Health Data Management for Internet of Medical Things

Abstract

AbstractPersonal health data can come from diverse sources and formats. The heterogeneity of these data sources and the multiple stakeholders involved requires robust, standards-compliant architectures that can adapt to rapidly changing data streams that may be enriched with new types of data from new data endpoints. This chapter surveys the existing state of the art in collecting and managing personal health data primarily available through the Internet of Medical Things (IoMT) devices. Various open-source solutions for generic personal data management require specific consideration if adopted to handle health data collected through IoMT. More specifically, for health data, there are burgeoning open-source initiatives that focus on novel machine learning techniques for personal health data on the “edge.” There are also various commercial solutions in this space, but they all operate in silos, which may need to change to achieve greater connectivity and interoperability. The chapter surveys existing solutions for personal health data management, discuss their strengths, and provides critical analyses of the aspects lacking in these systems. The chapter will conclude with future directions that would ultimately culminate in better IoMT integrated architectures for personal health data collection, storage, and management in a secure and privacy-preserving manner.KeywordsBlockchainConnectivityData managementData storageDistributed ledger technologyElectronic medical recordsFast healthcare interoperability of resourcesHealth dataInternet of medical thingsInteroperabilityMachine learningPersonal health dataPersonal health trainPrivacyRegulationSecuritySensor networksSocial online linked dataTechnical architecturesUse cases