Presentation (CTI)

OBJECTIVES: To determine the economic outcomes resulting from the implementation of an automated model for detecting administrative inefficiencies related to pharmacological therapy in patients with high-cost chronic diseases.
METHODS: A descriptive observational study was conducted during the years 2024 and 2025 based on the outcomes of an automated model designed to detect administrative inefficiencies. The model involved systematic cross-referencing of prescription orders, medication delivery times, and claim dates to identify records with prescribing errors or inconsistencies in medication claims. These cases were subsequently managed by the pharmaceutical care team. A univariate statistical analysis was performed using measures of central tendency, relative frequencies, and cumulative distributions. Statistical processing was conducted using the R software package (R Core Team, 4.5.0 (2025)).
RESULTS: A total of 8903 administrative inconsistencies were identified among a patient population with a mean age of 55 years (SD = 24), 66.4% of whom were female. The most frequent causes were treatment continuation despite physician-ordered suspension (34.1%), authorization errors (26%), prescription errors (11.6%), duplicate authorizations (9.4%) and other causes (11.6%). Pharmaceutical interventions in response to these findings resulted in an estimated total cost saving of USD 8,267,186.79, with an average monthly saving of USD 344,466.1. The greatest savings were observed in oncology drugs (USD 5,806,700.9), followed by inhalers and biologics for respiratory conditions (USD 1,383,219), antiresorptive agents (USD 296,776.6), immunoglobulins (USD 250,473.8), and immunosuppressants (USD 177,688.1).
CONCLUSIONS: The implementation of an automated model for detecting administrative inefficiencies optimized the pharmaceutical care process for patients with high-cost chronic conditions. It reduced the administrative burden associated with manual data reconciliation and allowed pharmacists to manage avoidable prescription and authorization errors more efficiently. Additionally, it demonstrated a substantial economic impact, contributing to improved efficiency in healthcare resource utilization.