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Chen K, Abtahi F, Carrero JJ, Fernandez-Llatas C, Seoane F. Process mining and data mining applications in the domain of chronic diseases: A systematic review. Artif Intell Med 2023; 144:102645. [PMID: 37783545 DOI: 10.1016/j.artmed.2023.102645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 08/24/2023] [Accepted: 08/28/2023] [Indexed: 10/04/2023]
Abstract
The widespread use of information technology in healthcare leads to extensive data collection, which can be utilised to enhance patient care and manage chronic illnesses. Our objective is to summarise previous studies that have used data mining or process mining methods in the context of chronic diseases in order to identify research trends and future opportunities. The review covers articles that pertain to the application of data mining or process mining methods on chronic diseases that were published between 2000 and 2022. Articles were sourced from PubMed, Web of Science, EMBASE, and Google Scholar based on predetermined inclusion and exclusion criteria. A total of 71 articles met the inclusion criteria and were included in the review. Based on the literature review results, we detected a growing trend in the application of data mining methods in diabetes research. Additionally, a distinct increase in the use of process mining methods to model clinical pathways in cancer research was observed. Frequently, this takes the form of a collaborative integration of process mining, data mining, and traditional statistical methods. In light of this collaborative approach, the meticulous selection of statistical methods based on their underlying assumptions is essential when integrating these traditional methods with process mining and data mining methods. Another notable challenge is the lack of standardised guidelines for reporting process mining studies in the medical field. Furthermore, there is a pressing need to enhance the clinical interpretation of data mining and process mining results.
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Affiliation(s)
- Kaile Chen
- Department of Clinical Science, Intervention and Technology, Karolinska Institutet, 17177 Stockholm, Sweden; School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Biomedical Engineering and Health Systems, Division of Ergonomics, KTH Royal Institute of Technology, 14157 Stockholm, Sweden.
| | - Farhad Abtahi
- Department of Clinical Science, Intervention and Technology, Karolinska Institutet, 17177 Stockholm, Sweden; School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Biomedical Engineering and Health Systems, Division of Ergonomics, KTH Royal Institute of Technology, 14157 Stockholm, Sweden; Department of Clinical Physiology, Karolinska University Hospital, 17176 Stockholm, Sweden
| | - Juan-Jesus Carrero
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Carlos Fernandez-Llatas
- Department of Clinical Science, Intervention and Technology, Karolinska Institutet, 17177 Stockholm, Sweden; SABIEN, ITACA, Universitat Politècnica de València, Spain
| | - Fernando Seoane
- Department of Clinical Science, Intervention and Technology, Karolinska Institutet, 17177 Stockholm, Sweden; Department of Clinical Physiology, Karolinska University Hospital, 17176 Stockholm, Sweden; Department of Medical Technology, Karolinska University Hospital, 17176 Stockholm, Sweden; Department of Textile Technology, University of Borås, 50190 Borås, Sweden
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Kropp T, Faeghi S, Lennerts K. Evaluation of patient transport service in hospitals using process mining methods: Patients' perspective. Int J Health Plann Manage 2023; 38:430-456. [PMID: 36374049 DOI: 10.1002/hpm.3593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 09/16/2022] [Accepted: 10/26/2022] [Indexed: 11/16/2022] Open
Abstract
Designing healthcare facilities and their processes is a complex task which influences the quality and efficiency of healthcare services. The ongoing demand for healthcare services and cost burdens necessitate the application of analytical methods to enhance the overall service efficiency in hospitals. However, the variability in healthcare processes makes it highly complicated to accomplish this aim. This study addresses the complexity in the patient transport service process at a German hospital, and proposes a method based on process mining to obtain a holistic approach to recognise bottlenecks and main reasons for delays and resulting high costs associated with idle resources. To this aim, the event log data from the patient transport software system is collected and processed to discover the sequences and the timeline of the activities for the different cases of the transport process. The comparison between the actual and planned processes from the data set of the year 2020 shows that, for example, around 36% of the cases were 10 or more minutes delayed. To find delay issues in the process flow and their root causes the data traces of certain routes are intensively assessed. Additionally, the compliance with the predefined Key Performance Indicators concerning travel time and delay thresholds for individual cases was investigated. The efficiency of assignment of the transport requests to the transportation staff are also evaluated which gives useful understanding regarding staffing potential improvements. The research shows that process mining is an efficient method to provide comprehensive knowledge through process models that serve as Interactive Process Indicators and to extract significant transport pathways. It also suggests a more efficient patient transport concept and provides the decision makers with useful managerial insights to come up with efficient patient-centred analysis of transportation services through data from supporting information systems.
