Test-retest reliability of DSM-5 substance use symptom checklists used in primary care and mental health care settings.

INTRODUCTION: Substance use disorders (SUDs) are underdiagnosed in healthcare settings. The Substance Use Symptom Checklist (SUSC) is a practical, patient-report questionnaire that has been used to assess SUD symptoms based on Diagnostic and Statistical Manual of Mental Disorders-5th edition (DSM-5) criteria. This study evaluates the test-retest reliability of SUSCs completed in primary and mental health care settings. METHODS: We identified 1194 patients who completed two SUSCs 1-21 days apart as part of routine care after reporting daily cannabis use and/or any past-year other drug use on behavioral health screens. Test-retest reliability of SUSC scores was evaluated within the full sample, subsamples who completed both checklists in primary care (n=451) or mental health clinics (n=512) where SUSC implementation differed, and subgroups defined by sex, insurance status, age, and substance use reported on behavioral health screens. RESULTS: In the full sample, test-retest reliability was high for indices reflecting the number of SUD symptoms endorsed (ICC=0.75, 95% CI:0.72-0.77) and DSM-5 SUD severity (kappa=0.72, 95% CI:0.69-0.75). These reliability estimates were higher in primary care (ICC=0.81, 95% CI:0.77-0.84; kappa=0.79, 95% CI:0.75-0.82, respectively) than in mental health clinics (ICC=0.74, 95% CI:0.70-0.78; kappa=0.73, 95% CI:0.68-0.77). Reliability differed by age and substance use reported on behavioral health screens, but not by sex or insurance status. CONCLUSIONS: The SUSC has good-to-excellent test-retest reliability when completed as part of routine primary or mental health care. Symptom checklists can reliably measure symptoms consistent with DSM-5 SUD criteria, which may aid SUD-related care in primary care and mental health settings.

Comparing Cannabis Use for Pain to Use for Other Reasons in Primary Care Patients.

BACKGROUND: Medical cannabis is commonly used for chronic pain, but little is known about differences in characteristics, cannabis use patterns, and perceived helpfulness among primary care patients who use cannabis for pain versus nonpain reasons. METHODS: Among 1688 patients who completed a 2019 cannabis survey administered in a health system in Washington state, where recreational use is legal, participants who used cannabis for pain (n = 375) were compared with those who used cannabis for other reasons (n = 558) using survey and electronic health record data. We described group differences in participant characteristics, use patterns, and perceptions and applied adjusted multinomial logistic and modified Poisson regression. RESULTS: Participants who used cannabis for pain were significantly more likely to report using applied (50.7% vs 10.6%) and beverage cannabis products (19.2% vs 11.6%), more frequent use (47.1% vs 33.1% for use ≥2 times per day; 81.6% vs 69.7% for use 4 to 7 days per week), and smoking tobacco cigarettes (19.2% vs 12.2%) than those who used cannabis for other reasons. They were also significantly more likely to perceive cannabis as very/extremely helpful (80.5% vs 72.7%), and significantly less likely to use cannabis for nonmedical reasons (4.8% vs 58.8%) or report cannabis use disorder symptoms (51.7% vs 61.1%). DISCUSSION: Primary care patients who use cannabis for pain use it more frequently, often in applied and ingested forms, and have more co-use of tobacco, which may differentially impact safety and effectiveness. These findings suggest the need for different approaches to counseling in clinical care.

High test-retest reliability of the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) questionnaire completed by primary care patients in routine care.

