportive cognitive structures, working memory problems, and stress on aggression by building a Structural Equation Model. It was expected that a direct cognitive pathway from aggression supportive cognitions directly to aggression would be identified. This hypothesis was supported. Meanwhile, the second hypothesis proposing an indirect relationship between stress and aggression was only partially supported, with maladaptive coping style being the only mediator identified. The current research demonstrated that aggression-supportive cognitive structures are the primary facilitators of aggressive behaviour. Meanwhile, the effect exerted by situational demands is contingent on the preferred coping style. Furthermore, despite the indirect nature, the influence of information processing was present for multiple precursors of aggression. Consequently, all these elements were included in the SIMBA and are suggested as primary targets for therapeutic aggression interventions. The results are discussed with attention to this proposed model, capturing further directions for future research