Identifying Opioid Use Disorder in the Emergency Department: Multi-System Electronic Health Record-Based Computable Phenotype Derivation and Validation Study

Fentanyl-positive urine drug screens in the emergency department: Association with intentional opioid misuse and racial disparities

Background

An increase in opioid-related overdoses, notably from potent synthetic opioids like fentanyl, prompted this consideration of characteristics of emergency department (ED) patients with evidence for illicit fentanyl use or exposure, the correlation with intentional opioid misuse, and subsequent ED management.

Methods

A retrospective review was performed of patients presenting to an urban academic medical center ED with evidence for illicit fentanyl use, determined by positive urine drug screens (UDS), from 6/2021 through 11/2021. Participant demographics, comorbidities, ED chief complaint and disposition, and evidence of intentional opioid misuse were considered. Secondary outcomes included provision of buprenorphine/naloxone and/or naloxone kits at discharge, ED recidivism, and six-month mortality. Bivariate comparisons and logistic regression models were performed.

Results

Among 409 unique patients, most were white and male with a mean age of 39.4. Approximately half presented with opioid-related complaints. Evidence of intentional opioid misuse was identified in 72.6 % of patients. Black patients had 79 % lower odds of intentional opioid misuse compared to white patients. Regarding ED management, 28.8 % were discharged with buprenorphine/naloxone and 14.0 % with a naloxone kit. Black patients had 63 % lower odds of receiving buprenorphine/naloxone compared to white patients after controlling for covariates. Nearly 6 % of the study population died within six months of the initial ED visit.

Conclusion

This fentanyl-focused review describes patient characteristics which largely mirror the epidemiology of the current opioid epidemic; however, despite evidence of objective exposure, it also suggests that Black patients may be less likely to use fentanyl intentionally. It also highlights potential disparities related to ED-based opioid misuse patient management.

Sensitivity and specificity of International Classification of Diseases algorithms (ICD-9 and ICD-10) used to identify opioid-related overdose cases: A systematic review and an example of estimation using Bayesian latent class models in the absence of gold standards

OBJECTIVES

This study aimed to summarize validity estimates of International Classification of Diseases (ICD) codes in identifying opioid overdose (OOD) among patient data from emergency rooms, emergency medical services, inpatient, outpatient, administrative, medical claims, and mortality, and estimate the sensitivity and specificity of the algorithms in the absence of a perfect reference standard.

METHODS

We systematically reviewed studies published before December 8, 2023, and identified with Medline and Embase. Studies reporting sufficient details to recreate a 2 × 2 table comparing the ICD algorithms to a reference standard in diagnosing OOD-related events were included. We used Bayesian latent class models (BLCM) to estimate the posterior sensitivity and specificity distributions of five ICD-10 algorithms and of the imperfect coroner's report review (CRR) in detecting prescription opioid-related deaths (POD) using one included study.

RESULTS

Of a total of 1990 studies reviewed, three were included. The reported sensitivity estimates of ICD algorithms for OOD were low (range from 25.0% to 56.8%) for ICD-9 in diagnosing non-fatal OOD-related events and moderate (72% to 89%) for ICD-10 in diagnosing POD. The last included study used ICD-9 for non-fatal and fatal and ICD-10 for fatal OOD-related events and showed high sensitivity (i.e. above 97%). The specificity estimates of ICD algorithms were good to excellent in the three included studies. The misclassification-adjusted ICD-10 algorithm sensitivity estimates for POD from BLCM were consistently higher than reported sensitivity estimates that assumed CRR was perfect.

CONCLUSION

Evidence on the performance of ICD algorithms in detecting OOD events is scarce, and the absence of bias correction for imperfect tests leads to an underestimation of the sensitivity of ICD code estimates.

Variability in Practice of Buprenorphine Treatment by Emergency Department Operational Characteristics

Introduction

We sought to describe emergency department (ED) buprenorphine treatment variability among EDs with varying operational characteristics.

Methods

We performed a retrospective cohort study of adult patients with opioid use disorder discharged from 12 hospital-based EDs within a large healthcare system as a secondary data analysis of a quality improvement study. Primary outcome of interest was buprenorphine treatment rate. We described treatment rates between EDs, categorized by tertile of operational characteristics including annual census, hospital and intensive care unit (ICU) admission rates, ED length of stay (LOS), and boarding time. Secondary outcomes were ED LOS and 30-day return rates.

