Agilum’s Comparative Rapid Cycle Analytics solution enables the leap to value-based care through a proprietary engine that compares treatments, drugs, processes, and costs to help identify the patterns that achieve the best outcomes to reduce the total cost of care.
A pharmacy-focused module powered by 140MM longitudinal patient records to help you make objective, real-time decisions and transform delivery of care
A tool that analyzes and reports drug remittance data across payer types and groups down to the NDC level. This powerful tool displays expenses, claim amounts, payer remittance for a focused group of high-cost drugs, and margins for high-cost drugs
A tool that uncovers micro-level differences on drug utilization metrics, then helps identify the need for formulary adjustments, especially on high-cost, high-volume drugs
As drug prices have skyrocketed, decisions about which drugs make it on the formulary have become increasingly important — and scrutiny and accountability have increased. Now, there is a solution that takes real-time data and turns it into real-world evidence you can use to make those decisions with confidence. Discover more by scheduling your online demo now.
Longitudinal data holds the power to improve care and reduce infection rates in hospitals, aid in accurate diagnosis and treatment decisions, and even reduce the cost and time involved in pharmaceutical research and development — if only we are willing to listen.
When it comes to hospitals deciding which medications to purchase, as well as determining which ones remain on the formularies, and which have the most value, the method is largely static and antiquated, having changed very little since the 1980s. Generally speaking, Pharmacy and Therapeutics committees, consisting of internal hospital leadership, vote to collectively decide which medications should be added or removed.
The problem with this method is that the decisions are largely subjective – influenced by anecdotal evidence about which drugs are most effective, which ones patients “prefer,” group consensus, relationships with sales reps, brand loyalty, and other unscientific factors.