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. Author manuscript; available in PMC: 2022 Nov 1.
Published in final edited form as: Contemp Clin Trials. 2021 Sep 2;110:106551. doi: 10.1016/j.cct.2021.106551

Effectiveness of a Smoking Cessation Algorithm Integrated into HIV Primary Care: Study Protocol for a Randomized Controlled Trial

Samantha M McKetchnie 1,2,3,*, Conall O’Cleirigh 1,2,4, Heidi M Crane 5, Samantha V Hill 6, David Prior 1, Matteo Peretti 1, Mariel Parman 7, Douglas Levy 4,8, Dustin Long 9, Karen Cropsey 7
PMCID: PMC8607828  NIHMSID: NIHMS1739701  PMID: 34481070

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.

Keywords: smoking cessation, HIV, tobacco use, algorithm

1. Introduction

In the United States, it is estimated that 14% of all adults smoke tobacco and that smoking is the cause of over 480,000 deaths each year.1 Smoking rates among people living with HIV (PLWH) are even higher – around 40%.2 In addition to increased smoking rates, PLWH are greatly impacted by the negative consequences of tobacco use, with almost 30% of cancers and 17% of myocardial infarctions experienced by PLWH linked to smoking.3 Additionally, previous research demonstrates that PLWH who are well engaged in HIV care will lose more years of life to smoking-related disease than to HIV itself.4,5 PLWH are less likely to respond well to antiretroviral therapy (ART) if they currently smoke tobacco,5,6 and smoking has been linked to decreased adherence to ART.7

Given the negative health consequences and high prevalence of smoking among PLWH, disseminating effective and acceptable smoking cessation strategies, particularly those that are scalable for busy HIV clinical care settings, is of utmost public health importance. HIV care providers frequently act as a primary medical contact for PLWH and would be ideal for addressing tobacco use during clinic visits. However, providers who are focused on HIV care may lack time in clinic visits to counsel patients on smoking, and are often not trained in smoking cessation treatment best practices.8,9 Additionally, current smoking cessation interventions geared towards PLWH are dose intensive and largely ignore smokers who are not currently seeking treatment.10-19

As a result, effective smoking cessation interventions utilized in clinical settings need to be well-integrated into standard care and entail minimal provider burden. An intervention delivery method that meets these criteria are computer-generated algorithms. Computer-generated algorithms are provider decision-making tools that have demonstrated effectiveness in improving providers’ ability to prescribe pharmacological options during clinic visits.20-23 These algorithms incorporate patient information with known treatment options and provide suggestions for possible treatment plans to providers.

In a series of two single-site pilot studies, we developed and tested a computer-generated algorithm for smoking cessation pharmacotherapy in HIV clinics. In both studies, the computer-generated algorithm utilized participant responses around current smoking and medication use to produce a tailored pharmacotherapy recommendation. This recommendation was then communicated to the patient’s care team, who had the option to prescribe the recommended pharmacotherapy. Results from these pilot studies indicate that a computer-generated algorithm may be a feasible and effective intervention to improve smoking cessation among PLWH.24,25

As a follow-up to these pilot programs, the current full-scale randomized controlled trial aims to test the effectiveness of a computer-generated smoking cessation algorithm relative to an enhanced treatment as usual condition. Our primary outcome will be smoking cessation, operationalized as 7-day point-prevalence cessation, but we also aim to characterize facilitators and barriers to integration of the smoking cessation algorithm, as well as provider prescribing behaviors. Additionally, we will estimate the cost-effectiveness of algorithm-guided treatment as compared to standard care. This trial responds directly to the NIH Office of AIDS Research (OAR) areas of high-priority research and represents an opportunity for significant innovation in the care of PLWH who smoke.26

2. Methods

2.1. Overview of Study Design

Adult PLWH who smoke and are enrolled in CFAR Network of Integrated Clinical Systems (CNICS) (N = 600) will be recruited from medical clinics that provide HIV care at The University of Alabama at Birmingham, University of Washington, and Fenway Health to participate in a trial on the effectiveness of a smoking cessation algorithm treatment integrated into HIV primary care. Participants will complete baseline surveys, which will be used to inform the algorithm-generated smoking cessation treatment recommendation for the treatment group. All participants, regardless of randomization allocation, will be asked to engage in short, weekly text message-based surveys for 11 weeks post-baseline, and 4 follow-up assessments (see Figure 1).

