Screening for Drug Use in Primary Care: Practical Implications of the New USPSTF Recommendation

Screening for psychotic experiences and psychotic disorders in general psychiatric settings: a systematic review and meta-analysis

Background

The absence of systematic screening for psychosis within general psychiatric services contribute to substantial treatment delays and poor long-term outcomes. We conducted a meta-analysis to estimate rates of psychotic experiences, clinical high-risk for psychosis syndrome (CHR-P), and psychotic disorders identified by screening treatment-seeking individuals to inform implementation recommendations for routine psychosis screening in general psychiatric settings.

Methods

PubMed and Web of Science databases were searched to identify empirical studies that contained information on the point prevalence of psychotic experiences, CHR-P, or psychotic disorders identified by screening inpatient and outpatient samples aged 12-64 receiving general psychiatric care. Psychotic experiences were identified by meeting threshold scores on validated self-reported questionnaires, and psychotic disorders and CHR-P by gold-standard structured interview assessments. A meta-analysis of each outcome was conducted using the Restricted Maximum Likelihood Estimator method of estimating effect sizes in a random effects model.

Results

41 independent samples (k=36 outpatient) involving n=25,751 patients (58% female, mean age: 24.1 years) were included. Among a general psychiatric population, prevalence of psychotic experiences was 44.3% (95% CI: 35.8-52.8%; 28 samples, n=21,957); CHR-P was 26.4% (95% CI: 20.0-32.7%; 28 samples, n=14,395); and psychotic disorders was 6.6% (95% CI: 3.3-9.8%; 32 samples, n=20,371).

Conclusions

High rates of psychotic spectrum illness in general psychiatric settings underscore need for secondary prevention with psychosis screening. These base rates can be used to plan training and resources required to conduct assessments for early detection, as well as build capacity in interventions for CHR-P and early psychosis in non-specialty mental health settings.

Subthreshold opioid use disorder prevention (STOP) trial: a cluster randomized clinical trial: study design and methods

BACKGROUND

Preventing progression to moderate or severe opioid use disorder (OUD) among people who exhibit risky opioid use behavior that does not meet criteria for treatment with opioid agonists or antagonists (subthreshold OUD) is poorly understood. The Subthreshold Opioid Use Disorder Prevention (STOP) Trial is designed to study the efficacy of a collaborative care intervention to reduce risky opioid use and to prevent progression to moderate or severe OUD in adult primary care patients with subthreshold OUD.

METHODS

The STOP trial is a cluster randomized controlled trial, randomized at the PCP level, conducted in 5 distinct geographic sites. STOP tests the efficacy of the STOP intervention in comparison to enhanced usual care (EUC) in adult primary care patients with risky opioid use that does not meet criteria for moderate-severe OUD. The STOP intervention consists of (1) a practice-embedded nurse care manager (NCM) who provides patient participant education and supports primary care providers (PCPs) in engaging and monitoring patient-participants; (2) brief advice, delivered to patient participants by their PCP and/or prerecorded video message, about health risks of opioid misuse; and (3) up to 6 sessions of telephone health coaching to motivate and support behavior change. EUC consists of primary care treatment as usual, plus printed overdose prevention educational materials and an educational video on cancer screening. The primary outcome measure is self-reported number of days of risky (illicit or nonmedical) opioid use over 180 days, assessed monthly via text message using items from the Addiction Severity Index and the Current Opioid Misuse Measure. Secondary outcomes assess other substance use, mental health, quality of life, and healthcare utilization as well as PCP prescribing and monitoring behaviors. A mixed effects negative binomial model with a log link will be fit to estimate the difference in means between treatment and control groups using an intent-to-treat population.

DISCUSSION

Given a growing interest in interventions for the management of patients with risky opioid use, and the need for primary care-based interventions, this study potentially offers a blueprint for a feasible and effective approach to improving outcomes in this population.

TRIAL REGISTRATION

Clinicaltrials.gov, identifier NCT04218201, January 6, 2020.