Results

There were 7,469 unique ED encounters for patients with opioid use disorder between January 2020-May 2021, of whom 759 (10.2%) were treated with buprenorphine. Buprenorphine treatment rates were higher in larger EDs and those with higher hospital and ICU admission rates. Emergency department LOS and 30-day ED return rate did not have consistent associations with buprenorphine treatment.

Conclusion

Rates of treatment with ED buprenorphine vary according to the operational characteristics of department. We did not observe a consistent negative relationship between buprenorphine treatment and operational metrics, as many feared. Additional funding and targeted resource allocation should be prioritized by departmental leaders to improve access to this evidence-based and life-saving intervention.

Adoption of Emergency Department-Initiated Buprenorphine for Patients With Opioid Use Disorder: Secondary Analysis of a Cluster Randomized Trial

Importance

Emergency department (ED) initiation of buprenorphine is safe and effective but underutilized in practice. Understanding the factors affecting adoption of this practice could inform more effective interventions.

Objective

To quantify the factors, including social contagion, associated with the adoption of the practice of ED initiation of buprenorphine for patients with opioid use disorder.

Design, Setting, and Participants

This is a secondary analysis of the EMBED (Emergency Department-Initiated Buprenorphine For Opioid Use Disorder) trial, a multicentered, cluster randomized trial of a clinical decision support intervention targeting ED initiation of buprenorphine. The trial occurred from November 2019 to May 2021. The study was conducted at ED clusters across health care systems from the northeast, southeast, and western regions of the US and included attending physicians, resident physicians, and advanced practice practitioners. Data analysis was performed from August 2022 to June 2023.

Exposures

This analysis included both the intervention and nonintervention groups of the EMBED trial. Graph methods were used to construct the network of clinicians who shared in the care of patients for whom buprenorphine was initiated during the trial before initiating the practice themselves, termed exposure.

Main Outcomes and Measures

Cox proportional hazard modeling with time-dependent covariates was performed to assess the association of the number of these exposures with self-adoption of the practice of ED initiation of buprenorphine while adjusting for clinician role, health care system, and intervention site status.

Results

A total of 1026 unique clinicians in 18 ED clusters across 5 health care systems were included. Analysis showed associations of the cumulative number of exposures to others initiating buprenorphine with the self-practice of buprenorphine initiation. This increased in a dose-dependent manner (1 exposure: hazard ratio [HR], 1.31; 95% CI, 1.16-1.48; 5 exposures: HR, 2.85; 95% CI, 1.66-4.89; 10 exposures: HR, 3.55; 95% CI, 1.47-8.58). Intervention site status was associated with practice adoption (HR, 1.50; 95% CI, 1.04-2.18). Health care system and clinician role were also associated with practice adoption.

Conclusions and Relevance

In this secondary analysis of a multicenter, cluster randomized trial of a clinical decision support tool for buprenorphine initiation, the number of exposures to ED initiation of buprenorphine and the trial intervention were associated with uptake of ED initiation of buprenorphine. Although systems-level approaches are necessary to increase the rate of buprenorphine initiation, individual clinicians may change practice of those around them.

Trial Registration

ClinicalTrials.gov Identifier: NCT03658642.

Enhanced phenotypes for identifying opioid overdose in emergency department visit electronic health record data

Background

Accurate identification of opioid overdose (OOD) cases in electronic healthcare record (EHR) data is an important element in surveillance, empirical research, and clinical intervention. We sought to improve existing OOD electronic phenotypes by incorporating new data types beyond diagnostic codes and by applying several statistical and machine learning methods.

Materials and Methods

We developed an EHR dataset of emergency department visits involving OOD cases or patients considered at risk for an OOD and ascertained true OOD status through manual chart reviews. We developed and validated prediction models using Random Forest, Extreme Gradient Boost, and Elastic Net models that incorporated 717 features involving primary and second diagnoses, chief complaints, medications prescribed, vital signs, laboratory results, and procedural codes. We also developed models limited to single data types.

