Objective-To assess the use of opioids by primary care physicians for the treatment of chronic pain.Methods-A written survey was completed by 248 primary care physicians. Outcomes of interest included type of opioids prescribed, common pain diagnoses treated, opioid prescribing concerns, treatment of patients with a history of substance use disorders and clinic-based protocols for pain management.Results-The mean age of the physicians who completed the questionnaire was 41 years. The majority were between the ages of 30 and 49 years (68%) with an equal number of men and women. Seventy percent were family physicians, 28.7% internists and less than 2% were community physicians and geriatricians. Physician concerns regarding opioid therapy included prescription drug abuse (84.2%), addiction (74.9%), adverse effects (68%), tolerance (60.7%), and medication interaction (32%). The survey found that the majority of the physicians were comfortable in prescribing narcotics to patients with terminal cancer. However, they were less comfortable prescribing narcotics to patients with low back pain and persons with a current or past history of drug or alcohol abuse. Physician management practices suggested that urine toxicology tests were underutilized with only 6.9% reporting obtaining this test before prescribing opioids and only 15.0% performing urine toxicology tests on patients already prescribed opioids. Logistic regression analysis revealed that whether or not physicians routinely conducted urine toxicology screens was significantly (p = 0.015) predicted by whether they had a system to track patients on opioids when prescribing narcotics. The primary limitation of the study is the reliance on physician self-report rather than objective measures of physician behavior. Conclusions-The survey suggests physicians are concerned about drug abuse, addiction, adverse effects, tolerance, and medication interaction. Their comfort level in prescribing opioids varies with the patient characteristics. Urine toxicology testing is underutilized in the primary care setting.

BACKGROUND:The use of opioid medications to manage chronic pain is complex and challenging, especially in primary care settings. Medication contracts are increasingly being used to monitor patient adherence, but little is known about the long-term outcomes of such contracts. OBJECTIVE:To describe the long-term outcomes of a medication contract agreement for patients receiving opioid medications in a primary care setting. DESIGN:Retrospective cohort study. SUBJECTS:All patients placed on a contract for opioid medication between 1998 and 2003 in an academic General Internal Medicine teaching clinic.MEASUREMENTS: Demographics, diagnoses, opiates prescribed, urine drug screens, and reasons for contract cancellation were recorded. The association of physician contract cancellation with patient factors and medication types were examined using the Chisquare test and multivariate logistic regression. RESULTS:A total of 330 patients constituting 4% of the clinic population were placed on contracts during the study period. Seventy percent were on indigent care programs. The majority had low back pain (38%) or fibromyalgia (23%). Contracts were discontinued in 37%. Only 17% were cancelled for substance abuse and noncompliance. Twenty percent discontinued contract voluntarily. Urine toxicology screens were obtained in 42% of patients of whom 38% were positive for illicit substances.CONCLUSIONS: Over 60% of patients adhered to the contract agreement for opioids with a median follow-up of 22.5 months. Our experience provides insight into establishing a systematic approach to opioid administration and monitoring in primary care practices. A more structured drug testing strategy is needed to identify nonadherent patients.

We implemented a quality improvement project for diabetes care in a faculty-resident internal medicine practice, using the Chronic Care Model framework. We created a planned visit clinic, used a stepwise medication algorithm, and self-management support. The intervention was effective for patients with glycohemoglobin A1c levels 10 or above (P = .0075) when compared with usual care after adjusting for all significant predictors. Compliance with foot examinations increased by 72% (P < .0001) and pneumococcal vaccinations by 25% (P = .0115). We believe that the Chronic Care Model can be successfully integrated into faculty-resident practices and provides a model for further exploration into disease management education in academic settings.

