Evaluation of Failure Causes in Employing Hospital Information Systems
محورهای موضوعی : Business Strategy
Hossein Sayyadi Tooranloo
1
,
Sepideh Saghafi
2
,
Arezoo Sadat Ayatollah
3
1 - Department of Management, Meybod University, Meybod, Iran
2 - Department of Public Adminstration, University of Tehran Kish International campus, Tehran, Iran
3 - Department of Information Technology Management, University of Science & Art,Yazd, Iran
کلید واژه: Failure mode and effects analysis (FMEA), Hospital Information Systems (HIS), Intuitionistic Fuzzy,
چکیده مقاله :
Today, the information systems play a critical role in business for each organization. Like other organizations, hospitals use information systems for data collection, data storage, data processing and the like to have long-term and short-term achievements. Despite the very benefits of implementing HIS and its costly implementation, the HIS project sometimes fails. The importance of the HIS failure and preventive practices in this regard have led researchers investigate the causes of failure for information systems in hospitals. In this paper, an FMEA-based model is presented in an intuitionisticfuzzy environment to evaluate the HIS failure factors. For this purpose, Data required to implement the proposed model were collected in 5 hospital, in Kerman (Iran). Based on research studies and survey of hospital academic experts, a total number of 27 failure modes were determined for the implementation HIS. The results of the proposed approach indicated that 8 factors are of paramount importance in terms of HIS failure causes: Individuals' lack of skill/knowledge, lack of integration between system and organizational activities, unrealistic planning, lack of IT management or weak project team (information system), improper software development, lack of managerial skills, misdiagnosis of roles and responsibilities, inconsistency between corporate culture and change requirements (compatibility).
Ahmadi, H., Nilashi, M., & Ibrahim, O. (2015). Organizational decision to adopt hospital information system: An empirical investigation in the case of Malaysian public hospitals. International journal of medical informatics, 84(3), 166-188.
Alcantud, J. C. R., Khameneh, A. Z., & Kilicman, A. (2020). Aggregation of infinite chains of intuitionistic fuzzy sets and their application to choices with temporal intuitionistic fuzzy information. Information Sciences, 514, 106-117.
Amin, I. M., Hussein, S. S., & Isa, W. A. R. W. M. (2011). Assessing user satisfaction of using hospital information system (HIS) in Malaysia. People, 12, 13.
Ammenwerth, E., Brender, J., Nykänen, P., Prokosch, H.-U., Rigby, M., & Talmon, J. (2004). Visions and strategies to improve evaluation of health information systems: Reflections and lessons based on the HIS-EVAL workshop in Innsbruck. International journal of medical informatics, 73(6), 479-491.
Ammenwerth, E., Ehlers, F., Hirsch, B., & Gratl, G. (2007). HIS-Monitor: An approach to assess the quality of information processing in hospitals. International journal of medical informatics, 76(2), 216-225.
Atanassov, K. T. (1983). Intuitionistic Fuzzy sets, in: VII ITKR’s Session, Sofia, Bulgarian.
Atanassov, K. (1999). Intuitionistic Fuzzy Sets (Physica-Verlag, Heidelberg, New York).
Atanassov, K., & Gargov, G. (1989). Interval valued intuitionistic fuzzy sets. Fuzzy sets and systems, 31(3), 343-349.
Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy sets and systems, 20(1), 87-96.
Atanassov, K. T. (1989). More on intuitionistic fuzzy sets. Fuzzy sets and systems, 33(1), 37-45.
Atanassov, K. T. (2000). Two theorems for intuitionistic fuzzy sets. Fuzzy sets and systems, 110(2), 267-269.
Balaraju, J., Raj, M. G., & Murthy, C. S. (2019). Fuzzy-FMEA risk evaluation approach for LHD machine–A case study. Journal of Sustainable Mining, 18(4), 257-268.
Ban, A. I. (2006). Nearest interval approximation of an intuitionistic fuzzy number Computational Intelligence, Theory and Applications (pp. 229-240): Springer.
