Screening for Adverse Drug Events: a Randomized Trial of Automated Calls Coupled with Phone-Based Pharmacist Counseling
Background Medication adverse events are important and common yet are often not identified by clinicians. We evaluated an automated telephone surveillance system coupled with transfer to a live pharmacist to screen potentially drug-related symptoms after newly starting medications for four common pr...
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Veröffentlicht in: | Journal of general internal medicine : JGIM 2019-02, Vol.34 (2), p.285-292 |
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Sprache: | eng |
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Zusammenfassung: | Background
Medication adverse events are important and common yet are often not identified by clinicians. We evaluated an automated telephone surveillance system coupled with transfer to a live pharmacist to screen potentially drug-related symptoms after newly starting medications for four common primary care conditions: hypertension, diabetes, depression, and insomnia.
Methods
Cluster randomized trial with automated calls to eligible patients at 1 and 4 months after starting target drugs from intervention primary care clinics compared to propensity-matched patients from control clinics. Primary and secondary outcomes were physician documentation of any adverse effects associated with newly prescribed target medication, and whether the medication was discontinued and, if yes, whether the reason for stopping was an adverse effect.
Results
Of 4876 eligible intervention clinic patients who were contacted using automated calls, 776 (15.1%) responded and participated in the automated call. Based on positive symptom responses or request to speak to a pharmacist, 320 patients were transferred to the pharmacist and discussed 1021 potentially drug-related symptoms. Of these, 188 (18.5%) were assessed as probably and 479 (47.1%) as possibly related to the medication. Compared to a propensity-matched cohort of control clinic patients, intervention patients were significantly more likely to have adverse effects documented in the medical record by a physician (277 vs. 164 adverse effects,
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ISSN: | 0884-8734 1525-1497 |
DOI: | 10.1007/s11606-018-4672-7 |