Abstract
This chapter introduces the healthcare environment and the need for data analytics and business intelligence in healthcare. It overviews the difference between data and information and how both play a major role in decision-making using a set of analytical tools that can be either descriptive and describe events that have happened in the past, diagnostic and provide a diagnosis, predictive and predict events, or prescriptive and prescribe a course of action.
The chapter then details the components of healthcare analytics and how they are used for decision-making improvement using metrics, indicators and dashboards to guide improvement in the quality of care and performance. Business intelligence technology and architecture are then explained with an overview of examples of BI applications in healthcare. The chapter ends with an outline of some software tools that can be used for BI in healthcare, a conclusion, and a list of references.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
R. Sharda, D. Delen, E. Turban, J. Aronson, and T. P. Liang, Businesss Intelligence and Analytics: Systems for Decision Support. 2014.
N. Kalé and N. Jones, Practical Analytics. Epistemy Press, 2015.
R. Sharda, D. Delen, and E. Turban, Business Intelligence: A Managerial Perspective on Analytics: A Managerial Perspective on Analytics. Pearson, 2015, pp. 416–416.
J. Akin, J. A. Johnson, J. P. Seale, and G. P. Kuperminc, “Using process indicators to optimize service completion of an ED drug and alcohol brief intervention program,” (in eng), Am J Emerg Med, vol. 33, no. 1, pp. 37–42, Jan 2015.
D. Steward, T. F. Glass, and Y. B. Ferrand, “Simulation-Based Design of ED Operations with Care Streams to Optimize Care Delivery and Reduce Length of Stay in the Emergency Department,” (in eng), J Med Syst, vol. 41, no. 10, p. 162, Sept 6 2017.
M. D. Basson, T. W. Butler, and H. Verma, “Predicting patient nonappearance for surgery as a scheduling strategy to optimize operating room utilization in a veterans' administration hospital,” (in eng), Anesthesiology, vol. 104, no. 4, pp. 826–34, Apr 2006.
C. J. Warner et al., “Lean principles optimize on-time vascular surgery operating room starts and decrease resident work hours,” (in eng), J Vasc Surg, vol. 58, no. 5, pp. 1417–22, Nov 2013.
R. Aslakson and P. Spronk, “Tasking the tailor to cut the coat: How to optimize individualized ICU-based palliative care?,” (in eng), Intensive Care Med, vol. 42, no. 1, pp. 119–21, Jan 2016.
J. Kesecioglu, M. M. Schneider, A. W. van der Kooi, and J. Bion, “Structure and function: planning a new ICU to optimize patient care,” (in eng), Curr Opin Crit Care, vol. 18, no. 6, pp. 688–92, Dec 2012.
W. J. Schellekens et al., “Strategies to optimize respiratory muscle function in ICU patients,” (in eng), Crit Care, vol. 20, no. 1, p. 103, Apr 19 2016.
T. L. Strome, Healthcare analytics for quality and performance improvement. Hoboken, NJ: Wiley, 2013.
C. Daluwatte, J. Vicente, L. Galeotti, L. Johannesen, D. G. Strauss, and C. G. Scully, “A novel ECG detector performance metric and its relationship with missing and false heart rate limit alarms,” (in eng), J Electrocardiol, vol. 51, no. 1, pp. 68–73, Jan - Feb 2018.
K. Honeyford, P. Aylin, and A. Bottle, “Should Emergency Department Attendances be Used With or Instead of Readmission Rates as a Performance Metric?: Comparison of Statistical Properties Using National Data,” (in eng), Med Care, Mar 29 2018.
D. A. Maldonado, A. Roychoudhury, and D. J. Lederer, “A novel patient-centered "intention-to-treat" metric of U.S. lung transplant center performance,” (in eng), Am J Transplant, vol. 18, no. 1, pp. 226–231, Jan 2018.
J. D. Markley, A. L. Pakyz, R. T. Sabo, G. Bearman, S. F. Hohmann, and M. P. Stevens, “Performance of a Novel Antipseudomonal Antibiotic Consumption Metric Among Academic Medical Centers in the United States,” (in eng), Infect Control Hosp Epidemiol, vol. 39, no. 2, pp. 229–232, Feb 2018.
