Journal of General Internal Medicine最新文献

Guidelines recommend chlordiazepoxide or diazepam over lorazepam for alcohol withdrawal due to lorazepam's increased risk of breakthrough symptoms and rebound phenomena, including seizures and delirium. Additionally, diazepam has a shorter time to peak effect than lorazepam, enabling more rapid symptom control and accurate titration to avoid over-sedation. The efficacy of chlordiazepoxide and diazepam for alcohol withdrawal was well established by the mid-1970s, while lorazepam was not approved in the USA until 1977. Despite its inferiority and later approval, lorazepam is the most widely used benzodiazepine for Emergency Department and inpatient alcohol withdrawal management. Therefore, it is notable that the first three studies comparing lorazepam to chlordiazepoxide and diazepam, published in 1983 and 1984, all concluded lorazepam is the "drug of choice." This conclusion is anomalous, as some lorazepam-treated patients developed seizures or delirium tremens, complications not observed with chlordiazepoxide or diazepam. These adverse outcomes were not disclosed in abstracts or discussions but seemingly intentionally concealed within the text. Evidence reveals these studies were ghostwritten by Wyeth, the pharmaceutical company that originally marketed lorazepam. Wyeth sales representatives likely distributed reprint copies of these deceptive "drug of choice" papers to physicians and physicians-in-training, thereby initiating the widespread reiterative intergenerational use of lorazepam for alcohol withdrawal.

Background: Addiction medical care is a core competency for internal medicine residents, yet most hospital systems lack an addiction medicine consult service to provide residents with practice-based learning opportunities.

Aim: Provide medical residents with clinical training in evidence-based approaches to treating substance use disorders.

Setting: Large, urban, academic tertiary care internal medicine residency program PARTICIPANTS: Internal medicine residents on inpatient services PROGRAM DESCRIPTION: Creation of an "educational consult" program where residents caring for patients with substance use disorders discuss cases with an addiction medicine provider to learn patient-relevant addiction medicine principles and management techniques PROGRAM EVALUATION: Over one academic year, 44 educational consults for residents were completed involving 50 unique trainees, mostly focusing on opioid (37, 84%) and alcohol (11, 25%) related topics. Follow-up survey data showed high levels of satisfaction (average 4.9/5 using a Likert scale), with 70% of respondents (20 of 28) reporting that the consult resulted in changes to patient care and 50% of respondents (14 of 28) reporting that the consult changed their approach to future patients.

Discussion: Educational consults are a feasible tool to provide trainees at the graduate medical education level with advanced clinical exposures in settings lacking addiction medicine consult services.

Background: Low-threshold care (LTC) practices for prescribing medication for opioid use disorder (MOUD) systematically remove treatment barriers, increasing access to lifesaving MOUD. Despite its promise, LTC operationalization is unclear and heterogeneous, and lacks standardized measures.

Objective: To develop and test LTC composite measures as useful predictors of implementation outcomes.

Design: This prospective cohort study was embedded within a California state MOUD practice change collaborative involving safety-net primary care clinics.

Participants: Data were collected at baseline, midpoint, and endpoint from 20 clinics.

Intervention: Clinics received a multifaceted implementation-support package designed to improve MOUD delivery.

Main measures: Four LTC scales (LTC12, LTC5, LTC3, LTC2) were developed and tested using team-reported one to five Likert items. Implementation outcomes included Reach (monthly new MOUD patients), Retention (monthly new MOUD patients engaged in treatment after initial diagnosis), and Adoption (active MOUD prescribers). Analyses included repeated-measures ANOVA for LTC change and Poisson GEE for incidence rate ratios, adjusting for panel size, medically underserved area designation, and time.

Key results: Clinics showed significant improvements in LTC scores over time. The LTC12 scale demonstrated the largest effect size (d = 1.18, p = .003). A 1-point increase on the LTC3 index was associated with a 37% increase in new patients receiving MOUD (IRR = 1.37, 95% CI [1.01,1.86], p = .047). A 1-point increase on LTC2 was associated with a 24% increase (IRR = 1.24, 95% CI [1.01,1.53], p = 0.049).

