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Toolkit for Data Integration and Decision Support in Medical Education

Last updated: December 14, 2020
Author: One45
Data Integration and Decision Support in Medical Education

Medical schools collect a LOT of data.  But for many schools, that data has become a burden to sift through, analyze and report on. For that data to shift from being an administrative burden to providing contextual, timely insights and intelligence to MedEd decision makers, it must be integrated and powered by a data warehouse to meet the demands.

For schools who are researching how to reduce the effort required to make higher-quality decisions – to support effective problem and opportunity identification, critical decision-making, strategy formulation, implementation, and evaluation – we’ve assembled a toolkit of information and resources specific to data integration and decision support for medical schools.  

#1. Consider how centralized, normalized data can provide your school with efficiencies and critical insights

A data warehouse is the key to powering timely, accurate and detailed information that can be acted on. Review ways that centralized data can provide you with the insights needed to make your data work for you. 

Blog: 6 ways a data warehouse can supercharge your medical school

Blog: The Power of Centralized MedEd Data

#2. Identify your sources of data that enable decision making

For medical schools considering a data transformation project, we’ve prepared this checklist to help you assess the data you have and the data you’re actually using to inform decisions.

Checklist & Data Sources Template

#3. Explore best practices in MedEd data-governance and measurement

Effective MedEd data governance and measurement are critical operational requirements for every medical school. In this interactive discussion with Courtney Marsden, Sr. Assessment and Evaluation Specialist, we explored how Northeast Ohio Medical University (NEOMED) is leveraging data across multiple systems to create real-time reporting and empower decision making. This discussion digs into lessons learned, best practises and impacts from this data transformation project

Webinar Recording:  Best Practices in MedEd Data Governance and Measurement – An Interview with NEOMED

#4. Understand the costs of your current data practices vs other possibilities.

Many medical schools have taken the first steps towards data integration to provide the basis for decision support in MedEd. Having a complete picture of your medical school’s performance at any time is a critical, but a difficult goal to accomplish. Data warehousing is key to solving this problem, but it’s no small feat to build. There are many steps required – from data collection, filtering, sanitizing, and shaping to permissioning, analyzing and visualizing that data.

Key considerations & cost calculator: Build vs. Buy a Data Warehouse 

#5. Review impact on positive accreditation outcomes and CQI by defining your data origins (types, source systems) 

Understanding and being able to examine where your data comes from is a powerful way to examine the alignment with accreditation standards and put your data to work in refining the continuous quality improvement process. Do you know what types of data are involved, what systems your data comes from? And then, have you clearly mapped how those data types and source systems align with accreditation standards for reviewing reports and outcomes?

Data to Standards Map: Get a customized visualization of your data mapped to accreditation standards

Blog Post: Moving towards CQI to Improve Accreditation Outcomes 

Blog Post: How MedEd Schools can Use Data and Analysis to Improve Accreditation Outcomes 

#6. Leverage data to improve program and learner outcomes

A Guide to Precision Medical Education

This guide covers the emerging, personalized, technology-enhanced future of medical education, precision medical education (or PME). It also provides insights on how integration of medical education data is critical for effective decision support and the ability to drive a cycle of true continuous quality improvement (CQI).

Blog post: Guide to Precision Medical Education 

Presentation: Presentation on Evolving Data Practices to Enable Precision Medical Education 

High quality decision making without the administrative burden

Imagine if your team was able to spend their time making sense of your data, enabling continuous quality improvement at the institution level and on an individual learner basis.

Imagine if the data required for accreditation, which is already collected and properly structured by schools, was coded to accreditation standards and centralized into a clean data warehouse. The accreditation process could move from a one-time, stressful experience, to a real-time dashboard focused on guided analysis. That same dashboard could be used to inform decisions on curriculum review, assess learner performance, preview program outcomes and eventually even guide financial analysis and research agendas.  

This data-informed future is within your grasp.