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.

Prevalence and Medication Treatment of Opioid Use Disorder Among Primary Care Patients with Hepatitis C and HIV.

BACKGROUND: Hepatitis C and HIV are associated with opioid use disorders (OUD) and injection drug use. Medications for OUD can prevent the spread of HCV and HIV. OBJECTIVE: To describe the prevalence of documented OUD, as well as receipt of office-based medication treatment, among primary care patients with HCV or HIV. DESIGN: Retrospective observational cohort study using electronic health record and insurance data. PARTICIPANTS: Adults ≥ 18 years with ≥ 2 visits to primary care during the study (2014-2016) at 6 healthcare systems across five states (CO, CA, OR, WA, and MN). MAIN MEASURES: The primary outcome was the diagnosis of OUD; the secondary outcome was OUD treatment with buprenorphine or oral/injectable naltrexone. Prevalence of OUD and OUD treatment was calculated across four groups: HCV only; HIV only; HCV and HIV; and neither HCV nor HIV. In addition, adjusted odds ratios (AOR) of OUD treatment associated with HCV and HIV (separately) were estimated, adjusting for age, gender, race/ethnicity, and site. KEY RESULTS: The sample included 1,368,604 persons, of whom 10,042 had HCV, 5821 HIV, and 422 both. The prevalence of diagnosed OUD varied across groups: 11.9% (95% CI: 11.3%, 12.5%) for those with HCV; 1.6% (1.3%, 2.0%) for those with HIV; 8.8% (6.2%, 11.9%) for those with both; and 0.92% (0.91%, 0.94%) among those with neither. Among those with diagnosed OUD, the prevalence of OUD medication treatment was 20.9%, 16.0%, 10.8%, and 22.3%, for those with HCV, HIV, both, and neither, respectively. HCV was not associated with OUD treatment (AOR = 1.03; 0.88, 1.21), whereas patients with HIV had a lower probability of OUD treatment (AOR = 0.43; 0.26, 0.72). CONCLUSIONS: Among patients receiving primary care, those diagnosed with HCV and HIV were more likely to have documented OUD than those without. Patients with HIV were less likely to have documented medication treatment for OUD.

Alcohol Use and Antiretroviral Adherence Among Patients Living with HIV: Is Change in Alcohol Use Associated with Change in Adherence?

Alcohol use increases non-adherence to antiretroviral therapy (ART) among persons living with HIV (PLWH). Dynamic longitudinal associations are understudied. Veterans Aging Cohort Study (VACS) data 2/1/2008-7/31/16 were used to fit linear regression models estimating changes in adherence (% days with ART medication fill) associated with changes in alcohol use based on annual clinically-ascertained AUDIT-C screening scores (range - 12 to + 12, 0 = no change) adjusting for demographics and initial adherence. Among 21,275 PLWH (67,330 observations), most reported no (48%) or low-level (39%) alcohol use initially, with no (55%) or small (39% ≤ 3 points) annual change. Mean initial adherence was 86% (SD 21%), mean annual change was - 3.1% (SD 21%). An inverted V-shaped association was observed: both increases and decreases in AUDIT-C were associated with greater adherence decreases relative to stable scores [p < 0.001, F (4, 21,274)]. PLWH with dynamic alcohol use (potentially indicative of alcohol use disorder) should be considered for adherence interventions.

RESUMEN: El consumo de alcohol aumenta el no-cumplimiento a la terapia antirretroviral (TARV) entre las personas que viven con VIH. No se han estudiado lo suficiente las dinámicas asociaciones longitudinales. Los datos del Estudio de la Envejecimiento de Cohorte de Veteranos (EECV) (1/2/2008­31/7/2016) fueron usados para encajar modelos de regresión lineal estimando los cambios en cumplimiento (% de días con medicaciones TARV surtidas) asociados con los cambios en el consumo de alcohol basado en los resultados anuales de las evaluaciones AUDIT-C, determinadas clínicamente, (una gama de -12 a + 12, 0 = cero cambio) adaptándose a las estadísticas demográficas y cumplimiento inicial. Entre 21,275 personas que viven con VIH (67,330 observaciones), la mayoría reportó ningún (48%) o bajos niveles del (39%) consumo de alcohol inicialmente, con ningún (55%) o muy pequeño (39% ≤ 3 puntos) cambio anual. la media inicial de cumplimiento fue 86% (DE 21%). La media de cambio anual fue -3.1% (DE 21%). Se observó una asociación de forma V invertida: tanto los aumentos como las disminuciones en AUDIT-C fueron asociados con mayor disminuciones de cumplimiento en comparación con resultados estables (p < 0.001, F (4, 21,274)). Personas que viven con VIH con el consumo dinámico de alcohol (potencialmente indicativo de un trastorno por consumo de alcohol) deben ser considerados por intervenciones de cumplimiento.