Figure 1:

Figure 1:

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Flow for RCT component of the study

In addition to the randomized controlled trial, the study team have developed an educational webinar focused on the intersection of HIV and smoking, produced by the study team in conjunction with the MGH Psychiatry Academy, a group focused on developing and hosting clinician educational programs. The educational webinar will be distributed to providers at all three implementation sites. Along with the educational webinar, providers at the implementation sites will be asked to complete brief surveys at the start of the study, as well as 6- and 18-months post-study start. These surveys will ask providers to detail their experiences of treating patients who smoke and are living with HIV, as well as their attitudes towards smoking cessation treatment. At each site, a Provider Champion – a clinic medical provider who is affiliated with the study – will present at clinic meetings on the study, the importance of addressing smoking in the context of HIV care, and resources available to providers (such as the educational webinar).

2.2. Specific Aims and Hypotheses

1. To compare the efficacy of Algorithm Treatment (AT) to Enhanced Treatment as Usual (eTAU) control group for smoking cessation among PLWH who smoke and are engaged in HIV clinical care. We hypothesize that the AT group will have a significantly higher proportion of abstinence (biologically confirmed primary outcomes), 24-hour quit attempts, and greater reduction in mean number of cigarettes smoked per day at 6-months post randomization compared to those in the eTAU group.

2. To characterize provider-, staff-, patient-, and clinical-level facilitators and barriers to integration of AT treatment in care. We hypothesize that, compared to the eTAU, written pharmacological prescriptions for smoking cessation and provider knowledge and confidence to treat nicotine dependence will be significantly greater in the AT group. We will also describe clinic characteristics such as size, staffing patterns (e.g., number and type of staff, providers, etc.), average number of annual visits by patients, amount of Ryan White funding, and average appointment time to help inform a future wide-scale implementation project across all CNICS sites.

3. To estimate the cost-effectiveness of the intervention relative to the primary smoking outcomes. This will be done by examining the incremental cost per quit of AT vs. eTAU at 6 months post intervention.

2.3. Participants

Participants are recruited from three clinics that provide HIV care to PLWH: the 1917 Clinic in Birmingham, AL; The Madison HIV Clinic at University of Washington in Seattle, WA; and Fenway Health in Boston, MA. All 3 clinics are part of CNICS, a cohort study that aims to integrate electronic clinical data from eight HIV clinics to better understand the relationship between patient and treatment factors with long-term health outcomes.27 All potential participants for this study are currently in CNICS. To be eligible, participants must meet all the following inclusion criteria: 1) age 18 or greater; 2) living with HIV; 3) receiving HIV care at one of the study implementation sites; 4) indicate smoking 5 or more cigarettes per day for the past month on the CNICS clinical assessment; and 5) not currently receiving smoking cessation treatment. Individuals who are unable to provide informed consent, do not have sufficient command of the English language, or are acutely mentally ill or intoxicated are excluded. Current motivation to quit smoking is not a requirement for inclusion in this study.

2.4. Procedures

2.4.1. Enrollment and baseline visit

PLWH complete the CNICS clinical assessment of patient reported outcomes and measures (PROs) including smoking as part of routine clinical care visits at CNICS sites in order to improve care.27,28 All individuals who endorse smoking cigarettes on their CNICS clinical assessment are contacted by study staff before the participant sees their provider and presented with information on the study. Those who are interested in participating are asked to complete a brief screening assessment to confirm eligibility. Individuals who are eligible will then be guided through the informed consent procedure and asked to complete a survey which, if randomized to the algorithm treatment (AT) condition, will be used to inform the smoking cessation recommendation algorithm. Randomization is conducted via the CNICS PRO platform which, upon completion of the initial survey, will automatically assign a participant to either the AT or eTAU condition using a randomization schema with allocations stratified by study implementation site. Participants in both conditions will also be asked to complete a carbon monoxide test, using a Vitalograph BreathCO monitor, with results of 4ppm or higher indicating recent smoking.29-32

