Optimize Up: Using Analytics for Clinical Decision Support Optimization

1. Optimize Up: Using Analytics for Clinical Decision Support Optimization Speakers Anwar Mohammad Sirajuddin, M.B.B.S., M.S. Memorial Hermann Health System, Houston, Texas Matthew Stanosheck, Director Cerner Corporation, Kansas City, Missouri

2. Disclosure • I, Anwar Mohammad Sirajuddin, and my spouse/partner have no relevant relationships with commercial interests to disclose. • I, Matthew Stanosheck disclose the following relevant relationship with commercial interests: • Employed by EHR Company Cerner

3. Learning Objectives • After participating in this session the learner should be better able to: • Identify tools for data extraction and data visualization • Identify a process for data validation • Use data drill down and visualizations to identify opportunities for CDS optimization • Work with clinical stakeholders to validate opportunities for CDS optimization and confirm that the optimization worked

4. CDS: Alert fatigue with intervention yields an impressive set of results. • A key component of the Electronic Health Record are Clinical Decision support alerts (CDS). • Provide treatment recommendations • Prevent adverse drug events • Cost containment • Improvement of reimbursement and regulatory compliance • Without a unified approach to implementing and monitoring of all these alerts, organizations may be inadvertently firing too many alerts or incorrectly. • With increased alert burden, clinical users may end up ignoring the alerts.

5. Methods • Data extraction tools and data visualization tools that are readily available commercially as well as through the EHR vendors, it is possible to identify potential areas of inefficient alerting to the clinical users. • We identified these opportunities by looking at two criteria; • High frequency alerts • High override rate. • Clinical decision support alerts need to be both meaningful and actionable.

6. Example 1:Warfarin-Heparin Interaction Alert

7. Monitor, Listen and Respond • Considered a nuisance alert • Bridging therapy • Feedback from physicians: • ”Make this alert smarter, PLEASE!” • New functionality • Incorporate INR value within interaction alert

8. Optimization Impact 86% reduction in DDI alerts for Warfarin-Heparin

9. Example 2:Dronedarone ADE Alert • Alert that fires on order for Dronedarone and the patient either missing baseline liver function tests or having elevated levels: Scenario 1 (Missing LFT) •ALT •AST •Bilirubin Total •Bilirubin Direct •Alkaline Phosphatase Scenario 2 (Elevated Levels) •ALT > 200 or •AST > 120

10. Alert Screenshot

11. Monitor, Listen and Respond • Analysis of data firings led to optimization investigation • Collaboration with physician and pharmacy specialists led to recommendation: • Bilirubin Direct is not a required liver function test if Bilirubin Total is present • Modification of rule to remove Bilirubin Direct requirement

12. Optimization Impact

13. Results • Automated the data extracts and visualization tool. • Significant reduction in the data processing times. • Leverage of the data visualizations and identify optimization opportunities for two CDS alerts. Focused on prevention of potential adverse drug events. • As a result of this optimization, we were able to reduce the total number of alerts by 91% and 74% respectively for the following two alerts: • Heparin-Warfarin Drug-Drug Interaction • Dronedarone and Hepatic Dysfunction

14. Results

15. Practical Application

16. CDS Alerts DashboardAll Facilities

17. CDS Alerts DashboardSingle Facility

18. Question 1 • Which one of the following is NOT a potential outcome of Clinical Decision support alerts (CDS). Answer Option • Creation of a continuity of care document • Prevention of adverse drug events • Improvement of reimbursement and regulatory compliance • Provide treatment recommendations

19. Answer • Creation of a continuity of care document • Prevention of adverse drug events • Improvement of reimbursement and regulatory compliance • Provide treatment recommendations • Explanation: A key component of the Electronic Health Record are Clinical Decision support alerts (CDS). Key outcomes of CDS Alerts can be: • Provide treatment recommendations, Prevent adverse drug events, Cost containment and Improvement of reimbursement and regulatory compliance • CDS’s alerts can not create a continuity of care document (CCD). Continuity of care document’s are created within the EMR but not within the CDS component.

20. Question 2 • In order to optimize Clinical Decision Support alerts, Memorial Hermann identified potential areas of inefficient alerting to the clinical users. • The main two criteria they reviewed were: • Low frequency alerts and high alert override rates • Low frequency alerts and low alerts override rates • High frequency alerts and low override rates • High frequency alerts and high alert override rates

21. Answer • Low frequency alerts and high alert override rates • Low frequency alerts and low alerts override rates • High frequency alerts and low override rates • High frequency alerts and high alert override rates • Explanation: Data extraction tools and data visualization tools identify potential areas of inefficient alerting to the clinical users. Knowing that CDS alerts need to be actionable and measurable, Memorial Hermann identified these opportunities by looking at two key criteria: • High frequency alerts • High override rate

22. AMIA is the professional home for more than 5,400 informatics professionals, representing frontline clinicians, researchers, public health experts and educators who bring meaning to data, manage information and generate new knowledge across the research and healthcare enterprise. AMIA 2018 Clinical Informatics Conference | amia.org

23. Thank you! • Anwar Mohammad Sirajuddin, M.B.B.S., M.S. Anwar.MohammadSirajuddin2@memorialhermann.org • Matthew Stanosheck mstanosheck@cerner.com