Results

A total of 1718 records involving 1485 patients were manually reviewed; 541 (36.4%) patients had one or more OOD. Prediction performance was similar for all models; sensitivity varied from 94% to 97%; and area under the receiver operating characteristic curve (AUC) was 98% for all methods. The primary diagnosis and chief complaint were the most important contributors to AUC performance; primary diagnoses and medication class contributed most to sensitivity; chief complaint, primary diagnosis, and vital signs were most important for specificity. Models limited to decision support data types available in real time demonstrated robust prediction performance.

Conclusions

Substantial prediction performance improvements were demonstrated for identifying OODs in EHR data. Our e-phenotypes could be applied in surveillance, retrospective empirical applications, or clinical decision support systems.

Computational phenotypes for patients with opioid-related disorders presenting to the emergency department

OBJECTIVE

We aimed to discover computationally-derived phenotypes of opioid-related patient presentations to the ED via clinical notes and structured electronic health record (EHR) data.

METHODS

This was a retrospective study of ED visits from 2013-2020 across ten sites within a regional healthcare network. We derived phenotypes from visits for patients ≥18 years of age with at least one prior or current documentation of an opioid-related diagnosis. Natural language processing was used to extract clinical entities from notes, which were combined with structured data within the EHR to create a set of features. We performed latent dirichlet allocation to identify topics within these features. Groups of patient presentations with similar attributes were identified by cluster analysis.

RESULTS

In total 82,577 ED visits met inclusion criteria. The 30 topics were discovered ranging from those related to substance use disorder, chronic conditions, mental health, and medical management. Clustering on these topics identified nine unique cohorts with one-year survivals ranging from 84.2-96.8%, rates of one-year ED returns from 9-34%, rates of one-year opioid event 10-17%, rates of medications for opioid use disorder from 17-43%, and a median Carlson comorbidity index of 2-8. Two cohorts of phenotypes were identified related to chronic substance use disorder, or acute overdose.

CONCLUSIONS

Our results indicate distinct phenotypic clusters with varying patient-oriented outcomes which provide future targets better allocation of resources and therapeutics. This highlights the heterogeneity of the overall population, and the need to develop targeted interventions for each population.

Racial and ethnic disparities in emergency department-initiated buprenorphine across five health care systems

BACKGROUND

Opioid overdose deaths have disproportionately impacted Black and Hispanic populations, in part due to disparities in treatment access. Emergency departments (EDs) serve as a resource for patients with opioid use disorder (OUD), many of whom have difficulty accessing outpatient addiction programs. However, inequities in ED treatment for OUD remain poorly understood.

METHODS

This secondary analysis examined racial and ethnic differences in buprenorphine access using data from EMBED, a study of 21 EDs across five health care systems evaluating a clinical decision support system for initiating ED buprenorphine. The primary outcome was receipt of buprenorphine, ED administered or prescribed. Hospital type (academic vs. community) was evaluated as an effect modifier. Hierarchical models with cluster effects for site and clinician were used to assess buprenorphine receipt by race and ethnicity.

RESULTS

Black patients were less likely to receive buprenorphine (6.4% [51/801] vs. White patients 8.5% [268/3154], odds ratio [OR] 0.59, 95% confidence interval [CI] 0.45-0.78). This association persisted after adjusting for age, insurance, gender, clinician X-waiver, hospital type, and urbanicity (adjusted OR [aOR] 0.64, 95% CI 0.48-0.84) but not when discharge diagnosis was included (aOR 0.75, 95% CI 0.56-1.02). Hispanic patients were more likely to receive buprenorphine (14.8% [122/822] vs. non-Hispanic patients, 11.6% [475/4098]) in unadjusted (OR 1.57, 95% CI 1.09-1.83) and adjusted models (aOR 1.41, 95% CI 1.08-1.83) but not including discharge diagnosis (aOR 1.32, 95% CI 0.99-1.77). Odds of buprenorphine were similar in academic and community EDs by race (interaction p = 0.97) and ethnicity (interaction p = 0.64).

CONCLUSIONS

Black patients with OUD were less likely to receive buprenorphine whereas Hispanic patients were more likely to receive buprenorphine in academic and community EDs. Differences were attenuated with discharge diagnosis, as fewer Black and non-Hispanic patients were diagnosed with opioid withdrawal. Barriers to medication treatment are heterogenous among patients with OUD; research must continue to address the multiple drivers of health inequities at the patient, clinician, and community level.