Routine Assessment of Symptoms of Substance Use Disorders in Primary Care: Prevalence and Severity of Reported Symptoms.

BACKGROUND: Most patients with substance use disorders (SUDs) never receive treatment and SUDs are under-recognized in primary care (PC) where patients can be treated or linked to treatment. Asking PC patients to directly report SUD symptoms on questionnaires might help identify SUDs but to our knowledge, this approach is previously untested. OBJECTIVE: To describe the prevalence and severity of DSM-5 SUD symptoms reported by PC patients as part of routine care. DESIGN: Cross-sectional study using secondary data. PARTICIPANTS: A total of 241,265 adult patients who visited one of 25 PC sites in an integrated health system in Washington state and had alcohol, cannabis, or other drug use screening documented in their EHRs (March 2015-July 2018) were included in main analyses if they had a positive screen for high-risk substance use defined as AUDIT-C score 7-12 points, or report of past-year daily cannabis use or any other drug use. MAIN MEASURES: The main outcome was number of SUD symptoms based on Diagnostic and Statistical Manual, 5th edition (DSM-5), reported on Symptom Checklists (0-11) for alcohol or other drugs: 2-3 mild; 4-5 moderate; 6-11 severe. RESULTS: Of screened patients, 16,776 (5.7%) reported high-risk use of alcohol (2.4%), cannabis (3.9%), and/or other drugs (1.7%), and 65.0-69.9% of those completed Symptom Checklists. Of those with high-risk alcohol use, 52.5% (95% CI 50.9-54.0%) reported ≥ 2 symptoms consistent with mild-severe alcohol use disorders. Of those reporting daily cannabis use, 29.8% (28.6-30.9%) reported ≥ 2 symptoms consistent with mild-severe SUDs. Of those reporting any other drug use, 37.5% (35.7-39.3%) reported ≥ 2 symptoms consistent with mild-severe SUDs. CONCLUSIONS AND RELEVANCE: Many PC patients who screened positive for high-risk substance use reported symptoms consistent with DSM-5 SUDs on self-report Symptom Checklists. Use of SUD Symptom Checklists could support PC providers in making SUD diagnoses and initiating discussions of substance use.

Effect of a Care Management Intervention on 12-Month Drinking Outcomes Among Patients With and Without DSM-IV Alcohol Dependence at Baseline.

BACKGROUND: The CHOICE care management intervention did not improve drinking relative to usual care (UC) for patients with frequent heavy drinking at high risk of alcohol use disorders. Patients with alcohol dependence were hypothesized to benefit most. We conducted preplanned secondary analyses to test whether the CHOICE intervention improved drinking relative to UC among patients with and without baseline DSM-IV alcohol dependence. METHODS: A total of 304 patients reporting frequent heavy drinking from 3 VA primary care clinics were randomized (stratified by DSM-IV alcohol dependence, sex, and site) to UC or the patient-centered, nurse-delivered, 12-month CHOICE care management intervention. Primary outcomes included percent heavy drinking days (%HDD) using 28-day timeline follow-back and a "good drinking outcome" (GDO)-abstaining or drinking below recommended limits and no alcohol-related symptoms on the Short Inventory of Problems at 12 months. Generalized estimating equation binomial regression models (clustered on provider) with interaction terms between dependence and intervention group were fit. RESULTS: At baseline, 59% of intervention and UC patients had DSM-IV alcohol dependence. Mean drinking outcomes improved for all subgroups. For participants with dependence, 12-month outcomes did not differ for intervention versus UC patients (%HDD 37% versus 38%, p = 0.76 and GDO 16% versus 16%, p = 0.77). For participants without dependence, %HDD did not differ between intervention (41%) and UC (31%) patients (p = 0.12), but the proportion with GDO was significantly higher among UC participants (26% versus 13%, p = 0.046). Neither outcome was significantly modified by dependence (interaction p values 0.19 for %HDD and 0.10 for GDO). CONCLUSIONS: Among participants with frequent heavy drinking, care management had no benefit relative to UC for patients with dependence, but UC may have had benefits for those without dependence. TRIAL REGISTRATION: ClinicalTrials.gov NCT01400581.

Level of Alcohol Use Associated with HIV Care Continuum Targets in a National U.S. Sample of Persons Living with HIV Receiving Healthcare.