Association between cannabis use disorder symptom severity and probability of clinically-documented diagnosis and treatment in a primary care sample

BACKGROUND

Brief cannabis screening followed by standardized assessment of symptoms may support diagnosis and treatment of cannabis use disorder (CUD). This study tested whether the probability of a medical provider diagnosing and treating CUD increased with the number of substance use disorder (SUD) symptoms documented in patients' EHRs.

METHODS

This observational study used EHR and claims data from an integrated healthcare system. Adult patients were included who reported daily cannabis use and completed the Substance Use Symptom Checklist, a scaled measure of DSM-5 SUD symptoms (0-11), during routine care 3/1/2015-3/1/2021. Logistic regression estimated associations between SUD symptom counts and: 1) CUD diagnosis; 2) CUD treatment initiation; and 3) CUD treatment engagement, defined based on Healthcare Effectiveness Data and Information Set (HEDIS) ICD-codes and timelines. We tested moderation across age, gender, race, and ethnicity.

RESULTS

Patients (N=13,947) were predominantly middle-age, male, White, and non-Hispanic. Among patients reporting daily cannabis use without other drug use (N=12,568), the probability of CUD diagnosis, treatment initiation, and engagement increased with each 1-unit increase in Symptom Checklist score (p's<0.001). However, probabilities of diagnosis, treatment, and engagement were low, even among those reporting ≥2 symptoms consistent with SUD: 14.0% diagnosed (95% CI: 11.7-21.6), 16.6% initiated treatment among diagnosed (11.7-21.6), and 24.3% engaged in treatment among initiated (15.8-32.7). Only gender moderated associations between Symptom Checklist and diagnosis (p=0.047) and treatment initiation (p=0.012). Findings were similar for patients reporting daily cannabis use with other drug use (N=1379).

CONCLUSION

Despite documented symptoms, CUD was underdiagnosed and undertreated in medical settings.

Implementation of substance use screening in rural federally-qualified health center clinics identified high rates of unhealthy alcohol and cannabis use among adult primary care patients

BACKGROUND

Screening for substance use in rural primary care clinics faces unique challenges due to limited resources, high patient volumes, and multiple demands on providers. To explore the potential for electronic health record (EHR)-integrated screening in this context, we conducted an implementation feasibility study with a rural federally-qualified health center (FQHC) in Maine. This was an ancillary study to a NIDA Clinical Trials Network study of screening in urban primary care clinics (CTN-0062).

METHODS

Researchers worked with stakeholders from three FQHC clinics to define and implement their optimal screening approach. Clinics used the Tobacco, Alcohol, Prescription Medication, and Other Substance (TAPS) Tool, completed on tablet computers in the waiting room, and results were immediately recorded in the EHR. Adult patients presenting for annual preventive care visits, but not those with other visit types, were eligible for screening. Data were analyzed for the first 12 months following implementation at each clinic to assess screening rates and prevalence of reported unhealthy substance use, and documentation of counseling using an EHR-integrated clinical decision support tool, for patients screening positive for moderate-high risk alcohol or drug use.

RESULTS

Screening was completed by 3749 patients, representing 93.4% of those with screening-eligible annual preventive care visits, and 18.5% of adult patients presenting for any type of primary care visit. Screening was self-administered in 92.9% of cases. The prevalence of moderate-high risk substance use detected on screening was 14.6% for tobacco, 30.4% for alcohol, 10.8% for cannabis, 0.3% for illicit drugs, and 0.6% for non-medical use of prescription drugs. Brief substance use counseling was documented for 17.4% of patients with any moderate-high risk alcohol or drug use.

CONCLUSIONS

Self-administered EHR-integrated screening was feasible to implement, and detected substantial alcohol, cannabis, and tobacco use in rural FQHC clinics. Counseling was documented for a minority of patients with moderate-high risk use, possibly indicating a need for better support of primary care providers in addressing substance use. There is potential to broaden the reach of screening by offering it at routine medical visits rather than restricting to annual preventive care visits, within these and other rural primary care clinics.