Algorithms to Identify Nonmedical Opioid Use

The rise in nonmedical opioid overdoses over the last two decades necessitates improved detection technologies. Manual opioid screening exams can exhibit excellent sensitivity for identifying the risk of opioid misuse but can be time-consuming. Algorithms can help doctors identify at-risk people. In the past, electronic health record (EHR)-based neural networks outperformed Drug Abuse Manual Screenings in sparse studies; however, recent data shows that it may perform as well or less than manual screenings. Herein, a discussion of several different manual screenings and recommendations is contained, along with suggestions for practice. A multi-algorithm approach using EHR yielded strong predictive values of opioid use disorder (OUD) over a large sample size. A POR (Proove Opiate Risk) algorithm provided a high sensitivity for categorizing the risk of opioid abuse within a small sample size. All established screening methods and algorithms reflected high sensitivity and positive predictive values. Neural networks based on EHR also showed significant effectiveness when corroborated with Drug Abuse Manual Screenings. This review highlights the potential of algorithms for reducing provider costs and improving the quality of care by identifying nonmedical opioid use (NMOU) and OUD. These tools can be combined with traditional clinical interviewing, and neural networks can be further refined while expanding EHR.

Using natural language processing to identify opioid use disorder in electronic health record data

BACKGROUND

As opioid prescriptions have risen, there has also been an increase in opioid use disorder (OUD) and its adverse outcomes. Accurate and complete epidemiologic surveillance of OUD, to inform prevention strategies, presents challenges. The objective of this study was to ascertain prevalence of OUD using two methods to identify OUD in electronic health records (EHR): applying natural language processing (NLP) for text mining of unstructured clinical notes and using ICD-10-CM diagnostic codes.

METHODS

Data were drawn from EHR records for hospital and emergency department patient visits to a large regional academic medical center from 2017 to 2019. International Classification of Disease, 10th Edition, Clinic Modification (ICD-10-CM) discharge codes were extracted for each visit. To develop the rule-based NLP algorithm, a stepwise process was used. First, a small sample of visits from 2017 was used to develop initial dictionaries. Next, EHR corresponding to 30,124 visits from 2018 were used to develop and evaluate the rule-based algorithm. A random sample of the results were manually reviewed to identify and address shortcomings in the algorithm, and to estimate sensitivity and specificity of the two methods of ascertainment. Last, the final algorithm was then applied to 29,212 visits from 2019 to estimate OUD prevalence.

RESULTS

While there was substantial overlap in the identified records (n = 1,381 [59.2 %]), overall n = 2,332 unique visits were identified. Of the total unique visits, 430 (18.4 %) were identified only by ICD-10-CM codes, and 521 (22.3 %) were identified only by NLP. The prevalence of visits with evidence of an OUD diagnosis in this sample, ascertained using only ICD-10-CM codes, was 1,811/29,212 (6.1 %). Including the additional 521 visits identified only by NLP, the estimated prevalence of OUD is 2,332/29,212 (7.9 %), an increase of 29.5 % compared to the use of ICD-10-CM codes alone. The estimated sensitivity and specificity of the NLP-based OUD classification were 81.8 % and 97.5 %, respectively, relative to gold-standard manual review by an expert addiction medicine physician.

CONCLUSION

NLP-based algorithms can automate data extraction and identify evidence of opioid use disorder from unstructured electronic healthcare records. The most complete ascertainment of OUD in EHR was combined NLP with ICD-10-CM codes. NLP should be considered for epidemiological studies involving EHR data.

User centered clinical decision support to implement initiation of buprenorphine for opioid use disorder in the emergency department: EMBED pragmatic cluster randomized controlled trial

OBJECTIVE

To determine the effect of a user centered clinical decision support tool versus usual care on rates of initiation of buprenorphine in the routine emergency care of individuals with opioid use disorder.

DESIGN

Pragmatic cluster randomized controlled trial (EMBED).

SETTING

18 emergency department clusters across five healthcare systems in five states representing the north east, south east, and western regions of the US, ranging from community hospitals to tertiary care centers, using either the Epic or Cerner electronic health record platform.

