Faculty Bibliography

Medical education assessment faces multifaceted challenges, including data complexity, resource constraints, bias, feedback translation, and educational continuity. Traditional approaches often fail to adequately address these issues, creating stressful and inequitable learning environments. This article introduces the concept of precision education, a data-driven paradigm aimed at personalizing the educational experience for each learner. It explores how artificial intelligence (AI), including its subsets machine learning (ML) and deep learning (DL), can augment this model to tackle the inherent limitations of traditional assessment methods.AI can enable proactive data collection, offering consistent and objective assessments while reducing resource burdens. It has the potential to revolutionize not only competency assessment but also participatory interventions, such as personalized coaching and predictive analytics for at-risk trainees. The article also discusses key challenges and ethical considerations in integrating AI into medical education, such as algorithmic transparency, data privacy, and the potential for bias propagation.AI's capacity to process large datasets and identify patterns allows for a more nuanced, individualized approach to medical education. It offers promising avenues not only to improve the efficiency of educational assessments but also to make them more equitable. However, the ethical and technical challenges must be diligently addressed. The article concludes that embracing AI in medical education assessment is a strategic move toward creating a more personalized, effective, and fair educational landscape. This necessitates collaborative, multidisciplinary research and ethical vigilance to ensure that the technology serves educational goals while upholding social justice and ethical integrity.

Precision education (PE) systematically leverages data and advanced analytics to inform educational interventions that, in turn, promote meaningful learner outcomes. PE does this by incorporating analytic results back into the education continuum through continuous feedback cycles. These data-informed sequences of planning, learning, assessing, and adjusting foster competence and adaptive expertise. PE cycles occur at individual (micro), program (meso), or system (macro) levels. This article focuses on program- and system-level PE.Data for PE come from a multitude of sources, including learner assessment and program evaluation. The authors describe the link between these data and the vital role evaluation plays in providing evidence of educational effectiveness. By including prior program evaluation research supporting this claim, the authors illustrate the link between training programs and patient outcomes. They also describe existing national reports providing feedback to programs and institutions, as well as 2 emerging, multiorganization program- and system-level PE efforts. The challenges encountered by those implementing PE and the continuing need to advance this work illuminate the necessity for increased cross-disciplinary collaborations and a national cross-organizational data-sharing effort.Finally, the authors propose practical approaches for funding a national initiative in PE as well as potential models for advancing the field of PE. Lessons learned from successes by others illustrate the promise of these recommendations.

IMPORTANCE/UNASSIGNED:Identifying the mechanisms of structural racism, such as racial and ethnic segregation, is a crucial first step in addressing the persistent disparities in access to live donor kidney transplantation (LDKT). OBJECTIVE/UNASSIGNED:To assess whether segregation at the candidate's residential neighborhood and transplant center neighborhood is associated with access to LDKT. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:In this cohort study spanning January 1995 to December 2021, participants included non-Hispanic Black or White adult candidates for first-time LDKT reported in the US national transplant registry. The median (IQR) follow-up time for each participant was 1.9 (0.6-3.0) years. MAIN OUTCOME AND MEASURES/UNASSIGNED:Segregation, measured using the Theil H method to calculate segregation tertiles in zip code tabulation areas based on the American Community Survey 5-year estimates, reflects the heterogeneity in neighborhood racial and ethnic composition. To quantify the likelihood of LDKT by neighborhood segregation, cause-specific hazard models were adjusted for individual-level and neighborhood-level factors and included an interaction between segregation tertiles and race. RESULTS/UNASSIGNED:Among 162 587 candidates for kidney transplant, the mean (SD) age was 51.6 (13.2) years, 65 141 (40.1%) were female, 80 023 (49.2%) were Black, and 82 564 (50.8%) were White. Among Black candidates, living in a high-segregation neighborhood was associated with 10% (adjusted hazard ratio [AHR], 0.90 [95% CI, 0.84-0.97]) lower access to LDKT relative to residence in low-segregation neighborhoods; no such association was observed among White candidates (P for interaction = .01). Both Black candidates (AHR, 0.94 [95% CI, 0.89-1.00]) and White candidates (AHR, 0.92 [95% CI, 0.88-0.97]) listed at transplant centers in high-segregation neighborhoods had lower access to LDKT relative to their counterparts listed at centers in low-segregation neighborhoods (P for interaction = .64). Within high-segregation transplant center neighborhoods, candidates listed at predominantly minority neighborhoods had 17% lower access to LDKT relative to candidates listed at predominantly White neighborhoods (AHR, 0.83 [95% CI, 0.75-0.92]). Black candidates residing in or listed at transplant centers in predominantly minority neighborhoods had significantly lower likelihood of LDKT relative to White candidates residing in or listed at transplant centers located in predominantly White neighborhoods (65% and 64%, respectively). CONCLUSIONS/UNASSIGNED:Segregated residential and transplant center neighborhoods likely serve as a mechanism of structural racism, contributing to persistent racial disparities in access to LDKT. To promote equitable access, studies should assess targeted interventions (eg, community outreach clinics) to improve support for potential candidates and donors and ultimately mitigate the effects of segregation.

