Twelve tips for creating a longitudinal quality improvement and safety education for early health professions students.

Despite the numerous calls for integrating quality improvement and patient safety (QIPS) curricula into health professions education, there are limited examples of effective implementation for early learners. Typically, pre-clinical QIPS experiences involve lectures or lessons that are disconnected from the practice of medicine. Consequently, students often prioritize other content they consider more important. As a result, they may enter clinical settings without essential QIPS skills and struggle to incorporate these concepts into their early professional identity formation. In this paper, we present twelve tips aimed at assisting educators in developing QIPS education early in the curricula of health professions students. These tips address various key issues, including aligning incentives, providing longitudinal experiences, incorporating real-world care outcomes, optimizing learning environments, communicating successes, and continually enhancing education and care delivery processes.

Secure Messaging Use Among Patients with Depression: An Analysis Using Real-World Data.

Background: Although depression is one of the most common mental health disorders outpacing other diseases and conditions, poor access to care and limited resources leave many untreated. Secure messaging (SM) offers patients an online means to bridge this gap by communicating nonurgent medical questions. We focused on self-care health management behaviors and delved into SM initiation as the initial act of engagement and SM exchanges as continuous engagement patterns. This study examined whether those with depression might be using SM more than those without depression. Methods: Patient portal data were obtained from a large academic medical center's electronic health records spanning 5 years, from January 2018 to December 2022. We organized and analyzed SM initiations and exchanges using the linear mixed-effects modeling technique. Results: Our predictors correlated with SM initiations, accounting for 25.1% of variance explained. In parallel, 24.9% of SM exchanges were attributable to these predictors. Overall, our predictors demonstrate stronger associations with SM exchanges. Discussion: We examined patients with and without depression across 2,629 zip codes over five years. Our findings reveal that the predictors affecting SM initiations and exchanges are multifaceted, with certain predictors enhancing its utilization and others impeding it. Conclusions: SM telehealth service provided support to patients with mental health needs to a greater extent than those without. By increasing access, fostering better communication, and efficiently allocating resources, telehealth services not only encourage patients to begin using SM but also promote sustained interaction through ongoing SM exchanges.

Developing the Expected Entrustment Score: Accounting for Variation in Resident Assessment.

BACKGROUND: Clinical competency committees (CCCs) and residency program leaders may find it difficult to interpret workplace-based assessment (WBA) ratings knowing that contextual factors and bias play a large role. OBJECTIVE: We describe the development of an expected entrustment score for resident performance within the context of our well-developed Observable Practice Activity (OPA) WBA system. DESIGN: Observational study PARTICIPANTS: Internal medicine residents MAIN MEASURE: Entrustment KEY RESULTS: Each individual resident had observed entrustment scores with a unique relationship to the expected entrustment scores. Many residents' observed scores oscillated closely around the expected scores. However, distinct performance patterns did emerge. CONCLUSIONS: We used regression modeling and leveraged large numbers of historical WBA data points to produce an expected entrustment score that served as a guidepost for performance interpretation.

See One, Do One, Forget One: Early Skill Decay After Paracentesis Training.

INTRODUCTION: Internal medicine residents perform paracentesis, but programs lack standard methods for assessing competence or maintenance of competence and instead rely on number of procedures completed. This study describes differences in resident competence in paracentesis over time. METHODS: From 2016 to 2017, internal medicine residents (n = 118) underwent paracentesis simulation training. Competence was assessed using the Paracentesis Competency Assessment Tool (PCAT), which combines a checklist, global scale, and entrustment score. The PCAT also delineates two categorical cut-point scores: the Minimum Passing Standard (MPS) and the Unsupervised Practice Standard (UPS). Residents were randomized to return to the simulation lab at 3 and 6 months (group A, n = 60) or only 6 months (group B, n = 58). At each session, faculty raters assessed resident performance. Data were analyzed to compare resident performance at each session compared with initial training scores, and performance between groups at 6 months. RESULTS: After initial training, all residents met the MPS. The number achieving UPS did not differ between groups: group A = 24 (40%), group B = 20 (34.5%), p = 0.67. When group A was retested at 3 months, performance on each PCAT component significantly declined, as did the proportion of residents meeting the MPS and UPS. At the 6-month test, residents in group A performed significantly better than residents in group B, with 52 (89.7%) and 20 (34.5%) achieving the MPS and UPS, respectively, in group A compared with 25 (46.3%) and 2 (3.70%) in group B (p < .001 for both comparison). DISCUSSION: Skill in paracentesis declines as early as 3 months after training. However, retraining may help interrupt skill decay. Only a small proportion of residents met the UPS 6 months after training. This suggests using the PCAT to objectively measure competence would reclassify residents from being permitted to perform paracentesis independently to needing further supervision.

Development of Resident-Sensitive Quality Measures for Inpatient General Internal Medicine.

