Process mining (PM) in the healthcare sector is a key discipline in terms of improving processes based on the data stored in clinical information systems. This discipline has been complemented by other techniques, such as process simulation (PS), in order to facilitate decision-making across the sector. In turn, this has enhanced the management of organizational indicators and strengthened the analysis, optimization, and improvement of processes. However, the combination of PM and PS in healthcare still presents a number of challenges, including the incorporation of additional medical specialties, the involvement of healthcare experts, the optimization of data quality, and the promotion of further research that seeks to strengthen the combination of these two areas. The present paper contributes to overcoming these challenges by introducing PROcess Mining & Simulation for Healthcare Analysis: PROMSHA-Methodology, a methodology that provides the necessary steps for process analysis by combining PM and PS in the field of healthcare. The methodology herein addresses certain challenges related to the quality of medical data and includes the participation of experts. To evaluate the usefulness of PROMSHA, a case study was conducted in the pediatric ophthalmology department of a Costa Rican hospital, in which PM techniques were applied to detect spaghetti models and repetitive activities, as well as to identify the causes of waiting lists and bottlenecks by means of several simulated scenarios.

Given the growing adoption of telemedicine in Chile—driven by the pandemic, technological advancements, and increased user familiarity—this study aimed to characterize oncology patients receiving care via this mode of care and analyze their behavior over time. Using a retrospective design, we evaluated the number of users, appointments, and average appointment frequency, segmented by age, insurance, and geographic region. Temporal variations were analyzed using two-proportion z-tests. The results revealed a reduction of over 58

Tele-oncology expanded rapidly during the COVID-19 pandemic and remains central to cancer care, yet evidence on guideline adherence in tele-oncology is limited, particularly in Latin America. In Chile, the Explicit Health Guarantees (GES) program mandates oncology care standards but provides no process model for monitoring adherence. We evaluated the conformance and temporal performance of telemedicine-based oncology pathways against a guideline-derived reference model using process mining, based on event logs from 182 patients treated in 2020–2023 in a Chilean hospital network. Conformance was computed using PM4Py with token-based replay and alignment-based fitness, complemented by cohort, abstraction-level and robustness analyses with bootstrap confidence intervals and multiple-comparison-adjusted testing. Overall conformance was low (4.95% of cases fully conforming; mean alignment fitness 0.19, 95% CI 0.16–0.22) against a highly precise reference model (precision 0.90). Censoring-aware analyses showed that low conformance was driven predominantly by genuine structural deviations rather than incomplete observation alone. Event-level cohort signatures were observed, but patient-level analyses suggested that these patterns should be interpreted as exploratory. Crude differences in care duration by out-of-model activity status were largely explained by differential observation time after exposure adjustment. Process-mining conformance checking provides reproducible indicators for governing tele-oncology pathways when metrics are interpreted alongside methodological caveats.

Process mining (PM) and process simulation (PS) are key disciplines for analyzing, optimizing, and improving processes to support decision-making. In recent years, the healthcare sector has significantly increased the adoption of PM tools and has shown growing interest in integrating techniques from other disciplines, such as simulation. However, the literature shows that the combination of PM and PS in healthcare remains limited and is concentrated in a small number of medical specialties. This study aims to analyze and demonstrate the potential of combining PM and PS for healthcare process analysis. The study presents a case study illustrating the integration of PM and PS in the analysis of an outpatient consultation process at a pediatric hospital in Costa Rica. The analysis is based on a dataset of 1814 cases from 2022 and was conducted using the Celonis and Apromore tools, within the context of a specific clinical specialty. The results reveal complex process models, rework activities, bottlenecks, and cases with low levels of conformance, as well as a lack of communication among staff, while highlighting the involvement of domain experts throughout the research. The study designs three simulation scenarios to evaluate process behavior and identifies limitations in the application of the tools used, mainly related to the complexity and variability inherent in healthcare processes.

Cancer poses a significant challenge within the healthcare domain due to its complexity. Analyzing cancer treatment pathways is crucial to identify treatments that are more specific and personalized for patients, as well as to predict potential outcomes and associated costs. Consequently, it is essential to distinguish between various patient types and their respective treatments. Process mining techniques, which utilize data analytics to understand care processes based on event logs, can enhance our comprehension of treatment sequences within different groups. This paper aims to explore variations in treatment pathways for breast cancer, perform process discovery techniques, visualize models, assess quality through conformance checking, and enhance the process model. The proposed approach is implemented through a case study of a real-world dataset extracted from Indonesian Health Insurance records, where breast cancer diagnoses account for a significant portion (25.8%). This research investigates care pathways of breast cancer within three distinct treatment groups: radiotherapy, chemotherapy, and a combination of both. Notably, chemotherapy emerges as the most common treatment, and the majority of patients fall within the age range of 50’ to 59’. Furthermore, the sequence of treatments within the chemotherapy and radiotherapy groups exhibits a substantial divergence of 25%. The outcomes emphasize the applicability of the mining approach, illustrating the potential patterns of breast cancer treatment pathways.

