Benefits and required capabilities of BI-tools in the private healthcare

ABSTRACT

Increasing amount of data in the healthcare sector requires specific tools that enable to process data in the rapidly changing environment. Especially in the private healthcare sector there is a clear need for appropriate tools to support decision-making process and thus, enhance profit making. Based on an empirical investigation of eight private healthcare sector organizations, we gain understanding on use of BI-tools in the private healthcare sector and utilization of them in decision-making. We analyse the capability factors and features of the BI-tools in the private healthcare industry sector, using the information systems (IS) success model by DeLone and McLean [1] as our theoretical lenses for identifying the gained benefits and the success of BI-tool utilization.

References

1. DeLone, W., and McLean, E. 2003. The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. Journal of Management Information Systems / Spring 2003, Vol. 19, No. 4, pp. 9--30. Bates, D., Saria, S., Ohno-Machado, L., Shah, A., Escobar, G. (2014). Big Data
2. In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients. Health Affairs 33.7, Jul2014, pp.1123--31
3. Cases, M., Furlong, L., Albanell, J., Altman, R. B., Bellazzi, R., Boyer, S., Brand, A., Brookes, A. J., Brunak, S., Clark, T. W., Gea, J., Ghazal, P., Graf, Guigó, R., Klein, T. E., López-Bigas, Maojo, Mons, B., Musen, M. and Oliveira, J. L. (2013). Improving data and knowledge management to better integrate health care and research. Journal of Internal Medicine. Oct 2013, Vol.274 Issue 4, pp.321--328
4. Katsaliaki K. and Mustafee N. (2011). Applications of simulation within the healthcare context. Journal of the Operational Research Society. Aug2011, Vol.62, Issue 8, pp.1431--1451
5. Stewart, R. and Davis K. (2016). 'Big data' in mental health research: current status and emerging possibilities. Social Psychiatry and Psychiatric Epidemiology. Aug2016, Vol.51, Issue 8, pp.1055--1072
6. Krumholz, H.M. (2014). Big Data And New Knowledge In Medicine: The Thinking, Training And Tools Needed For A Learning Health System. Health Affairs 33.7. Jul2014. pp.1163--70
7. Swanson, R. C., Cattaneo, A., Bradley, E., Chunharas, S., Atun, R., Abbas, K. M., Katsaliaki, K., Mustafee, N., Meier, B. M. and Best, A. (2012). Rethinking health systems strengthening: key systems thinking tools and strategies for transformational change. Oxford Journals. Medicine & Health. Health Policy and Planning Vol.27, 2012. Issue supply 4. pp. iv54--iv61.
8. Spruit, M., Vroon, R. and Batenburg, R. (2014). Towards healthcare business intelligence in long-term care. An explorative case study in the Netherlands. Computers in Human Behavior, 2014. Vol.30 pp.698--707.
9. Demirkan, H. 2013. A Smart Healthcare Systems Framework. IT Professional. Sept/Oct 2013. Vol.15, Issue 5, pp.38--45
10. Elbashir M., Collier P., Sutton S., Davern M. and Leech S. (2013). Enhancing the Business Value of Business Intelligence: The Role of Shared Knowledge and Assimilation. Journal of Information Systems. Vol.27, No. 2. Fall 2013, pp.87--105
11. Wullianallur R. and Viju R., (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems. 2014, 2:3.
12. Suomi, R. and Tähkäpää, J. (2002). The Strategic Role of ICT in the Competition Between Public and Private Health Care Sectors in the Nordic Welfare Societies - Case Finland. Proceedings of the 35th Hawaii International Conference on System Sciences - 2002
13. Lönnqvist A. and Pirttimäki V. (2006). The measurement of business intelligence. Information Systems Management , Vol.23, 2006. Issue 1, pp.32--40
14. Hawking, P. and Sellitto, C. (2010). Business Intelligence (BI) Critical Success Factors. 21st Australasian Conference on Information Systems. 1--3 Dec 2010, Brisbane.