BACKGROUND: The Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) is a three-item screening measure of unhealthy alcohol use that is widely used in healthcare settings. Evidence shows high test-retest reliability of the AUDIT-C in research samples, but most studies had limited external validity and used small samples that could not be used to evaluate reliability across demographic subgroups and/or screening modalities. This study evaluates the test-retest reliability of the AUDIT-C completed in routine care in a large primary care sample, including across demographic subgroups defined by age, sex, race, ethnicity, and screening modality (i.e., completed in-clinic or online). METHODS: We used electronic health record (EHR) data from Kaiser Permanente Washington. The sample included 18,491 adult primary care patients who completed two AUDIT-C screens 1-21 days apart as part of routine care in 2021. Test-retest reliability was evaluated for AUDIT-C total scores (0-12) and for a binary measure indicating unhealthy alcohol use (scores ≥3 women, ≥4 men). Using previously established cutoffs, we interpreted reliability coefficients >0.75 as indicating "excellent" reliability. RESULTS: AUDIT-C screens completed in routine care and documented in EHRs had excellent test-retest reliability for total scores (ICC = 0.87, 95% CI: 0.87-0.87) and the binary indicator of unhealthy alcohol use (κ = 0.79, 95% CI: 0.78-0.80). Reliability coefficients were good to excellent across all demographic groups and for in-clinic and online modalities. Higher reliability was seen when both screens were completed through online patient portals (ICC = 0.93, 95% CI: 0.93-0.93) versus in-clinic (ICC = 0.81, 95% CI: 0.79-0.82) or when one screen was completed using each modality (ICC = 0.83, 95% CI: 0.82-0.83). Lower reliability was seen in American Indian/Alaska Native (ICC = 0.82, 95% CI: 0.75-0.87) and multiracial individuals (ICC = 0.82, 95% 0.80-0.84). CONCLUSIONS: In real-world routine care conditions, AUDIT-C screens have excellent test-retest reliability across demographic subgroups and modalities (online and in-clinic). Future research should examine why reliability varies slightly across modalities and demographic subgroups.

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.

Prevalence of Cannabis Use Disorder and Reasons for Use Among Adults in a US State Where Recreational Cannabis Use Is Legal.

Importance: Medical and nonmedical cannabis use and cannabis use disorders (CUD) have increased with increasing cannabis legalization. However, the prevalence of CUD among primary care patients who use cannabis for medical or nonmedical reasons is unknown for patients in states with legal recreational use. Objective: To estimate the prevalence and severity of CUD among patients who report medical use only, nonmedical use only, and both reasons for cannabis use in a state with legal recreational use. Design, Setting, and Participants: This cross-sectional survey study took place at an integrated health system in Washington State. Among 108 950 adult patients who completed routine cannabis screening from March 2019 to September 2019, 5000 were selected for a confidential cannabis survey using stratified random sampling for frequency of past-year cannabis use and race and ethnicity. Among 1688 respondents, 1463 reporting past 30-day cannabis use were included in the study. Exposure: Patient survey-reported reason for cannabis use in the past 30 days: medical use only, nonmedical use only, and both reasons. Main Outcomes and Measures: Patient responses to the Composite International Diagnostic Interview-Substance Abuse Module for CUD, corresponding to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition CUD severity (0-11 symptoms) were categorized as any CUD (≥2 symptoms) and moderate to severe CUD (≥4 symptoms). Adjusted analyses were weighted for survey stratification and nonresponse for primary care population estimates and compared prevalence of CUD across reasons for cannabis use. Results: Of 1463 included primary care patients (weighted mean [SD] age, 47.4 [16.8] years; 748 [weighted proportion, 61.9%] female) who used cannabis, 42.4% (95% CI, 31.2%-54.3%) reported medical use only, 25.1% (95% CI, 17.8%-34.2%) nonmedical use only, and 32.5% (95% CI, 25.3%-40.8%) both reasons for use. The prevalence of CUD was 21.3% (95% CI, 15.4%-28.6%) and did not vary across groups. The prevalence of moderate to severe CUD was 6.5% (95% CI, 5.0%-8.6%) and differed across groups: 1.3% (95% CI, 0.0%-2.8%) for medical use, 7.2% (95% CI, 3.9%-10.4%) for nonmedical use, and 7.5% (95% CI, 5.7%-9.4%) for both reasons for use (P = .01). Conclusions and Relevance: In this cross-sectional study of primary care patients in a state with legal recreational cannabis use, CUD was common among patients who used cannabis. Moderate to severe CUD was more prevalent among patients who reported any nonmedical use. These results underscore the importance of assessing patient cannabis use and CUD symptoms in medical settings.