Assessing the diagnostic utility of the Cannabis Use Disorder Identification Test - Revised (CUDIT-R) among veterans with medical and non-medical cannabis use

BACKGROUND

Few studies examine the utility of the Cannabis Use Disorder Identification Test - Revised (CUDIT-R) in relation to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, (DSM-5) criteria for cannabis use disorder (CUD). This study assesses the performance of the CUDIT-R among a sample of Veterans with and without medical cannabis use.

METHODS

We approached and consented primary care patients presenting to one of three Department of Veterans Affairs (VA) Medical Centers. Veterans with at least monthly cannabis use and complete CUD data at baseline were included in this analysis (n=234). CUDIT-R scores were compared against Alcohol Use Disorder and Associated Disabilities Interview Schedule-5 (DSM-5) CUD as the standard to calculate measures of validity (sensitivity, specificity), identify optimal CUDIT-R cutoff values, and assess the diagnostic proficiency of the CUDIT-R using receiver operating characteristic (ROC) curves. We further stratified analyses by active medical cannabis card holder status and DSM-5 CUD severity (any, moderate, and severe).

RESULTS

Among the entire sample, 38.9% qualified for any DSM-5 CUD, with 10.7% and 3.0% meeting criteria for moderate and severe CUD, respectively. We identified optimal CUDIT-R scores at 10 for any DSM-5 CUD (sensitivity=0.58; specificity=0.80), at 12 for moderate CUD (sensitivity=0.72; specificity=0.82), and at 14 for severe CUD (sensitivity=0.71; specificity=0.87). ROC curves showed higher CUDIT-R validity among non-card holders compared with medical cannabis card holders.

CONCLUSION

The present study identified optimal CUDIT-R cutoff scores for Veterans who use cannabis. Varying DSM-5 validity measures inform the need for population-specific CUDIT-R cutoff values.

Psychometric Performance of a Substance Use Symptom Checklist to Help Clinicians Assess Substance Use Disorder in Primary Care

Importance

Substance use disorders (SUDs) are underrecognized in primary care, where structured clinical interviews are often infeasible. A brief, standardized substance use symptom checklist could help clinicians assess SUD.

Objective

To evaluate the psychometric properties of the Substance Use Symptom Checklist (hereafter symptom checklist) used in primary care among patients reporting daily cannabis use and/or other drug use as part of population-based screening and assessment.

Design, Setting, and Participants

This cross-sectional study was conducted among adult primary care patients who completed the symptom checklist during routine care between March 1, 2015, and March 1, 2020, at an integrated health care system. Data analysis was conducted from June 1, 2021, to May 1, 2022.

Main Outcomes and Measures

The symptom checklist included 11 items corresponding to SUD criteria in the Diagnostic and Statistical Manual for Mental Disorders (Fifth Edition) (DSM-5). Item response theory (IRT) analyses tested whether the symptom checklist was unidimensional and reflected a continuum of SUD severity and evaluated item characteristics (discrimination and severity). Differential item functioning analyses examined whether the symptom checklist performed similarly across age, sex, race, and ethnicity. Analyses were stratified by cannabis and/or other drug use.

Results

A total of 23 304 screens were included (mean [SD] age, 38.2 [5.6] years; 12 554 [53.9%] male patients; 17 439 [78.8%] White patients; 20 393 [87.5%] non-Hispanic patients). Overall, 16 140 patients reported daily cannabis use only, 4791 patients reported other drug use only, and 2373 patients reported both daily cannabis and other drug use. Among patients with daily cannabis use only, other drug use only, or both daily cannabis and other drug use, 4242 (26.3%), 1446 (30.2%), and 1229 (51.8%), respectively, endorsed 2 or more items on the symptom checklist, consistent with DSM-5 SUD. For all cannabis and drug subsamples, IRT models supported the unidimensionality of the symptom checklist, and all items discriminated between higher and lower levels of SUD severity. Differential item functioning was observed for some items across sociodemographic subgroups but did not result in meaningful change (<1 point difference) in the overall score (0-11).

