BACKGROUND AND OBJECTIVES: The study's objective was to identify patients at risk for ineffective pain management using patient-specific opioid prescription data. METHODS: We conducted a retrospective review of payer opioid prescription data and patient charts from a rural family medicine group with a residency program. Sixty-one patients were identified who had received three or more prescriptions from two or more providers over a 6-month period. We noted the number of prescriptions and providers, type of medication prescribed, evidence of a medication management agreement (MMA), early refills, increasing doses, frequent telephone calls, reports of lost or stolen medication, history of drug abuse, and proper chart documentation. RESULTS: Seventy percent of the patients were female, 35% had a history of drug abuse, 15% had MMAs, and only 55% had accurate office chart documentation. The number of prescriptions in a 6-month period averaged 8.4 (SD=5.5, range 3 to 28). The average number of prescribers was 3.7 (SD=1.8, range 2 to 10). Patients using non-opioid analgesics had 3.2 fewer prescriptions per 6 months and were less likely to have six or more prescriptions (OR=0.24, 95% CI=0.08--0.73) than those on opioids alone. Concurrent use of non-opioid analgesics, escalating opioid dosage, and number of providers best predicted the number of opioid prescriptions. CONCLUSIONS: Payer data was useful in identifying patients who receive larger-than-expected numbers of opioid prescriptions and factors associated with those larger prescription numbers.
BACKGROUND AND OBJECTIVES: The study's objective was to identify patients at risk for ineffective pain management using patient-specific opioid prescription data. METHODS: We conducted a retrospective review of payer opioid prescription data and patient charts from a rural family medicine group with a residency program. Sixty-one patients were identified who had received three or more prescriptions from two or more providers over a 6-month period. We noted the number of prescriptions and providers, type of medication prescribed, evidence of a medication management agreement (MMA), early refills, increasing doses, frequent telephone calls, reports of lost or stolen medication, history of drug abuse, and proper chart documentation. RESULTS: Seventy percent of the patients were female, 35% had a history of drug abuse, 15% had MMAs, and only 55% had accurate office chart documentation. The number of prescriptions in a 6-month period averaged 8.4 (SD=5.5, range 3 to 28). The average number of prescribers was 3.7 (SD=1.8, range 2 to 10). Patients using non-opioid analgesics had 3.2 fewer prescriptions per 6 months and were less likely to have six or more prescriptions (OR=0.24, 95% CI=0.08--0.73) than those on opioids alone. Concurrent use of non-opioid analgesics, escalating opioid dosage, and number of providers best predicted the number of opioid prescriptions. CONCLUSIONS: Payer data was useful in identifying patients who receive larger-than-expected numbers of opioid prescriptions and factors associated with those larger prescription numbers.
Authors: Alicia Grattan; Mark D Sullivan; Kathleen W Saunders; Cynthia I Campbell; Michael R Von Korff Journal: Ann Fam Med Date: 2012 Jul-Aug Impact factor: 5.166
Authors: Gerald Cochran; Adam J Gordon; Craig Field; Jennifer Bacci; Ranjita Dhital; Thomas Ylioja; Maxine Stitzer; Thomas Kelly; Ralph Tarter Journal: Res Social Adm Pharm Date: 2015-05-08
Authors: Gerald Cochran; Jennifer L Bacci; Thomas Ylioja; Valerie Hruschak; Sharon Miller; Amy L Seybert; Ralph Tarter Journal: J Am Pharm Assoc (2003) Date: 2016-03-24
Authors: Bianca Blanch; Nicholas A Buckley; Leigh Mellish; Andrew H Dawson; Paul S Haber; Sallie-Anne Pearson Journal: Drug Saf Date: 2015-06 Impact factor: 5.606