Implementation and workflow strategies for integrating digital therapeutics for alcohol use disorders into primary care: a qualitative study.

BACKGROUND: Alcohol use disorders (AUD) are prevalent and often go untreated. Patients are commonly screened for AUD in primary care, but existing treatment programs are failing to meet demand. Digital therapeutics include novel mobile app-based treatment approaches which may be cost-effective treatment options to help fill treatment gaps. The goal of this study was to identify implementation needs and workflow design considerations for integrating digital therapeutics for AUD into primary care. METHODS: We conducted qualitative interviews with clinicians, care delivery leaders, and implementation staff (n = 16) in an integrated healthcare delivery system in the United States. All participants had experience implementing digital therapeutics for depression or substance use disorders in primary care. Interviews were designed to gain insights into adaptations needed to optimize existing clinical processes, workflows, and implementation strategies for use with alcohol-focused digital therapeutics. Interviews were recorded and transcribed and then analyzed using a rapid analysis process and affinity diagramming. RESULTS: Qualitative themes were well represented across health system staff roles. Participants were enthusiastic about digital therapeutics for AUD, anticipated high patient demand for such a resource, and made suggestions for successful implementation. Key insights regarding the implementation of digital therapeutics for AUD and unhealthy alcohol use from our data include: (1) implementation strategy selection must be driven by digital therapeutic design and target population characteristics, (2) implementation strategies should seek to minimize burden on clinicians given the large numbers of patients with AUD who are likely to be interested in and eligible for digital therapeutics, and (3) digital therapeutics should be offered alongside many other treatment options to accommodate individual patients' AUD severity and treatment goals. Participants also expressed confidence that previous implementation strategies used with other digital therapeutics such as clinician training, electronic health record supports, health coaching, and practice facilitation would be effective for the implementation of digital therapeutics for AUD. CONCLUSIONS: The implementation of digital therapeutics for AUD would benefit from careful consideration of the target population. Optimal integration requires tailoring workflows to meet anticipated patient volume and designing workflow and implementation strategies to meet the unique needs of patients with varying AUD severity.

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.

Cannabis and nicotine co-use among primary care patients in a state with legal cannabis access.

The aim of this exploratory analysis was to evaluate cannabis exposure, reasons for use and problematic cannabis use among adult primary care patients in Washington state (United States) who co-use cannabis and nicotine (tobacco cigarettes and/or nicotine vaping) compared to patients who endorse current cannabis use only. As part of a NIDA Clinical Trials Network (CTN) parent study, patients who completed a cannabis screen as part of routine primary care were randomly sampled (N = 5,000) to a receive a confidential cannabis survey. Patients were stratified and oversampled based on the frequency of past-year cannabis use and for Black, indigenous, or other persons of color. Patients who endorsed past 30-day cannabis use are included here (N = 1388). Outcomes included; prevalence of cannabis use, days of cannabis use per week and times used per day, methods of use, THC:CBD content, non-medical and/or medical use, health symptoms managed, and cannabis use disorder (CUD) symptom severity. We conducted unadjusted bivariate analyses comparing outcomes between patients with cannabis and current nicotine co-use to patients with cannabis-only use. Nicotine co-use (n = 352; 25.4 %) was associated with differences in method of cannabis use, THC:CBD content, days of use per week and times used per day, number of health symptoms managed, and CUD severity (all p < 0.001), compared to primary care patients with cannabis-only use (n = 1036). Interventions targeting cannabis and nicotine co-use in primary care are not well-established and further research is warranted given findings of more severe cannabis use patterns and the adverse health outcomes associated with co-use.

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.