Background Social needs screening in primary care may be valuable for addressing non-medical health-related factors, such as housing insecurity, that interfere with optimal medical care. Yet it is unclear if patients welcome such screening and how comfortable they are having this information included in electronic health records (EHR). Objective To assess patient attitudes toward inclusion of social needs information in the EHR and key correlates, such as sociodemographic status, self-rated health, and trust in health care. Design, participants, and main measures In a cross-sectional survey of patients attending a primary care clinic for annual or employment exams, 218/560 (38%) consented and completed a web survey or personal interview between 8/20/20-8/23/21. Patients provided social needs information using the Accountable Care Communities Screening Tool. For the primary outcome, patients were asked, “Would you be comfortable having these kinds of needs included in your health record (also known as your medical record or chart)?” Analyses Regression models were estimated to assess correlates of patient comfort with including social needs information in medical records. Key results The median age was 45, 68.8% were female, and 78% were white. Median income was $75,000 and 84% reported education beyond high school. 85% of patients reported they were very or somewhat comfortable with questions about social needs, including patients reporting social needs. Social need ranged from 5.5% (utilities) to 26.6% (housing), and nonwhite and gender-nonconforming patients reported greater need. 20% reported “some” or “complete” discomfort with social needs information included in the EHR. Adjusting for age, gender, race, education, trust, and self-rated health, each additional reported social need significantly increased discomfort with the EHR for documenting social needs. Conclusions People with greater social needs were more wary of having this information placed in the EHR. This is a concerning finding, since one rationale for collecting social need data is to use this information (presumably in the EHR) for addressing needs.

Quality improvement (QI) plays a vital role in practice management, patient care, and reimbursement. The authors implemented a 3-year longitudinal curriculum that combined QI didactics, intervention development, and implementation at university-based, community-based, and Veterans Administration–based practices. Highlights included Plan-Do-Study-Act cycle format, team-based collaboration to brainstorm interventions, interdisciplinary QI council to select and plan interventions, system-wide intervention implementation across entire clinic populations with outcome monitoring, and intervention modifications based on challenges. A pre–post survey assessed residents’ confidence in QI skills and interdisciplinary team participation, while quarterly quality data assessed patient outcomes. All 150 internal medicine residents participated. Confidence in QI and interdisciplinary team participation improved significantly ( P < .001). Patient outcomes improved for 6 of 9 targeted projects and were sustained at 1 year. This curriculum is a systems-based innovation designed to improve patient care and encourage interdisciplinary teamwork and can be adopted by residencies seeking to improve engagement in QI.

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Background Resident continuity-clinic (RCC) is a crucial component of ambulatory training in primary care.The no-show rate (NSR) in a large academic center with 60 residents averaged 27% in academic year (AY) 2018, despite an automated phone/text reminder system 3 days prior to appointment, resulting in fragmented care, reduced access and decreased learning opportunities for residents. Objectives To determine whether telephone outreach targeting patients predicted to be at high-risk to no-show can reduce NSR for RCC appointments. Methods A validated machine-learning prediction model developed by data scientists at UPMC for Primary care, generated a daily list of high-risk patients (i.e. =20% risk to no-show).

BackgroundUPP-GIM, a large urban academic primary-care practice and a patient-centered medical home, treats approximately 1800 diabetics. Despite multiple interventions, the number of patients with A1C=9% remained at 14%, and dilated retina exam (DRE) rate was only 64%.Objectives1. Create a comprehensive team-based model that engages patients throughout their continuum of care, focusing on patients with A1C=9%.2. Incrementally improve glycemic control, diabetes outcomes and DRE rate.MethodsIn January 2014, a multi-disciplinary diabetes team was created to redesign care. A comprehensive care model was established and implemented in phases. All clinic staff and physicians participated. It included engaging patients with a pre-visit phone call, improving at-visit efficiency to maximize addressing all patient needs, including downloading and printing glucose meter reports and adding tele-ophthalmology, and intensifying inter-visit care with algorithm-based medication titration. (see figure)Processes and outcomes were tracked over 18 months. Control charts were used to evaluate processes and outcome measures as phases were implemented and quarterly reports for outcomes.ResultsMean A1C in the cohort of patients starting with A1C=9% (n=246) improved from 10.8 (±1.7) to 9.5 (±1.9). Appointment show rate improved from 68% to 73%.Encounters with diabetic educators rose from 20% to 54%. Meter download compliance reached almost 90% and DRE rates improved from 64% to 76%.ConclusionsEngaging uncontrolled diabetic patients before, during, and after visits with a team-based approach and novel day-of-visit efficiencies improved glycemic control and eye exam rates. Continued monitoring is needed to ensure the durability of these gains.Figure 1Comprehensive Care Model: Phases and Timeline.Figure 2Patient characteristics.Figure 3Control Charts showing results of Pre-Visit planning (PVP), Glucometer downloads (At-Visit), Trend of A1C ≥9%, and eye exam rates in our diabetes population.Figure 4Insulin Titration Algorithm used by Diabetic educators for inter-visit care.