Effectiveness of a smoking cessation algorithm integrated into HIV primary care: Study protocol for a randomized...

Area of Science:

• Public Health
• Clinical Medicine
• Health Informatics

Background:

• Smoking cessation interventions are crucial for individuals living with HIV.
• HIV care providers often lack training in delivering smoking cessation treatments.
• Computer-generated algorithms can assist providers in selecting effective treatments.

Purpose of the Study:

• To evaluate a computer-generated smoking cessation pharmacotherapy recommendation algorithm.
• To compare the algorithm's effectiveness against enhanced usual care in HIV primary care settings.

Main Methods:

• 600 adult smokers living with HIV recruited from three US clinics.
• Participants undergo baseline and follow-up visits with assessments and carbon monoxide monitoring.
• Intervention group receives algorithm-generated pharmacotherapy recommendations via EHR for up to 12 weeks.

Main Results:

• The study aims to demonstrate increased treatment utilization and smoking abstinence.
• Data collection includes self-report surveys and carbon monoxide monitoring.

Conclusions:

• An integrated algorithm may enhance smoking cessation treatment adoption and success in HIV care.
• Successful implementation could support broader use in EHR-equipped HIV clinics and the CFAR Network.