Wed. Nov 20th, 2019

Health Big Data and EA Convergence Workshop

Health Big Data and EA Convergence Workshop

27-29 October 2019

Thimphu

Background

Khesar Gyalpo University of Medical Sciences of Bhutan (KGUMSB) in collaboration with Ministry of Health (MoH), MIT (Massachusetts Institute of Technology) Critical Data Team, Asia eHealth Information Network (AeHIN), and Bhutan Foundation are organizing the “Big Data Bhutan and AeHIN Convergence Workshop on Enterprise Architecture” from 27-29 October 2019. The workshop is being organized back-to-back with the ‘International Conference on Medical and Health Sciences’ with 2019 conference’s theme as “Mental Health Matters: Everyone’s Responsibility.

This is a ground-breaking Datathon workshop to be held in Bhutan for health. Big data has been the buzzword in the digital world. Healthcare industry is predicted to undergo yet another high-tech makeover in next few years with improvement in electronic health record system, availability of high-power computers and development of Artificial Intelligence (AI). AI is going to transform delivery of healthcare services and it is going to become the new normal across virtually every sector of the healthcare industry.

The major AI applications in healthcare include diagnostics, robotic surgeries and virtual nursing assistants. It is estimated that healthcare AI business will reach $6.6 billion in value by 2021 and that adoption of AI in US alone could save healthcare industry $150 billion annually by 2026.

Following are few example cases of AI application in healthcare:

  • Predicting ICU transfers–saving lives by early detection of deteriorating condition with AI
  • Medical testing– predicting relevant medical test results with AI. AI based solutions can be used to help clinicians make better decisions by narrowing the types of tests that are likely to be useful for a patient.
  • Predicting hospital acquired infection– recommended treatments and preventive measures for future patients. Using AI driven models, providers can predict which patients are most likely to develop central-line infections by looking at a variety of data including patient information, treatment history and staff history.
  • Predicting hospital readmissions – proactive assessment of readmission risk. AI is most suitable candidate to perform the task where the data inputs are complex and may elude clinicians.
  • Improve diagnosis of clinicians – AI excels at categorizing data, especially once it has been exposed to large amounts of data on the subject. Medical imaging analysis and patient medical records, genetics, and more can all be combined to improve diagnostic outcomes. Moreover, AI tools can use similar information to craft unique treatment approaches and offer recommendations to doctors. It is also going to speed up the image reading time thereby saving clinician’s time.
  • Improve clinical workflow – better decisions with AI based decision support and diagnosis. AI based decision support and diagnosis can help clinicians make better decisions by incorporating more data into decision-making process and by learning patterns that are outside the clinician’s purview.

These are only few examples but its application ranges from public health surveillance to supporting better evidence-based policy decision making. While developed nations are leveraging on these innovative technologies, for most developing countries, big data and AI are still unheard or poorly understood. The United Nations approved the Sustainable Development Goals for 2030 and many of the goals require the ability to process large volumes of data to monitor progress. Big data will be an important tool in achieving and sustaining the SDGs.

The motivation for us organizing this important workshop in Bhutan comes from His Majesty the 5th Druk Gyaplo’s addresses at the 1st convocation ceremony of KGUMSB and 14th RUB’s Convocation ceremony. His Majesty The King emphasized the importance of embracing and adopting innovative technological development of Artificial Intelligence, Quantum Computing, Blockchain, Machine Learning, Big Data, IOT (the Internet of Things), Virtual Reality and Augmented Reality in Bhutan. His Majesty stated “These technologies excite me as they present opportunities for the future. Among the many countries in the world, those which prepare for this change and build the foundations to take advantage of such technological advancements will prosper and develop. Those which are not able to do so will invariably be left behind.”

 

Therefore, Bhutan should embrace modern technology and build our capacity to leverage the advantages of modern technologies to improve healthcare services and other socio-economic development. The academic institutions like universities and research organizations should play a key role in the introduction and training of Bhutanese in the application and use of these modern technologies. Therefore, KGUMSB has taken the initiative to organize this workshop on Big Data for Health in Bhutan.

 

The main objectives of organizing the workshop are to:

  • Introduce principles and application of Big Data for health in Bhutan
  • Create awareness on how innovative Big Data science such as Artificial Intelligence (AI) or Machine Learning (ML) are applied to improve gamut of healthcare services
  • Develop Bhutan health data enterprise architecture framework to align collection, storage and maintenance of all health related data for application of AI or ML.
  • Share experiences and challenges of other countries where AI and ML are applied for improving and digitizing healthcare services
  • Introduce and provide hands-on training on open source data science tools
  • Facilitate networking and future collaboration with the world-renowned experts and Bhutanese partners
  • Build foundation for establishing AI and Computer Simulation Centre for Medical Education and Healthcare Services at KGUMB

Workshop Program

This workshop is split into 3 different sessions:

  1. Big Data for medical and health Sciences students on 27 October 2019. This workshop is exclusively for students from the Faculty of Nursing and Public Health (FNPH), Faculty of Postgraduate Medicine (FoPGM), Faculty of Traditional Medicine (FoTM), Royal Thimphu College (RTC), Appollo Bhutan Institute of Nursing, Aura Academy of Health Sciences and visiting nursing students from Deakin University, Melbourne Australia.
  2. Asia eHealth Information Network Convergence Workshop on Enterprise Architecture on 28 October 2019 for delegates from Asian countries.
  3. Big Data for Health on 29 October 2019 for Healthcare workers and other relevant sectors of Bhutan.

For more details please refer the workshop program given in the Annexure 1.

 Speakers

A total of 21 world renown experts in diverse field of application of AI will be speaking during the workshop. These experts come from 7 different countries (USA, Australia, Singapore, Japan, Thailand, Philippines, and Myanmar) and will be sharing their experiences and challenges. Therefore, this workshop will be offering very enriching learning experiences to the Bhutanese participants.

