Data Driven Quality Improvement of Health Professions Education: Design and Development of CLUE - An Interactive Curriculum Data Visualization Tool.

Curriculum Mapping and dynamic visualization is quickly becoming an integral aspect of quality improvement in support of innovations which drive curriculum quality assurance processes in medical education. CLUE (Curriculum Explorer) a highly interactive, engaging and independent platform was developed to support curriculum transparency, enhance student engagement, and enable granular search and display. Reflecting a design based approach to meet the needs of the school's varied stakeholders, CLUE employs an iterative and reflective approach to drive the evolution of its platform, as it seeks to accommodate the ever-changing needs of our stakeholders in the fast pace world of medicine and medical education today. CLUE exists independent of institutional systems and in this way, is uniquely positioned to deliver a data driven quality improvement resource, easily adaptable for use by any member of our health care professions.

A Conceptual Analytics Model for an Outcome-Driven Quality Management Framework as Part of Professional Healthcare Education.

BACKGROUND: Preparing the future health care professional workforce in a changing world is a significant undertaking. Educators and other decision makers look to evidence-based knowledge to improve quality of education. Analytics, the use of data to generate insights and support decisions, have been applied successfully across numerous application domains. Health care professional education is one area where great potential is yet to be realized. Previous research of Academic and Learning analytics has mainly focused on technical issues. The focus of this study relates to its practical implementation in the setting of health care education. OBJECTIVE: The aim of this study is to create a conceptual model for a deeper understanding of the synthesizing process, and transforming data into information to support educators' decision making. METHODS: A deductive case study approach was applied to develop the conceptual model. RESULTS: The analytics loop works both in theory and in practice. The conceptual model encompasses the underlying data, the quality indicators, and decision support for educators. CONCLUSIONS: The model illustrates how a theory can be applied to a traditional data-driven analytics approach, and alongside the context- or need-driven analytics approach.