Samantha M McKetchnie1, Conall O'Cleirigh2, Heidi M Crane3, Samantha V Hill4, David Prior5, Matteo Peretti5, Mariel Parman6, Douglas E Levy7, Dustin Long8, Karen Cropsey6. 1. The Fenway Institute, Fenway Community Health, Boston, MA, United States of America; Behavioral Medicine Program, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States of America; School of Social Work, Boston College, Newton, MA, United States of America. Electronic address: Smarquez1@mgh.harvard.edu. 2. The Fenway Institute, Fenway Community Health, Boston, MA, United States of America; Behavioral Medicine Program, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States of America; Harvard Medical School, Harvard University, Boston, MA, United States of America. 3. Department of Medicine, University of Washington, Seattle, WA, United States of America. 4. Department of Pediatrics, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States of America. 5. The Fenway Institute, Fenway Community Health, Boston, MA, United States of America. 6. Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, United States of America. 7. Harvard Medical School, Harvard University, Boston, MA, United States of America; Health Policy Research Center, Mongan Institute, Massachusetts General Hospital, Boston, MA, United States of America. 8. Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, United States of America.
Abstract
BACKGROUND: While addressing smoking cessation in the context of HIV primary care may increase the acceptability of smoking cessation treatment for patients, HIV care providers have not been trained in offering these treatments. Tools that aid providers in treatment selection, such as computer-generated algorithms, may address barriers to providing effective and efficient treatment options to their patients. OBJECTIVE: To test the effectiveness of a computer-generated smoking cessation pharmacotherapy recommendation algorithm fully integrated into HIV primary care against an enhanced usual care condition. METHODS: Six hundred adult smokers living with HIV will be recruited from 3 medical clinics that provide HIV care in Birmingham, AL, Seattle, WA, and Boston, MA. Participants will be asked to complete a baseline visit and 4 follow-up visits, which will include self-report assessments and carbon monoxide monitoring. Additionally, participants have the option to respond to weekly text-message based surveys sent over an 11-week period between baseline and end of treatment. Participants randomized to the AT condition will have a tailored, algorithm-generated smoking cessation pharmacotherapy recommendation delivered to their HIV care provider via EHR, with the potential to receive up to 12 weeks of smoking cessation pharmacotherapy. CONCLUSIONS: A smoking cessation pharmacotherapy recommendation algorithm integrated into HIV primary care may increase treatment utilization and smoking abstinence among smokers living with HIV. If successful, the intervention would be ready for use across the entire CFAR Network of Integrated Clinical Systems network and, more broadly, in HIV clinics that utilize an EHR system.
BACKGROUND: While addressing smoking cessation in the context of HIV primary care may increase the acceptability of smoking cessation treatment for patients, HIV care providers have not been trained in offering these treatments. Tools that aid providers in treatment selection, such as computer-generated algorithms, may address barriers to providing effective and efficient treatment options to their patients. OBJECTIVE: To test the effectiveness of a computer-generated smoking cessation pharmacotherapy recommendation algorithm fully integrated into HIV primary care against an enhanced usual care condition. METHODS: Six hundred adult smokers living with HIV will be recruited from 3 medical clinics that provide HIV care in Birmingham, AL, Seattle, WA, and Boston, MA. Participants will be asked to complete a baseline visit and 4 follow-up visits, which will include self-report assessments and carbon monoxide monitoring. Additionally, participants have the option to respond to weekly text-message based surveys sent over an 11-week period between baseline and end of treatment. Participants randomized to the AT condition will have a tailored, algorithm-generated smoking cessation pharmacotherapy recommendation delivered to their HIV care provider via EHR, with the potential to receive up to 12 weeks of smoking cessation pharmacotherapy. CONCLUSIONS: A smoking cessation pharmacotherapy recommendation algorithm integrated into HIV primary care may increase treatment utilization and smoking abstinence among smokers living with HIV. If successful, the intervention would be ready for use across the entire CFAR Network of Integrated Clinical Systems network and, more broadly, in HIV clinics that utilize an EHR system.
Authors: Jack E Burkhalter; Carolyn M Springer; Rosy Chhabra; Jamie S Ostroff; Bruce D Rapkin Journal: Nicotine Tob Res Date: 2005-08 Impact factor: 4.244
Authors: Cassandra A Stanton; Elizabeth E Lloyd-Richardson; George D Papandonatos; Marcel A de Dios; Raymond Niaura Journal: AIDS Educ Prev Date: 2009-06