OBJECTIVES: Real-world data (RWD) provides complimentary and supplemental data to discern effectiveness and value of therapeutic interventions. Fragmented data ecosystems remain a challenge for developing real-world evidence (RWE). In particular, medications dispensed via specialty pharmacies (SP) may not be well captured in traditional claims databases or, if captured, may represent a subgroup of patients. We sought to identify whether tokenization and matching between SP data and administrative claims data was feasible and representative for a nasal formulation of metoclopramide (nasal MCP) using the treatment of for diabetic gastroparesis (DGP).

METHODS: 1,596 nasal MCP patients existed in the SP database. Patients were tokenized and de-identified via Datavant. Patients were matched to Symphony Integrated DataVerse® (IDV) utilizing the tokens. A total of 879 (55%) patients were identified in IDV. For these patients, pre and post Gimoti health care utilization (HCRU) was analyzed to determine the impact pre- and post-Gimoti prescription. Also, a comparison analysis was conducted to understand HCRU differences between nasal MCP vs oral MCP after treatment. The analyses compared all-cause and symptoms-related (gastroparesis and nausea-vomiting) visits for physician's office, hospital outpatient, inpatient hospitalization, and emergency department visits reported via the Symphony IDV.

RESULTS: Based on this novel approach, RWD was utilized to understand the impact of a particular treatment and a proper sample size for an analysis was identified to be able to conduct an analysis on a patient population that initial was not identifiable in multiple claims databases.

CONCLUSIONS: Identification of patients outside the routine healthcare sources is important to understand patient journeys, HCRU and experiences with healthcare settings, and decisions concerning new treatments; this particularly becomes more imperative for hard-to-find patients such as DGP treated with nasal MCP. The comprehensive, population-level information on health, medical, and HCRU derived from RWD can help inform healthcare decision-making.