Most people with opioid use disorder (OUD) never receive treatment. Medication treatment of OUD in primary care is recommended as an approach to increase access to care. The PRimary Care Opioid Use ...Disorders treatment (PROUD) trial tests whether implementation of a collaborative care model (Massachusetts Model) using a nurse care manager (NCM) to support medication treatment of OUD in primary care increases OUD treatment and improves outcomes. Specifically, it tests whether implementation of collaborative care, compared to usual primary care, increases the number of days of medication for OUD (implementation objective) and reduces acute health care utilization (effectiveness objective). The protocol for the PROUD trial is presented here.
PROUD is a hybrid type III cluster-randomized implementation trial in six health care systems. The intervention consists of three implementation strategies: salary for a full-time NCM, training and technical assistance for the NCM, and requiring that three primary care providers have DEA waivers to prescribe buprenorphine. Within each health system, two primary care clinics are randomized: one to the intervention and one to Usual Primary Care. The sample includes all patients age 16-90 who visited the randomized primary care clinics from 3 years before to 2 years after randomization (anticipated to be > 170,000). Quantitative data are derived from existing health system administrative data, electronic medical records, and/or health insurance claims ("electronic health records," EHRs). Anonymous staff surveys, stakeholder debriefs, and observations from site visits, trainings and technical assistance provide qualitative data to assess barriers and facilitators to implementation. The outcome for the implementation objective (primary outcome) is a clinic-level measure of the number of patient days of medication treatment of OUD over the 2 years post-randomization. The patient-level outcome for the effectiveness objective (secondary outcome) is days of acute care utilization e.g. urgent care, emergency department (ED) and/or hospitalizations over 2 years post-randomization among patients with documented OUD prior to randomization.
The PROUD trial provides information for clinical leaders and policy makers regarding potential benefits for patients and health systems of a collaborative care model for management of OUD in primary care, tested in real-world diverse primary care settings. Trial registration # NCT03407638 (February 28, 2018); CTN-0074 https://clinicaltrials.gov/ct2/show/NCT03407638?term=CTN-0074&draw=2&rank=1.
Several mathematical rules by which bone adapts to mechanical loading have been proposed. Previous work focused mainly on negative feedback models, e.g., bone adapts to increased loading after a ...minimum strain effective (MES) threshold has been reached. The MES algorithm has numerous caveats, so we propose a different model, according to which bone adapts to changes in its mechanical environment based on the principle of cellular accommodation. With the new algorithm we presume that strain history is integrated into cellular memory so that the reference state for adaptation is constantly changing. To test this algorithm, an experiment was performed in which the ulnae of Sprague–Dawley rats were loaded in axial compression. The animals received loading for 15 weeks with progressively decreasing loads, increasing loads, or a constant load. The results showed the largest increases in geometry in the decreasing load group, followed by the constant load group. Bone formation rates (BFRs) were significantly greater in the decreasing load group during the first 2 weeks of the study as compared to all other groups (
P
<
0.05
). After the first few weeks of mechanical loading, the BFR in the loaded ulnae returned to the values of the nonloaded ulnae. These experimental results closely fit the predicted results of the cellular accommodation algorithm. After the initial weeks of loading, bone stopped responding so the degree of adaptation was proportional to the initial peak load magnitude.
Behavioral health and other preventable factors account for nearly half of all premature deaths in the United States. Motivational interviewing (MI) is effective for engaging ambivalent patients in ...behavior change. However, many clinicians report barriers to receiving MI training, including time, cost, and travel. This study examined the effect of a 2-day virtual MI training built around didactic and real-play learning activities.
Thirty interprofessional clinicians from eight Veterans Affairs medical centers and their community-based outpatient clinics across 4 US states attended a 2-day virtual MI training. Participants completed a posttraining evaluation and a 3-month posttraining evaluation.
Participants reported that they learned new knowledge and skills, and they would be able to apply these to their practice (M > 4).They reported high satisfaction with the training and platform and found the technology easy to use (M > 4). In the 3-month posttraining survey, participants reported that they were using MI in their practice (M = 4.19) and that they would like additional support, such as additional reading (n = 8).
This study demonstrates the effect of a 2-day virtual MI training built around didactic and real-play learning activities. Virtual training particularly enhances training opportunities in rural settings. Our training removed travel and payment as barriers to participation.