15. Popovič, A., Hackney R., Coelho P.S. and Jaklič J. (2012). Towards business intelligence systems success: effects of maturity and culture on analytical decision making. Decision Support Systems. Vol.54, pp.729--739
16. Turban, E., Aronson, J. E., Liang, T-P. and Sharda, R. (2008): Business intelligence: a managerial approach. Pearson Prentice Hall, New Jersey.
17. Nykänen, E., Järvenpää, M., and Teittinen, H. (2016). Business intelligence in decision making in Finnish enterprises. Nordic Journal of Business, 65 (2), pp.24--44.
18. Pirttimäki, V. (2006). Business Intelligence as a Managerial Tool in Large Finnish Companies. Tampere University of Technology. Publication 646. Doctoral thesis. Tampere, Finland.
19. Gilad B. and Gilad T. (1985). A systems Approach to Business Intelligence. Business Horizons. Vol.28, No. 5, pp.65--70
20. Côrte-Real, N., Ruivo, P. and Oliveira, T. (2014). The diffusion stages of business intelligence & analytics (BI&A): A systematic mapping study. Procedia Technology. Vol.16, pp.172--179
21. Jinpon, P., Jaroensutasinee M. and Jaroensutasinee K. (2011). Business Intelligence and its Applications in the Public Healthcare System. Walailak Journal of Science and Technology (WJST). 2011. Vol8, Issue 2, pp.97--110
22. Yeoh, W. and Koronios, A. (2010). Critical success factors for business intelligence systems. Journal of computer information systems, Spring 2010, pp.23--32
23. Wang, H. & Wang S. "A Knowledge Management Approach to Data Mining Process for Business Intelligence," Industrial Management & Data Systems, 108(5), 2008, 622--634.
24. Chen H., Chiang R. and Storey V. 2012. Business Intelligence and Analytics: From Big Data To Big Impact. MIS Quarterly Vol. 36 No. 4, pp. 1165--1188/December 2012.
25. Chaudhuri, S., Dayal, U. and Narasayya, V. (2011). An Overview of Business Intelligence Technology. Communications of the ACM. Vol.54, pp.88--98
26. Watson, H. J., and Wixom, B. H. 2007. "The Current State of Business Intelligence," IEEE Computer (40:9), pp. 96--99.
27. Doan, A., Ramakrishnan, R., and Halevy, A. Y. (2011). "Crowdsourcing Systems on the World-Wide Web," Communications of the ACM (54:4), pp. 86--96.
28. O'Reilly, T. 2005. "What Is Web 2.0? Design Patterns and Business Models for the Next Generation of Software," September 30, (http://www.oreillynet.com/pub/a/oreilly/tim/ news/2005/09/30/what-is-web-20.html).
29. Kankanhalli, A., Hahn J., Tan, S., and Gao, G. (2016). Big data and analytics in healthcare: Introduction to the special section. Information Systems Frontiers. April 2016, Volume 18, Issue 2, pp 233--235.
30. Brandão, A., Pereira, E., Esteves, M., Portela, F., Santos, M. F., Abelha, A. and Machado, J. (2016). A Benchmarking Analysis of Open-Source Business Intelligence Tools in Healthcare Environments. Information, 7(4), pp.57
31. Yeoh, W. and Popovič, A. (2015). Extending the Understanding of Critical Success Factors for Implementing Business Intelligence Systems. Journal of the Association for Information Science and Technology.. Volume 67, Issue 1. January 2016. Pages 134--147
32. McLellan, E., MacQueen, K. M. and Neidig, J L. (2003). Beyond the Qualitative Interview: Data Preparation and Transcription. Sage Journals, Feb2003, pp.63--84
33. Walter, A., Ritter, T. and Gemünden, H.G. (2001). Value creation in buyer-seller relationships. Industrial Marketing Management 30(4), pp.365--377.
34. Ratia, M. and Myllärniemi, J. (2017). Business Intelligence Tools for Private Healthcare Data-Driven Value Creation. Proceedings IFKAD 2017. St. Petersburg, Russia, 7--9 June 2017. pp. 408--419.
35. Myllärniemi, J. (2013). Knowledge processes in healthcare network - a case study. 8th International Forum on Knowledge Asset Dynamics, IFKAD 2013, 12.-4.6.2013, Zagreb, Croatia. Zagreb, Croatia: Institute of Knowledge Asset Management, pp.1223--1236
36. Malmi, T. (1999). Activity-based costing diffusion across organizations: an exploratory empirical analysis of Finnish firms. Accounting, Organizations and Society. Vol.24, pp.649--672