Organizations are increasingly incorporating new strategies to ensure that the complex processes involved in the healthcare sector are fully understood and enhanced. This includes the adoption of process mining (PM) and process simulation (PS), which have been used separately or in combination in the healthcare field to assist decision-makers in process optimization. Although both PM and PS have demonstrated valuable contributions to healthcare, existing literature lacks analysis of the benefits, limitations, and tools derived from their combination. The present article undertakes a literature review, based on the PRISMA methodology, in which both areas are analyzed in terms of their application to healthcare. By reviewing distinct scholar databases, 31 research studies were selected for analysis, in which it was possible to characterize case studies, techniques, tools, perspectives and algorithms, as well as to identify key limitations. The results indicate a stronger focus on medical fields like cardiology and emergency departments, with a preference for software tools such as ProM, Disco, Arena, and CPNTools. The use of real data predominates, and the two most identified and detailed limitations are data quality issues and the involvement of healthcare experts throughout the analysis. Moreover, there is an increasing interest in publications on these topics within Latin America. Finally, through the findings of the present article, the authors propose opportunities for future research,like the development of case studies in medical fields that have not been addressed, and the availability of software that integrates PM as well as PS, that may improve the usefulness of the combination of these disciplines in the field of healthcare.

Over the years, patient satisfaction has become a key factor when evaluating the quality of healthcare. There is a constant desire to further analyze patients' needs and expectations, with the aim of improving their healthcare experience. With tools such as customer journey maps (CJM), two objectives are addressed: a) identify how patients interact through the phases of the care cycle and b) execute multiple analyses to deliver a wide variety of outcomes focused on improving patient experience, through the combination of these tools with emerging disciplines such as process mining. We propose a new method based on a predefined framework to optimize the creation of healthcare indicators through process mining. Our method focuses on identifying touchpoints, defining, calculating, validating, and visualizing Key Performance Indicators (KPIs) in a clinical process. The proposed method was applied to analyze an emergency room process as a case study. Results demonstrate the usefulness of the method to discover the main process interaction points, to create key indicators, and generate dashboards that support the goal of understanding and optimizing patient care.

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.

Mobile applications (Apps) may become effective aids in health care. Health Apps could reduce barriers such as access and costs and could be used to monitor symptoms, behaviors and even treatments. There is more evidence of their usefulness in nutrition, cardiovascular and mental health. Despite this, its current use is predominantly for information purposes. Healthcare App quality evaluation should consider both clinical and technological aspects since the evidence on its clinical effectiveness is still incipient and they have associated risks. In Chile, the use of mobile technology and Apps is increasing, but there are no regulations for their use. There are few national institutions oriented to the creation and development of Apps for healthcare, highlighting the Digital Transformation Committee, part of the Corporation for the Promotion of Production (CORFO) and the National Center for Health Information Systems (CENS). General recommendations for healthcare App development and use have been established. In this process, it would be beneficial to include actors involved in care. Given the progress of healthcare Apps worldwide and nationally, it is important that health professionals develop digital skills to maximize the potential benefit of these technologies.

Mobile applications (Apps) may become effective aids in health care. Health Apps could reduce barriers such as access and costs and could be used to monitor symptoms, behaviors and even treatments. There is more evidence of their usefulness in nutrition, cardiovascular and mental health. Despite this, its current use is predominantly for information purposes. Healthcare App quality evaluation should consider both clinical and technological aspects since the evidence on its clinical effectiveness is still incipient and they have associated risks. In Chile, the use of mobile technology and Apps is increasing, but there are no regulations for their use. There are few national institutions oriented to the creation and development of Apps for healthcare, highlighting the Digital Transformation Committee, part of the Corporation for the Promotion of Production (CORFO) and the National Center for Health Information Systems (CENS). General recommendations for healthcare App development and use have been established. In this process, it would be beneficial to include actors involved in care. Given the progress of healthcare Apps worldwide and nationally, it is important that health professionals develop digital skills to maximize the potential benefit of these technologies.

Clinical guidelines are recommendations of how to diagnose, treat, and manage a patient's medical condition. Health organizations must measure adherence to clinical guidelines to enhance the quality of service, but due to the complexity of the medical environment, there is no simple way of measuring adherence to clinical guidelines. This scoping review will systematically assess the criteria used to measure adherence to clinical guidelines in the past 20 years and explore the suitability of using process mining techniques. We will use a workflow protocol based on declarative and temporal constraints to translate the narrative text rules in the publications into a high-level process model. This approach will enable us to explore the main patterns and gaps identified when measuring adherence to clinical guidelines and how they affect the adoption of process mining techniques. The main contributions of this paper are a) a comprehensive analysis of the criteria used for measuring adherence, considering a diverse set of medical conditions b) a framework that will classify the level of complexity of the rules used to measure adherence based on declarative and temporal constraints c) list of key trends and gaps identified in the literature and how they relate to the use of process mining techniques in healthcare.