Barki, H., Rivard, S., & Talbot, J. (1993). Toward an assessment of software development risk. Journal of management information systems, 10(2), 203-225.
Beuscart-Zéphir, M.-C., Anceaux, F., Crinquette, V., & Renard, J.-M. (2001). integrating users’ activity modeling in the design and assessment of hospital electronic patient records: the example of anesthesia. International journal of medical informatics, 64(2), 157-171.
Boehm, B. W. (1991). Software risk management: principles and practices. IEEE software, 8(1), 32-41.
Boran, F. E., Genç, S., Kurt, M., & Akay, D. (2009). A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Systems with Applications, 36(8), 11363-11368.
Borzekowski, R. (2009). Measuring the cost impact of hospital information systems: 1987–1994. Journal of health economics, 28(5), 938-949.
Bowles, J. B., & Peláez, C. E. (1995). Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliability Engineering & System Safety, 50(2), 203-213.
Braglia, M., Frosolini, M., & Montanari, R. (2003). Fuzzy criticality assessment model for failure modes and effects analysis. International Journal of Quality & Reliability Management, 20(4), 503-524.
Breton, M., Lamothe, L., & Denis, J.-L. (2014). How healthcare organisations can act as institutional entrepreneurs in a context of change. Journal of health organization and management, 28(1), 77-95.
Buhaescu, T. (1989). Some observations on intuitionistic fuzzy relations. Paper presented at the Intimerat Seminar on Functional Equations.
Carvalho, J. V., Rocha, Á., van de Wetering, R., & Abreu, A. (2019). A Maturity model for hospital information systems. Journal of Business Research, 94, 388-399.
Chanamool, N., & Naenna, T. (2016). Fuzzy FMEA application to improve decision-making process in an emergency department. Applied Soft Computing, 43, 441-453.
Chang, C.-L., Wei, C.-C., & Lee, Y.-H. (1999). Failure mode and effects analysis using fuzzy method and grey theory. Kybernetes, 28(9), 1072-1080.
Chang, C.L., Wei, C.C. & Lee, Y.H., (2001). Failure mode and effects analysis using fuzzy method and grey theory, Integr. Manuf. Syst. 12(3), 211–216.
Chatzoglou, P. D., Vraimaki, E., Diamantidis, A., & Sarigiannidis, L. (2010). Computer acceptance in Greek SMEs. Journal of Small Business and Enterprise Development, 17(1), 78-101.
Chen, L.H. & Ko, W.C. (2007). Fuzzy linear programming models for new product design using QFD with FMEA, Appl. Math. Modell.
Chen, R.-F., & Hsiao, J.-L. (2012). an investigation on physicians’ acceptance of hospital information systems: a case study. International journal of medical informatics, 81(12), 810-820.
Chen, T.-Y., & Li, C.-H. (2011). Objective weights with intuitionistic fuzzy entropy measures and computational experiment analysis. Applied Soft Computing, 11(8), 5411-5423.
Daniela, R., & Dospinescu, O. (2004). The Adoption Electronic Banking Services in Developing Countries. Department of Business Information Systems, 20-35.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management science, 35(8), 982-1003.
Deschrijver, G., Cornelis, C., & Kerre, E. E. (2004). On the representation of intuitionistic fuzzy t-norms and t-conorms. IEEE Transactions on fuzzy systems, 12(1), 45-61.
Deschrijver, G., & Kerre, E. (2002). On the relationship between intuitionistic fuzzy sets and some other extensions of fuzzy set theory. Journal of Fuzzy Mathematics, 10(3), 711-725.
Engin, M., & Gürses, F. (2019). Adoption of Hospital Information Systems in Public Hospitals in Turkey: An Analysis with the Unified Theory of Acceptance and Use of Technology Model. International Journal of Innovation and Technology Management, 16(06), 1950043.
Ewusi-Mensah, K. (1997). Critical issues in abandoned information systems development projects. Communications of the ACM, 40(9), 74-80.