Barriers and facilitators to changing drinking and receiving alcohol-related care: Interviews with Veterans Health Administration primary care patients who indicated interest but did not enroll in an alcohol care management intervention trial.

Background: Most people with alcohol use disorder do not receive treatment, and primary care (PC)-based management of alcohol use disorder is a key strategy to close this gap. Understanding PC patients' perspectives on changing drinking and receiving alcohol-related care is important for this goal, particularly among those who decline alcohol-related care. This study examined perspectives on barriers and facilitators to changing drinking and receiving alcohol-related care among Veterans Health Administration (VA) PC patients who indicated interest but did not enroll in the Choosing Healthier drinking Options In primary CarE trial (CHOICE), which tested a PC-based alcohol care management intervention. Methods: VA PC patients with frequent heavy drinking who indicated interest in CHOICE but did not enroll were invited to participate. Twenty-seven patients completed in-person, semi-structured interviews. Interview transcripts were analyzed using iterative deductive and inductive content analysis. Results: Participants were mostly men (96%) and White (59%), and the mean age was 48. Seventy-four percent met criteria for alcohol use disorder, and the median number of past-week standard drinks was 41.5. Participants reported fewer alcohol-related problems, lower importance of/readiness to change drinking, and higher confidence in their ability to change than patients who enrolled in the CHOICE trial. Barriers fell into 5 domains: drinking fulfills need(s); reducing drinking or treatment is not needed; treatment is not effective/not acceptable; alcohol-related stigma; and practical barriers. Facilitators fell into 4 domains: reasons to change drinking; social support; treatment is acceptable/meets patients' needs; and practical facilitators. Participants discussed how Veteran identity and military experiences impacted drinking and willingness to receive care, which amplified multiple barriers/facilitators. Conclusions: This study identified barriers and facilitators to changing drinking and receiving alcohol-related care among VA PC patients who indicated interest but did not enroll in an alcohol care management trial. Findings can inform patient-centered interventions and support clinicians in engaging patients in care.

Sex Differences in Comorbid Mental and Substance Use Disorders Among Primary Care Patients With Opioid Use Disorder.

OBJECTIVE: The authors sought to characterize the 3-year prevalence of mental disorders and nonnicotine substance use disorders among male and female primary care patients with documented opioid use disorder across large U.S. health systems. METHODS: This retrospective study used 2014-2016 data from patients ages ≥16 years in six health systems. Diagnoses were obtained from electronic health records or claims data; opioid use disorder treatment with buprenorphine or injectable extended-release naltrexone was determined through prescription and procedure data. Adjusted prevalence of comorbid conditions among patients with opioid use disorder (with or without treatment), stratified by sex, was estimated by fitting logistic regression models for each condition and applying marginal standardization. RESULTS: Females (53.2%, N=7,431) and males (46.8%, N=6,548) had a similar prevalence of opioid use disorder. Comorbid mental disorders among those with opioid use disorder were more prevalent among females (86.4% vs. 74.3%, respectively), whereas comorbid other substance use disorders (excluding nicotine) were more common among males (51.9% vs. 60.9%, respectively). These differences held for those receiving medication treatment for opioid use disorder, with mental disorders being more common among treated females (83% vs. 71%) and other substance use disorders more common among treated males (68% vs. 63%). Among patients with a single mental health condition comorbid with opioid use disorder, females were less likely than males to receive medication treatment for opioid use disorder (15% vs. 20%, respectively). CONCLUSIONS: The high rate of comorbid conditions among patients with opioid use disorder indicates a strong need to supply primary care providers with adequate resources for integrated opioid use disorder treatment.

Influence of a national transgender health care directive on receipt of alcohol-related care among transgender Veteran Health Administration patients with unhealthy alcohol use.

