A realist evaluation of prospective entrustment decisions in paediatric residency clinical competency committees.

PURPOSE: Making entrustment decisions (granting more responsibility, advancement and graduation) are important actions in medical training that pose risks to trainees and patients if not done well. A previous realist synthesis of the existing literature revealed that clinical competency committees (CCCs) do not typically make deliberate entrustment decisions, instead defaulting to the promotion and graduation of trainees in the absence of red flags. This study sought further understanding of these areas through empirical data. METHODS: The authors conducted a realist inquiry to better understand how CCC prospective entrustment decision-making is carried out in paediatric residency programs. They conducted four CCC meeting observations and 18 interviews with CCC members at eight sites in an effort to confirm, disconfirm, and elaborate an existing theory that was based on a literature synthesis. RESULTS: The literature-based theory held up well against the empiric data collected in this study. Therefore, the authors did not modify that theory and instead developed three new demi-regularities (recurring patterns in data when conducting realist work) that add detail and nuance to their previous understanding of this model. These new demi-regularities focus on (i) expounding on how deliberate actions of CCCs focus more on resident development than on resident entrustment; (ii) elucidating that effortful work is not only about reconciling a paucity of data or incongruent data but also working hard to 'do the right thing' for residents; and (iii) describing how programs consider bias, equity and fairness, with a wide range of intentionality from being reactive to being proactive. CONCLUSION: This study offers evidence of deliberate CCC efforts to support resident development. Moving forward, a similar focus should be more consistently placed on equitable entrustment and advancement decisions to balance both of these foundational goals.

Competency-based medical education: Connecting training outcomes to patient care.

Competency-based medical education (CBME) is a patient-centered and learner-focused approach to education where curricula are delivered in a manner tailored to the individuals' learning needs, and assessment focuses on ensuring trainees achieve requisite and clearly specified learning outcomes. Despite calls to focus assessment on what matters for patients. In this article, the authors explore one aspect of this next era: the use of electronic health record clinical performance indicators, such as Resident-Sensitive Quality Measures (RSQMs) and TRainee Attributable and Automatable Care Evaluations in Real-time (TRACERs), for learner assessment. They elaborate on both the promise and the potential limitations of using such measures in a program of learner assessment.

How do validity experts conceptualise argumentation? It's a rhetorical question.

INTRODUCTION: Health professions education (HPE) has adopted the conceptualization of validity as an argument. However, the theoretical and practical aspects of how validity arguments should be developed, used and evaluated in HPE have not been deeply explored. Articulating the argumentation theory undergirding validity and validation can help HPE better operationalise validity as an argument. To better understand this, the authors explored how HPE validity scholars conceptualise assessment validity arguments and argumentation, seeking to understand potential consequences of these views on validation practices. METHODS: The authors used critical case sampling to identify HPE assessment validity experts in three ways: (1) participation in a prominent validity research group, (2) appearing in a bibliometric study of HPE validity publications and (3) authorship of recent HPE validity literature. Qualitative semi-structured interviews were conducted with 16 experts in HPE assessment validity from four different countries. The authors used reflexive thematic analysis to develop themes relevant to their research question. RESULTS: The authors developed three themes grounded in participants' responses: (1) In theory, HPE validity is a social and situated argument. (2) In practice, the absence of audience and evaluation stymies the social nature of HPE validity. (3) Lack of validity argumentation creates and maintains power differentials within HPE. Participants articulated that current HPE validation practices are rooted in post-positivist epistemology when they should be situated (i.e. context-dependent), audience-centric and inclusive. DISCUSSION: When discussing validity argumentation in theory, participants' descriptions reflect an interpretivist lens for evaluation that is misaligned with real-world validity practices. This misalignment likely arises from HPE's adoption of "validity as an argument" as a slogan, without integrating theoretical and practical principles of argumentation theory.

Ambulatory Long Block: A Model of Precision Education and Assessment for Internal Medicine Residents.

