CLOSEDLOOP PLATFORM
Better data science for better health.
Everything you need to build, deploy, and maintain impactful AI/ML-driven operations at scale.
The first data science platform purpose-built for healthcare.
The only data science platform to win the Centers for Medicare & Medicaid (CMS) AI Health Outcomes Challenge and be ranked #1 Best in KLAS two years in a row, ClosedLoop provides you with all the capabilities and expertise needed to make AI work for healthcare.
Data Ingestion & Normalization
Data Preparation
Training & Validation (AutoML)
Deployment & Monitoring (ML Ops)
Explainability & Reporting (XAI)
Data Ingestion & Normalization
Every healthcare organization’s data environment is unique — and messy. ClosedLoop speeds up the time-to-value for deploying data science operations and programs by productizing all of the processes necessary to ingest and normalize healthcare data.
- HIPAA-compliant, HITRUST, and SOC 2 Type II-certified data transfer and storage
- Ingestion and standardization of nearly every type of raw healthcare data
- Automatic mapping of data to 20+ healthcare terminologies (e.g. CCSR, CPT, LOINC)
- Automatic cleanup of medical codes
Data Preparation
Forcing generic data science tools to work in a healthcare context requires your team to build infrastructure instead of delivering insights to decision-makers. ClosedLoop enables rapid delivery of value with tools that transform raw data into the complex variables needed for applicability to a healthcare setting.
- ML feature versioning, change logs, rollback, and archiving
- Dynamic index dates, look-back periods, and prediction windows
- Define ML features once for reuse across exploratory data analysis, model training, and production
- Generation of multiple ML features from a single logical expression
- Natural language processing (NLP) for clinical notes
Training & Validation (AutoML)
The true value of data science lies in iteratively building and refining models to deliver accurate and explainable health insights, not in building tools and solutions. Focus your data science efforts on delivering explainable health insights with the only platform designed specifically for healthcare.
- Elastic scaling compute, able to handle models with thousands of features
- Automated cross-validation and hyperparameter optimization
- Control over algorithm selection, including XGBoost, random forest, elastic net, and other open-source ML algorithms
- Auto-generated precision / recall and ROC curves, calibration plots, and AUC calculation
- Algorithmic bias checks
Deployment & Monitoring (MLOps)
Building models is easier than keeping them running. ClosedLoop gives you the ability to instantly monitor performance to understand when and why model accuracy drops, and act quickly to restore it.
- Performance and feature drift monitoring across model iterations
- Auditable individual-level predictions and contributing factors
- Model run and data ingestion reporting
- Model staging, versioning, rollback, and archiving
Explainability & Reporting
Clinicians and other key stakeholders won’t act on predictions they don’t trust. With ClosedLoop’s award-winning explainability, you can help ensure adoption of AI/ML-driven insights that are not only accurate but also understandable and trustworthy.
- Individual- and population-level contributing factors
- Fully configurable report generation
- Longitudinal risk tracking
- Patented factor evidence for granular explanation of individual-level risk predictions with Shapley value significance testing
RELATED RESOURCES
A Framework for Measuring the ROI and Health Equity Impact of AI-Enabled Health Programs
Discover a useful framework that your organization can use to evaluate programs’ ROI and impact on health equity, …
How ClosedLoop Won the CMS AI Challenge
ClosedLoop CTO, Dave DeCaprio, details how we won the CMS AI Health Outcomes Challenge, what obstacles we encountered …
ClosedLoop Named Best in KLAS 2023
ClosedLoop ranked #1 in Healthcare Artificial Intelligence: Data Science Solutions for the second year in a row. …