INTRODUCTION: Transgender persons are vulnerable to under-receipt of recommended health care due to chronic exposure to systemic stressors (e.g., discriminatory laws and health system practices). Scant information exists on receipt of alcohol-related care for transgender populations, and whether structural interventions to reduce transgender discrimination in health care improve receipt of recommended treatment. This study evaluated the effect of the Veteran Health Administration (VA) Transgender Healthcare Directive-a national policy to reduce structural discrimination-on receipt of evidence-based alcohol-related care for transgender VA patients with unhealthy alcohol use. METHODS: The study used an interrupted time series with control design to compare monthly receipt of alcohol-related care among transgender patients with unhealthy alcohol use (Alcohol Use Disorders Identification Test Consumption ≥5) documented in their electronic health record before (10/1/2009-5/31/2011) and after (7/1/2011-7/31/2017) implementation of VA's Transgender Healthcare Directive. A propensity-score matched sample of non-transgender patients with unhealthy alcohol use served as a comparison group to control for concurrent secular trends. Mixed effects segmented logistic regression models estimated changes in level and slope (i.e., rate of change) in receipt of any evidence-based alcohol-related care, including brief intervention, specialty addictions treatment, and alcohol use disorder medications. RESULTS: The matched sample (mean age = 47.5 [SD = 15.0]; 75% non-Hispanic White race/ethnicity) included 2074 positive alcohol screens completed by 1377 transgender patients and 6,l99 positive alcohol screens completed by 6185 non-transgender patients. Receipt of alcohol-related care increased for transgender patients from 78.5% (95% CI: 71.3%-85.6%) at the start of study to 83.0% (75.9%-90.1%) immediately before the directive and decreased slightly from 81.6% (77.4%-85.9%) immediately after the directive to 80.1% (76.8-85.4) at the end of the study. Changes in level and slope comparing periods before and after the directive were not statistically significant, nor were they statistically significantly different from the matched sample of non-transgender patients. CONCLUSIONS: Health systems must urgently employ and evaluate policies to address structural stigma that produces and reproduces disparities in health and health care. Although VA's directive was not associated with increased receipt of alcohol-related care, that receipt of alcohol-related care among transgender patients is comparable to non-transgender patients is promising.

Comparison of Medical Cannabis Use Reported on a Confidential Survey vs Documented in the Electronic Health Record Among Primary Care Patients.

Importance: Patients who use cannabis for medical reasons may benefit from discussions with clinicians about health risks of cannabis and evidence-based treatment alternatives. However, little is known about the prevalence of medical cannabis use in primary care and how often it is documented in patient electronic health records (EHR). Objective: To estimate the primary care prevalence of medical cannabis use according to confidential patient survey and to compare the prevalence of medical cannabis use documented in the EHR with patient report. Design, Setting, and Participants: This study is a cross-sectional survey performed in a large health system that conducts routine cannabis screening in Washington state where medical and nonmedical cannabis use are legal. Among 108 950 patients who completed routine cannabis screening (between March 28, 2019, and September 12, 2019), 5000 were randomly selected for a confidential survey about cannabis use, using stratified random sampling for frequency of past-year use and patient race and ethnicity. Data were analyzed from November 2020 to December 2021. Exposures: Survey measures of patient-reported past-year cannabis use, medical cannabis use (ie, explicit medical use), and any health reason(s) for use (ie, implicit medical use). Main Outcomes and Measures: Survey data were linked to EHR data in the year before screening. EHR measures included documentation of explicit and/or implicit medical cannabis use. Analyses estimated the primary care prevalence of cannabis use and compared EHR-documented with patient-reported medical cannabis use, accounting for stratified sampling and nonresponse. Results: Overall, 1688 patients responded to the survey (34% response rate; mean [SD] age, 50.7 [17.5] years; 861 female [56%], 1184 White [74%], 1514 non-Hispanic [97%], and 1059 commercially insured [65%]). The primary care prevalence of any past-year patient-reported cannabis use on the survey was 38.8% (95% CI, 31.9%-46.1%), whereas the prevalence of explicit and implicit medical use were 26.5% (95% CI, 21.6%-31.3%) and 35.1% (95% CI, 29.3%-40.8%), respectively. The prevalence of EHR-documented medical cannabis use was 4.8% (95% CI, 3.45%-6.2%). Compared with patient-reported explicit medical use, the sensitivity and specificity of EHR-documented medical cannabis use were 10.0% (95% CI, 4.4%-15.6%) and 97.1% (95% CI, 94.4%-99.8%), respectively. Conclusions and Relevance: These findings suggest that medical cannabis use is common among primary care patients in a state with legal use, and most use is not documented in the EHR. Patient report of health reasons for cannabis use identifies more medical use compared with explicit questions about medical use.