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Affiliation(s)
- Tobias Kropp
- Institute for Technology and Management in Construction, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Shiva Faeghi
- Institute for Technology and Management in Construction, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Kunibert Lennerts
- Institute for Technology and Management in Construction, Karlsruhe Institute of Technology, Karlsruhe, Germany
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Valero-Ramon Z, Fernandez-Llatas C, Collantes G, Valdivieso B, Billis A, Bamidis P, Traver V. Analytical exploratory tool for healthcare professionals to monitor cancer patients' progress. Front Oncol 2023; 12:1043411. [PMID: 36698423 PMCID: PMC9869047 DOI: 10.3389/fonc.2022.1043411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 12/09/2022] [Indexed: 01/11/2023] Open
Abstract
Introduction Cancer is a primary public concern in the European continent. Due to the large case numbers and survival rates, a significant population is living with cancer needs. Consequently, health professionals must deal with complex treatment decision-making processes. In this context, a large quantity of data is collected during cancer care delivery. Once collected, these data are complex for health professionals to access to support clinical decision-making and performance review. There is a need for innovative tools that make clinical data more accessible to support cancer health professionals in these activities. Methods Following a co-creation, an interactive approach thanks to the Interactive Process Mining paradigm, and data from a tertiary hospital, we developed an exploratory tool to present cancer patients' progress over time. Results This work aims to collect and report the process of developing an exploratory analytical Interactive Process Mining tool with clinical relevance for healthcare professionals for monitoring cancer patients' care processes in the context of the LifeChamps project together with a graphical and navigable Process Indicator in the context of prostate cancer patients. Discussion The tool presented includes Process Mining techniques to infer actual processes and present understandable results visually and navigable, looking for different types of patients, trajectories, and behaviors.
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Affiliation(s)
- Zoe Valero-Ramon
- Institute of Information and Communication Technologies - Technological Innovation for Health and Well-being (ITACA-SABIEN), Universitat Politècnica de València, Valencia, Spain,*Correspondence: Zoe Valero-Ramon,
| | - Carlos Fernandez-Llatas
- Institute of Information and Communication Technologies - Technological Innovation for Health and Well-being (ITACA-SABIEN), Universitat Politècnica de València, Valencia, Spain,Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden
| | | | | | - Antonis Billis
- Lab of Medical Physics and Digital Innovation, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Panagiotis Bamidis
- Lab of Medical Physics and Digital Innovation, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vicente Traver
- Institute of Information and Communication Technologies - Technological Innovation for Health and Well-being (ITACA-SABIEN), Universitat Politècnica de València, Valencia, Spain
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Valero-Ramon Z, Fernandez-Llatas C, Valdivieso B, Traver V. Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining. SENSORS (BASEL, SWITZERLAND) 2020; 20:E5330. [PMID: 32957673 PMCID: PMC7570892 DOI: 10.3390/s20185330] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/02/2020] [Accepted: 09/14/2020] [Indexed: 12/16/2022]
Abstract
Rich streams of continuous data are available through Smart Sensors representing a unique opportunity to develop and analyse risk models in healthcare and extract knowledge from data. There is a niche for developing new algorithms, and visualisation and decision support tools to assist health professionals in chronic disease management incorporating data generated through smart sensors in a more precise and personalised manner. However, current understanding of risk models relies on static snapshots of health variables or measures, rather than ongoing and dynamic feedback loops of behaviour, considering changes and different states of patients and diseases. The rationale of this work is to introduce a new method for discovering dynamic risk models for chronic diseases, based on patients' dynamic behaviour provided by health sensors, using Process Mining techniques. Results show the viability of this method, three dynamic models have been discovered for the chronic diseases hypertension, obesity, and diabetes, based on the dynamic behaviour of metabolic risk factors associated. This information would support health professionals to translate a one-fits-all current approach to treatments and care, to a personalised medicine strategy, that fits treatments built on patients' unique behaviour thanks to dynamic risk modelling taking advantage of the amount data generated by smart sensors.
