Browsing by Author "Sepúlveda, Marcos"

Now showing 1 - 12 of 12
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    Analysis of Emergency Room Episodes Duration Through Process Mining
    (2019) Rojas, Eric; Cifuentes Soto, Andrés Alonso; Burattin, A.; Muñoz Gama, Jorge; Sepúlveda, Marcos; Capurro, Daniel
    This study presents the proposal of a performance analysis method for ER Processes through Process Mining. This method helps to determine which activities, sub-processes, interactions and characteristics of episodes explain why the process has long episode duration, besides providing decision makers with additional information that will help to decrease waiting times, reduce patient congestion and increment quality of provided care. By applying the exposed method to a case study, it was discovered that when a loop is formed between the Examination and Treatment sub-processes, the episode duration lengthens. Moreover, the relationship between case severity and the number of repetitions of the Examination-Treatment loop was also studied. As the case severity increases, the number of repetitions increases as well.
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    Analysis of Multidisciplinary Collaboration in Primary Healthcare: The Chilean Case
    (2017) Saint-Pierre, Cecilia; Sepúlveda, Marcos; Herskovic, Valeria
    Around 10% of the population suffers from diabetes, and this percentage is expected to rise. Healthcare guidelines propose a multidisciplinary, collaborative approach for treatment. However, there is little data to understand whether healthcare professionals are actually collaborating and how this collaboration takes place. We analyzed 4 years of data from 3 healthcare centers in Chile, corresponding to 2,838 patients. Patients were classified according to the composition of the healthcare team into four categories: highly multidisciplinary teams, specialized teams, physician-nurse centered teams, and non-collaborative treatment. Our results show that team prevalence is related to patient and healthcare center characteristics.
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    Analyzing medical emergency processes with process mining: the stroke case
    (2019) Fernandez-Llatas, Carlos; Ibanez-Sanchez, Gema; Celda, Angeles; Mandingorra, Jesus; Aparici-Tortajada, Lucia; Martinez-Millana, Antonio; Munoz-Gama, Jorge; Sepúlveda, Marcos; Rojas, Eric; Gálvez, Víctor; Capurro, Daniel; Traver, Vicente
    Medical emergencies are one of the most critical processes that occurs in a hospital. The creation of adequate and timely triage protocols, can make the difference between the life and death of the patient. One of the most critical emergency care protocols is the stroke case. This disease demands an accurate and quick diagnosis for ensuring an immediate treatment in order to limit or even, avoid, the undesired cognitive decline. The aim of this paper is perform an analysis of how Process Mining techniques can support health professionals in the interactive analysis of emergency processes considering critical timing of Stroke, using a Question Driven methodology. To demonstrate the possibilities of Process Mining in the characterization of the emergency process, we have used a real log with 9046 emergency episodes from 2145 stroke patients that occurred from January of 2010 to June of 2017. Our results demonstrate how Process Mining technology can highlight the differences of the stroke patient flow in emergency, supporting professionals in the better understanding and improvement of quality of care.
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    Backpack Process Model (BPPM): A Process Mining Approach for Curricular Analytics
    (MDPI, 2021) Salazar Fernandez, Juan Pablo; Muñoz Gama, Jorge; Maldonado Mahauad, Jorge; Bustamante, Diego; Sepúlveda, Marcos
    Curricular analytics is the area of learning analytics that looks for insights and evidence on the relationship between curricular elements and the degree of achievement of curricular outcomes. For higher education institutions, curricular analytics can be useful for identifying the strengths and weaknesses of the curricula and for justifying changes in learning pathways for students. This work presents the study of curricular trajectories as processes (i.e., sequence of events) using process mining techniques. Specifically, the Backpack Process Model (BPPM) is defined as a novel model to unveil student trajectories, not by the courses that they take, but according to the courses that they have failed and have yet to pass. The usefulness of the proposed model is validated through the analysis of the curricular trajectories of N = 4466 engineering students considering the first courses in their program. We found differences between backpack trajectories that resulted in retention or in dropout; specific courses in the backpack and a larger initial backpack sizes were associated with a higher proportion of dropout. BPPM can contribute to understanding how students handle failed courses they must retake, providing information that could contribute to designing and implementing timely interventions in higher education institutions.
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    Building Process-Oriented Data Science Solutions for Real-World Healthcare
    (MDPI, 2022) Fernandez-Llatas, Carlos; Martin, Niels; Johnson, Owen; Sepúlveda, Marcos; Helm, Emmanuel; Muñoz Gama, Jorge
    The COVID-19 pandemic has highlighted some of the opportunities, problems and barriers facing the application of Artificial Intelligence to the medical domain. It is becoming increasingly important to determine how Artificial Intelligence will help healthcare providers understand and improve the daily practice of medicine. As a part of the Artificial Intelligence research field, the Process-Oriented Data Science community has been active in the analysis of this situation and in identifying current challenges and available solutions. We have identified a need to integrate the best efforts made by the community to ensure that promised improvements to care processes can be achieved in real healthcare. In this paper, we argue that it is necessary to provide appropriate tools to support medical experts and that frequent, interactive communication between medical experts and data miners is needed to co-create solutions. Process-Oriented Data Science, and specifically concrete techniques such as Process Mining, can offer an easy to manage set of tools for developing understandable and explainable Artificial Intelligence solutions. Process Mining offers tools, methods and a data driven approach that can involve medical experts in the process of co-discovering real-world evidence in an interactive way. It is time for Process-Oriented Data scientists to collaborate more closely with healthcare professionals to provide and build useful, understandable solutions that answer practical questions in daily practice. With a shared vision, we should be better prepared to meet the complex challenges that will shape the future of healthcare.
