Large-scale assessment of missed opportunity risks in a complex hospital setting.

In this research, we apply a large-scale logistic regression analysis to assess the patient missed opportunity risks at a complex VA (US Department of Veterans Affairs) hospital in three categories, namely, no-show alone, no-show combined with late patient cancellation and no-show combined with late patient and clinic cancellations. The analysis includes unique explanatory variables related to VA patients for predicting missed opportunity risks. Furthermore, we develop two aggregated weather indices by combining many weather measures and include them as explanatory variables. The results indicate that most of the explanatory variables considered are significant factors for predicting the missed opportunity risks. Patients with afternoon appointment, higher percentage service connected, and insurance, married patients, shorter lead time and appointments with longer appointment length are consistently related to lower risks of missed opportunity. Furthermore, the VA patient-related factors and the two proposed weather indices are useful predictors for the risks of no-show and patient cancellation. More importantly, this research presents an effective procedure for VA hospitals and clinics to analyze the missed opportunity risks within the complex VA information technology system, and help them to develop proper interventions to mitigate the adverse effects caused by the missed opportunities.