PURPOSE:The era of precision education is increasingly leveraging electronic health record (EHR) data to assess residents' clinical performance. But precision in what the EHR-based resident performance metrics are truly assessing is not fully understood. For instance, there is limited understanding of how EHR-based measures account for the influence of the team on an individual's performance-or conversely how an individual contributes to team performances. This study aims to elaborate on how the theoretical understandings of supportive and collaborative interdependence are captured in residents' EHR-based metrics. METHOD:Using a mixed methods study design, the authors conducted a secondary analysis of 5 existing quantitative and qualitative datasets used in previous EHR studies to investigate how aspects of interdependence shape the ways that team-based care is provided to patients. RESULTS:Quantitative analyses of 16 EHR-based metrics found variability in faculty and resident performance (both between and within resident). Qualitative analyses revealed that faculty lack awareness of their own EHR-based performance metrics, which limits their ability to act interdependently with residents in an evidence-informed fashion. The lens of interdependence elucidates how resident practice patterns develop across residency training, shifting from supportive to collaborative interdependence over time. Joint displays merging the quantitative and qualitative analyses showed that residents are aware of variability in faculty's practice patterns and that viewing resident EHR-based measures without accounting for the interdependence of residents with faculty is problematic, particularly within the framework of precision education. CONCLUSIONS:To prepare for this new paradigm of precision education, educators need to develop and evaluate theoretically robust models that measure interdependence in EHR-based metrics, affording more nuanced interpretation of such metrics when assessing residents throughout training.

Precision education (PE) uses personalized educational interventions to empower trainees and improve learning outcomes. While PE has the potential to represent a paradigm shift in medical education, a theoretical foundation to guide the effective implementation of PE strategies has not yet been described. Here, the authors introduce a theoretical foundation for the implementation of PE, integrating key learning theories with the digital tools that allow them to be operationalized. Specifically, the authors describe how the master adaptive learner (MAL) model, transformative learning theory, and self-determination theory can be harnessed in conjunction with nudge strategies and audit and feedback dashboards to drive learning and meaningful behavior change. The authors also provide practical examples of these theories and tools in action by describing precision interventions already in use at one academic medical center, concretizing PE's potential in the current clinical environment. These examples illustrate how a firm theoretical grounding allows educators to most effectively tailor PE interventions to fit individual learners' needs and goals, facilitating efficient learning and, ultimately, improving patient and health system outcomes.