We evaluated associations between levels of alcohol use and HIV care continuum components using national Veterans Aging Cohort Study data for all patients with HIV and AUDIT-C screening (2/1/2008-9/30/2014). Poisson regression models evaluated associations between alcohol use levels (non-drinking, low-, medium-, high-, and very high-level drinking) and: (1) engagement with care (documented CD4 cells/µl or viral load copies/ml labs), (2) ART treatment (≥ 1 prescription), and (3) viral suppression (HIV RNA < 500 copies/ml) within one year. Among 33,224 patients, alcohol use level was inversely associated with all care continuum outcomes (all p < 0.001). Adjusted prevalence of care engagement ranged from 77.8% (95% CI 77.1-78.4%) for non-drinking to 69.1% (66.6-71.6%) for high-level drinking. The corresponding range for ART treatment was 74.0% (73.3-74.7%) to 60.1% (57.3-62.9%) and for viral suppression was 57.3% (56.5-58.1%) to 38.3% (35.6-41.1%). Greater alcohol use is associated with suboptimal HIV treatment across the HIV care continuum.

Patient characteristics associated with treatment initiation and engagement among individuals diagnosed with alcohol and other drug use disorders in emergency department and primary care settings.

Background: Treatment initiation and engagement rates for alcohol and other drug (AOD) use disorders differ depending on where the AOD use disorder was identified. Emergency department (ED) and primary care (PC) are 2 common settings where patients are identified; however, it is unknown whether characteristics of patients who initiate and engage in treatment differ between these settings. Methods: Patients identified with an AOD disorder in ED or PC settings were drawn from a larger study that examined Healthcare Effectiveness Data and Information Set (HEDIS) AOD treatment initiation and engagement measures across 7 health systems using electronic health record data (n = 54,321). Multivariable generalized linear models, with a logit link, clustered on health system, were used to model patient factors associated with initiation and engagement in treatment, between and within each setting. Results: Patients identified in the ED had higher odds of initiating treatment than those identified in PC (adjusted odds ratio [aOR] = 1.89, 95% confidence interval [CI] = 1.73-2.07), with no difference in engagement between the settings. Among those identified in the ED, compared with patients aged 18-29, older patients had higher odds of treatment initiation (age 30-49: aOR = 1.25, 95% CI = 1.12-1.40; age 50-64: aOR = 1.42, 95% CI = 1.26-1.60; age 65+: aOR = 1.27, 95% CI = 1.08-1.49). However, among those identified in PC, compared with patients aged 18-29, older patients were less likely to initiate (age 30-49: aOR = 0.81, 95% CI = 0.71-0.94; age 50-64: aOR = 0.68, 95% CI = 0.58-0.78; age 65+: aOR = 0.47, 95% CI = 0.40-0.56). Women identified in ED had lower odds of initiating treatment (aOR = 0.80, 95% CI = 0.72-0.88), whereas sex was not associated with treatment initiation in PC. In both settings, patients aged 65+ had lower odds of engaging compared with patients aged 18-29 (ED: aOR = 0.61, 95% CI = 0.38-0.98; PC: aOR = 0.42, 95% CI = 0.26-0.68). Conclusion: Initiation and engagement in treatment differed by sex and age depending on identification setting. This information could inform tailoring of future AOD interventions.

Psychiatric comorbidity and Healthcare Effectiveness Data and Information Set (HEDIS) measures of alcohol and other drug treatment initiation and engagement across 7 health care systems.

Background: Psychiatric comorbidity is common among patients with alcohol and other drug (AOD) use disorders. To better understand how psychiatric comorbidity influences AOD treatment access in health care systems, the present study examined treatment initiation and engagement among a large, diverse sample of patients with comorbid psychiatric and AOD use disorders. Methods: This study utilized data from a multisite observational study examining Healthcare Effectiveness Data and Information Set (HEDIS) measures of initiation and engagement in treatment (IET) among patients with AOD use disorders from 7 health care systems. Participants were aged 18 or older with at least 1 AOD index diagnosis between October 1, 2014, and August 15, 2015. Data elements extracted from electronic health records and insurance claims data included patient demographic characteristics, ICD-9 (International Classification of Diseases, Ninth Revision) diagnostic codes, and procedure codes. Descriptive analyses and multivariate logistic regression models were used to examine the relationship between patient-level factors and IET measures. Results: Across health care systems, out of a total of 86,565 patients who had at least 1 AOD index diagnosis during the study period, 66.2% (n = 57,335) patients also had a comorbid psychiatric disorder. Among patients with a comorbid psychiatric disorder, 34.9% (n = 19,998) initiated AOD treatment, and of those, 10.3% (n = 2,060) engaged in treatment. After adjusting for age, sex, and race/ethnicity, patients with comorbid psychiatric disorders were more likely to initiate (odds ratio [OR] = 3.20, 95% confidence interval [CI] = 3.08, 3.32) but no more likely to engage (OR = 0.56, 95% CI = 0.51, 0.61) in AOD treatment, compared with those without a comorbid psychiatric disorder. Conclusions: Findings suggest that identification of comorbid psychiatric disorders may increase initiation in AOD treatment. However, innovative efforts are needed to enhance treatment engagement both generally and especially for individuals without diagnosed psychiatric conditions.

