Nathaniel M Rickles1, Amy L Huang2, Michelle B Gunther3, Winney J Chan4. 1. University of Connecticut, School of Pharmacy, 69 N. Eagleville Rd, Unit 3092, Storrs, CT 06269-3092, USA. Electronic address: nathaniel.rickles@uconn.edu. 2. Northeastern University, School of Pharmacy, 360 Huntington Ave., 140 The Fenway- 218TF Boston, MA 02115, USA. Electronic address: huang.amy@husky.neu.edu. 3. Northeastern University, School of Pharmacy, 360 Huntington Ave., 140 The Fenway- 218TF Boston, MA 02115, USA. Electronic address: gunther.michelle@husky.northeastern.edu. 4. Northeastern University, School of Pharmacy, 360 Huntington Ave., 140 The Fenway- 218TF Boston, MA 02115, USA. Electronic address: Chan.win@husky.neu.edu.
Abstract
BACKGROUND: Prescription opioid abuse has rapidly increased in recent years and is now considered a national epidemic by the United States government. Community pharmacies are at the forefront of opioid abuse, given their role in dispensing opioid prescriptions. Despite this role, however, there are few known guidelines to help community pharmacists navigate the process of detecting and managing prescription opioid abuse. OBJECTIVES: To develop and evaluate a candidate guideline, based on clinical experience and existing literature, to help community pharmacists monitor and manage potential opioid prescription abuse. METHODS: We developed an algorithm based on literature and expert advice. The algorithm was reviewed by two discussion groups and six community pharmacy stakeholders through in-depth interviews, and revised based on feedback. RESULT: Key themes identified from the discussions were that the algorithm should encompass the following: (1) start with ensuring authenticity of the prescription; (2) employ state prescription drug monitoring program (PDMP) as a primary screening tool to detect those at risk for prescription opioid abuse; (3) employ the additional abuse detection steps of clinical profile review and observation of the person picking up the prescription; (4) involve protocols of sharing concerns with the patient, making contact with the prescriber, and/or return of the prescription if appropriate, and (5) be easy to follow and significantly enhanced through color coding. CONCLUSION: Future steps should explore the feasibility of using the algorithm in different community settings, and determine the algorithm's impact on the number of prescription opioids dispensed and the number of individuals referred to prescribers for discussions about possible prescription opioid abuse.
BACKGROUND: Prescription opioid abuse has rapidly increased in recent years and is now considered a national epidemic by the United States government. Community pharmacies are at the forefront of opioid abuse, given their role in dispensing opioid prescriptions. Despite this role, however, there are few known guidelines to help community pharmacists navigate the process of detecting and managing prescription opioid abuse. OBJECTIVES: To develop and evaluate a candidate guideline, based on clinical experience and existing literature, to help community pharmacists monitor and manage potential opioid prescription abuse. METHODS: We developed an algorithm based on literature and expert advice. The algorithm was reviewed by two discussion groups and six community pharmacy stakeholders through in-depth interviews, and revised based on feedback. RESULT: Key themes identified from the discussions were that the algorithm should encompass the following: (1) start with ensuring authenticity of the prescription; (2) employ state prescription drug monitoring program (PDMP) as a primary screening tool to detect those at risk for prescription opioid abuse; (3) employ the additional abuse detection steps of clinical profile review and observation of the person picking up the prescription; (4) involve protocols of sharing concerns with the patient, making contact with the prescriber, and/or return of the prescription if appropriate, and (5) be easy to follow and significantly enhanced through color coding. CONCLUSION: Future steps should explore the feasibility of using the algorithm in different community settings, and determine the algorithm's impact on the number of prescription opioids dispensed and the number of individuals referred to prescribers for discussions about possible prescription opioid abuse.
Authors: Alina Cernasev; Shane Desselle; Kenneth C Hohmeier; Joanne Canedo; Britney Tran; James Wheeler Journal: Int J Environ Res Public Health Date: 2021-06-09 Impact factor: 3.390