Prospective Analytics: The Next Thing in Healthcare Analytics

1. Healthcare Analytics Advances to the Prospective Stage for Outcomes Improvement – Anne-Marie Bickmore

2. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Healthcare Analytics A new term that is quickly gaining traction is prospective analytics. And for good reason. This new type of analytics offers an unprecedented opportunity to use data to affect decisions, actions, and outcomes at the point of care. It takes advantage of retrospective and predictive analytics to support clinical decision making in a more expansive way, by looking at the potential results of all options.

3. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Healthcare Analytics You should understand that without a proven track record in retrospective and predictive analytics, jumping right into prospective analytics is like walking onstage to perform a Beethoven symphony with a world-class orchestra at Carnegie Hall with no prior musical experience.

4. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Healthcare Analytics To get to this new level, organizations must first establish a foundation by gaining experience with other types of analytics. Just as becoming an accomplished musician – for all but the very rare savant – requires learning to read music, working with an expert instructor, and practicing for many, many hours.

5. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Healthcare Analytics Currently, there are three types of analytics that organizations should use with the petabytes of clinical, financial, and operational data they currently generate to elevate quality, improve outcomes, and lower costs. It’s important to not only become proficient in all three, but to do it in the proper order. Let’s look at each of them in depth.

6. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Retrospective Analytics Healthcare organizations need to perform this as the foundational layer for all other analytics. This must be mastered first. It’s the equivalent of learning to read music. Retrospective analytics provides a look at what has already happened, helping a healthcare organization understand why those events happened.

7. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Retrospective Analytics Clinicians can use retrospective data to view past actions – such as whether a panel of patients with sepsis received medication A or medication B – and what the outcomes were with each. They can also confirm that the sample size used in the analysis was statistically valid.

8. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Retrospective Analytics Retrospective analysis can be extremely effective at helping the organization standardize care and remove variations. It’s main limitations are: 1. Leads to conclusions that are restricted to choices already made. 2. Takes a long time for those conclusions to become policy at the point of care, usually after many rounds of discussion.

9. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Predictive Analytics Predictive analytics takes a higher level, forward-looking view. It takes the conclusions from retrospective analytics and gives the organization the ability to speculate on options. For example, let’s take a heart failure readmission scenario.

10. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Predictive Analytics Predictive analytics help identify heart failure patients with a high risk for returning to the hospital. Once we identify those patients, we alert a case manager and interventions can focus on decreasing the likelihood of patients returning as readmissions. In our music analogy, predictive analytics is the equivalent to practicing scales and techniques that improve the musician’s skills.

11. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Predictive Analytics With predictive analytics, the outcomes aren’t known or guaranteed. The organization is simply looking at the likelihood of an event to occur if it follows a particular course. It then relies on other processes to determine what action to take. While predictive analytics can generate new possibilities, it is still not a decision-making tool.

12. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Next Level Analytics Prospective analytics takes the knowledge gained through retrospective and predictive analytics. Then they drill down to show bedside clinicians (or administrators) all available options for changing the current state, as well as the associated consequences. This is why organizations are so excited about this new methodology.

13. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Next Level Analytics For a good clinical example consider the two basic types of appendectomies: • Simple appendectomy removes an inflamed appendix • Complex appendectomy removes a ruptured appendix Because the symptoms are the same for both types, the clinical team doesn’t know which type they’re addressing until they begin to operate. The standard is to code all appendectomies as simple upon admission.

14. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Next Level Analytics The challenge is that the surgical procedure and the post-op care for a simple appendectomy are very different than for a complex one. Different medications are required and the length of stay is longer for complex appendectomies. If the procedure isn’t re-coded, the quality measures will be based on the wrong set of standards.

15. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Next Level Analytics Prospective analytics collates data as it is entered into the electronic health record (EHR) and alerts the clinician to the possibility that this procedure initially coded as a simple appendectomy may actually be a complex appendectomy. Also provided are the best practice care options for the complex appendectomy patient vs. the simple appendectomy. It’s a form of decision support.

16. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Next Level Analytics Prospective and predictive analytics has many possible applications on the operational side, such as: • Predicting patient loads in ER based on previous events. • What services are likely to be needed. • How to allocate radiology resources based on predicted load. It is still an educated guess but it allows organizations to plan for future events.

17. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Next Level Analytics Retrospective analytics are good at identifying problems. Predictive analytics are good at anticipating problems. The prospective approach delivers its value by validating the gut instinct of clinicians and healthcare administrators, with real-time, evidence-based solutions to problems, i.e., empirical data.

18. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Sharing Knowledge One advantage of prospective analytics is that it can integrate the multiple variables associated with each patient and disease process, and identify likely outcomes based on existing data and past analysis. Consider the heart failure example. The system could secure a follow- up appointment in a timeframe most suited for the patient.

19. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Blending Prospective Analytics into Workflows Healthcare is filled with great initiatives that never come to fruition because they don’t fit within the workflows of healthcare professionals. It’s important to proactively bring the results of prospective analytics to clinicians and others as part of their normal course of work, rather than forcing them to seek it out.

20. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Blending Prospective Analytics into Workflows Consider that the typical primary care physician has roughly 12 to 15 minutes to spend with a patient. He/she doesn’t want to spend that time looking up information. The most updated information presented at the point of care should include all possible outcomes. This is what drives clinical quality improvement and ensures consistency at the lowest reasonable cost.

21. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Driving Adoption Prospective analytics offers tremendous possibilities. Keep in mind that not everyone in the organization is forward-thinking. If the organization starts with retrospective analytics to support the conclusions drawn by prospective analytics, it will be much easier to sell to the skeptics. Soon you’ll have them making beautiful, and cost-effective, music together.

22. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. More about this topic Three Approaches to Predictive Analytics in Healthcare David Crockett – Senior Director of Research and Predictive Analytics 3 Reasons Why Comparative Analytics, Predictive Analytics, and NLP Won’t Solve Healthcare’s Problems - Dale Sanders – Executive Vice President, Software The Practical Use of the Healthcare Analytics Adoption Model Jarod Crapo – Vice President Cut Through the Confusion: The 5 Types of Healthcare Analytics Solutions Paul Horstmeier – Senior Vice President How Clinical Analytics Will Improve the Cost and Quality of Healthcare Delivery Dan Burton – CEO, Health Catalyst Link to original article for a more in-depth discussion. Healthcare Analytics Advances to the Prospective Stage for Outcomes Improvement

24. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Anne-Marie Bickmore joined Health Catalyst in December 2012. Prior to coming to Catalyst, she worked for Lantana Consulting as the lead Project Manager (2011- 2012), Director of Informatics at Swedish American Hospital Rockford, IL (2010- 2011), and Intermountain Healthcare serving in multiple leadership roles both clinical and IT (1999-2011). Anne Marie has dual Bachelor’s degrees in Psychology and Nursing from the University of Utah. Other Clinical Quality Improvement Resources Click to read additional information at www.healthcatalyst.com