Optimizing measurement of misdiagnosis-related harms using symptom-disease pair analysis of diagnostic error (SPADE): comparison groups to maximize SPADE validity

Abstract

Diagnostic errors in medicine represent a significant public health problem but continue to be challenging to measure accurately, reliably, and efficiently. The recently developed Symptom-Disease Pair Analysis of Diagnostic Error (SPADE) approach measures misdiagnosis related harms using electronic health records or administrative claims data. The approach is clinically valid, methodologically sound, statistically robust, and operationally viable without the requirement for manual chart review. This paper clarifies aspects of the SPADE analysis to assure that researchers apply this method to yield valid results with a particular emphasis on defining appropriate comparator groups and analytical strategies for balancing differences between these groups. We discuss four distinct types of comparators (intra-group and inter-group for both look-back and look-forward analyses), detailing the rationale for choosing one over the other and inferences that can be drawn from these comparative analyses. Our aim is that these additional analytical practices will improve the validity of SPADE and related approaches to quantify diagnostic error in medicine.

Keywords: delayed diagnosis; diagnostic errors; missed diagnosis; outcome assessment, health care; quality improvement; stroke.

Figure 1.. Comparison group options in SPADE analysis.

Comparison groups can be used in both directions of SPADE analysis to assess the relationships between symptoms and diseases and infer expected visit rates to enhance the validity of the proposed symptom-disease pair as a true diagnostic-error dyad. Symptom and disease comparison groups can be drawn from within a cohort of interest (intra-group comparison) or from a separate (inter-group comparison) cohort. Using the look-back approach, researchers can use an inter-group disease comparator (A) or an intra-group symptom comparator (B). Using the look-forward approach, researchers can use an intra-group disease comparator (C) or an inter-group symptom comparator (D). We use the dizzy-stroke exemplar to illustrate these four types of comparators. Solid arrows indicate a symptom-disease pair of interest; dotted arrows indicate the comparison group. Note that the intra-group comparisons are “one-to-many” (single cohort, multiple outcomes) (B, C), while the inter-group comparisons are “many-to-one” (multiple cohorts, single outcomes) (A, D). Rationales for choosing one type of comparator over another can be found in Table 1, but the most commonly used forms shown are intra-group (B, C).