Volume 4, Issue 2

Integrating SBIRT into Primary Care: Aligning Technology with the Health Care Team
Hannah Knudsen, PhD
University of Kentucky

When considering the question posed by Dr. Roman for this issue of The Bridge—“What could each of us do to enhance physician involvement in identifying and assisting individuals with substance use disorders?”—I was initially pessimistic about my ability to make any useful suggestions. It is a challenging question. My initial reaction as a researcher was to start mentally listing all the barriers to physician involvement, of which there are many. But then I found myself re-visiting a failed grant application that we submitted a couple of years ago that sought to implement SBIRT in our academic medical center’s internal medicine clinics.

We knew, based on data from our state, that there was a clear need for greater screening to identify patients who might benefit from further intervention. Epidemiological data had shown that Kentucky had slightly lower rates of illicit drug use (7.4%) and binge drinking (21.6%) relative to the nation, but much higher rates of smoking (34.0%) [1]. At the time, Kentucky had the sixth-highest rate of nonmedical prescription pain reliever use, and Kentucky binge drinkers had the highest mean number of binge episodes in the US [2].

From our perspective, improving care for patients with SUDs might be enhanced through an SBIRT model that integrated technology and medical extenders within the primary care clinic setting. When we were developing this grant application, our medical center had not yet transitioned to electronic health records, so integrating technology into the clinic had some novelty. The technology itself was actually relatively simple—we proposed to develop a web-based app deployed on an iPad® that relied upon ultra-brief (i.e., single item) screening measures of tobacco [3] , alcohol [4, 5], non-medical prescription and illicit drug use [6] that could be collected by a nurse at the time that other vital signs were measured. Positive responses to the alcohol and/or drug screeners would lead to two additional brief screening tools, the three-item AUDIT-C [7, 8] and/or the 10-item DAST [9, 10]. The app would then calculate risk scores, and for patients with scores indicative of risk, the app would generate examples of brief advice for the physician to use when he or she met with the patient in the examination room. Specifically, the physician would be prompted to: (1) deliver brief advice messages based on the risk level and (2) refer patients with positive screens to a medical extender, in this case a behavioral health specialist, who would then conduct a more thorough assessment and make additional referrals to treatment. We hoped that by including a “button” on the iPad® that would send an automated, encrypted email to a behavioral health specialist that physicians could easily link patients to additional care.

The study team developed this approach to SBIRT by reviewing the literature and soliciting feedback from providers within the Internal Medicine department. From the literature, it was clear that physicians’ lack of time was a key barrier to the implementation of SBIRT [11-15]. Previous efforts had been hampered by paper screening tools that took considerable time to administer and then to score [16, 17]. Automating the scoring process would represent one easy advantage of building an SBIRT app. Given the barriers related to clinician time, we sought the shortest possible initial screening questions and then relatively brief measures for individuals who screened positive on the single items. In part, we wanted to reduce administration time relative to other screening tools, such as the NIDA-Modified ASSIST (NM-ASSIST). NIDA’s website (http://www.drugabuse.gov/nmassist/?q=nida_questionnaire) includes automatic scoring, but for patients who report any drug use, the additional items are far more numerous than the DAST. The literature also has documented numerous other barriers: 1) many primary care physicians (PCPs) lack diagnostic skills and confidence about intervening with patients [18-20]; 2) many PCPs fear that screening may identify patients with SUDs for whom PCPs lack tools and access to specialists to provide adequate help [11, 13]; and 3) referral processes are often inconvenient [21]. Again, through automation of the referral process, we hoped that an iPad®-based app could enhance physicians’ confidence in intervening with patients and reduce the challenges in referring at-risk patients to appropriate follow-up care.

When we presented this model to physicians in the clinical department, their feedback was also consistent with the literature. The limited amount of time required, coupled with the involvement of the nurse in administering the screening questions, was viewed as a strength because it reduced the time burden for physicians. Physicians noted that they often felt that asking patients about substance use could open a “can of worms” that was time-consuming and challenging to address. Physicians described challenges in making referrals, in part because our medical center has very limited offerings for substance abuse treatment (primarily limited to services for pregnant women and some buprenorphine treatment through the psychiatry department). The inclusion of an automated referral mechanism to someone with expertise in behavioral health assessment and treatment was seen as a key strength.

The research questions that we hoped to address were related to both the acceptability of the iPad® tool (i.e., would PCPs like the tool?) and rates of implementation (i.e., would the tool increase the rates of screening and referral?). Layered over these basic questions were additional research questions about implementation strategies, to determine whether a web-based training coupled with period email reminders would yield greater use of the iPad® app than a basic “Quick Start” handout, which is similar to the instructions that accompany many new technological devices. Our logic was that a web-based training package was more sustainable than face-to-face trainings because new providers could be easily trained even after the proposed study ended. 
I still like the relative simplicity of what we proposed, although grant reviewers did not particularly like the design, primarily due to concerns that SBIRT for drug use had not been endorsed by the US Preventive Services Task Force. The literature for using SBIRT to address tobacco [22] and alcohol use [23-25] is considerably stronger than the research base for drug use; data for drug-related SBIRT largely comes from observational studies [26, 27] rather than randomized trials [28]. At the same time, there is a dearth of alternative approaches for identifying individuals with problematic drug use through the primary care system. Evidence continues to build that single item screening questions perform well in primary care settings for identifying individuals with SUDs [29].

Interestingly, the single item drug use screener and DAST-10 were recently endorsed as potential core elements for inclusion in electronic health records [30], which offers some consolation that we might have been on the right track with the content of our app. Recent research also suggests that perhaps the brief intervention itself could be computerized, which would further reduce the burden on PCPs [31].

In considering the implementation of SBIRT, I am reminded of the classic work of Everett Rogers who described key characteristics of innovation that can promote their use. He argued that innovations were more likely to be adopted if: (1) they were better than the status quo, (2) were compatible with both users’ norms and context, (3) were simple to use, (4) could be experimented with prior to making a decision about adoption; and (5) had observable effects that others would notice and endorse. We tried to attend to these issues when considering our model of implementation, although the observability issue is challenging in the context of protecting privacy in health care settings.

I think these issues raise questions that need to be asked as health systems and researchers continue to consider how to integrate SBIRT into primary care. Questions for health systems include: Is SBIRT better than the clinic’s current practice? How can SBIRT be integrated without disrupting work flow in the clinic? What is the simplest model of SBIRT that can be used with good results? Can SBIRT be implemented for a trial period so that providers can try it and then provide feedback about how to improve its implementation? Perhaps by integrating these questions into the planning process, health systems and researchers will be able to expand the reach of SBIRT and connect more patients to much needed care.

1. SAMHSA. Table 45. – Selected Drug Use, Perceptions of Great Risk, Average Annual Marijuana Initiates, Past Year Substance Dependence or Abuse, Needing But Not Receiving Treatment, and Past Year Mental Health Measures in Kentucky, by Age Group: Estimated Numbers (in Thousands), Annual Averages Based on 2008-2009 NSDUHs.  2008 and 2009 [cited 2012 January 3]; Available from: http://www.oas.samhsa.gov/2k9State/WebOnlyTables/KY.pdf.
2. Centers for Disease Control and Prevention. (2012). Vital signs: Binge drinking prevalence, frequency, and intensity among adults--United States, 2010. MMWR, 61, 14-19.
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