TY - GEN
T1 - Evaluation of operational process variables in healthcare using process mining and data visualization techniques
AU - Aguirre, Jimmy Armas
AU - Torres, André Coronado
AU - Pescoran, Misael Evangelista
AU - Mayorga, Santiago Aguirre
N1 - Publisher Copyright:
© 2019 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.
PY - 2019
Y1 - 2019
N2 - In this paper, a reference model is proposed for the evaluation of operational processes variables in healthcare using process mining and data visualization techniques. For this reason, the PM2 methodology is used as a reference to conduct projects oriented to the evaluation of data collected in business processes, including data visualization techniques, with the purpose of reducing the acquisition time of knowledge related to the processes of institutions of the healthcare sector. The proposed model is based on the application of a set of data visualization techniques to reduce the knowledge acquisition gap presented by process mining. The model consists of 5 stages: 1. Extraction, 2. Event processing, 3. Process mining, 4. Data visualization and 5. Evaluation of results. A testing scenario was defined in a Clinic network in Lima (Peru) to validate the proposed model and the surgery process was chosen, since it is critical for the organization. The results showed the existing bottleneck in the surgery process, between the activities of registering and preparing the patient. This allowed to take corrective measures between the activities to optimize the process cycle time. Likewise, a sequence was identified in the activities that had not been previously detected in the process documentation, these represented 2.6% difference, so the documented process was modified to achieve a 99.6% affinity.
AB - In this paper, a reference model is proposed for the evaluation of operational processes variables in healthcare using process mining and data visualization techniques. For this reason, the PM2 methodology is used as a reference to conduct projects oriented to the evaluation of data collected in business processes, including data visualization techniques, with the purpose of reducing the acquisition time of knowledge related to the processes of institutions of the healthcare sector. The proposed model is based on the application of a set of data visualization techniques to reduce the knowledge acquisition gap presented by process mining. The model consists of 5 stages: 1. Extraction, 2. Event processing, 3. Process mining, 4. Data visualization and 5. Evaluation of results. A testing scenario was defined in a Clinic network in Lima (Peru) to validate the proposed model and the surgery process was chosen, since it is critical for the organization. The results showed the existing bottleneck in the surgery process, between the activities of registering and preparing the patient. This allowed to take corrective measures between the activities to optimize the process cycle time. Likewise, a sequence was identified in the activities that had not been previously detected in the process documentation, these represented 2.6% difference, so the documented process was modified to achieve a 99.6% affinity.
KW - Business process
KW - Data visualization
KW - Healthcare
KW - Process mining
UR - http://www.scopus.com/inward/record.url?scp=85073629711&partnerID=8YFLogxK
U2 - 10.18687/LACCEI2019.1.1.286
DO - 10.18687/LACCEI2019.1.1.286
M3 - Conference contribution
AN - SCOPUS:85073629711
T3 - Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
BT - 17th LACCEI International Multi-Conference for Engineering, Education, and Technology
PB - Latin American and Caribbean Consortium of Engineering Institutions
T2 - 17th LACCEI International Multi-Conference for Engineering, Education, and Technology, LACCEI 2019
Y2 - 24 July 2019 through 26 July 2019
ER -