Development and Implementation of Clinical Outcome Measures for Automated Collection Within Specialty Pharmacy Practice.

BACKGROUND: Johns Hopkins Specialty Pharmacy Services recognized the need to identify and develop standardized collection methods for clinical outcome measures (COMs) to demonstrate program quality and value to third-party payers, manufacturers, and internal stakeholders. OBJECTIVE: To define specialty COMs and develop a framework for standardized data collection and reporting. METHODS: COMs for specialty pharmacy disease states (cystic fibrosis; hepatitis C; inflammatory conditions in dermatology, gastroenterology and rheumatology; and multiple sclerosis) were identified through a literature search, collaboration with specialty pharmacists, and committee review. Once identified, these measures were distributed to internal and external stakeholders that included specialty clinic team members, drug manufacturers, and third-party payers for input and validation. A standardized process for discrete documentation and data collection of these measures was implemented using case management software, electronic medical record integration, and informatics support. RESULTS: 28 COMs were identified. The various data sources used to collect the COMs were incorporated into an automated virtual dashboard to allow for regular review and sharing with clinicians, leadership, and other key stakeholders. The virtual dashboard included COMs with data derived from electronic medical records (n = 9), patient-reported outcomes based on responses to pharmacist-delivered questions (n = 11), and pharmacist assessment of outcomes (n = 8). The completed virtual dashboard was further refined to allow for reporting of both population and patient-level outcome results on a quarterly basis. CONCLUSIONS: This project describes methods to standardize documentation, data collection, and reporting of clinical outcomes data for multiple specialty conditions in a health system-integrated specialty pharmacy program. Through literature review and stakeholder consultation, a variety of potential COMs were identified for further evaluation of feasibility and value considering documentation and data collection requirements. Incorporation of COMs into a virtual dashboard will help facilitate the evaluation of program effectiveness, quality improvement planning, and sharing with stakeholders. Additional opportunities exist to further standardize COMs across the pharmacy industry to allow for future benchmarking and standardized evaluation of patient care programs. DISCLOSURES: No funding supported the writing of this article. The authors have no relevant conflicts of interest to disclose. This study was presented as a poster presentation at the APhA Annual Meeting, March 2018, Nashville, TN, and as a platform presentation at the Eastern States Conference, May 2018, Hershey, PA.

Assessing the Population-Level Correlation of Medication Regimen Complexity and Adherence Indices Using Electronic Health Records and Insurance Claims.

BACKGROUND: Nonadherence to medication regimens can lead to adverse health care outcomes and increasing costs. OBJECTIVES: To (a) assess the level of medication complexity at an outpatient setting using population-level electronic health record (EHR) data and (b) evaluate its association with medication adherence measures derived from medication-dispensing claims. METHODS: We linked EHR data with insurance claims of 70,054 patients who had an encounter with a U.S. midwestern health system between 2012 and 2013. We constructed 3 medication-derived indices: medication regimen complexity index (MRCI) using EHR data; medication possession ratio (MPR) using insurance pharmacy claims; and prescription fill rates (PFR; 7 and 30 days) using both data sources. We estimated the partial correlation between indices using Spearman's coefficient (SC) after adjusting for age and sex. RESULTS: The mean age (SD) of 70,054 patients was 37.9 (18.0) years, with an average Charlson Comorbidity Index of 0.308 (0.778). The 2012 data showed mean (SD) MRCI, MPR, and 30-day PFR of 14.6 (17.8), 0.624 (0.310), and 81.0 (27.0), respectively. Patients with previous inpatient stays were likely to have high MRCI scores (36.3 [37.9], P < 0.001) and were less adherent to outpatient prescriptions (MPR = 50.3 [27.6%], P < 0.001; 30-day PFR = 75.7 [23.6%], P < 0.001). However, MRCI did not show a negative correlation with MPR (SC = -0.31, P < 0.001) or with 30-day PFR (SC = -0.17, P < 0.001) at significant levels. CONCLUSIONS: Medication complexity and adherence indices can be calculated on a population level using linked EHR and claims data. Regimen complexity affects patient adherence to outpatient medication, and strength of correlations vary modestly across populations. Future studies should assess the added values of MRCI, MPR, and PFR to population health management efforts. DISCLOSURES: No outside funding supported this study. The authors have nothing to disclose. The abstract of this work was presented at INFORMS Healthcare Conference, held on July 27-29, 2019, in Cambridge, MA.