PARTICIPANTS

599 attending emergency physicians caring for 5047 adult patients presenting with opioid use disorder.

INTERVENTION

A user centered, physician facing clinical decision support system seamlessly integrated into user workflows in the electronic health record to support initiating buprenorphine in the emergency department by helping clinicians to diagnose opioid use disorder, assess the severity of withdrawal, motivate patients to accept treatment, and complete electronic health record tasks by automating clinical and after visit documentation, order entry, prescribing, and referral.

MAIN OUTCOME MEASURES

Rate of initiation of buprenorphine (administration or prescription of buprenorphine) in the emergency department among patients with opioid use disorder. Secondary implementation outcomes were measured with the RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) framework.

RESULTS

1 413 693 visits to the emergency department (775 873 in the intervention arm and 637 820 in the usual care arm) from November 2019 to May 2021 were assessed for eligibility, resulting in 5047 patients with opioid use disorder (2787 intervention arm, 2260 usual care arm) under the care of 599 attending physicians (340 intervention arm, 259 usual care arm) for analysis. Buprenorphine was initiated in 347 (12.5%) patients in the intervention arm and in 271 (12.0%) patients in the usual care arm (adjusted generalized estimating equations odds ratio 1.22, 95% confidence interval 0.61 to 2.43, P=0.58). Buprenorphine was initiated at least once by 151 (44.4%) physicians in the intervention arm and by 88 (34.0%) in the usual care arm (1.83, 1.16 to 2.89, P=0.01).

CONCLUSIONS

User centered clinical decision support did not increase patient level rates of initiating buprenorphine in the emergency department. Although streamlining and automating electronic health record workflows can potentially increase adoption of complex, unfamiliar evidence based practices, more interventions are needed to look at other barriers to the treatment of addiction and increase the rate of initiating buprenorphine in the emergency department in patients with opioid use disorder.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03658642.

Sustained Implementation of a Multicomponent Strategy to Increase Emergency Department-Initiated Interventions for Opioid Use Disorder

STUDY OBJECTIVE

There is strong evidence supporting emergency department (ED)-initiated buprenorphine for opioid use disorder, but less is known about how to implement this practice. Our aim was to describe implementation, maintenance, and provider adoption of a multicomponent strategy for opioid use disorder treatment in 3 urban, academic EDs.

METHODS

We conducted a retrospective analysis of electronic health record data for adult patients with opioid use disorder-related visits before (March 2017 to November 2018) and after (December 2018 to July 2020) implementation. We describe patient characteristics, clinical treatment, and process measures over time and conducted an interrupted time series analysis using a patient-level multivariable logistic regression model to assess the association of the interventions with buprenorphine use and other outcomes. Finally, we report provider-level variation in prescribing after implementation.

RESULTS

There were 2,665 opioid use disorder-related visits during the study period: 28% for overdose, 8% for withdrawal, and 64% for other conditions. Thirteen percent of patients received medications for opioid use disorder during or after their ED visit overall. Following intervention implementation, there were sustained increases in treatment and process measures, with a net increase in total buprenorphine of 20% in the postperiod (95% confidence interval 16% to 23%). In the adjusted patient-level model, there was an immediate increase in the probability of buprenorphine treatment of 24.5% (95% confidence interval 12.1% to 37.0%) with intervention implementation. Seventy percent of providers wrote at least 1 buprenorphine prescription, but provider-level buprenorphine prescribing ranged from 0% to 61% of opioid use disorder-related encounters.

CONCLUSION

A combination of strategies to increase ED-initiated opioid use disorder treatment was associated with sustained increases in treatment and process measures. However, adoption varied widely among providers, suggesting that additional strategies are needed for broader uptake.

Two data-driven approaches to identifying the spectrum of problematic opioid use: A pilot study within a chronic pain cohort

BACKGROUND

Although electronic health records (EHR) have significant potential for the study of opioid use disorders (OUD), detecting OUD in clinical data is challenging. Models using EHR data to predict OUD often rely on case/control classifications focused on extreme opioid use. There is a need to expand this work to characterize the spectrum of problematic opioid use.

