In the rapidly evolving field of healthcare management, the analysis of medical claims data has become an essential component for improving the quality and equity of healthcare services. The nature of care delivery in the United states is heavily influenced by its fragmentation--care is often spread across multiple disconnected providers (e.g., primary-care physicians, specialists). Settings with greater care fragmentation have been shown to inhibit effective communication and coordination between care team members, thus contributing to higher costs and lower quality of treatment [13,33,21,1,7]. Despite the well-understood impacts of fragmentation, there are still few quantitative tools that can capture the mechanisms of care delivery networks at scale [14]. Standard analyses of local infrastructure features, often executed using tabular data, are limited in their ability to distill complex dynamics between physicians.

We are facing a global healthcare crisis today as the healthcare cost is ever climbing, but with the aging population, government fiscal revenue is ever dropping. To create a more efficient and effective healthcare system, three technical challenges immediately present themselves: healthcare access, healthcare equity, and healthcare efficiency. An autonomous mobile clinic solves the healthcare access problem by bringing healthcare services to the patient by the order of the patient's fingertips. Nevertheless, to enable a universal autonomous mobile clinic network, a three-stage technical roadmap needs to be achieved: In stage one, we focus on solving the inequity challenge in the existing healthcare system by combining autonomous mobility and telemedicine. In stage two, we develop an AI doctor for primary care, which we foster from infancy to adulthood with clean healthcare data. With the AI doctor, we can solve the inefficiency problem. In stage three, after we have proven that the autonomous mobile clinic network can truly solve the target clinical use cases, we shall open up the platform for all medical verticals, thus enabling universal healthcare through this whole new system.

The tech industry today is abuzz with the potential of AI in healthcare – this discussion is drawing in the interest of very diverse stakeholders, from healthcare providers to drug developers to health insurers and to general public at large. This is not merely hype – the kind of substantial investments pouring in are a proof. According to Accenture, the AI healthcare market is projected to reach $6.6 billion by 2021, expected to result in about $150 billion cost savings annually. Accenture also broke down potential annual benefits for 2026 within the healthcare industry where robot-assisted surgery could easily cut costs by $40 billion while virtual nursing assistants, dosage error reduction, clinical trials and automated image diagnosis could save $20 billion, $16 billion, $13 billion and $3 billion respectively. Gurpreet Singh, a U.S. health services leader at PWC, sees three main areas where major chunks of investment will be heavily focused; digitization, engagement and diagnostics.

The tech industry today is abuzz with the potential of AI in healthcare – this discussion is drawing in the interest of very diverse stakeholders, from healthcare providers to drug developers to health insurers and to general public at large. This is not merely hype – the kind of substantial investments pouring in are a proof. According to Accenture, the AI healthcare market is projected to reach $6.6 billion by 2021, expected to result in about $150 billion cost savings annually. Accenture also broke down potential annual benefits for 2026 within the healthcare industry where robot-assisted surgery could easily cut costs by $40 billion while virtual nursing assistants, dosage error reduction, clinical trials and automated image diagnosis could save $20 billion, $16 billion, $13 billion and $3 billion respectively. Gurpreet Singh, a U.S. health services leader at PWC, sees three main areas where major chunks of investment will be heavily focused; digitization, engagement and diagnostics.

Founded in 2014, Catalia Health has raised $7.8 million, with most of the money coming from two Seed rounds and a grant last year. The company is developing an interactive robotic "coach" called Mabu, which seems similar to the AI-driven robots being marketed toward elderly populations we covered recently. In this case, Mabu is intended to work with all sorts of patients but targeted for those living with chronic diseases such as diabetes to help manage their medical conditions. Mabu ensures medications are taken regularly but also collects information related to daily health based on a Q&A with the patient. Data collected is then sent to a care team for analysis and action.