Flowers, S. (1996). Software failure. Management Failure.
Ford, E. W., Menachemi, N., Huerta, T. R., & Yu, F. (2010). Hospital IT adoption strategies associated with implementation success: implications for achieving meaningful use. Journal of Healthcare Management, 55(3), 175-188.
GARCIA, P. A., A., SCHIRRU, R., FRUTUOSO EMelo, P., F. A (2005). Fuzzy data environment analysis approach for FMEA, Progress in Nuclear Energy, 46(3-4), 359-373.
Gartner, D., Zhang, Y., & Padman, R. (2018). Cognitive workload reduction in hospital information systems. Health care management science, 21(2), 224-243.
Gau, W.-L., & Buehrer, D. J. (1993). Vague sets. IEEE transactions on systems, man, and cybernetics, 23(2), 610-614.
Gupta, A. (2007). Modern trends in planning and designing of hospitals: Principles and practice. Indian Journal of Medical Research, 126(2), 167-169.
Hamborg, K.-C., Vehse, B., & Bludau, H.-B. (2004). Questionnaire based usability evaluation of hospital information systems. Electronic journal of information systems evaluation, 7(1), 21-30.
Hamidfar, M. (2008). Adoption of electronic patient records by Iranian hospitals staff.
Handayani, P. W., Hidayanto, A. N., Ayuningtyas, D., & Budi, I. (2016). Hospital information system institutionalization processes in indonesian public, government-owned and privately owned hospitals. International journal of medical informatics, 95, 17-34.
He, X., Li, Y., Qin, K., & Meng, D. (2020). Distance measures on intuitionistic fuzzy sets based on intuitionistic fuzzy dissimilarity functions. Soft Computing, 24(1), 523-541.
Ireson, W. G., Coombs, C. F., & Moss, R. Y. (1995). Handbook of reliability engineering and management, 2nd ed.: McGraw-Hill Professional, New York, n. Y.
Ismail, A., Jamil, A. T., Rahman, A. F. A., Bakar, J. M. A., Saad, N. M., & Saadi, H. (2010). The implementation of Hospital Information System (HIS) in tertiary hospitals in malaysia: a qualitative study. Malaysian Journal of Public Health Medicine, 10(2), 16-24.
Ismail, N. I., Abdullah, N. H., & Shamsuddin, A. (2015). Adoption of hospital information system (HIS) in Malaysian public hospitals. Procedia-Social and Behavioral Sciences, 172, 336-343.
Jiang, J. J., & Klein, G. (1999). Information system project-selection criteria variations within strategic classes. IEEE Transactions on Engineering Management, 46(2), 171-176.
Jiang, W., Xie, C., Zhuang, M., & Tang, Y. (2017). Failure mode and effects analysis based on a novel fuzzy evidential method. Applied Soft Computing, 57, 672-683.
Joshi, M., & Nash, D. B. (2005). The healthcare quality book: Vision, strategy, and tools: Foundation of the Amer College.
Kappelman, L. A., McKeeman, R., & Zhang, L. (2006). Early warning signs of IT project failure: The dominant dozen. Information systems management, 23(4), 31-36.
Keil, M., Cule, P. E., Lyytinen, K., & Schmidt, R. C. (1998). A framework for identifying software project risks. Communications of the ACM, 41(11), 76-83.
Kensing, F., Sigurdardottir, H., & Stoop, A. (2007). MUST-a participatory method for designing sustainable health IT. Paper presented at the Medinfo 2007: Proceedings of the 12th World Congress on Health (Medical) Informatics; Building Sustainable Health Systems.
Keskin, G.A. and Özkan, C. (2009), “An alternative evaluation of FMEA: fuzzy ART algorithm”, QUality and Reliability Engineering International, Vol. 25 No. 6, pp. 647-661.
Khalifa, M., & Alswailem, O. (2015). Hospital Information Systems (HIS) Acceptance and Satisfaction: A Case Study of a Tertiary Care Hospital. Procedia Computer Science, 63, 198-204.
