Jason M. Satterfield, PhD Professor of Medicine University of California San Francisco

1. Computer-Facilitated 5A’s for Smoking Cessation: Using Technology to Improve Implementation and Fidelity Jason M. Satterfield, PhD Professor of Medicine University of California San Francisco Jason.Satterfield@ucsf.edu Funded by NIH/NIDA Grant 1R01DA034253 awarded to Dr. Satterfield No Conflicts of Interest to Report

2. Our Big Picture.... • How can we improve the availability and fidelity of EBBP’s in primary care? • Obstacles: Time, lack of expertise, competing demands • How can we get primary care practices to adopt technology tools to improve patient care?

3. EBBP: 5A’s Ask Advise Assess Assist Arrange Technology as Implementation and Fidelity Tool Tech: Registration Tablet

4. Implementation Outcomes Intervention Strategies Evidence Based Practices Implementation Strategies Systems Environment Organizational Group/Learning Supervision Providers Consumers Outcomes Implementation Outcomes Fidelity Reach Acceptability Sustainability Uptake Costs Workforce Service Outcomes Efficiency Safety Effectiveness Equity Patient-Centered Timeliness Client Outcomes Symptoms Functioning Satisfaction Quality of Life *IOM Standards of Care Implementation Research Methods Source: Proctor, Landsverk, Aarons, Chambers, Glisson, Mittman (2009); Proctor, Silmere, et al., (2011);

5. What’s the Current Gold Standard? • Ask about smoking at every visit. • Advise smokers to quit or at least cut down. • Assessa patient’s level of readiness to quit. • Assist patients to enhance motivation to quit and/or develop a plan for cessation including counseling, meds, NRT, quitlines, and other tools. • Arrange a follow-up appointment to check progress and continue the conversation.

6. The 5A’s Work, But… • 75-80% of providers ask and advise • Only 63.4% assess and 56.4% assist. (Park ER., JAMA Intern Med, 2015) • The fifth ‘A’ - arrange a follow-up and referral - has been followed by only 10.4% of providers. (Park ER., JAMA Intern Med, 2015) • Not enough time, expertise, need something to help (like a tablet….)?

7. Social Norms & Influence Behavioral Intention to Use Actual Use Perceived Usefulness Attitude Perceived Ease of Use Facilitating Conditions Technology Acceptance Model (TAM; Davis 1989, 1993)

9. 5A’s RCT • 3 clinics, stratified randomization by provider • Intervention = 5A’s program on tablet • Tailored health handout for pt • Summary and tx recs for provider • Control = Usual care • Primary outcome: 5A’s adherence rates

10. Intervention Details • The CF5A’s intervention follows clinical practice guidelines for 5A’s and incorporates elements of motivational interviewing and behavioral psychology. • It uses branching logic to tailor the program to the patient’s needs. • It creates 2 products – a personalized handout for the patient and a clinical summary and tip sheet for the provider.

12. 3 Study Clinics • #1: 18,000 pts with 40,200 visits/yr; 32% medicare, 15% Medi-Cal; ~12% smokers • 30 faculty physicians, 63 residents, 3 fellows, and 5 NPs • #2: 6,500 pts with 25,000 visits/yr; 32% uninsured, 36% Medi-Cal; ~25% smokers • 22 faculty, 51 residents, 2 fellows, and 7 NP’s • #3: 2,600pts with 12,000 visits/yr; ~40% smokers • 30 faculty, 6 fellows, and 4 NP’s

14. Patient Eligibility • English or Spanish speaking • Smoked at least 1 cig in past 7 days • Smoked at least 100 cigs in lifetime • Must be in office for PC visit • Could participate up to 3 times in one year

15. Primary Outcomes • 5A’s Fidelity • Pt report of provider behavior • In person or phone interview • 6 outcomes • Ask, Advise, Assess, Any Assist (9Q’s), Arrange, All 5A’s

16. RCT Results: Consort Diagram

17. RCT Results • N=221 providers; n=961 unique patients • N=549 control; N=412 intervention • N=1,340 PC encounters • N=1,011 completed post-visit interview (75.4%)

18. Provider Demographicsn=221

20. GEE Analysis of Primary Outcomes • The data represent a 4-level nested structure (sites, providers, patients, visits) • Preliminary models determined that the provider-level clustering could be ignored • Final GEE models had visits clustered with patients and modeled site variability via a fixed main effect • Modeled X variables: group, visit, group*visit, site

21. GEE Analysis of Patient-Reported Outcomes Describing Provider-Delivery of 5AsN=961 patients with n=1340 visits

22. GEE Analysis of Pt-Reported Outcomes Describing Provider and/or Tablet-Delivery N=961 patients with n=1340 visits

23. Conclusions • CF5A’s was effective in increasing the likelihood of patients receiving higher fidelity 5A’s delivered by either the provider or tablet. • CF5A’s was effective in changing provider 5A’s behavior for Assess, Assist, Arrange (1st visit), and All 5A’s (1st visit).

24. Translating Research into Practice • Practice Implications for stakeholders • CF5A’s is an acceptable and efficient strategy to get EBP to pts • Need linkages with EHR • Need better longitudinal follow and smart adaptations • Generalizable Findings • Waiting rooms provide a key venue for digital intervention delivery • Additional HRB’s can be addressed • Link digital tool to other essential functions like registration

25. Thank You • Our CF5A’s UCSF and SFGH Team • Project Coordinator: Nick Alvarado • Co-I’s: Nicole Appelle, Paula Lum, Soraya Azari, Ralph Gonzales, Ricardo Muñoz, Steve Gregorich, Anna Napoles, Maya Vijayaraghavan • Fellows: Sara Kalkhoran, Jessica Bloome • NIH/NIDA implementation collaboratory