This document discusses how business intelligence (BI) is being used in healthcare. It provides examples of how BI can help improve patient outcomes by leveraging large amounts of patient data to identify best practices and clinical pathways. Key performance indicators identified through BI analysis can also guide hospitals in quality improvement initiatives and reducing costs. While data collection challenges remain, the document concludes that as healthcare data collection improves, BI will play an increasingly important role in making data-driven decisions to enhance both patient care and healthcare business operations.

1. Healthcare in BI
ETHAN CHANDLER AND MICHAEL FARRIS

2. Introduction and Background
 Business Intelligence (BI) has made a way into all industries
 Many processes involved
 Patient healthcare
 Business in healthcare
 Healthcare provider data
 Discovering how parts work together show how BI in healthcare is its own
industry
 BI can leverage patient care for the success of the healthcare provider and
the patient
 BI is changing healthcare via analytics and process development

3. Current Reporting Used in Healthcare
 Many kinds of reports have been generated throughout history for
patients
 Several methods can be used to help develop and create
measurements (Ferr, Amyot, & Corrales, 2010, p. 3)
 “These tend to specialize on determined clinical areas or diseases” (Ferr, Amyot,
& Corrales, 2010, p. 4)
 There are complications with what is provided to patients
 Doctors want to avoid malpractice
 Software can be complex and restrictive
 Reports need to be detailed enough to give a good report and protect doctors
from malpractice

4. Data Collection in Healthcare
 Healthcare industry moving to digitize all its documentation and
operations the more insights can be gained
 Healthcare and government regulations ensure privacy through HIPPA
 Data is siloed into individual healthcare providers records. By aggregating
this data more insights can be gained
 Collection of data post care is imperative to help better understand the
data while patients are under the care of a provider
 Data reliability and uniformity is difficult in Healthcare due to the many
different users inputting data on one patient
 The quantity of data captured for patients can create noise in the data.

5. Post Care Data and Collection
Methods
 Creating an entire picture of care is difficult as patients are reluctant to
report after leaving care
 Phone calls and emails can be sent to ensure quality and outcome but are
unreliable and at times incorrect
 Outcomes are measured in accuracy, and in the healthcare industry,
accuracy is vital to a treatment’s success (Wiens & Shenoy, 2018)
 A target data set can help train a model for a specific purpose, but a testing set
is needed to test the model against a more general set of data, which may
contain outliers or unclean data
 Business Intelligence techniques can then be used to visualize the
outcomes of the machine learning and understand at a higher level what is
happening

6. Key Performance Indicators in
Healthcare
 Analysis for specific KPI's can be difficult given the subjective nature of
medicine and patients
 Age and Chronic conditions can be important pointers to KPIs for
providers to link to health issues
 KPI's help drive quicker decisions from providers on clinical pathways
 KPI's can guide hospitals in creating programs to help specific tasks or
processes
 One hospital cut viruses caught in the hospital by seeing an increase in viruses
after admission
 By creating a program for care providers and visitors to wash hands more often
the patients likelihood of getting the virus was cut in half. (Ferranti 2010)

7. BI and its Role in Patient Outcome
 Big data is currently the best solution for determining best care pathways
 Clinical pathways are designed by BI to create a process to treat specific
ailments with a predefined care path proven to be successful in most patients
 Tracking patients past their in-patient care creates better data because long
term outcome is involved in the analysis.
 When a physician chooses a clinical pathway before it was based on their own
data, now a physician can use aggregated data from many experiences to pick
a clinical pathway that is data proven and has a greater chance of success. (J.
McGlothlin, 2018)
 Studies and analysis done to create these paths could be outdated by the time
the necessary data is collected and put into use for the ailment

8. BI and its Role in Patient Outcome
 Quality Improvement is an important factor to Healthcare because as our
collective knowledge of medicine grows so does the possible paths to care
for the patient
 Dashboards that help inform providers of specific concerns and KPIs in a
specific treatment can greatly assist patient outcome
 Using BI to cut out waste can help create fewer shortages and better
prepare healthcare professionals for staffing, supplies, and knowledge
 Cost is prohibitive because of the amount of data required to produce
reliable information to help drive clinical pathways