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Affiliation(s)
- Zoe Valero-Ramon
- SABIEN-ITACA Universitat Politècnica de València, Camino de Vera S/N, 46022 Valencia, Spain; (C.F.-L.); (V.T.)
| | - Carlos Fernandez-Llatas
- SABIEN-ITACA Universitat Politècnica de València, Camino de Vera S/N, 46022 Valencia, Spain; (C.F.-L.); (V.T.)
- CLINTEC-Karolinska Institutet, 171 77 Solna, Sweden
| | | | - Vicente Traver
- SABIEN-ITACA Universitat Politècnica de València, Camino de Vera S/N, 46022 Valencia, Spain; (C.F.-L.); (V.T.)
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Martin N, De Weerdt J, Fernández-Llatas C, Gal A, Gatta R, Ibáñez G, Johnson O, Mannhardt F, Marco-Ruiz L, Mertens S, Munoz-Gama J, Seoane F, Vanthienen J, Wynn MT, Boilève DB, Bergs J, Joosten-Melis M, Schretlen S, Van Acker B. Recommendations for enhancing the usability and understandability of process mining in healthcare. Artif Intell Med 2020; 109:101962. [PMID: 34756220 DOI: 10.1016/j.artmed.2020.101962] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 07/19/2020] [Accepted: 09/22/2020] [Indexed: 11/28/2022]
Abstract
Healthcare organizations are confronted with challenges including the contention between tightening budgets and increased care needs. In the light of these challenges, they are becoming increasingly aware of the need to improve their processes to ensure quality of care for patients. To identify process improvement opportunities, a thorough process analysis is required, which can be based on real-life process execution data captured by health information systems. Process mining is a research field that focuses on the development of techniques to extract process-related insights from process execution data, providing valuable and previously unknown information to instigate evidence-based process improvement in healthcare. However, despite the potential of process mining, its uptake in healthcare organizations outside case studies in a research context is rather limited. This observation was the starting point for an international brainstorm seminar. Based on the seminar's outcomes and with the ambition to stimulate a more widespread use of process mining in healthcare, this paper formulates recommendations to enhance the usability and understandability of process mining in healthcare. These recommendations are mainly targeted towards process mining researchers and the community to consider when developing a new research agenda for process mining in healthcare. Moreover, a limited number of recommendations are directed towards healthcare organizations and health information systems vendors, when shaping an environment to enable the continuous use of process mining.
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Affiliation(s)
- Niels Martin
- Research Foundation Flanders (FWO), Belgium; Hasselt University, Belgium; Vrije Universiteit Brussel, Belgium.
| | | | | | - Avigdor Gal
- Technion - Israel Institute of Technology, Israel.
| | - Roberto Gatta
- Centre Hopitalier Universitaire de Vaudois, Switzerland; Università degli Studi di Brescia, Italy.
| | | | | | | | | | | | | | - Fernando Seoane
- Karolinska Institutet, Sweden; Karolinska University Hospital, Sweden; University of Borås, Sweden.
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Process-Oriented Instrument and Taxonomy for Teaching Surgical Procedures in Medical Training: The Ultrasound-Guided Insertion of Central Venous Catheter. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17113849. [PMID: 32485808 PMCID: PMC7312770 DOI: 10.3390/ijerph17113849] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Revised: 05/18/2020] [Accepted: 05/20/2020] [Indexed: 11/30/2022]
Abstract
Procedural training is relevant for physicians who perform surgical procedures. In the medical education field, instructors who teach surgical procedures need to understand how their students are learning to give them feedback and assess them objectively. The sequence of steps of surgical procedures is an aspect rarely considered in medical education, and state-of-the-art tools for giving feedback and assessing students do not focus on this perspective. Process Mining can help to include this perspective in this field since it has recently been used successfully in some applications. However, these previous developments are more centred on students than on instructors. This paper presents the use of Process Mining to fill this gap, generating a taxonomy of activities and a process-oriented instrument. We evaluated both tools with instructors who teach central venous catheter insertion. The results show that the instructors found both tools useful to provide objective feedback and objective assessment. We concluded that the instructors understood the information provided by the instrument since it provides helpful information to understand students’ performance regarding the sequence of steps followed.
