Camilo Alvarez1, Eric Rojas2, Michael Arias3, Jorge Munoz-Gama4, Marcos Sepúlveda5, Valeria Herskovic6, Daniel Capurro7. 1. Computer Science Department, School of Engineering, Pontificia Universidad Católica de Chile, Chile. Electronic address: cealvarez@uc.cl. 2. Computer Science Department, School of Engineering, Pontificia Universidad Católica de Chile, Chile. Electronic address: eric.rojas@uc.cl. 3. Computer Science Department, School of Engineering, Pontificia Universidad Católica de Chile, Chile. Electronic address: m.arias@uc.cl. 4. Computer Science Department, School of Engineering, Pontificia Universidad Católica de Chile, Chile. Electronic address: jmun@ing.puc.cl. 5. Computer Science Department, School of Engineering, Pontificia Universidad Católica de Chile, Chile. Electronic address: marcos@ing.puc.cl. 6. Computer Science Department, School of Engineering, Pontificia Universidad Católica de Chile, Chile. Electronic address: vherskov@ing.puc.cl. 7. Internal Medicine Department, School of Medicine, Pontificia Universidad Católica de Chile, Chile. Electronic address: dcapurro@med.puc.cl.
Abstract
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.
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.
Authors: Sen Yang; Aleksandra Sarcevic; Richard A Farneth; Shuhong Chen; Omar Z Ahmed; Ivan Marsic; Randall S Burd Journal: J Biomed Inform Date: 2018-07-31 Impact factor: 6.317
Authors: Eric Rojas; Andres Cifuentes; Andrea Burattin; Jorge Munoz-Gama; Marcos Sepúlveda; Daniel Capurro Journal: Int J Environ Res Public Health Date: 2019-04-10 Impact factor: 3.390
Authors: Emmanuel Helm; Anna M Lin; David Baumgartner; Alvin C Lin; Josef Küng Journal: Int J Environ Res Public Health Date: 2020-02-19 Impact factor: 3.390
Authors: Michael Arias; Eric Rojas; Santiago Aguirre; Felipe Cornejo; Jorge Munoz-Gama; Marcos Sepúlveda; Daniel Capurro Journal: Int J Environ Res Public Health Date: 2020-09-10 Impact factor: 3.390