The following is a guest article by Steven Lazer, CTO at Dell Technologies Healthcare
With Edge and IoT devices generating massive quantities of data from hospital acute care bedside monitors, connected wearable medical devices, sensors, and mobile applications, the Health IoT opportunity is accelerating in fundamental ways.
To fully leverage data coming from use cases such as the monitoring the well-being of patients and devices remotely and tele-sitters to improve patient safety and reduce fall risk in post-acute care step-down patients, health leaders are starting to deploy distributed analytics to derive actionable insights from IoT devices at the Edge of the data fabric.
Right now, most data being generated in healthcare environments is typically sent to, and analyzed in, a central location – whether it be a data center, public cloud or some other organizational repository. Transmitting data from an endpoint to specific servers for analysis comes with challenges that include network latency, bandwidth costs, network hardware expenditure, man-hours to configure and monitor, data security in transit, and compliance risks.
At the same time, there is an increasing opportunity for decision-making at the point of capture — for example, enabling emergency medical services to process patient data in the ambulance without having to transmit it to the hospital for analysis first. In an example like this, the urgency for immediate diagnostic responses is imperative.
All these factors build the case for bringing the building blocks of cloud analytics closer to where the IoT devices are located so that these intelligent systems can take actions based on the results of the analysis. In simple terms, we’re talking about bringing the analytics to the data, rather than sending the data to the analytics.
Connective Analytics take us one step further: allowing us to filter and categorize data by leveraging AI and Machine Learning at the Edge. AI advancements can help to aggregate data points, leading to learnings that can be used to update relevant algorithms. This allows for continuous improvement in pattern recognition capabilities, automating the ability to flag abnormalities to trigger a behavior or alarm. We’re moving toward a future where real-time AI learning occurs as patients (and their data) interact with their healthcare providers, creating a continuous real-time patient record – 24/7/365.
Healthcare organizations will evolve from reactively collecting data points at the point of care to proactively designing algorithms that can turn IoT-generated data into edge intelligence through distributed analytics. Not only will this lead to faster, more accurate, less costly clinical decisions at the edge, but IoT analytics can filter and prioritize biometric inputs before transmitting this information.
With filtered patient data, clinicians can deliver more efficient care to focus on patient outcomes. Applying patient-specific algorithms to human data can narrow the range of data collected while still not missing what would be “normal” for the patient. The initial machine learning period for the algorithms will likely surface false positive data points requiring review, but as more data becomes available, these algorithms will continue to help streamline decision-making and improve the information provided to clinicians.
Dell Technologies provides Connective Analytic Solutions to deploy, orchestrate, and operationalize machine learning against data sets in the healthcare IoT ecosystem – enabling refinement in place, reducing the requirement to move patient data, and resulting in faster and more efficient outcomes. Using analytics as an aggregator and filter at the Edge can result in the following:
- Expanding chronic disease management and preventative medicine with sensors that can alert providers to clinically meaningful changes and recommend early intervention.
- Reducing readmissions and improving patient safety by monitoring discharge instruction adherence through computer vision and other IoT solutions in acute care or home-based recovery settings.
- Enhancing precision medicine research by integrating wearables, whether into clinical trials or closing the gaps in episodic care.
- Automatically removing PHI from data to allow information aggregation across patient populations for incorporation into population health management strategies.
- Managing pharmaceuticals and improving drug supply chain safety by tracking medication from manufacture to consumption.
- Helping patients suffering from chronic diseases leverage telehealth opportunities, such as virtual health consultations for condition assessments and remote monitoring.
We’ve certainly moved well beyond the lean operations and financial health IoT use cases introduced a decade ago. Instead, we can now envision a greater goal: moving from disease management to disease prevention; that’s the power of IoT devices at the healthcare Edge. It’s enhanced productivity. It’s improved resource utilization. With better insights from Connective Analytics, healthcare organizations can move toward their vision of becoming a smarter digital healthcare organization.
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About Steven Lazer
Steven Lazer is the Global Healthcare CTO for Dell Technologies Healthcare & Life Sciences. He concentrates on translating healthcare organizational requirements into best-practice Health IT strategies aligned with a robust partner ecosystem including clinical ISVs, system integrators, and channel partners. Steven has over 30 years of experience in Health IT and has personally driven many successful EMR implementations.
About Dell Technologies
Dell Technologies provides solutions to help healthcare organizations realize their digital transformation – from the point of care to the data center to the cloud. Our solutions provide transformative and essential infrastructure that make the future of healthcare real – including healthcare Edge and IoT solutions. Dell Technologies is proud sponsor of Healthcare Scene. Visit us at www.DellTechnologies.com/Healthcare and read our latest whitepaper: “Evolving from Chronic Disease Management to Preventative Care: Healthcare IoT Data at the Edge”.