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Ibanez-Sanchez G, Fernandez-Llatas C, Martinez-Millana A, Celda A, Mandingorra J, Aparici-Tortajada L, Valero-Ramon Z, Munoz-Gama J, Sepúlveda M, Rojas E, Gálvez V, Capurro D, Traver V. Toward Value-Based Healthcare through Interactive Process Mining in Emergency Rooms: The Stroke Case. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16101783. [PMID: 31137557 PMCID: PMC6572362 DOI: 10.3390/ijerph16101783] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Revised: 05/10/2019] [Accepted: 05/14/2019] [Indexed: 12/19/2022]
Abstract
The application of Value-based Healthcare requires not only the identification of key processes in the clinical domain but also an adequate analysis of the value chain delivered to the patient. Data Science and Big Data approaches are technologies that enable the creation of accurate systems that model reality. However, classical Data Mining techniques are presented by professionals as black boxes. This evokes a lack of trust in those techniques in the medical domain. Process Mining technologies are human-understandable Data Science tools that can fill this gap to support the application of Value-Based Healthcare in real domains. The aim of this paper is to perform an analysis of the ways in which Process Mining techniques can support health professionals in the application of Value-Based Technologies. For this purpose, we explored these techniques by analyzing emergency processes and applying the critical timing of Stroke treatment and a Question-Driven methodology. To demonstrate the possibilities of Process Mining in the characterization of the emergency process, we used a real log with 9046 emergency episodes from 2145 stroke patients that occurred from January 2010 to June 2017. Our results demonstrate how Process Mining technology can highlight the differences between the flow of stroke patients compared with that of other patients in an emergency. Further, we show that support for health professionals can be provided by improving their understanding of these techniques and enhancing the quality of care.
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Affiliation(s)
| | - Carlos Fernandez-Llatas
- SABIEN-ITACA, Universitat Politècnica de València, 46022 València, Spain.
- Unidad Mixta de Reingeniería de Procesos Sociosanitarios (eRPSS), Instituto de Investigaciń Sanitaria del Hospital Universitario y Politecnico La Fe, Bulevar Sur S/N, 46026 València, Spain.
| | | | - Angeles Celda
- Hospital General de Valencia, Av. de les Tres Creus, 2, 46014 València, Spain.
| | - Jesus Mandingorra
- Hospital General de Valencia, Av. de les Tres Creus, 2, 46014 València, Spain.
- School of Nursing, Universidad Católica de Valencia, 46022 València, Spain.
| | | | - Zoe Valero-Ramon
- SABIEN-ITACA, Universitat Politècnica de València, 46022 València, Spain.
| | - Jorge Munoz-Gama
- School of Engineering, Pontificia Universidad Católica de Chile, Santiago 8320000, Chile.
| | - Marcos Sepúlveda
- School of Engineering, Pontificia Universidad Católica de Chile, Santiago 8320000, Chile.
| | - Eric Rojas
- School of Medicine, Pontificia Universidad Católica de Chile, Santiago 8320000, Chile.
| | - Víctor Gálvez
- School of Engineering, Pontificia Universidad Católica de Chile, Santiago 8320000, Chile.
| | - Daniel Capurro
- School of Medicine, Pontificia Universidad Católica de Chile, Santiago 8320000, Chile.
| | - Vicente Traver
- SABIEN-ITACA, Universitat Politècnica de València, 46022 València, Spain.
- Unidad Mixta de Reingeniería de Procesos Sociosanitarios (eRPSS), Instituto de Investigaciń Sanitaria del Hospital Universitario y Politecnico La Fe, Bulevar Sur S/N, 46026 València, Spain.
