Technological Innovation in Reducing Health Disparities

1. Robert S. Gold, Dean University of Maryland School of Public Health Prepared for 40th Anniversary Celebration and Annual Meeting November 1, 2007 Technological Innovation in Reducing Health Disparities Eta Sigma Gamma

2. Vision: High Quality Interventions • How do we maintain and improve on the efficiency of current public health interventions without dramatically increasing cost? • How do we contribute to the reduction in the disparity in morbidity and mortality for underserved populations? • How do we take appropriate advantage of advanced technologies to accomplish the first two?

3. Before we begin . . .

4. If we can decode this Why can’t we decode this?

5. If we can figure out how to read this Why can’t we figure out how to read this?

6. If we can land here Why can’t we land here?

7. If we can build this Why can’t we build this?

8. If this is a cultural phenomenon Why can’t we figure out how to effectively apply this kind of technology for education?

9. If we have so many best practices Why can’t we figure out how to get educators to use them effectively?

10. How do we get there? • Recognize challenges • Apply effective technology strategies & tools • Use interactive and appropriate learning strategies • Generate a plan for strategic deployment

11. Which technology is best? • We should focus on critical outcomes, not technology. • Key to effective interventions is: • focusing on the needs of the populations • the requirements of the content, and • the constraints faced by the practitioner, before selecting a delivery system.

12. Technology givens! • Continuous and rapid change • Regularly redefining effective and best practices • Benefits realized only when effectively integrated into lifestyle and practice • We are not always prepared Source: http://www.oswego.org/ocsd-web/tech/techplan/staff-dev.htm

13. What we do! • My philosophical orientation! • We must remove all barriers . . .

14. Caveat • We don’t always need more powerful hardware • Sometimes we need: • Better pedagogy • More effective application of learning theory • Discovery, exploration, user generated automata

15. My fears revolve around: • Individualism ~ Dehumanization • Content focus ~ Technology focus

16. Concerns • Cost • Access • Functionality

17. The technologies: • Visualization technologies • Biomedical technologies • Communication technologies • Data technologies • Educational technologies • Science fiction technologies

18. Visualization technologies • Transform information into a visual form, enabling the viewer to easily understand the information using interactive graphics and visual design • Although an old idea, two things create new opportunities • Increasing computing power • Increasing amount of information online

19. Visualization – new techniques • Scientific visualization • Visualization of text • Visualization of histories • Visualization of data - GIS • Visualization of social networks • Real-time visualization • Visualization of concepts

20. Scientific visualization • Molecular models • Bioinformatics • Medical imaging • A technique to organize information allowing analysis

21. Text visualization • Explore the fabric and meaning of text in context • A technique to organizing information to enhance comprehension

22. Visualization of “histories” • A mechanism to graphically illustrate and explore relationships between people, events, concepts • A technique to organize information to see relationships

23. Geographic Information Systems • Tracking disease • Overlaying population group and geographic data • Evaluation of change over time

24. Social Network Analysis • Diseases are often spread through social contact • Contact information is often key in controlling an epidemic, man-made or otherwise • There is a long history of the use of DM tools in the study of social networks: Social networks as graphs. *Center for Discrete Mathematics and Theoretical Computer Science / National Science Foundation

25. Spread Of Opinion • Of relevance to bioterrorism. • Dynamic models of how opinions spread through social networks • Your opinion changes at time t+1 if the number of neighboring vertices with the opposite opinion at time t exceeds threshold • Widely studied • Relevant variants: confidence in your opinion (= immunity); probabilistic change of opinion *Center for Discrete Mathematics and Theoretical Computer Science / National Science Foundation

26. Social networks • NEJM – social network analysis and spread of obesity • Network analysis Source: Christakis NA, Fowler JH (2007) The Spread of Obesity in a Large Social Network over 32 Years. N Engl J Med. 357(4):370-9.

27. Biomedical technologies • Bioengineering • Genomics and Epigenetics • Nanotechnology

28. Bioengineering • Thought controlled smart prostheses • Biomechanics of blood flow • Biomechanics of muscle and soft tissue • Engineering molecular biosensors

29. Genomics and Epigenetics • Functional genomics • Epigenetics

30. Nanotechnology • Applied science working on the atomic and molecular level • Nanomedicine • e.g., bioavailability

31. Communication technologies

32. Data technologies • Computer clusters • Grid computing

33. Educational technologies • In this environment we have: • Access to national online networks and data • Data mining and visualization tools • Knowledge based approaches / knowledge management • Simulation and modeling • Grid-based computing • Other non-hardware based strategies

34. Educational technologies • Self-paced interactive multimedia tailored to educational diagnosis • Simulation technologies • Expert systems / decision support systems • Knowledge management systems • Groupware • Data technologies / techniques • GIS • Other – the brain

35. Appropriate technology can revolutionize: • The delivery system of products and services • The way we communicate • The way we use and view television • The way we individualize and personalize education • The way we internalize, understand, and use massive amounts of data

36. Science fiction technologies Personalized medicine?

37. I believe • that all thought, memory, feeling, and emotion are the result of biochemical reactions. • If true, then all behavioral choices are also chemical reactions • Therefore, what is the future of • health disparities research? • Public health education?

38. Two universal principles • Virtually all diseases / health states have a genetic component • There are no perfect human specimens • All of us carry a significant number of DNA glitches For us, next three pillars of genomic futures • Genomics to biology • Genomics to health • Genomics to society Source: Francis S. Collins, MD, PhD, National Human Genome Research Institute, NIH

39. Genomics to biology • Define the structure of human variation • Sequence lots of additional genomes • Reduce the cost of sequencing • Identify all functional elements of the genome • Identify all the proteins of the cell, and their interactions • Develop a computational model of the cell

40. Genomics to health • Define genetic and environmental risk factors for all common disease • Develop a strategy for individualized preventive medicine • Develop gene-based therapeutics for single gene and complex disorders • Educate health professionals • Define causes of health disparities

41. Genomics to society • Ensure genetic privacy and protection against genetic discrimination • Define genetic factors that influence behavior • Ensure appropriate patenting and licensing practices to benefit the public • Understand the relationship of genomics, race, and ethnicity • Define boundaries of the appropriate application of genomics in the non-medical arena

42. What’s next • 2010 – mainstreaming of individualized preventive medicine • Predictive genetic tests • Interventions to reduce risk • Pharmacogenomics is standard of care • But • Will access be inequitable? Will disparities persist? • Will genetic discrimination be allowed?

43. 2020 – genomic therapeutic revolution in full swing • Gene-based designer drugs (diabetes, alzheimer’s) • Gene therapy standard of care • Sequencing a complete human genome costs $1,000 • But • Intense debate underway on non-medical uses of genetics