HIT Hazard Manager: for Proactive Hazard Control

1. HIT Hazard Manager: for Proactive Hazard Control James WalkerMD, Principal Investigator, Geisinger Health System Andrea Hassol MSPH, Project Director, Abt Associates September 10, 2012 AHRQ Contract: HHSA290200600011i,#14

2. Accident Analysis “Most reporting systems concentrate on analyzing adverse events; this means that injury has already occurred before any learning takes place.” DeRosier, et al. (2002)Using Health Care Failure Mode and Effect Analysis. JC Journal on Quality Improvement. 28(5):248-269.

3. Accident Analysis Analysis (e.g., RCA) Patient Harm

4. Near-Miss Analysis “Most reporting systems concentrate on analyzing adverse events; this means that injury has already occurred before any learning takes place. More progressive systems also concentrate on analyzing close calls, which affords the opportunity to learn from an event that did not result in a tragic outcome.”DeRosier, et al. (2002)Using Health Care Failure Mode and Effect Analysis. JC Journal on Quality Improvement. 28(5):248-269.

5. Near-Miss Analysis Analysis (e.g., RCA) Patient Harm Near Miss

6. Proactive Hazard Control “Most reporting systems concentrate on analyzing adverse events; this means that injury has already occurred before any learning takes place. More progressive systems also concentrate on analyzing close calls, which affords the opportunity to learn from an event that did not result in a tragic outcome. Systems also exist that permit proactive evaluation of vulnerabilities before close calls occur.”DeRosier, et al. (2002)Using Health Care Failure Mode and Effect Analysis. JC Journal on Quality Improvement. 28(5):248-269.

7. Error in Design or Implementation HIT-Related Hazards Interaction between HIT and other healthcare systems Proactive Hazard Control Hazard Identified? No Yes Yes Hazard Resolved? ”Un-Forced” HIT-Use Error No Adverse Effect No HIT-Use-Error Trap Yes Use-Error Trap Identifiable Patient Harm? Yes Care-Process Compromise? Hazard in Production Patient Harm No No No Adverse Effect Near Miss

8. Proactive Hazard Control: A Case • Pre-implementation Analysis: New CPOE cannot interface safely with the existing best-in-class pharmacy system. • Replace the pharmacy system with the one that is integrated with the CPOE: Expensive 9-month delay • Years later, David Classen studied 62 HER implementations and concluded that CPOE and pharmacy systems from different vendors can never be safely interfaced.

9. The Hazard Ontology Why a standard language (ontology) for HIT hazards? To decrease the cost and increase the effectiveness of hazard control. Example: Much of the budget of the Aviation Safety Information Analysis and Sharing (ASIAS) system is devoted to normalizing data—because every airline uses a different ontology and can’t afford to change.

10. Health It Hazard Manager – AHRQ ACTION Task Order • Design & Alpha-Test (266 hazards) • Geisinger • Beta-Test (Website) • Geisinger • Abt Associates • ECRI PSO • Beta-Test Evaluation • Abt Associates • Geisinger

11. Hazard Manager Beta-Test 7 sites: integrated delivery systems, large and small hospitals, urban and rural • Usability (individual interviews) • Inter-rater Scenario Testing (individual web or in-person sessions) • Ontology of hazard attributes (group conference) • Usefulness (group conference) • Automated Reports (group conference) 4 vendors offered critiques All-Project meeting: 6 test sites, 4 vendors, AHRQ, ONC, FDA

12. HIT Hazard Manager 2.0 Demo

14. Hazard Ontology • Discovery: when and how the hazard was discovered; stage of discovery • Causation: usability, data quality, decision support, vendor factors, local implementation, other organizational factors • Impact: risk and impact of care process compromise; seriousness of patient harm • Hazard Control: control steps; who will approve and implement the control plan

22. Beta-Test Analytic Methods • Content analysis of 495 Short Hazard Descriptions • Frequencies of hazard ontology factors: combinations often selected together; factors never selected • Inter-rater differences in entries of mock hazard scenarios/vignettes • Suggestions from testers to improve ontology clarity, comprehensiveness, mutual exclusivity • Content analysis of “Other Specify” entries

23. Example: Unforced User Error • Unforced User Error was the second most frequently chosen factor (79 hazards). • In 55 instances, another factor was also chosen: * Multiple selections possible • Inter-rater testing revealed differing attitudes about the role of health IT in preventing user errors.

24. Ontology Revision: “Use Error” Use Error was often due to the absence of protections or safeguards to prevent errors: • Added a new factor to Decision Support: “Missing Recommendation or Safeguard” • Re-defined “Unforced User Error” as “Use Error in the absence of other factors”

25. Hazard Manager Benefits

26. Value: Care-Delivery Organizations • Prior to an upgrade, learn about hazards others have reported. • Identify hazards that occur at the interface of two vendors’ products. • Control hazards proactively. • Estimate the risk hazards pose and prioritize hazard-control efforts. • Inform user-group interactions with vendors. • Protect confidentiality.

27. Value: HIT Vendors • Identify the 90% of hazards that their customers do not currently report. • Learn which products interact hazardously with their own. • Prioritize hazard control efforts. • Identify hazards early in the release of new versions. • Preserve confidentiality.

28. Value: Policy Makers • Identify and categorize common hazards that occur at the interface of specific types of products (e.g., pharmacy and order entry). • Move hazard identification earlier in the IT lifecycle (especially prior to production use). • Monitor the success of hazard control in reducing health IT hazards and decreasing their impact on patients.

29. Beta-Test Final Report available on AHRQ website: healthit.ahrq.gov/HealthITHazardManagerFinalReport For more information:andrea_hassol@abtassoc.com