reducing the distance between data & knowledge
DESCRIPTION
Payne on Reducing The Distance Between Data & Knowledge: Realizing the Promise of HIT and Biomedical InformaticsTRANSCRIPT
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 InformaticsExecutive Director, Center for IT Innovations in HealthcareCo-Director, Center for Clinical and Translational Science, Biomedical Informatics ProgramCo-Director, Comprehensive Cancer Center, Biomedical Informatics Shared Resource
OSU Center for Personalize HealthcareNational ConferenceOctober 6, 2011
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
Clinical Encounters
HIT + Biomedical Informatics
Research
The Problem
3
Data Generation
Management,Integration,
Delivery
KnowledgeGeneration
IncreasingDistance
EHR
EDW
Contributing Factors (1)
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
Regulatory, Technical, and Cultural Barriers Between Data and Knowledge Generation
Care Providers
ResearchersHIT +
Biomedical Informatics
Clinical InvestigatorsCI, Imaging, CRI, TBI, PHI
Bioinformatics, TBI, CRI
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
“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
“The whole is more than the sum of its parts.” - Aristotle
Point
Counter-Point
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?
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.
Clinical InformaticsPublic Health Informatics
Translational BioinformaticsClinical Research Informatics
Creating a Learning Healthcare System: Learning from Every Patient and Improving Care
Instrument Patient Encounters
(Data + Tissue)
Generate Hypotheses
Verify and Validate Hypotheses
Formalize Evidence
Apply Evidence
Improve Patient Care
(Quality + Outcomes)
Learn from every patient encounter so that we can improve their care, their families
care, and their communities care
Many Sources of Data
9
Molecular Phenotype
EnvironmentEnterprise Systems and Data Repositories:
EHR, CTMS, Data Warehouses
Emergent SourcesPHR, Instruments, Etc.
The Biomedical Informatics Continuum: From Data to Knowledge
10
Data Information Knowledge
+ Context + Application
Significant Barriers
11
Technical
Regulatory
Cultural Achieving shared language and understanding
between stakeholders
The Construction of the Tower of Babel (Hendrick van Clev) Source: Wikimedia Commons
Strategies & Future Directions: HIT
12
Informatics “translation”
Holistic approach to planning, implementation and management
Adoption of knowledge management practices as a core competency
Transition to agile, lightweight technologies as the “edge” of enterprise systems
• Eliminating traditional boundaries• Focusing on economies of scale
across mission areas• Bridging applied informatics and
HIT practice• Semantics• NLP• Temporal Reasoning• IR• Visualization
• Enabling end-user self service
Strategies & Future Directions: BMI
13
• Answering people-centric questions:
• Workflow• Usability• Software Design Patterns
• True platform integration:• SOA and Cloud Computing• Semantic web• Knowledge engineering• Visualization and HCI
• Reasoning:• Data mining• Text mining/NLP• Data integration• Knowledge discovery
• Enable all stakeholders to ask and answer questions
• Includes informaticians
Strategies & Future Directions: Culture
14
Harmonization of regulatory frameworks: Early successes related to universal bio-specimen collection projects and
GWAS/PWAS study paradigms
HIT and BMI must be partners: Technology and methodological silos are major barriers
Socio-technical approach to platform adoption: Adoption means more than being on-time and under-budget
Clinical Encounters
HIT + Biomedical Informatics
Research
Revisiting “The Problem”
15
Data Generation
Management,Integration,
Delivery
KnowledgeGeneration
IncreasingDistance
EHR
EDW
Clinical Encounters
Research
Towards a Solution: A Systems Approach to Biomedicine
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Data Generation
KnowledgeGeneration
HIT & Biomedical Informatics “Fabric”
17
Thank you for your time and attention!• [email protected]• http://bmi.osu.edu/~payne