Starship Health Technologies (United States)

The wide range of potential real-world applications (e.g. smart city, traffic management, crash detection) for the Multi- Camera Vehicle Tracking (MCVT) problem makes it a worthwhile research topic in the computer vision field. In general, there are two approaches to address the MCVT problem: the global approach, which processes detections to create unified tracks directly, and the more commonly used two-step hierarchical approach, which involves separate stages for intracamera and inter-camera tracking. Typically, the two-step hierarchical MCVT approach can be further divided into four modules: object detection, feature extraction, single camera tracking and multi camera tracking. Each module plays a distinct role in enhancing the overall effectiveness of MCVT solutions. To date, there has only been limited research thoroughly examining how these modules individually affect the overall tracking performance. This paper presents an ablation study on the MCVT problem as a case study using the CityFlow V2 dataset. Using a benchmark MCVT framework, various state-of-art algorithms for each module have been implemented back-to-back to assess the impact of these algorithms. The effectiveness of these algorithms is assessed through two key metrics: IDF1 score performance and computational complexity. The study provides a comprehensive comparison study to understand the contributions of different algorithms in each module. Among all those modules, automatically generated spatial-temporal constraints maintains the computational efficiency while also contribute a lot on IDF1 score performance which could be the focusing point for future research on real-time real-world application.

Cancer care is becoming more complex each year, making the patient’s path to, and through the care continuum confusing and daunting. Patient-navigation programs for cancer patients have grown in recent years, but their navigation support is fragmented in many ways and often incomplete, lessening their effectiveness. Digital tools to support the cancer patient navigator can make them more effective and allow more patients to receive navigation support. A multifaceted analysis of the needs for such tools is presented. The analysis methods include literature review, domain analysis, and qualitative interviews with a sample of ten cancer patient navigators.

A mobile health (mHealth) smartphone app can help persons returning from incarceration better manage cardiovascular disease (CVD) and CVD risk factors during their re-entry period. A successful intervention to educate and increase self-efficacy in this population around cardiovascular health has potential to produce broad population health benefits. A participatory design (PD) approach was used to define the features and functionality of such an intervention in the form of a smart phone app. The PD process forced the research team to view CV health in the broader context of other challenges that RCs face day to day. It also led to a broadening of the process to include other stakeholders that work closely with RCs, and to explore how these challenges affect the re-entry process. Ultimately, PD led to an app design that emphasized a common problem-solving approach that can be applied to a broad range of problems. An interactive prototype of the app’s interface was assessed by a sample of RCs, users, with consensus that the problem-areas were appropriate and that the app was easy to use and to navigate. A quantitative assessment with the System Usability Survey instrument, yielded an average score of 81.5, placing it in the 90th percentile.

Introduction:Care coordination (CC) is an important fulcrum for pursuing a range of health care goals. Current research and policy analyses have focused on aggregated data rather than on understanding what happens within individual cases. At the case level, CC emerges as a complex network of communications among providers over time, crossing and recrossing many organizational boundaries. Micro-level analysis is needed to understand where and how CC fails, as well as to identify best practices and root causes of problems.Coordination Process Diagramming:Coordination Process Diagramming (CPD) is a new framework for representing and analyzing CC arcs at the micro level, separating an arc into its participants and roles, communication structure, organizational structures, and transitions of care, all on a common time line.Conclusion:Comparative CPD analysis across a sample of CC arcs identifies common CC problems and potential root causes, showing the potential value of the framework. The analyses also suggest intervention strategies that could be applied to attack the root causes of CC problems, including organizational changes, education and training, and additional health information technology development.