Conclusions and Relevance

In this cross-sectional study, a symptom checklist, administered to primary care patients who reported daily cannabis and/or other drug use during routine screening, discriminated SUD severity as expected and performed well across subgroups. Findings support the clinical utility of the symptom checklist for standardized and more complete SUD symptom assessment to help clinicians make diagnostic and treatment decisions in primary care.

Integrating Routine Screening for Opioid Use Disorder into Primary Care Settings: Experiences from a National Cohort of Clinics

BACKGROUND

The U.S. Preventive Services Task Force recommends routine population-based screening for drug use, yet screening for opioid use disorder (OUD) in primary care occurs rarely, and little is known about barriers primary care teams face.

OBJECTIVE

As part of a multisite randomized trial to provide OUD and behavioral health treatment using the Collaborative Care Model, we supported 10 primary care clinics in implementing routine OUD screening and conducted formative evaluation to characterize early implementation experiences.

DESIGN

Qualitative formative evaluation.

APPROACH

Formative evaluation included taking detailed observation notes at implementation meetings with individual clinics and debriefings with external facilitators. Observation notes were analyzed weekly using a Rapid Assessment Process guided by the Consolidated Framework for Implementation Research, with iterative feedback from the study team. After clinics launched OUD screening, we conducted structured fidelity assessments via group interviews with each site to evaluate clinic experiences with routine OUD screening. Data from observation and structured fidelity assessments were combined into a matrix to compare across clinics and identify cross-cutting barriers and promising implementation strategies.

KEY RESULTS

While all clinics had the goal of implementing population-based OUD screening, barriers were experienced across intervention, individual, and clinic setting domains, with compounding effects for telehealth visits. Seven themes emerged characterizing barriers, including (1) challenges identifying who to screen, (2) complexity of the screening tool, (3) staff discomfort and/or hesitancies, (4) workflow barriers that decreased screening follow-up, (5) staffing shortages and turnover, (6) discouragement from low screening yield, and (7) stigma. Promising implementation strategies included utilizing a more universal screening approach, health information technology (HIT), audit and feedback, and repeated staff trainings.

CONCLUSIONS

Integrating population-based OUD screening in primary care is challenging but may be made feasible via implementation strategies and tailored practice facilitation that standardize workflows via HIT, decrease stigma, and increase staff confidence regarding OUD.

Validity of the Single-Item Screen-Cannabis (SIS-C) for Cannabis Use Disorder Screening in Routine Care

Importance

Cannabis use is prevalent and increasing, and frequent use intensifies the risk of cannabis use disorder (CUD). CUD is underrecognized in medical settings, but a validated single-item cannabis screen could increase recognition.

Objective

To evaluate the Single-Item Screen-Cannabis (SIS-C), administered and documented in routine primary care, compared with a confidential reference standard measure of CUD.

Design, Setting, and Participants

This diagnostic study included a sample of adult patients who completed routine cannabis screening between January 28 and September 12, 2019, and were randomly selected for a confidential survey about cannabis use. Random sampling was stratified by frequency of past-year use and race and ethnicity. The study was conducted at an integrated health system in Washington state, where adult cannabis use is legal. Data were analyzed from May 2021 to March 2022.

Exposures

The SIS-C asks about frequency of past-year cannabis use with responses (none, less than monthly, monthly, weekly, daily or almost daily) documented in patients' medical records.

Main Outcomes and Measures

The Diagnostic and Statistical Manual, Fifth Edition (DSM-5) Composite International Diagnostic Interview-Substance Abuse Module (CIDI-SAM) for past-year CUD was completed on a confidential survey and considered the reference standard. The SIS-C was compared with 2 or more criteria on the CIDI-SAM, consistent with CUD. All analyses were weighted, accounting for survey design and nonresponse, to obtain estimates representative of the health system primary care population.

