An evaluation of the risk factors associated with implementing projects of health information technology by fuzzy combined ANP-DEMATEL

Background Application of a Clinical Information System (CIS) like Electronic Patient Record (EPR), PACS system and CPOE has turned into one of the most important criteria of priorities of health care systems. The aims of the clinical information system include improving the physicians’ efficiency level, integrating the caring process, and expanding the fuzzy quality of the services offered to patients. Achievement of these benefits in reality is not an easy task, and there are lots of plans in this field which are doomed to failure. About 50% of the implementation plans of clinical information systems in health care organizations have failed, and this trend is significantly affecting industrial countries. Proper implementation of hospital information systems lies in identifying and assessing the relationships among the most important risk factors of fuzzy. The present study aimed to provide an applicable model for identifying, ranking and evaluating the risk factors associated with projects of clinical information technology in hospitals of Shiraz University of Medical Sciences. Method This is an applied study which evaluates the risk factors associated with implementation of clinical information technology projects in hospitals of Shiraz Medical Sciences University. The participants consisted of professionals and senior experts of clinical information technology. Fuzzy logic was used in this study. We also applied ANP-DEMATEL combined model with fuzzy procedure to provide the analytic model of the study Results According to the study findings, lack of top-executive supports, and unstable organizational environment were the two most important risk factors, while the main organizational factors and technology were also highly important. In addition, the factors associated with technology had the highest influence on the other studied risk factors. Conclusion Hospital authorities can benefit from this proposed model to reduce the risk of implementing the projects of clinical information technology and improve the success coefficient of the risk of such projects.

[1]  M. Asgharpour,et al.  Evaluation of the government's support policies for the pharmaceutical industry in the midst of sanctions and the covid-19 pandemic , 2022, Journal of Health Administration.

[2]  E. Supriyanto,et al.  Evaluation of Factors to Respond to the COVID-19 Pandemic Using DEMATEL and Fuzzy Rule-Based Techniques , 2021, Int. J. Fuzzy Syst..

[3]  R. Alizadeh,et al.  Challenges in Creating Business Value from Health Information Systems (HIS): A Hybrid Fuzzy Approach , 2021 .

[4]  U. Hübner,et al.  The Effect of Innovation Capabilities of Health Care Organizations on the Quality of Health Information Technology: Model Development With Cross-sectional Data , 2021, JMIR medical informatics.

[5]  Jeffrey Soar,et al.  An Exploratory Study of the Readiness of Public Healthcare Facilities in Developing Countries to Adopt Health Information Technology (HIT)/e-Health: the Case of Ghana , 2020, Journal of Healthcare Informatics Research.

[6]  A.S. Laar,et al.  Assessment of mobile health technology for maternal and child health services in rural Upper West Region of Ghana. , 2019, Public health.

[7]  Navid Sahebjamnia,et al.  An enhanced risk assessment framework for business continuity management systems , 2016 .

[8]  Paul N. Gorman,et al.  Barriers and facilitators to exchanging health information: a systematic review , 2016, Int. J. Medical Informatics.

[9]  Karmen S. Williams,et al.  Characteristics of Local Health Departments Associated with Implementation of Electronic Health Records and other Informatics Systems , 2016, Public health reports.

[10]  Özer Uygun,et al.  An integrated DEMATEL and Fuzzy ANP techniques for evaluation and selection of outsourcing provider for a telecommunication company , 2015, Comput. Ind. Eng..

[11]  Mehrbakhsh Nilashi,et al.  Prioritizing critical factors to successful adoption of total hospital information system , 2015 .

[12]  Slobodan Zecevic,et al.  A novel hybrid MCDM model based on fuzzy DEMATEL, fuzzy ANP and fuzzy VIKOR for city logistics concept selection , 2014, Expert Syst. Appl..

[13]  Tsu-Ming Yeh,et al.  Factors in determining wind farm location: Integrating GQM, fuzzy DEMATEL, and ANP , 2014 .

[14]  P. Ray,et al.  Health Care Provider Adoption of eHealth: Systematic Literature Review , 2013, Interactive journal of medical research.

[15]  Gülçin Büyüközkan,et al.  A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers , 2012, Expert Syst. Appl..

[16]  Cengiz Kahraman,et al.  Fuzzy Multi-Criteria Decision Making: Theory and Applications with Recent Developments , 2008 .

[17]  Guy Paré,et al.  Prioritizing Clinical Information System Project Risk Factors: A Delphi Study , 2008, Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008).