Ultraviolet-A1 phototherapy in Asian skin: A review of 159 cases in Singapore.

BACKGROUND: Ultraviolet-A1 phototherapy has been used to treat many inflammatory dermatoses. AIMS: To determine the efficacy and safety of ultraviolet-A1 phototherapy in Asian skin. MATERIALS AND METHODS: We performed a review of records of patients undergoing ultraviolet-A1 phototherapy at our dermatology unit in Singapore from January 2007 to January 2011. Their electronic medical records were reviewed and a standardized questionnaire was filled up for data collection and tabulation. Chi-square or Fisher's exact tests were used to compare the difference in response between various groups for each characteristic. P value of < 0.05 was considered statistically significant. RESULTS: Our study comprised of 159 patients, of which 103 were patients with hand and foot eczema, 21 with atopic dermatitis, 17 with scleroderma and the remaining with miscellaneous dermatoses. Of these patients, 47.6% of patients with hand and feet eczema had good response after 10 sessions, which increased to 75% after 20 sessions and to 84.6% after 30 sessions. After 10 sessions, 47.6% of patients with atopic dermatitis had good response, which increased to 66.7% after 20 sessions. After 30 sessions, all the three remaining patients with atopic dermatitis experienced good response. For patients with scleroderma, only 11.8 and 10% had good response after 10 and 20 sessions, respectively, which increased to 40% after 30 sessions. LIMITATIONS: Limitations of our study include its retrospective design and, consequently, the lack of standardized treatment protocol, as well as subjective assessment in terms of clinical improvement. CONCLUSIONS: Ultraviolet-A1 phototherapy appears to be efficacious for the treatment of hand and foot eczema as well as atopic dermatitis. However, in patients with scleroderma, the response was partial and needed a longer duration of treatment.

Prevalence and Characterization of Yoga Mentions in the Electronic Health Record.

BACKGROUND: There is a growing patient population using yoga as a therapeutic intervention, but little is known about how yoga interfaces with health care in clinical settings. PURPOSE: To characterize how yoga is documented at a large academic medical center and to systematically identify clinician-derived therapeutic use cases of yoga. METHODS: We designed a retrospective observational study using a yoga cohort (n = 30,976) and a demographically matched control cohort (n = 92,919) from the electronic health records at Penn Medicine between 2006 and 2016. We modeled the distribution of yoga notes among patients, clinicians, and clinical service departments, built a multinomial Naïve Bayes classifier to separate the notes by context-dependent use of the word yoga, and modeled associations between clinician recommendations to use yoga and 754 diagnostic codes with Fisher's exact test, setting an false discovery rate (FDR)-adjusted P-value ≤ .05 (ie, q-value) as the significance threshold. RESULTS: Yoga mentions in the electronic health record have increased 10.4-fold during the 10-year study period, with 2.6% of patients having at least 1 mention of yoga in their notes. In total, 30,976 patients, 2398 clinicians, and 41 clinical service departments were affiliated with yoga notes. The majority of yoga notes are in primary care. Nine diagnoses met the significance criteria for having an association with clinician recommendations to use yoga including Parkinson's disease (Odds ratio [OR], 6.3 [3.7 to 11.4]; q-value < 0.001), anxiety (OR, 5.8 [3.8 to 9.0]; q-value < 0.001), and backache (OR, 3.8 [2.4 to 6.3]; q-value = 0.001). CONCLUSIONS: There is a widespread and growing trend to include yoga as part of the clinical record. In practice, clinicians are recommending yoga as a nonpharmacological intervention for a subset of common chronic diseases.

