Healthcare Cost Savings From the Automated Identification of Administrative Inefficiencies in Patients With High-Cost Chronic Diseases
Author(s)
Juliana Madrigal Cadavid, MSc, Alejandra Rendon, MSc, Isabel Uribe, MD, Marisella Londoño, Head Nurse, Juan Esteban Tabares, MSc, Jorge Ivan Estrada, MSc. PhD Candidate.
Helpharma, Medellin, Colombia.
Helpharma, Medellin, Colombia.
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 in 2024 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, version 4.2, 2022).
RESULTS: A total of 6,034 administrative inconsistencies were identified among a patient population with a mean age of 55.6 years (SD = 22.8), 64.5% of whom were female. The most frequent causes were treatment continuation despite physician-ordered suspension (34.1%; n = 2,058), authorization errors (26.8%; n = 1,619), duplicate authorizations (13.9%; n = 838), prescription errors (11.2%; n = 675), and other causes (14.0%; n = 844). Pharmaceutical interventions in response to these findings resulted in an estimated total cost saving of USD 4,819,021.83, with an average monthly saving of USD 401,585.20. The greatest savings were observed in oncology drugs (USD 3,444,892.60), followed by inhalers and biologics for respiratory conditions (USD 679,735.60), immunoglobulins (USD 196,332.70), antiresorptive agents (USD 183,815.90), and immunosuppressants (USD 97,460.50).
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 more efficiently manage avoidable prescription and authorization errors. Additionally, it demonstrated a substantial economic impact, contributing to improved efficiency in healthcare resource utilization.
METHODS: A descriptive observational study was conducted in 2024 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, version 4.2, 2022).
RESULTS: A total of 6,034 administrative inconsistencies were identified among a patient population with a mean age of 55.6 years (SD = 22.8), 64.5% of whom were female. The most frequent causes were treatment continuation despite physician-ordered suspension (34.1%; n = 2,058), authorization errors (26.8%; n = 1,619), duplicate authorizations (13.9%; n = 838), prescription errors (11.2%; n = 675), and other causes (14.0%; n = 844). Pharmaceutical interventions in response to these findings resulted in an estimated total cost saving of USD 4,819,021.83, with an average monthly saving of USD 401,585.20. The greatest savings were observed in oncology drugs (USD 3,444,892.60), followed by inhalers and biologics for respiratory conditions (USD 679,735.60), immunoglobulins (USD 196,332.70), antiresorptive agents (USD 183,815.90), and immunosuppressants (USD 97,460.50).
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 more efficiently manage avoidable prescription and authorization errors. Additionally, it demonstrated a substantial economic impact, contributing to improved efficiency in healthcare resource utilization.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
HSD55
Topic
Health Service Delivery & Process of Care, Real World Data & Information Systems
Disease
No Additional Disease & Conditions/Specialized Treatment Areas