[18]  Guy Paré,et al.  Research Paper: A Risk Assessment of Two Interorganizational Clinical Information Systems , 2006, J. Am. Medical Informatics Assoc..

[19]  Cengiz Kahraman,et al.  Project risk evaluation using a fuzzy analytic hierarchy process: An application to information technology projects: Research Articles , 2006 .

[20]  Cengiz Kahraman,et al.  Project risk evaluation using a fuzzy analytic hierarchy process: An application to information technology projects , 2006, Int. J. Intell. Syst..

[21]  J D Kleinke,et al.  Dot-gov: market failure and the creation of a national health information technology system. , 2005, Health affairs.

[22]  J. Marc Overhage,et al.  Viewpoint Paper: Communities' Readiness for Health Information Exchange: The National Landscape in 2004 , 2005, J. Am. Medical Informatics Assoc..

[23]  Peter E. D. Love,et al.  Management of risks in information technology projects , 2004, Ind. Manag. Data Syst..

[24]  Mark Keil,et al.  How Software Project Risk Affects Project Performance: An Investigation of the Dimensions of Risk and an Exploratory Model , 2004, Decis. Sci..

[25]  C. K. Barsukiewicz,et al.  Electronic medical records: are physicians ready? , 1999, Journal of healthcare management / American College of Healthcare Executives.

[26]  Kalle Lyytinen,et al.  A framework for identifying software project risks , 1998, CACM.

[27]  P Starr,et al.  Smart technology, stunted policy: developing health information networks. , 1997, Health affairs.

[28]  Gwo-Hshiung Tzeng,et al.  TRANSPORTATION INVESTMENT PROJECT SELECTION WITH FUZZY MULTIOBJECTIVES , 1993 .

[29]  Thomas L. Saaty,et al.  Decision Making, Scaling, and Number Crunching , 1989 .

[30]  Muhammad Tariq Majeed,et al.  Do information and communication technologies (ICTs) contribute to health outcomes? An empirical analysis , 2018, Quality & Quantity.

[31]  Luis Villa,et al.  A literature review for large-scale health information system project planning, implementation and evaluation , 2017, Int. J. Medical Informatics.

[32]  Jorge Vareda Gomes,et al.  Successful IS/IT Projects in Healthcare: Pretesting a Questionnaire , 2016, CENTERIS/ProjMAN/HCist.

[33]  Adam Alami,et al.  Why Do Information Technology Projects Fail? , 2016, CENTERIS/ProjMAN/HCist.

[34]  Philbert Nduwimfura,et al.  A Review of Risk Management for Information Systems Outsourcing , 2016 .

[35]  Basit Shahzad,et al.  Identification of Risk Factors in Large Scale Software Projects: A Quantitative Study , 2014, Int. J. Knowl. Soc. Res..

[36]  Selçuk Çebi,et al.  A fuzzy risk assessment model for hospital information system implementation , 2012, Expert Syst. Appl..

[37]  Ming-Lang Tseng,et al.  Evaluation of customer perceptions on airline service quality in uncertainty , 2011 .

[38]  Robbie T. Nakatsu,et al.  A comparative study of important risk factors involved in offshore and domestic outsourcing of software development projects: A two-panel Delphi study , 2009, Inf. Manag..

[39]  M. Jaana,et al.  Prioritizing the Risk Factors Influencing the Success of Clinical Information System Projects , 2008, Methods of Information in Medicine.

[40]  Cengiz Kahraman,et al.  Fuzzy Multi-Criteria Decision Making , 2008 .

[41]  Sun-Jen Huang,et al.  An empirical analysis of risk components and performance on software projects , 2007, J. Syst. Softw..

[42]  J Brender,et al.  Factors Influencing Success and Failure of Health Informatics Systems , 2006, Methods of Information in Medicine.

[43]  Michael F. Chiang,et al.  Software engineering risk factors in the implementation of a small electronic medical record system: the problem of scalability , 2002, AMIA.

[44]  J. Mann.  Identifying and Explaining Risk Factors Associated with Information Systems Projects in Thailand : A Model and Research Propositions , 2002 .

[45]  F. Payton,et al.  Interorganizational health care systems implementations: an exploratory study of early electronic commerce initiatives. , 2001, Health care management review.

[46]  B. Kaplan,et al.  Culture counts: how institutional values affect computer use. , 2000, M.D. computing : computers in medical practice.

[47]  K. Pemble.  Regional health information networks: the Wisconsin Health Information Network, a case study. , 1994, Proceedings. Symposium on Computer Applications in Medical Care.