Practical assessment of DSM-5 alcohol use disorder criteria in routine care: High test-retest reliability of an Alcohol Symptom Checklist.

BACKGROUND: Alcohol use disorder (AUD) is underdiagnosed and undertreated in medical settings, in part due to a lack of AUD assessment instruments that are reliable and practical for use in routine care. This study evaluates the test-retest reliability of a patient-report Alcohol Symptom Checklist questionnaire when it is used in routine care, including primary care and mental health specialty settings. METHODS: We performed a pragmatic test-retest reliability study using electronic health record (EHR) data from Kaiser Permanente Washington, an integrated health system in Washington state. The sample included 454 patients who reported high-risk drinking on a behavioral health screen and completed two Alcohol Symptom Checklists 1 to 21 days apart. Subgroups of these patients who completed both checklists in primary care (n = 271) or mental health settings (n = 79) were also examined. The primary measure was an Alcohol Symptom Checklist on which patients self-reported whether they experienced each of the 11 AUD criteria within the past year, as defined by the Diagnostic and Statistical Manual of Mental Disorders-5th edition (DSM-5). RESULTS: Alcohol Symptom Checklists completed in routine care and documented in EHRs had excellent test-retest reliability for measuring AUD criterion counts (ICC = 0.79, 95% CI: 0.76 to 0.82). Test-retest reliability estimates were also high and not significantly different for the subsamples of patients who completed both checklists in primary care (ICC = 0.82, 95% CI: 0.77 to 0.85) or mental health settings (ICC = 0.74, 95% CI: 0.62 to 0.83). Test-retest reliability was not moderated by having a past two-year AUD diagnosis, nor by the age or sex of the patient completing it. CONCLUSIONS: Alcohol Symptom Checklists can reliably and pragmatically assess AUD criteria in routine care among patients who screen positive for high-risk drinking. The Alcohol Symptom Checklist may be a valuable tool in supporting AUD-related care and monitoring AUD criteria longitudinally in routine primary care and mental health settings.

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.

Approaches for implementing digital interventions for alcohol use disorders in primary care: A qualitative, user-centered design study.