Results

Of 5000 sampled adult patients, 1688 responded to the cannabis survey (34% response rate). Patients were predominantly middle-aged (weighted mean [SD] age, 50.7 [18.1]), female or women (weighted proportion [SE], 55.9% [4.1]), non-Hispanic (weighted proportion [SE], 96.7% [1.0]), and White (weighted proportion [SE], 74.2% [3.7]). Approximately 6.6% of patients met criteria for past-year CUD. The SIS-C had an area under receiver operating characteristic curve of 0.89 (95% CI, 0.78-0.96) for identifying CUD. A threshold of less than monthly cannabis use balanced sensitivity (0.88) and specificity (0.83) for detecting CUD. In populations with a 6% prevalence of CUD, predictive values of a positive screen ranged from 17% to 34%, while predictive values of a negative screen ranged from 97% to 100%.

Conclusions and Relevance

In this diagnostic study, the SIS-C had excellent performance characteristics in routine care as a screen for CUD. While high negative predictive values suggest that the SIS-C accurately identifies patients without CUD, low positive predictive values indicate a need for further diagnostic assessment following positive results when screening for CUD in primary care.

Clinical documentation of patient-reported medical cannabis use in primary care: Toward scalable extraction using natural language processing methods

Background: Most states have legalized medical cannabis, yet little is known about how medical cannabis use is documented in patients' electronic health records (EHRs). We used natural language processing (NLP) to calculate the prevalence of clinician-documented medical cannabis use among adults in an integrated health system in Washington State where medical and recreational use are legal. Methods: We analyzed EHRs of patients ≥18 years old screened for past-year cannabis use (November 1, 2017-October 31, 2018), to identify clinician-documented medical cannabis use. We defined medical use as any documentation of cannabis that was recommended by a clinician or described by the clinician or patient as intended to manage health conditions or symptoms. We developed and applied an NLP system that included NLP-assisted manual review to identify such documentation in encounter notes. Results: Medical cannabis use was documented for 16,684 (5.6%) of 299,597 outpatient encounters with routine screening for cannabis use among 203,489 patients seeing 1,274 clinicians. The validated NLP system identified 54% of documentation and NLP-assisted manual review the remainder. Language documenting reasons for cannabis use included 125 terms indicating medical use, 28 terms indicating non-medical use and 41 ambiguous terms. Implicit documentation of medical use (e.g., "edible THC nightly for lumbar pain") was more common than explicit (e.g., "continues medical cannabis use"). Conclusions: Clinicians use diverse and often ambiguous language to document patients' reasons for cannabis use. Automating extraction of documentation about patients' cannabis use could facilitate clinical decision support and epidemiological investigation but will require large amounts of gold standard training data.

Screening for Unhealthy Alcohol and Drug Use in General Medicine Settings

Unhealthy alcohol and drug use are among the top 10 causes of preventable death in the United States, but they are infrequently identified and addressed in medical settings. Guidelines recommend screening adult primary care patients for alcohol and drug use, and routine screening should be a component of high-quality clinical care. Brief, validated screening tools accurately detect unhealthy alcohol and drug use, and their thoughtful implementation can facilitate adoption and optimize the quality of screening results. Recommendations for implementation include patient self-administered screening tools, integration with electronic health records, and screening during routine primary care visits.

Cannabis use assessment and documentation in healthcare: Priorities for closing the gap

Several factors, including the lack of a systematic cannabis use assessment within healthcare systems, have led to significant under-documentation of cannabis use and its correlates in medical records, the unpreparedness of clinicians, and poor quality of cannabis-related electronic health record data, limiting its utilization in research. Multiple steps are required to overcome the existing knowledge gaps and accommodate the health needs implied by the increasing cannabis use prevalence. These steps include (1) enhancing clinician and patient education on the importance of cannabis use assessment and documentation, (2) implementing a standardized approach for comprehensive cannabis use assessment within and across healthcare systems, (3) improving documentation of cannabis use and its correlates in medical records and electronic health records by building in prompts, (4) developing and validating reliable computable phenotypes of cannabis use, (5) conducting research utilizing electronic health data to study a wide array of related health outcomes, (6) and establishing evidence-based guidelines to inform clinical practices and policies. Integrating comprehensive cannabis use assessment and documentation within healthcare systems is necessary to enhance patient care and improve the quality of electronic health databases. Employing electronic health record data in cannabis-related research is crucial to accelerate research in light of the existing knowledge gaps on a wide array of health outcomes. Thus, improving and modernizing cannabis use assessment and documentation in healthcare is an integral step on which research conduct and evidence generation primarily rely.