Health of Spanish centenarians: a cross-sectional study based on electronic health records.

BACKGROUND: With the number of centenarians increasing exponentially in Spain, a deeper knowledge of their socio-demographic, clinical, and healthcare use characteristics is important to better understand the health profile of the very elderly. METHODS: We conducted a retrospective, cross-sectional observational study in the EpiChron Cohort (Aragón, Spain) aimed at analyzing the socio-demographic, clinical, drug use and healthcare use characteristics of 1680 centenarians during 2011-2015, using data from electronic health records and clinical-administrative databases. RESULTS: Spanish centenarians (79.1% women) had 101.6 years on average. Approximately 80% of centenarians suffered from multimorbidity, with an average of 4.0 chronic conditions; 50% were exposed to polypharmacy, with an average of 4.8 medications; only 6% of centenarians were free of chronic diseases and only 7% were not on medication. Centenarians presented a cardio-cerebrovascular pattern in which hypertension, heart failure, cerebrovascular disease and dementia were the most frequent conditions. Primary care was the most frequently visited healthcare level (79% of them), followed by medical specialist consultations (23%), hospitalizations (13%), and emergency service use (9%). CONCLUSIONS: Multimorbidity is the rule rather than the exception in Spanish centenarians. Addressing medical care in the very elderly from a holistic geriatric view is critical in order to preserve their health, and avoid the negative effects of polypharmacy.

Health Care Use Over 3 Years After Adolescent SBIRT.

BACKGROUND: Most studies on adolescent screening, brief intervention, and referral to treatment (SBIRT) have examined substance use outcomes. However, it may also impact service use and comorbidity-an understudied topic. We address this gap by examining effects of SBIRT on health care use and comorbidities. METHODS: In a randomized trial sample, we assessed 3 SBIRT care modalities: (1) pediatrician-delivered, (2) behavioral clinician-delivered, and (3) usual. Medical comorbidity and health care use were compared between a brief-intervention group with access to SBIRT for behavioral health (combined pediatrician and behavioral clinician arms) and a group without (usual care) over 1 and 3 years. RESULTS: Among a sample of eligible adolescents (n = 1871), the SBIRT group had fewer psychiatry visits at 1 year (incidence rate ratio [iRR] = 0.76; P = .05) and 3 years (iRR = 0.65; P < .05). Total outpatient visits did not differ in year 1. The SBIRT group was less likely to have mental health diagnoses (odds ratio [OR] = 0.69; 95% confidence interval [CI] = 0.48-1.01) or chronic conditions (OR = 0.66; 95% CI = 0.45-0.98) at 1 year compared with those in usual care. At 3 years, the SBIRT group had fewer total outpatient visits (iRR = 0.85; P < .05) and was less likely to have substance use diagnoses (OR = 0.64; 95% CI = 0.45-0.91) and more likely to have substance use treatment visits (iRR = 2.04; P < .01). CONCLUSIONS: Providing SBIRT in pediatric primary care may improve health care use and health, mental health, and substance use outcomes. We recommend further exploring the effects of SBIRT on these outcomes.

Development and validation of a dynamic inpatient risk prediction model for clinically significant hypokalemia using electronic health record data.

