Journal of General Internal Medicine

Since FDA approval of HIV pre-exposure prophylaxis (PrEP) for HIV prevention, attention has been focused on PrEP implementation. The CDC estimates that 1.2 million U.S. adults might benefit from PrEP, but only a minority are using PrEP, so there is a significant unmet need to increase access for those at risk for HIV. Given the large numbers of individuals who have indications for PrEP, there are not enough practicing specialists to meet the growing need for providers trained in providing PrEP. Moreover, since PrEP is a preventive intervention for otherwise healthy individuals, primary care providers (PCPs) should be primary prescribers of PrEP. There are important clinical considerations that providers should take into account when planning to prescribe PrEP, which are highlighted in the clinical case discussed. A growing body of research also suggests that some providers may be cautious about prescribing PrEP because of concerns regarding its “real-world” effectiveness, anticipated unintended consequences associated with its use, and ambiguity as to who should prescribe it. This review summarizes findings from studies that have assessed prescriber behavior regarding provision of PrEP, and offers recommendations on how to optimize PrEP implementation in primary care settings. Development and dissemination of educational interventions for PCPs and potential PrEP users are needed, including improved methods to assist clinicians in identifying appropriate PrEP candidates, and programs to promote medication adherence and access to social and behavioral health services. PCPs are well-positioned to prescribe PrEP and coordinate health-related services to improve the sexual health of their patients, but tailored educational programs are needed.

Disease management (DM) has been promoted to improve health outcomes and lower costs for patients with chronic disease. Unfortunately, most of the studies that support claims of DM’s success suffer from a number of biases, the most important of which is selection bias, or bias in the type of patients enrolling. To quantify the differences between those who do and do not enroll in DM. This was an observational study of the health care use, costs, and quality of care of 27,211 members of a large health insurer who were identified through claims as having asthma, diabetes, or congestive heart failure, were considered to be at high risk for incurring significant claims costs, and were eligible to join a disease management program involving health coaching. We used health coach call records to determine which patients participated in at least one coaching call and which refused to participate. We used claims data for the 12 months before the start of intervention to tabulate costs and utilization metrics. In addition, we calculated HEDIS quality scores for the year prior to the start of intervention. The patients who enrolled in the DM program differed significantly from those who did not on demographic, cost, utilization and quality parameters prior to enrollment. For example, compared to non-enrollees, diabetes enrollees had nine more prescriptions per year and higher HbA1c HEDIS scores (0.70 vs. 0.61, p < 0.001). These findings illuminate the serious problem of selection into DM programs and suggest that the effectiveness levels found in prior evaluations using methodologies that don’t address this may be overstated.

Medical students preparing for the United States Medical Licensing Exam (USMLE) Step 2 Clinical Knowledge (CK) Exam frequently use the UWorld Step 2 CK Question Bank (QBank). Over 90% of medical students use UWorld QBanks to prepare for at least one USMLE. Although several questions in the QBank mention race, ethnicity, or immigration status, their contributions to the QBank remain underexamined. We conducted a systematic, mixed-methods content analysis to assess whether and how disease conditions might be racialized throughout this popular medical education resource. We screened 3537 questions in the QBank between May 28 and August 11, 2020, for mentions of race, ethnicity, or immigration status. We performed multinomial logistic regression to assess the likelihood of each racial/ethnic category occurring in either the question stem, answer explanation, or both. We used an inductive technique for codebook development and determined code frequencies. We reviewed the frequency and distribution of race or ethnicity in question stems, answer choices, and answer explanations; assessed associations between disease conditions and racial and ethnic categories; and identified whether and how these associations correspond to race-, ethnicity-, or migration-based care. References to Black race occurred most frequently, followed by Asian, White, and Latinx groups. Mentions of race/ethnicity varied significantly by location in the question: Asian race had 6.40 times greater odds of occurring in the answer explanation only (95% CI 1.19–34.49; p < 0.031) and White race had 9.88 times greater odds of occurring only in the question stem (95% CI 2.56–38.08; p < 0.001). Qualitative analyses suggest frequent associations between disease conditions and racial, ethnic, and immigration categories, which often carry implicit or explicit biological and genetic explanations. Our analysis reveals patterns of race-based disease associations that have potential for systematic harm, including promoting incorrect race-based associations and upholding cultural conventions of White bodies as normative.

Evidence-based programs such as mailed fecal immunochemical test (FIT) outreach can only affect health outcomes if they can be successfully implemented. However, attempts to implement programs are often limited by organizational-level factors. As part of the Strategies and Opportunities to Stop Colon Cancer in Priority Populations (STOP CRC) pragmatic trial, we evaluated how organizational factors impacted the extent to which health centers implemented a mailed FIT outreach program. Eight health centers participated in STOP CRC. The intervention consisted of customized electronic health record tools and clinical staff training to facilitate mailing of an introduction letter, FIT kit, and reminder letter. Health centers had flexibility in how they delivered the program. We categorized the health centers’ level of implementation based on the proportion of eligible patients who were mailed a FIT kit, and applied configurational comparative methods to identify combinations of relevant organizational-level and program-level factors that distinguished among high, medium, and low implementing health centers. The factors were categorized according to the Consolidated Framework for Implementation Research model. FIT tests were mailed to 21.0–81.7% of eligible participants at each health center. We identified a two-factor solution that distinguished among levels of implementation with 100% consistency and 100% coverage. The factors were having a centralized implementation team (inner setting) and mailing the introduction letter in advance of the FIT kit (intervention characteristics). Health centers with high levels of implementation had the joint presence of both factors. In health centers with medium levels of implementation, only one factor was present. Health centers with low levels of implementation had neither factor present. Full implementation of the STOP CRC intervention relied on a centralized implementation team with dedicated staffing time, and the advance mailing of an introduction letter. ClinicalTrials.gov Identifier: NCT01742065 Registered 05 December 2012–Prospectively registered

The term “holdover admissions” refers to patients admitted by an overnight physician and whose care is then transferred to a new primary team the next morning. Descriptions of the holdover process in internal medicine are sparse. To identify important factors affecting the quality of holdover handoffs at an internal medicine (IM) residency program and to compare them to previously identified factors for other handoffs. We undertook a qualitative study using structured focus groups and interviews. We analyzed data using qualitative content analysis. IM residents, IM program directors, and hospitalists at a large academic medical center. A nine-question open-ended interview guide. We identified 13 factors describing holdover handoffs. Five factors—physical space, standardization, task accountability, closed-loop verification, and resilience—were similar to those described in prior handoff literature in other specialties. Eight factors were new concepts that may uniquely affect the quality of the holdover handoff in IM. These included electronic health record access, redundancy, unwritten thoughts, different clinician needs, diagnostic uncertainty, anchoring, teaching, and feedback. These factors were organized into five overarching themes: physical environment, information transfer, responsibility, clinical reasoning, and education. The holdover handoff in IM is complex and has unique considerations for achieving high quality. Further exploration of safe, efficient, and educational holdover handoff practices is necessary.