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The Future is Cyber-Healthcare


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invited talk at iPHEM16, Innovation in Pre-hospital Emergency Medicine, Kent Surrey and Sussex Air Ambulance Trust, July 2016, Brighton, United Kingdom

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The Future is Cyber-Healthcare

  1. 1. The Future is Cyber-Healthcare 1 Payam Barnaghi Institute for Communication Systems (ICS)/ 5G Innovation Centre University of Surrey Guildford, United Kingdom
  2. 2. The Future is Cyber-Healthcare?
  3. 3. 3 IBM Mainframe 360, source Wikipedia
  4. 4. Apollo 11 Command Module (1965) had 64 kilobytes of memory operated at 0.043MHz. An iPhone 5s has a CPU running at speeds of up to 1.3GHz and has 512MB to 1GB of memory Cray-1 (1975) produced 80 million Floating point operations per second (FLOPS) 10 years later, Cray-2 produced 1.9G FLOPS An iPhone 5s produces 76.8 GFLOPS – nearly a thousand times more Cray-2 used 200-kilowatt power Source: Nick T.,, 2014 image source:
  5. 5. Computing Power 5 −Smaller size −More Powerful −More memory and more storage −"Moore's law" over the history of computing, the number of transistors in a dense integrated circuit has doubled approximately every two years.
  6. 6. Smaller in size but larger in scale 6
  7. 7. The old Internet timeline 7Source: Internet Society
  8. 8. The World Wide Web 8 Tim Berners-Lee
  9. 9. Connectivity and information exchange was (and is ) the main motivation behind the Internet; but Content and Services are now the key elements; and all started growing rapidly by the introduction of the World Wide Web (and linked information and search and discovery services). 9
  10. 10. Early days of the web 10
  11. 11. The Internet/Web in the early days 1111
  12. 12. Source: Intel, 2012
  13. 13. 13P. Barnaghi et al., "Digital Technology Adoption in the Smart Built Environment", IET Sector Technical Briefing, The Institution of Engineering and Technology (IET), I. Borthwick (editor), March 2015.
  14. 14. 14 Sensor devices are becoming widely available - Programmable devices - Off-the-shelf gadgets/tools
  15. 15. Internet of Things: The story so far RFID based solutions Wireless Sensor and Actuator networks , solutions for communication technologies, energy efficiency, routing, … Smart Devices/ Web-enabled Apps/Services, initial products, vertical applications, early concepts and demos, … Motion sensor Motion sensor ECG sensor Physical-Cyber-Social Systems, Linked-data, semantics, M2M, More products, more heterogeneity, solutions for control and monitoring, … Future: Cloud, Big (IoT) Data Analytics, Interoperability, Enhanced Cellular/Wireless Com. for IoT, Real-world operational use-cases and Industry and B2B services/applications, more Standards…
  16. 16. 1G AMPS, NMT, TACS 2G GSM. GPRS, TDMA IS-136, CDMA IS-95, PDC 3G UMTS, CDMA2000, 4G 5G LTE, LTE-A People Things Voice Text Data 5G technologies and standards Connection + Control M2M/IoT Communication technologies
  17. 17. 5G –Vertical Applications 17 Image source: The Brain with David Eagleman, BBC Speed of light?
  18. 18. The IoT is a dynamic, online and rapidly changing world 18 Conventional (Big) Data Analytics IoT Data Analytics Image sources: ABC Australia and Motion sensor Motion sensor ECG sensor
  19. 19. Live data 19 3D Map- Alexandra Institute, Aarhus, Denmark
  20. 20. Live events 20
  21. 21. Extracting city events 21 Nazli FarajiDavar, Payam Barnaghi, "A Deep Multi-View Learning Framework for City Event Extraction from Twitter Data Streams", submitted to ACM Transactions on Intelligent Systems and Technology (TIST), Nov. 2015.
  22. 22. Medical/Health Data − The average person is likely to generate more than one million gigabytes of health-related data in their lifetime. This is equivalent to 300 million books. − Medical data is expected to double every 73 days by 2020. − 80% of health data is invisible to current systems because it’s unstructured. − Less than 50% of medical decisions meet evidence-based standards. (source: The rand corporation) 22Source: IBM Research
  23. 23. Unstructured data! Heterogeneity, multi-modality and volume are among the key issues. Often natural language! We need interoperable and machine-interpretable solutions… 23
  24. 24. Device/Data interoperability 24
  25. 25. Medical/Health decision making − One in five diagnoses are incorrect or incomplete and nearly 1.5 million medication errors are made in the US every year. − Medical journals publish new treatments and discoveries every day. − The amount of medical information available is doubling every five years and much of this data is unstructured - often in natural language. − 81 percent of physicians report that they spend five hours per month or less reading journals. 25Source: IBM Research
  26. 26. Medical/Health data in decision making − Patient histories can give clues. − Electronic medical record data provide lots of information. − Current observation and measurement data and fast analysis of the data can help (combined with other data/medical records). − This needs fast/accurate/secure data: − Collection/retrieval − Communication − Sharing/Integration − Processing/Analysis − Visualisation/presentation 26