Purpose: The purpose of this study was to develop a dynamic risk prediction model for inpatient hypokalemia and evaluate its predictive performance. Methods: A retrospective cohort included all admissions aged 18 years and above from 2 large tertiary hospitals in Florida over a 22-month period. Hypokalemia was defined as a potassium value of less than 3 mmol/L, and subsequent initiation of potassium supplements. Twenty-five risk factors (RF) identified from literature were operationalized using discrete electronic health record (EHR) data elements. For each of the first 5 hospital days, we modeled the probability of developing hypokalemia at the subsequent hospital day using logistic regression. Predictive performance of our model was validated with 100 bootstrap datasets and evaluated by the C statistic and Hosmer-Lemeshow goodness-of-fit test. Results: A total of 4511 hypokalemia events occurred over 263 436 hospital days (1.71%). Validated C statistics of the prediction model ranged from 0.83 (Day 1 model) to 0.86 (Day 3), while p-values for the Hosmer-Lemeshow test spanned from 0.005 (Day 1) to 0.27 (Day 4 and 5). For the Day 3 prediction, 9.9% of patients with risk scores in the 90th percentile developed hypokalemia and accounted for 60.4% of all hypokalemia events. After controlling for baseline potassium values, strong predictors included diabetic ketoacidosis, increased mineralocorticoid activity, polyuria, use of kaliuretics, use of potassium supplements and watery stool. Conclusion: This is the first risk prediction model for hypokalemia. Our model achieved excellent discrimination and adequate calibration ability. Once externally validated, this risk assessment tool could use real-time EHR information to identify individuals at the highest risk for hypokalemia and support proactive interventions by pharmacists.

Assessment of bleeding incidences associated with rivaroxaban therapy in adults with solid tumors.

PURPOSE: Report bleeding incidences associated with rivaroxaban in adult patients with solid tumor malignancies requiring anticoagulation therapy. METHODS: This retrospective review was conducted at Indiana University Health, University Hospital and the Simon Cancer Center in Indianapolis, IN from January 2013 - February 2016. Patients were included if they had a solid tumor malignancy and prescribed rivaroxaban. Data were collected on 144 patients. Major bleeding was defined as bleeding requiring treatment (local, systemic treatment, blood cell transfusions) or hospitalization and minor bleeding was defined as bleeding not requiring treatment or hospitalization. RESULTS: Sixty-four (44%) patients experienced bleeding while on rivaroxaban. There were six cancer types that had a higher incidence of bleeding: bladder, breast, melanoma, pancreas, prostate, and renal cell cancers; 40% (6/15) of patients with bladder cancer experienced bleeding; 54% (7/13) with breast cancer experienced bleeding; 40% (4/10) of patients with melanoma experienced bleeding; 58% (11/19) of patients with pancreatic cancer experienced bleeding; 45% (10/22) of patients with prostate cancer experienced bleeding; and 56% (5/9) of patients with renal cell carcinoma experienced bleeding. No other data collected identified increased incidence of bleeding. CONCLUSIONS: Patients on rivaroxaban with a diagnosis of bladder, breast, melanoma, pancreas, prostate, or renal cell cancers had a higher incidence of bleeding compared to other solid tumors. Major bleeding was higher in bladder, breast, pancreas, and renal cell carcinomas, while minor bleeding was higher in patients with melanoma and prostate cancer.

Prescription and over-the-counter medication record integration: A holistic patient-centered approach.

OBJECTIVE: Nonprescription or over-the-counter (OTC) medications play a crucial role in a patient's ability to make choices to manage his or her own health care. Often these nonprescription choices are unknown to health care practitioners and thus are often not included as part of patients' health records. The exclusion of these therapies can pose risks to patient safety. There is a significant need to address the capture of OTC medication use in both the physician and pharmacy electronic health records (EHRs) and during the practitioner-patient medication history and reconciliation process. DATA SOURCES: On October 26, 2016, the National Association of Chain Drug Stores Foundation hosted a forum for a select group of health care industry professionals to work on a series of recommendations to improve the documentation of OTC medication use by patients. This diverse group of more than a dozen stakeholders included community pharmacy leaders from multiple chain pharmacies, pharmacy system vendors, e-prescribing vendors, health information technology experts, and multiple industry representatives, including individuals from the public sector. This commentary is a summary of the insights and suggestions where the group was in general agreement. SUMMARY: Successful OTC medication EHR integration will require system-level buy-in across multiple layers of the health care industry to improve patient safety. Forum participants suggested incremental steps that could be taken by multiple stakeholders to lay the foundation for integration within an appropriate regulatory structure. CONCLUSION: Based on the results of the stakeholder forum, an agreement formed around one approach that the group supported as feasible: the creation of a consistent and uniform identification method for OTC medications in cooperation with government regulatory authorities.

