A structured approach to EHR surveillance of diagnostic error in acute care: an exploratory analysis of two institutionally-defined case cohorts

Abstract

Objectives To test a structured electronic health record (EHR) case review process to identify diagnostic errors (DE) and diagnostic process failures (DPFs) in acute care. Methods We adapted validated tools (Safer Dx, Diagnostic Error Evaluation Research [DEER] Taxonomy) to assess the diagnostic process during the hospital encounter and categorized 13 postulated e-triggers. We created two test cohorts of all preventable cases (n=28) and an equal number of randomly sampled non-preventable cases (n=28) from 365 adult general medicine patients who expired and underwent our institution’s mortality case review process. After excluding patients with a length of stay of more than one month, each case was reviewed by two blinded clinicians trained in our process and by an expert panel. Inter-rater reliability was assessed. We compared the frequency of DE contributing to death in both cohorts, as well as mean DPFs and e-triggers for DE positive and negative cases within each cohort. Results Twenty-seven (96.4%) preventable and 24 (85.7%) non-preventable cases underwent our review process. Inter-rater reliability was moderate between individual reviewers (Cohen’s kappa 0.41) and substantial with the expert panel (Cohen’s kappa 0.74). The frequency of DE contributing to death was significantly higher for the preventable compared to the non-preventable cohort (56% vs. 17%, OR 6.25 [1.68, 23.27], p<0.01). Mean DPFs and e-triggers were significantly and non-significantly higher for DE positive compared to DE negative cases in each cohort, respectively. Conclusions We observed substantial agreement among final consensus and expert panel reviews using our structured EHR case review process. DEs contributing to death associated with DPFs were identified in institutionally designated preventable and non-preventable cases. While e-triggers may be useful for discriminating DE positive from DE negative cases, larger studies are required for validation. Our approach has potential to augment institutional mortality case review processes with respect to DE surveillance.

Developing, pilot testing, and refining requirements for 3 EHR-integrated interventions to improve diagnostic safety in acute care: a user-centered approach

“… 25–29 Of note, healthcare communication and collaboration was added to the original 5 Safer Dx dimensions based on recommendations from our steering committee. 25 , 29 Based on an in-depth analysis of a cohort of cases with DEs using this framework, 25 , 29 our systems engineers identified common and impactful diagnostic process failures (eg, failure or delay in ordering needed tests) at our institution that would serve as potential targets of intervention within the corresponding Safer Dx dimension (eg, diagnostic test performance and interpretation). 25 , 29 …”

“…Next, we searched the literature and assessed the available infrastructure at our institution to compile a comprehensive list of interventions ( Table 1 , fourth column) that could both satisfy initial user requirements ( Table 1 , second column) and prevent key diagnostic process failures ( Table 1 , third column) identified at our institution. 7 , 25 , 29 …”

Diagnostic Errors in Hospitalized Patients

“…Depending on the research approach and the types of underlying patient populations sampled, rates of diagnostic errors in these high-risk groups have been estimated to be approximately 5% to 20%, or even higher. 6,[24][25][26][27][28][29][30][31] For example, a retrospective study of 391 cases of unplanned 7-day readmissions found that 5.6% of cases contained at least 1 diagnostic error during the index admission. 32 In a study conducted at 6 Belgian acute-care hospitals, 56% of patients requiring an unplanned transfer to a higher level of care were determined to have had an adverse event, and of these adverse events, 12.4% of cases were associated with errors in diagnosis.…”

“…53 This facilitates selection and development of strategies at the institutional level that are most likely to improve patient outcomes. 24…”

“…These estimates may be much higher than previously predicted using traditional chart review approaches. 6,24 For example, a recently published study of 2809 random admissions in 11 Massachusetts hospitals identified 978 adverse events but only 10 diagnostic errors (diagnostic error rate, 0.4%). 19 This was likely because the trigger method used in the study did not specifically examine the diagnostic process as critically as done by the Safer Dx framework and DEER taxonomy tools, thereby underestimating the total number of diagnostic errors.…”