By 2014 the Internet of Things (IoT) had entered the mainstream via smart home and elder care applications. Medical devices introduced at the annual Consumer Electronics Show made their way into the homes and the lives of consumers. Industrial and organizational applications followed. IoT consumer devices replaced single-function appliances like scales that provided some data but offered no real knowledge management. The adoption is so great that today estimates for active IoT devices range from 10 to 40 billion (Jovanović.2021).
IoT made it convenient to monitor and manage, in real time, almost anything that can be measured. The Internet of Medical Tech (IoMT) has paved the way for reimagining holistic health. Forbes reports about 700 million IoT devices are in use in medical facilities. Business Insider estimates the global healthcare IoT market value at around $100B. The Big Four tech enterprises — Alphabet, Amazon, Apple, and Microsoft — are accelerating their pursuit of the healthcare market, and they're starting to hone their strategies in specific corners of the ecosystem, particularly in IoMT.
How might it work? Teams of physicians work virtually with patients, assessing Electronic Medical Records (EMR), Electronic Health Records (EHR), and Personal Health Records (PHR). The collection of records comprise a person’s past and present health situation. Individuals can provide data from every aspect of their lives so that they have minimal health complications and healthier lives. Better quality of health lowers medical risks and healthcare costs.
But imagine the future in which all data sources about a person’s health situation are brought together in one fantastic graph database. Sources of medical information can be updated on a moment by moment basis through the IoMT. Consumer devices can include wearables, sensor clothing, and headsets, creating an immense amount of data to analyze. In addition to IoMT, EMR, EHR, and PHR, physicians and specialists notes will be included in the mix, and the patient journal entries and conversations.
Relational databases are a poor fit for this type of data situation, and the diversity of kinds of data sources can frustrate the extraction of insights needed to help patients, to manage risks and to prevent the spread of disease. A graph database supports the proliferation of sources and forms of data, especially those from IoMT. Graph intelligence provides more clarity and faster insights.
A real-world health graph is easy to visualize. Entities like Health Care Providers (HCPs), prescribers, caregivers, Integrated Delivery Networks (IDNs) and all their members, along with IoMT products make up the nodes. Edges are the natural relationships and connections between these entities.
