Abstract
Healthcare is one of the most data-rich and data-generating industries. Yet, these data tend to be discontinuous, incomplete, lacking standardization, as well as erroneous and unusable. Pressure is increasing for healthcare organizations to provide value-based care to all consumers and stakeholders alike. In order to address these challenges, business analytics and intelligence (BA/BI) are critical strategic tools which need to be used methodically and systematically to analyse the wealth of seemingly disparate healthcare data sets to provide opportunities for enhancement of healthcare’s overall performance through data-driven decision-making. The following addresses this need by presenting a systematic framework for the application of business analytics and intelligence and reports on a pilot study to test this framework at one of the largest not-for-profit tertiary hospitals in Melbourne, Australia.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Azma, F., & Mostafapour, M. A. (2012). Business intelligence as a key strategy for development organizations. Procedia Technology, 1, 102–106.
Ball, M. J. (2000). Nursing informatics: Where caring and technology meet (3rd ed.. [with] forewords by Sue Karen Donaldson and Ulla Gerdin. ed). New York: Springer Verlag.
Borlawsky, T., LaFountain, J., Petty, L., Saltz, J., & Payne, P. (2007). Leveraging an existing data warehouse to annotate workflow models for operations research and optimization. Paper presented at the AMIA... Annual Symposium proceedings/AMIA Symposium. AMIA Symposium.
Boyatzis, R. (1998). Transforming qualitative research. Thousand Oaks: Sage.
Burke, D. E., & Menachemi, N. (2004). Opening the black box: Measuring hospital information technology capability. Health Care Management Review, 29(3), 207–217.
Burns, D. (2007). Systemic action research: A strategy for whole system change. Bristol: Policy Press.
Chen, Y., Matsumura, Y., Nakagawa, K., Ji, S., Nakano, H., Teratani, T., et al. (2007). Analysis of yearly variations in drug expenditure for one patient using data warehouse in a hospital. Journal of Medical Systems, 31(1), 17–24.
Das, S., Yaylacicegi, U., & Menon, N. M. (2010). The effect of information technology investments in healthcare: A longitudinal study of its lag, duration, and economic value. IEEE Transactions on Engineering Management, 58(1), 124–140. https://doi.org/10.1109/TEM.2010.2048906.
Dhaval, R., Buskirk, J., Backer, J., Sen, C., Gordillo, G., & Kamal, J. (2006). Leveraging an information warehouse to create translational research environment for wound care center. Paper presented at the AMIA... Annual Symposium proceedings/AMIA Symposium. AMIA Symposium.
Eaton, S., Ostrander, M., Santangelo, J., & Kamal, J. (2007). Managing data quality in an existing medical data warehouse using business intelligence technologies. Paper presented at the AMIA... Annual Symposium proceedings/AMIA Symposium. AMIA Symposium.
Elsworthy, A. M., Claessen, S. M., Graham, B., Guo, Y., Innes, K. C., Loggie, C. L., et al.. (2013). ICD-10-AM: The international statistical classification of diseases and related health problems, 10th revision, Australian modification: Tabular list of diseases.
Evans, J. R., & Lindner, C. H. (2012). Business analytics: The next frontier for decision sciences. Decision Line, 43(2), 4–6.
Ferranti, J. M., Langman, M. K., Tanaka, D., McCall, J., & Ahmad, A. (2010). Bridging the gap: Leveraging business intelligence tools in support of patient safety and financial effectiveness. Journal of the American Medical Informatics Association, 17(2), 136–143.
Frimpong, J. A., Jackson, B. E., Stewart, L. M., Singh, K. P., Rivers, P. A., & Bae, S. (2013). Health information technology capacity at federally qualified health centers: A mechanism for improving quality of care. BMC Health Services Research, 13(1), 1.
Gagnon, M. P., Godin, G., Gagne, C., Fortin, J. P., Lamothe, L., Reinharz, D., & Cloutier, A. (2003). An adaptation of the theory of interpersonal behaviour to the study of telemedicine adoption by physicians. International Journal of Medical Informatics, 71(2–3), 103–115. https://doi.org/10.1016/S1386-5056(03)00094-7.
Golfarelli, M., Rizzi, S., & Cella, I. (2004). Beyond data warehousing: What’s next in business intelligence? Paper presented at the Proceedings of the 7th ACM international workshop on Data warehousing and OLAP.
Grant, A., Moshyk, A., Diab, H., Caron, P., Lorenzi, F. d., Bisson, G., et al. (2006). Integrating feedback from a clinical data warehouse into practice organisation. International Journal of Medical Informatics, 75(3), 232–239.
Hanauer, D., Zheng, K., Ramakrishnan, N., & Keller, B. (2011). Opportunities and challenges in association and episode discovery from electronic health records. IEEE Intelligent Systems, 26(5), 83–87.
Hevner, A., & Chatterjee, S. (2010). Design research in information systems. New York: Springer Science+Business Media.
