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.

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.

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.

Sparse Detector Configuration in SiPM Digital Photon Counting PET: a Feasibility Study.

PURPOSE: To investigate the minimum number of SiPM detectors required for solid-state digital photon counting (DPC) oncologic whole-body 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography (PET)/X-ray computed tomography (CT). PROCEDURES: A DPC PET/CT (Vereos, Philips) with 23,040 1-to-1 crystal-to-detector couplings was utilized. [18F]FDG PET/CT of a uniformity phantom and 10 oncology patients selected by block randomization from a large clinical trial were included (457 ± 38 MBq, 64 ± 22 min p.i, body mass index (BMI) of 14-41). Sparse-ring PET configurations with 50 % detector reduction in tangential and axial directions were analyzed and compared to the current full ring configuration. Resulting images were reviewed blindly and quantitatively over detectable lesions and the liver. RESULTS: One hundred twelve lesions (d = 10 to 95 mm) were analyzed in the patient population. All lesions remained visible and were demonstrated without compromised image quality under all BMIs in the 50 % sparse detector configurations despite the DPC PET system sensitivity reduction to 1/4th. An excellent consistency of SUVmax measurements of lesions with an average of 5 % SUVmax difference was found between dPET of full and sparse configurations. CONCLUSIONS: The feasibility of either expanding the axial field of view (FOV) by a factor of two or halving the number of detectors was demonstrated for solid-state digital photon counting PET, thus either potentially enabling cost reduction or extended effective axial FOV without increased cost.

How Long of a Dynamic 3'-Deoxy-3'-[18F]fluorothymidine ([18F]FLT) PET Acquisition Is Needed for Robust Kinetic Analysis in Breast Cancer?

PURPOSE: To quantitatively evaluate the minimally required scanning time of 3'-deoxy-3'-[18F]fluorothymidine ([18F]FLT) positron emission tomography (PET) dynamic acquisition for accurate kinetic assessment of the proliferation in breast cancer tumors. PROCEDURES: Within a therapeutic intervention trial, 26 breast tumors of 8 breast cancer patients were analyzed from 30-min dynamic [18F]FLT-PET acquisitions. PET/CT was acquired on a Gemini TF 64 system (Philips Healthcare) and reconstructed into 26 frames (8 × 15 s, 6 × 30 s, 5 × 1 min, 5 × 2 min, and 2 × 5 min). Maximum activity concentrations (Bq/ml) of volume of interests over tumors and plasma in descending aorta were obtained over time frames. Kinetic parameters were estimated using in-house developed software with the two-tissue three-compartment irreversible model (2TCM) (K1, k2, k3, and Ki; k4 = 0) and Patlak model (Ki) based on different acquisition durations (Td) (10, 12, 14, 16, 20, 25, and 30 min, separately). Different linear regression onset time (T0) points (1, 2, 3, 4, and 5 min) were applied in Patlak analysis. Ki of the 30-min data set was taken as the gold standard for comparison. Pearson product-moment correlation coefficient (R) of 0.9 was chosen as a limit for the correlation. RESULTS: The correlation of kinetic parameters between the gold standard and the abbreviated dynamic data series increased with longer Td from 10 to 30 min. k2 and k3 using 2TCM and Ki using Patlak model revealed poor correlations for dynamic PET with Td ≤ 14 min (k2: R = 0.84, 0.85, 0.86; k3: R = 0.67, 0.67, 0.67; Ki: R = 0.72, 0.78, 0.87 at Td = 10, 12, and 14 min, respectively). Excellent correlations were shown for all kinetic parameters when Td ≥ 16 min regardless of the kinetic model and T0 value (R > 0.9). CONCLUSIONS: This study indicates that a 16-min dynamic PET acquisition appears to be sufficient to provide accurate [18F]FLT kinetics to quantitatively assess the proliferation in breast cancer lesions.