Khajouei, R., Abbasi, R., & Mirzaee, M. (2018). Errors and causes of communication failures from hospital information systems to electronic health record: A record-review study. International journal of medical informatics, 119, 47-53.
Kimiafar, K., Moradi, G., Sadoughi, F., & Hosseini, F. (2007). A study on the user's views on the quality of teaching hospitals information system of Mashhad University of Medical Sciences-2006. Journal of Health Administration, 10(29), 31-36.
Krawczak, M., & Szkatuła, G. (2020). On matching of intuitionistic fuzzy sets. Information Sciences, 517, 254-274.
Kumar, P. S. (2020). Algorithms for solving the optimization problems using fuzzy and intuitionistic fuzzy set. International Journal of System Assurance Engineering and Management, 11(1), 189-222.
Kumru, M., & Kumru, P. Y. (2013). Fuzzy FMEA application to improve purchasing process in a public hospital. Applied Soft Computing, 13(1), 721-733.
Kutlu, A. C., & Ekmekçioğlu, M. (2012). Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert Systems with Applications, 39(1), 61-67.
Lærum, H., Karlsen, T. H., & Faxvaag, A. (2004). Use of and attitudes to a hospital information system by medical secretaries, nurses and physicians deprived of the paper-based medical record: a case report. BMC medical informatics and decision making, 4(1), 18.
Lapczynski, P. H. (2004). An integrated model of technology acceptance for mobile computing: Pace University.
Laudon, K., & Laudon, J. (2001). Information systems management: organization and technology. 7a. Edicion, Prentice Hall.
Lee, H. W., Ramayah, T., & Zakaria, N. (2012). External factors in hospital information system (HIS) adoption model: a case on Malaysia. Journal of medical systems, 36(4), 2129-2140.
Lee, T.-T., Mills, M. E., Bausell, B., & Lu, M.-H. (2008). Two-stage evaluation of the impact of a nursing information system in Taiwan. International journal of medical informatics, 77(10), 698-707.
Li, Y., Shan, Y., Liu, P. (2015). An extended TODIM method for group decision making with the interval intuitionistic fuzzy sets. Math. Probl. Eng. Article ID 672140.
Littlejohns, P., Wyatt, J. C., & Garvican, L. (2003). Evaluating computerised health information systems: hard lessons still to be learnt. Bmj, 326(7394), 860-863.
Liu, C.-T., Yang, P.-T., Yeh, Y.-T., & Wang, B.-L. (2006). The impacts of smart cards on hospital information systems—An investigation of the first phase of the national health insurance smart card project in Taiwan. International journal of medical informatics, 75(2), 173-181.
Liu, H.-C., Liu, L., Liu, N., & Mao, L.-X. (2012). Risk evaluation in failure mode and effects analysis with extended VIKOR method under fuzzy environment. Expert Systems with Applications, 39(17), 12926-12934.
Lyytinen, K., Mathiassen, L., & Ropponen, J. (1998). Attention shaping and software risk—a categorical analysis of four classical risk management approaches. Information Systems Research, 9(3), 233-255.
Mangeli, M., Shahraki, A., & Saljooghi, F. H. (2019). Improvement of risk assessment in the FMEA using nonlinear model, revised fuzzy TOPSIS, and support vector machine. International Journal of Industrial Ergonomics, 69, 209-216.
Mbananga, N., Madale, R., & Becker, P. (2002). Evaluation of hospital information system in the Northern Province in South Africa. Durban: Health Systems Trust.
McFarlan, F. W. (1981). Portfolio approach to information-systems. Harvard business review, 59(5), 142-150.
McDermott, R., Mikulak, R. J., & Beauregard, M. (1996). The basics of FMEA: SteinerBooks.
McGonigle, D., & Mastrian, K. (2014). Nursing informatics and the foundation of knowledge: Jones & Bartlett Publishers.
