Table 4, Change ideas for preventing and minimizing diagnostic error - Advances in Patient Safety: From Research to Implementation (Volume 2: Concepts and Methodology)

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Henriksen K, Battles JB, Marks ES, et al., editors. Advances in Patient Safety: From Research to Implementation (Volume 2: Concepts and Methodology). Rockville (MD): Agency for Healthcare Research and Quality (US); 2005 Feb.

Advances in Patient Safety: From Research to Implementation (Volume 2: Concepts and Methodology).

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Table 4Change ideas for preventing and minimizing diagnostic error

Change Idea	Rationale/Description	Challenges
Upstream feedback to earlier providers who have may have failed to make correct diagnosis	Promotes culture of safety, accountability, continuous and blame-free learning, and communication “Hard-wires” organizational and practitioner learning from diagnosis evolution and delays Feedback from specialists poised to see missed diagnosis could be especially useful Build in “feedback from the feedback” to capture reflective practitioner assessment of why errors may have occurred and considerations for future prevention Permits aggregation for tracking, uncovering patterns, learning across cases, elucidating pitfalls, measuring improvements	Logistical requirements for implementation and surveillance screening to identify errors Avoiding “tampering,” from availability bias that neglects base rates (ordering aortogram on every chest pain patient to “rule out” dissection) Protecting confidentiality, legal liabilities, blame-free atmosphere Ways to extend to previous hospitals and physicians outside of own institution To be highest leverage needs to be coupled with reporting, case conferences

Safety nets to mitigate harm from diagnostic uncertainty and error	Well designed observation and followup venues and systems (e.g., admit for observation, followup calls or e-mail from MD in 48 hours, automated 2 wk phone followup to ensure tests obtained) for high-risk, uncertain diagnoses Educating and empowering patients to have lower threshold for seeking followup care or advice, including better defining and specifying particular warning symptoms	Logistics Resource constraints (bed, test availability, clinician time) Avoiding false positive errors, inappropriate use of scarce/costly resources Not creating excessive patient worry/anxiety

Timely and reliable abnormal test result followup systems	Fail-safe, prospectively designed systems to identify which results are critical abnormals (panic and routine), who to communicate result to, how to deliver Uniform approach across various test types and disciplines (lab, pathology, radiology, cardiology) Leverage information technologies to automate and increase reliability Involve patients to ensure timely notification of results, and contacting provider when this fails Modeled on Massachusetts Patient Safety Coalition program 78	Achieving consensus on test types and cut-off thresholds On-call, cross-coverage issues for critical panic results Defining responsibilities: e.g., for ED patients, for lab personnel Practitioner efficiency issues: avoiding excess duplicate work; efficient documentation

Fail-safe protocols for preliminary/resident and definitive readings of tests	Must be well-defined and organized system for supervision, creating and communicating final reports Need for system for amending and alerting critical changes to clinicians “After-hours” systems for readings, amending	How to best recognize and convey variations in expertise of attendings who write final reports Quality control poses major unmet challenges

Prospectively defining red flag diagnoses and situations and instituting prospective readiness	Create “pull” systems for patients with particular medical problems to ensure standardized, expedited diagnostic evaluations (so don't have to “push” to quickly obtain) Like AMI thrombolytic “clot box,” in-place for ready activation the moment patient first presents Embodies/requires coordinated multidisciplinary approach (e.g., pharmacy, radiology, specialists)	Difficulties in evidence-based delineation of diagnoses, situations, patient selection, criteria, and standardized actions Avoiding excessive work-up, diverting resources from other problems

Automated screening check lists to avoid missing key history, physical, lab data	Less reliance on human memory for more thorough questioning Queries triggered by individual patient features Could be customized based on presenting problem (i.e., work exposures for lung symptoms, travel history for fever)	Evidence of efficacy to-date unconvincing; unclear value of unselective “review of systems” Sorting out, avoiding false positive errors from data with poor signal:noise ratio Effectively implementing with teamwork and integrated information technology

High-level patient education and engagement with diagnosis probabilities and uncertainties	Since diagnosis often so complex and difficult, this needs to be shared with patients in ways to minimize disappointments and surprises Support and enhance patients taking initiative to question diagnosis, particularly if not responding as expected	Potentially more time-consuming for practitioners Avoiding unnecessary testing Doing in way that balances need for preserving patient confidence in their physicians and advice, with education and recognition of diagnosis fallabilities

Test and leverage information technology tools to avoid known cognitive and care process pitfalls	Better design of ways to streamline documentation (including differential diagnosis) and access/display of historical data Easing documentation time demands to give practitioners more time to talk to patients and think about their problems Facilitating real-time access to medical knowledge sources Sophisticated decision support tools that use complex rules and individualized patients data Prompts to suggest consideration of medication effects in differential, based on linkages to patient's medication profile, lab results	Shortcomings of first generation of “artificial intelligence” diagnosis software Challenges coupling knowledge bases with individual patient characteristics Paucity of standardized, accepted, sharable clinical alerts/rules New errors and distractions introduced by intrusion of computer into clinical encounter Alleged atrophy of unaided cognitive skills

From: Diagnosing Diagnosis Errors: Lessons from a Multi-institutional Collaborative Project

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Schiff GD, Kim S, Abrams R, et al. Diagnosing Diagnosis Errors: Lessons from a Multi-institutional Collaborative Project. In: Henriksen K, Battles JB, Marks ES, et al., editors. Advances in Patient Safety: From Research to Implementation (Volume 2: Concepts and Methodology). Rockville (MD): Agency for Healthcare Research and Quality (US); 2005 Feb. Table 4, Change ideas for preventing and minimizing diagnostic error.
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