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Pieces Predict

Artificial intelligence, machine learning, and analytics predict outcomes so healthcare providers can provide care early and efficiently.

Pieces Predict is a cloud-based software platform that uses artificial intelligence, natural language processing, and clinically-reviewed algorithms to extract critical, actionable data from the full-breadth of electronic health records. By reducing manual effort, hospitals and healthcare systems can work more efficiently. Predicting outcomes leads to early detection and intervention to prevent disease and decrease patient deterioration.

Features

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 risks to the patient, the likelihood of readmission, and probable outcomes based on social determinants.

Activation

Pieces Predict provides the necessary alerts to the care team with detailed recommendations for safe, timely treatment, prioritization of action ,and discharge.

Monitoring

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

Learning

Pieces provides Clinician-in-the-LoopTM 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.

Pieces Predict is tailored to your specific needs through a system of modules.

Universal

Identify and mitigate risk factors that can contribute to excess length of stay

reLOS

Reducing Length of Stay.

Identify and mitigate risk factors that can contribute to excess length of stay

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reADMT

Reduce Readmittance

Social Determinants of Health and other factors score patients on the likelihood of readmittance

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SDoH

Social Determinant of Health

Combines SDoH with clinical and environmental factors to provide an overall portrait of patient vulnerability and risk

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OPD

Opioid Module

Reviews all clinical documentation and existing data for real-time identification, monitoring, and tracking of the total opioid patient population

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Adult

Adult patients need specific protocols

aCo19

Adult COVID-19

Reviews all clinical documentation and existing data for real-time identification, monitoring, and tracking of the total COVID-19 patient population

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aEWS

Adult Early Warning System

Intervene with ample leadtime to alter potential acute clinical deterioration

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Pediatric

Pediatric patients have specific needs and protocols

pediCo19

Pediatric COVID-19

Reviews all clinical documentation and existing data for real-time identification, monitoring, and tracking of COVID-19 pediatric patients

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pediEWS

Pediatric Early Warning System

Intervene with ample leadtime to alter potential acute clinical deterioration

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pediSep

Pediatric Sepsis

Predicts the likely presence of sepsis on arrival in pediatric patients

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pediAST

Pediatric Asthma

Predicts the likelihood of an Emergency Department visit or hospitalization within 90 days of visit

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

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.”

Pieces for Health Systems

If you are a Health System, Pieces Predict is just one part of our solution. We can help improve patient care, protect your bottom line, and connect you and your patients to community resources.

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Ready to learn more?

Pieces Predict is flexible and can be tailored to your specific needs. We’d love to schedule a demo.

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