Background: Digital interventions, such as smartphone apps, can be effective in treating alcohol use disorders (AUD). However, efforts to integrate digital interventions into primary care have been challenging. To inform successful implementation, we sought to understand how patients and clinicians preferred to use apps in routine primary care. Methods: This study combined user-centered design and qualitative research methods, interviewing 18 primary care patients with AUD and nine primary care clinicians on topics such as prior experiences with digital tools, and design preferences regarding approaches for offering apps for AUD in primary care. Interviews were recorded and transcribed for template analysis whereby a priori codes were based on interview topics and refined through iterative coding. New codes and cross-cutting themes emerged from the data. Results: Patient participants with AUD indicated they would be more likely to engage in treatment if primary care team members were involved in their use of apps. They also preferred to see clinicians "invested" and recommended that clinicians ask about app use and progress during follow-up appointments or check-ins. Clinician participants valued the opportunity to offer apps to their patients but noted that workflows would need to be tailored to individual patient needs. Time pressures, implementation complexity, and lack of appropriate staffing were cited as barriers. Clinicians proposed concrete solutions (e.g., education, tools, and staffing models) that could improve their ability to use apps within the constraints of primary care and suggested that some patients could potentially use apps without clinician support. Conclusions: A user-centered approach to engaging patients in digital alcohol interventions in primary care may require personalized support for both initiation and follow-up. Meeting patients' needs likely require increased staffing and efficient workflows in primary care. Health systems should consider offering multiple pathways for enrolling patients in apps to accommodate individual preferences and contextual barriers. Plain Language Summary: Healthcare systems have begun using app-based treatments to help patients manage their health conditions, including alcohol use disorders. Some apps have been tested in research studies and appear to be effective. However, it is difficult for healthcare teams to offer apps to patients. Clinicians must engage in new activities that they have not done before, such as "teaching" patients to use apps and checking in on their use of the apps. Identifying how to use apps in routine healthcare is critical to their successful implementation. This study interviewed 27 people, including healthcare providers and patients in primary care, to uncover the most optimal ways to offer apps to patients with alcohol use disorders. The interviews combined the use of qualitative research methods and user-centered design. Results suggest that to use to address alcohol use disorders, primary care teams should be prepared to offer personalized support to patients. Both patient and clinician interviewees said that the steps required to use apps must be intuitive and simple. Patients could gain more benefits if clinicians introduced the apps and guided patients to use them, as opposed to making apps available for patients to download and use on their own. However, the exact approach to offering apps would depend on a given patient's preferences and the extent that staffing was available in the clinic to support patients. Health systems should be prepared to offer and support patients in their use of apps, which should accommodate patient preferences and the constraints of the clinic.

Practical Assessment of Alcohol Use Disorder in Routine Primary Care: Performance of an Alcohol Symptom Checklist.

BACKGROUND: Alcohol use disorder (AUD) is highly prevalent but underrecognized and undertreated in primary care settings. Alcohol Symptom Checklists can engage patients and providers in discussions of AUD-related care. However, the performance of Alcohol Symptom Checklists when they are used in routine care and documented in electronic health records (EHRs) remains unevaluated. OBJECTIVE: To evaluate the psychometric performance of an Alcohol Symptom Checklist in routine primary care. DESIGN: Cross-sectional study using item response theory (IRT) and differential item functioning analyses of measurement consistency across age, sex, race, and ethnicity. PATIENTS: Patients seen in primary care in the Kaiser Permanente Washington Healthcare System who reported high-risk drinking on the Alcohol Use Disorder Identification Test Consumption screening measure (AUDIT-C ≥ 7) and subsequently completed an Alcohol Symptom Checklist between October 2015 and February 2020. MAIN MEASURE: Alcohol Symptom Checklists with 11 items assessing AUD criteria defined in the Diagnostic and Statistical Manual for Mental Disorders, 5th edition (DSM-5), completed by patients during routine medical care and documented in EHRs. KEY RESULTS: Among 11,464 patients who screened positive for high-risk drinking and completed an Alcohol Symptom Checklist (mean age 43.6 years, 30.5% female), 54.1% reported ≥ 2 DSM-5 AUD criteria (threshold for AUD diagnosis). IRT analyses demonstrated that checklist items measured a unidimensional continuum of AUD severity. Differential item functioning was observed for some demographic subgroups but had minimal impact on accurate measurement of AUD severity, with differences between demographic subgroups attributable to differential item functioning never exceeding 0.42 points of the total symptom count (of a possible range of 0-11). CONCLUSIONS: Alcohol Symptom Checklists used in routine care discriminated AUD severity consistently with current definitions of AUD and performed equitably across age, sex, race, and ethnicity. Integrating symptom checklists into routine care may help inform clinical decision-making around diagnosing and managing AUD.

Approaches for Implementing App-Based Digital Treatments for Drug Use Disorders Into Primary Care: A Qualitative, User-Centered Design Study of Patient Perspectives.