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    Interactive Process Mining for Medical Training
    (2020) Muñoz, Gama Jorge; Victor, Galvez; Rene de la Fuente; Sepúlveda, Marcos; Fuentes, Ricardo
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    PALIA-ER: Bringing question-driven process mining closer to the emergency room
    (2017) Rojas, Eric; Fernández-Llatas, Carlos; Traver, Vicente; Muñoz-Gama, Vicente; Sepúlveda, Marcos; Herskovic, Valeria; Capurro, Daniel
    This paper presents PALIA-ER, a web-based tool for question-driven process mining in Emergency Room. PALIA-ER uses Palia discovery algorithm and includes model simplification and filtering features specially domain-specific for ER. Most PALIA-ER functionalities can be easily applied to other interdisciplinary contexts such as other healthcare units, education, or logistics.
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    Process mining in healthcare: A literature review
    (2016) Sepúlveda, Marcos
    Process Mining focuses on extracting knowledge from data generated and stored in corporate information systems in order to analyze executed processes. In the healthcare domain, process mining has been used in different case studies, with promising results. Accordingly, we have conducted a literature review of the usage of process mining in healthcare. The scope of this review covers 74 papers with associated case studies, all of which were analyzed according to eleven main aspects, including: process and data types; frequently posed questions; process mining techniques, perspectives and tools; methodologies; implementation and analysis strategies; geographical analysis; and medical fields. The most commonly used categories and emerging topics have been identified, as well as future trends, such as enhancing Hospital Information Systems to become process-aware. This review can: (i) provide a useful overview of the current work being undertaken in this field; (ii) help researchers to choose process mining algorithms, techniques, tools, methodologies and approaches for their own applications; and (iii) highlight the use of process mining to improve healthcare processes.
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    Process-oriented instrument and taxonomy for teaching surgical procedures in medical training: The ultrasound-guided insertion of central venous catheter
    Gálvez, Víctor; De La Fuente Sanhueza, René Francisco; Meneses, César; Leiva, Luis; Fagalde, Gonzalo; Herskovic, Valeria; Fuentes, Ricardo; Muñoz Gama, Jorge; Sepúlveda, Marcos
    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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    Street Address Correction Based on Spelling Techniques
    (2005) Mois, Patricio; Sepúlveda, Marcos; Proschle, Humberto
    In the 90's, Geographic Information Systems started having a remarkable demand, since they are an innovative technology that allows visualizing information in a spatial way, along with its geographic distribution., Digital maps enterprises offer a variety of services, among them stand out the ratification of addresses: to check clients' databases for detection and correction of wrong entries, and then to validate the integrity of every new record that is inserted. This work details the development of an algorithm that improves the process of ratification using spelling techniques, with the goal of minimizing the human intervention required in the process, without sacrificing quality. The benefits are better response times and reduction of service costs.
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    The Sequence of Steps: A Key Concept Missing in Surgical Training—A Systematic Review and Recommendations to Include It
    (MDPI, 2023) Galvez-Yanjari, Víctor; de la Fuente, René; Muñoz-Gama, Jorge; Sepúlveda, Marcos
    © 2023 by the authors.Background: Surgical procedures have an inherent feature, which is the sequence of steps. Moreover, studies have shown variability in surgeons’ performances, which is valuable to expose residents to different ways to perform a procedure. However, it is unclear how to include the sequence of steps in training programs. Methods: We conducted a systematic review, including studies reporting explicit teaching of a standard sequence of steps, where assessment considered adherence to a standard sequence, and where faculty or students at any level participated. We searched for articles on PubMed, EMBASE, CINAHL, Web of Science, and Google Scholar databases. Results: We selected nine articles that met the inclusion criteria. The main strategy to teach the sequence was to use videos to demonstrate the procedure. The simulation was the main strategy to assess the learning of the sequence of steps. Non-standardized scoring protocols and written tests with variable validity evidence were the instruments used to assess the learning, and were focused on adherence to a standard sequence and the omission of steps. Conclusions: Teaching and learning assessment of a standard sequence of steps is scarcely reported in procedural skills training literature. More research is needed to evaluate whether the new strategies to teach and assess the order of steps work. We recommend the use of Surgical Process Models and Surgical Data Science to incorporate the sequence of steps when teaching and assessing procedural skills.
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    Towards a Taxonomy of Human Resource Allocation Criteria
    (2018) Muñoz Gama, Jorge; Sepúlveda, Marcos ; Arias, Michael
    Allocating the most appropriate resource to execute the activities of a business process is a key aspect within the organizational perspective. An optimal selection of the resources that are in charge of executing the activities may contribute to improve the efficiency and the performance of the business processes. Despite the existence of resource metamodels that seek to provide a better representation of resources, a detailed classification of the allocation criteria that have been used to evaluate resources is missing. In this paper, we provide an initial proposal for a resource allocation criteria taxonomy. This taxonomy is based on an extensive literature review that yielded 2,370 articles regarding the existing resource allocation approaches within the business process management discipline, from which 95 articles were considered for the analysis. The proposed taxonomy points out the most frequently used criteria for assessing the resources from January 2005 to July 2016.