Clinician-in-the-Loop™

Our clinicians continuously make improvements to our healthcare AI.

Our licensed clinicians constantly monitor and “teach” the Pieces Artificial Intelligence (AI) so it keeps getting smarter and more reliable with machine learning.

Benefits of Clinician-in-the-Loop

NLP Accuracy

Real-world experience helps create highly accurate healthcare Natural Language Processing (NLP) models for healthcare solutions.

Reduced Errors with Machine Learning

With machine learning, we continuously refine and teach the algorithms to interpret the clinical data more accurately.

Efficient Refinement of the AI

On-line and offline corrections to the healthcare AI are made more efficiently by our clinicians.

Increased Trust

Experienced clinicians refining the AI and machine learning allows for interpretation of healthcare data to be more nuanced and reliable.

Our Clinician-in-the-Loop in Action

Advantages of Healthcare AI Early Warning Systems

Pieces Predict™ healthcare AI can surface impending clinical deterioration and is continuously refined by our clinicians. Real-world clinical experience helps refine our algorithms so you can have confidence that our summaries are accurate and sensitive. We work to deliver the right balance so when Pieces Predicts alerts you to intervene early, you can trust the recommendation will prevent clinical deterioration.

Refinement of SDoH Algorithm

What is happening with a patient outside the hospital is critical to understanding the proper treatment once a patient is in the hospital. These Social Determinants of Health are a critical part of our algorithms and are constantly being refined by our clinicians. If it is found that a patient has food insecurity, that factors into the treatment plan. Where malnutrition may be a fairly obvious concern, our algorithms can uplevel related concerns about diabetes or other health issues. We consistently make refinements to those clinical correlations.

Refinement to Length of Stay Work Flows

Our clinicians understand the workings of a hospital and the workflows. We put that knowledge to work within Pieces Predict. We update and refine our healthcare algorithms to increase efficiencies so patients can get discharged in the most streamlined process possible. Our clinical experience feeds into defining what steps are needed to be taken in which order. Reduced excess length of stay can support a hospital’s capacity issues, without adding more beds.

Learn more about Clinician-in-the-Loop