1. Bridging the Gap Empowering Caregivers With Real Time Access to Aggregate Patient Safety Data Jeffrey M. Ferranti, MD, MS Director, Computerized Patient Safety Initiatives Director, Enterprise Data Warehouse Duke University Health System

2. The Recent IOM Report • Preventing Medication Errors (2006) 1 • Employ error detection methodologies in all care settings • Assess the medication use process through active monitoring • Computerized detection of ADEs should complement voluntary reporting • “Health care systems should capture information on medication safety and use this information to improve the safety of their care delivery systems.”1 1. Institute of Medicine. Preventing Medication Errors: Quality Chasm Series. Washington, DC, National Academy Press, 2006.

3. Utilizing Data To Facilitate Safety and Quality Efforts • IT Safety Systems • The Data Warehouse (DSR) • Business Intelligence Tools • Six Sigma Analytics • Performance Services • The Balanced Score Card • Patient Safety Office Technology Operations

4. Error Monitoring at DukeA Multi Faceted Approach • The Qualitative Approach  Safety Reporting (SRS) • Provides qualitative data that informs safety and quality initiatives • Recently upgraded to SRS 2.0 which standardizes the reporting process • Not a reliable metric. • The Quantitative Approach  ADE-S • Computerized detection of ADEs; Excellent Metric • Based on the gold standard of manual chart review • Standardized scoring system, published inter-rater reliability

5. The Qualitative ApproachVoluntary Safety Reporting System (SRS)

6. Error Monitoring at DukeA Multi Faceted Approach • The Qualitative Approach  Safety Reporting (SRS) • Provides qualitative data that informs safety and quality initiatives • Recently upgraded to SRS 2.0 to facilitate front line reporting • Not a reliable metric. • The Quantitative Approach  ADE-S • Computerized detection of ADEs; Excellent Metric • Based on the gold standard of manual chart review • Standardized scoring system, published inter-rater reliability

7. The Quantitative ApproachComputerized ADE Surveillance ADE Database Lab Pharmacy Naranjo Pharmacist Review ADT Orders SI Event Report Generator CDR Kappa Daily Reports Possible ADEs to Pharmacist for Review

8. Bridging the GapDuke’s Strategy for Operationalizing Safety Data • Empower caregivers with direct access to safety data (COGNOS) • Predictive Analytics

11. OLAP Processing / Data Exploration

12. Bridging the GapDuke’s Strategy for Operationalizing Safety Data • Empower caregivers with direct access to safety data (COGNOS) • Predictive Analytics

13. ADE-S / SRS Trend Report for Narcotics Transition Period / Surveillance Data Unavailable

14. Six Sigma Opiate Pilot ProjectDevelop a Predicative Risk Model X Y 63,033 Total Patients Exposed to Narcotics Patient Age Patient LOS Patient Gender Clinical Service Morphine (Y/N) Fentanyl (Y/N) Midazalam (Y/N) Etc … 322 Narcotic ADEs SI>=3

15. Six Sigma Opiate Pilot ProjectDevelop a Predictive Risk Model 63,033 LOS > 4 Days Age > 51 Fentanyl Fluoxetine 322 Narcotic ADEs SI>=3 0.5 % (1/200) 8.7 % 9.1 % 7.5 % LOS > 4 Days Age > 51 Fentanyl Scopolamine Thoracic Surgery Any Narcotic Sertraline Midazolam

16. Six Sigma Opiate ProjectDevelop a Predictive Risk Model LOS > 4 Days Age > 51 Fentanyl Fluoxetine • If we can predict who is at high risk we can target interventions more effectively Acute Pain Consults Increased Monitoring 9.1 % Frequent Re-Assessment Pharmacy Consults

17. Clinical Information is an Enterprise Asset

18. Questions