BACKGROUND: Graduate medical education (GME) training has long-lasting effects on patient care quality. Despite this, few GME programs use clinical care measures as part of resident assessment. Furthermore, there is no gold standard to identify clinical care measures that are reflective of resident care. Resident-sensitive quality measures (RSQMs), defined as "measures that are meaningful in patient care and are most likely attributable to resident care," have been developed using consensus methodology and piloted in pediatric emergency medicine. However, this approach has not been tested in internal medicine (IM). OBJECTIVE: To develop RSQMs for a general internal medicine (GIM) inpatient residency rotation using previously described consensus methods. DESIGN: The authors used two consensus methods, nominal group technique (NGT) and a subsequent Delphi method, to generate RSQMs for a GIM inpatient rotation. RSQMs were generated for specific clinical conditions found on a GIM inpatient rotation, as well as for general care on a GIM ward. PARTICIPANTS: NGT participants included nine IM and medicine-pediatrics (MP) residents and six IM and MP faculty members. The Delphi group included seven IM and MP residents and seven IM and MP faculty members. MAIN MEASURES: The number and description of RSQMs generated during this process. KEY RESULTS: Consensus methods resulted in 89 RSQMs with the following breakdown by condition: GIM general care-21, diabetes mellitus-16, hyperkalemia-14, COPD-13, hypertension-11, pneumonia-10, and hypokalemia-4. All RSQMs were process measures, with 48% relating to documentation and 51% relating to orders. Fifty-eight percent of RSQMs were related to the primary admitting diagnosis, while 42% could also be related to chronic comorbidities that require management during an admission. CONCLUSIONS: Consensus methods resulted in 89 RSQMs for a GIM inpatient service. While all RSQMs were process measures, they may still hold value in learner assessment, formative feedback, and program evaluation.

Growth mindset in competency-based medical education.

The ongoing adoption of competency-based medical education (CBME) across health professions training draws focus to learner-centred educational design and the importance of fostering a growth mindset in learners, teachers, and educational programs. An emerging body of literature addresses the instructional practices and features of learning environments that foster the skills and strategies necessary for trainees to be partners in their own learning and progression to competence and to develop skills for lifelong learning. Aligned with this emerging area is an interest in Dweck's self theory and the concept of the growth mindset. The growth mindset is an implicit belief held by an individual that intelligence and abilities are changeable, rather than fixed and immutable. In this paper, we present an overview of the growth mindset and how it aligns with the goals of CBME. We describe the challenges associated with shifting away from the fixed mindset of most traditional medical education assumptions and practices and discuss potential solutions and strategies at the individual, relational, and systems levels. Finally, we present future directions for research to better understand the growth mindset in the context of CBME.

Twelve tips to maximize the value of a clinical competency committee in postgraduate medical education.

Medical education has shifted to a competency-based paradigm, leading to calls for improved learner assessment methods and validity evidence for how assessment data are interpreted. Clinical competency committees (CCCs) use the collective input of multiple people to improve the validity and reliability of decisions made and actions taken based on assessment data. Significant heterogeneity in CCC structure and function exists across postgraduate medical education programs and specialties, and while there is no "one-size-fits-all" approach, there are ways to maximize value for learners and programs. This paper collates available evidence and the authors' experiences to provide practical tips on CCC purpose, membership, processes, and outputs. These tips can benefit programs looking to start a CCC and those that are improving their current CCC processes.

Entrustment and mapping of observable practice activities for resident assessment.

Entrustable Professional Activities (EPAs) and the Next Accreditation System reporting milestones reduce general competencies into smaller evaluable parts. However, some EPAs and reporting milestones may be too broad to use as direct assessment tools. We describe our internal medicine residency curriculum and assessment system, which uses entrustment and mapping of observable practice activities (OPAs) for resident assessment. We created discrete OPAs for each resident rotation and learning experience. In combination, these serve as curricular foundation and tools for assessment. OPA performance is measured via a 5-point entrustment scale, and mapped to milestones and EPAs. Entrustment ratings of OPAs provide an opportunity for immediate structured feedback of specific clinical skills, and mapping OPAs to milestones and EPAs can be used for longitudinal assessment, promotion decisions, and reporting. Direct assessment and demonstration of progressive entrustment of trainee skill over time are important goals for all training programs. Systems that use OPAs mapped to milestones and EPAs provide the opportunity for achieving both, but require validation.

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.

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.

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.

Reconstructing Neurath's Ship: A Case Study in Reevaluating Equity in a Program of Assessment.

Inequity in assessment has been described as a "wicked problem"-an issue with complex roots, inherent tensions, and unclear solutions. To address inequity, health professions educators must critically examine their implicit understandings of truth and knowledge (i.e., their epistemologies) with regard to educational assessment before jumping to solutions. The authors use the analogy of a ship (program of assessment) sailing on different seas (epistemologies) to describe their journey in seeking to improve equity in assessment. Should the education community repair the ship of assessment while sailing or should the ship be scrapped and built anew? The authors share a case study of a well-developed internal medicine residency program of assessment and describe efforts to evaluate and enable equity using various epistemological lenses. They first used a postpositivist lens to evaluate if the systems and strategies aligned with best practices, but found they did not capture important nuances of what equitable assessment entails. Next, they used a constructivist approach to improve stakeholder engagement, but found they still failed to question the inequitable assumptions inherent to their systems and strategies. Finally, they describe a shift to critical epistemologies, seeking to understand who experiences inequity and harm to dismantle inequitable systems and create better ones. The authors describe how each unique sea promoted different adaptations to their ship, and challenge programs to sail through new epistemological waters as a starting point for making their own ships more equitable.

