HMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
Payne
1. Reducing The Distance Between Data & Knowledge: Realizing the Promise of HIT and Biomedical Informatics Philip R.O. Payne, Ph.D. Associate Professor & Chair, Department of Biomedical Informatics Executive Director, Center for IT Innovations in Healthcare Co-Director, Center for Clinical and Translational Science, Biomedical Informatics Program Co-Director, Comprehensive Cancer Center, Biomedical Informatics Shared Resource OSU Center for Personalize Healthcare National Conference October 6, 2011
2. Outline Problem Statement The Promise of HIT and Biomedical Informatics Creating a Learning Healthcare System Generating Knowledge Strategies and Future Directions HIT Biomedical Informatics Cultural Discussion 2
3. The Problem Data Generation EHR Increasing Distance Management, Integration, Delivery EDW Knowledge Generation 3
4. Contributing Factors (1) Regulatory, Technical, and Cultural Barriers Between Data and Knowledge Generation High performance systems require rapid adaptation Increasing demand for better, faster, safer, more cost effective therapies Simultaneous demand for increased controls over secondary use of clinical data Artificial partitioning of access to data for knowledge generation purposes Critical overlaps and potential sources of conflict between these factors CI, Imaging, CRI, TBI, PHI Clinical Investigators Bioinformatics, TBI, CRI
5. Contributing Factors (2) 5 Historical precedence for reductionism in the biomedical and life sciences Break-down problems into fundamental units Study units and generate knowledge Reassemble knowledge into systems-level models Influences policy, education, research, and practice Recent scientific paradigms have illustrated major problems with this type of approach Systems biology/medicine Reductionist approach to data, information, and knowledge management is still prevalent HIT vs. Informatics Informatics sub-disciplines
6. Point “The whole is more than the sum of its parts.” - Aristotle Counter-Point “To make progress in understanding all this, we probably need to begin with simplified (oversimplified?) models and ignore the critics' tirade that the real world is more complex. The real world is always more complex, which has the advantage that we shan't run out of work.” - John Ball Is it time for a systems-approach to secondary use of data in healthcare? If so, how do we reduce the “distance” between data and knowledge generation?
7. The Promise of Healthcare IT (HIT) Delivering timely and contextually appropriate data, information, and knowledge in support of basic science, clinical and translational research, clinical care, and public health.
8. Creating a Learning Healthcare System: Learning from Every Patient and Improving Care Clinical Informatics Public Health Informatics Translational Bioinformatics Clinical Research Informatics Learn from every patient encounter so that we can improve their care, their families care, and their communities care
9. Many Sources of Data 9 Emergent Sources PHR, Instruments, Etc. Molecular Phenotype Enterprise Systems and Data Repositories: EHR, CTMS, Data Warehouses Environment
14. Achieving shared language and understanding between stakeholdersThe Construction of the Tower of Babel (Hendrick van Clev) Source: Wikimedia Commons
The clinical template should be used for presentation to a clinical audience and focused on clinical topics. Examples include clinical grand rounds, clinical conferences and seminars and internal clinical meetings.
