Rajesh Vargheese

A look at challenges and opportunities of Big Data analytics in healthcare

Big Data analytics can revolutionize the healthcare industry. It can improve operational efficiencies, help predict and plan responses to disease epidemics, improve the quality of monitoring of clinical trials, and optimize healthcare spending at all levels from patients to hospital systems to governments. This paper provides an overview of Big Data, applicability of it in healthcare, some of the work in progress and a future outlook on how Big Data analytics can improve overall quality in healthcare systems.

An IoT/IoE enabled architecture framework for precision on shelf availability: Enhancing proactive shopper experience

In todays customer centric world, enabling shopper experience is a very important factor in maintaining customer loyalty. On shelf availability (OSA) of the product that the customer is looking for is a key metric in ensuring shopper experience. Current manual approaches to enabling OSA have gaps and might fall short of customer expectations and impact bottom line of retailers. We propose an automated Internet of Things/Internet of Everything (IoT/IoE) enabled model for ensuring OSA. The use of sensors and big data analytics helps process data from various information sources to generate information about out-of-stock products on the shelf and notifies the store associates to take actions to stock up the products on the shelves. The precision level of such approaches could vary and hence we look at multiple sensor configurations that can be used in the retail segment to address the OSA needs. In addition, we also propose an algorithm for enabling OSA and an integrated architecture to precisely identify and predict trends towards out-of-stock situations. The algorithm also emphasizes on multi stage cross verification to reduce false alarms. Such a model is capable of improving operational efficiencies, reducing operational cost and improving shopper experience.

Securing B2B Pervasive Information Sharing between Healthcare Providers: Enabling the Foundation for Evidence based Medicine☆

Abstract Evidence based Medicine is emerging as a key process in Health care to enable insights driven quality care for individual patients. The core of making evidence based medicine a reality is information sharing between healthcare entities. However, the sharing of information across health care providers has security implications. The old methods of securing enterprises using perimeter defense models are likely to fall short; hence new innovative methods such as dynamic learning threat defense systems that perform rapid detection based on flow, signature, behavior, packet capturing techniques in addition to security policies and protection schemes are required to ensure security of the information exchanged as well as other threats to information and systems within the enterprise. Ensuring the confidentiality, integrity and availability of the data is the foundation to enable such pervasive information sharing that is required for enabling evidence based care model to achieve better health outcomes.

Leveraging Cloud based Virtual Care as a Tool Kit for Mitigating Risk of Exposure during a Pandemic

Abstract Pandemic is an epidemic occurring over a very wide area affecting large number of people when new strains emerge for which people have very less resistance and there are no readymade vaccines available. Pandemic can spread in a short amount of time from person to person and especially, the care givers that the patients with flu interact are at higher risk of exposure. Virtual care, leveraging collaboration technologies can be extremely useful in such situations to maintain essential services for patients and communities but limit the risk to your staff that is providing services by enabling social distancing. Leveraging dependent information vectors, analytics can be used to open up local clinics that are closer to affected population and can be operated remotely by providers to provide care at the same time not exposing themselves to the direct contact with patients. We will look at some unique ways that virtual care methods such as Telehealth, m-health and remote care can be leveraged to efficiently reduce the risk. These include, but not limited to such as video analysis based pre-evaluation of patients, remotely controlled lockers for sample collection, gesture and voice based non touch controls.