Data Mining technology as a tool for supporting analytical decision making process in Health Information Management System (HIMS)
DOI:
https://doi.org/10.54536/ajaset.v5i2.98Keywords:
Hospital, HIMS, Data Mining, DMHIMSAbstract
Wide spread use of information system in the delivery of managed healthcare system and the challenges of identifying and disseminating relevant healthcare information, complex and diverse data and knowledge forms and tasks coupled with the prevalence of legacy systems require automated approaches for effective and efficient utilization of massive amount of data to support in strategic planning and decision-making and assist the strategic management mechanisms. Despite the fact that data mining is progressively used in information systems as a technology to support analytical decision making, it is however still barely used in hospital information system to support analytical decision making process. Hence, this paper presents the usefulness of data mining technology in Hospital Information Management System (HIMS). Data mining technology offered capabilities to increase the productivity of medical personnel, analyze care outcomes, lower healthcare costs, improve healthcare quality by using fast and better clinical decision making and generally assist the strategic management mechanisms.
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References
Abraham, C., Nishihara, E. and Akiyama. M. (2011). Transforming Healthcare with Information
Technology in Japan: A Review of Policy, People, and Progress. International Journal
of Medical Informatics,80 (3): 157-170.
Aggarwal, N., Kumar, A., Khatter, H. and Aggarwal, V. (2012). Analysis the effect of data
mining techniques on database. Advances in Engineering Software, 47(1): 164-169.
Buntin, M.B., Burke, M. F., Hoaglin, M. C. and Blumenthal, D. (2011). The Benefits of
HealthInformation Technology: A Review of the Recent Literature Shows
Predominantly Positive Results. Health Affairs,30 (3): 464-471.
Chiasson, M.W. and Davidson, E. (2005). Taking Industry Seriously in Information
SystemsResearch. MIS Quarterly, 29 (4): 591-605.
Claver, E., González, R. and Llopis, J. (2000). An Analysis of Research in Information
Systems(1981–1997). Information & Management, 37 (4): 181-195.
Dutta, A, and Heda, S (2000). Information systems architecture to support managed care
business process. Decision Support Systems, 30: 217-225.
Goksen, Y., Eminagaoglu, M. and Dogan, O. (2011). Data Mining in Medical Records for the
Enhancement of Strategic Decisions: A Case Study. Scientific Bulletin – Economic
Sciences, 10(1): 135-144.
Gorunescu, F. (2011). Data Mining: Concepts, Models, and Techniques. India: Springer.
Guleria, P. and Sood, M. (2014). Data Mining in Education: A Review on the Knowledge Discovery
Perspective. International Journal of Data Mining & Knowledge Management Process, 4(5):
- 60.
Hastie, T., Tibshirani, R. and Friedman, J. (2009). The elements of statistical learning: Data mining,
inference, and prediction. Second Edition. Springer, New York, USA.
Haux, R. (2004). Strategic Information Management in Hospitals: An Introduction to Hospital
Information Systems. New York, NY, USA: Springer Verlag.
Haux, R. (2006). Health Information Systems - Past, Present, Future. International Journal of
Medical Informatics,75 (3-4): 268-281.
Health Information and Management Systems Society (2008). Enabling Healthcare Reform using
Information Technology. http://www.himss.org.
Khademolqorani, S. and Hamadani, A. Z. (2013). An Adjusted Decision Support System
through Data Mining and Multiple Criteria Decision Making. Procedia - Social and
Behavioral Sciences, 3: 388-395.
Lavrac, N., Mladenic, D., Bohanec, M. and Moyle, S. (2003). Data Mining and Decision
Support: Integration and Collaboration. Springer.
Lawal, N. T. A, Odeniyi, O. A. and Kayode, A. A. (2015): Application of Data Mining
and Knowledge Management for Business improvement: An Exploratory Study.
International Journal of Applied Information Systems, 8(3):13-19.
Lee, J. W., Lee, J. B., Park, M. and Song, S. H. (2005). An extensive comparison of recent
classification tools applied to microarray data. Computational Statistics & Data
Analysis, 48(4): 869–885.
Liao, S-H., Chu, P-H. and Hsiao, P-Y. (2012). Data mining techniques and applications –
A decade review from 2000 to 2011. Expert Systems with Applications, 39:11303-
Li, J., Wei, L., Li, G. and Xu, W. (2011) An evolution strategy-based multiple kernels
multi criteria programming approach: The case of credit decision making, Decision
Support System, 51: 292-298.
Ludwick, D.A. and Doucette, J. (2009). Adopting Electronic Medical Records in Primary
Care:Lessons Learned from Health Information Systems Implementation Experience
in Seven Countries. International Journal of Medical Informatics, 78(1): 22-31.
Maclennan, J., Tang, Z. and Crivat, B. (2008). Data mining with Microsoft SQL server
,Indianapolis, Wiley.
MIT Sloan. (2012). Center for Information Systems Research - MIT Sloan School of
Management. Accessed September 05, 2020, http://cisr.mit.edu.
Novak, J. and Judah, A. (2011). Towards a Health Productivity Reform Agenda for
Australia.http://www.achr.com.au.
Obenshain, M. K. (2004). Application of Data Mining Techniques to Healthcare Data.
In: Statistics for Hospital Epidemiology, Birnbaum, D. (ed.), 25(8): 690-695.
Rada, R. (2002). Information systems for Healthcare enterprises. Hypermedia Solutions
Limited.
Rupnik. R., Kukar. M. and Krisper, M., (2007) Integrating data mining and decision support
through data mining based decision support system. Journal of Computer Information
Systems, 47(3):89-104.
Rupnik, R. and Jaklic, J. (2009). The Deployment of Data Mining into Operational Business
Processes, In Data Mining and Knowledge Discovery in Real Life Applications, Julio
Ponce and AdemKarahoca (Ed.), InTech, pp. 373-388.
Sakthimurugan, T. and Poonkuzhali, S. (2012). An Effective Retrieval of Medical Records using Data Mining Techniques. International Journal of Pharmaceutical Science
andHealth Care, 2 (2): 72–77.
Seng, J.-L. and Chen, T. C. (2010). An analytic approach to select data mining for business decision.
Expert Systems with Applications, 37, 8042-8057.
Shaker, H., El-Sappagh, S., El-Masri, A. M. and Riad, M. E. (2013). Data Mining and Knowledge
Discovery: Applications, Techniques, Challenges and Process Models in Healthcare.
International Journal of Engineering Research and Applications, 3(3): 900-906.
Sheng, O. R. Liu (2000). Decision support for healthcare in a new information age. Decision Support
Systems, 30:101-103.
Ting, S. L., Shum, C. C., Kwok, S. K., Tsang, A. H. C. and Lee, W. B. (2009). Data Mining in
Biomedicine: Current Applications and Further Directions for Research. Journal of
Software Engineering & Applications, 2, 150-159
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