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PurposeThe use of telemedicine (TM) has accelerated in recent years, yet research on the implementation and effectiveness of TM-delivered medication treatment for opioid use disorder (MOUD) has been limited. This study investigated the feasibility of implementing a care coordination model involving MOUD delivered via an external TM provider for the purpose of expanding access to MOUD for patients in rural settings.MethodsThe study tested a care coordination model in 6 rural primary care sites by establishing referral and coordination between the clinic and a TM company for MOUD. The intervention spanned approximately 6 months from July/August 2020 to January 2021, coinciding with the peak of the COVID-19 pandemic. Each clinic tracked patients with OUD in a registry during the intervention period. A pre-/post-intervention design (N = 6) was used to assess the clinic-level outcome as patient-days on MOUD based on patient electronic health records.FindingsAll clinics implemented critical components of the intervention, with an overall TM referral rate of 11.7% among patients in the registry. Five of the 6 sites showed an increase in patient-days on MOUD during the intervention period compared to the 6-month period before the intervention (mean increase per 1,000 patients: 132 days, P = .08, Cohen's d = 0.55). The largest increases occurred in clinics that lacked MOUD capacity or had a greater number of patients initiating MOUD during the intervention period.ConclusionsTo expand access to MOUD in rural settings, the care coordination model is most effective when implemented in clinics that have negligible or limited MOUD capacity.

Telemedicine (TM) enabled by digital health technologies to provide medical services has been considered a key solution to increasing health care access in rural communities. With the immediate need for remote care due to the COVID-19 pandemic, many health care systems have rapidly incorporated digital technologies to support the delivery of remote care options, including medication treatment for individuals with opioid use disorder (OUD). In responding to the opioid crisis and the COVID-19 pandemic, public health officials and scientific communities strongly support and advocate for greater use of TM-based medication treatment for opioid use disorder (MOUD) to improve access to care and have suggested that broad use of TM during the pandemic should be sustained. Nevertheless, research on the implementation and effectiveness of TM-based MOUD has been limited. To address this knowledge gap, the National Drug Abuse Treatment Clinical Trials Network (CTN) funded (via the NIH HEAL Initiative) a study on Rural Expansion of Medication Treatment for Opioid Use Disorder (Rural MOUD; CTN-0102) to investigate the implementation and effectiveness of adding TM-based MOUD to rural primary care for expanding access to MOUD. In preparation for this large-scale, randomized controlled trial incorporating TM in rural primary care, a feasibility study is being conducted to develop and pilot test implementation procedures. In this commentary, we share some of our experiences, which include several challenges, during the initial two-month period of the feasibility study phase. While these challenges could be due, at least in part, to adjusting to the COVID-19 pandemic and new workflows to accommodate the study, they are notable and could have a substantial impact on the larger, planned pragmatic trial and on TM-based MOUD more broadly. Challenges include low rates of identification of risk for OUD from screening, low rates of referral to TM, digital device and internet access issues, workflow and capacity barriers, and insurance coverage. These challenges also highlight the lack of empirical guidance for best TM practice and quality remote care models. With TM expanding rapidly, understanding implementation and demonstrating what TM approaches are effective are critical for ensuring the best care for persons with OUD.

BackgroundSynergies between technology and health care in the United States are accelerating, increasing opportunities to leverage these technologies to improve patient care.MethodsThis study was a collaboration between an academic study team, a rural primary care clinic, and a local nonprofit informatics company developing tools to improve patient care through population management. Our team created a text messaging management tool, then developed methods for and tested the feasibility of bidirectional text messaging to remind eligible patients about the need for lipid testing. We measured patient response to the text messages, then interviewed 8 patients to explore their text messaging experience.ResultsOf the 129 patients the clinic was able to contact by phone, 29.4% had no cell phone or text-messaging capabilities. An additional 20% refused to participate. Two thirds of the 28 patients who participated in the text messaging intervention (67.9%) responded to at least 1 of the up to 3 messages. Seven of 8 interviewed patients had a positive text-messaging experience.ConclusionsBidirectional text messaging is a feasible and largely acceptable form of communication for test reminders that has the potential to reach large numbers of patients in clinical care.

ObjectiveThe patient-centered medical home (PCMH) includes comprehensive chronic illness and preventive services, including identifying patients who are overdue for colorectal cancer screening (CRCS). The association between PCMH implementation and CRCS during the Systems of Support to Increase Colorectal Cancer Screening Trial (SOS) is described.MethodsThe SOS enrolled 4664 patients from 21 clinics from August 2008 to November 2009. Patients were randomized to usual care, mailed fecal kits, kits plus brief assistance, or kits plus assistance and navigation. A PCMH model that included a workflow for facilitating CRCS was implemented at all study clinics in late 2009. Patients enrolled early had little exposure to the PCMH, whereas patients enrolled later were exposed during most of their first year in the trial. Logistic regression models were used to assess the association between PCMH exposure and CRCS.ResultsUsual care patients with ≥8 months in the PCMH had higher CRCS rates than those with ≤4 months in the PCMH (adjusted difference, 10.1%; 95% confidence interval, 5.7-14.6). SOS interventions led to significant increases in CRCS, but the magnitude of effect was attenuated by exposure to the PCMH (P for interaction = .01).ConclusionExposure to a PCMH was associated with higher CRCS rates. Automated mailed and centrally delivered stepped interventions increased CRCS rates, even in the presence of a PCMH.

