Cite as: Kamel Boulos MN. IBM Watson Health: how cognitive technologies have begun transforming clinical medicine and healthcare (Oral session IV – Patient safety tools, Thursday 19 May 2016, 15:45-16:45, Hotel Puijonsarvi, Kuopio). In: Proceedings of the 4th Nordic Conference on Research in Patient Safety and Quality in Healthcare (NSQH2016), Kuopio, Finland, 18-20 May 2016 (organised by University of Eastern Finland), p.29. URL: http://www.uef.fi/NSQH2016 (In: Nykanen I (ed.). The 4th Nordic Conference on Research in Patient Safety and Quality in Healthcare. Kuopio, Finland, May 18-20, 2016. Program and Abstracts. Publications of the University of Eastern Finland. Report and Studies in Health Sciences 21. 2016, p.29 (of 119 p.). ISBN: 978-952-61-2130-7 (nid.), ISSNL: 1798-5722, ISSN: 1798-5730.) IBM Watson health: how cognitive technologies have begun transforming clinical medicine and healthcare Maged N Kamel Boulos ABSTRACT Background: IBM Watson Health (http://www.ibm.com/smarterplanet/us/en/ibmwatson/health/) belongs to a new generation of smart cognitive computing technologies (a type of artificial intelligence) that are poised to transform the way healthcare is delivered, and to vastly improve clinical outcomes, quality of care and patient safety. Objectives: Our goal was to collect and document the huge potential of a range of emerging and exemplary uses of IBM Watson in healthcare in both developed and developing country settings. Methods: A survey of current peer reviewed and grey literature has been conducted, looking for reports and case studies involving the use of IBM Watson in different health and healthcare applications. Results, conclusions and clinical implications: With its ability to make sense of unstructured medical information by analysing the meaning and context of natural language, and uncovering important knowledge buried within large volumes of data and information, including medical images, IBM Watson is exceptionally well suited for clinical and healthcare decision support, where there are often elements of ambiguity and uncertainty. It has been (or is currently being) successfully deployed in many developed countries in the West, as well as in developing countries, such as India and South Africa. IBM Watson unlocks a complex case by acquiring information from multiple sources, e.g., accessing the electronic patient record, then parsing all related medical evidence at up to 60 million pages per second. After processing all of this information, Watson offers relevant and prioritised suggestions to the decision-maker, e.g., helping clinicians identify the best diagnosis and treatment options in complex oncology cases, and providing hospital managers with new operational insights. The ultimate goals are to reduce cost, medical errors, mortality rates, and help improve patients' quality of life.

1. IBM Watson Health:
How cognitive technologies have begun
transforming clinical medicine and healthcare
19-20 MAY, KUOPIO, FINLAND
Maged N. Kamel Boulos, MBBCh, PhD, SMIEEE
UHI Professor and Chair of Digital Health, Scotland, UK
maged.kamelboulos@uhi.ac.uk / mnkboulos@ieee.org
t: @mnkboulos
♦ What is IBM Watson Health? ♦ Open to developers ♦ Applications & examples
♦ In developing countries ♦ Research evidence ♦ Alternative platform ♦ Conclusions

2. What is IBM Watson / Cognitive computing?
• IBM Watson belongs to a class of AI (artificial intelligence) that (partially) simulates
human thought processes and is known as 'cognitive computing'.
• Cognitive computing includes self-learning systems that mimic to a great (but not full)
extent the way the human brain works by harnessing data mining, pattern recognition
and natural language processing.
• Watson uses natural language capabilities, hypothesis generation, big data analysis and machine
learning, and evidence-based learning and reasoning to support healthcare professionals as they make
clinical decisions. The clinician starts by posing a query to the system, describing symptoms and other
related factors. Watson then begins to parse the input to identify key pieces of information. The system
supports medical terminology by design, extending Watson's natural language processing capabilities.
• Watson next mines the patient data to find relevant facts about family history, current medications,
other existing conditions, etc. It combines this information with current patient findings from tests and
instruments, including medical images, which it can also "read", and then examines all available data
sources to form hypotheses and test them. Watson can incorporate electronic medical record data,
patient information, clinicians' notes, treatment guidelines, research, clinical studies and journal
articles into the data available for analysis.
• Watson will then provide a list of potential diagnoses along with a score that indicates the level of
confidence for each hypothesis.

3. • "The ability to take context into account during
the hypothesis generation and scoring phases of
the processing pipeline allows Watson to address
these complex problems, helping the doctor and
patient make more informed and accurate
decisions" (and avoid over-testing).
http://www.ibm.com/innovation/
uk/watson/watson_in_healthcare
.shtml
• An enabler for precision
medicine and P4
medicine.
• Helps in designing better
treatments.

