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Primary care encounters are common among patients at risk for suicide. Prior suicide care programs that incorporate depression screening, suicide risk assessment, and collaborative safety planning show promise in nonprimary care settings. This secondary analysis of a stepped-wedge, cluster randomized implementation trial examines the effectiveness of a primary care–based suicide care program embedded alongside a substance use care program in reducing suicide attempts within 90 days of the primary care visit. Visual Abstract. Effectiveness of Integrating Suicide Care in Primary Care: Primary care encounters are common among patients at risk for suicide. Prior suicide care programs that incorporate depression screening, suicide risk assessment, and collaborative safety planning show promise in nonprimary care settings. This secondary analysis of a stepped-wedge, cluster randomized implementation trial examines the effectiveness of a primary care–based suicide care program embedded alongside a substance use care program in reducing suicide attempts within 90 days of the primary care visit. Background: Primary care encounters are common among patients at risk for suicide. Objective: To evaluate the effectiveness of implementing population-based suicide care (SC) in primary care for suicide attempt prevention. Design: Secondary analysis of a stepped-wedge, cluster randomized implementation trial. (ClinicalTrials.gov: NCT02675777) Setting: 19 primary care practices within a large health care system in Washington State, randomly assigned launch dates. Patients: Adult patients (aged ≥18 years) with primary care visits from January 2015 to July 2018. Intervention: Practice facilitators, electronic medical record (EMR) clinical decision support, and performance monitoring supported implementation of depression screening, suicide risk assessment, and safety planning. Measurements: Clinical practice and patient measures relied on EMR and insurance claims data to compare usual care (UC) and SC periods. Primary outcomes included documented safety planning after population-based screening and suicide risk assessment and suicide attempts or deaths (with self-harm intent) within 90 days of a visit. Mixed-effects logistic models regressed binary outcome indicators on UC versus SC, adjusted for randomization stratification and calendar time, accounting for repeated outcomes from the same site. Monthly outcome rates (percentage per 10 000 patients) were estimated by applying marginal standardization. Results: During UC, 255 789 patients made 953 402 primary care visits and 228 255 patients made 615 511 visits during the SC period. The rate of safety planning was higher in the SC group than in the UC group (38.3 vs. 32.8 per 10 000 patients; rate difference, 5.5 [95% CI, 2.3 to 8.7]). Suicide attempts within 90 days were lower in the SC group than in the UC group (4.5 vs. 6.0 per 10 000 patients; rate difference, −1.5 [CI, −2.6 to −0.4]). Limitation: Suicide care was implemented in combination with care for depression and substance use. Conclusion: Implementation of population-based SC concurrent with a substance use program resulted in a 25% reduction in the suicide attempt rate in the 90 days after primary care visits. Primary Funding Source: National Institute of Mental Health. [ABSTRACT FROM AUTHOR]

Background: The National Institute on Alcohol Abuse and Alcoholism (NIAAA) recommends the paper-based or computerized Alcohol Symptom Checklist to assess alcohol use disorder (AUD) symptoms in routine care when patients report high-risk drinking. However, it is unknown whether Alcohol Symptom Checklist response characteristics differ when it is administered online (eg, remotely via an online electronic health record [EHR] patient portal before an appointment) versus in clinic (eg, on paper after appointment check-in). Objective: This study evaluated the psychometric performance of the Alcohol Symptom Checklist when completed online versus in clinic during routine clinical care. Methods: This cross-sectional, psychometric study obtained EHR data from the Alcohol Symptom Checklist completed by adult patients from an integrated health system in Washington state. The sample included patients who had a primary care visit in 2021 at 1 of 32 primary care practices, were due for annual behavioral health screening, and reported high-risk drinking on the behavioral health screen (Alcohol Use Disorder Identification Test–Consumption score ≥7). After screening, patients with high-risk drinking were typically asked to complete the Alcohol Symptom Checklist—an 11-item questionnaire on which patients self-report whether they had experienced each of the 11 AUD criteria listed in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) over a past-year timeframe. Patients could complete the Alcohol Symptom Checklist online (eg, on a computer, smartphone, or tablet from any location) or in clinic (eg, on paper as part of the rooming process at clinical appointments). We examined sample and measurement characteristics and conducted differential item functioning analyses using item response theory to examine measurement consistency across these 2 assessment modalities. Results: Among 3243 patients meeting eligibility criteria for this secondary analysis (2313/3243, 71% male; 2271/3243, 70% White; and 2014/3243, 62% non-Hispanic), 1640 (51%) completed the Alcohol Symptom Checklist online while 1603 (49%) completed it in clinic. Approximately 46% (752/1640) and 48% (764/1603) reported ≥2 AUD criteria (the threshold for AUD diagnosis) online and in clinic (P =.37), respectively. A small degree of differential item functioning was observed for 4 of 11 items. This differential item functioning produced only minimal impact on total scores used clinically to assess AUD severity, affecting total criteria count by a maximum of 0.13 criteria (on a scale ranging from 0 to 11). Conclusions: Completing the Alcohol Symptom Checklist online, typically prior to patient check-in, performed similarly to an in-clinic modality typically administered on paper by a medical assistant at the time of the appointment. Findings have implications for using online AUD symptom assessments to streamline workflows, reduce staff burden, reduce stigma, and potentially assess patients who do not receive in-person care. Whether modality of DSM-5 assessment of AUD differentially impacts treatment is unknown. [ABSTRACT FROM AUTHOR]