Meli, P. L. (2008). Perspectives of health information management faculty use of an e-learning laboratory and technology acceptance: University of Central Florida.
Mirghafoori, S. H., Tooranloo, H. S., & Saghafi, S. (2020). Diagnosing and routing electronic service quality improvement of academic libraries with the FMEA approach in an intuitionistic fuzzy environment. The Electronic Library.
Mohanty, R., Rana, S., & Kolay, S. (1999). Hospital information system in medicare:an experience at Tata Main Hospital Jamshedpur. Indian Journal of Occupational and Environmental Medicine, 3(4), 187-190.
Mozaffar, H., Williams, R., Cresswell, K., & Sheikh, A. (2018). Anglicization of hospital information systems: Managing diversity alongside particularity. International journal of medical informatics, 119, 88-93.
Murray-Webster, R., & Thiry, M. (2000). Managing programmes of projects. Gower handbook of project management, 3, 47-64.
Nauman, A. B., Aziz, R., & Ishaq, A. (2005). Information systems development failure: a case study to highlight the IS development complexities in simple, low risk projects in developing countries. Paper presented at the The Second International Conference on Innovations in Information Technology. Dubai: UAE University.
Ngan, R. T., Ali, M., Tamir, D. E., Rishe, N. D., & Kandel, A. (2020). Representing complex intuitionistic fuzzy set by quaternion numbers and applications to decision making. Applied Soft Computing, 87, 105961.
Nilashi, M., Ahmadi, H., Ahani, A., Ravangard, R., & bin Ibrahim, O. (2016). Determining the importance of hospital information system adoption factors using fuzzy analytic network process (ANP). Technological Forecasting and Social Change, 111, 244-264.
Özogul, C. O., Karsak, E. E., & Tolga, E. (2009). A real options approach for evaluation and justification of a hospital information system. Journal of Systems and Software, 82(12), 2091-2102.
Park, J. H., Cho, H. J., & Kwun, Y. C. (2013). Extension of the VIKOR method to dynamic intuitionistic fuzzy multiple attribute decision making. Computers & Mathematics with Applications, 65(4), 731-744.
Park, J. H., Park, I. Y., Kwun, Y. C., & Tan, X. (2011). Extension of the TOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environment. Applied Mathematical Modelling, 35(5), 2544-2556.
Qi, X.-W., Liang, C.-Y., Cao, Q.-W., Ding, Y. (2011). Automaticcon vergent approach in interval valued intuitionistic fuzzy multi-attribute group decision making. J. Syst. Eng. Electron, 33(1), 110–115.
Qin, J., Xi, Y., & Pedrycz, W. (2020). Failure mode and effects analysis (FMEA) for risk assessment based on interval type-2 fuzzy evidential reasoning method. Applied Soft Computing, 89, 106134.
Rafie, M., & Namin, F. S. (2015). Prediction of subsidence risk by FMEA using artificial neural network and fuzzy inference system. International Journal of Mining Science and Technology, 25(4), 655-663.
Ratnaningtyas, D. D., & Surendro, K. (2013). Information quality improvement model on hospital information system using Six Sigma. Procedia Technology, 9, 1166-1172.
Reichertz, P. L. (2006). Hospital information systems—Past, present, future. International journal of medical informatics, 75(3), 282-299.
Robertson, I. D., & Saveraid, T. (2008). Hospital, radiology, and picture archiving and communication systems. Veterinary radiology & ultrasound, 49(s1).
Safa’a, I. H. (2012). Critical Risk Factors for Information System (IS) Projects between Sink and Swim (Vol. 2, pp. 1270-1279): IJCSET.
Salahuddin, L., Ismail, Z., Hashim, U. R., Ismail, N. H., Raja Ikram, R. R., Abdul Rahim, F., & Hassan, N. H. (2020). Healthcare practitioner behaviours that influence unsafe use of hospital information systems. Health informatics journal, 26(1), 420-434.