Future of Personalized Cardiovascular Medicine: JACC State-of-the-Art Review.

Previous decades have seen significant progress in the biological understanding of cardiovascular disease, as well as major advances in computational and information technologies. However, anticipated improvements in outcomes, quality, and cost of cardiovascular medicine at the individual and population levels from these advances have lagged expectations. Further, trends showing widening gaps in the pace of technological development and its successful uptake and application in practice suggests that substantial systemic changes are needed. Recent declines in key U.S. health outcomes have added further urgency to seek scalable approaches that deliver the right treatment to the right patient and to develop information-driven policies that improve health. The clinical care and research enterprises are currently in the midst of assimilating changes entrained by a "fourth industrial revolution" marked by the convergence of biology, physical sciences, and information science. These changes, if managed appropriately, can simultaneously enable cost-effective personalized medical care as well as more effective and targeted population health interventions. In this paper derived from a lecture in honor of cardiologist Paul Dudley White, the author explores how White's prescient insights into prevention and treatment continue to resonate today as we seek to assimilate ubiquitous computing, sophisticated sensor technologies, and bidirectional digital communication into the practice of cardiology. How the ongoing acceleration in basic science and information technologies can be wedded to the principles articulated by White as we pursue scalable approaches to personalized medicine is also examined.

Racial disparities in discontinuation of long-term opioid therapy following illicit drug use among black and white patients.

BACKGROUND: Among patients prescribed long-term opioid therapy (LTOT) for chronic pain, no study has yet examined how clinicians respond to evidence of illicit drug use and whether the decision to discontinue opioids is influenced by a patient's race. METHODS: Among outpatients of black and white race initiating LTOT through the VA between 2000 and 2010, we reviewed electronic medical records to determine whether opioids were discontinued within 60 days of a positive urine drug test. Logistic regression was used to examine differences by race. RESULTS: Among 15,366 patients of black (48.1%) or white (51.9%) race initiating LTOT from 2000 to 2010, 20.5% (25.5% of blacks vs. 15.8% of whites, P <. 001) received a urine drug test within the first 6 months of treatment; 13.8% tested positive for cannabis and 17.4% for cocaine. LTOT was discontinued in 11.4% of patients who tested positive for cannabis and in 13.1% of those who tested positive for cocaine. Among patients testing positive for cannabis, blacks were 2.1 times more likely than whites to have LTOT discontinued (adjusted odds ratio [AOR] 2.06, 95% confidence interval [CI] 1.04-4.08). Among patients testing positive for cocaine, blacks were 3.3 times more likely than whites to have LTOT discontinued (AOR 3.30, CI 1.28-8.53). CONCLUSIONS: Among patients testing positive for illicit drug use while receiving LTOT, clinicians are substantially more likely to discontinue opioids when the patient is black. A more universal approach to administering and responding to urine drug testing is urgently needed.

Forecasting the Maturation of Electronic Health Record Functions Among US Hospitals: Retrospective Analysis and Predictive Model.