Hevner, A., March, S., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105.
HIMMS Analytics Database. (2009). Available from HIMSS Retrieved 5/03/2012 http://www.himssanalytics.org/hcproider/emradoption.asp
Institute of Medicine (U.S.). Committee on Quality of Health Care in America. (2001). Crossing the quality chasm: A new health system for the 21st century. Washington, D.C.: The National Academies Press.
Kelley, T. F., Brandon, D. H., & Docherty, S. L. (2011). Electronic nursing documentation as a strategy to improve quality of patient care. Journal of Nursing Scholarship, 43(2), 154–162. https://doi.org/10.1111/j.1547-5069.2011.01397.x.
Krumholz, H. M. (2014). Big data and new knowledge in medicine: The thinking, training, and tools needed for a learning health system. Health Affairs (Millwood), 33(7), 1163–1170.
Levinson, D. R. (2010). Adverse events in Hospitals: National incidence among medicare beneficiaries. Washington, DC: US Department of Health and Human Services, Office of the Inspector General.
Lin, Y., Brown, R., Yang, H., Li, S., Lu, H., & Chen, H. (2011). Data mining large-scale electronic health records for clinical support. IEEE Intelligent Systems, 26(5), 87–90.
Miller, K. (2012). Big data analytics in biomedical research. Biomedical Computation Review, 2, 14–21.
Moghimi, H., Schaffer, J. L., & Wickramasinghe, N. (2016). Exploring the possibilities for intelligent risk detection in healthcare contexts. International Journal of Networking and Virtual Organisations, 16(2), 171–190.
Ostrander, M., Parikh, D., & Tennant, M. (2007). Improving daily workflow to improve insurance rejection handling by leveraging an existing data warehouse. Paper presented at the AMIA... Annual Symposium proceedings/AMIA Symposium. AMIA Symposium.
Perer, A. (2012). Healthcare analytics for clinical and non-clinical settings. Proceedings of CHI Conference.
Porter, M. E., & Teisberg, E. O. (2006). Redefining health care: Creating value-based competition on results. Boston: Harvard Business School Press.
Proctor, P. R., & Compton, W. D. (2010). Engineering and the healthcare delivery system. In Handbook of healthcare delivery systems (p. 2-1-2-8). New York: CRC Press.
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 2(1), 3.
Rajiv-Kohli, V. G. (2008). Business value of IT: An essay on expanding research directions to keep up with the times. Journal of the Association for Information Systems, 9(1), 1.
Resetar, E., Noirot, L., Reichley, R., Storey, P., Skiles, A., Traynor, P., et al. (2006). Using business intelligence to monitor clinical quality metrics. Paper presented at the AMIA... Annual Symposium proceedings/AMIA Symposium. AMIA Symposium.
Roohan, P. J. (2006). Integration of data and management tools into the New York state medicaid managed care encounter data system. The Journal of Ambulatory Care Management, 29(4), 291–299.
Rouse, W. B. (2010). Engineering the system of healthcare delivery. Amsterdam: IOS Press.
Ryan, L. (2009). System Overhaul. Retrieved 1/09/2014, 2014, from http://www.the-hospitalist.org/details/article/184492/System_Overhaul.html
Services, U. S. D. o. H. a. H. (2010). Electronic health records and meaningful use.
Shafqat, S., Kishwer, S., Rasool, R. U., et al. (2018). Big data analytics enhanced healthcare systems: A review. The Journal of Supercomputing. https://doi.org/10.1007/s11227-017-2222-4.
Silvia Piai, J. D. (2008). Western Europe, Healthcare Sector, IT Spending Forecast Update, 2007–2011.
Wang, Y., Kung, L., & Byrd, T. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations technology. Forecasting and Social Change, 126, 3–13. https://doi.org/10.1016/j.techfore.2015.12.019.
Ward, M. J., Marsolo, K. A., & Froehle, C. M. (2014). Applications of business analytics in healthcare. Business Horizons, 57(5), 571–582.
Wickramasinghe, N., & Schaffer, J. (2010) Realizing value driven ehealth solutions IBM Center for the Business of Government http://www.businessofgovernment.org/report/realizing-value-driven-e-health-solutions
Wickramasinghe, N., Bali, R. K., Lehaney, B., Schaffer, J., & Gibbons, M. C. (2009). Healthcare knowledge management primer. Hoboken: Taylor and Francis.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Wickramasinghe, N. (2020). Enabling Value-Based Health Care with Business Analytics and Intelligence. In: Wickramasinghe, N., Bodendorf, F. (eds) Delivering Superior Health and Wellness Management with IoT and Analytics. Healthcare Delivery in the Information Age. Springer, Cham. https://doi.org/10.1007/978-3-030-17347-0_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-17347-0_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-17346-3
Online ISBN: 978-3-030-17347-0
eBook Packages: MedicineMedicine (R0)