Student High Value Care Initiative: a Longitudinal Model for Student-Led Implementation and Scholarship.

BACKGROUND: Educating medical trainees to practice high value care is a critical component to improving quality of care and should be introduced at the beginning of medical education. AIM: To create a successful educational model that provides medical students and junior faculty with experiential learning in quality improvement and mentorship opportunities, and produce effective quality initiatives. SETTING: A tertiary medical center affiliated with a medical school in New York City. PARTICIPANTS: First year medical students, junior faculty in hospital medicine, and a senior faculty course director. PROGRAM DESCRIPTION: The Student High Value Care initiative is a longitudinal initiative comprised of six core elements: (1) project development, (2) value improvement curriculum, (3) mentorship, (4), Institutional support, (5) scholarship, and (6) student leadership. PROGRAM EVALUATION: During the first 3 years, 68 medical students and ten junior faculty participated in 10 quality improvement projects. Nine projects were successful in their measured outcomes, with statistically significant improvements. Nine had an abstract accepted to a regional or national meeting, and seven produced publications in peer-reviewed literature. DISCUSSION: In the first 3 years of the initiative, we successfully engaged medical students and junior faculty to create and support the implementation of successful quality improvement initiatives. Since that time, the program continues to offer meaningful mentorship and scholarship opportunities.

Sociome Data Commons: A scalable and sustainable platform for investigating the full social context and determinants of health.

Background/Objective: Non-clinical aspects of life, such as social, environmental, behavioral, psychological, and economic factors, what we call the sociome, play significant roles in shaping patient health and health outcomes. This paper introduces the Sociome Data Commons (SDC), a new research platform that enables large-scale data analysis for investigating such factors. Methods: This platform focuses on "hyper-local" data, i.e., at the neighborhood or point level, a geospatial scale of data not adequately considered in existing tools and projects. We enumerate key insights gained regarding data quality standards, data governance, and organizational structure for long-term project sustainability. A pilot use case investigating sociome factors associated with asthma exacerbations in children residing on the South Side of Chicago used machine learning and six SDC datasets. Results: The pilot use case reveals one dominant spatial cluster for asthma exacerbations and important roles of housing conditions and cost, proximity to Superfund pollution sites, urban flooding, violent crime, lack of insurance, and a poverty index. Conclusion: The SDC has been purposefully designed to support and encourage extension of the platform into new data sets as well as the continued development, refinement, and adoption of standards for dataset quality, dataset inclusion, metadata annotation, and data access/governance. The asthma pilot has served as the first driver use case and demonstrates promise for future investigation into the sociome and clinical outcomes. Additional projects will be selected, in part for their ability to exercise and grow the capacity of the SDC to meet its ambitious goals.

Framework for Integrating Equity Into Machine Learning Models: A Case Study.

Predictive analytic models leveraging machine learning methods increasingly have become vital to health care organizations hoping to improve clinical outcomes and the efficiency of care delivery for all patients. Unfortunately, predictive models could harm populations that have experienced interpersonal, institutional, and structural biases. Models learn from historically collected data that could be biased. In addition, bias impacts a model's development, application, and interpretation. We present a strategy to evaluate for and mitigate biases in machine learning models that potentially could create harm. We recommend analyzing for disparities between less and more socially advantaged populations across model performance metrics (eg, accuracy, positive predictive value), patient outcomes, and resource allocation and then identify root causes of the disparities (eg, biased data, interpretation) and brainstorm solutions to address the disparities. This strategy follows the lifecycle of machine learning models in health care, namely, identifying the clinical problem, model design, data collection, model training, model validation, model deployment, and monitoring after deployment. To illustrate this approach, we use a hypothetical case of a health system developing and deploying a machine learning model to predict the risk of mortality in 6 months for patients admitted to the hospital to target a hospital's delivery of palliative care services to those with the highest mortality risk. The core ethical concepts of equity and transparency guide our proposed framework to help ensure the safe and effective use of predictive algorithms in health care to help everyone achieve their best possible health.