题名

運用任務-科技適配模式建構醫療人員排班系統雛型

并列篇名

Applying task-technology fit model to construct a medical staff scheduling system prototype

DOI

10.6840/cycu201900629

作者

蔡子滔

关键词

醫療人員排班系統 ; 任務-科技適配模式 ; 約班及約假 ; 人員排班 ; Medical staff scheduling system ; Task-technology fit model ; Predetermined schedule ; Day-off reservation ; Staff scheduling

期刊名称

中原大學工業與系統工程學系學位論文

卷期/出版年月

2019年

学位类别

碩士

导师

陳平舜

内容语文

繁體中文

中文摘要

本研究藉由任務-科技適配模式(Task-Technology Fit Model, TTF)提供之科技特性衡量資訊科技系統,作為建構一個醫療人員排班系統雛型之參考,用於確認功能需求及人機介面設計。經由評估所提出之醫療人員排班系統後,本研究排除任務-科技適配模式所提供之13點科技特性中的兼容性,並新增2點科技特性作為考量,即客製化及擴充性。 根據修訂任務-科技適配模式之科技特性,本研究為個案醫院的醫事放射師,開發出彈性設置每日各勤務之人力需求、約班及約假的功能。基於彈性、客製化及擴充性,本研究開發的系統能容納多種演算法,以產生更好的滿足軟和硬限制之醫療人員班表。此外,排班人員能自所建構的限制中,選擇所需的軟和硬限制。在需求或法規變更的情況下,本醫療人員排班系統雛型能輕易擴充或修改限制。因此,本研究結果提供一個雛型,讓系統開發人員能為每個醫療人員的單位開發醫療人員排班系統。

英文摘要

This research uses the task-technology fit model (TTF) to measure the technological characteristics of information technology systems as a reference for constructing a medical staff scheduling system prototype for identifying function requirements and designing human interfaces. After the evaluation of the proposed medical staff scheduling system has been executed, this research excludes compatibility from the 13 technological characteristics of the TTF, and adds two technological characteristics into consideration, customization and scalability. Based on the revised technological characteristics of the TTF, this research develops the flexible scheduling functions to satisfy daily manpower requirements and allow predetermined schedules and day-off reservations for hospital's radiologists. Based on flexibility, customization, and scalability, the developed system is able to accommodate multiple algorithms to generate a better medical staff schedule, which satisfies soft and hard constraints. Furthermore, the scheduler can choose the required soft and hard constraints from all constructed constraints. The developed medical staff scheduling system prototype will be easily extended to add or modify constraints in case of requirement or regulation changes. Therefore, the results of this study provide a fundamental prototype for system developers to develop a customized medical staff scheduling system for each medical staff unit.

