Exploring a Broader View of the Clinical AI Continuous Improvement Lifecycle
LUCEM HEALTH WHITE PAPER
Framing your thinking for the continuous improvement lifecycle
Every clinical AI developer understands the importance of monitoring, refining, and improving AI models after they’re deployed. Without effective continuous learning and improvement processes, clinical AI models can’t adapt to new sources of data or evolving medical techniques, and they can’t measure model-generated predictions against actual patient outcomes. As a result, they may become less accurate, and therefore less effective, over time — no matter how sound the models or how much high — quality data was used to train them initially.
This white paper highlights solutions for the main hurdles associated with implementing a continuous improvement lifecycle, such as:
Outcomes data residing in different siloed systems, applications, and organizations.
Outcomes data not consistently and properly labeled. Privacy regulations limit how patient data can be used and shared.
Lag time between initial model prediction and relevant outcomes data become available for continual learning.
Download the Continuous Improvement Lifecycle White Paper
Lucem Health unlocks healthcare innovation by turning the science of clinical AI into trusted, practical, and effective point-of-care solutions. With a technology platform that takes healthcare data from any source, connects it to any clinical AI model or algorithm, and seamlessly delivers insights through familiar clinical workflows, we bring the full power and potential of clinical AI from the lab to the front lines of healthcare —augmenting a clinician’s process to identify at-risk patients earlier, so they can deliver better care and improve patient outcomes.