Bobbi Jo H. Yarborough

Increased access to naloxone has contributed to reductions in opioid-related overdose. However, many individuals prescribed naloxone do not fill their prescription. Better understanding modifiable patient-level barriers to naloxone receipt may inform health system–level implementation strategies that improve naloxone availability. A quality improvement survey was emailed to adult members of Kaiser Permanente Northwest with a valid email address who had an order for naloxone between September 15, 2023 and September 15, 2024 that appeared unfilled in pharmacy records. Using χ² tests for categorical variables and analysis of variance for continuous variables, the authors compared individuals who filled vs did not fill a naloxone prescription. A total of 481 responses were received (19% response rate). More than half (57.5%) of respondents reported not obtaining naloxone at a pharmacy or other community resource. Primary reasons included: perceived lack of need (55.3%), unawareness of practitioner recommendation (14.9%), uncertainty about its purpose (13.1%), cost concerns (7.3%), and unfamiliarity with naloxone (4.4%). Fewer than 10 respondents cited logistical barriers. Results from this quality improvement survey are consistent with previous studies of barriers to naloxone use. Among patients who did not fill a prescription for naloxone, many did not assess their personal risk of overdose to be high, and some resented a perceived implication that they might be abusing opioids. Others were unaware that a naloxone prescription was written until notified by a pharmacist. Cost and logistical barriers were less common concerns, each for less than 10% of those not filling a prescription. Clear, nonjudgmental communication about the risk for opioid-related overdose, even among people using their opioids appropriately, and education about the purpose of naloxone may improve access.

Background: Age-adjusted suicide rates have increased in the U.S. over the past 25 years. Algorithm-based methods for identifying individuals at risk for suicide based on electronic health record and claims data have been validated but few studies have evaluated implementation or effects on population-level suicide attempt rates. Methods: This hybrid type I effectiveness-implementation pragmatic clinical trial will test a suicide risk identification model in behavioral health clinics at three large health systems. Local decision-makers will determine implementation specifics at each site. Clinics within each health system will be randomized to determine order of implementation. A stepped-wedge design using repeated measures pre/post-implementation maximizes statistical efficiency and power with fewer participants compared to a parallel design while allowing all clinics to participate. A pre-implementation period will serve as the baseline. The primary outcome will be the rate of suicide attempt per 1000 visits at 90- and 180-days following a behavioral health visit in which an individual was identified by the suicide risk model compared with the baseline period (no use of suicide risk model). Secondary outcomes include identification of suicide risk and recognition of individuals at risk for suicide (e.g., completed risk assessment), both compared to the baseline period. Generalized linear mixed models will be used to account for clustering within clinics and repeated measures over time, adjusting for relevant covariates to estimate the effect of the suicide risk model on outcomes. Implementation outcomes, including system-level determinants and clinician acceptance and use of the suicide risk model, will also be measured. Conclusions: Few suicide risk models derived from administrative and clinical data have been tested in real world care settings. This trial will determine whether the use of such a risk model reduces suicide attempts compared to usual care. By describing important implementation factors, use of such risk models, if effective, may be accelerated for other health care systems. Trial registration: ClinicalTrials.gov NCT06060535.

Safety planning for suicide prevention is an important quality metric for Zero Suicide implementation. We describe the development, validation, and application of electronic health record (EHR) programs to measure uptake of safety planning practices across six integrated healthcare systems as part of a Zero Suicide evaluation study. Safety planning was documented in narrative notes and structured EHR templates using the Stanley Brown Safety Planning Intervention (SBSPI) in response to a high-risk cutoff score on the Columbia Suicide Severity Rating Scale (CSSRS). Natural Language Processing (NLP) metrics were developed and validated using chart review to characterize practices documented in narrative notes. We applied NLP to measure frequency of documentation in the narrative text and standard programming methods to examine structured SBSPI templates from 2010-2022. Chart reviews found three safety planning practices documented in narrative notes that were delivered to at least half of patients at risk: professional contacts, lethal means counseling for firearms, and lethal means counseling for medication access/storage. NLP methods were developed to identify these practices in clinical text with high levels of accuracy (Sensitivity, Specificity, & PPV ≥ 82%). Among visits with a high-risk CSSRS, 40% (Range 2-73% by health system) had an SBSPI template within 1 year of implementation. This is one of the first reports describing development of measures that leverage electronic health records to track use of suicide prevention safety plans. There are opportunities to use the methods developed here in future evaluations of safety planning.

Background: Suicide risk prediction models derived from electronic health records (EHR) are a novel innovation in suicide prevention but there is little evidence to guide their implementation. Methods: In this qualitative study, 30 clinicians and 10 health care administrators were interviewed from one health system anticipating implementation of an automated EHR-derived suicide risk prediction model and two health systems piloting different implementation approaches. Site-tailored interview guides focused on respondents’ expectations for and experiences with suicide risk prediction models in clinical practice, and suggestions for improving implementation. Interview prompts and content analysis were guided by Consolidated Framework for Implementation Research (CFIR) constructs. Results: Administrators and clinicians found use of the suicide risk prediction model and the two implementation approaches acceptable. Clinicians desired opportunities for early buy-in, implementation decision-making, and feedback. They wanted to better understand how this manner of risk identification enhanced existing suicide prevention efforts. They also wanted additional training to understand how the model determined risk, particularly after patients they expected to see identified by the model were not flagged at-risk and patients they did not expect to see identified were. Clinicians were concerned about having enough suicide prevention resources for potentially increased demand and about their personal liability; they wanted clear procedures for situations when they could not reach patients or when patients remained at-risk over a sustained period. Suggestions for making risk model workflows more efficient and less burdensome included consolidating suicide risk information in a dedicated module in the EHR and populating risk assessment scores and text in clinical notes. Conclusion: Health systems considering suicide risk model implementation should engage clinicians early in the process to ensure they understand how risk models estimate risk and add value to existing workflows, clarify clinician role expectations, and summarize risk information in a convenient place in the EHR to support high-quality patient care.

Background: Suicide risk prediction models derived from electronic health records (EHR) and insurance claims are a novel innovation in suicide prevention but patient perspectives on their use have been understudied. Methods: In this qualitative study, between March and November 2020, 62 patients were interviewed from three health systems: one anticipating implementation of an EHR-derived suicide risk prediction model and two others piloting different implementation approaches. Site-tailored interview guides focused on patients’ perceptions of this technology, concerns, and preferences for and experiences with suicide risk prediction model implementation in clinical practice. A constant comparative analytic approach was used to derive themes. Results: Interview participants were generally supportive of suicide risk prediction models derived from EHR data. Concerns included apprehension about inducing anxiety and suicidal thoughts, or triggering coercive treatment, particularly among those who reported prior negative experiences seeking mental health care. Participants who were engaged in mental health care or case management expected to be asked about their suicide risk and largely appreciated suicide risk conversations, particularly by clinicians comfortable discussing suicidality. Conclusion: Most patients approved of suicide risk models that use EHR data to identify patients at-risk for suicide. As health systems proceed to implement such models, patient-centered care would involve dialogue initiated by clinicians experienced with assessing suicide risk during virtual or in person care encounters. Health systems should proactively monitor for negative consequences that result from risk model implementation to protect patient trust.