The association between medical comorbidity and Healthcare Effectiveness Data and Information Set (HEDIS) measures of treatment initiation and engagement for alcohol and other drug use disorders.

Background: Medical comorbidity may influence treatment initiation and engagement for alcohol and other drug (AOD) use disorders. We examined the association between medical comorbidity and Healthcare Effectiveness Data and Information Set (HEDIS) treatment initiation and engagement measures.Methods: We used electronic health record and insurance claims data from 7 US health care systems to identify patients with AOD use disorders between October 1, 2014, and August 15, 2015 (N = 86,565). Among patients identified with AOD use disorders in outpatient and emergency department (ED) settings, we examined how Charlson/Deyo comorbidity index scores and medical complications of AOD use were associated with treatment initiation. Among those who initiated treatment in inpatient and outpatient/ED settings, we also examined how comorbidity and AOD use-related medical complications were associated with treatment engagement. Analyses were conducted using generalized estimating equation logistic regression modeling.Results: Among patients identified as having an AOD diagnosis in outpatient and ED settings (n = 69,965), Charlson/Deyo comorbidity index scores of 2 or more were independently associated with reduced likelihood of initiation (risk ratio [RR] = 0.80, 95% confidence interval [CI] = 0.74, 0.86; reference score = 0), whereas prior-year diagnoses of cirrhosis (RR = 1.25, 95% CI = 1.12, 1.35) and pancreatic disease (RR = 1.34, 95% CI = 1.15, 1.56) were associated with greater likelihood of initiation. Among those who were identified in outpatient/ED settings and initiated, higher comorbidity scores were associated with lower likelihood of engagement (score 1: RR = 0.85, 95% CI = 0.76, 0.94; score 2+: RR = 0.61, 95% CI = 0.53, 0.71).Conclusion: Medical comorbidity was associated with lower likelihood of initiating or engaging in AOD treatment, but cirrhosis and pancreatic disease were associated with greater likelihood of initiation. Interventions to improve AOD treatment initiation and engagement for patients with comorbidities are needed, such as integrating medical and AOD treatment.

Factors associated with Healthcare Effectiveness Data and Information Set (HEDIS) alcohol and other drug measure performance in 2014-2015.

Background: Only 10% of patients with alcohol and other drug (AOD) disorders receive treatment. The AOD Initiation and Engagement in Treatment (AOD-IET) measure was added to the national Healthcare Effectiveness Data and Information Set (HEDIS) to improve access to care. This study identifies factors related to improving AOD-IET rates. Methods: We include data from 7 health systems with differing geographic, patient demographic, and organizational characteristics; all used a common Virtual Data Warehouse containing electronic health records and insurance claims data. Multilevel logistic regression models examined AOD-IET among adults (18+). Results: A total of 86,565 patients had an AOD diagnosis qualifying for the HEDIS denominator. The overall initiation rate was 27.9% with wide variation; the overall engagement rate was 11.5% and varied from 4.5% to 17.9%. Women versus men (odds ratio [OR] = 0.81, 95% confidence interval [CI] = 0.76-0.86); Hispanics (OR = 0.85, 95% CI = 0.79-0.91), black/African Americans (OR = 0.82, 95% CI = 0.75-0.90), and Asian Americans (OR = 0.83, 95% CI = 0.72-0.95) versus whites; and patients aged 65+ versus 18-29 (OR = 0.82, 95% CI = 0.74-0.90) had lower odds of initiation. Patients aged 30-49 versus 18-29 (OR = 1.11, 95% CI = 1.04-1.19) and those with prior psychiatric (OR = 1.26, 95% CI = 1.18-1.35) and medical (OR = 1.18, 95% CI = 1.10-1.26) conditions had higher odds of initiation. Identification in primary care versus other departments was related to lower odds of initiation (emergency department [ED]: OR = 1.55, 95% CI = 1.45-1.66; psychiatry/AOD treatment: OR = 3.58, 95% CI = 3.33-3.84; other outpatient: OR = 1.19, 95% CI = 1.06-1.32). Patients aged 30-49 versus 18-29 had higher odds of engagement (OR = 1.26, 95% CI = 1.10-1.43). Patients aged 65+ versus 18-29 (OR = 0.51, 95% CI = 0.43-0.62) and black/African Americans versus whites (OR = 0.64, 95% CI = 0.53-0.77) had lower odds. Those initiating treatment in psychiatry/AOD treatment versus primary care (OR = 7.02, 95% CI = 5.93-8.31) had higher odds of engagement; those in inpatient (OR = 0.40, 95% CI = 0.32-0.50) or other outpatient (OR = 0.73, 95% CI = 0.59-0.91) settings had lower odds. Discussion: Rates of initiation and engagement varied but were low. Findings identified age, race/ethnicity, co-occurring conditions, and department of identification as key factors associated with AOD-IET. Focusing on these could help programs develop interventions that facilitate AOD-IET for those less likely to receive care.

