Data Analytics to Bedside

Baylor Scott & White Quality Alliance is putting our investment in high-powered data analytics to work by refining algorithms to generate actionable patient data that is then distributed to the hands of our physicians. Analytics to Bedside or A2B initially targeted high-risk patients – top 5% attributed to us through our managed care contract agreements including the Baylor Scott & White Health North Texas division employee health plan. Initial success was realized, but continued improvement efforts have allowed for the progression from a retrospective, reactive model for both our care coordination efforts and our office direct outreach efforts which focused on patients who have already had a risk occurrence to now using a risk-stratification model where an algorithm is applied to integrated data from multiple sources (inpatient and outpatient EHR’s, claims data, and inpatient and outpatient practice management systems) and predicts who will have a high probability of a risk occurrence (rising risk) as well as identifies those in need of wellness exams and preventative measures. These sophisticated risk or wellness stratification models allow us to identify patients with unmet needs and allocate the appropriate level of care resources to promote wellness and minimize or prevent a risk occurrence before it happens.

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Patients in the greatest need category – or top 5% (high-risk) are enrolled in complex disease management, medication reconciliation, and transitional care protocols through a RN care manager. Those identified within the next highest 15% (rising-risk) of patients are enrolled in a patient centered medical home approach utilizing transitional care services, and gaps-in-care protocols through a MA Health Coordinator. Those identified within the lowest (80%) risk are proactively outreached and offered wellness and gaps-in-care services. It is in this way that we achieve higher quality care that (1.) is wellness oriented and compliant with population contracts, (2.) is strategically matched to appropriate care resources to reduce unnecessary, higher-cost interventions, and (3.) increases wellness for all populations using single workflow approach across the risk spectrum (high-risk to low-risk).