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Process Mining Dashboard in Operating Rooms: Analysis of Staff Expectations with Analytic Hierarchy Process. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16020199. [PMID: 30642000 PMCID: PMC6352092 DOI: 10.3390/ijerph16020199] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Revised: 01/07/2019] [Accepted: 01/09/2019] [Indexed: 12/14/2022]
Abstract
The widespread adoption of real-time location systems is boosting the development of software applications to track persons and assets in hospitals. Among the vast amount of applications, real-time location systems in operating rooms have the advantage of grounding advanced data analysis techniques to improve surgical processes, such as process mining. However, such applications still find entrance barriers in the clinical context. In this paper, we aim to evaluate the preferred features of a process mining-based dashboard deployed in the operating rooms of a hospital equipped with a real-time location system. The dashboard allows to discover and enhance flows of patients based on the location data of patients undergoing an intervention. Analytic hierarchy process was applied to quantify the prioritization of the dashboard features (filtering data, enhancement, node selection, statistics, etc.), distinguishing the priorities that each of the different roles in the operating room service assigned to each feature. The staff in the operating rooms (n = 10) was classified into three groups: Technical, clinical, and managerial staff according to their responsibilities. Results showed different weights for the features in the process mining dashboard for each group, suggesting that a flexible process mining dashboard is needed to boost its potential in the management of clinical interventions in operating rooms. This paper is an extension of a communication presented in the Process-Oriented Data Science for Health Workshop in the Business Process Management Conference 2018.
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Question-Driven Methodology for Analyzing Emergency Room Processes Using Process Mining. APPLIED SCIENCES-BASEL 2017. [DOI: 10.3390/app7030302] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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10
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Fernandez-Llatas C, Martinez-Millana A, Martinez-Romero A, Benedi JM, Traver V. Diabetes care related process modelling using Process Mining techniques. Lessons learned in the application of Interactive Pattern Recognition: coping with the Spaghetti Effect. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:2127-30. [PMID: 26736709 DOI: 10.1109/embc.2015.7318809] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Diabetes is one of the metabolic disorders with more growth expectations in next decades. The literature points to a correct self-management, to an appropriate treatment and to an adequate healthy lifestyle as a way to dramatically improve the quality of life of patients with diabetes. The implementation of a holistic diabetes care system, using rising information technologies for deploying cares based on the thesis of the Evidence-Based Medicine can be a effective solution to provide an adequate and continuous care to patients. However, the design and deployment of computer readable careflows is not a easy task. In this paper, we propose the use of Interactive Pattern Recognition techniques for the iterative design of those protocols and we analyze the problems of using Process Mining to infer careflows and how to how to cope with the resulting Spaghetti Effect.
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Fernandez-Llatas C, Lizondo A, Monton E, Benedi JM, Traver V. Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems. SENSORS (BASEL, SWITZERLAND) 2015; 15:29821-40. [PMID: 26633395 PMCID: PMC4721690 DOI: 10.3390/s151229769] [Citation(s) in RCA: 74] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Revised: 11/17/2015] [Accepted: 11/20/2015] [Indexed: 11/18/2022]
Abstract
The definition of efficient and accurate health processes in hospitals is crucial for ensuring an adequate quality of service. Knowing and improving the behavior of the surgical processes in a hospital can improve the number of patients that can be operated on using the same resources. However, the measure of this process is usually made in an obtrusive way, forcing nurses to get information and time data, affecting the proper process and generating inaccurate data due to human errors during the stressful journey of health staff in the operating theater. The use of indoor location systems can take time information about the process in an unobtrusive way, freeing nurses, allowing them to engage in purely welfare work. However, it is necessary to present these data in a understandable way for health professionals, who cannot deal with large amounts of historical localization log data. The use of process mining techniques can deal with this problem, offering an easily understandable view of the process. In this paper, we present a tool and a process mining-based methodology that, using indoor location systems, enables health staff not only to represent the process, but to know precise information about the deployment of the process in an unobtrusive and transparent way. We have successfully tested this tool in a real surgical area with 3613 patients during February, March and April of 2015.
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Affiliation(s)
- Carlos Fernandez-Llatas
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera S/N, Valencia 46022, Spain.
