Quantifying health and healthcare
Updated: Feb 12, 2022
The heart of digital transformation is quantification or more specifically the capacity to continuously measure (sample) processes, environments, systems, people, etc. It aligns with engineering’s dictum “In God we trust, all else we measure” and is indispensable in the operation of modern systems, real-time analysis (including advanced analytics and artificial intelligence), and decision-making. Quantification and artificial intelligence methods including deep learning help us understand and model dynamic systems, processes, and interactions subject to multiple influences and dependencies. In medicine, the physiological changes detected and deciphered by these systems are often overlooked by traditional healthcare providers relying on legacy practices.
Quantification begins with sensors that capture, pre-process, and continuously stream measurements for interpretation, analysis, and response. The challenge with these systems is in transforming and interpreting massive volumes of complex data into useful actionable information. It’s a daunting task in fields like medicine where technology is capturing and deriving unprecedentedly complex data sets that challenge even expert practitioners.
For example, Biobeat’s remote patient monitoring patches and systems collect millions of data points per patient every day. These include blood pressure, one-lead ECG, heart rate, heart rate variability, pulse pressure, systemic vascular resistance, mean arterial pressure, stroke volume, cardiac output, cardiac index, blood saturation (SpO2), respiratory rate, and skin temperature. It’s a remarkable technological achievement and unprecedented continuous quantification of patient cardiovascular and respiratory performance.
It’s also too much information for doctors and nurses to synthesize and put to practical use, so Biobeat applies advanced analytics and artificial intelligence to transform data into actionable insights for guiding patient care. The system’s advanced AI and Analytics individually score each patient based on staff customized thresholds to perform automated triage, prioritize care, and alert on early patient deterioration. Specialists benefit from anytime-anywhere access to patient real-time monitoring data and AI-based analytical tools. Similarly, the system helps clinicians implement data-driven personalized care strategies based on evaluating earlier than previously possible how individual patients respond to treatments.
Together, these innovative technologies are revolutionizing health management, diagnostics, and care delivery. They also raise important concerns about their deployment and integration with existing practices. I will discuss these in upcoming posts.