Value of biopsy in a cohort of children with high-titer celiac serologies: observation of dynamic policy differences between Europe and North America.

BACKGROUND: Healthcare systems implement change at different rates because of differences in incentives, organizational processes, key influencers, and management styles. A comparable set of forces may play out at the national and international levels as demonstrated in significant differences in the diagnostic management of pediatric Celiac Disease (CD) between European and North American practitioners. METHODS: We use retrospective clinical cohorts of 27,868 serum tissue transglutaminase (tTG) immunoglobulin A levels and 7907 upper gastrointestinal endoscopy pathology reports to create a dataset of 793 pathology reports with matching tTG results between July 1 of 2014 and July 1 of 2018. We use this dataset to characterize histopathological findings in the duodenum, stomach and esophagus of patients as a function of serum tTG levels. In addition, we use the dataset to estimate the local and national cost of endoscopies performed in patients with serum tTG levels greater than 10 times the upper limit of normal. RESULTS: Using evidence from a US tertiary care center, we show that in the cohort of pediatric patients with high pre-test probability of CD as determined by serum tTG levels, biopsy provides no additional diagnostic value for CD, and that it counter-intuitively introduces diagnostic uncertainty in a number of patients. We estimate that using the European diagnostic algorithms could avoid between 4891 and 7738 pediatric endoscopies per year in the US for evaluation of CD. CONCLUSIONS: This study considers the North American and European management guidelines for the diagnosis of pediatric CD and highlights the slow adoption in North America of evidence-based algorithms developed and applied in Europe for triage of endoscopy and biopsy. We suggest that system dynamics influences that help maintain the status quo in North America include a variety of social and economic factors in addition to medical evidence. This work contributes to the growing body of evidence that the dynamics that largely favor maintaining status quo management policies in a variety of systems extend to clinical medicine and potentially influence clinical decisions at the level of individual patients and the population.

Modeling Pathology Workload and Complexity to Manage Risks and Improve Patient Quality and Safety.

Anatomic pathology (AP) laboratories provide critical diagnostic information that help determine patient treatments and outcomes, but the risks of AP operations and their impact on patient safety and quality of care remain poorly recognized and undermanaged. Hospital-based laboratories face an operational and risk management challenge because clinical work of unknown quantity and complexity arrives with little advance notice, which results in fluctuations in workload that can push operations beyond planned capacity, leading to diagnostic delays and potential errors. Modeling the dynamics of workload and complexity in AP offers the opportunity to better use available information to manage risks. We developed a stock-and-flow model of a typical AP laboratory operation and identified key exogenous inputs that drive AP work. To test the model, we generated training and validations data sets by combining data from the electronic medical records and laboratory information systems over multiple years. We demonstrate the implementation of 10-day AP work forecast generated on a daily basis, and show its performance in comparison with actual work. Although the model somewhat underpredicts work as currently implemented, it provides a framework for prospective management of resources to ensure quality during workload surges. Although full implementation requires additional model development, we show that AP workload largely depends on few and accessible clinical inputs. Recognizing that level loading of work in a hospital is not practical, predictive modeling of work can empower laboratories to triage, schedule, or mobilize resources more effectively and better manage risks that reduce the quality or timeliness of diagnostic information.