Previous eras of assessment in medical education have been defined by how assessment is done, from knowledge exams popularized in the 1960s to the emergence of work-based assessment in the 1990s to current efforts to integrate multiple types and sources of performance data through programmatic assessment. Each of these eras was a response to why assessment was performed (e.g., assessing medical knowledge with exams; assessing communication, professionalism, and systems competencies with work-based assessment). Despite the evolution of assessment eras, current evidence highlights the graduation of trainees with foundational gaps in the ability to provide high-quality care to patients presenting with common problems, and training program leaders report they graduate trainees they would not trust to care for themselves or their loved ones. In this article, the authors argue that the next era of assessment should be defined by why assessment is done: to ensure high-quality, equitable care. Assessment should place focus on demanding graduates possess the knowledge, skills, attitudes, and adaptive expertise to meet the needs of all patients and ensuring that graduates are able to do this in an equitable fashion. The authors explore 2 patient-focused assessment approaches that could help realize the promise of this envisioned era: entrustable professional activities (EPAs) and resident sensitive quality measures (RSQMs)/TRainee Attributable and Automatable Care Evaluations in Real-time (TRACERs). These examples illustrate how the envisioned next era of assessment can leverage existing and new data to provide precision education assessment that focuses on providing formative and summative feedback to trainees in a manner that seeks to ensure their learning outcomes prepare them to ensure high-quality, equitable patient outcomes.

BACKGROUND:While recognized as a key HIV prevention strategy, preexposure prophylaxis (PrEP) availability and accessibility are not well documented globally. We aimed to describe PrEP drug registration status and the availability of PrEP services across HIV care sites participating in the International epidemiology Databases to Evaluate AIDS (IeDEA) research consortium. METHODS:We used country-level PrEP drug registration status from the AIDS Vaccine Advocacy Coalition and data from IeDEA surveys conducted in 2014, 2017 and 2020 among participating HIV clinics in seven global regions. We used descriptive statistics to assess PrEP availability across IeDEA sites serving adult patients in 2020 and examined trends in PrEP availability among sites that responded to all three surveys. RESULTS:Of 199 sites that completed the 2020 survey, PrEP was available in 161 (81%). PrEP availability was highest at sites in North America (29/30; 97%) and East Africa (70/74; 95%) and lowest at sites in Central (10/20; 50%) and West Africa (1/6; 17%). PrEP availability was higher among sites in countries where PrEP was officially registered (146/161; 91%) than where it was not (14/32; 44%). Availability was higher at health centers (109/120; 90%) and district hospitals (14/16; 88%) compared to regional/teaching hospitals (36/63). Among the 94 sites that responded to all three surveys, PrEP availability increased from 47% in 2014 to 60% in 2017 and 76% in 2020. CONCLUSION/CONCLUSIONS:PrEP availability has substantially increased since 2014 and is now available at most IeDEA sites. However, PrEP service provision varies markedly across global regions.

The goal of medical education is to produce a physician workforce capable of delivering high-quality equitable care to diverse patient populations and communities. To achieve this aim amidst explosive growth in medical knowledge and increasingly complex medical care, a system of personalized and continuous learning, assessment, and feedback for trainees and practicing physicians is urgently needed. In this perspective, the authors build on prior work to advance a conceptual framework for such a system: precision education (PE).PE is a system that uses data and technology to transform lifelong learning by improving personalization, efficiency, and agency at the individual, program, and organization levels. PE "cycles" start with data inputs proactively gathered from new and existing sources, including assessments, educational activities, electronic medical records, patient care outcomes, and clinical practice patterns. Through technology-enabled analytics, insights are generated to drive precision interventions. At the individual level, such interventions include personalized just-in-time educational programming. Coaching is essential to provide feedback and increase learner participation and personalization. Outcomes are measured using assessment and evaluation of interventions at the individual, program, and organizational level, with ongoing adjustment for repeated cycles of improvement. PE is rooted in patient, health system, and population data; promotes value-based care and health equity; and generates an adaptive learning culture.The authors suggest fundamental principles for PE, including promoting equity in structures and processes, learner agency, and integration with workflow (harmonization). Finally, the authors explore the immediate need to develop consensus-driven standards: rules of engagement between people, products, and entities that interact in these systems to ensure interoperability, data sharing, replicability, and scale of PE innovations.