S. Stevanovic and B. Pervan, “A GPS Phase-Locked Loop Performance Metric Based on the Phase Discriminator Output,” (in eng), Sensors (Basel), vol. 18, no. 1, Jan 19 2018.
P. Kaushik, “Physician Burnout: A Leading Indicator of Health Performance and "Head-Down" Mentality in Medical Education-I,” (in eng), Mayo Clin Proc, vol. 93, no. 4, p. 544, Apr 2018.
K. D. Olson, “In Reply-Physician Burnout: A Leading Indicator of Health Performance and "Head-Down" Mentality in Medical Education-I and II,” (in eng), Mayo Clin Proc, vol. 93, no. 4, pp. 545–547, Apr 2018.
J. Peck and O. Viswanath, “Physician Burnout: A Leading Indicator of Health Performance and “Head-Down” Mentality in Medical Education-II,” (in eng), Mayo Clin Proc, vol. 93, no. 4, pp. 544–545, Apr 2018.
Center for Disease Control. (2018, April 22). Developing Evaluation Indicators. Available: https://www.cdc.gov/std/Program/pupestd/Developing%20Evaluation%20Indicators.pdf
Z. Azadmanjir, M. Torabi, R. Safdari, M. Bayat, and F. Golmahi, “A Map for Clinical Laboratories Management Indicators in the Intelligent Dashboard,” (in eng), Acta Inform Med, vol. 23, no. 4, pp. 210–4, Aug 2015.
M. C. Schall, Jr., L. Cullen, P. Pennathur, H. Chen, K. Burrell, and G. Matthews, “Usability Evaluation and Implementation of a Health Information Technology Dashboard of Evidence-Based Quality Indicators,” (in eng), Comput Inform Nurs, vol. 35, no. 6, pp. 281–288, Jun 2017.
P. Stattin et al., “Dashboard report on performance on select quality indicators to cancer care providers,” (in eng), Scand J Urol, vol. 50, no. 1, pp. 21–8, 2016.
Datapine.com. (2018, September 17). Healthcare Dashboards Examples. Available:https://www.datapine.com/dashboard-examples-and-templates/healthcare#patient-satisfaction-dashboard
L. Madsen, “Business Intelligence An Introduction,” in Healthcare Business Intelligence: A Guide to Empowering Successful Data Reporting and Analytics: Wiley, 2012.
L. Madsen, “The Tenets of Healthcare BI,” in Healthcare Business Intelligence: A Guide to Empowering Successful Data Reporting and Analytics: Wiley, 2012.
R. H. AlHazme, A. M. Rana, and M. De Lucca, “Development and implementation of a clinical and business intelligence system for the Florida health data warehouse,” (in eng), Online J Public Health Inform, vol. 6, no. 2, p. e182, 2014.
T. S. Cook and P. Nagy, “Business intelligence for the radiologist: making your data work for you,” (in eng), J Am Coll Radiol, vol. 11, no. 12 Pt B, pp. 1238–40, Dec 2014.
B. Pinto and B. I. Fox, “Clinical and Business Intelligence: Why It’s Important to Your Pharmacy,” (in eng), Hosp Pharm, vol. 51, no. 7, p. 604, Jul 2016.
C. Howson, R. L. Sallam, J. L. Richardson, J. Tapadinhas, C. J. Doine, and A. Woodward, “Magic Quadrant for Analytics and Business Intelligence Platforms,” Gartner Group February 26, 2018. Available: https://b2bsalescafe.files.wordpress.com/2018/03/magic-quadrant-for-analytics-and-business-intelligence-platforms.pdf.
B. Aziza. (2018) Gartner Magic Quadrant: Who’s Winning In The Data And Machine Learning Space. Forbes. Available: https://www.forbes.com/sites/ciocentral/2018/02/28/gartner-magic-quadrant-whos-winning-in-the-data-machine-learning-space/#7dca83b37dab
K. Monica, “Top Healthcare Business Intelligence Companies by Hospital Users,” ed, 2017.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2019 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
El Morr, C., Ali-Hassan, H. (2019). Healthcare, Data Analytics, and Business Intelligence. In: Analytics in Healthcare. SpringerBriefs in Health Care Management and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-04506-7_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-04506-7_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04505-0
Online ISBN: 978-3-030-04506-7
eBook Packages: Business and ManagementBusiness and Management (R0)