Economic Impact of a Pharmacotherapeutic Management Program for Oncological Patients

Author(s)

Uribe I1, Rendon A1, Madrigal Cadavid J2, Restrepo AM1, Herrera R1, Abad JM3, Estrada Acevedo JI1
1Helpharma, Medellin, Antioquia, Colombia, 2Helpharma, Medellin, ANT, Colombia, 3SURA EPS, Medellin, Colombia

OBJECTIVES: To describe the economic results of clinical follow-up and pharmacotherapeutic management of oncology patients.

METHODS: Descriptive cross-sectional study in patients diagnosed with cancer undergoing treatment with oral chemotherapy, evaluated by a team of physicians and pharmacists during 2023. Through continuous pharmacotherapeutic follow-up, medication errors and problems in pharmacological adherence were detected and intervened to guarantee the pertinence, adherence, safety, and effectiveness of the treatments, and the rational use of health resources. A univariate analysis was performed using the statistical package R Core Team Version 4.2 (2022).

RESULTS: 9709 patients were evaluated, and 262 cases were detected to intervene in 239 patients (2.5%), with a mean age of 59 years (SD=17), 66% women. A total of 48.5% of the cases corresponded to therapeutic non-adherence and accumulation of medicines at home, 38.6% prescription errors, 11.8% pharmacological duplications, and 1.2% chronification of unnecessary treatment. The drugs associated were mainly letrozole 9.5%, anastrozole 8.8%, capecitabine 8%, abiraterone 6.1%, temozolamide 4.6% and lenalidomide 4.6%. These pharmaceutical interventions represented savings equivalent to COP 196,8736,803 (501,125.42 USD).

CONCLUSIONS: Clinical follow-up and pharmacotherapeutic management in oncology patients allow for obtaining satisfactory clinical results and a significant economic impact on the health system.

Conference/Value in Health Info

2024-11, ISPOR Europe 2024, Barcelona, Spain

Value in Health, Volume 27, Issue 12, S2 (December 2024)

Code

RWD94

Topic

Clinical Outcomes, Methodological & Statistical Research, Real World Data & Information Systems

Topic Subcategory

Artificial Intelligence, Machine Learning, Predictive Analytics, Clinical Outcomes Assessment, Health & Insurance Records Systems

Disease

Drugs, No Additional Disease & Conditions/Specialized Treatment Areas

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