An Evidence-Based Decision Support Framework for Clinician Medical Scheduling

被引:25
作者
Cho, Minsu [1 ]
Song, Minseok [1 ]
Yoo, Sooyoung [2 ]
Reijers, Hajo A. [3 ]
机构
[1] Pohang Univ Sci & Technol, Dept Ind & Management Engn, Pohang 37673, South Korea
[2] Seoul Natl Univ, Bundang Hosp, Hlth ICT Res Ctr, Seoul 13620, South Korea
[3] Univ Utrecht, Dept Informat & Comp Sci, NL-3512 Utrecht, Netherlands
基金
新加坡国家研究基金会;
关键词
Simulation modeling; process mining; personal clinician schedules; experimental analyses; waiting time for consultation; OUTPATIENT PROCESS ANALYSIS; BUSINESS PROCESS REDESIGN; HEALTH-CARE; PREFERENCES;
D O I
10.1109/ACCESS.2019.2894116
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:15239 / 15249
页数:11
相关论文
共 33 条
[1]  
Analyzer P., PROCESS ANAL WEBSITE
[2]  
[Anonymous], 2013, INT SERIES OPERATION, DOI DOI 10.1007/978-1-4614-9512-3_12
[3]   Evidence Based Medicine and Shared Decision Making: The challenge of getting both evidence and preferences into health care [J].
Barratt, Alexandra .
PATIENT EDUCATION AND COUNSELING, 2008, 73 (03) :407-412
[4]   Patients' preferences for attributes related to health care services at hospitals in Amhara Region, northern Ethiopia: a discrete choice experiment [J].
Berhane, Adugnaw ;
Enquselassie, Fikre .
PATIENT PREFERENCE AND ADHERENCE, 2015, 9 :1293-1301
[5]  
Bose RPJC, 2013, 2013 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DATA MINING (CIDM), P127, DOI 10.1109/CIDM.2013.6597227
[6]   A web-based appointment system to reduce waiting for outpatients: A retrospective study [J].
Cao, Wenjun ;
Wan, Yi ;
Tu, Haibo ;
Shang, Fujun ;
Liu, Danhong ;
Tan, Zhijun ;
Sun, Caihong ;
Ye, Qing ;
Xu, Yongyong .
BMC HEALTH SERVICES RESEARCH, 2011, 11
[7]   Using computational modeling to assess the impact of clinical decision support on cancer screening improvement strategies within the community health centers [J].
Carney, Timothy Jay ;
Morgan, Geoffrey P. ;
Jones, Josette ;
McDaniel, Anna M. ;
Weaver, Michael ;
Weiner, Bryan ;
Haggstrom, David A. .
JOURNAL OF BIOMEDICAL INFORMATICS, 2014, 51 :200-209
[8]  
Cayirli T, 2003, PROD OPER MANAG, V12, P519, DOI 10.1111/j.1937-5956.2003.tb00218.x
[9]   Evaluating the effect of best practices for business process redesign: An evidence-based approach based on process mining techniques [J].
Cho, Minsu ;
Song, Minseok ;
Comuzzi, Marco ;
Yoo, Sooyoung .
DECISION SUPPORT SYSTEMS, 2017, 104 :92-103
[10]  
Cho M, 2015, INT J IND ENG-THEORY, V22, P480