Graph databases can power IoT in health care
Learn why graph databases are perfect for IoT.
Until very recently, objects of common use (such as wearable devices, cars, watches, refrigerators and health-monitoring devices) did not produce or handle data at a large scale and lacked internet connectivity.
However, furnishing such objects with computer chips and sensors that enable data collection and transmission over the internet has created a network of billions of physical devices around the world, commonly known as Internet of Things (IoT).
In the health care field specifically, IoT has become nothing short of a revolutionary movement. Devices like fitness or health-tracking wearable devices, biosensors, clinical devices for monitoring vital signs, and more create a continuous stream of data.
This makes them a major contributor toward revealing critical information that is potentially beneficial in improving the health and lifestyle of the population at large.
However, as with any technological disruption, IoT has led to an emergence of new datasets with extremely painful data management demands.
Having everything connected means that even the simplest of IoT applications will demand extremely open, flexible, and fundamentally connected data models. With the rich ecosystem of products that IoT presents, the management of such an information system is systematically different from before.
Why graph databases are perfect for IoT
The traditional relational databases fail miserably when dealing with high volume, sensitive, and interconnected data ingested into organizational systems at a very high velocity from disparate sources. They cannot deliver in real-time, a capability that is critical for virtual health care, due to technical limitations such as complex joins.
Typical examples include master data management, ensuring compliance with GDPR, HIPAA and other regulations, failing to uncover or discover patterns in real-time in fraud detection, implementing symbolic AI/reasoning, and more. The list of cases where traditional databases fail is endless.
Graph databases are schema-less and built of nodes to store data entities, with edges to store relationships between them. They are a perfect choice for understanding complex, connected, and dynamic systems.
As each smart device in a virtual cloud of devices is likely to have multi-faceted interrelationships with other devices, graph technology allows these relationships to be manifested more realistically, without the need to force fit into arbitrary relational models.
Graphs are especially useful for discovering previously unknown or little understood relationships. These relationships can include those arising from behavioral patterns or coincident patterns of change.
This significantly advances the ability to unveil insights on everything in IoT, including data control and security, and facilitate real-time analytics on the complex relationships between connected devices.
A good example would be that of discovering fraud rings in real-time, which is prevalent in banking and relevant in health care. Conducting entity link analysis to detect organized or collusive activities, kickbacks, fake referrals, and other health care frauds is almost impossible to achieve with traditional databases.
This is because queries to find relations are incredibly complex to build, expensive to run, and scaling them in a way that supports real-time access poses significant technical challenges.
In order to leverage the vital relationships and growing swarm of real-time devices that make up IoT, graph databases are optimized to not only query the data quickly, but also to preserve relationship data for perpetual real-time performance.
Graphs enable us to ask questions we haven’t even considered asking before we had technologies optimized for providing these answers. However, graph databases still largely remain an untapped asset that can truly help the health care industry in dealing with real-time, saturated, and complex data.
Graph and IoT – role in health care
- Graph databases can handle high volume, sensitive, and interconnected data from disparate sources (such as connected devices, wearables, biosensors, fitness trackers, electronic medical records, claims, laboratories and pharmacies) and analyze it in real-time.
- All these sources of data make for valuable patients’ health-related data inflow. This can include the minutest of details such as social determinants of health (SDOH), pulse rate, laboratory data, allergies, vital signs, immunizations, medications, MRI scans and more.
- Real-time analysis of this data can help with early intervention and treatment for patients within the comfort of their homes and offices.
Graphs can easily manage data inflow from IoT devices and analyze it in real time. By integrating this data stream with historic IoT data and other sources like EMRs and PHRs in graph, early intervention and treatment can be provided to patients within the comfort of their homes and offices.
More importantly, in the current COVID-19 pandemic situation, a strategic graph database-IoT based solution will not only help in providing virtual personalized health care, but can also help to prevent the spread of this contagious disease and the life-threatening risks associated with it.
Here is an example of how this could work: A mechanism has been built in the medical system for real-time streaming of all the patient vitals from wearable and home care devices into Graph DB, which also has an input of other connected patient data like EHR/EMR, gaps in care, biometrics, lab data, X-rays, MRI scans and more.
Additionally, important features, such sending an SOS to the concerned clinician for immediate action the moment a patient’s vitals show an abnormality, are built in as feeds to the Graph DB.
So, if an otherwise healthy individual were to suddenly develop a fast pulse, high blood pressure or undergo a change in weight, an SOS event would be sent to a cardiologist or nurse the moment this real-time event is received by the graph DB.
The clinician would then immediately take a holistic view of the patient’s condition, taking into consideration all types of patient’s details, along-with the newly received vitals, and decide the appropriate course of action.
All of this can happen in a matter of minutes. The event can also trigger a real-time stratification of the patient from low risk to medium or high risk through an ML model on/via Graph and alert an engagement specialist to do the appropriate outreach for enrolling the member into a related care management program without any delay.
The road ahead
With the advent of more and more processing power through technologies like quantum computing, high speed connectivity enabled through 5G networks, and added level of digital intelligence enabled by AI, IoT networks are expected to make the very fabric of the world around us much smarter and more responsive.
The devices will go beyond just transmitting data from patient to doctor but would also help to assist in areas such as medical adherence, remote monitoring, early warning, and telehealth.
With the adoption of FHIR standards, health care interoperability will improve, leading to the growth of rich, clinical data. As patients’ datasets evolve and AI algorithms mature, the promise of assisted diagnosis, disease prediction and precision medicine will become real.
This is precisely where advances in graph technology coupled with IoT will help to build the foundation for evolving the next generation of connected health care.