Background: Few studies have investigated first-year nursing students' perspectives after they received real-time online demonstration (RTOD) for fundamental nursing skills education. Method: A mixed-methods study was conducted with prospective second-year nursing students after they completed a one-semester RTOD class in their first year. With permission from the original authors, an online questionnaire, the Self-Structured Questionnaire (SSQ), was administered to 277 students in undergraduate and higher-diploma programs, followed by two focus group interviews with 13 students. Survey and focus group data were analyzed using descriptive statistics and thematic analysis, respectively. Results: Regarding students' barriers in administrative, individual, and technological areas, three themes emerged from the focus groups: (1) learning quality; (2) connection; and (3) impediments. Conclusion: RTOD contributed to fundamental nursing skills education. However, there was room for improvement. [J Nurs Educ. 2024;63(1):43–47.] [ABSTRACT FROM AUTHOR]

Background: The authors tested the efficacy of the "I-STAND" intervention for reducing sitting time, a novel and potentially health-promoting approach, in older adults with obesity. Methods: The authors recruited 60 people (mean age = 68 ± 4.9 years, 68% female, 86% White; mean body mass index = 35.4). The participants were randomized to receive the I-STAND sitting reduction intervention (n = 29) or healthy living control group (n = 31) for 12 weeks. At baseline and at 12 weeks, the participants wore activPAL devices to assess sitting time (primary outcome). Secondary outcomes included fasting glucose, blood pressure, and weight. Linear regression models assessed between-group differences in the outcomes. Results: The I-STAND participants significantly reduced their sitting time compared with the controls (–58 min per day; 95% confidence interval [–100.3, –15.6]; p =.007). There were no statistically significant changes in the secondary outcomes. Conclusion: I-STAND was efficacious in reducing sitting time, but not in changing health outcomes in older adults with obesity. [ABSTRACT FROM AUTHOR]

Objective: Addressing firearm access is recommended when patients are identified as being at risk of suicide. However, the practice of assessing firearm access is controversial, and no national guidelines exist to inform practice. This study qualitatively explored patient perspectives on a routine question about firearm access to optimize the patient centeredness of this practice in the context of suicide risk.Methods: Electronic health record data were used to identify primary care patients reporting depressive symptoms, including suicidal thoughts, within 2 weeks of sampling. Participants completed a semistructured telephone interview (recorded and transcribed), which focused broadly on the experience of being screened for suicidality and included specific questions to elicit beliefs and opinions about being asked a standard firearm access question. Directive (deductive) and conventional (inductive) content analysis was used to analyze responses to the portion of the interview focused on firearm assessment and disclosure.Results: Thirty-seven patients in Washington State ages 20-95 completed the qualitative interview by phone. Organizing themes included apprehensions about disclosing access to firearms related to privacy, autonomy, and firearm ownership rights; perceptions regarding relevance of the firearm question, informed by experiences with suicidality and common beliefs and misconceptions about the inevitability of suicide; and suggestions for connecting questions about firearms and other lethal means to suicide risk.Conclusions: Clarifying the purpose and use of routine firearm access assessment, contextualizing firearm questions within injury prevention broadly, and addressing misconceptions about suicide prevention may help encourage disclosure of firearm access and increase the patient centeredness of this practice. [ABSTRACT FROM AUTHOR]

