Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
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.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.sapharm.2018.02.004 | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!