IMPORTANCE: Experts recommend that alcohol use disorders (AUDs) be managed in primary care, but effective approaches are unclear. OBJECTIVE: To test whether 12 months of alcohol care management, ...compared with usual care, improved drinking outcomes among patients with or at high risk for AUDs. DESIGN, SETTING, AND PARTICIPANTS: This randomized clinical trial was conducted at 3 Veterans Affairs (VA) primary care clinics. Between October 11, 2011, and September 30, 2014, the study enrolled 304 outpatients who reported heavy drinking (≥4 drinks per day for women and ≥5 drinks per day for men). INTERVENTIONS: Nurse care managers offered outreach and engagement, repeated brief counseling using motivational interviewing and shared decision making about treatment options, and nurse practitioner–prescribed AUD medications (if desired), supported by an interdisciplinary team (CHOICE intervention). The comparison was usual primary care. MAIN OUTCOMES AND MEASURES: Primary outcomes, assessed by blinded telephone interviewers at 12 months, were percentage of heavy drinking days in the prior 28 days measured by timeline follow-back interviews and a binary good drinking outcome, defined as abstinence or drinking below recommended limits in the prior 28 days (according to timeline follow-back interviews) and no alcohol-related symptoms in the past 3 months as measured by the Short Inventory of Problems. RESULTS: Of 304 participants, 275 (90%) were male, 206 (68%) were white, and the mean (SD) age was 51.4 (13.8) years. At baseline, both the CHOICE intervention (n = 150) and usual care (n = 154) groups reported heavy drinking on 61% of days (95% CI, 56%-66%). During the 12-month intervention, 137 of 150 patients in the intervention group (91%) had at least 1 nurse visit, and 77 of 150 (51%) had at least 6 nurse visits. A greater proportion of patients in the intervention group than in the usual care group received alcohol-related care: 42% (95% CI, 35%-49%; 63 of 150 patients) vs 26% (95% CI, 19%-35%; 40 of 154 patients). Alcohol-related care included more AUD medication use: 32% (95% CI, 26%-39%; 48 of 150 patients in the intervention group) vs 8% (95% CI, 5%-13%; 13 of 154 patients in the usual care group). No significant differences in primary outcomes were observed at 12 months between patients in both groups. The percentages of heavy drinking days were 39% (95% CI, 32%-47%) and 35% (95% CI, 28%-42%), and the percentages of patients with a good drinking outcome were 15% (95% CI, 9%-22%; 18 of 124 patients) and 20% (95 % CI, 14%-28%; 27 of 134 patients), in the intervention and usual care groups, respectively (P = .32-.44). Findings at 3 months were similar. CONCLUSIONS AND RELEVANCE: The CHOICE intervention did not decrease heavy drinking or related problems despite increased engagement in alcohol-related care. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT01400581
Background: Most states have legalized medical cannabis, yet little is known about how medical cannabis use is documented in patients' electronic health records (EHRs). We used natural language ...processing (NLP) to calculate the prevalence of clinician-documented medical cannabis use among adults in an integrated health system in Washington State where medical and recreational use are legal. Methods: We analyzed EHRs of patients ≥18 years old screened for past-year cannabis use (November 1, 2017-October 31, 2018), to identify clinician-documented medical cannabis use. We defined medical use as any documentation of cannabis that was recommended by a clinician or described by the clinician or patient as intended to manage health conditions or symptoms. We developed and applied an NLP system that included NLP-assisted manual review to identify such documentation in encounter notes. Results: Medical cannabis use was documented for 16,684 (5.6%) of 299,597 outpatient encounters with routine screening for cannabis use among 203,489 patients seeing 1,274 clinicians. The validated NLP system identified 54% of documentation and NLP-assisted manual review the remainder. Language documenting reasons for cannabis use included 125 terms indicating medical use, 28 terms indicating non-medical use and 41 ambiguous terms. Implicit documentation of medical use (e.g., "edible THC nightly for lumbar pain") was more common than explicit (e.g., "continues medical cannabis use"). Conclusions: Clinicians use diverse and often ambiguous language to document patients' reasons for cannabis use. Automating extraction of documentation about patients' cannabis use could facilitate clinical decision support and epidemiological investigation but will require large amounts of gold standard training data.
Objective:The authors sought to characterize the 3-year prevalence of mental disorders and nonnicotine substance use disorders among male and female primary care patients with documented opioid use ...disorder across large U.S. health systems.Methods:This retrospective study used 2014–2016 data from patients ages ≥16 years in six health systems. Diagnoses were obtained from electronic health records or claims data; opioid use disorder treatment with buprenorphine or injectable extended-release naltrexone was determined through prescription and procedure data. Adjusted prevalence of comorbid conditions among patients with opioid use disorder (with or without treatment), stratified by sex, was estimated by fitting logistic regression models for each condition and applying marginal standardization.Results:Females (53.2%, N=7,431) and males (46.8%, N=6,548) had a similar prevalence of opioid use disorder. Comorbid mental disorders among those with opioid use disorder were more prevalent among females (86.4% vs. 74.3%, respectively), whereas comorbid other substance use disorders (excluding nicotine) were more common among males (51.9% vs. 60.9%, respectively). These differences held for those receiving medication treatment for opioid use disorder, with mental disorders being more common among treated females (83% vs. 71%) and other substance use disorders more common among treated males (68% vs. 63%). Among patients with a single mental health condition comorbid with opioid use disorder, females were less likely than males to receive medication treatment for opioid use disorder (15% vs. 20%, respectively).Conclusions:The high rate of comorbid conditions among patients with opioid use disorder indicates a strong need to supply primary care providers with adequate resources for integrated opioid use disorder treatment.
