HIT Hazard Manager: for Proactive Hazard Control

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Slide 1

HIT Hazard Manager: for Proactive Hazard Control

James Walker, MD, Principal Investigator, Geisinger Health System
Andrea Hassol, MSPH, Project Director, Abt Associates.

September 10, 2012

AHRQ Contract: HHSA290200600011i,#14

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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.

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Accident Analysis

Image: A yellow triangle is labeled "Patient Harm." Two arrows bent at a right angle to each other point up from the triangle to a text box that reads "Analysis (e.g., RCA)."

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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.

Slide 5

Near-Miss Analysis

Image: A yellow triangle is labeled "Patient Harm." Two arrows bent at a right angle to each other point up from the triangle to a text box that reads "Analysis (e.g., RCA)." The longer arrow, pointing up, extends below the triangle and ends in a small text box that reads "Near Miss."

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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.

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Proactive Hazard Control

Image: A flow chart shows the proactive hazards control process:

HIT [Health Information Technology]-Related Hazards:

• Error in design implementation.
• Interaction between HIT and other healthcare systems.

Hazard Identified?

• Yes → Hazard Resolved?
  • Yes → No Adverse Effect.
  • No → Hazard in Production.
• No → Hazard in Production →
  • HIT-Use-Error Trap →
  • "Un-Forced" HIT-Use-Error →
  • Care-Process Compromise?
    • No → No Adverse Effect.
    • Yes → Identifiable Patient Harm?
      • Yes → Patient Harm.
      • No → Near Miss.

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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.

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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.

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Health It Hazard Manager—AHRQ ACTION Task Order

• Design & Alpha-Test (266 hazards):
  • Geisinger.
• Beta-Test (Web site):
  • Geisinger.
  • Abt Associates.
  • ECRI PSO.
• Beta-Test Evaluation:
  • Abt Associates.
  • Geisinger.

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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.

Slide 12

HIT Hazard Manager 2.0 Demo

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HIT Hazard Manager

Image: A screenshot shows the Description page of the HIT Hazard Manager system, where a short, public, and detailed, private, description of the hazard may be entered.

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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.

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HIT Hazard Manager

Image: A screenshot shows the Discovery page of the HIT Hazard Manager system, where information about how the hazard was discovered may be entered.

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HIT Hazard Manager

Image: A screenshot shows the Causation page of the HIT Hazard Manager system where information on usability, data quality, decision support, and vendor and other factors may be entered.

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HIT Hazard Manager

Image: A screenshot shows the Impact page of the HIT Hazard Manager system. A pull-down menu to select the most serious/worst harm that could happen if the hazard is not fixed is highlighted.

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HIT Hazard Manager

Image: A screenshot shows the Impact page of the HIT Hazard Manager system. A pull-down menu to select the estimated duration of harm to the patient is highlighted.

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HIT Hazard Manager

Image: A screenshot shows the Hazard Control Plan page of the HIT Hazard Manager system. A pull-down menu to select how quickly the hazard must be controlled is highlighted.

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HIT Hazard Manager

Image: A screenshot shows the Hazard Control Plan page of the HIT Hazard Manager system.

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HIT Hazard Manager

Image: A screenshot shows the Plan Approval page of the HIT Hazard Manager system, where information about who needs to approve and implement the plan may be selected.

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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.

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Example: Unforced User Error

• Unforced User Error was the second most frequently chosen factor (79 hazards).

• In 55 instances, another factor was also chosen:

  Usability	Data Quality	CDS	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.

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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."

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Hazard Manager Benefits

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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.

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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.

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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.

Slide 29

Beta-Test Final Report available on AHRQ Web site.

For more information: andrea_hassol@abtassoc.com