Predictors of Healthcare Effectiveness Data and Information Set (HEDIS) treatment initiation and engagement among patients with opioid use disorder across 7 health systems.

Background: The prevalence of opioid use disorder (OUD) has increased rapidly in the United States and improving treatment access is critical. Among patients with OUD, we examined factors associated with the Healthcare Effectiveness Data and Information Set (HEDIS) performance measures of alcohol and other drug (AOD) treatment initiation and engagement. Methods: Electronic health record and claims data between October 1, 2014, and August 15, 2015, from 7 health systems were used to identify patients (n = 11,490) with a new index OUD diagnosis (no AOD diagnosis prior <60 days) based on International Classification of Diseases (ICD)-9 codes. Multivariable generalized linear models with a logit link clustered on health system were used to examine the associations of patient demographic and clinical characteristics, and department of index diagnosis, with HEDIS measures of treatment initiation and engagement. Results: The prevalence of OUD among all AOD diagnoses varied across health systems, as did rates of AOD initiation (5.7%-21.6%) and engagement (7.6%-24.6%). Those diagnosed in the emergency department (adjusted odds ratio [aOR] = 1.58, 95% confidence interval [CI] = 1.27,1.97) or psychiatry/AOD treatment (aOR = 2.92, 95% CI = 2.47,3.46) were more likely to initiate treatment compared with primary care. Older patients were less likely to initiate (age 50-64 vs. age 18-29: aOR = 0.42, 95% CI = 0.35, 0.51; age 65+ vs. age 18-29: aOR = 0.34, 95% CI = 0.26, 0.43), as were women (aOR = 0.72, 95% CI = 0.62, 0.85). Patients diagnosed in psychiatry/AOD treatment (aOR = 2.67, 95% CI = 1.98, 3.60) compared with primary care were more likely to engage in treatment. Those identified in an inpatient setting (aOR = 0.19, 95% CI = 0.14, 0.27 vs. primary care), those with medical comorbidity (aOR = 0.70, 95% CI = 0.52, 0.95), and older patients (age 50-64 vs. 18-29: aOR = 0.64, 95% CI = 0.46, 0.88; age 65+ vs. 18-29: aOR = 0.36, 95% CI = 0.22, 0.57) were less likely to engage in treatment. Conclusions: Rates of initiation and engagement for OUD patients vary widely with noticeable room for improvement, particularly in this critical time of the opioid crisis. Targeting patient and system factors may improve health system performance, which is key to improving patient outcomes.

Healthcare Effectiveness Data and Information Set (HEDIS) measures of alcohol and drug treatment initiation and engagement among people living with the human immunodeficiency virus (HIV) and patients without an HIV diagnosis.