METHODS

Using a large academic medical center database, we developed 2 data-driven methods of OUD detection: (1) a Comorbidity Score developed from a Phenome-Wide Association Study of phenotypes associated with OUD and (2) a Text-based Score using natural language processing to identify OUD-related concepts in clinical notes. We evaluated the performance of both scores against a manual review with correlation coefficients, Wilcoxon rank sum tests, and area-under the receiver operating characteristic curves. Records with the highest Comorbidity and Text-based scores were re-evaluated by manual review to explore discrepancies.

RESULTS

Both the Comorbidity and Text-based OUD risk scores were significantly elevated in the patients judged as High Evidence for OUD in the manual review compared to those with No Evidence (p = 1.3E-5 and 1.3E-6, respectively). The risk scores were positively correlated with each other (rho = 0.52, p < 0.001). AUCs for the Comorbidity and Text-based scores were high (0.79 and 0.76, respectively). Follow-up manual review of discrepant findings revealed strengths of data-driven methods over manual review, and opportunities for improvement in risk assessment.

CONCLUSION

Risk scores comprising comorbidities and text offer differing but synergistic insights into characterizing problematic opioid use. This pilot project establishes a foundation for more robust work in the future.

Initiation of buprenorphine for opioid use disorder in the hospital setting: Practice models, challenges, and legal considerations

DISCLAIMER

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.

PURPOSE

To provide health-system pharmacists with published examples of strategies utilized to offer buprenorphine to inpatients with opioid use disorder (OUD) along with information on challenges and legal considerations.

SUMMARY

Hospitals and emergency departments (EDs) are a constant source of healthcare for patients with OUD. As a result, hospital practitioners can screen, diagnose, begin treatment, and facilitate transfer of care to the outpatient setting. Offering sublingual buprenorphine in the hospital can bridge the gap before outpatient care is established. Multiple studies have shown that initiating treatment in the ED or during inpatient hospitalization results in 47% to 74% of patients utilizing medication-assisted treatment at day 30 of follow-up, statistically superior to the rates achieved with brief interventions or referral alone. Moreover, initiating buprenorphine treatment in the ED has been shown to decrease healthcare costs. Despite the benefits of offering buprenorphine in the inpatient setting, several challenges must be solved by hospital administration, such as achieving clinician readiness to prescribe buprenorphine, developing relationships with outpatient providers of buprenorphine, and creating an efficient workflow. Treatment of OUD with buprenorphine is heavily regulated on the federal level. Pharmacists can participate in the development of these programs and ensure compliance with applicable laws.

CONCLUSION

As health systems continue to care for patients with OUD, starting buprenorphine in the inpatient setting can improve the transition to outpatient treatment. Several institutions have developed programs with positive results. With an understanding of the typical barriers and relevant laws when initiating buprenorphine in the hospital setting, health-system pharmacists can assist in the development and operation of these initiatives.

Using Natural Language Processing and Machine Learning to Identify Hospitalized Patients with Opioid Use Disorder

Opioid use disorder (OUD) represents a global public health crisis that challenges classic clinical decision making. As existing hospital screening methods are resource-intensive, patients with OUD are significantly under-detected. An automated and accurate approach is needed to improve OUD identification so that appropriate care can be provided to these patients in a timely fashion. In this study, we used a large-scale clinical database from Mass General Brigham (MGB; formerly Partners HealthCare) to develop an OUD patient identification algorithm, using multiple machine learning methods. Working closely with an addiction psychiatrist, we developed a set of hand-crafted rules for identifying information suggestive of OUD from free-text clinical notes. We implemented a natural language processing (NLP)-based classification algorithm within the Medical Text Extraction, Reasoning and Mapping System (MTERMS) tool suite to automatically label patients as positive or negative for OUD based on these rules. We further used the NLP output as features to build multiple machine learning and a neural classifier. Our methods yielded robust performance for classifying hospitalized patients as positive or negative for OUD, with the best performing feature set and model combination achieving an F1 score of 0.97. These results show promise for the future development of a real-time tool for quickly and accurately identifying patients with OUD in the hospital setting.