9. Conclusion
 As data collection becomes easier Healthcare Business Intelligence will
become more advanced
 By using patient outcomes and key performance indicators hospitals can
not only be competitive but also deliver a better level of care to the patient
 Analysis is both useful for current patients but creating better data
collection practices alongside the analysis will help create future growth in
data reliability
 Healthcare BI will continue to grow in its ability to make data driven
decisions but also grow Healthcare companies' revenue

10. References
 Ferr, D., Amyot, D., & Corrales, C. V. (2010). Towards A Business Intelligence FrameworkFor Healthcare Safety. The Journal of Internet
Banking and Commerce, 15(3), 1-9.
 Machado, J. M., & Abelha, A. (2016). Applying Business Intelligence to Clinical and Healthcare Organizations. Hershey PA: Medical
Information Science Reference.” Retrieved from
http://search.ebscohost.com.proxy.kennesaw.edu/login.aspxdirect=true&db=n5h&AN=16PU2021651517&site=eds-live&scope=site
 Healthcare Business Intelligence - Global Market Outlook 2017-2026. (2019). M2PressWIRE. Retrieved from
http://search.ebscohost.com.proxy.kennesaw.edu/login.aspx?direct=true&db=n5h&AN=16PU2021651517&site=eds-live&scope=site
 Ratia, M., Myllärniemi, J. and Helander, N. (2018), "The new era of business intelligence: Big Data potential in the private health care value
creation", Meditari Accountancy Research, Vol. 26 No. 3, pp. 531-546. Retrieved from https://doi-
org.proxy.kennesaw.edu/10.1108/MEDAR-08-2017-0200
 Jeffrey M Ferranti, Matthew K Langman, David Tanaka, Jonathan McCall, Asif Ahmad, Bridging the gap: leveraging business intelligence
tools in support of patient safety and financial effectiveness, Journal of the American Medical Informatics Association, Volume 17, Issue
2, March 2010, Pages 136–143, Retrieved From https://doi-org.proxy.kennesaw.edu/10.1136/jamia.2009.002220
 J. McGlothlin, (2018) "Accelerating Analytics for Clinical Pathways to Drive Cost Reduction and Quality Improvement," 2018 IEEE
International Conference on Information Reuse and Integration (IRI), Salt Lake City, UT, 2018, pp. 426-435. Retrieved From
http://ieeexplore.ieee.org.proxy.kennesaw.edu/stamp/stamp.jsp?tp=&arnumber=8424740&isnumber=8424671
 Nurullah, A. S., Northcott, H.C., & Harvey, M.D. (2014). Public assessment of key performance indicators of healthcare in a Canadian
province: the effect of age and chronic health problems. SPringerPlus, 3(1), 28-28
 Wiens, J., & Shenoy, E. S. (2018). Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology. Clinical
Infectious Diseases, 66(1), 149-153.

11. Reading List
 BI in Healthcare Management https://healthitanalytics.com/features/leveraging-business-intelligence-for-healthcare-management
 Patient Care and Expenses with BIhttps://www.datapine.com/blog/business-intelligence-in-healthcare/
 Healthcare BI and its design https://www.healthcatalyst.com/healthcare-business-intelligence-data-warehouse
 Video Interviews on HealthIT Data Sources https://www.himss.org/clinical-business-intelligence
 Information on Healthcare Spending in BI https://www.villanovau.com/resources/bi/business-intelligence-in-healthcare/
 Current Applications in Healthcare BI https://emerj.com/ai-sector-overviews/business-intelligence-healthcare-current-applications/
 Information reagaurding Cutting Costs with BI https://healthtechmagazine.net/article/2019/11/how-predictive-analytics-can-help-
cut-costs
 Current data that is used in Dashboards and BI for Healthcare from data.gov https://dashboard.healthit.gov/index.php
 Value of Dashboards in Healthcare https://www.healthcatalyst.com/value-of-healthcare-dashboards
 Research on BI Improvements in Healthcare https://pdfs.semanticscholar.org/4f4f/632a3e18550c06b780bb5790ab6169f8376b.pdf