Research Article
Influencing Data Availability in IoT Enabled Cloud based e-Health in a 30 day Readmission Context
@INPROCEEDINGS{10.4108/icst.collaboratecom.2014.257621, author={Rajesh Vargheese and Yannis Viniotis}, title={Influencing Data Availability in IoT Enabled Cloud based e-Health in a 30 day Readmission Context}, proceedings={3rd International Workshop on Collaborative Cloud}, publisher={IEEE}, proceedings_a={COLLABCLOUD}, year={2014}, month={11}, keywords={collaboration cloud management internet of things internet of everything ioe iot e-health predictive analytics sensors policy m2m 30 day readmission}, doi={10.4108/icst.collaboratecom.2014.257621} }
- Rajesh Vargheese
Yannis Viniotis
Year: 2014
Influencing Data Availability in IoT Enabled Cloud based e-Health in a 30 day Readmission Context
COLLABCLOUD
ICST
DOI: 10.4108/icst.collaboratecom.2014.257621
Abstract
The US healthcare Affordable Care Act established the 30 day readmission protection program as one of the base lines of measuring quality of care at hospitals and post discharge. With reduced payment penalties for hospitals with excessive readmissions, hospitals have increased their focus on managing post discharge care. With the emphasis on prevention and proactive care, integrated approaches that have the ability to collect relevant data from patients, process it efficiently and timely and predict risk patterns in advance and enable seamless collaboration between the patients and the care team is required. Internet of things enabled collaborative cloud based e-health is evolving as one of the key transformation approaches in helping to address the 30 day readmission avoidance efforts. While the sensors provide critical data, there are significant constraints in terms of processing, power, storage and overall context. The power and capabilities of the cloud can augment the local visibility of sensors by providing capabilities that the sensors lack. In this work, we define these capabilities as the five P’s: Provisioning, Policy Management, Processing, Protection and Prediction. We argue that the blind spots in the unavailability of data or compromised data can result in missed opportunities for proactive care; ours proposed architecture ensures data availability, processing availability and integrity and thus is very important in a 30 day readmission context.