hit hazard manager: for proactive hazard control james walker md, principal investigator, geisinger...
TRANSCRIPT
HIT Hazard Manager: for Proactive Hazard Control
James Walker MD, Principal Investi gator, Geisinger Health SystemAndrea Hassol MSPH, Project Director, Abt Associates
September 10, 2012
AHRQ Contract: HHSA290200600011i,#14
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.
Patient Harm
Analysis (e.g., RCA)
Accident Analysis
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.
Patient Harm
Near Miss
Analysis (e.g., RCA)
Near-Miss Analysis
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.
”Un-Forced” HIT-Use Error
Error in Design or Implementation
Interaction between HIT and other healthcare systems
Proactive Hazard Control
Care-Process Compromise?
Identifiable Patient Harm?
Patient Harm
No Adverse Effect Near Miss
Hazard in
Production
No Adverse Effect
Yes
Yes
HIT-Related Hazards
Yes Yes
NoNo
No
No
Use-Error Trap
Hazard Identified?
Hazard Resolved?
HIT-Use-Error Trap
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.
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.
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
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 critiquesAll-Project meeting: 6 test sites, 4 vendors, AHRQ, ONC, FDA
HIT Hazard Manager 2.0Demo
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
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
Example: Unforced User Error
Unforced User Error was the second most frequently chosen factor (79 hazards).
In 55 instances, another factor was also chosen:
UsabilityData
QualityCDS
Software Design
Other Org. Factors
22 9 12 9 33
* Multiple selections possible
Inter-rater testing revealed differing attitudes about the role of health IT in preventing user errors.
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”
Hazard Manager Benefits
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.
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.
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.
For more information:[email protected]
Beta-Test Final Report available on AHRQ website:
healthit.ahrq.gov/HealthITHazardManagerFinalReport