As a result of the impacts from the SARS-COV-2 (COVID-19) pandemic on clinical care, additional enrollment options were added as well. Procedures for each visit have been adjusted to allow for either in-person or remote engagement, as each clinic instituted changes in clinical care – including increased telehealth visits, due to varying levels of restriction on face-to-face contact between staff and patients in response to the pandemic. For example, options for completing the CNICS clinical assessment have expanded beyond with-in clinic completion to also include completion on personal devices at home prior to appointments, an approach most useful for the increasing numbers of telehealth appointments that have occurred as a result of social distancing recommendations. As a result, in addition to same-day within-clinic enrollment options, study staff can also contact PLWH who are eligible by phone or HIPAA-compliant Zoom to complete the enrollment and facilitate completion of the baseline assessment on a personal device. Those whose baseline visit is completed remotely are not asked to complete a carbon monoxide test.

2.4.2. Follow-up

Once enrolled, text messages will be scheduled via Qualtrics for participants in both the intervention and control conditions who agree to take part in the weekly 4-item self-report survey. Twelve weeks after the baseline visit, participants will be asked to complete an End of Treatment assessment, which will include self-report surveys and carbon monoxide monitoring. The same procedure is followed for the 1-, 3-, and 6-month post-treatment assessments. For each of these post-baseline assessments, participants who have a medical appointment that coincides with the timing of the study visit will complete the assessment at the time of their clinic visit and will also complete a carbon monoxide test. Participants who do not have an upcoming medical appointment will be given the options of completing the self-report surveys over the phone or, preferably, can be sent a unique URL to complete it online on their own device (smart phone, tablet, or computer). Participants will receive $20 for their baseline visit; $40 for each of the End of Treatment, 1-Month Follow-Up, and 3-Month Follow-Up assessments they complete; and $50 for completing the 6-month follow-up assessment, for a total of up to $190 per participant. Study enrollment began on 8/17/2020 and the trial is actively recruiting at this time. The Institutional Review Boards at The University of Alabama at Birmingham, University of Washington, and Fenway Health have approved this study and provide ongoing site-specific oversight.

2.5. Intervention Conditions

2.5.1. Algorithm Treatment (AT).

All participants will complete a survey with questions designed to inform the algorithm, but only those randomized to the AT condition will have their algorithm results sent to their medical provider. The algorithm is structured so that participants who report current motivation to quit smoking will be prescribed varenicline (if no contraindications), bupropion, or a combination of bupropion and nicotine replacement therapy (NRT). Those who do not report current motivation to quit smoking will still be recommended NRT, with dosing based on past quit attempts and current smoking habits.

Given differences in clinic flow and electronic health records (EHRs) across the different sites, the approach to messaging varies across clinics but typically involves an electronic provider message via their EHR. One site also provides this information via a paper delivered to the provider to minimize missed EHR messages. The message sent to the medical provider shows the recommended pharmacotherapy choice (include titrating dosing, if necessary) in the form of a pre-populated prescription, any contraindications for use, and a brief rationale for why the particular medication was recommended. The rationale is included to give providers the tools they need to make an informed decision about prescribing. The provider will have the option to not prescribe a pharmacotherapy treatment, prescribe an alternative pharmacotherapy treatment, or sign the order for the algorithm-recommended pharmacotherapy treatment. The algorithm is designed to suggest effective treatment for smoking cessation regardless of current motivation to quit and to provide the most effective level of care while accounting for contraindications. If providers prescribe the algorithm recommended medication, participants can receive the medication at no-cost by filling the prescription at the clinic pharmacies. All participants will receive a brief handout on behavioral strategies and tips for smoking cessation, and will also be referred to the national quit line.33

2.5.2. Enhanced Treatment as Usual (eTAU).

Individuals in the eTAU condition will complete a survey with questions designed to inform the algorithm, but the results of the algorithm will not be sent to their medical provider. Independent of the study, a medical provider may elect to prescribe smoking cessation pharmacotherapy as part of standard of care. All participants will receive a brief handout on behavioral strategies and tips for smoking cessation, and will also be referred to the national quit line.33

2.6. Assessments

2.6.1. Screening.

Potential participants will be identified through responses to CNICS clinical assessments. Those who appear to meet inclusion criteria will be approached as they complete the assessment in clinic prior to seeing their provider. Participants who complete the assessment remotely for telehealth visits or who are seeing providers at sites that are still requiring pre-screening to address COVID-19 restrictions will be called prior to the appointment to confirm eligibility.