Conclusions: Our findings provide preliminary empirical support for a replicable measure of LTC in primary care settings. Longer scales showed greater internal consistency and sensitivity to change, while brief scales predicted patient reach outcomes. These measures may be useful for clinical programs to gauge the extent to which their MOUD services align with low threshold care principles and to guide quality improvement efforts. Future research should validate these scales in larger, diverse cohorts and test causal impact.

Background: Deprescribing reduces potentially harmful or unnecessary medications in older adults. Successful implementation requires understanding both patient and provider perspectives, although few studies have compared their preferences.

Objective: To examine general practitioner (GP) and patient preferences for deprescribing and medication-related decision-making by investigating factors associated with preference concordance and the relationship with patient-provider trust.

Design: Cross-sectional survey study conducted in primary care settings within the German-speaking region of Switzerland.

Participants: Sixty-five patients aged ≥ 65 years taking ≥ 5 medications and their 10 GPs completed questionnaires.

Main measures: Patient and GP preferences for deprescribing medications were assessed through two questions; for patients: "Thinking about your current medication list, are there any medications that you would like to stop taking or reduce the dose of?" and for GPs: "Would you stop or reduce the dose of any of the medications that the patient is currently taking?" We assessed concordance between GPs' and patients' deprescribing preferences and analyzed associations between trust and deprescribing preferences using univariate Generalized Estimating Equations with Poisson distribution and finite-sample correction, accounting for clustering at the GP level.

Key results: Similar proportions of patients (38%) and GPs (35%) wanted to deprescribe at least one medication, but only eight GP-patient dyads wanted to deprescribe, with just one dyad selecting the same medication. The most frequently identified medications to deprescribe were dietary supplements (GPs = 11/42, 26%; patients = 4/35, 11%), cardiovascular system medications (GPs = 9/42, 21%; patients = 15/35, 43%), and nervous system medications (GPs = 8/42, 19%; patients = 9/35, 26%). GPs' primary reason for not deprescribing was believing patients wanted to continue their medications (83%), while patients believed doctors only prescribe necessary ones (38%).

Conclusions: Despite similar interest in deprescribing, GPs and patients rarely selected the same medications to stop. These findings suggest that clear discussions about medication necessity and preferences could improve deprescribing decisions.

Background: In July 2022, the Ohio Department of Medicaid (ODM) launched a statewide Regional Quality Improvement (QI) Hub program using a hub-and-spoke model to embed Learning Health System (LHS) capabilities in primary care. The Ohio State University (OSU) Hub piloted the approach across ten affiliated sites, aiming to raise overall hypertension control by ≥ 10 percentage points and promote more consistent levels of hypertension control across patient populations by June 2027. This report describes the first 18 months post-implementation.

Methods: A mixed-methods, formative evaluation followed the Exploration-Preparation-Implementation-Sustainment (EPIS) framework. The hub provided centralized data extraction (bi-weekly Epic pulls), an R Shiny dashboard, and tailored coaching on Plan-Do-Study-Act cycles (PDSA) while participating in statewide learning collaboratives. Blood pressure (BP) control rates were tracked with statistical process control (SPC) charts. Site-level logistic regressions tested interaction effects between demographics (non-Hispanic Black vs. White) and implementation period.

Results: Among 22,563 hypertensive adults (73,264 encounters), the baseline centerline was 72.9% controlled BP. Two SPC shifts, April 2024 to 77.3% and July 2024 to 79.1%, produced a + 6.2 percentage-point (+ 8.5%) relative improvement, equating to ~ 4542 additional controlled BP encounters beyond baseline expectations. Site-level absolute changes ranged from -3.9% to + 14.6%. Across sites, six narrowed and four widened the Black-White BP control gap. Site-level sensitivity analysis showed no significant change in the gap. Qualitative data highlighted the importance of near real-time feedback and flexible coaching; staffing turnover constrained progress in several under-resourced clinics.

Conclusion: Implementing a hub-and-spoke LHS model within a large academic health system was feasible and associated with sustained gains in hypertension control over 18 months. Centralized analytics, adaptive QI support, and statewide peer learning were key enablers, whereas workforce instability remained a barrier. Early results support the model's potential to build LHS capacity in primary care, with ongoing work needed to strengthen uniform impact and long-term sustainability.