- Unidad Mixta de Reingeniería de Procesos Sociosanitarios (eRPSS), Instituto de Investigación Sanitaria del Hospital Universitario y Politecnico La Fe, Bulevar Sur S/N, Valencia 46026, Spain.
| | - Aroa Lizondo
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera S/N, Valencia 46022, Spain.
| | - Eduardo Monton
- My Sphera S.L. Ronda Auguste y Louis Lumiere 23, Nave 13, Parque Tecnologico, Paterna 46980, Spain.
| | - Jose-Miguel Benedi
- Pattern Recognition and Human Language Technology (PRHTL), Universitat Politecnica de Valencia, Camino de Vera S/N, Valencia 46022, Spain.
| | - Vicente Traver
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera S/N, Valencia 46022, Spain.
- Unidad Mixta de Reingeniería de Procesos Sociosanitarios (eRPSS), Instituto de Investigación Sanitaria del Hospital Universitario y Politecnico La Fe, Bulevar Sur S/N, Valencia 46026, Spain.
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Fernandez-Llatas C, Valdivieso B, Traver V, Benedi JM. Using process mining for automatic support of clinical pathways design. Methods Mol Biol 2015; 1246:79-88. [PMID: 25417080 DOI: 10.1007/978-1-4939-1985-7_5] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
The creation of tools supporting the automatization of the standardization and continuous control of healthcare processes can become a significant helping tool for clinical experts and healthcare systems willing to reduce variability in clinical practice. The reduction in the complexity of design and deployment of standard Clinical Pathways can enhance the possibilities for effective usage of computer assisted guidance systems for professionals and assure the quality of the provided care. Several technologies have been used in the past for trying to support these activities but they have not been able to generate the disruptive change required to foster the general adoption of standardization in this domain due to the high volume of work, resources, and knowledge required to adequately create practical protocols that can be used in practice. This chapter proposes the use of the PALIA algorithm, based in Activity-Based process mining techniques, as a new technology to infer the actual processes from the real execution logs to be used in the design and quality control of healthcare processes.
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Fernández-Llatas C, Benedi JM, García-Gómez JM, Traver V. Process mining for individualized behavior modeling using wireless tracking in nursing homes. SENSORS (BASEL, SWITZERLAND) 2013; 13:15434-51. [PMID: 24225907 PMCID: PMC3871075 DOI: 10.3390/s131115434] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2013] [Revised: 10/30/2013] [Accepted: 11/04/2013] [Indexed: 11/18/2022]
Abstract
The analysis of human behavior patterns is increasingly used for several research fields. The individualized modeling of behavior using classical techniques requires too much time and resources to be effective. A possible solution would be the use of pattern recognition techniques to automatically infer models to allow experts to understand individual behavior. However, traditional pattern recognition algorithms infer models that are not readily understood by human experts. This limits the capacity to benefit from the inferred models. Process mining technologies can infer models as workflows, specifically designed to be understood by experts, enabling them to detect specific behavior patterns in users. In this paper, the eMotiva process mining algorithms are presented. These algorithms filter, infer and visualize workflows. The workflows are inferred from the samples produced by an indoor location system that stores the location of a resident in a nursing home. The visualization tool is able to compare and highlight behavior patterns in order to facilitate expert understanding of human behavior. This tool was tested with nine real users that were monitored for a 25-week period. The results achieved suggest that the behavior of users is continuously evolving and changing and that this change can be measured, allowing for behavioral change detection.
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Affiliation(s)
- Carlos Fernández-Llatas
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA). Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain; E-Mails: (J.M.G.-G.); (V.T.)
- Unidad Mixta de Reingeniería de Procesos Sociosanitarios (eRPSS), Instituto de Investigación Sanitaria del Hospital Universitario y Politécnico La Fe, Bulevar Sur S/N, Valencia 46026, Spain
| | - José-Miguel Benedi
- Instituto Tecnológico de Informática (ITI), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain; E-Mail:
| | - Juan M. García-Gómez
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA). Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain; E-Mails: (J.M.G.-G.); (V.T.)
| | - Vicente Traver
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA). Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain; E-Mails: (J.M.G.-G.); (V.T.)
- Unidad Mixta de Reingeniería de Procesos Sociosanitarios (eRPSS), Instituto de Investigación Sanitaria del Hospital Universitario y Politécnico La Fe, Bulevar Sur S/N, Valencia 46026, Spain
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