BACKGROUND: Using a self-service kiosk to measure blood pressure (BP) has the potential to increase patients awareness of their BP control and free up medical assistant (MA) time. The objective of this study was to evaluate BP kiosk acceptability and usability, as well as its effects on the workflow of patient BP self-measurement in a primary care clinic. METHODS: We used qualitative and quantitative assessments of kiosk implementation via meetings with clinic leaders, focus groups with clinic providers and staff, observations of kiosk users, and surveys of kiosk users at 2 and 8 months. RESULTS: Most patients were comfortable using the kiosk (82% at 2 months, 87% at 8 months). Initial provider concerns included accuracy, but most gained confidence after comparing it with other monitors and reviewing the literature supporting its accuracy. Patients and providers saw many benefits: easier BP checks, increased patient engagement, and saved MA time for other tasks. The clinic addressed early concerns (eg, infection control, confusing instructions, perceived loss of personal touch). Most patients (86%) supported the clinic continuing to use the kiosks. CONCLUSIONS: Providers, staff, and patients adapted to the use of BP kiosks, providing value by engaging patients in their own care and saving MA time. The clinic decided to keep the self-service kiosk after the pilot period.

There is increasing interest in medical text messaging interventions being used to achieve positive patient outcomes across a range of clinical research and health practice environments. Short messaging service (SMS) is a low-cost tool that provides an easy communication route to engage potentially broad populations through text messaging, and is part of the growing social trend toward increased adoption of personal communication technologies by patient populations. Testing the effectiveness and impact of various communication strategies requires navigation of a complex web of clinical and research regulations and oversight mechanisms. We describe a case study of the implementation of SMS to provide bidirectional communications between physicians and patients involved in routine care reminders to illustrate the review processes and governance structures needed. By mapping the regulatory and approval processes required to manage and steward a research study across clinical and community boundaries, we provide a guide for other translational health researchers who may utilize similar kinds of personally owned technology interventions as research tools.

PURPOSE We conducted a randomized controlled trial to compare the effectiveness of adding various forms of enhanced external support to practice facilitation on primary care practices' clinical quality measure (CQM) performance. METHODS Primary care practices across Washington, Oregon, and Idaho were eligible if they had fewer than 10 full-time clinicians. Practices were randomized to practice facilitation only, practice facilitation and shared learning, practice facilitation and educational outreach visits, or practice facilitation and both shared learning and educational outreach visits. All practices received up to 15 months of support. The primary outcome was the CQM for blood pressure control. Secondary outcomes were CQMs for appropriate aspirin therapy and smoking screening and cessation. Analyses followed an intention-to-treat approach. RESULTS Of 259 practices recruited, 209 agreed to be randomized. Only 42% of those offered educational outreach visits and 27% offered shared learning participated in these enhanced supports. CQM performance improved within each study arm for all 3 cardiovascular disease CQMs. After adjusting for differences between study arms, CQM improvements in the 3 enhanced practice support arms of the study did not differ significantly from those seen in practices that received practice facilitation alone (omnibus P = .40 for blood pressure CQM). Practices randomized to receive both educational outreach visits and shared learning, however, were more likely to achieve a blood pressure performance goal in 70% of patients compared with those randomized to practice facilitation alone (relative risk = 2.09; 95% CI, 1.16-3.76). CONCLUSIONS Although we found no significant differences in CQM performance across study arms, the ability of a practice to reach a target level of performance may be enhanced by adding both educational outreach visits and shared learning to practice facilitation. Key words: primary health care; cardiovascular disease; quality improvement; chronic illness; prevention; health promotion; quantitative methods; health services; organizational change; practice-based research, INTRODUCTION Past efforts to transform primary care have included practice redesign based on medical home principles and adoption of electronic health records. (1-4) More recently, primary care practices face increasing [...]