Sociodemographic Characteristics Associated With and Prevalence and Frequency of Cannabis Use Among Adults in the US

IMPORTANCE

Cannabis use has increased, but there are few studies on frequent and daily cannabis use among US adults. Individuals who engage in higher frequency use may suffer more health consequences.

OBJECTIVE

To examine frequency of cannabis use and associated factors among US adults.

DESIGN, SETTING, AND PARTICIPANTS

This survey study included data from 21 US states and 2 US territories reported in the Behavioral Risk Factor Surveillance System surveys from 2016 to 2019. Cross-sectional data on US adults ages 18 years and older were used to estimate demographic, socioeconomic, and behavioral risk factors for cannabis use, taking into account the survey strata and sampling weights for the 4 years of combined data. Using a multivariable ordinal logistic analysis, the association of demographic, socioeconomic status, and behavioral risk factors with past month cannabis frequency were examined.

EXPOSURES

Sociodemographic characteristic, ie, age, gender, race and ethnicity, educational attainment, employment status, and annual household income.

MAIN OUTCOMES AND MEASURES

Ordinal categorization of number of days of cannabis use in the past 30 days in terms of nonuse, infrequent use (1-5 days), frequent use (6-29 days), and daily use.

RESULTS

Among the 387 179 respondents, 58 009 (27.9%) were ages 18 to 34 years, 186 923 (50.3%) were ages 35 to 64 years, and 142 225 (21.8%) were age 65 years or older (mean [SD] age, 48.3 [0.1] years). The sample included 28 345 (9.8%) Black, 36 697 (22.6%) Hispanic, and 292 210 (57.3%) White respondents. Smoking was the most common form of cannabis use. The frequency of cannabis use varied significantly by age, gender, race, marital status, education, and employment. Higher frequency cannabis use was associated with younger age (ages 18-34 years: adjusted odds ratio [aOR],4.12; 95% CI, 3.63-4.68; ages 35-64 years: aOR,2.22; 95% CI, 1.98-2.49), Black (aOR, 1.46; 95% CI, 1.33-1.71) and Native American (aOR, 1.25; 95% CI, 1.04-1.52) race, and less educational attainment (high school or less: aOR,1.09; 95% CI, 1.02-1.17; some college: aOR,1.27; 95% CI, 1.19-1.35). Being married (aOR, 0.54; 95% CI, 0.51-0.58) or identifying as Asian (aOR, 0.60; 95% CI, 0.51-0.71) or Hispanic (aOR, 0.71; 95% CI, 0.65-0.77) was associated with lower-frequency cannabis use after accounting for other baseline factors.

CONCLUSIONS AND RELEVANCE

This nationally based study found that higher-frequency cannabis use is more common among young and racial minority populations, as well as respondents with low socioeconomic status. Given the known and emerging negative health effects of cannabis use, more attention may need to be paid to high-frequency use among underserved populations in the form of screening, risk stratification, and treatment.

Prevalence of substance use and substance-related disorders among US Veterans Health Administration patients

BACKGROUND

Substance use and related disorders are common among US Veterans, but the population burden of has never been directly assessed among Veterans Health Administration (VA) patients. We surveyed VA patients to measure substance use and related disorders in the largest US integrated healthcare system.

METHODS

We surveyed N = 6000 outpatients from 30 geographically-representative VA healthcare systems. We assessed substance use (lifetime, past 12-month, daily in past 3 months) and past 12-month disorders following DSM-5 criteria and estimated the association with Veteran characteristics (age, gender, race/ethnicity, socioeconomic status, VA utilization).