ABSTRACT: High-quality precision education (PE) aims to enhance outcomes for learners and society by incorporating longitudinal data and analytics to shape personalized learning strategies. However, existing educational data collection methods often suffer from fragmentation, leading to gaps in understanding learner and program performance. In this article, the authors present a novel approach to PE at the University of Cincinnati, focusing on the Ambulatory Long Block, a year-long continuous ambulatory group-practice experience. Over the last 17 years, the Ambulatory Long Block has evolved into a sophisticated data collection and analysis system that integrates feedback from various stakeholders, as well as learner self-assessment, electronic health record utilization information, and clinical throughput metrics. The authors detail their approach to data prioritization, collection, analysis, visualization, and feedback, providing a practical example of PE in action. This model has been associated with improvements in both learner performance and patient care outcomes. The authors also highlight the potential for real-time data review through automation and emphasize the importance of collaboration in advancing PE. Generalizable principles include designing learning environments with continuity as a central feature, gathering both quantitative and qualitative performance data from interprofessional assessors, using this information to supplement traditional workplace-based assessments, and pairing it with self-assessments. The authors advocate for criterion referencing over normative comparisons, using user-friendly data visualizations, and employing tailored coaching strategies for individual learners. The Ambulatory Long Block model underscores the potential of PE to drive improvements in medical education and health care outcomes.

Using Theory-Informed Group Processes to Make TIMELESS Promotion Decisions.

ABSTRACT: Competency-based medical education (CBME) depends on effective programs of assessment to achieve the desired outcomes and goals of training. Residency programs must be able to defend clinical competency committee (CCC) group decisions about learner readiness for practice, including decisions about time-variable resident promotion and graduation. In this article, the authors describe why CCC group decision-making processes should be supported by theory and review 3 theories they used in designing their group processes: social decision scheme theory, functional theory, and wisdom of crowds. They describe how these theories were applied in a competency-based, time-variable training pilot-Transitioning in Internal Medicine Education Leveraging Entrustment Scores Synthesis (TIMELESS) at the University of Cincinnati internal medicine residency program in 2020-2022-to increase the defensibility of their CCC group decision-making. This work serves as an example of how use of theory can bolster validity arguments supporting group decisions about resident readiness for practice.

Competency-based medical education: The spark to ignite healthcare's escape fire.

High-value care is what patients deserve and what healthcare professionals should deliver. However, it is not what happens much of the time. Quality improvement master Dr. Don Berwick argued more than two decades ago that American healthcare needs an escape fire, which is a new way of seeing and acting in a crisis situation. While coined in the U.S. context, the analogy applies in other Western healthcare contexts as well. Therefore, in this paper, the authors revisit Berwick's analogy, arguing that medical education can, and should, provide the spark for such an escape fire across the globe. They assert that medical education can achieve this by fully embracing competency-based medical education (CBME) as a way to place medicine's focus on the patient. CBME targets training outcomes that prepare graduates to optimize patient care. The authors use the escape fire analogy to argue that medical educators must drop long-held approaches and tools; treat CBME implementation as an adaptive challenge rather than a technical fix; demand genuine, rich discussions and engagement about the path forward; and, above all, center the patient in all they do.

Lessons From Organic Chemistry: The Case for Considering Both High Standards and Equity in Assessment.

ABSTRACT: In this commentary, the authors explore the tension of balancing high performance standards in medical education with the acceptability of those standards to stakeholders (e.g., learners and patients). The authors then offer a lens through which this tension might be considered and ways forward that focus on both patient outcomes and learner needs.In examining this phenomenon, the authors argue that high performance standards are often necessary. Societal accountability is key to medical education, with the public demanding that training programs prepare physicians to provide high-quality care. Medical schools and residency programs, therefore, require rigorous standards to ensure graduates are ready to care for patients. At the same time, learners' experience is important to consider. Making sure that performance standards are acceptable to stakeholders supports the validity of assessment decisions.Equity should also be central to program evaluation and validity arguments when considering performance standards. Currently, learners across the continuum are variably prepared for the next phase in training and often face inequities in resource availability to meet high passing standards, which may lead to learner attrition. Many students who face these inequities come from underrepresented or disadvantaged backgrounds and are essential to ensuring a diverse medical workforce to meet the needs of patients and society. When these students struggle, it contributes to the leaky pipeline of more socioeconomically and racially diverse applicants.The authors posit that 4 key factors can balance the tension between high performance standards and stakeholder acceptability: standards that are acceptable and defensible, progression that is time variable, requisite support structures that are uniquely tailored for each learner, and assessment systems that are equitably designed.