4. Individual patient and population perspectives
https://www.ibm.com/smarterplanet/us/en/ibmwatson/health/population
https://www.ibm.com/smarterplanet/us/en/ibmwatson/health/solutions/patient-
engagement

5. Not a replacement of clinicians, but a
"cognitive prosthesis" for them

6. https://www.youtube.com/watch?v=cFUg-b9qeIk
Video station

7. http://www.ibm.com/smarterplanet/us/en/
ibmwatson/watson-oncology.html

8. Open to developers
https://www.ibm.com/smarterplanet/
us/en/ibmwatson/developercloud/
https://www.ibm.com/marketplace/cloud/simplify-
crowd-sourced-research-studies/us/en-us
http://www.ibm.com/smarterplanet/
us/en/ibmwatson/knowledge-
studio.html

9. http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/services-catalog.html

10. http://www.ibm.com/smarterplanet/
us/en/ibmwatson/ecosystem.html
Third-party
health app
examples
http://theeliza.space/

11. http://www.medtronic.com/us-en/about-3/medtronic-ibm-
watson-health.html
Pathway Genomics OME™ app:
https://www-03.ibm.com/press/us/en/pressrelease/48766.wss
http://www-03.ibm.com/press/uk/en/pressrelease/49725.wss
https://youtu.be/mULwISf9YCw
https://nutrino.co/
and many more...
'Virtual Doctor' for diabetes
Novo Nordisk A/S

12. Not just in the Western world, but also in
developing countries
https://www.manipalhospitals.com/IBM-Watson/
Video: https://youtu.be/hbqDknMc_Bo
Bangalore, India

13. Not just in the Western world, but also in
developing countries
http://www.metropolitan.co.za/
South Africa
Metropolitan Health is using
IBM Watson Engagement Advisor
Video:
https://www.youtube.com/watch?v=vLE7VuppRzU

14. Peer-reviewed research
evidence examples
"Watson has been applied to a
few pilot studies in the areas of
drug target identification and
drug repurposing. Results
suggest that Watson can
accelerate identification of novel
drug candidates and novel drug
targets by harnessing the
potential of big data."
http://dx.doi.org/10.1016/j.clinthera.2015.12.001

15. Peer-reviewed research
evidence (Cont'd)
IBM Watson in oncology
http://dx.doi.org/10.1007/978-1-4939-
3283-2_10 (see pp.213-215)

16. Peer-reviewed research evidence (Cont'd)
Reducing readmission rates:
Using IBM Watson to
automate outreach to
selected high-risk patients
can significantly reduce
hospital readmission rates
and healthcare costs
http://dx.doi.org/10.1089/pop.2015.0014

17. Alternative platform: Google DeepMind Health
https://deepmind.com/health.html
• Google has been given access to an estimated 1.6 million NHS patient records from the Royal Free, Barnet and Chase
Farm hospitals in London, going back over the past five years and continuing until 2017. The records include full names, as
well as patient histories, but the data remain encrypted, meaning that Google employees should not be able to identify
anyone, according to the Royal Free Trust.
• Google DeepMind is a British AI start-up founded in 2010 and acquired by Google in 2014.

18. Conclusions
• IBM started developing Watson in 2005. It reached a mature stage in 2011, heralding
a new era of cognitive computing applications in many industries, including health
and healthcare.
• With its ability to make sense of unstructured medical information by analysing the
meaning and context of natural language, and uncovering important knowledge
buried within large volumes of data and information, including medical images and
audio, IBM Watson is exceptionally well suited for clinical, healthcare and personal
health decision support applications, where there are often elements of ambiguity
and uncertainty.
• Thanks to its openness to developers, it has been (or is currently being) successfully
deployed, in hospital environments and in consumer apps, in many developed
countries in the West, as well as in developing countries, such as India and South
Africa.

19. Conclusions
• As of May 2016, limited peer-reviewed literature exists about IBM Watson in health and
medicine, and most available material is in the form of grey literature (thousands of news
items, Web articles/reports and videos). However, more peer-reviewed research is expected
in the near feature, as IBM Watson Health rapidly establishes itself as a key enabler
technology for 'P5 medicine' (Precision, Predictive, Preventive, Personalised and
Participatory) globally.
• IBM Watson unlocks a complex case by acquiring information from multiple sources, e.g.,
accessing the electronic patient record, then parsing all related medical evidence at up to 60
million pages per second. After processing all of this information, Watson offers relevant
and prioritised suggestions to the decision-maker, e.g., helping clinicians identify the best
diagnosis and treatment options in complex oncology cases, and providing hospital
managers with new operational insights (individual patient and population perspectives).
• The ultimate goals of Watson-powered applications are to reduce healthcare costs,
medical errors, morbidity and mortality rates, and help improve patients and populations'
quality of life.

20. Appendix: Custom hardware
• Both platforms, IBM Watson and
Google DeepMind, employ their
own proprietary specialised
hardware to offer their cloud-
based AI services to developers
and users.
Google's latest Tensor Processing Unit (TPU)
https://cloudplatform.googleblog.com/2016/05/Google
-supercharges-machine-learning-tasks-with-custom-
chip.html
IBM Watson uses a
cluster of massively
parallel POWER7
processors/servers