Eric Rojas

Process mining in healthcare

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.

A Framework for Recommending Resource Allocation Based on Process Mining

Dynamically allocating the most appropriate resource to execute the different activities of a business process is an important challenge in business process management. An ineffective allocation may lead to an inadequate resources usage, higher costs, or a poor process performance. Different approaches have been used to solve this challenge: data mining techniques, probabilistic allocation, or even manual allocation. However, there is a need for methods that support resource allocation based on multi-factor criteria. We propose a framework for recommending resource allocation based on Process Mining that does the recommendation at sub-process level, instead of activity-level. We introduce a resource process cube that provides a flexible, extensible and fine-grained mechanism to abstract historical information about past process executions. Then, several metrics are computed considering different criteria to obtain a final recommendation ranking based on the BPA algorithm. The approach is applied to a help desk scenario to demonstrate its usefulness.

Clinical Processes and Its Data, What Can We Do with Them?

Global healthcare services have evolved over time, and nowadays they are expected to follow high-quality optimized standards. Analyzing healthcare processes has become a relevant field of study, and different techniques and tools have been developed to promote improvements in the efficiency and effectiveness of these processes. There is a research field called process mining that can be used to extract knowledge from the event data stored in the hospital information systems. With the help of this, it is possible to discover the real executed process, examine its performance and analyze the resource interaction during its execution. The goal of this article is to provide a bibliographic survey about the use of process mining algorithms, techniques, and tools in the analysis of healthcare processes, providing a general overview about the main approaches previously used and the information required to apply them in the medical field. We provide important insights about data, algorithms, techniques and methodologies that are required to help answer medical expert questions about their processes, motivating and inspiring a broader usage. So, if we have the information and it is possible to analyze and understand the healthcare processes, why are we not doing it?

pMineR: An Innovative R Library for Performing Process Mining in Medicine

Process Mining is an emerging discipline investigating tasks related with the automated identification of process models, given real-world data (Process Discovery). The analysis of such models can provide useful insights to domain experts. In addition, models of processes can be used to test if a given process complies (Conformance Checking) with specifications. For these capabilities, Process Mining is gaining importance and attention in healthcare.

Discovering role interaction models in the Emergency Room using Process Mining

OBJECTIVES A coordinated collaboration among different healthcare professionals in Emergency Room (ER) processes is critical to promptly care for patients who arrive at the hospital in a delicate health condition, claiming for an immediate attention. The aims of this study are (i) to discover role interaction models in (ER) processes using process mining techniques; (ii) to understand how healthcare professionals are currently collaborating; and (iii) to provide useful knowledge that can help to improve ER processes. METHODS A four step method based on process mining techniques is proposed. An ER process of a university hospital was considered as a case study, using 7160 episodes that contains specific ER episode attributes. RESULTS Insights about how healthcare professionals collaborate in the ER was discovered, including the identification of a prevalent role interaction model along the major triage categories and specific role interaction models for different diagnoses. Also, common and exceptional professional interaction models were discovered at the role level. CONCLUSIONS This study allows the discovery of role interaction models through the use of real-life clinical data and process mining techniques. Results show a useful way of providing relevant insights about how healthcare professionals collaborate, uncovering opportunities for process improvement.

Generating and Comparing Knowledge Graphs of Medical Processes Using pMineR

Process mining focuses on extracting knowledge, under the form of models, from data generated and stored in information systems. The analysis of generated models can provide useful insights to domain experts. In addition, models of processes can be used to test if a considered process complies with some given specifications. For these reasons, process mining is gaining significant importance in the healthcare domain, where the complexity and flexibility of processes makes extremely hard to evaluate and assess how patients have been treated. In this paper we describe how pMineR, an R library designed and developed for performing process mining in the medical domain, is currently exploited in Hospitals for supporting domain experts in the analysis of the extracted knowledge models. In its current release, pMineR can encode extracted processes under the form of directed graphs, which are easy to interpret and understand by experts of the domain. It also provides graphical comparison between different processes, allows to model the adherence to a given clinical guidelines and to estimate performance and the workload of the available resources in healthcare.

Business process analysis in advertising: An extension to a methodology based on process mining projects

Nowadays organizations generate large amount of data. Only a few make a good use to optimize the performance of the business. Process mining appears as a branch of the data science that tries to understand the actual operational processes in the organizations through different algorithms, allowing the discovery of process models to give insight of the processes and understand how they can be improved. In this work different process mining techniques are applied to a company dedicated to the advertisement market, specifically the process of dealing with contract issues with customers. The Process Mining Project Methodology was followed to execute a case study. Additional to the basic methodology, elements from the others areas of studies were added to generate better results and have a better understanding of the problem. The case study includes three scenarios with three different hypotheses that were validated through our method.