Care variation Prediction
Variation in care is a major factor influencing increased population healthcare costs. While some variation in care is expected and appropriate for certain cohorts, variation can be a sign of decreased access for patients, inappropriate treatment, poor access to treatment alternatives, and reimbursements based ...
Variation in care is a major factor influencing increased population healthcare costs. While some variation in care is expected and appropriate for certain cohorts, variation can be a sign of decreased access for patients, inappropriate treatment, poor access to treatment alternatives, and reimbursements based on volume and increased treatment costs.
The deficit
Executives and physicians face the menacing task of controlling care variation to control spending in healthcare organizations. Due to the multiple complicating factors contributing to this cost and quality driver, sophisticated prediction to proactively manage costs related to unstandardized care is an immediate need.
Integrates data sources
Integrates disparate data sources and helps users understand and control care variation using predictive modeling, and applies algorithms via the KenSci Machine Learning Platform to fully leverage the rich and underlying patterns embedded within the claims data to derive accurate and actionable insights.
Variance insight alerts
Can alert an Executive and physician that an increased population cost is attributed directly to care variance from the use of certain kinds of supplies, providing insights that allow for opportunities to consider standardizing supply chain and logistics for products moving forward.
Continuous learning
Learns from a pool of historical clinical data to drill down to determine Month over Month variation in cohort characteristics and treatment for diseases and procedures.
Maximize efficiency
Reduces clinical variance and care utilization critical to maximizing the efficiency and efficacy of the healthcare system – ultimately bringing down costs and making care more affordable and accessible for everyone.