BACKGROUND: The Meaningful Use (MU) program has promoted electronic health record adoption among US hospitals. Studies have shown that electronic health record adoption has been slower than desired in certain types of hospitals; but generally, the overall adoption rate has increased among hospitals. However, these studies have neither evaluated the adoption of advanced functionalities of electronic health records (beyond MU) nor forecasted electronic health record maturation over an extended period in a holistic fashion. Additional research is needed to prospectively assess US hospitals' electronic health record technology adoption and advancement patterns. OBJECTIVE: This study forecasts the maturation of electronic health record functionality adoption among US hospitals through 2035. METHODS: The Healthcare Information and Management Systems Society (HIMSS) Analytics' Electronic Medical Record Adoption Model (EMRAM) dataset was used to track historic uptakes of various electronic health record functionalities considered critical to improving health care quality and efficiency in hospitals. The Bass model was used to predict the technological diffusion rates for repeated electronic health record adoptions where upgrades undergo rapid technological improvements. The forecast used EMRAM data from 2006 to 2014 to estimate adoption levels to the year 2035. RESULTS: In 2014, over 5400 hospitals completed HIMSS' annual EMRAM survey (86%+ of total US hospitals). In 2006, the majority of the US hospitals were in EMRAM Stages 0, 1, and 2. By 2014, most hospitals had achieved Stages 3, 4, and 5. The overall technology diffusion model (ie, the Bass model) reached an adjusted R-squared of .91. The final forecast depicted differing trends for each of the EMRAM stages. In 2006, the first year of observation, peaks of Stages 0 and 1 were shown as electronic health record adoption predates HIMSS' EMRAM. By 2007, Stage 2 reached its peak. Stage 3 reached its full height by 2011, while Stage 4 peaked by 2014. The first three stages created a graph that exhibits the expected "S-curve" for technology diffusion, with inflection point being the peak diffusion rate. This forecast indicates that Stage 5 should peak by 2019 and Stage 6 by 2026. Although this forecast extends to the year 2035, no peak was readily observed for Stage 7. Overall, most hospitals will achieve Stages 5, 6, or 7 of EMRAM by 2020; however, a considerable number of hospitals will not achieve Stage 7 by 2035. CONCLUSIONS: We forecasted the adoption of electronic health record capabilities from a paper-based environment (Stage 0) to an environment where only electronic information is used to document and direct care delivery (Stage 7). According to our forecasts, the majority of hospitals will not reach Stage 7 until 2035, absent major policy changes or leaps in technological capabilities. These results indicate that US hospitals are decades away from fully implementing sophisticated decision support applications and interoperability functionalities in electronic health records as defined by EMRAM's Stage 7.

Developing and deploying a community healthcare worker-driven, digitally- enabled integrated care system for municipalities in rural Nepal.

Integrating care at the home and facility level is a critical yet neglected function of healthcare delivery systems. There are few examples in practice or in the academic literature of affordable, digitally-enabled integrated care approaches embedded within healthcare delivery systems in low- and middle-income countries. Simultaneous advances in affordable digital technologies and community healthcare workers offer an opportunity to address this challenge. We describe the development of an integrated care system involving community healthcare worker networks that utilize a home-to-facility electronic health record platform for rural municipalities in Nepal. Key aspects of our approach of relevance to a global audience include: community healthcare workers continuously engaging with populations through household visits every three months; community healthcare workers using digital tools during the routine course of clinical care; individual and population-level data generated routinely being utilized for program improvement; and being responsive to privacy, security, and human rights concerns. We discuss implementation, lessons learned, challenges, and opportunities for future directions in integrated care delivery systems.

Advancement of clinical pharmacy practice through intervention capture.

PURPOSE: The methods and processes utilized to deploy the Pharmacists Achieve Results with Medications Documentation (PhARMD) Project intervention template across the largest integrated healthcare system in the United States are described. SUMMARY: The PhARMD Project team at the Department of Veterans Affairs (VA) designed, developed, and deployed a standardized template within VA's electronic health record (EHR) that allows the clinical pharmacy specialist (CPS) to efficiently document select interventions made during patient care encounters that specifically contribute to the overall care provided and patient outcomes. The template is completed by the CPSs as part of progress note documentation within the EHR. Using point-and-click functionality, a CPS selects the check boxes corresponding to specific interventions made during that patient care encounter. This improves workflow and negates the need to document interventions in a separate software system, streamlining documentation. The implementation and use of the PhARMD template at each VA facility are voluntary. From October 1, 2016, to September 30, 2017, 4,728 CPSs documented 3,805,323 interventions during 2,384,771 patient care encounters. These interventions were documented across 592,126 unique patients, with a mean of 6.4 interventions per patient during this period. Most interventions (95%) were performed by CPSs functioning as advanced practice providers and with autonomous prescriptive authority authorized under their scope of practice. CONCLUSION: The PhARMD template demonstrated that the capture of clinical pharmacy interventions and outcomes can be achieved across a large integrated healthcare system by thousands of CPSs in numerous practice settings.

