End Diagnostic Overshadowing: Understanding and Reducing Diagnostic Error in Patients With Disabilities

People with disabilities (PWD) experience increased risk of diagnostic error-sometimes due to attributing symptoms to disability rather than a potentially new or co-morbid conditions. As well, some diagnoses are prone to error. Based on literature we identified the following twenty-six diagnoses prone to error with ICD-10 codes: Aortic aneurysm and dissection I71.0 - I71.9; Arterial thromboembolism I74.0 - I74.9; Venous thromboembolism I82.0-I82.99 and I82.A-I82.C; Congestive heart failure I50.1-150.9; Stroke All I60, I61, I62, I63, I64; Myocardial infarction I21.0-I21.9 and I21.A-I21.B; Spinal abscess G06.0, G06.1 and G06.2; Meningitis and encephalitis G04 -G04.91; Endocarditis I33.0-I33.9 and I38; Sepsis A41.0-A41.9; Pneumonia J12.0-J95.851; Lung cancer C34.0-C34.92; Melanoma C43.0- C43.9; Colorectal cancer C18.0-C18.9; Breast cancer C50 to C50.929, and C79.81; Prostate cancer C61; Pediatric Arterial ischemic stroke I63.0-163.9xx; Appendicitis K35-K35.8xx; Asthma J45.2-J45.998; Retinal blastoma C69.20, C69.21, C60.22; Brain tumor C71.0-C71.9; Polyateritis M30.0-M30.8; Congenital heart disease Q20 - Q28 (Q24.9 particularly important); Duchense muscular dystrophy G71.0-G71.9; Inflammatory bowel disease K51.0-K51.9; Scleroderma M34.0-M34.9.The goal of this research is to identify and create understanding of what underlies and contributes to increased risk of diagnostic error with these diagnoses. The investigators plan to develop ways to reduce diagnostic error, specifically ways to identify people with disabilities at risk of diagnostic error (DE). The investigators will also develop education programs and decision supports targeted to healthcare professionals. If it is effective, ways to reduce diagnostic error will have been developed among people with disabilities.

Aim 1: Identify and create understanding of mechanisms underlying diagnostic overshadowing. We will conduct baseline and post-analysis of CPT codes related to diagnostic processes to examine differences between patients aged ≥3 years with and without the specific disabilities listed above, along with demographic and clinical characteristics associated with health outcomes (e.g., race, ethnicity, gender, insurance type, specific diagnoses). Based on differences identified in baseline CPT analyses, we will conduct follow-up chart reviews and targeted interviews and develop Joint Commission-style individual mock tracers, following the care of the listed specific populations of PWD, and systems-of-care tracers focused on evaluating the extent to which care systems incorporate accessibility, effective communication, reasonable accommodations, trauma-informed care, and other processes that support timely and accurate diagnosis of conditions prone to diagnostic error. Mock tracer teams will provide formative evaluation of care to involved staff. Using inductive thematic analysis45 of notes from chart reviews, interviews, and mock tracers, we will identify mechanisms underlying diagnostic overshadowing. We will evaluate CPT codes (quantitative), chart reviews (mixed methods), and interview and tracer results (qualitative) at Year 5 compared with Year 1 to determine changes.

Our hypothesis is that there will be statistical difference in diagnostic processes between people with the specified disabilities and people without the specified disabilities.

Aim 2: Co-produce a framework of mechanisms underlying diagnostic overshadowing to develop educational programs and EHR decision supports. We will collaborate with stakeholders to refine, confirm, and prioritize mechanisms underlying diagnostic overshadowing identified in Aim 1 and use these findings for the co-production of educational programs and EHR decision supports. We will evaluate these mitigation efforts through: (1) pre- and post-knowledge assessments related to use of the educational programs; and (2) descriptive pre- and post-data on the use of specific EHR decision supports.47 Our hypothesis is that we will have information that can be used to develop algorithms for identifying PWD from the specific populations at risk of DO/DE as evidenced by diagnostic process data from the Safer DX Checklist and usage of CPT E/M code.

Our Hypotheses are that there will be statistical change in time to diagnostic evaluation for PWD from the specified 5 population groups and with the specified diagnoses prone to error. We will Evaluate for change after implementation of algorithms to identify patients with the specified disabilities at risk for DO/DE.