Background: Problematic use of alcohol and other drugs (AOD) is highly prevalent among people living with the human immunodeficiency virus (PLWH), and untreated AOD use disorders have particularly detrimental effects on human immunodeficiency virus (HIV) outcomes. The Healthcare Effectiveness Data and Information Set (HEDIS) measures of treatment initiation and engagement are important benchmarks for access to AOD use disorder treatment. To inform improved patient care, we compared HEDIS measures of AOD use disorder treatment initiation and engagement and health care utilization among PLWH and patients without an HIV diagnosis. Methods: Patients with a new AOD use disorder diagnosis documented between October 1, 2014, and August 15, 2015, were identified using electronic health records (EHR) and insurance claims data from 7 health care systems in the United States. Demographic characteristics, clinical diagnoses, and health care utilization data were also obtained. AOD use disorder treatment initiation and engagement rates were calculated using HEDIS measure criteria. Factors associated with treatment initiation and engagement were examined using multivariable logistic regression models. Results: There were 469 PLWH (93% male) and 86,096 patients without an HIV diagnosis (60% male) in the study cohort. AOD use disorder treatment initiation was similar in PLWH and patients without an HIV diagnosis (10% vs. 11%, respectively). Among those who initiated treatment, few engaged in treatment in both groups (9% PLWH vs. 12% patients without an HIV diagnosis). In multivariable analysis, HIV status was not significantly associated with either AOD use disorder treatment initiation or engagement. Conclusions: AOD use disorder treatment initiation and engagement rates were low in both PLWH and patients without an HIV diagnosis. Future studies need to focus on developing strategies to efficiently integrate AOD use disorder treatment with medical care for HIV.

The prevalence of Healthcare Effectiveness Data and Information Set (HEDIS) initiation and engagement in treatment among patients with cannabis use disorders in 7 US health systems.

Background: Cannabis use disorders (CUDs) have increased with more individuals using cannabis, yet few receive treatment. Health systems have adopted the Healthcare Effectiveness Data and Information Set (HEDIS) quality measures of initiation and engagement in alcohol and other drug (AOD) dependence treatment, but little is known about the performance of these among patients with CUDs. Methods: This cohort study utilized electronic health records and claims data from 7 health care systems to identify patients with documentation of a new index CUD diagnosis (no AOD diagnosis ≤60 days prior) from International Classification of Diseases, Ninth revision, codes (October 1, 2014, to August 31, 2015). The adjusted prevalence of each outcome (initiation, engagement, and a composite of both) was estimated from generalized linear regression models, across index identification settings (inpatient, emergency department, primary care, addiction treatment, and mental health/psychiatry), AOD comorbidity (patients with CUD only and CUD plus other AOD diagnoses), and patient characteristics. Results: Among 15,202 patients with an index CUD diagnosis, 30.0% (95% confidence interval [CI]: 29.2-30.7%) initiated, 6.9% (95% CI: 6.2-7.7%) engaged among initiated, and 2.1% (95% CI: 1.9-2.3%) overall both initiated and engaged in treatment. The adjusted prevalence of outcomes varied across index identification settings and was highest among patients diagnosed in addiction treatment, with 25.0% (95% CI: 22.5-27.6%) initiated, 40.9% (95% CI: 34.8-47.0%) engaged, and 12.5% (95% CI: 10.0-15.1%) initiated and engaged. The adjusted prevalence of each outcome was generally highest among patients with CUD plus other AOD diagnosis at index diagnosis compared with those with CUD only, overall and across index identification settings, and was lowest among uninsured and older patients. Conclusion: Among patients with a new CUD diagnosis, the proportion meeting HEDIS criteria for initiation and/or engagement in AOD treatment was low and demonstrated variation across index diagnosis settings, AOD comorbidity, and patient characteristics, pointing to opportunities for improvement.

Increased Rates of Documented Alcohol Counseling in Primary Care: More Counseling or Just More Documentation?

BACKGROUND: Clinical performance measures often require documentation of patient counseling by healthcare providers. Little is known about whether such measures encourage delivery of counseling or merely its documentation. OBJECTIVE: To assess changes in provider documentation of alcohol counseling and patient report of receiving alcohol counseling in the Veterans Administration (VA) from 2009 to 2012. DESIGN: Retrospective time-series analysis. PARTICIPANTS: A total of 5413 men who screened positive for unhealthy alcohol use at an outpatient visit and responded to a confidential mailed survey regarding alcohol counseling from a VA provider in the prior year. MAIN MEASURES: Rates of provider documentation of alcohol counseling in the electronic health record and patient report of such counseling on the survey were assessed over 4 fiscal years. Annual rates were calculated overall and with patients categorized into four mutually exclusive groups based on their own reports of alcohol counseling (yes/no) and whether alcohol counseling was documented by a provider (yes/no). KEY RESULTS: Provider documentation of alcohol counseling increased 23.6% (95% CI: 17.0, 30.2), from 59.4% to 83.0%, while patient report of alcohol counseling showed no significant change (4.0%, 95% CI: -2.3, 10.3), increasing from 66.1% to 70.1%. An 18.7% (95% CI: 11.7, 25.7) increase in the proportion of patients who reported counseling that was documented by a provider largely reflected a 14.7% decline (95% CI: 8.5, 20.8) in the proportion of patients who reported alcohol counseling that was not documented by a provider. The proportion of patients who did not report counseling but whose providers documented it did not show a significant change (4.9%, 95%CI: 0.0, 9.9). CONCLUSIONS: If patient report is accurate, increased rates of documented alcohol counseling in the VA from 2009 to 2012 predominantly reflected improved documentation of previously undocumented counseling rather than delivery of additional counseling or increased documentation of counseling that did not meaningfully occur.

