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Development and use of a computer program to detect potentially inappropriate prescribing in older adults residing in Canadian long-term care facilities

BACKGROUND: Inappropriate prescribing has been estimated to be as high as 40% in long-term care. The purpose of this study was to develop a computer program that identifies potentially inappropriate drug prescriptions and to test its reliability. METHODS: Potentially inappropriate prescriptions were...

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Detalles Bibliográficos
Autores principales: Papaioannou, Alexandra, Bedard, Michel, Campbell, Glenda, Dubois, Sacha, Ferko, Nicole, Heckman, George, Flett, Norman
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2002
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC131054/
https://www.ncbi.nlm.nih.gov/pubmed/12379159
http://dx.doi.org/10.1186/1471-2318-2-5
Descripción
Sumario:BACKGROUND: Inappropriate prescribing has been estimated to be as high as 40% in long-term care. The purpose of this study was to develop a computer program that identifies potentially inappropriate drug prescriptions and to test its reliability. METHODS: Potentially inappropriate prescriptions were identified based on modified McLeod guidelines. A database from one pharmacy servicing long-term care facilities in Ontario was utilized for this cross-sectional study. Prescription information was available for the 356 long-term care residents and included: the date the prescription was filled, the quantity of drug prescribed and the eight-digit drug identification number. The pharmacy database was linked to the computer-based program for targeting potential inappropriate prescriptions. The computer program's reliability was assessed by comparing its results to a manual search conducted by two independent research assistants. RESULTS: There was complete agreement between the computer and manual abstraction for the total number of potentially inappropriate prescriptions detected. In total, 83 potentially inappropriate prescriptions were identified. Fifty-three residents (14.9%) received at least one potentially inappropriate prescription. Of those, twenty (37.7%) received two potential inappropriate prescriptions and eight (15.1%) received 3 or more potential inappropriate prescriptions. The most common potential inappropriate prescriptions were identified as long-term use of non-steroidal anti-inflammatory agents and tricyclic antidepressants with active metabolites. CONCLUSION: A computer program can accurately and automatically detect inappropriate prescribing in residents of long-term care facilities. This tool may be used to identify potentially inappropriate drug combinations and educate health care professionals.