Screening for elder mistreatment in emergency departments: current progress and recommendations for next steps

Emergency Department (ED) visits provide an important but seldom realized opportunity to identify elder mistreatment. Many screening tools exist, including several that are brief and may be effective, but few have been specifically designed for or tested in EDs. In addition to the absence of validated tools, other challenges with implementing ED elder mistreatment screening include difficulty integrating anything longer than a few questions into a busy clinical encounter and resources required to respond to positive screens. The Electronic Health Record (EHR) offers a critical tool to facilitate elder mistreatment screening through required data entry and real-time monitoring of compliance and results. We describe current work in the field and recommend next steps including design and testing of a two-step screening process, implementation research to accelerate adoption, development of ED-based interventions and referral protocols for positive cases, and consideration of the important role of pre-hospital providers in case identification.

Healthcare utilization patterns among persons who use drugs during the COVID-19 pandemic

Persons with drug use disorders are an underserved and stigmatized population, and the COVID-19 pandemic could exacerbate these issues. The discussion around those with drug use disorders in the midst of the pandemic has focused on the need to ensure uninterrupted treatment access; however, very few in this population actually receive treatment, and retention is a substantial issue among those who do. Evidence from other chronic conditions suggests persons at high risk for severe COVID-19 complications are foregoing care due to fear of contracting the virus. Persons with drug use disorders tend to fall into this high-risk category, and thus may be avoiding healthcare facilities. Our data suggest this is true. If so, adverse outcomes, and increased severity of use disorders and associated health complications, could become prevalent. Clinicians should identify persons with drug use disorders who may be foregoing treatment, and engage them using methods that minimize the risk of COVID-19 transmission.

•

COVID-19 may exacerbate issues among persons with drug use disorders (DUDs).

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Focus so far has been on need to ensure uninterrupted treatment access for DUDs.

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Very few persons with DUDs receive treatment, but many engage with health system.

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This population tends to be at high-risk for severe COVID-19 complications.

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Those with DUDs may be avoiding healthcare facilities, while conditions worsen.

Assessment of Probable Opioid Use Disorder Using Electronic Health Record Documentation

IMPORTANCE

Electronic health records are a potentially valuable source of information for identifying patients with opioid use disorder (OUD).

OBJECTIVE

To evaluate whether proxy measures from electronic health record data can be used reliably to identify patients with probable OUD based on Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) criteria.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cross-sectional study analyzed individuals within the Geisinger health system who were prescribed opioids between December 31, 2000, and May 31, 2017, using a mixed-methods approach. The cohort was identified from 16 253 patients enrolled in a contract-based, Geisinger-specific medication monitoring program (GMMP) for opioid use, including patients who maintained or violated contract terms, as well as a demographically matched control group of 16 253 patients who were prescribed opioids but not enrolled in the GMMP. Substance use diagnoses and psychiatric comorbidities were assessed using automated electronic health record summaries. A manual medical record review procedure using DSM-5 criteria for OUD was completed for a subset of patients. The analysis was conducted beginning from June 5, 2017, until May 29, 2020.

MAIN OUTCOMES AND MEASURES

The primary outcome was the prevalence of OUD as defined by proxy measures for DSM-5 criteria for OUD as well as the prevalence of comorbidities among patients prescribed opioids within an integrated health system.

RESULTS

Among the 16 253 patients enrolled in the GMMP (9309 women [57%]; mean [SD] age, 52 [14] years), OUD diagnoses as defined by diagnostic codes were present at a much lower rate than expected (291 [2%]), indicating the necessity for alternative diagnostic strategies. The DSM-5 criteria for OUD can be assessed using manual medical record review; a manual review of 200 patients in the GMMP and 200 control patients identifed a larger percentage of patients with probable moderate to severe OUD (GMMP, 145 of 200 [73%]; and control, 27 of 200 [14%]) compared with the prevalence of OUD assessed using diagnostic codes.

CONCLUSIONS AND RELEVANCE

These results suggest that patients with OUD may be identified using information available in the electronic health record, even when diagnostic codes do not reflect this diagnosis. Furthermore, the study demonstrates the utility of coding for DSM-5 criteria from medical records to generate a quantitative DSM-5 score that is associated with OUD severity.