2.6.2. Questions to Inform Algorithm.

Immediately after completing informed consent procedures, participants will be asked to complete an up to 18-item questionnaire that will be used to inform the algorithm-generated pharmacotherapy recommendation. These questions assess average cigarettes smoked per day, number of cigarettes smoked the previous day, current smoking cessation treatment, previous quit attempts, and timing of first cigarette of the day.

2.6.3. Descriptive Measures.

Participants are asked to complete a short demographic questionnaire at the baseline visit, which includes highest level of education and employment status.

2.6.4. Smoking History.

Assessment of smoking history includes age of smoking initiation, age of regular use, use of other tobacco products, number of years smoked, previous quit attempts, previous smoking cessation treatment, and family history of smoking and medical problems related to smoking. Smoking history is assessed only at baseline.

2.6.5. Current Smoking Behavior.

Current smoking behavior is assessed via several self-reported measures: The Fagerstrom Test for Nicotine Dependence,34 Questionnaire of Smoking Urges – Brief Form,35 Hughes-Hatsukami Withdrawal Scale36, and Thoughts about Abstinence.37 Questions about current smoking behavior, including number of cigarettes smoked in the past week and number of 24-hour quit attempts (secondary outcomes), are asked at baseline, end of treatment, and follow-up visits. Additionally, at follow-up, participants will be asked about use of smoking cessation medication and other resources to support abstinence, including counseling or quit-line services. Those who endorse use of smoking cessation medication during the study period will be asked about use patterns.

2.6.6. Biological Measures.

HIV disease markers, specifically HIV viral load and CD4 count, will be obtained from the CNICS data repository. Additionally, participants who attend study appointments – baseline, end of treatment, and follow-ups – in-person will be asked to complete an assessment of expelled carbon monoxide at each visit as a measure of the study primary outcome.

2.6.7. Weekly Text Message-Based Surveys.

At their baseline visit, participants will be asked if they would like to opt in to receiving weekly, text message-based surveys. These weekly surveys will consist of 3 consistent questions −1) number of cigarettes smoked the previous day, use of smoking cessation medications, and smoking cessation medication adherence (where applicable) – and 1 question that rotates each week to allow for more diverse data collection. The rotating questions include HIV medication adherence (administered on weeks 1, 4, 7, and 10), confidence in smoking cessation (weeks 2, 5, 8, and 11), and feelings of anxiety (weeks 3, 6, and 9). Participation in the weekly surveys is optional. Those who would like to participate but who do not have a phone with text message capabilities can choose to receive a weekly call instead, where a study staff member administers the survey questions over the phone.

2.6.7. Treatment Satisfaction.

At the 3-Month Follow-Up, participants will be asked to complete an 18-item survey about their satisfaction with the clinical care they received related to smoking cessation and their satisfaction with the use of smoking cessation pharmacotherapy (where applicable).38,39

2.6.8. Facilitators, Barriers, and Provider Attitudes Towards Smoking Cessation Treatment.

Medical providers at all 3 implementation sites will be sent a survey at the start of the study, as well as 6- and 18-months post study start. The survey includes questions around providers’ confidence in their ability to counsel patients in smoking cessation and their willingness to include smoking cessation as part of their routine patient assessment in the future. Additionally, providers are asked to assess potential the impact of barriers and facilitators to providing patients with smoking cessation treatment – including provider training, safety/tolerability of smoking cessation pharmacotherapy, patient finances, and patient motivation to quit smoking.

2.6.9. Cost-effectiveness Outcomes.

Cost data are collected on cost of setting up the algorithm in the EHR, time and monetary cost of training providers in smoking cessation counseling best practices, time providers spend discussing smoking cessation with patients, time research assistants spend completing intervention-related tasks, and cost of pharmacotherapy prescribed to participants in the study. Effectiveness measures will be carbon monoxide-verified smoking cessation as measured at 6 months follow-up (the primary study outcome).