Background: Criteria for alcohol use disorders (AUD) in the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) were intended to result in a similar prevalence of AUD as DSM-IV. We evaluated the prevalence of AUD using DSM-5 and DSM-IV criteria, and compared characteristics of patients who met criteria for: neither DSM-5 nor DSM-IV AUD, DSM-5 alone, DSM-IV alone, or both, among Veterans Administration (VA) outpatients in the Considering Healthier drinking Options In primary CarE (CHOICE) trial. Methods: VA primary care patients who reported frequent heavy drinking and enrolled in the CHOICE trial were interviewed at baseline using the DSM-IV Mini International Neuropsychiatric Interview for AUD, as well as questions about socio-demographics, mental health, alcohol craving, and substance use. We compared characteristics across 4 mutually exclusive groups based on DSM-5 and DSM-IV criteria. Results: Of 304 participants, 13.8% met criteria for neither DSM-5 nor DSM-IV AUD; 12.8% met criteria for DSM-5 alone, and 73.0% met criteria for both DSM-IV and DSM-5. Only 1 patient (0.3%) met criteria for DSM-IV AUD alone. Patients meeting both DSM-5 and DSM-IV criteria had more negative drinking consequences, mental health symptoms and self-reported readiness to change compared with those meeting DSM-5 criteria alone or neither DSM-5 nor DSM-IV criteria. Conclusions: In this sample of primary care patients with frequent heavy drinking, DSM-5 identified 13% more patients with AUD than DSM-IV. This group had a lower mental health symptom burden and less self-reported readiness to change compared to those meeting criteria for both DSM-IV and DSM-5 AUD. [ABSTRACT FROM AUTHOR]

Background: Most patients with alcohol use disorders (AUDs) never receive alcohol treatment, and experts have recommended management of AUDs in primary care. The Choosing Healthier Drinking Options In primary CarE (CHOICE) trial was a randomized controlled effectiveness trial of a novel intervention for primary care patients at high risk for AUDs. This report describes the conceptual and scientific foundation of the CHOICE model of care, critical elements of the CHOICE trial design consistent with the Template for Intervention Description and Replication (TIDieR), results of recruitment, and baseline characteristics of the enrolled sample. Methods: The CHOICE intervention is a multi-contact, extended counseling intervention, based on the Chronic Care Model, shared decision-making, motivational interviewing, and evidence-based options for managing AUDs, designed to be practical in primary care. Outpatients who received care at 3 Veterans Affairs primary care sites in the Pacific Northwest and reported frequent heavy drinking (≥4 drinks/day for women; ≥5 for men) were recruited (2011-2014) into a trial in which half of the participants would be offered additional alcohol-related care from a nurse. CHOICE nurses offered 12 months of patient-centered care, including proactive outreach and engagement, repeated brief motivational interventions, monitoring with and without alcohol biomarkers, medications for AUDs, and/or specialty alcohol treatment as appropriate and per patient preference. A CHOICE nurse practitioner was available to prescribe medications for AUDs. Results: A total of 304 patients consented to participate in the CHOICE trial. Among consenting participants, 90% were men, the mean age was 51 (range 22-75), and most met DSM-IV criteria for alcohol abuse (14%) or dependence (59%). Many participants also screened positive for tobacco use (44%), depression (45%), anxiety disorders (30-41%) and non-tobacco drug use disorders (19%). At baseline, participants had a median AUDIT score of 18 [Interquartile range (IQR) 14-24] and a median readiness to change drinking score of 5 (IQR 2.75-6.25) on a 1-10 Likert scale. Conclusion: The CHOICE trial tested a patient-centered intervention for AUDs and recruited primary care patients at high risk for AUDs, with a spectrum of severity, co-morbidity, and readiness to change drinking. Trial registration The trial is registered at clinicaltrial.gov (NCT01400581). [ABSTRACT FROM AUTHOR]

