Sooyoung Yoo1, Minsu Cho2, Eunhye Kim1, Seok Kim1, Yerim Sim2, Donghyun Yoo3, Hee Hwang1, Minseok Song4. 1. Center for Medical Informatics, Seoul National University Bundang Hospital, South Korea. 2. School of Business Administration, Ulsan National Institute of Science and Technology, South Korea. 3. Patient's Affairs, Seoul National University Bundang Hospital, South Korea. 4. Department of Industrial & Management Engineering, POSTECH (Pohang University of Science & Technology), South Korea. Electronic address: mssong@postech.ac.kr.
Abstract
INTRODUCTION: Many hospitals are increasing their efforts to improve processes because processes play an important role in enhancing work efficiency and reducing costs. However, to date, a quantitative tool has not been available to examine the before and after effects of processes and environmental changes, other than the use of indirect indicators, such as mortality rate and readmission rate. METHODS: This study used process mining technology to analyze process changes based on changes in the hospital environment, such as the construction of a new building, and to measure the effects of environmental changes in terms of consultation wait time, time spent per task, and outpatient care processes. Using process mining technology, electronic health record (EHR) log data of outpatient care before and after constructing a new building were analyzed, and the effectiveness of the technology in terms of the process was evaluated. RESULTS: Using the process mining technique, we found that the total time spent in outpatient care did not increase significantly compared to that before the construction of a new building, considering that the number of outpatients increased, and the consultation wait time decreased. These results suggest that the operation of the outpatient clinic was effective after changes were implemented in the hospital environment. We further identified improvements in processes using the process mining technique, thereby demonstrating the usefulness of this technique for analyzing complex hospital processes at a low cost. CONCLUSION: This study confirmed the effectiveness of process mining technology at an actual hospital site. In future studies, the use of process mining technology will be expanded by applying this approach to a larger variety of process change situations.
INTRODUCTION: Many hospitals are increasing their efforts to improve processes because processes play an important role in enhancing work efficiency and reducing costs. However, to date, a quantitative tool has not been available to examine the before and after effects of processes and environmental changes, other than the use of indirect indicators, such as mortality rate and readmission rate. METHODS: This study used process mining technology to analyze process changes based on changes in the hospital environment, such as the construction of a new building, and to measure the effects of environmental changes in terms of consultation wait time, time spent per task, and outpatient care processes. Using process mining technology, electronic health record (EHR) log data of outpatient care before and after constructing a new building were analyzed, and the effectiveness of the technology in terms of the process was evaluated. RESULTS: Using the process mining technique, we found that the total time spent in outpatient care did not increase significantly compared to that before the construction of a new building, considering that the number of outpatients increased, and the consultation wait time decreased. These results suggest that the operation of the outpatient clinic was effective after changes were implemented in the hospital environment. We further identified improvements in processes using the process mining technique, thereby demonstrating the usefulness of this technique for analyzing complex hospital processes at a low cost. CONCLUSION: This study confirmed the effectiveness of process mining technology at an actual hospital site. In future studies, the use of process mining technology will be expanded by applying this approach to a larger variety of process change situations.
Authors: Hansol Chang; Jae Yong Yu; Sun Young Yoon; Sung Yeon Hwang; Hee Yoon; Won Chul Cha; Min Seob Sim; Ik Joon Jo; Taerim Kim Journal: J Clin Med Date: 2020-11-26 Impact factor: 4.241