This slide deck discusses 3 potentially disruptive future trends in healthcare that affect how we manage information.

1. FUTURE OF
HEALTHCARE:
3 DISRUPTIVE
TRENDS
Sean Koon, MD

2. What Should We
be Thinking
About Now?
To be Better
Prepared for the
Future?

3. Overview • Explore the concept of “Disruptive Innovation”
and contrast it with “Incremental Innovation”
• Discuss why this concept is important to our
work in health informatics
• Propose three “disruptive” trends, with
examples

4. Incremental
Innovations
• Often come from a focus on “continuous
improvement” in quality and can use “process
improvement” tools (Corso & Pellegrini, 2007)
• Have the benefit of established:
– Policies
– Processes
– Oversight
– Standards
– Resources

5. Incremental
Innovations
• Ontologies
• Interoperability
• Privacy and Security
• Health Information Exchange
• Improved DataVisualizations
• Dashboards with attention to human factors
• Improvement in NLP/use of unstructured data
• Database speed and innovations for efficient access
• Improved interfaces by applying usability standards
• Linkages between Major datasets
• Phenotyping algorithms
• Integration of Standards
• Provider alerts

6. “Disruptive”
Innovation
• Not well defined in the literature
• Typically used describe a product’s impact on
marketplace (in terms of investments)
• Can be a lower quality product that has some other
key benefit
• It’s disruptive because it doesn’t fit well in
established structures and because it can compete
for resources
• May solve important problems, but also creates new
problems to solve
• For this talk, we are interested innovation that is
disruptive to the healthcare operations and
informatics work

7. How can you
tell if an
innovation
will be
“disruptive”?
• Nagy, Schuessler, and Dubinsky (2016)
state that an innovation will be more
disruptive if it:
1. Involves a central aspect of our work
and
2. Involves:
a. New capability (“Functionality”)
b. New process (“Technical Standard”)
c. New Ownership
Do we see this with things like telehealth visits or
smartphone monitoring?

8. Three
Disruptive
Trends for
the Future
• Episodic to Continuous
• Unidirectional to
Multidirectional
• General to Specific

9. Trend 1:
Episodic to
Continuous
Blurring of Clinic and Home Environments
– Telemedicine facilitates more convenient and frequent
interactions
– Monitoring devices support both continuous evaluation and
remote care
– Chronic disease epidemic
Continuous Updating of Knowledge Resources
– The advent of the EHR means that every new data point
supports continuous learning
– Internet news sources, online patient communities, and
online medical libraries all develop the patient as a source of
information
Who will be responsible for this information?
What tools are needed to summarize and create
knowledge from monitoring data? What standards
are needed?

10. Trend 2:
Unidirectional to
Multidirectional
• Medical knowledge
– Traditional ‘directive model’: Information comes primarily from medical
providers:
RCT’s  Guidelines DoctorPatient
– New Model:
Patient Communities, “Patient’s like me”
Wikipedia, social media, ‘google’
Big data
• Patient Data
– Electronic data capture tools, personal monitoring devices
– Personal health record: Patient contributing to the health record directly
– Predictive analytics
How do we manage the Quality of information and
reconcile the Conflicts from all of these sources?
What will Evidence Based Medicine mean in the
FUTURE?

11. Trend 3: General
to Specific
• Precision Medicine
– “-omics”, behavioral data, environmental data
– Electronic data capture tools, the “Quantified Self”
• Learning Healthcare System
– Large datasets support more specific
recommendations
• Patient Centered Outcomes
– studying outcomes that can be aligned with
individual patients’ values

12. Summary:
• Three disruptive trends are proposed:
– Episodic to Continuous: Patient care
(especially chronic disease), knowledge
implementation
– Information that comes from Multiple
Sources and flows in Multiple Directions
– Treatment that is more Specific and
Individualized to a patient’s body,
circumstance, and personal preferences
Challenge:
In which ways might these 3 disruptive
trends change the way we plan our work in
other areas?
-Decision support -Privacy
-NLP -Security
-EHR design -User Interface
-Standards -Storage and Retrieval-
Dashboards -Informatics research
and more…

13. Conclusion There are many exciting opportunities in
biomedical informatics!
Disruptive innovations can impact many
other efforts
How can we use our understanding of
disruptive trends to plan for the future?
Thank you….

14. References
• Corso, M., & Pellegrini, L. (2007). Continuous and Discontinuous Innovation:
Overcoming the Innovator Dilemma. Creativity and Innovation Management,
16(4), 333–347. https://doi.org/10.1111/j.1467-8691.2007.00459.x
• Cypreste, R.,Walsh, K., & Bedford, M. (2015). Evidence-based medicine: what
does the future hold? Postgraduate MedicalJournal, 91(1077), 359–360.
• Desai, A. (2015). Scanning the HIM Environment:AHIMA’s 2015 Report Offers
Insight on Emerging IndustryTrends and Challenges. Journal of AHIMA, 86(5),
38–43.
• Dorsey, E. R., &Topol, E. J. (2016). State ofTelehealth. New EnglandJournal of
Medicine, 375(2), 154–161. https://doi.org/10.1056/NEJMra1601705
• Han, D.,Wang, S., Jiang, C., Jiang, X., Kim, H.-E., Sun, J., & Ohno-Machado, L.
(2015).Trends in biomedical informatics: automated topic analysis of JAMIA
articles. Journal of the American Medical Informatics Association, 22(6), 1153–1163.
https://doi.org/10.1093/jamia/ocv157
• Mazzocchi, F. (2015). Could Big Data be the end of theory in science?: A few
remarks on the epistemology of data-driven science. EMBO Reports, 16(10),
1250–1255. https://doi.org/10.15252/embr.201541001
• Nagy, D., Schuessler, J., & Dubinsky,A. (2016). Defining and identifying
disruptive innovations. Industrial Marketing Management, 57, 119–126.
https://doi.org/10.1016/j.indmarman.2015.11.017
• Welch, B. M., & Kawamoto, K. (2013). Clinical decision support for genetically
guided personalized medicine: a systematic review. Journal of the American
Medical InformaticsAssociation, 20(2), 388–400. https://doi.org/10.1136/amiajnl-
2012-000892