The Next Era of Assessment: Can Ensuring High-Quality, Equitable Patient Care Be the Defining Characteristic?

ABSTRACT: 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.

Finding Medicine's Moneyball: How Lessons From Major League Baseball Can Advance Assessment in Precision Education.

ABSTRACT: Precision education (PE) leverages longitudinal data and analytics to tailor educational interventions to improve patient, learner, and system-level outcomes. At present, few programs in medical education can accomplish this goal as they must develop new data streams transformed by analytics to drive trainee learning and program improvement. Other professions, such as Major League Baseball (MLB), have already developed extremely sophisticated approaches to gathering large volumes of precise data points to inform assessment of individual performance.In this perspective, the authors argue that medical education-whose entry into precision assessment is fairly nascent-can look to MLB to learn the possibilities and pitfalls of precision assessment strategies. They describe 3 epochs of player assessment in MLB: observation, analytics (sabermetrics), and technology (Statcast). The longest tenured approach, observation, relies on scouting and expert opinion. Sabermetrics brought new approaches to analyzing existing data in a way that better predicted which players would help the team win. Statcast created precise, granular data about highly attributable elements of player performance while helping to account for nonplayer factors that confound assessment such as weather, ballpark dimensions, and the performance of other players. Medical education is progressing through similar epochs marked by workplace-based assessment, learning analytics, and novel measurement technologies. The authors explore how medical education can leverage intersectional concepts of MLB player and medical trainee assessment to inform present and future directions of PE.

A realist synthesis of prospective entrustment decision making by entrustment or clinical competency committees.

INTRODUCTION: The real-world mechanisms underlying prospective entrustment decision making (PEDM) by entrustment or clinical competency committees (E/CCCs) are poorly understood. To advance understanding in this area, the authors conducted a realist synthesis of the published literature to address the following research question: In E/CCC efforts to make defensible prospective entrustment decisions (PEDs), what works, for whom, under what circumstances and why? METHODS: Realist work seeks to understand the contexts (C), mechanisms (M) and outcomes (O) that explain how and why things work (or do not). In the authors' study, contexts included individual E/CCC members, E/CCC structures and processes, and training programmes. The outcome (i.e. desired outcome) was a PED. Mechanisms were a substantial focus of the analysis and informed the core findings. To define a final corpus of 52 included papers, the authors searched four databases, screened all results from those searches and performed a full-text review of a subset of screened papers. Data extraction focused on developing context-mechanism-outcome configurations from the papers, which were used to create a theory for how PEDM leads to PEDs. RESULTS: PEDM is often driven by default (non-deliberate) decision making rather than a deliberate process of deciding whether a trainee should be entrusted or not. When defaulting, some E/CCCs find red flags that sometimes lead to being more deliberate with decision making. E/CCCs that seek to be deliberate describe PEDM that can be effortful (when data are insufficient or incongruent) or effortless (when data are robust and tell a congruent story about a trainee). Both information about trainee trustworthiness and the sufficiency of data about trainee performance influence PEDM. Several moderators influence what is considered to be sufficient data, how trustworthiness data are viewed and how PEDM is carried out. These include perceived consequences and associated risks, E/CCC member trust propensity, E/CCC member personal knowledge of and experience with trainees and E/CCC structures and processes. DISCUSSION: PEDM is rarely deliberate but should be. Data about trainee trustworthiness are foundational to making PEDs. Bias, equity and fairness are nearly absent from the papers in this synthesis, and future efforts must seek to advance understanding and practice regarding the roles of bias, equity and fairness in PEDM.

