Pieces Predict™

Virtual assistance for care teams, directly in your patient list.

Enhanced by Clinician in the Loop(™), Pieces Predict enables care teams to intervene early and efficiently, without digging through the patient chart. By reducing manual effort, providers and care managers can work more efficiently within hospitals and healthcare systems.

Pieces Predict supports healthcare providers at the point of care:

Reducing time and cognitive effort performing redundant manual chart reviews
Identifying discharge barriers and predicting discharge dates
Aligning care teams with auto-generated snapshots of a patient’s current status
Improving detection of critical care deterioration
Identifying social determinants of health in real-time

Align Care Teams

Identified Discharge
Barriers

Create more capacity by reducing your excess length of stay

AI-Generated Predicted Discharge Date

Prioritize which patients can go home the soonest

Automated Patient
Summary

Leverage generative AI to support increasing provider satisfaction and enable earlier interventions for better patient outcomes.

How it Works

Identification

Using artificial intelligence (AI), Pieces Predict rapidly identifies at-risk patients in the Electronic Medical Record (EMR) using their relevant clinical, social, and economic determinants.

Prediction

Predictive models and deep clinical algorithms analyze structured and unstructured patient data to make intelligent predictions about the likelihood of readmission, and other possible factors.

Activation

Pieces Predict provides timely information to the care team to help prioritize actions and discharges.

Monitoring

Pieces Predict continuously monitors patient data within the EMR to detect changes and identify needed interventions.

Learning

Pieces provides Clinician-in-the-Loop^TM which augments machine learning with licensed clinicians. The Pieces data-science team provides ongoing analysis for all customers.

Integrated Social Determinants
of Health (SDoH) Data

Pieces is continuously monitoring and updating the relationship between SDoH and health outcomes for better-informed recommendations and predictions.

Mae Centeno, DNP, RN, CCNS, ACNS, BC

Former System Vice President of Chronic Care Continuum, Baylor Scott & White Health

“We decided to partner with Pieces because their solution offered valuable NLP technology which discovers critical elements embedded in the EHR notes to offer key information about readmissions, without spending hours reading the chart.”

Ready to learn more?

Less time in the EHR means more time with the patient, enhancing provider and patient satisfaction. Let’s schedule a time to meet.