Samra, H., Li, A., Soh, B., & Zain, M. A. (2020). Utilisation of hospital information systems for medical research in Saudi Arabia: A mixed-method exploration of the views of healthcare and IT professionals involved in hospital database management systems. Health Information Management Journal, 49(2-3), 117-126.
Samy, G. N., Ahmad, R., & Ismail, Z. (2009). Threats to health information security. Paper presented at the Information Assurance and Security, 2009. IAS'09. Fifth International Conference on Information Assurance and Security.
Schmidt, R., Lyytinen, K., & Mark Keil, P. C. (2001). Identifying software project risks: An international Delphi study. Journal of management information systems, 17(4), 5-36.
Segismundo, A., & Augusto Cauchick Miguel, P. (2008). Failure mode and effects analysis (FMEA) in the context of risk management in new product development: A case study in an automotive company. International Journal of Quality & Reliability Management, 25(9), 899-912.
Sligo J, Gauld R, Roberts V, Villa L. A literature review for large-scale health information system project planning, implementation and evaluation. International journal of medical informatics. 2017; 97: 86-97.
Shortliffe, E. H., & Barnett, G. O. (2014). Biomedical data: Their acquisition, storage, and use Biomedical informatics (pp. 39-66): Springer.
Stoyanova, D. (1993). More on Cartesian product over intuitionistic fuzzy sets. BUSEFAL, 54, 9-13.
Sulaiman, H., & Wickramasinghe, N. (2014). Assimilating Healthcare Information Systems in a Malaysian Hospital. CAIS, 34, 77.
Szmidt, E., & Kacprzyk, J. (2000). Distances between intuitionistic fuzzy sets. Fuzzy sets and systems, 114(3), 505-518.
Szmidt, E., & Kacprzyk, J. (2001). Entropy for intuitionistic fuzzy sets. Fuzzy sets and systems, 118(3), 467-477.
Thakare, V., & Khire, G. (2014). Role of emerging technology for building smart hospital information system. Procedia Economics and Finance, 11, 583-588.
Triantaphyllou, E., Shu, B., Sanchez, S. N., & Ray, T. (1998). Multi-criteria decision making: an operations research approach. Encyclopedia of electrical and electronics engineering, 15(1998), 175-186.
Vegoda, P. R. (1987). Introduction to hospital information systems. International journal of clinical monitoring and computing, 4(2), 105-109.
Wang, Y.-M., Chin, K.-S., Poon, G. K. K., & Yang, J.-B. (2009). Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean. Expert Systems with Applications, 36(2), 1195-1207.
Wu, J.-Z., & Zhang, Q. (2011). Multicriteria decision making method based on intuitionistic fuzzy weighted entropy. Expert Systems with Applications, 38(1), 916-922.
Xu, Z. (2007). Intuitionistic fuzzy aggregation operators. IEEE Transactions on fuzzy systems, 15(6), 1179-1187.
Yardley, D. (2002). How to Ensure Your Next It Project Is a Success: Learning the Lessons of Project Failure.
Yucel, G., Cebi, S., Hoege, B., & Ozok, A. F. (2012). A fuzzy risk assessment model for hospital information system implementation. Expert Systems with Applications, 39(1), 1211-1218.
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
Zhang, Q.-s., Jiang, S., Jia, B., & Luo, S. (2010). Some information measures for interval-valued intuitionistic fuzzy sets. Information Sciences, 180(24), 5130-5145.
Zhang, H., Xie, J., Song, Y., Ge, J., & Zhang, Z. (2020). A novel ranking method for intuitionistic fuzzy set based on information fusion and application to threat assessment. Iranian Journal of Fuzzy Systems, 17(1), 91-104.
Zhao, X., & Wei, G. (2013). Some intuitionistic fuzzy Einstein hybrid aggregation operators and their application to multiple attribute decision making. Knowledge-Based Systems, 37, 472-479.
Zmud, R. W. (1980). Management of large software development efforts. MIS quarterly, 45-55.