主题分类 電機資訊學院 > 工業與系統工程學系
工程學 > 工程學總論
参考文献
  1. 蔡嘉哲,運用萬用啟發式演算法解醫護人員排班問題,中原大學工業與系統工程學系碩士論文,2015。
    連結:
  2. 參考文獻
  3. Awadallah, M. A., Bolaji, A. L., and Al-Betar, M. A. (2015). A hybrid artificial bee colony for a nurse rostering problem. Applied Soft Computing, 35, 726-739.
  4. Burke, E. K., De Causmaecker, P., Vanden Berghe, G., and Van Landeghem, H. (2004). The state of the art of nurse rostering. Journal of Scheduling, 7(6), 441-499.
  5. Burke, E. K., Li, J. P., and Qu, R. (2010). A hybrid model of integer programming and variable neighbourhood search for highly-constrained nurse rostering problems. European Journal of Operational Research, 203(2), 484-493.
  6. Cheang, B., Li, H., Lim, A., and Rodrigues, B. (2003). Nurse rostering problems - a bibliographic survey. European Journal of Operational Research, 151(3), 447-460.
  7. Chen, H., Rong, W. E., Ma, X. Y., Qu, Y., and Xiong, Z. (2017). An extended technology acceptance model for mobile social gaming service popularity analysis. Mobile Information Systems, Article ID 3906953.
  8. Chen, P. S., Lin, Y. J., and Peng, N. C. (2016). A two-stage method to determine the allocation and scheduling of medical staff in uncertain environments. Computers & Industrial Engineering, 99, 174-188.
  9. Chen, P. S., Yu, C. J., and Chen, G. Y. H. (2015). Applying task-technology fit model to the healthcare sector: A case study of hospitals' computed tomography patient-referral mechanism. Journal of Medical Systems, 39(8), 80-93.
  10. D'Ambra, J., Wilson, C. S., and Akter, S. (2013). Application of the task-technology fit model to structure and evaluate the adoption of e-books by academics. Journal of the American Society for Information Science and Technology, 64(1), 48-64.
  11. Davis, F. D., Bagozzi, R. P., and Warshaw, P. R. (1989). User acceptance of computer-technology - a comparison of 2 theoretical-models. Management Science, 35(8), 982-1003.
  12. Dishaw, M. T., and Strong, D. M. (1999). Extending the technology acceptance model with task-technology fit constructs. Information & Management, 36(1), 9-21.
  13. Goodhue, D. L. (1995). Understanding user evaluations of information systems. Management Science, 41(12), 1827-1844.
  14. Goodhue, D. L. (1998). Development and measurement validity of a task-technology fit instrument for user evaluations of information systems. Decision Sciences, 29(1), 105-138.
  15. Goodhue, D. L., and Thompson, R. L. (1995). Task-technology fit and individual-performance. Mis Quarterly, 19(2), 213-236.
  16. Jin, S. H., Yun, H. Y., Jeong, S. J., and Kim, K. S. (2017). Hybrid and cooperative strategies using harmony search and artificial immune systems for solving the nurse rostering problem. Sustainability, 9(7), 1090-1108.
  17. Kennedy, J., and Eberhart, R. C. (1995). Particle swarm optimization. IEEE International Conference on Neural Networks, 1942-1948.
  18. Lin, H. C. (2017). Nurses' satisfaction with using nursing information systems from technology acceptance model and information systems success model perspectives a reductionist approach. Computers Informatics Nursing, 35(2), 91-99.
  19. Lin, W. S., and Wang, C. H. (2012). Antecedences to continued intentions of adopting e-learning system in blended learning instruction: A contingency framework based on models of information system success and task-technology fit. Computers & Education, 58(1), 88-99.
  20. Mischek, F., and Musliu, N. (2019). Integer programming model extensions for a multi-stage nurse rostering problem. Annals of Operations Research, 275(1), 123-143.
  21. Nadri, H., Rahimi, B., Afshar, H. L., Samadbeik, M., and Garavand, A. (2018). Factors affecting acceptance of hospital information systems based on extended technology acceptance model: A case study in three paraclinical departments. Applied Clinical Informatics, 9(2), 238-247.
  22. Ngai, E. W. T., Poon, J. K. L., and Chan, Y. H. C. (2007). Empirical examination of the adoption of WebCT using TAM. Computers & Education, 48(2), 250-267.
  23. Rahimian, E., Akartunali, K., and Levine, J. (2017). A hybrid Integer programming and variable neighbourhood search algorithm to solve nurse rostering problems. European Journal of Operational Research, 258(2), 411-423.
  24. Rajeswari, M., Amudhavel, J., Pothula, S., and Dhavachelvan, P. (2017). Directed bee colony optimization algorithm to solve the nurse rostering problem. Computational Intelligence and Neuroscience, Article ID 6563498.
  25. Todorovic, N., and Petrovic, S. (2013). Bee colony optimization algorithm for nurse rostering. IEEE Transactions on Systems Man Cybernetics-Systems, 43(2), 467-473.
  26. Valouxis, C., Gogos, C., Goulas, G., Alefragis, P., and Housos, E. (2012). A systematic two phase approach for the nurse rostering problem. European Journal of Operational Research, 219(2), 425-433.
  27. Wang, J., and Li, D. (2019). Task scheduling based on a hybrid heuristic algorithm for smart production line with fog computing. Sensors, 19(5), 1023-1040.
  28. Wu, T. H., Yeh, J. Y., and Lee, Y. M. (2015). A particle swarm optimization approach with refinement procedure for nurse rostering problem. Computers & Operations Research, 54, 52-63.
  29. Xu, S. Z. (2019). A petri net-based hybrid heuristic scheduling algorithm for flexible manufacturing system. International Journal of Simulation Modelling, 18(2), 325-334.
  30. Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NICSO 2010), 284, 65-74.
  31. Yu, T. K., and Yu, T. Y. (2010). Modelling the factors that affect individuals' utilisation of online learning systems: An empirical study combining the task technology fit model with the theory of planned behaviour. British Journal of Educational Technology, 41(6), 1003-1017.
  32. Zheng, Z. R., Liu, X. Y., and Gong, X. J. (2017). A simple randomized variable neighbourhood search for nurse rostering. Computers & Industrial Engineering, 110, 165-174.
  33. 曾智揚,建構啟發式演算法求解有軟硬限制之最佳化問題:以醫護人員排班問題為例,中原大學工業與系統工程學系碩士論文,2017。