BACKGROUND: Digital interventions, such as websites and smartphone apps, can be effective in treating drug use disorders (DUDs). However, their implementation in primary care is hindered, in part, by a lack of knowledge on how patients might like these treatments delivered to them. OBJECTIVE: This study aims to increase the understanding of how patients with DUDs prefer to receive app-based treatments to inform the implementation of these treatments in primary care. METHODS: The methods of user-centered design were combined with qualitative research methods to inform the design of workflows for offering app-based treatments in primary care. Adult patients (n=14) with past-year cannabis, stimulant, or opioid use disorder from 5 primary care clinics of Kaiser Permanente Washington in the Seattle area participated in this study. Semistructured interviews were recorded, transcribed, and analyzed using qualitative template analysis. The coding scheme included deductive codes based on interview topics, which primarily focused on workflow design. Inductive codes emerged from the data. RESULTS: Participants wanted to learn about apps during visits where drug use was discussed and felt that app-related conversations should be incorporated into the existing care whenever possible, as opposed to creating new health care visits to facilitate the use of the app. Nearly all participants preferred receiving clinician support for using apps over using them without support. They desired a trusting, supportive relationship with a clinician who could guide them as they used the app. Participants wanted follow-up support via phone calls or secure messaging because these modes of communication were perceived as a convenient and low burden (eg, no copays or appointment travel). CONCLUSIONS: A user-centered implementation of treatment apps for DUDs in primary care will require health systems to design workflows that account for patients' needs for structure, support in and outside of visits, and desire for convenience.

Receipt of evidence-based alcohol-related care in a national sample of transgender patients with unhealthy alcohol use: Overall and relative to non-transgender patients.

BACKGROUND/OBJECTIVE: Evidence-based alcohol-related care-brief intervention for all patients with unhealthy alcohol use and specialty addictions treatment and/or pharmacotherapy for patients with alcohol use disorder (AUD)-should be routinely offered. Transgender persons may be particularly in need of alcohol-related care, given common experiences of social and economic hardship that may compound the adverse effects of unhealthy alcohol use. We examined receipt of alcohol-related care among transgender patients compared to non-transgender patients in a large national sample of Veterans Health Administration (VA) outpatients with unhealthy alcohol use. METHODS: We extracted electronic health record data for patients from all VA facilities who had an outpatient visit 10/1/09-7/31/17 and a documented positive screen for unhealthy alcohol use (AUDIT-C ≥ 5). We identified transgender patients with a validated approach using transgender-related diagnostic codes. We fit modified Poisson models, adjusted for demographics and comorbidities, to estimate the average predicted prevalence of brief intervention (documented 0-14 days following most recent positive screening), specialty addictions treatment for AUD (documented 0-365 days following screening), and filled prescriptions for medications to treat AUD (documented 0-365 days following screening) for transgender patients, and compared to that of non-transgender patients. RESULTS: Among transgender Veterans with unhealthy alcohol use (N = 1392), the adjusted prevalence of receiving brief intervention was 75.4% (95% CI 72.2-78.5), specialty addictions treatment for AUD was 15.7% (95% CI 13.7-17.7), and any AUD pharmacotherapy was 19.0% (95% CI 17.1-20.8). Receipt of brief intervention did not differ for transgender relative to non-transgender patients (Prevalence Ratio [PR] 1.01, 95% CI 0.98-1.04, p = 0.574). However, transgender patients were more likely to receive specialty addictions treatment (PR 1.24, 95% CI 1.12-1.37, p < 0.001) and pharmacotherapy (PR 1.16, 95% CI 1.06-1.28, p = 0.002). CONCLUSIONS: Findings suggest the majority of transgender VHA patients with unhealthy alcohol use receive brief intervention, though a quarter still do not. Nonetheless, rates of specialty addictions treatment and pharmacotherapy are low overall, although transgender patients may be receiving this care at greater rates than non-transgender patients. Further research is needed to investigate these findings and to increase receipt of evidence-based care overall.