PURPOSE Six key elements of opioid medication management redesign in primary care have been previously identified. Here, we examine the effect of implementing these Six Building Blocks on opioid-prescribing practices. METHODS Six rural-serving organizations with 20 clinic locations received support for 15 months during the period October 2015 to May 2017 to implement the Six Building Blocks. Patients undergoing long-term opioid therapy (LtOT) at these study sites were compared with patients undergoing LtOT enrolled in a regional health plan who did not receive care at the study sites but who resided in the same primary care service areas (control group). Outcomes were monthly trend in the proportion of patients undergoing LtOT prescribed a >100 morphine equivalent dose (MED) of opioids daily and the total number of patients receiving an opioid prescription. An interrupted time series using difference- indifference analysis was used for tests of significance. RESULTS The proportion of patients prescribed a >100 MED of opioids daily decreased 2.2% (11.8% to 9.6%) among patients at the intervention clinics and 1.3% (14.0% to 12.7%) among patients in the control group. The rate of decrease was significantly greater among study patients than among patients in the control group (P = .018). The rate of decrease in the number of patients on LtOT at intervention clinics increased during the intervention period compared with the preintervention period (P CONCLUSIONS Efforts to redesign opioid medication management in primary care resulted in a significant decrease in opioid prescribing. Future research is needed to determine if these results are generalizable to other settings and to assess implications for patient-reported outcomes., INTRODUCTION Use of opioid medication long term for chronic pain has contributed to an epidemic of opioid overdose and addiction. (1) Although rates of opioid prescribing have moderated in recent [...]

Purpose: To investigate the prevalence of opioid use disorder (OUD) and medication treatment for OUD (MOUD) receipt in rural primary care settings and identify characteristics associated with MOUD among patients with OUD. Methods: Secondary analyses based on electronic health records of all adult patients who visited 1 of the 6 rural primary care clinic sites from October 2019 to January 2021. Mixed effects logistic regression was conducted to assess MOUD receipt (Y/N) in relation to patient characteristics (eg, demographics, other substance use disorders [SUDs], mental health disorders, and chronic pain) and the number of MOUD prescribers per clinic. Findings: The prevalence of OUD varied from 0.7% to 8.2% (Mean [SD] = 3.3% [95% CI: 0.4, 6.1]) among 36,762 primary care patients across 6 clinic sites. Among 1,164 patients with OUD, on average 50.1% received MOUD (95% CI: 28.0, 72.3). Patients in clinics with more than 3 MOUD prescribers had more than 3 times the odds of receiving MOUD (OR = 3.42; 95% CI, 1.22‐9.62) as those in clinics with fewer than 3 prescribers. MOUD was positively associated with younger age (18‐30 [OR = 6.97; 95% CI, 3.37‐14.42], 31‐64 [OR = 5.03; 95% CI, 2.64‐9.57], relative to those 65 and older), having other co‐occurring SUDs (OR = 3.77; 95% CI, 2.57‐5.52), being male (OR = 1.50; 95% CI, 1.12‐2.01), and negatively associated with having chronic pain disorders (OR = 0.69; 95% CI, 0.50‐0.94). Conclusions: The prevalence of OUD and MOUD are high but vary considerably across rural primary care clinics; primary care MOUD prescribers play a key role on MOUD access in rural settings. [ABSTRACT FROM AUTHOR]

Background and Aims: International Classification of Diseases (ICD) diagnosis codes are often used in research to identify patients with opioid use disorder (OUD), but their accuracy for this purpose is not fully evaluated. This study describes application of ICD‐10 diagnosis codes for opioid use, dependence and abuse from an electronic health record (EHR) data extraction using data from the clinics' OUD patient registries and clinician/staff EHR entries. Design: Cross‐sectional observational study. Setting: Four rural primary care clinics in Washington and Idaho, USA. Participants: 307 patients. Measurements This study used three data sources from each clinic: (1) a limited dataset extracted from the EHR, (2) a clinic‐based registry of patients with OUD and (3) the clinician/staff interface of the EHR (e.g. progress notes, problem list). Data source one included records with six commonly applied ICD‐10 codes for opioid use, dependence and abuse: F11.10 (opioid abuse, uncomplicated), F11.20 (opioid dependence, uncomplicated), F11.21 (opioid dependence, in remission), F11.23 (opioid dependence with withdrawal), F11.90 (opioid use, unspecified, uncomplicated) and F11.99 (opioid use, unspecified with unspecified opioid‐induced disorder). Care coordinators used data sources two and three to categorize each patient identified in data source one: (1) confirmed OUD diagnosis, (2) may have OUD but no confirmed OUD diagnosis, (3) chronic pain with no evidence of OUD and (4) no evidence for OUD or chronic pain. Findings F11.10, F11.21 and F11.99 were applied most frequently to patients who had clinical diagnoses of OUD (64%, 89% and 79%, respectively). F11.20, F11.23 and F11.90 were applied to patients who had a diagnostic mix of OUD and chronic pain without OUD. The four clinics applied codes inconsistently. Conclusions: Lack of uniform application of ICD diagnosis codes make it challenging to use diagnosis code data from EHR to identify a research population of persons with opioid use disorder. [ABSTRACT FROM AUTHOR]