How Does TIMELESS Training Impact Resident Motivation for Learning, Assessment, and Feedback? Evaluating a Competency-Based Time-Variable Training Pilot.

PURPOSE: As competency-based medical education has become the predominant graduate medical education training model, interest in time-variable training has grown. Despite multiple competency-based time-variable training (CBTVT) pilots ongoing in the United States, little is known about how this training approach impacts learners. The authors aim to explore how their CBTVT pilot program impacted resident motivation for learning, assessment, and feedback. METHOD: The authors performed a qualitative educational case study on the Transitioning in Internal Medicine Education Leveraging Entrustment Scores Synthesis (TIMELESS) program at the University of Cincinnati from October 2020 through March 2022. Semistructured interviews were conducted with TIMELESS residents (n = 9) approximately every 6 months to capture experiences over time. The authors used inductive thematic analysis to develop themes and compared their findings with existing theories of learner motivation. RESULTS: The authors developed 2 themes: TIMELESS had variable effects on residents' motivation for learning and TIMELESS increased resident engagement with and awareness of the program of assessment. Participants reported increased motivation to learn and seek assessment, though some felt a tension between performance (e.g., advancement through the residency program) and growth (e.g., improvement as a physician). Participants became more aware of the quality of assessments they received, in part due to TIMELESS increasing the perceived stakes of assessment, and reported being more deliberate when assessing other residents. CONCLUSIONS: Resident motivation for learning, assessment, and feedback was impacted in ways that the authors contextualize using current theories of learner motivation (i.e., goal orientation theory and attribution theory). Future research should investigate how interventions, such as coaching, guided learner reflection, or various CBTVT implementation strategies, can help keep learners oriented toward mastery learning rather than toward performance.

Frameworks for Integrating Learning Analytics With the Electronic Health Record.

ABSTRACT: The information systems designed to support clinical care have evolved separately from those that support health professions education. This has resulted in a considerable digital divide between patient care and education, one that poorly serves practitioners and organizations, even as learning becomes ever more important to both. In this perspective, we advocate for the enhancement of existing health information systems so that they intentionally facilitate learning. We describe three well-regarded frameworks for learning that can point toward how health care information systems can best evolve to support learning. The Master Adaptive Learner model suggests ways that the individual practitioner can best organize their activities to ensure continual self-improvement. The PDSA cycle similarly proposes actions for improvement but at a health care organization's workflow level. Senge's Five Disciplines of the Learning Organization, a more general framework from the business literature, serves to further inform how disparate information and knowledge flows can be managed for continual improvement. Our main thesis holds that these types of learning frameworks should inform the design and integration of information systems serving the health professions. An underutilized mediator of educational improvement is the ubiquitous electronic health record. The authors list learning analytic opportunities, including potential modifications of learning management systems and the electronic health record, that would enhance health professions education and support the shared goal of delivering high-quality evidence-based health care.

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.

TRainee Attributable & Automatable Care Evaluations in Real-time (TRACERs): A Scalable Approach for Linking Education to Patient Care.

Competency-based medical education (CBME) is an outcomes-based approach to education and assessment that focuses on what competencies trainees need to learn in order to provide effective patient care. Despite this goal of providing quality patient care, trainees rarely receive measures of their clinical performance. This is problematic because defining a trainee's learning progression requires measuring their clinical performance. Traditional clinical performance measures (CPMs) are often met with skepticism from trainees given their poor individual-level attribution. Resident-sensitive quality measures (RSQMs) are attributable to individuals, but lack the expeditiousness needed to deliver timely feedback and can be difficult to automate at scale across programs. In this eye opener, the authors present a conceptual framework for a new type of measure - TRainee Attributable & Automatable Care Evaluations in Real-time (TRACERs) - attuned to both automation and trainee attribution as the next evolutionary step in linking education to patient care. TRACERs have five defining characteristics: meaningful (for patient care and trainees), attributable (sufficiently to the trainee of interest), automatable (minimal human input once fully implemented), scalable (across electronic health records [EHRs] and training environments), and real-time (amenable to formative educational feedback loops). Ideally, TRACERs optimize all five characteristics to the greatest degree possible. TRACERs are uniquely focused on measures of clinical performance that are captured in the EHR, whether routinely collected or generated using sophisticated analytics, and are intended to complement (not replace) other sources of assessment data. TRACERs have the potential to contribute to a national system of high-density, trainee-attributable, patient-centered outcome measures.