As revenue pressures intensify, keeping commercially insured patients in-network is critical to every health system’s survival – but many organizations lack the insights to understand why patients go elsewhere.
Network Explorer’s new filters help close that gap with granular visibility into the factors that drive out-of-network care. Built on industry-best machine learning models – for site of service accuracy, referral patterns, provider specialty imputation and patient origin – these enhancements help users distinguish between unavoidable out-of-network visits and network misalignment – enabling health systems to act with greater precision.
What's New: Filters That Pinpoint Referral Patterns by Visit Type and Payer
New filters reveal key patterns in referral behavior based on care setting and payer:
- Distinguish Emergency vs. Planned Visits. The new Emergency Visit filter allows users to distinguish between planned and emergency care. Because physicians have limited influence over a patient’s choices during an emergency, excluding these visits from referral views enables a clearer understanding of each provider’s referral activity enhanced by Trilliant Health’s machine learning models. With this filter, users can more precisely identify where alignment efforts are needed – and where out-of-network visits are unavoidable.
- Analysis of Specific Payers. With the Payer Name filter, users can now analyze visits by the payer, helping to identify patterns of payer steerage in shoppable services, network inadequacy and opportunities to strengthen relationships with key payers. This enables users to pinpoint areas where network performance can be improved and informs strategic decision-making around payer partnerships.
Why It Matters: Helps Distinguish Avoidable Leakage From Unavoidable Care
As health systems navigate tightening margins and increased competition for commercially insured patients, understanding why care is delivered out-of-network is just as important as knowing where. These new filters help move beyond surface-level metrics to uncover the underlying drivers of patient journeys – whether it is an emergency, payer influence or a breakdown in provider alignment.
Without the ability to distinguish between emergency and planned visits, network integrity analyses can be misleading – overstating risk and obscuring opportunities to improve network performance. Similarly, without visibility into patterns of out-of-network referrals across payers, it is difficult to know whether gaps are the result of referral leakage, consumer preference or payer steerage.
With clearer visibility into these dynamics, health systems can focus their strategy where it matters: reducing avoidable leakage, strengthening network integrity and improving performance across key provider and payer relationships.
Get Started Today
Already a client? Log in now to explore the new filters and gain deeper insights into referral dynamics, emergency care and payer influence in your network.
New to Network Explorer? Schedule a demo to learn how these capabilities can help you reduce leakage, improve alignment and strengthen your network’s performance.
