Rapid health data repository allocation using predictive machine learning

Health-related data is stored in a number of repositories that are managed and controlled by different entities. For instance, Electronic Health Records are usually administered by governments. Electronic Medical Records are typically controlled by health care providers, whereas Personal Health Records are managed directly by patients. Recently, Blockchain-based health record systems largely regulated by technology have emerged as another type of repository. Repositories for storing health data differ from one another based on cost, level of security and quality of performance. Not only has the type of repositories increased in recent years, but the quantum of health data to be stored has increased. For instance, the advent of wearable sensors that capture physiological signs has resulted in an exponential growth in digital health data. The increase in the types of repository and amount of data has driven a need for intelligent processes to select appropriate repositories as data is collected. However, the storage allocation decision is complex and nuanced. The challenges are exacerbated when health data are continuously streamed, as is the case with wearable sensors. Although patients are not always solely responsible for determining which repository should be used, they typically have some input into this decision. Patients can be expected to have idiosyncratic preferences regarding storage decisions depending on their unique contexts. In this paper, we propose a predictive model for the storage of health data that can meet patient needs and make storage decisions rapidly, in real-time, even with data streaming from wearable sensors. The model is built with a machine learning classifier that learns the mapping between characteristics of health data and features of storage repositories from a training set generated synthetically from correlations evident from small samples of experts. Results from the evaluation demonstrate the viability of the machine learning technique used. © The Author(s) 2020

A scalable framework for healthcare monitoring application using the Internet of Medical Things

Internet of Things (IoT) is finding application in many areas, particularly in health care where an IoT can be effectively used in the form of an Internet of Medical Things (IoMT) to monitor the patients remotely. The quality of life of the patients and health care outcomes can be improved with the deployment of an IoMT because health care professionals can monitor conditions; access the electronic medical records and communicates with each other. This remote monitoring and consultations might reduce the traditional stressful and costly exercise of frequent hospitalization. Also, the rising costs of health care in many developed countries have influenced the introduction of the Healthcare Monitoring Application (HMA) to their existing health care practices. To materialize the HMA concepts for successful deployment for civilian and commercial use with ease, application developers can benefit from a generic, scalable framework that provides significant components for building an HMA. In this chapter, a generic maintainable HMA is advanced by amalgamating the advantages of event-driven and the layered architecture. The proposed framework is used to establish an HMA with an end-to-end Assistive Care Loop Framework (ACLF) to provide a real-time alarm and assistance to monitor pregnant women. © 2020 John Wiley & Sons, Ltd

Visions and Challenges in Managing and Preserving Data to Measure Quality of Life

Health-related data analysis plays an important role in self-knowledge, disease prevention, diagnosis, and quality of life assessment. With the advent of data-driven solutions, a myriad of apps and Internet of Things (IoT) devices (wearables, home-medical sensors, etc) facilitates data collection and provide cloud storage with a central administration. More recently, blockchain and other distributed ledgers became available as alternative storage options based on decentralised organisation systems. We bring attention to the human data bleeding problem and argue that neither centralised nor decentralised system organisations are a magic bullet for data-driven innovation if individual, community and societal values are ignored. The motivation for this position paper is to elaborate on strategies to protect privacy as well as to encourage data sharing and support open data without requiring a complex access protocol for researchers. Our main contribution is to outline the design of a self-regulated Open Health Archive (OHA) system with focus on quality of life (QoL) data.Comment: DSS 2018: Data-Driven Self-Regulating System

Smart and secure medical device gateway for managing patient recovery

Patients recuperating from orthopedic surgery require frequent monitoring and hospital visits with a wealth of personal medical data generated both on and off-site, making it challenging to maintain records. This paper discusses a secure blockchain-based data management software to enable safe remote access without compromising patient information. The BlockTrack software developed at our group will be customized to interface with modules for orthopedic recuperation monitoring. Modules can consist of ultrasonic bone health monitoring sensors, connected to relay nodes that can transmit patient data to the BlockTrack mobile app, which then intercepts the information to be stored securely on a cloud-based Blockchain network. Each record will have a unique ID enabled by Blockchain, for secure access and review of patient information by other parties, including doctors and pharmacists. Key findings are discussed with a goal to further develop this solution

Secure spontaneous emergency access to personal health record

We propose a system which enables access to the user's Personal Health Record (PHR) in the event of emergency. The access typically occurs in an ad-hoc and spontaneous manner and the user is usually unconscious, hence rendering the unavailability of the user's password to access the PHR. The proposed system includes a smart card carried by the user at all time and it is personalized with a pseudo secret, an URL to the PHR Server, a secret key shared with the PHR Server and a number of redemption tokens generated using a hash chain. In each emergency session, a one-time use redemption token is issued by the smart card, allowing the emergency doctor to retrieve the user's PHR upon successful authentication of his credentials and validation of the redemption token. The server returns the PHR encrypted with a one-time session key which can only be decrypted by the emergency doctor. The devised interaction protocol to facilitate emergency access to the user's PHR is secure and efficient

Decision Modeling for Healthcare Enterprise IT Architecture Utilizing Cloud Computing

In this paper, we present an overview of cloud computing, examine the potential uses for cloud computing in healthcareenvironments, and propose a framework to guide architectural selection decisions regarding information systems in bothlarge and small healthcare organizations. The framework provides insight to both practitioners and academics by extendingour understanding of the decisions regarding computing architectures within the healthcare system