Medication errors continue to be the leading cause of avoidable patient harm in hospitals. This paper sets out a framework to assure medication safety that combines machine learning and safety engineering methods. It uses safety analysis to proactively identify potential causes of medication error, based on expert opinion. As healthcare is now data rich, it is possible to augment safety analysis with machine learning to discover actual causes of medication error from the data, and to identify where they deviate from what was predicted in the safety analysis. Combining these two views has the potential to enable the risk of medication errors to be managed proactively and dynamically. We apply the framework to a case study involving thoracic surgery, e.g. oesophagectomy, where errors in giving beta-blockers can be critical to control atrial fibrillation. This case study combines a HAZOP-based safety analysis method known as SHARD with Bayesian network structure learning and process mining to produce the analysis results, showing the potential of the framework for ensuring patient safety, and for transforming the way that safety is managed in complex healthcare environments.

The main challenge in the pathway analysis of cancer treatments is the complexity of the process. Process mining is one of the approaches that can be used to visualize and analyze these complex pathways. In this study, our purpose was to use process mining to explore variations in the treatment pathways of endometrial cancer. We extracted patient data from a hospital information system, created the process model, and analyzed the variations of the 62-day pathway from a General Practitioner referral to the first treatment in the hospital. We also analyzed the variations based on three different criteria: the type of the first treatment, the age at diagnosis, and the year of diagnosis. This approach should be of interest to others dealing with complex medical and healthcare processes.

Emerging research has linked psychological well-being with many physiological markers as well as morbidity and mortality. In this analysis, the relationship between components of eudaimonic well-being and serum sphingolipids levels was investigated using data from a large national survey of middle-aged American adults (Midlife in the United States). Health behaviors (i.e., diet, exercise, and sleep) were also examined as potential mediators of these relationships. Serum levels of total ceramides - the main molecular class of sphingolipids previously associated with several disease conditions - were inversely linked with environmental mastery. In addition, significant correlations were found between specific ceramide, dihydroceramide, and hexosylceramides species with environmental mastery, purpose in life, and self-acceptance. Using hierarchical regression and mediation analyses, health behaviors appeared to mediate these associations. However, the link between ceramides and environmental mastery was partially independent of health behaviors, suggesting the role of additional mediating factors. These findings point to sphingolipid metabolism as a novel pathway of health benefits associated with psychological well-being. In particular, having a sense of environmental mastery may promote restorative behaviors and benefit health via improved blood sphingolipid profiles.

Disease trajectories model patterns of disease over time and can be mined by extracting diagnosis codes from electronic health records (EHR). Process mining provides a mature set of methods and tools that has been used to mine care pathways using event data from EHRs and could be applied to disease trajectories. This paper presents a literature review on process mining related to mining disease trajectories using EHRs. Our review identified 156 papers of potential interest but only four papers which directly applied process mining to disease trajectory modelling. These four papers are presented in detail covering data source, size, selection criteria, selections of the process mining algorithms, trajectory definition strategies, model visualisations, and the methods of evaluation. The literature review lays the foundations for further research leveraging the established benefits of process mining for the emerging data mining of disease trajectories.

Nowadays, assessing and improving customer experience has become a priority, and has emerged as a key differentiator for business and organizations worldwide. A customer journey (CJ) is a strategic tool, a map of the steps customers follow when engaging with a company or organization to obtain a product or service. The increase of the need to obtain knowledge about customers' perceptions and feelings when interacting with participants, touchpoints, and channels through different stages of the customer life cycle. This study aims to describe the application of process mining techniques in healthcare as a tool to asses customer journeys. The appropriateness of the approach presented is illustrated through a case study of a key healthcare process. Results depict how a healthcare process can be mapped through the CJ components, and its analysis can serve to understand and improve the patient's experience.

BACKGROUND:Vitamin K antagonist medications (VKA) are essential for the prevention of thromboembolic events, but their effectiveness is influenced by multiple factors, such as the type of medication chosen.AIM:To evaluate the efficacy in anticoagulant control of the bioequivalent and non-bioequivalent drugs of acenocoumarol compared to the reference drug. To evaluate the efficacy of warfarin bioequivalents available in Chile. To contrast the overall anticoagulant control efficacy between acenocoumarol and warfarin.MATERIAL AND METHODS:The results of 69333 outpatient oral anticoagulation controls were analyzed. Patient were separated in groups according to the drug that they used. Subsequently, the proportions of controls outside the range for each of acenocoumarol and warfarin bioequivalent drugs were compared. Acenocoumarol non-bioequivalent drugs were also compared with the reference drug. Acenocoumarol was compared with warfarin.RESULTS:Acenocoumarol bioequivalent drugs and the reference drug had a similar proportion of controls outside the range (Odds ratios (OR) 0.812; 0.969; 0.974 and 0.963). Non-bioequivalent drugs had a higher proportion than the reference drug (OR 1.561 and 2.037). Both warfarin brands have a similar proportion of controls outside of the range (OR 1.050). Acenocoumarol compared to warfarin had a significant higher proportion of controls outside the range (OR 1.191).CONCLUSIONS:The pharmacological presentation of vitamin K antagonists could influence anticoagulant control. Therefore, it is not prudent to switch these presentations frequently.