Impact of Social Determinants of Health, Health Literacy, Self-perceived Risk, and Trust in the Emergency Physician on Compliance with Follow-up.

INTRODUCTION: Patients presenting to the emergency department (ED) with "low-risk" acute coronary syndrome (ACS) symptoms can be discharged with outpatient follow-up. However, follow-up compliance is low for unknown nonclinical reasons. We hypothesized that a patient's social factors, health literacy, self-perceived risk, and trust in the emergency physician may impact follow-up compliance. METHODS: This was a prospective study of a convenience sample of discharged ED patients presenting with chest pain and given a follow-up appointment prior to departing the ED. Patients were asked about social and demographic factors and to estimate their own risk for heart disease; they also completed the Short Assessment of Health Literacy-English (SAHL-E) and the Trust in Physician Scale (TiPS). RESULTS: We enrolled146 patients with a follow-up rate of 36.3%. Patients who had a low self-perceived heart disease risk (10% or less) were significantly less likely to attend follow-up than those with a higher perceived risk (23% vs 44%, P = 0.01). Other factors did not significantly predict follow-up rates. CONCLUSION: In an urban county ED, in patients who were deemed low risk for ACS and discharged, only self-perception of risk was associated with compliance with a follow-up appointment.

Robustly Aligning a Shape Model and Its Application to Car Alignment of Unknown Pose.

Precisely localizing in an image a set of feature points that form a shape of an object, such as car or face, is called alignment. Previous shape alignment methods attempted to fit a whole shape model to the observed data, based on the assumption of Gaussian observation noise and the associated regularization process. However, such an approach, though able to deal with Gaussian noise in feature detection, turns out not to be robust or precise because it is vulnerable to gross feature detection errors or outliers resulting from partial occlusions or spurious features from the background or neighboring objects. We address this problem by adopting a randomized hypothesis-and-test approach. First, a Bayesian inference algorithm is developed to generate a shape-and-pose hypothesis of the object from a partial shape or a subset of feature points. For alignment, a large number of hypotheses are generated by randomly sampling subsets of feature points, and then evaluated to find the one that minimizes the shape prediction error. This method of randomized subset-based matching can effectively handle outliers and recover the correct object shape. We apply this approach on a challenging data set of over 5,000 different-posed car images, spanning a wide variety of car types, lighting, background scenes, and partial occlusions. Experimental results demonstrate favorable improvements over previous methods on both accuracy and robustness.