Barriers to and Facilitators of Alcohol Use Disorder Pharmacotherapy in Primary Care: A Qualitative Study in Five VA Clinics.

BACKGROUND: Three medications are FDA-approved and recommended for treating alcohol use disorders (AUD) but they are not offered to most patients with AUD. Primary care (PC) may be an optimal setting in which to offer and prescribe AUD medications, but multiple barriers are likely. OBJECTIVE: This qualitative study used social marketing theory, a behavior change approach that employs business marketing techniques including "segmenting the market," to describe (1) barriers and facilitators to prescribing AUD medications in PC, and (2) beliefs of PC providers after they were segmented into groups more and less willing to prescribe AUD medications. DESIGN: Qualitative, interview-based study. PARTICIPANTS: Twenty-four providers from five VA PC clinics. APPROACH: Providers completed in-person semi-structured interviews, which were recorded, transcribed, and analyzed using social marketing theory and thematic analysis. Providers were divided into two groups based on consensus review. KEY RESULTS: Barriers included lack of knowledge and experience, beliefs that medications cannot replace specialty addiction treatment, and alcohol-related stigma. Facilitators included training, support for prescribing, and behavioral staff to support follow-up. Providers more willing to prescribe viewed prescribing for AUD as part of their role as a PC provider, framed medications as a potentially effective "tool" or "foot in the door" for treating AUD, and believed that providing AUD medications in PC might catalyze change while reducing stigma and addressing other barriers to specialty treatment. Those less willing believed that medications could not effectively treat AUD, and that treating AUD was the role of specialty addiction treatment providers, not PC providers, and would require time and expertise they do not have. CONCLUSIONS: We identified barriers to and facilitators of prescribing AUD medications in PC, which, if addressed and/or capitalized on, may increase provision of AUD medications. Providers more willing to prescribe may be the optimal target of a customized implementation intervention to promote changes in prescribing.

Binge drinking and perceived ethnic discrimination among Hispanics/Latinos: Results from the Hispanic community health study/study of Latinos sociocultural ancillary study.

The study assessed whether overall perceived ethnic discrimination and four unique discrimination types were associated with binge drinking in participants from the Hispanic Community Health Study/Study of Latinos who also completed the Hispanic Community Health Study/Study of Latinos Sociocultural Ancillary Study (n = 5,313). In unadjusted analyses that were weighted for sampling strategy and design, each unit increase in discrimination type was associated with a 12-63% increase in odds of binge drinking; however, after adjusting for important demographic variables including age, sex, heritage group, language, and duration of U.S. residence, there was no longer an association between discrimination and binge drinking. Further research still needs to identify the salient factors that contribute to increased risk for binge drinking among Hispanics/Latinos.

Association Between Receipt of Brief Alcohol Intervention and Quality of Care among Veteran Outpatients with Unhealthy Alcohol Use.

BACKGROUND: Brief alcohol intervention, including advice to reduce or abstain from drinking, is widely recommended for general medical outpatients with unhealthy alcohol use, but it is challenging to implement. Among other implementation challenges, providers report reluctance to deliver such interventions, citing concerns about negatively affecting their patient relationships. OBJECTIVE: The purpose of this study was to determine whether patient-reported receipt of brief intervention was associated with patient-reported indicators of high-quality care among veteran outpatients with unhealthy alcohol use. DESIGN: Cross-sectional secondary data analysis was performed using the Veterans Health Administration (VA) Survey of Healthcare Experiences of Patients (SHEP). PARTICIPANTS: The study included veteran outpatients who (1) responded to the outpatient long-form SHEP (2009-2011), (2) screened positive for unhealthy alcohol use (Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) questionnaire score ≥ 3 for women, ≥ 4 for men), and (3) responded to questions assessing receipt of brief intervention and quality of care. MAIN MEASURES: We used logistic regression models to estimate the adjusted predicted prevalence of reporting two indicators of high-quality care--patient ratings of their VA provider and of overall VA healthcare (range 0-10, dichotomized as ≥ 9 indicating high quality)--for both patients who did and did not report receipt of brief intervention (receiving alcohol-related advice from a provider) within the previous year. KEY RESULTS: Among 10,612 eligible veterans, 43.8% reported having received brief intervention, and 84.2% and 79.1% rated their quality of care as high from their provider and the VA healthcare system, respectively. In adjusted analyses, compared to veterans who reported receiving no brief intervention, a higher proportion of veterans reporting receipt of brief intervention rated the quality of healthcare from their provider (86.9% vs. 82.0%, p < 0.01) and the VA overall (82.7% vs. 75.9%, p < 0.01) as high. CONCLUSIONS: In this cross-sectional analysis of veterans with unhealthy alcohol use, a higher proportion of those who reported receipt of brief intervention reported receiving high-quality care compared to those who reported having received no such intervention. These findings do not support provider concerns that delivering brief intervention adversely affects patients' perceptions of care.