RESULTS

Alcohol was the most commonly-reported substance (24% used past 12 months, 11% daily in past 3 months, 10% met criteria for alcohol use disorder), followed by cannabis (42% lifetime use, 12% use in past 12 months, 5% daily use in past 3 months, 3% met criteria for cannabis use disorder). Overall, 5% met criteria for non-alcohol drug use disorder (13% for substance use disorder (SUD)). SUD prevalence was highest for young Veterans and those who were unemployed or otherwise not employed for wages. Past 12-month cannabis use was common, even among older adults (65-74 years: 10%; 75 and older: 2%).

CONCLUSIONS

Prevalence data are important inputs into decisions around population health monitoring, treatment capacity, and quality measurement strategies. Substance use and SUD are more prevalent than previously reported, and VA may need to screen for non-alcohol drugs to identify patients who need care. More tailored assessment may be needed for cannabis use, high-prevalence subgroups, and older adults.

Primary healthcare approach to substance abuse management

Substance abuse is common amongst patients attending primary healthcare settings. Despite the substantial impact on one’s health, substance abuse is often underdiagnosed by primary care providers owing to a lack of training and time for screening. Self-reported screening tools are easy to administer and efficient to make a substance abuse diagnosis in primary care settings. Comorbid mental illness and intimate partner violence are common amongst patients presenting with substance abuse in primary care. An early diagnosis and a brief behavioural change counselling are effective in managing substance abuse before it develops into dependency. A brief motivational communication rather than a confrontation during substance abuse screening, counselling and treatment is important to achieve optimum patient outcomes.

Comparison of Methods for Alcohol and Drug Screening in Primary Care Clinics

IMPORTANCE

Guidelines recommend that adult patients receive screening for alcohol and drug use during primary care visits, but the adoption of screening in routine practice remains low. Clinics frequently struggle to choose a screening approach that is best suited to their resources, workflows, and patient populations.

OBJECTIVE

To evaluate how to best implement electronic health record (EHR)-integrated screening for substance use by comparing commonly used screening methods and examining their association with implementation outcomes.

DESIGN, SETTING, AND PARTICIPANTS

This article presents the outcomes of phases 3 and 4 of a 4-phase quality improvement, implementation feasibility study in which researchers worked with stakeholders at 6 primary care clinics in 2 large urban academic health care systems to define and implement their optimal screening approach. Site A was located in New York City and comprised 2 clinics, and site B was located in Boston, Massachusetts, and comprised 4 clinics. Clinics initiated screening between January 2017 and October 2018, and 93 114 patients were eligible for screening for alcohol and drug use. Data used in the analysis were collected between January 2017 and October 2019, and analysis was performed from July 13, 2018, to March 23, 2021.

INTERVENTIONS

Clinics integrated validated screening questions and a brief counseling script into the EHR, with implementation supported by the use of clinical champions (ie, clinicians who advocate for change, motivate others, and use their expertise to facilitate the adoption of an intervention) and the training of clinic staff. Clinics varied in their screening approaches, including the type of visit targeted for screening (any visit vs annual examinations only), the mode of administration (staff-administered vs self-administered by the patient), and the extent to which they used practice facilitation and EHR usability testing.

MAIN OUTCOMES AND MEASURES

Data from the EHRs were extracted quarterly for 12 months to measure implementation outcomes. The primary outcome was screening rate for alcohol and drug use. Secondary outcomes were the prevalence of unhealthy alcohol and drug use detected via screening, and clinician adoption of a brief counseling script.

