An ad hoc committee will plan and conduct a public workshop to explore the data quality issues and strategies central to the increasing capture and use of digital clinical and patient-reported data for knowledge development. The workshop will engage leading experts in reviewing the challenges, defining key questions, and exploring a strategic framework for progress on the issue of health data quality in a learning health system. Questions/topics of consideration could include What are the data quality requirements to support the various knowledge generation processes required by the learning health system (quality monitoring, sentinel event detection, disease surveillance, clinical research)? What is known about the current state of digital health data quality? What implications does this have for short term uses? What analytical methods are available to assess data quality? What novel analytical methods will need to be developed in order to meet learning health system data-use needs? What are the essential components of a strategy to achieve the necessary data quality levels? What lessons have been learned by those organizations already undertaking learning health system—type efforts? What foundational work has been done that can be built on/leveraged to better meet learning health system data quality needs?
Through a series of expert presentations and discussions, workshop participants addressed issues of matching data quality to use, how these needs align with current data sources, and what the potential and challenges are for leveraging digital health data for learning—both the short and long term. The final workshop session included a moderated discussion geared toward describing ways forward on the issues highlighted earlier in the workshop.
This publication summarizes the proceedings of the workshop on Digital Data Priorities for Continuous Learning in Health and Health Care, the 12th in the Learning Health System Series of publications by the Roundtable