Bootstrapping-based approaches to estimate the frequency, duration and risk factors for diagnostic delays

Oregon Convention Center 

We develop a bootstrapping-based approach to estimate a range of statistical measures of diagnostic delays and missed diagnostic opportunities from large administrative data sources or electronic health records. Our approach utilizes the observed and expected patterns of healthcare visits with signs and symptoms of a disease during the period prior to the initial diagnosis where diagnostic delays are expected to occur. In order to account for uncertainty in identifying a diagnostic opportunity versus coincidental symptoms, resampling is used to randomly select which individual visits represent a diagnostic delay in a given trial. We describe three different resampling algorithms that can be used to select which healthcare visits represent a diagnostic delay based on the specific clinical characteristics of a given disease.

Using this approach, we estimate individual-level metrics that summarize the frequency and duration of diagnostic delays. We also use this procedure to model patient and healthcare setting risk factors for diagnostic delay. We apply our approach to a wide range of infectious and non-infectious diseases and summarize how each of our proposed algorithms impact t