  27. 27. IBM Watson 27 Watson can process the patient data to find relevant facts about family history, current medications and other existing conditions. It can combines this information with current findings from tests and instruments and then examines all available data sources to form hypotheses and test them. Watson can also incorporate treatment guidelines, electronic medical record data, doctor's and nurse's notes, research, clinical studies, journal articles, and patient information into the data available for analysis. Source: IBM Watson can read 40 million documents in 15 seconds.
  28. 28. Sensely 28 Source:
  29. 29. kHealth for Asthma 29 Source: Kno.e.sis, Wright State University
  30. 30. Healthcare data analytics 30
  31. 31. Technology Integrated Health Management (TIHM) 31 Internet of Things testbed for healthcare
  32. 32. The Health Challenge: Dementia  16,801 people with dementia in Surrey – set to rise to 19,000 by 2020 (estimated) - nationally 850,000 - estimated 1m by 2025 (Alzheimer’s Society)  Estimated to cost £26bn p/a in the UK (Alzheimer’s Society): health and social care (NHS and private) + unpaid care  Devices in the IoT will provide actionable data on agitation, mood, sleep, appetite, weight loss, anxiety and wandering – all have a big impact on quality of life and wellbeing TIHM
  33. 33. The Health Challenge: Falls  Surrey spends £10m a year on fracture care – with 95% of hip fractures caused by falls  People with dementia suffer significantly higher fall rates that cause injury – with falls the most common cause of injury- related deaths in the over-75s  Devices in the IoT will monitor location, activity and incident, supporting health/care staff and carers, enabling early intervention TIHM
  34. 34. The Health Challenge: Carers  5.4m carers supporting ill, older or disabled family members, friends and partners in England - expected to rise by 40% over the next 20 years.  Value of such informal care estimated at £120bn a year – but carer ‘burnout’ a key reason why loved ones require admission to a care/nursing home.  Devices in the IoT will support carers in their caring asks – and support their own health and wellbeing. TIHM
  35. 35.  Infrastructure  Interoperability, integration  Security  Data governance  Scalability Technical Challenge
  36. 36. Innovation Partners Nine companies with 25+ devices and services, including monitors, sensors, apps, hubs, virtual assistants, location devices and wearables
  37. 37. Device/Data interoperability 37
  38. 38. Gate way 1 Gate way 2 Gate way 3 Proprietary Cloud/Data Services TIHM Cloud Hy pe rC A T Hy pe rC A T Hy pe rC A T Multiple providers/multiple gateway (not ideal)
  39. 39. TIHM Middleware Connectivity/Device Association Layer Data Exchange/Interoperability Layer Service/Application Layer Blue toot h WiFI ZigB EE TIHM Cloud Hyp erC AT RES T API Proprietary Cloud/Data Services Hy pe rC AT Hy pe rC AT
  40. 40. 40 “Each single data item is important.” “Relying merely on data from sources that are unevenly distributed, without considering background information or social context, can lead to imbalanced interpretations and decisions.” ?
  41. 41. KAT- Knowledge Acquisition Toolkit F. Ganz, D. Puschmann, P. Barnaghi, F. Carrez, "A Practical Evaluation of Information Processing and Abstraction Techniques for the Internet of Things", IEEE Internet of Things Journal, 2015. 41
  42. 42. Gateway Gatewa y Data Analytics Engine IoT Test Bed Cloud External NHS, GP IT systems Possible links to Other Test Beds HyperCat Gateway HyperCat HyperCat HyperCat Data-driven and patient centered Healthcare Applications TIHM Testbed Architecture
  43. 43.  Extend into homes – year 1 via two CCG areas, rolling out across four more CCGs in year 2  Reach 350 homes – with a control group of 350 – via dementia register  Focus on most effective product combinations – with potential for more via an open call Roll Out NE Hants & Farnham Living Lab Guildford & Waverley Rest of Surrey And beyond… TIHM
  44. 44. In Conclusion − Great opportunities and many applications; − Enhanced and (near-) real-time insights; − Supporting more automated decision making and in-depth analysis of events and occurrences by combining various sources of data; − Providing more and better information to citizens; − Citizens in control − Transparency and data management issues (privacy, security, trust, …) − Reliability and dependability of the systems 45
  45. 45. More connected wearable devcies 46
  46. 46. Boundary between human, technology and devices 47
  47. 47. Cognitive systems era 48 connected and intelligent systems
  48. 48. Accumulated and connected knowledge? 49 Image courtesy: IEEE Spectrum
  49. 49. Other challenges and topics that I didn't talk about Security Privacy Trust, resilience and reliability Noise and incomplete data Cloud and distributed computing Networks, test-beds and mobility Mobile computing Applications and use-case scenarios 50
  50. 50. Q&A − Thank you. @pbarnaghi