ClosedLoop

ClosedLoop operates a data science platform designed exclusively for healthcare organizations seeking to predict and prevent adverse health events. The company provides AI and machine learning tools that enable providers, payers, pharmaceutical companies, and digital health organizations to identify individuals at risk before they experience costly or harmful health outcomes.

The platform features three core components: ClosedLoop Predict for individual-level risk prediction, data science automation tools that reduce data preparation time, and a Healthcare Content Library containing templates for predictive models, machine learning features, and data transformation specific to healthcare use cases. The system incorporates social determinants of health data and provides explainable AI outputs designed for clinical decision-making.

ClosedLoop has earned recognition as a four-time consecutive Best in KLAS winner for Healthcare AI/Data Science Solutions (2022-2025) and won the CMS AI Challenge. The company serves health plans, ACOs, provider networks, pharmaceutical companies, and digital health organizations across the healthcare sector.

Organizations should consider ClosedLoop when they need to move beyond rules-based risk stratification to AI-driven predictive analytics with clinical explainability. The platform's healthcare-specific design and content library reduce the time data science teams spend on infrastructure and allow them to focus on model refinement and deployment.

Expect a partnership approach focused on operationalizing AI for specific use cases like care management targeting, cost prediction, and preventive intervention prioritization. The platform serves organizations managing population health under value-based contracts who need accurate predictions to allocate limited care management resources effectively.

ClosedLoop focuses exclusively on healthcare, serving providers, ACOs, payers, pharmaceutical companies, and digital health organizations. The platform addresses healthcare-specific challenges including care management prioritization, high-cost patient identification, medication adherence prediction, and social determinants of health integration. The company's content library includes healthcare-specific data models, and its explainable AI approach addresses clinical adoption requirements unique to healthcare settings where transparency in algorithmic decision-making is essential for provider trust and regulatory compliance.

Mid-to-large healthcare payers, ACOs, health systems, and provider networks operating under value-based care contracts who have data science teams and need to predict individual-level health risks. Organizations seeking to move from retrospective reporting to proactive intervention targeting, particularly those managing complex populations where social determinants of health significantly impact outcomes.