2.7. Data Analysis

2.7.1. Power.

To determine the number of participants needed for the randomized controlled trial, we conducted a power analysis, based on a 2-sided test at the 0.05 significance level using normal approximation methods. The primary aim of the study is to compare the proportion of participants with 7-day point-prevalence abstinence in the AT group versus the eTAU control group at 6 months. Assuming 10% of intervention patients and 4% of control patients are abstinent at 6 months, as seen in the pilot studies,24,25 at least participants are needed in each group (560 total) to detect group differences in proportions at 80% power. For this trial, we increased the number of participants in each group to 300 (600 total) to cover a larger range of possible cessation rates.

2.7.1. Aim 1: Compare the efficacy of AT to eTAU control group for smoking cessation among PLWH smokers engaged in HIV clinical care.

Using Pearson’s chi-square test for 2 independent proportions, we will analyze the difference in the proportion of participants with 7-day point-prevalence cessation at 6 months between the treatment arms. Participants for whom we do not have 6-month follow-up data will be assumed to be smoking. We do not anticipate any difference in baseline variables between arms, but if differences do exist, we will utilize a multivariate logistic regression model with cessation as the outcome, treatment group as the primary independent variable, and imbalanced variables which are known to be correlated with cessation as additional independent variables. The same approach will be taken for 24-hour quit attempt – dichotomized yes/no – at 6 months. For cigarette reduction, we will look at the change in number of cigarettes smoked per day at baseline and 6-month follow-up. Either a 2 independent sample t-test or the non-parametric Wilcoxon Rank-sum test as appropriate will be used to test for a difference in the change in cigarettes per day between the 2 groups. If necessary, an ordinary least squares regression model with change in cigarettes per day as the outcome adjusting for baseline cigarettes per day will be used to account for imbalance in measured covariates between treatment groups.

In addition to these primary analyses, we will examine outcomes by sex and racial groups to determine moderator effects. For sensitivity analyses, we will exclude participants without 6-month follow-up data. We will also 1) consider count regressions models that can account for time from enrollment until 6 month visit (such as Poisson regression40) if actual timing of planned 6-month follow-up visits differs enough between participants; 2) repeat all analyses using end of treatment and 6-month follow-up outcomes; 3) consider employing models that treat site as a clustering variable (such as Cochran-Mantel-Haenzel41 and Generalized Estimating Equation42); and 4) fit longitudinal models with data from all timepoints. These longitudinal modals will examine the interaction between treatment group and time. Exploratory analyses will be conducted stratified by site, cigarettes per day at enrollment, and quit goal.

2.7.2. Aim 2: Characterize provider-, staff-, patient-, and clinic-level facilitators and barriers to integration of AT treatment.

We will compare provider behavior (e.g., tobacco screening, number of prescriptions written) and attitudes toward providing smoking cessation treatment at baseline to those at 6-month follow-up, using proportions and means as appropriate. We will also examine, via descriptive analyses, facilitators and barriers to integration of AT.

2.7.3. Aim 3: Estimate the cost-effectiveness of the intervention relative to enhanced treatment as usual.

The primary cost-effectiveness analysis will examine the incremental cost per quit, taking the provider organization’s perspective over the 6 month study period. This approach will be taken to ensure comparability with published studies and to ensure policy relevance to health care organizations providing care to PLWH. We are also gathering data for the purposes of societal perspective analyses, as the intervention may impact a wide range of stakeholders, including patients, providers, and insurance companies. The incremental cost per quit of AT vs. eTAU is estimated as: (Total costs at 6 months for AT - Total costs at 6 months for eTAU)/(Total successful quits at 6 months for AT - Total successful quits at 6 months for eTAU). The major direct costs (or benefits) associated with AT are: 1) algorithm programming and implementation in the EHR; 2) staff/provider training; 3) time providers spend providing smoking cessation treatment to patients and monitoring adverse outcomes (if any), and 4) smoking cessation medications provided by the health care system (both the cost of the medication itself and the cost of getting medications to the patient). In keeping the primary perspective of our analyses, we will principally track costs borne by the health care system. We will explore the collection of cost data relevant to insurers or patients to understand how program sustainability varies by economic perspective, including a societal perspective. To that end, indirect costs will include the value of the time the patient spends in tobacco treatment. Research costs will be excluded. To maximize data accuracy, cost information will be collected prospectively. Per unit costs for personnel time will be based on national average wages where possible. Costs for medications will be based on prices that apply in non-research settings. We will use Monte Carlo simulation43 methods and sensitivity analyses to develop confidence bounds and identify inputs with the greatest effect on cost-effectiveness.