Interrupted Time Series of User-centered Clinical Decision Support Implementation for Emergency Department-initiated Buprenorphine for Opioid Use Disorder

OBJECTIVES

Adoption of emergency department (ED) initiation of buprenorphine (BUP) for opioid use disorder (OUD) into routine emergency care has been slow, partly due to clinicians' unfamiliarity with this practice and perceptions that it is complicated and time-consuming. To address these barriers and guide emergency clinicians through the process of BUP initiation, we implemented a user-centered computerized clinical decision support system (CDS). This study was conducted to assess the feasibility of implementation and to evaluate the preliminary efficacy of the intervention to increase the rate of ED-initiated BUP.

METHODS

An interrupted time series study was conducted in an urban, academic ED from April 2018 to February 2019 (preimplementation phase), March 2019 to August 2019 (implementation phase), and September 2019 to December 2019 (maintenance phase) to study the effect of the intervention on adult ED patients identified by a validated electronic health record (EHR)-based computable phenotype consisting of structured data consistent with potential cases of OUD who would benefit from BUP treatment. The intervention offers flexible CDS for identification of OUD, assessment of opioid withdrawal, and motivation of readiness to start treatment and automates EHR activities related to ED initiation of BUP (including documentation, orders, prescribing, and referral). The primary outcome was the rate of ED-initiated BUP. Secondary outcomes were launch of the intervention, prescription for naloxone at ED discharge, and referral for ongoing addiction treatment.

RESULTS

Of the 141,041 unique patients presenting to the ED over the preimplementation and implementation phases (i.e., the phases used in primary analysis), 906 (574 preimplementation and 332 implementation) met OUD phenotype and inclusion criteria. The rate of BUP initiation increased from 3.5% (20/574) in the preimplementation phase to 6.6% (22/332) in the implementation phase (p = 0.03). After the temporal trend of the number of physician's with X-waiver training and other covariates were adjusted for, the relative risk of BUP initiation rate was 2.73 (95% confidence interval [CI] = 0.62 to 12.0, p = 0.18). Similarly, the number of unique attendings who initiated BUP increased modestly 7/53 (13.0%) to 13/57 (22.8%, p = 0.10) after offering just-in-time training during the implementation period. The rate of naloxone prescribed at discharge also increased (6.5% preimplementation and 11.5% implementation; p < 0.01). The intervention received a system usability scale score of 82.0 (95% CI = 76.7 to 87.2).

CONCLUSION

Implementation of user-centered CDS at a single ED was feasible, acceptable, and associated with increased rates of ED-initiated BUP and naloxone prescribing in patients with OUD and a doubling of the number of unique physicians adopting the practice. We have implemented this intervention across several health systems in an ongoing trial to assess its effectiveness, scalability, and generalizability.

Progress Report on EMBED: A Pragmatic Trial of User-Centered Clinical Decision Support to Implement EMergency Department-Initiated BuprenorphinE for Opioid Use Disorder

Buprenorphine (BUP) can safely and effectively reduce craving, overdose, and mortality rates in people with opioid use disorder (OUD). However, adoption of ED-initiation of BUP has been slow partly due to physician perception this practice is too complex and disruptive. We report progress of the ongoing EMBED (EMergency department-initiated BuprenorphinE for opioid use Disorder) project. This project is a five-year collaboration across five healthcare systems with the goal to develop, integrate, study, and disseminate user-centered Clinical Decision Support (CDS) to promote the adoption of Emergency Department (ED)-initiation of buprenorphine/naloxone (BUP) into routine emergency care. Soon to enter its third year, the project has already completed multiple milestones to achieve its goals including (1) user-centered design of the CDS prototype, (2) integration of the CDS into an automated electronic health record (EHR) workflow, (3) data coordination including derivation and validation of an EHR-based computable phenotype, (4) meeting all ethical and regulatory requirements to achieve a waiver of informed consent, (5) pilot testing of the intervention at a single site, and (6) launching a parallel group-randomized 18-month pragmatic trial in 20 EDs across 5 healthcare systems. Pilot testing of the intervention in a single ED was associated with increased rates of ED-initiated BUP and naloxone prescribing and a doubling of the number of unique physicians adopting the practice. The ongoing multi-center pragmatic trial will assess the intervention’s effectiveness, scalability, and generalizability with a goal to shift the emergency care paradigm for OUD towards early identification and treatment.