Lack of real-time, actionable data leaves many organizations struggling to measure the effectiveness of population health improvements. For example, data available to the University of Texas Medical Branch (UTMB Health), a safety net organization with 125,000 Medicaid patients, was often typically delayed, at least six weeks, slowing the improvement process. The data also did not convey the scope of potential opportunities, hindering chances for improvement on Delivery System Reform Incentive Payment (DSRIP), a value-based care payment reimbursement model program for Medicaid and low-income uninsured patients. With the help of data analytics, UTMB Health was able to focus on improvement efforts for these populations and boost reimbursement based on DSRIP performance. UTMB Health also earned $2.1 million in pay-for-performance dollars, achieved after the analytics application was implemented and demonstrated improvement on 23 of 32 performance measures—nearly 72 percent.