3. DISCUSSION

This study responds to multiple NIH OAR research priorities, specifically the need to address HIV-related comorbidities and to develop interventions that leverage multiple scientific domains.26 As PLWH are 2-3 times more likely to smoke cigarettes than the general population and tobacco use has significant negative impacts on the health of PLWH, identifying smoking cessation treatments that are effective and accessible is of high public health importance.6,20,21 Additionally, though HIV care providers often act as a primary point of medical contact for PLWH, many HIV care providers are underprepared or lack time to counsel their patients on smoking cessation treatment.9 Interventions to address this gap in treatment must integrate elements of behavioral, biomedical, and implementation science, with a focus on providing medical professionals with the information they need to counsel their patients efficiently on effective treatment options. Research around use of algorithm-guided treatment recommendations suggest that algorithms can act as an important decisional aid to providers who have not received expert training on a particular condition or chronic disease.44,45 Specifically for smoking cessation, algorithm-guided pharmacotherapy recommendations may serve as an appropriate and acceptable tool for providers who want to address cigarette use among their patients but have not been trained to do so.

As this research is being conducted during the COVID-19 pandemic, elements of the protocol have been temporarily adjusted to ensure the safety of our participants and study staff. Due to the variation in clinic restrictions, as well as state policies across implementation sites, each site has adapted study procedures to best respond to clinic-specific concerns. As of early 2021, the 1917 Clinic in Birmingham, AL and the Madison HIV Clinic in Seattle, WA have resumed most of their in-clinic visits, with a minority of patients using telehealth for their medical appointments. Fenway Health in Boston, MA is seeing the majority of patients via telehealth, with a small subset of individuals attending medical appointments in clinic. The Madison HIV Clinic is enrolling all participants in person, while the 1917 Clinic and Fenway Health are enrolling both in-person and remotely.

The use of algorithm-generated treatment recommendations for intervening on smoking carries multiple strengths, including scalability and efficiency. The protocol outlined above can be easily scaled up for application in large care centers that treat patients with HIV. Additionally, the brevity of the intervention lends itself to use in busy clinics where more intensive smoking cessation interventions might not be feasible or well-received. If successful, the implementation of this treatment algorithm could be expanded across the entire CNICS network and other HIV clinics through EHR platforms and would provide all practitioners the ability to deliver high quality smoking cessation treatment to PLWH during routine clinic visits. The proposed cost-effectiveness analysis will provide critical information for additional sites as they contemplate implementing the AT program. Finally, as use of EHR is rapidly increasing in clinic settings and the capabilities of EHR are expanding to include automated messaging, interventions that are fully integrated into electronic systems represent the future of implementation science.

Further research should be done to adapt algorithm-focused treatment for smoking cessation for use in clinics without robust assessment of smoking behaviors. All implementation sites for this trial are CNICS-affiliated, and participants are recruited from a sample of individuals who are engaged in routine CNICS assessment, which includes routine assessment of smoking. Clinics that are not CNICS-affiliated or do not routinely assess smoking behaviors of their patients may not be able to utilize the algorithm protocol in its current form, as the algorithm pulls smoking behavior information from routine assessment to generate the tailored pharmacotherapy recommendation.

4. CONCLUSIONS

Leveraging two previous pilot studies,24,25 the current study aims to test the effectiveness of algorithm-guided smoking cessation pharmacotherapy recommendations fully integrated into HIV clinical care. By providing medical personnel with an accessible and effective tool for prescribing smoking cessation treatment, we hope to improve rates of smoking abstinence among PLWH in our clinics and increase provider confidence in navigating the issue of smoking among their patients. If successful, the intervention would be ready for use across the entire CNICS network and, more broadly, in HIV clinics that utilize an EHR system.

Acknowledgements

Funding for this project came from the National Institute on Drug Abuse R01DA044112. The National Institute on Drug Abuse and the National Institutes of Health had no role in the writing of the report or in the decision to submit the manuscript for publication.

Footnotes

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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