*Corresponding author:Amit Walinjkar, Faculty of Computer Science and Electronics Engineering, University of Essex, Colchester, Essex, UK
Received:September 25, 2018; Published: October 10, 2018
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Electronic Health Records (EHR) is an essential element in human healthcare monitoring systems these days. As a large amount of data continues being archived and uploaded to healthcare repositories, virtually every second across the globe, vast amount of data mining tasks continue being modelled and modified to extract valuable decision support information. The Health Level 7 (HL7) consortium provides the framework and related standards for the exchange, integration, sharing, and retrieval of electronic health information that supports clinical practice, management and delivery. With the large number of Internet of Things (IoT) health care kits becoming available it has become increasingly difficult to log the realtime patient monitoring information to healthcare repositories. As patients continue being monitored in real-time it has become essential that the trauma events information such as stroke or cardiac arrhythmia be uploaded to the EHR in real-time. Currently available monitoring devices can monitor and analyse an abnormal condition but may not be able to upload these events in real-time. The proposed research focused on developing real-time interoperability tools and services, which can enable wearable IoT devices to interact with the EHR in real-time and can provide real-time decision support.
The Fast Healthcare Interoperability Resources (FHIR) specification was used to develop and encode trauma related information in terms of FHIR resources, conceptual and logical models using clinFHIR tools. A HAPIFHIR application was implemented on an IoT device which could upload real-time ECG, PPG and relevant trauma information on a test FHIR server. The HAPIFHIR application code could encapsulate ECG arrhythmia, vital signs and trauma events in a single observation and could upload it to the HAPIFHIR server. Several such observations could be linked to a patient context and could be observed in real time in EHR. The ECG, the PPG, vital signs and trauma events were encoded according to Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT) specifications. The alerts and alarms mechanism could assist the emergency response teams at the hospitals to prepare for an emergency well in time. An analogue front-end biomedical device was used for data acquisition and signal processing and the IoT devices were networked over wireless network to upload the events and observations to the FHIR server in real time. The system focussed on ‘preventive care’ as the next generation personalized health-care monitoring devices continue becoming available.
Keywords : IoT Healthcare; Trauma analysis; HL7; ECG FHIR; SNOMED-CT FHIR; HAPI FHIR; clinFHIR