Background The AUDIT-C is an extensively validated screen for unhealthy alcohol use (i.e. drinking above recommended limits or alcohol use disorder), which consists of three questions about alcohol consumption. AUDIT-C scores ⩾4 points for men and ⩾3 for women are considered positive screens based on US validation studies that compared the AUDIT-C to "gold standard" measures of unhealthy alcohol use from independent, detailed interviews. However, results of screening-positive or negative based on AUDIT-C scores-can be inconsistent with reported drinking on the AUDIT-C questions. For example, individuals can screen positive based on the AUDIT-C score while reporting drinking below US recommended limits on the same AUDIT-C. Alternatively, they can screen negative based on the AUDIT-C score while reporting drinking above US recommended limits. Such inconsistencies could complicate interpretation of screening results, but it is unclear how often they occur in practice. Methods This study used AUDIT-C data from respondents who reported past-year drinking on one of two national US surveys: a general population survey (N = 26,610) and a Veterans Health Administration (VA) outpatient survey (N = 467,416). Gender-stratified analyses estimated the prevalence of AUDIT-C screen results-positive or negative screens based on the AUDIT-C score-that were inconsistent with reported drinking (above or below US recommended limits) on the same AUDIT-C. Results Among men who reported drinking, 13.8% and 21.1% of US general population and VA samples, respectively, had screening results based on AUDIT-C scores (positive or negative) that were inconsistent with reported drinking on the AUDIT-C questions (above or below US recommended limits). Among women who reported drinking, 18.3% and 20.7% of US general population and VA samples, respectively, had screening results that were inconsistent with reported drinking. Limitations This study did not include an independent interview gold standard for unhealthy alcohol use and therefore cannot address how often observed inconsistencies represent false positive or negative screens. Conclusions Up to 21% of people who drink alcohol had alcohol screening results based on the AUDIT-C score that were inconsistent with reported drinking on the same AUDIT-C. This needs to be addressed when training clinicians to use the AUDIT-C. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: This pilot study evaluated whether use of evidence-based implementation strategies to integrate care for cannabis and other drug use into primary care (PC) as part of Behavioral Health Integration (BHI) increased diagnosis and treatment of substance use disorders (SUDs).Methods: Patients who visited the three pilot PC sites were eligible. Implementation strategies included practice coaching, electronic health record decision support, and performance feedback (3/2015-4/2016). BHI introduced annual screening for past-year cannabis and other drug use, a Symptom Checklist for DSM-5 SUDs, and shared decision-making about treatment options. Main analyses tested whether the proportions of PC patients diagnosed with, and treated for, new cannabis or other drug use disorders (CUDs and DUDs, respectively), differed significantly pre- and post-implementation.Results: Of 39,599 eligible patients, 57% and 59% were screened for cannabis and other drug use, respectively. Among PC patients reporting daily cannabis use (2%) or any drug use (1%), 51% and 37%, respectively, completed an SUD Symptom Checklist. The proportion of PC patients with newly diagnosed CUD increased significantly post-implementation (5 v 17 per 10,000 patients, p < 0.0001), but not other DUDs (10 vs 13 per 10,000, p = 0.24). The proportion treated for newly diagnosed CUDs did not increase post-implementation (1 vs 1 per 10,000, p = 0.80), but did for those treated for newly diagnosed other DUDs (1 vs 3 per 10,000, p = 0.038).Conclusions: A pilot implementation of BHI to increase routine screening and assessment for SUDs was associated with increased new CUD diagnoses and a small increase in treatment of new other DUDs. [ABSTRACT FROM AUTHOR]

Patients with opioid use disorder (OUD) have increased emergency and hospital utilization. The PROUD trial showed that implementation of office-based addiction treatment (OBAT) increased OUD medication treatment compared to usual care, but did not decrease acute care utilization in patients with OUD documented pre-randomization (clinicaltrials.gov/study/NCT03407638). This paper reports secondary emergency and hospital utilization outcomes in patients with documented OUD in the PROUD trial. This cluster-randomized implementation trial was conducted in 12 clinics from 6 diverse health systems (March 2015-February 2020). Patients who visited trial clinics and had an OUD diagnosis within 3 years pre-randomization were included in primary analyses; secondary analyses added patients with OUD who were new to the clinic or with newly-documented OUD post-randomization. Outcomes included days of emergency care and hospital utilization over 2 years post-randomization. Explanatory outcomes included measures of OUD treatment. Patient-level analyses used mixed-effect regression with clinic-specific random intercepts. Among 1988 patients with documented OUD seen pre-randomization (mean age 49, 53 % female), days of emergency care or hospitalization did not differ between intervention and usual care; OUD treatment also did not differ. In secondary analyses among 1347 patients with OUD post-randomization, there remained no difference in emergency or hospital utilization despite intervention patients receiving 32.2 (95 % CI 4.7, 59.7) more days of OUD treatment relative to usual care. Implementation of OBAT did not reduce emergency or hospital utilization among patients with OUD, even in the sample with OUD first documented post-randomization in whom the intervention increased treatment. • Treatment of opioid use disorder (OUD) has the potential to decrease emergency and hospital care. • This study evaluated implementation of a program in which nurses support medication treatment of OUD in primary care. • Although patients entering intervention clinics post-randomization had more OUD treatment but did not have decreased acute care. [ABSTRACT FROM AUTHOR]

