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Barriers, solutions, and effect of using pharmacogenomics data to support opioid prescribing

Opioid use and misuse are continued issues facing clinicians across all aspects of health care. As clinicians struggle to effectively manage opioid prescribing, pharmacogenomics (PGx) further offers the prescriber an improved ability to understand the potential for an individual patient’s genetics t...

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Detalles Bibliográficos
Autores principales: Bright, David, Petry, Natasha, Roath, Eric, Reckow, Elizabeth, Chavour, Sreekanth
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Academy of Managed Care Pharmacy 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10390958/
https://www.ncbi.nlm.nih.gov/pubmed/33252002
http://dx.doi.org/10.18553/jmcp.2020.26.12.1597
Descripción
Sumario:Opioid use and misuse are continued issues facing clinicians across all aspects of health care. As clinicians struggle to effectively manage opioid prescribing, pharmacogenomics (PGx) further offers the prescriber an improved ability to understand the potential for an individual patient’s genetics to influence opioid efficacy and safety. When PGx data are available at the point of initial prescribing, clinicians can apply that data to drug therapy selection. However, barriers continue to exist relative to PGx data sharing and interpretation, which have created difficulties for widespread PGx implementation. This article briefly describes potential barriers to PGx data integration, strategies to overcome those barriers, and the potential positive effect of successful data sharing on opioid prescribing. Prescription drug monitoring programs (PDMPs) have been successfully operationalized to share controlled substance prescribing data across health care settings. Such data sharing enables clinicians to, among other things, better understand risks associated with misuse. Because a relatively limited volume of PGx data is currently pertinent to opioid prescribing, such PGx data could be added to PDMPs as a way to communicate genetic information within current technology platforms. Not only would this integrate into existing clinical workflow models where PDMP data are accessed at this point of prescribing and/or dispensing, but associated clinical guidance for PGx data interpretation in the context of opioids could be integrated into the workflow process. Such clinical decision support could be provided directly through the PDMP interface for uniformity or could be provided via systems that access PDMP data. Clinical, economic, and policy implications of the inclusion of PGx data within PDMPs are also discussed. Through harnessing PDMP for data sharing, multiple barriers to PGx implementation could be mitigated, and clinicians may have better access to PGx data to optimize opioid prescribing.