Targeted Injury Detection System for Adverse Drug Events: An AHRQ-Funded Patient Safety Initiative

1. Targeted Injury Detection System for Adverse Drug Events: An AHRQ-Funded Patient Safety Initiative TIDS-ADE The Quality ColloquiumAugust 20, 2008Andrew Masica, MD, MSCIBaylor Health Care System-Dallas, TX

2. TIDS-ADE Background • Trigger tool methodology focused mechanism for risk reduction event precipitates a response Example: IHI • Adverse drug events common/costly usually actionable clinical/IT interface

3. Project Goals • Develop a functional trigger tool for ADEs in hospitalized patients that can be disseminated broadly • Detection at multiple time points related to event occurrence (before, during, or after) • Potential benefits in clinical care setting: 1. Prevention of ADEs 2. Mitigation of ongoing ADEs 3. Capture of “true” ADE rate • Toolkit for real-world implementation

4. Definitions • Trigger = alert: any event prompting further investigation by clinician. • ADE criteria = if event attributed to drug and: reaches a level of harm that is durable or requires a change in the treatment plan due to unacceptable level of risk for harm or patient discomfort • Example of “unacceptable risk” for patient harm: -INR ≥ 6.0 and active warfarin order -event prompted discontinuation of drug=ADE • Broader concept of ADEs

5. Organizational Structure Coordinating Center RTIConference Calls AHRQ In-person meetings Site leads • Site System Leadership • Patient Safety • Health Care Improvement • Project champion • -oversight • -data management • Local Test Site • Pharmacy champion • Pharmacy IT • Pharmacy Staff

6. Site leader meetings Activation of IT/programming resources Project introduction to site staff Validation of triggers Launch 3-9 months prior to start 2-6 months prior to start 1-4 months prior to start 4-6 weeks prior to start Begin pilot Implementation: Site Environments Task Timing

7. Implementation: Triggers • Choice/set-up of triggers: higher yield alerts (Classen, Evans JAMA 1991) core set of 1520 consensus, tiered TIDS alerts tailoring to local site capabilities/priorities • Trigger validation steps (3-phases): programmer’s bank of “dummy data” real-time pre-launch tests by site IT pharmacist post-launch troubleshooting for obvious “misses” • Uniform process for evaluating trigger utility

8. TIDS-ADE Workflow Central Pharmacy Floor Virtual • Alert Work List • Patient ID • Date/location • Trigger details • Triage • Alert Review • Chart • Patient • Intervention • Trigger Evaluation • Respond to ?’s • Data Warehouse • Biweekly meetings 15 minutes 1-2 minutes (review) 1-2 minutes 1 minute (response) Per alert

10. Results† Test site average: 5-10 alerts per 100 patient days †Preliminary data from alpha-site testing

11. Trigger Evaluation Was the alert useful?

12. Trigger Evaluation Did the alert detect an adverse event or trend?

13. Trigger Evaluation Did the alert change patient care?

14. Trigger evaluation Did a drug cause the adverse event or trend?

15. TIDS-ADE: Trigger Summary Results can guide refinement of alerts.

16. Impact on ADE Detection rates • Expanded definition of ADEs for project: patient harm or unacceptable risk for patient harm • TIDS Alerts considered to have detected an ADE if: alert detected an adverse event or trend adverse event or trend was caused by a drug Baylor Grapevine • >40 cases meeting both conditions over 10 weeks • Approximately 4-5 ADEs detected per week with TIDS 2.3 ADEs per 100 admissions • Voluntary reporting: <0.5 ADEs per 100 admissions

17. Lower ADE rate at Baylor? • Sites in published literature: 3-6 ADEs per 100 admissions academic centers/training programs mature EHRs/CPOE experience with trigger tool methodology vs. • Community setting paper based with varying degrees of IT support staffing limitations acceptance of trigger approach to ADEs verification process can be difficult

18. Additional Outcomes • Qualitative Feedback level of detail in alert felt to be beneficial favorable view of alerts with trending evaluation piece undermined perceived usefulness sharp learning curve fits well into existing practice patterns • Quantitative 80 hours of programming time for study triggers 45 minutes pharmacist time daily

19. Lessons learned from TIDS-ADE • High risk situations can be captured prospectively with use of a trigger tool • Need to resource multi-site collaboration general framework for implementation • Outcomes are influenced by site characteristics performance of specific triggers ADE detection rate • Dynamic evaluation process for alerts is critical optimizes performance of the triggering system reduction in alert fatigue

20. TIDS-ADE: Future Directions • Full analysis/toolkit development in progress • Incorporation of broader ADE definition into daily patient care • Clarify endpoints for “successful” triggers • Cross-cutting projectrealistic planning for resource allocations

21. TIDS-ADE Leaders/Sites…thanks to all!