RESULTS

Patients of the 6 clinics had a mean (SD) age ranging from 48.9 (17.3) years at clinic B2 to 59.1 (16.7) years at clinic B3, were predominantly female (52.4% at clinic A1 to 64.6% at clinic A2), and were English speaking. Racial diversity varied by location. Of the 93,114 patients with primary care visits, 71.8% received screening for alcohol use, and 70.5% received screening for drug use. Screening at any visit (implemented at site A) in comparison with screening at annual examinations only (implemented at site B) was associated with higher screening rates for alcohol use (90.3%-94.7% vs 24.2%-72.0%, respectively) and drug use (89.6%-93.9% vs 24.6%-69.8%). The 5 clinics that used a self-administered screening approach had a higher detection rate for moderate- to high-risk alcohol use (14.7%-36.6%) compared with the 1 clinic that used a staff-administered screening approach (1.6%). The detection of moderate- to high-risk drug use was low across all clinics (0.5%-1.0%). Clinics with more robust practice facilitation and EHR usability testing had somewhat greater adoption of the counseling script for patients with moderate-high risk alcohol or drug use (1.4%-12.5% vs 0.1%-1.1%).

CONCLUSIONS AND RELEVANCE

In this quality improvement study, EHR-integrated screening was feasible to implement in all clinics and unhealthy alcohol use was detected more frequently when self-administered screening was used at any primary care visit. The detection of drug use was low at all clinics, as was clinician adoption of counseling. These findings can be used to inform the decision-making of health care systems that are seeking to implement screening for substance use.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02963948.

Is telemedicine the answer to rural expansion of medication treatment for opioid use disorder? Early experiences in the feasibility study phase of a National Drug Abuse Treatment Clinical Trials Network Trial

Telemedicine (TM) enabled by digital health technologies to provide medical services has been considered a key solution to increasing health care access in rural communities. With the immediate need for remote care due to the COVID-19 pandemic, many health care systems have rapidly incorporated digital technologies to support the delivery of remote care options, including medication treatment for individuals with opioid use disorder (OUD). In responding to the opioid crisis and the COVID-19 pandemic, public health officials and scientific communities strongly support and advocate for greater use of TM-based medication treatment for opioid use disorder (MOUD) to improve access to care and have suggested that broad use of TM during the pandemic should be sustained. Nevertheless, research on the implementation and effectiveness of TM-based MOUD has been limited. To address this knowledge gap, the National Drug Abuse Treatment Clinical Trials Network (CTN) funded (via the NIH HEAL Initiative) a study on Rural Expansion of Medication Treatment for Opioid Use Disorder (Rural MOUD; CTN-0102) to investigate the implementation and effectiveness of adding TM-based MOUD to rural primary care for expanding access to MOUD. In preparation for this large-scale, randomized controlled trial incorporating TM in rural primary care, a feasibility study is being conducted to develop and pilot test implementation procedures. In this commentary, we share some of our experiences, which include several challenges, during the initial two-month period of the feasibility study phase. While these challenges could be due, at least in part, to adjusting to the COVID-19 pandemic and new workflows to accommodate the study, they are notable and could have a substantial impact on the larger, planned pragmatic trial and on TM-based MOUD more broadly. Challenges include low rates of identification of risk for OUD from screening, low rates of referral to TM, digital device and internet access issues, workflow and capacity barriers, and insurance coverage. These challenges also highlight the lack of empirical guidance for best TM practice and quality remote care models. With TM expanding rapidly, understanding implementation and demonstrating what TM approaches are effective are critical for ensuring the best care for persons with OUD.

The Need for Biochemical Testing for Alcohol in Integrated Addiction Treatment Settings During the Opioid Epidemic

As the opioid crisis drives expansion of integrated opioid use disorder (OUD) treatment programs in generalist settings, these programs will contend with significant rates of co-occurring alcohol use. The authors present a brief literature review and commentary regarding nondisordered and disordered alcohol use in OUD treatment settings and biochemical detection techniques. Biochemical testing for alcohol in integrated OUD treatment settings is both important for detecting alcohol use disorder and feasible. Breathalyzer testing may assist with management of acutely intoxicated patients. Biochemical testing for alcohol is an important part of integrated OUD treatment. More research is needed on the impact of alcohol use on OUD treatment outcomes and the role of breathalyzer testing in management of intoxicated patients in the outpatient setting.