IntroductionAdolescents with heart disease report difficulty in communication about their health as a major inhibiting factor in their care. MHealth technologies collect health data in daily life and ...enable health data sharing between the provider and patient. The adolescent population has a high level of engagement with mobile devices and a willingness to use them for health-related activities.HypothesisWe hypothesized that our novel gamified mHealth platform Heart Hero can engage adolescent patients in the collection of cardiac health data in their daily life.MethodsWe designed the research app using ResearchKit to collect continuous physiological data from the Apple Watch and daily survey data on well-being, stress, medical adherence, and cardiac symptoms. Patients were provided the app, iPhone, and Apple Watch and enrolled for 27 days. A final in-app survey was provided to assess feedback. We enrolled 28 patients total who were scheduled for outpatient cardiopulmonary exercise testing.ResultsMean age was 14.3 years old (SD +/-3.08) with a 1:1 M:F ratio. 61% of patients were ≥ 15 years of age. 94% of patients completed the final survey. Subjects on average completed 64% (SEM +/-5) of the daily quizzes with an average daily adherence of over 50% wearing the watch. 100% reported they liked using the watch and app, and 89% would like to continue wearing the Apple Watch. 53% reported the study encouraged them to exercise more while 21% reported encouragement to walk more. Fig 1 demonstrates A) Apple Watch and b) daily survey data collected from a patient throughout the study.ConclusionsIn conclusion, Heart Hero is a mHealth platform which can successfully be used to collect continuous health data from the adolescent population with high engagement characterized by adherence and positive patient feedback. Adherence to the app was notably superior to the initial 5 ResearchKit applications enrolling adult patients.
With increasing rates of opioid use disorder (OUD) and overdose deaths in the US, increased access to medications for OUD (MOUD) is paramount. Rigorous effectiveness evaluations of large-scale ...implementation initiatives using quasi-experimental designs are needed to inform expansion efforts.
To evaluate a US Department of Veterans Affairs (VA) initiative to increase MOUD use in nonaddiction clinics.
This quality improvement initiative used interrupted time series design to compare trends in MOUD receipt. Primary care, pain, and mental health clinics in the VA health care system (n = 35) located at 18 intervention facilities and nonintervention comparison clinics (n = 35) were matched on preimplementation MOUD prescribing trends, clinic size, and facility complexity. The cohort of patients with OUD who received care in intervention or comparison clinics in the year after September 1, 2018, were evaluated. The preimplementation period extended from September 1, 2017, through August 31, 2018, and the postimplementation period from September 1, 2018, through August 31, 2019.
The multifaceted implementation intervention included education, external facilitation, and quarterly reports.
The main outcomes were the proportion of patients receiving MOUD and the number of patients per clinician prescribing MOUD. Segmented logistic regression evaluated monthly proportions of MOUD receipt 1 year before and after initiative launch, adjusting for demographic and clinical covariates. Poisson regression models examined yearly changes in clinician prescribing over the same time frame.
Overall, 7488 patients were seen in intervention clinics (mean SD age, 53.3 14.2 years; 6858 91.6% male; 1476 19.7% Black, 417 5.6% Hispanic; 5162 68.9% White; 239 3.2% other race including American Indian or Alaska Native, Asian, Native Hawaiian or other Pacific Islander, and multiple races; and 194 2.6% unknown) and 7558 in comparison clinics (mean SD age, 53.4 14.0 years; 6943 91.9% male; 1463 19.4% Black; 405 5.4% Hispanic; 5196 68.9% White; 244 3.2% other race; 250 3.3% unknown). During the preimplementation year, the proportion of patients receiving MOUD in intervention clinics increased monthly by 5.0% (adjusted odds ratio AOR, 1.05; 95% CI, 1.03-1.07). Accounting for this preimplementation trend, the proportion of patients receiving MOUD increased monthly by an additional 2.3% (AOR, 1.02; 95% CI, 1.00-1.04) during the implementation year. Comparison clinics increased by 2.6% monthly before implementation (AOR, 1.03; 95% CI, 1.01-1.04), with no changes detected after implementation. Although preimplementation-year trends in monthly MOUD receipt were similar in intervention and comparison clinics, greater increases were seen in intervention clinics after implementation (AOR, 1.04; 95% CI, 1.01-1.08). Patients treated with MOUD per clinician in intervention clinics saw greater increases from before to after implementation compared with comparison clinics (incidence rate ratio, 1.50; 95% CI, 1.28-1.77).
A multifaceted implementation initiative in nonaddiction clinics was associated with increased MOUD prescribing. Findings suggest that engagement of clinicians in general clinical settings may increase MOUD access.
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