The \u27Uberization\u27 of Healthcare: The Forthcoming Legal Storm Over Mobile Health Technology\u27s Impact on the Medical Profession

The nascent field of mobile health technology is still very small but is predicted to grow exponentially as major technology companies such as Apple, Google, Samsung, and even Facebook have announced mobile health initiatives alongside influential healthcare provider networks. Given the highly regulated nature of healthcare, significant legal barriers stand in the way of mobile health’s potential ascension. I contend that the most difficult legal challenges facing this industry will be restrictive professional licensing and scope of practice laws. The primary reason is that mobile health threatens to disrupt historical power dynamics within the healthcare profession that have legally enshrined physicians as the primary decision-makers and economic earners within the healthcare industry. In the near term, mobile health represents a potential redistribution of medical authority and financial power to technology companies and lesser-trained medical providers (nurses, physician assistants, etc.) at the expense of physicians. In the long-term, mobile health threatens to transform healthcare into a primarily “providerless” service, in the same fashion mobile taxi app company Uber envisions “driverless” taxis. Therefore, I conclude that while mobile health holds out tantalizing potential to improve upon the cost and accessibility of healthcare, there will be significant resistance to licensing and scope of practice reforms until broader political economy questions concerning the long-term viability of the medical profession are answered

The \u27Uberization\u27 of Healthcare: The Forthcoming Legal Storm Over Mobile Health Technology\u27s Impact on the Medical Profession

The nascent field of mobile health technology is still very small but is predicted to grow exponentially as major technology companies such as Apple, Google, Samsung, and even Facebook have announced mobile health initiatives alongside influential healthcare provider networks. Given the highly regulated nature of healthcare, significant legal barriers stand in the way of mobile health’s potential ascension. I contend that the most difficult legal challenges facing this industry will be restrictive professional licensing and scope of practice laws. The primary reason is that mobile health threatens to disrupt historical power dynamics within the healthcare profession that have legally enshrined physicians as the primary decision-makers and economic earners within the healthcare industry. In the near term, mobile health represents a potential redistribution of medical authority and financial power to technology companies and lesser-trained medical providers (nurses, physician assistants, etc.) at the expense of physicians. In the long-term, mobile health threatens to transform healthcare into a primarily “providerless” service, in the same fashion mobile taxi app company Uber envisions “driverless” taxis. Therefore, I conclude that while mobile health holds out tantalizing potential to improve upon the cost and accessibility of healthcare, there will be significant resistance to licensing and scope of practice reforms until broader political economy questions concerning the long-term viability of the medical profession are answered

Applications of a Biomechanical Patient Model for Adaptive Radiation Therapy

Biomechanical patient modeling incorporates physical knowledge of the human anatomy into the image processing that is required for tracking anatomical deformations during adaptive radiation therapy, especially particle therapy. In contrast to standard image registration, this enforces bio-fidelic image transformation. In this thesis, the potential of a kinematic skeleton model and soft tissue motion propagation are investigated for crucial image analysis steps in adaptive radiation therapy. The first application is the integration of the kinematic model in a deformable image registration process (KinematicDIR). For monomodal CT scan pairs, the median target registration error based on skeleton landmarks, is smaller than (1.6 ± 0.2) mm. In addition, the successful transferability of this concept to otherwise challenging multimodal registration between CT and CBCT as well as CT and MRI scan pairs is shown to result in median target registration error in the order of 2 mm. This meets the accuracy requirement for adaptive radiation therapy and is especially interesting for MR-guided approaches. Another aspect, emerging in radiotherapy, is the utilization of deep-learning-based organ segmentation. As radiotherapy-specific labeled data is scarce, the training of such methods relies heavily on augmentation techniques. In this work, the generation of synthetically but realistically deformed scans used as Bionic Augmentation in the training phase improved the predicted segmentations by up to 15% in the Dice similarity coefficient, depending on the training strategy. Finally, it is shown that the biomechanical model can be built-up from automatic segmentations without deterioration of the KinematicDIR application. This is essential for use in a clinical workflow

The “Uberization” of Healthcare: The Forthcoming Legal Storm over Mobile Health Technology’s Impact on the Medical Profession

The article examines the potential of mobile health to transform the delivery of healthcare through allowing non-physicians providing care independent of physicians and outside of traditional clinics and hospitals in the United States. It discusses licensing and scope of practice laws from large information technology (IT) corporations

The “Uberization” of Healthcare: The Forthcoming Legal Storm over Mobile Health Technology’s Impact on the Medical Profession

The article examines the potential of mobile health to transform the delivery of healthcare through allowing non-physicians providing care independent of physicians and outside of traditional clinics and hospitals in the United States. It discusses licensing and scope of practice laws from large information technology (IT) corporations

Improved 3D MR Image Acquisition and Processing in Congenital Heart Disease

Congenital heart disease (CHD) is the most common type of birth defect, affecting about 1% of the population. MRI is an essential tool in the assessment of CHD, including diagnosis, intervention planning and follow-up. Three-dimensional MRI can provide particularly rich visualization and information. However, it is often complicated by long scan times, cardiorespiratory motion, injection of contrast agents, and complex and time-consuming postprocessing. This thesis comprises four pieces of work that attempt to respond to some of these challenges. The first piece of work aims to enable fast acquisition of 3D time-resolved cardiac imaging during free breathing. Rapid imaging was achieved using an efficient spiral sequence and a sparse parallel imaging reconstruction. The feasibility of this approach was demonstrated on a population of 10 patients with CHD, and areas of improvement were identified. The second piece of work is an integrated software tool designed to simplify and accelerate the development of machine learning (ML) applications in MRI research. It also exploits the strengths of recently developed ML libraries for efficient MR image reconstruction and processing. The third piece of work aims to reduce contrast dose in contrast-enhanced MR angiography (MRA). This would reduce risks and costs associated with contrast agents. A deep learning-based contrast enhancement technique was developed and shown to improve image quality in real low-dose MRA in a population of 40 children and adults with CHD. The fourth and final piece of work aims to simplify the creation of computational models for hemodynamic assessment of the great arteries. A deep learning technique for 3D segmentation of the aorta and the pulmonary arteries was developed and shown to enable accurate calculation of clinically relevant biomarkers in a population of 10 patients with CHD

Deep Learning Methods for Industry and Healthcare

L'abstract è presente nell'allegato / the abstract is in the attachmen