Continua medical-grade data for remote monitoring of health, medical and fitness
Remote monitoring of individuals through personal connected health devices encompasses capturing and securing data in dynamic mobile environments outside of the clinical environment: such as the tracking, storing and forwarding of a person’s vital signs data and their measurement time-stamps as they travel over different time zones, within environments with no or poor connectivity and in instances of an abrupt loss of connectivity or power. This includes protecting and securing a person’s privacy while ensuring responsiveness of concerns encountered during the monitoring of their data. Achieving medical-grade interoperability means:
- Multiple sensor types of data, e.g., vital signs sensor data from a glucose monitor, a blood pressure cuff, a pulse-oximeter, thermometer or any other CDG remote monitoring device, may be sent separately or combined as multi-measurements and be clearly understood by healthcare providers and the care support systems they use end-to-end – all the while the context of the data remains.
- Healthcare providers have an authentic and holistic view and understanding of the data because the chemical, biological and medical science data is understood through its comprehensive nomenclature coding system, as per widely used standards.
- The data model can accommodate the needs of use cases across the healthcare system, so deployments can handle data that serve the most demanding Use Cases in this respect. Other deployments may work with simpler sub sets as appropriate. This to accommodate care scenarios that involve complex hospital treatment as well as scenarios that do not, like prevention programs. As an example, for an ICU patient there is much more relevant data to “blood pressure” than for a general practitioner visit.
- Time order of measurements is preserved regardless of poor connectivity conditions that occur from time to time and the individual moving between time zones (and possibly switching between home and travel measurement devices).
- Privacy and security provisions as well as authentication of the individual and the devices meet the legal and other needs of the healthcare sector.
- Interoperability between devices, gateways and services has been achieved through harmonization with existing adopted healthcare standards and protocols understood by regulatory agencies, governments, EHRs, clinicians, etc. and as validated with the muster and rigor of a globally accepted conformity acceptance scheme requiring the very best practices for compliance and interoperability.
To achieve the above, the CDG achieves medical-grade interoperability by specifying an end-to-end information and communication technology (ICT) framework for personal connected health solutions based on recognized open standards, to create a secure and interoperable health data exchange. They enable the secure flow of medical-grade data among sensors, gateways, and services by providing clear guidance on their interoperability by adding the necessary missing features within the underlying standards or specifications. The value that the CDG’s provide is secure medical data exchange fully harmonized with existing adopted healthcare standards and protocols understood by regulatory agencies, governments, EHRs, clinicians, etc.
As a result, Continua's medical-grade data model is both generic and extensible. This was the core tenet of the Continua Design Guidelines (CDG) envisioned in 2006 by Continua Health Alliance: to foster the integration of medical-grade health/sensor data to flow from a multitude of vital signs devices used by consumers to health services, all the way into local, regional or national EHRs and data lake services in a safe and secure manner. Compared to other data models where there is a different structure defined for every measurement type (such as Bluetooth LE, Open mHealth and many proprietary models), Continua's data model has an extensible structure composed of:
- a limited set of physiological measurements (where only the Type, Value, Unit and Time-stamp are required), and
- clearly defined attributes that efficiently describe and model hundreds of different health/sensor measurement types (i.e., Temperature, Glucose, Blood Pressure, Insulin Pump, CGM, Fitness, with many more clearly specified codes that define the science of each type as specified by the medical community).
- Continua’s medical-grade data model is analogous to FHIR in that it defines a small set of base types that can be used to model more complex devices. The Continua approach supports a dynamic and scalable ecosystem – something that is untenable with other existing data models.
This means that gateway developers (computer and mobile app developers) who implement to the CDG data model won't need to update their product coding over time in order to handle different measurement types sent from another vendor's Continua compliant device - the gateway will always work. And, through Continua's Design Guidelines, all of this maps directly into the HL7 record set: including version 2, version 3 and now FHIR. This allows for easy and uniform trans-coding to legacy systems without loss or distortion of clinical meaning.
For more information, please see review http://www.pchalliance.org/about-continua