An Evidence-Based Decision Support Framework for Clinician Medical Scheduling
In healthcare management, waiting time for consultation is an important measure that has strong associations with patient's satisfaction (i.e., the longer patients wait for consultation, the less satisfied they are). To this end, it is required to optimize medical scheduling for clinicians. A t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8621008/ |
| _version_ | 1818613246637113344 |
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| author | Minsu Cho Minseok Song Sooyoung Yoo Hajo A. Reijers |
| author_facet | Minsu Cho Minseok Song Sooyoung Yoo Hajo A. Reijers |
| author_sort | Minsu Cho |
| collection | DOAJ |
| description | In healthcare management, waiting time for consultation is an important measure that has strong associations with patient's satisfaction (i.e., the longer patients wait for consultation, the less satisfied they are). To this end, it is required to optimize medical scheduling for clinicians. A typical approach for deriving the optimized schedules is to perform experiments using discrete event simulation. The existing work has developed how to build a simulation model based on process mining techniques. However, applying this method for outpatient processes straightforwardly, in particular medical scheduling, is challenging: 1) the collected data from electronic health record system requires a series of processes to acquire simulation parameters from the raw data; and 2) even if the derived simulation model fully reflects the reality, there is no systematic approach to deriving effective improvements for simulation analysis, i.e., experimental scenarios. To overcome these challenges, this paper proposes a novel decision support framework for a clinician's schedule using simulation analysis. In the proposed framework, a data-driven simulation model is constructed based on process mining analysis, which includes process discovery, patient arrival rate analysis, and service time analysis. Also, a series of steps to derive the optimal improvement method from the simulation analysis is included in the framework. To demonstrate the usefulness of our approach, we present the case study results with real-world data in a hospital. |
| first_indexed | 2024-12-16T15:59:04Z |
| format | Article |
| id | doaj.art-5fde17d5453345dab7447ac786f58fef |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T15:59:04Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5fde17d5453345dab7447ac786f58fef2022-12-21T22:25:30ZengIEEEIEEE Access2169-35362019-01-017152391524910.1109/ACCESS.2019.28941168621008An Evidence-Based Decision Support Framework for Clinician Medical SchedulingMinsu Cho0Minseok Song1https://orcid.org/0000-0002-6813-8853Sooyoung Yoo2Hajo A. Reijers3Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, South KoreaDepartment of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, South KoreaHealth ICT Research Center, Seoul National University Bundang Hospital, Bundang-gu, South KoreaDepartment of Information and Computing Sciences, Utrecht University, Utrecht, The NetherlandsIn healthcare management, waiting time for consultation is an important measure that has strong associations with patient's satisfaction (i.e., the longer patients wait for consultation, the less satisfied they are). To this end, it is required to optimize medical scheduling for clinicians. A typical approach for deriving the optimized schedules is to perform experiments using discrete event simulation. The existing work has developed how to build a simulation model based on process mining techniques. However, applying this method for outpatient processes straightforwardly, in particular medical scheduling, is challenging: 1) the collected data from electronic health record system requires a series of processes to acquire simulation parameters from the raw data; and 2) even if the derived simulation model fully reflects the reality, there is no systematic approach to deriving effective improvements for simulation analysis, i.e., experimental scenarios. To overcome these challenges, this paper proposes a novel decision support framework for a clinician's schedule using simulation analysis. In the proposed framework, a data-driven simulation model is constructed based on process mining analysis, which includes process discovery, patient arrival rate analysis, and service time analysis. Also, a series of steps to derive the optimal improvement method from the simulation analysis is included in the framework. To demonstrate the usefulness of our approach, we present the case study results with real-world data in a hospital.https://ieeexplore.ieee.org/document/8621008/Simulation modelingprocess miningpersonal clinician schedulesexperimental analyseswaiting time for consultation |
| spellingShingle | Minsu Cho Minseok Song Sooyoung Yoo Hajo A. Reijers An Evidence-Based Decision Support Framework for Clinician Medical Scheduling IEEE Access Simulation modeling process mining personal clinician schedules experimental analyses waiting time for consultation |
| title | An Evidence-Based Decision Support Framework for Clinician Medical Scheduling |
| title_full | An Evidence-Based Decision Support Framework for Clinician Medical Scheduling |
| title_fullStr | An Evidence-Based Decision Support Framework for Clinician Medical Scheduling |
| title_full_unstemmed | An Evidence-Based Decision Support Framework for Clinician Medical Scheduling |
| title_short | An Evidence-Based Decision Support Framework for Clinician Medical Scheduling |
| title_sort | evidence based decision support framework for clinician medical scheduling |
| topic | Simulation modeling process mining personal clinician schedules experimental analyses waiting time for consultation |
| url | https://ieeexplore.ieee.org/document/8621008/ |
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