Uncompensated Care provided by Physicians at an Academic Medical Center during 2007-2008 using an Opportunity Cost Model

This project was aimed at defining, quantifying and analyzing the value of uncompensated care provided by physicians as part of the Yale Medical Group for the 2008 fiscal year. Using an opportunity cost model, uncompensated care was calculated for each department as a total of bad debt and free care and then compared to existing estimates of such care. Another aim of this study was to conduct an interdepartmental comparison of the value of such care as a percentage of departmental earnings. To undertake this study, a literature search was performed to determine previous estimates and models of uncompensated care by physicians. Primary financial data (including charges, payments and write-offs for Bad Debt and Free Care) from the Yale Medical Group for fiscal year 2008 was then collected, fed into the opportunity cost model and compared to published estimates. The results of this study showed that, as a whole, physicians at the Yale Medical Group provided $6,510,373.65 of Uncompensated Care (or 2.75% of Total Payments) with a departmental range of 0.57%-15.29% of Total payments. These results show that Faculty physicians at Yale provided a larger amount of Uncompensated care than the published estimates obtained from random sampling of almost 4000 physicians. The results also reveal large differences in levels of uncompensated care between departments at Yale

Optimizing Mental Health Care by Increasing Access Services through Evidenced-based mHealth Applications

BACKGROUND: The COVID pandemic has disrupted mental health services leading to an increased need for mental health support. Nearly half of all adults in the United States have reported worry and stress leading to a rise in feelings of anxiety. Currently, there is a mental health workforce shortage, impacting the available treatment services. Mobile Health (mHealth) applications can help bridge the gap in the availability of these services and potentially improve health outcomes through education, social support, self-managed care, and patient-provider communication. PURPOSE: The purpose is to optimize mental health care and access to services by leveraging the use of mHealth applications. METHODS: The Technology Acceptance Model (TAM) guided this quality improvement project using technology to support mental health. A college representative sent a Qualtrics© survey to adult participants who were students, employees, or staff on the campus. In addition, all participants had regular access to a smartphone and no previous experience with the Sanvello© digital application. The participants self-enrolled in the project by providing consent, demographic information, and responses to the Generalized Anxiety Disorder 7 (GAD-7) instrument. After completing the GAD-7, participants received download and use instructions for the Sanvello© smartphone application. Following the use of the smartphone application, participants were surveyed for their perceptions on the use of mHealth for mental health care. RESULTS: Pre-intervention findings included an aggregate GAD-7 median score of 18.96 indicating high levels of anxiety, a gap in the resources for those with anxiety, worry, and stress symptoms, and that participants reported receptivity to education as well as preventative and early treatment. 50 participated in the pre-intervention survey and received instructions on the use of the Sanvello© app. Post intervention survey participants (n=32) reported they used the free version of the application. Most indicated that they used the application less than once a week, with 50% of participants indicating they felt the mHealth application improved access to mental health care services. Following the intervention, an aggregate GAD-7 score of 15.52 was noted. Qualitative thematic analysis noted four dominant themes: ease of use, time, technical functionality, and engagement in the use of the application. CONCLUSION: Providing care through technological tools such as mHealth applications can reform how we support access to mental health services while delivering evidence-based care. This use of technology permits greater flexibility for patients and mental health care providers while optimizing access to mental health services

Formalization of the General Video Temporal Synchronization Problem

In this work, we present a theoretical formalization of the temporal synchronization problem and a method to temporally synchronize multiple stationary video cameras with overlapping views of the same scene. The method uses a two stage approach that first approximates the synchronization by tracking moving objects and identifying curvature points. The method then proceeds to refine the estimate using a consensus based matching heuristic to find frames that best agree with the pre-computed camera geometries from stationary background image features. By using the fundamental matrix and the trifocal tensor in the second refinement step, we improve the estimation of the first step and handle a broader more generic range of input scenarios and camera conditions. The method is relatively simple compared to current techniques and is no harder than feature tracking in stage one and computing accurate geometries in stage two. We also provide a robust method to assist synchronization in the presence of inaccurate geometry computation, and a theoretical limit on the accuracy that can be expected from any synchronization syste

What You See Is What You Detect: Towards better Object Densification in 3D detection

Recent works have demonstrated the importance of object completion in 3D Perception from Lidar signal. Several methods have been proposed in which modules were used to densify the point clouds produced by laser scanners, leading to better recall and more accurate results. Pursuing in that direction, we present, in this work, a counter-intuitive perspective: the widely-used full-shape completion approach actually leads to a higher error-upper bound especially for far away objects and small objects like pedestrians. Based on this observation, we introduce a visible part completion method that requires only 11.3\% of the prediction points that previous methods generate. To recover the dense representation, we propose a mesh-deformation-based method to augment the point set associated with visible foreground objects. Considering that our approach focuses only on the visible part of the foreground objects to achieve accurate 3D detection, we named our method What You See Is What You Detect (WYSIWYD). Our proposed method is thus a detector-independent model that consists of 2 parts: an Intra-Frustum Segmentation Transformer (IFST) and a Mesh Depth Completion Network(MDCNet) that predicts the foreground depth from mesh deformation. This way, our model does not require the time-consuming full-depth completion task used by most pseudo-lidar-based methods. Our experimental evaluation shows that our approach can provide up to 12.2\% performance improvements over most of the public baseline models on the KITTI and NuScenes dataset bringing the state-of-the-art to a new level. The codes will be available at \textcolor[RGB]{0,0,255}{\url{{https://github.com/Orbis36/WYSIWYD}