Factors Underlying Quality Problems with Alcohol Screening Prompted by a Clinical Reminder in Primary Care: A Multi-site Qualitative Study.

BACKGROUND: Population-based alcohol screening is recommended in primary care, and increasingly incentivized by policies, yet is challenging to implement. The U.S. Veterans Health Administration (VA) achieved high rates of screening using a national performance measure and associated electronic clinical reminder to prompt and facilitate screening and document results. However, the sensitivity of alcohol screening for identifying unhealthy alcohol use is low in VA clinics. OBJECTIVE: We aimed to understand factors that might contribute to low sensitivity of alcohol screening. DESIGN: This was an observational, qualitative study. PARTICIPANTS: Participants included clinical staff responsible for conducting alcohol screening and nine independently managed primary care clinics of a single VA medical center in the Northwestern U.S. APPROACH: Four researchers observed clinical staff as they conducted alcohol screening. Observers took handwritten notes, which were transcribed and coded iteratively. Template analysis identified a priori and emergent themes. KEY RESULTS: We observed 72 instances of alcohol screening conducted by 31 participating staff. Observations confirmed known challenges to implementation of care using clinical reminders, including workflow and flexibility limitations. Three themes specific to alcohol screening emerged. First, most observed screening was conducted verbally, guided by the clinical reminder, although some variability in approaches to screening (e.g., paper-based or laminate-based screening) was observed. Second, specific verbal screening practices that might contribute to low sensitivity of clinical screening were identified, including conducting non-verbatim screening and making inferences, assumptions, and/or suggestions to input responses. Third, staff introduced and adapted screening questions to enhance patient comfort. CONCLUSIONS: This qualitative study in nine clinics found that implementation of alcohol screening facilitated by a clinical reminder resulted primarily in verbal screening in which questions were not asked vertbatim and were otherwise adapted. Non-verbal approaches to screening, or patient self-administration, may enhance validity and standardization of screening while simultaneously addressing limitations of the clinical reminder and issues related to perceived discomfort.

Probability and predictors of patients converting from negative to positive screens for alcohol misuse.

BACKGROUND: Medicare reimburses providers for annual alcohol screening. However, the benefit of rescreening patients a year after a negative screen for alcohol misuse is unknown. We hypothesized that some subgroups of patients who screen negative would have a very low probability of converting to a positive subsequent screen (e.g., <0.1%), calling into question the value of annual alcohol screening for some patient subgroups. METHODS: This retrospective cohort study estimated the probability of converting to a positive screen for alcohol misuse a year after a negative screen among outpatients from 30 Veterans Health Administration (VA) medical centers. Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) alcohol screening scores (range 0 to 12 points) from 2004 to 2008 were obtained from electronic health record data. Eligible patients screened negative on their initial screen (AUDIT-C scores 0 to 3 for men; 0 to 2 for women). The main outcome was a positive subsequent screen (AUDIT-C scores ≥4 men; ≥3 women). RESULTS: Among 21,081 women and 323,913 men who screened negative on an initial screen, 5.4% and 6.0%, respectively, screened positive a year later. The adjusted probability of converting to a positive subsequent screen varied from 2.1 to 38.9% depending on age, gender, and initial negative screen score. Women, older patients, and those with initial AUDIT-C scores of 0 were least likely to a convert to a positive subsequent screen, while younger men with AUDIT-C scores of 3 were most likely to a convert to a positive subsequent screen. CONCLUSIONS: The probability of a positive subsequent screen varied depending on age, gender, and initial negative screen score but exceeded 2% in all patient subgroups. Annual rescreening appears reasonable for all VA patients who had a negative screen the year prior.

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.