Background and Objective: Population-based alcohol screening, followed by brief intervention for patients who screen positive for unhealthy alcohol use, is widely recommended for primary care settings and considered a top prevention priority, but is challenging to implement. However, new policy initiatives in the U.S., including the Affordable Care Act, may help launch widespread implementation. While the nationwide Veterans Health Administration (VA) has achieved high rates of documented alcohol screening and brief intervention, research has identified quality problems with both. We conducted a qualitative key informant study to describe local implementation of alcohol screening and brief intervention from the perspectives of frontline adopters in VA primary care in order to understand the process of implementation and factors underlying quality problems.Methods: A purposive snowball sampling method was used to identify and recruit key informants from 5 VA primary care clinics in the northwestern U.S. Key informants completed 20-30 minute semi-structured interviews, which were recorded, transcribed, and qualitatively analyzed using template analysis.Results: Key informants (N=32) included: clinical staff (n=14), providers (n=14), and administrative informants (n=4) with varying participation in implementation of and responsibility for alcohol screening and brief intervention at the medical center. Ten inter-related themes (5 a priori and 5 emergent) were identified and grouped into 3 applicable domains of Greenhalgh's conceptual framework for dissemination of innovations, including values of adopters (theme 1), processes of implementation (themes 2 and 3), and post-implementation consequences in care processes (themes 4-10). While key informants believed alcohol use was relevant to health and important to address, the process of implementation (in which no training was provided and electronic clinical reminders "just showed up") did not address critical training and infrastructure needs. Key informants lacked understanding of the goals of screening and brief intervention, believed referral to specialty addictions treatment (as opposed to offering brief intervention) was the only option for following up on a positive screen, reported concern regarding limited availability of treatment resources, and lacked optimism regarding patients' interest in seeking help.Conclusions: Findings suggest that the local process of implementing alcohol screening and brief intervention may have inadequately addressed important adopter needs and thus may have ultimately undermined, instead of capitalized on, staff and providers' belief in the importance of addressing alcohol use as part of primary care. Additional implementation strategies, such as training or academic detailing, may address some unmet needs and help improve the quality of both screening and brief intervention. However, these strategies may be resource-intensive and insufficient for comprehensively addressing implementation barriers. [ABSTRACT FROM AUTHOR]

Background: Cannabis and other drug use is associated with adverse health events, but little is known about the association of routine clinical screening for cannabis or other drug use and acute care utilization. This study evaluated whether self-reported frequency of cannabis or other drug use was associated with subsequent acute care.Method: This retrospective cohort study used EHR and claims data from 8 sites in Washington State that implemented annual substance use screening. Eligible adult primary care patients (N = 47,447) completed screens for cannabis (N = 45,647) and/or other drug use, including illegal drug use and prescription medication misuse, (N = 45,255) from 3/3/15-10/1/2016. Separate single-item screens assessed frequency of past-year cannabis and other drug use: never, less than monthly, monthly, weekly, daily/almost daily. An indicator of acute care utilization measured any urgent care, emergency department visits, or hospitalizations ≤19 months after screening. Adjusted Cox proportional hazards models estimated risk of acute care.Results: Patients were predominantly non-Hispanic White. Those reporting cannabis use less than monthly (Hazard Ratio [HR] = 1.12, 95 % CI = 1.03-1.21) or daily (HR = 1.24; 1.10-1.39) had greater risk of acute care during follow-up than those reporting no use. Patients reporting other drug use less than monthly (HR = 1.34; 1.13-1.59), weekly (HR = 2.21; 1.46-3.35), or daily (HR = 2.53; 1.86-3.45) had greater risk of acute care than those reporting no other drug use.Conclusion: Population-based screening for cannabis and other drug use in primary care may have utility for understanding risk of subsequent acute care. It is unclear whether findings will generalize to U.S. states with broader racial/ethnic diversity. [ABSTRACT FROM AUTHOR]