AI-Enabled Medical Education: Threads of Change, Promising Futures, and Risky Realities Across Four Potential Future Worlds.

BACKGROUND: The rapid trajectory of artificial intelligence (AI) development and advancement is quickly outpacing society's ability to determine its future role. As AI continues to transform various aspects of our lives, one critical question arises for medical education: what will be the nature of education, teaching, and learning in a future world where the acquisition, retention, and application of knowledge in the traditional sense are fundamentally altered by AI? OBJECTIVE: The purpose of this perspective is to plan for the intersection of health care and medical education in the future. METHODS: We used GPT-4 and scenario-based strategic planning techniques to craft 4 hypothetical future worlds influenced by AI's integration into health care and medical education. This method, used by organizations such as Shell and the Accreditation Council for Graduate Medical Education, assesses readiness for alternative futures and effectively manages uncertainty, risk, and opportunity. The detailed scenarios provide insights into potential environments the medical profession may face and lay the foundation for hypothesis generation and idea-building regarding responsible AI implementation. RESULTS: The following 4 worlds were created using OpenAI's GPT model: AI Harmony, AI conflict, The world of Ecological Balance, and Existential Risk. Risks include disinformation and misinformation, loss of privacy, widening inequity, erosion of human autonomy, and ethical dilemmas. Benefits involve improved efficiency, personalized interventions, enhanced collaboration, early detection, and accelerated research. CONCLUSIONS: To ensure responsible AI use, the authors suggest focusing on 3 key areas: developing a robust ethical framework, fostering interdisciplinary collaboration, and investing in education and training. A strong ethical framework emphasizes patient safety, privacy, and autonomy while promoting equity and inclusivity. Interdisciplinary collaboration encourages cooperation among various experts in developing and implementing AI technologies, ensuring that they address the complex needs and challenges in health care and medical education. Investing in education and training prepares professionals and trainees with necessary skills and knowledge to effectively use and critically evaluate AI technologies. The integration of AI in health care and medical education presents a critical juncture between transformative advancements and significant risks. By working together to address both immediate and long-term risks and consequences, we can ensure that AI integration leads to a more equitable, sustainable, and prosperous future for both health care and medical education. As we engage with AI technologies, our collective actions will ultimately determine the state of the future of health care and medical education to harness AI's power while ensuring the safety and well-being of humanity.

User-Centered Evaluation and Design Recommendations for an Internal Medicine Resident Competency Assessment Dashboard.

OBJECTIVES: Clinical Competency Committee (CCC) members employ varied approaches to the review process. This makes the design of a competency assessment dashboard that fits the needs of all members difficult. This work details a user-centered evaluation of a dashboard currently utilized by the Internal Medicine Clinical Competency Committee (IM CCC) at the University of Cincinnati College of Medicine and generated design recommendations. METHODS: Eleven members of the IM CCC participated in semistructured interviews with the research team. These interviews were recorded and transcribed for analysis. The three design research methods used in this study included process mapping (workflow diagrams), affinity diagramming, and a ranking experiment. RESULTS: Through affinity diagramming, the research team identified and organized opportunities for improvement about the current system expressed by study participants. These areas include a time-consuming preprocessing step, lack of integration of data from multiple sources, and different workflows for each step in the review process. Finally, the research team categorized nine dashboard components based on rankings provided by the participants. CONCLUSION: We successfully conducted user-centered evaluation of an IM CCC dashboard and generated four recommendations. Programs should integrate quantitative and qualitative feedback, create multiple views to display these data based on user roles, work with designers to create a usable, interpretable dashboard, and develop a strong informatics pipeline to manage the system. To our knowledge, this type of user-centered evaluation has rarely been attempted in the medical education domain. Therefore, this study provides best practices for other residency programs to evaluate current competency assessment tools and to develop new ones.