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

16

Data Generation

KnowledgeGeneration

HIT & Biomedical Informatics “Fabric”

17

Thank you for your time and attention!• philip.payne@osumc.edu• http://bmi.osu.edu/~payne