Prediction of Future Chronic Opioid Use Among Hospitalized Patients.

BACKGROUND: Opioids are commonly prescribed in the hospital; yet, little is known about which patients will progress to chronic opioid therapy (COT) following discharge. We defined COT as receipt of ≥ 90-day supply of opioids with < 30-day gap in supply over a 180-day period or receipt of ≥ 10 opioid prescriptions over 1 year. Predictive tools to identify hospitalized patients at risk for future chronic opioid use could have clinical utility to improve pain management strategies and patient education during hospitalization and discharge. OBJECTIVE: The objective of this study was to identify a parsimonious statistical model for predicting future COT among hospitalized patients not on COT before hospitalization. DESIGN: Retrospective analysis electronic health record (EHR) data from 2008 to 2014 using logistic regression. PATIENTS: Hospitalized patients at an urban, safety net hospital. MAIN MEASUREMENTS: Independent variables included medical and mental health diagnoses, substance and tobacco use disorder, chronic or acute pain, surgical intervention during hospitalization, past year receipt of opioid or non-opioid analgesics or benzodiazepines, opioid receipt at hospital discharge, milligrams of morphine equivalents prescribed per hospital day, and others. KEY RESULTS: Model prediction performance was estimated using area under the receiver operator curve, accuracy, sensitivity, and specificity. A model with 13 covariates was chosen using stepwise logistic regression on a randomly down-sampled subset of the data. Sensitivity and specificity were optimized using the Youden's index. This model predicted correctly COT in 79% of the patients and no COT correctly in 78% of the patients. CONCLUSIONS: Our model accessed EHR data to predict 79% of the future COT among hospitalized patients. Application of such a predictive model within the EHR could identify patients at high risk for future chronic opioid use to allow clinicians to provide early patient education about pain management strategies and, when able, to wean opioids prior to discharge while incorporating alternative therapies for pain into discharge planning.

Substance Use Disorder Treatment Following Clinician-Initiated Discontinuation of Long-Term Opioid Therapy Resulting from an Aberrant Urine Drug Test.

BACKGROUND: It is unclear whether substance use disorder (SUD) treatment is offered to, or utilized by, patients who are discontinued from long-term opioid therapy (LTOT) following aberrant urine drug tests (UDTs). OBJECTIVE: To describe the proportion of patients who were referred to, and engaged in, SUD treatment following LTOT discontinuation and to examine differences in SUD treatment referral and engagement based on the substances that led to discontinuation. DESIGN: From a sample of 600 patients selected from a national cohort of Veterans Health Administration patients who were discontinued from LTOT, we used manual chart review to identify 169 patients who were discontinued because of a UDT that was positive for alcohol, cannabis, or other illicit or non-prescribed controlled substances. MAIN MEASURES: We extracted sociodemographic, clinical, and health care utilization data from patients' electronic medical records. KEY RESULTS: Forty-three percent of patients (n = 73) received an SUD treatment referral following LTOT discontinuation and 20% (n = 34) engaged in a new episode of SUD treatment in the year following discontinuation. Logistic regression models controlling for sociodemographic and clinical variables demonstrated that patients who tested positive for cannabis were less likely than patients who tested positive for non-cannabis substances to receive referrals for SUD treatment (aOR = 0.44, 95% CI = 0.23-0.84, p = 0.01) or engage in SUD treatment (aOR = 0.42, 95% CI = 0.19-0.94, p = 0.04). Conversely, those who tested positive for cocaine were more likely to receive an SUD treatment referral (aOR = 3.32, 95% CI = 1.57-7.06, p = 0.002) and engage in SUD treatment (aOR = 2.44, 95% CI = 1.00-5.96, p = 0.05) compared to those who did not have a cocaine-positive UDT. CONCLUSIONS: There may be substance-specific differences in clinician referrals to, and patient engagement in, SUD treatment. This suggests a need for more standardized implementation of clinical guidelines that recommend SUD care, when appropriate, following LTOT discontinuation.