Background: Up to forty-five percent of Americans do not fill prescriptions secondary to cost. Medication non-adherence leads to morbidity and mortality (~$100-300B annually). The majority of physicians cannot price medications within 25% of cost. Our institution alone sees more than 600 prescription-cash-pay patients (under/uninsured or no Part D) each month.

Purpose: To empower clinicians to reduce patient outpatient medication costs.

Description: Our team partnered with GoodRx to provide prescription pricing and discount information. We used this data to create a new proprietary algorithm-based tool to further reduce prescription cost. Leveraging a combination of therapeutic interchange and permuative analysis of medication dose, formulation, quantity, pharmacy, and available discounts – e.g. HCTZ 25 mg is “optimized” with a minimum of 3,500 permutations, a standard-use functionality of 23,625 permutations, and a maximum of 2,956,500 permutations – we are able to identify the most high-value therapeutic choice for a particular patient. Initial testing was promising.

For instance: A 38-year-old male with hypertriglyceridemia-induced pancreatitis was admitted for the fourth time in 14 months for hypertriglyceridemia-induced pancreatitis secondary to medication non-adherence. He was without insurance — receiving institutional charity care for hospitalization costs but paying cash for outpatient medication costs. Using the tool, the medical team was able to reduce the patient’s 90-day outpatient medication cost from $1,287.00 to $61.79 (95% reduction). Not believing such savings were possible, the patient’s wife filled the prescriptions while the patient was still hospitalized and excitedly showed him a picture of the receipt showing the reduced cost. With medications in hand, he was able to discharge; he expressed gratitude. By reducing his readmissions, the institution saved >$20,000/year, freeing future charity care funds and scarce hospital resources for other patients in need.

Given promising initial results such as this, we secured internal grant funding to develop an automated version of the tool. We then partnered with our hospital medicine providers and social workers to identify patients who might benefit from a “cost consult.” Thus far the initial pilot has helped 24 patients save >$5,000 annually (88%).

Conclusions: Our initial pilot demonstrates the potential for an algorithm-based outpatient cost reduction tool in combination with a network of motivated hospital medicine clinicians and social workers to reduce patient outpatient medication costs. We anticipate such cost reduction will improve primary medication adherence; reduce morbidity; improve hospital metrics; improve patient satisfaction, quality of life, and economic productivity; and reduce systemic costs. This novel approach to the problem of medication expense shows promise to dramatically improve the delivery of high-value patient care.