The details of the expert speakers are given in AeHIN portal:

 http://www.asiaehealthinformationnetwork.org/bhutan/

Target audience

Since this is a first ever Big Data workshop in Bhutan, the organizers have tried to be as inclusive as possible by inviting participants from many governmental, academic institutions, and autonomous bodies including private sectors. Although, the workshop is focused mainly on application of AI for health data, we have invited participants from other sectors to create awareness and education so that similar workshops and collaborative research can be carried in their own sectors.

Funding Support

Bhutan foundation and MoH has generously funded local logistics whereas, AeHIN and MIT Critical Data Team are funding their own travel, local accommodation costs.

Annexure 1: Workshop Program

Big Data Bhutan and Convergence Workshop on Enterprise Architecture

27 October 2019: Big Data for Health-Students

08.30─09.00 Registration
09.00─09.20 Welcome Remarks Dr Neyzang Wangmo
10.20-10.00 Data Science to Improve Population Health Leo Anthony Celi MD MS MPH
10.00─10.30 Quality Improvement in Healthcare Ngiam Kee Yuan

Peggy Lai MD MPH

David Pilcher MBBS

10.3010.50 Photo session followed by coffee break
10.50─11.10 Global Surgery: Past, Present, and Future

 

Mark Shrime MD PhD
11.10 – 11.30 SurgiBox: The Operating Room in a Backpack

 

Olivia Waring

 

11.30─12.00 Multi-stakeholder Approaches to Achieve Global Health Mark Shrime MD PhD

Shion Seino

Euma Ishii

12.0013.00 Lunch break
13.00─13.20 Nursing in the 21stCentury Tiffany Wang RN
13.20─13.40 Respiratory Therapists: Role in Emergency Medicine and Intensive Care Joseph Byers RRT

 

13.40-14.10 Fostering a Culture of Innovation Michael Morley MD

Katharine Morley MD

Kenneth Paik MD MS MBA

14.1014.30 Tea/Coffee break
14.30─14.50 Emergency Ultrasound Alon Dagan MD
14.50-15.20 Artificial Intelligence for Healthcare Mengling Feng PhD

Wei-Hung Weng PhD

15.20-15.30 Wrap-up
15.30-16.00 Networking

 

28 October 2019: AeHIN Convergence Workshop on Enterprise Architecture

Time Activity Facilitator
07.30─08.30 Registration
08.30─09.00 Opening Ceremonies To be confirmed
09.00─09.30 Review of Bhutan Convergence Workshop 1 Output
09.30─10.30 Current State of Bhutan eHealth Situation (Overview and Digital Health Challenges in Program Implementation)

 

Many Bhutan digital/ehealth players are already digitizing healthcare and have applications/programs in their agencies. This session aims to allow a chance for these players to present their ongoing initiatives.

 

Mr Garab Dorji

 

10.3010.45 Coffee break
10.45─11.15 AeHIN Mind the GAPS Playbook

 

Recognizing the challenges faced by governments with governing and managing their health data, AeHIN gathered countries together to formulate a strategy. This strategy is now called Mind the GAPS to represent the key investments governments need to make on governance, architecture, program management, standards and interoperability.

Dr Boonchai Kijsanayotin

 

11.15─12.00 Proposed Bhutan Enterprise Architecture Framework

 

The proposed enterprise architecture framework for Bhutan will show how it can enable health interoperability in the country

 

Mr Steven Uggowitzer

 

12.00─13.00 Lunch break
13.00─14.00 §  Country Approaches of GAPS Implementation

§  Governance and Architecture: Phillipine Experience and Challenges (10 mins)

§  Program Management & People: Malaysia Experience and Challenges (10 mins)

§  Standards & Interoperability: Thailand Experience and Challenges (10 mins)

§  Experience from Country Convergence Workshops: Myanmar Experience and Challenges (10 mins)

§  Telemedicine: Indian Experience and Challenges (10 mins)

§  Open Forum: What can we learn from other countries? (10 mins)

 

Dr Portia Fernandez-Marcelo

Dr Alvin Marcelo

Dr Boonchai Kijsanayotin

Ms Aye Aye Sein

 

14.00 – 15.00

Mind the GAPS Workshop Proper (Breakout Sessions)

Mind the GAPS country experiences will only work if there is a shared blueprint for all stakeholders to see, understand, and follow. This session will guide the eHealth stakeholders locate their roles and plot their applications on the GAPS framework and interoperability blueprint for better understanding on how they can contribute to the national ehealth program or big data program.

§ Governance (Facilitator: Dr Alvin Marcelo /Dr PornpimolKijsanayotin)

§ Architecture (Dr FazilahAllaudin/ Dr Portia Marcelo)

§ Program Management (Mr Jai Ganesh/ Ms Aye Aye Sein)

§ Standards &Interoperabilty (Facilitator: Mr Mark Landry/ Mr Steven Uggowitzer)

 

Mr Steven Uggowitzer

Dr Portia Fernandez-Marcelo

Dr Alvin Marcelo

Dr BoonchaiKijsanayotin

Ms Aye Aye Sein

 

15.0015.15 Coffee break
15.15─16.15

Mind the GAPS Workshop Presentation (Plenary Session)

Presentation of group work and synthesis

 

 

29 October 2019: Big Data for Health

Time Activity Facilitator
09.00─09.15 Welcome remarks
09.15─09.35 The Future of Healthcare Ngiam Kee Yuan
09.35─09.55 Opportunities and Challenges of Medical Artificial Intelligence Leo Anthony Celi, MDMS MPH
09.55─10.15 Deployment of medical artificial intelligence: Industry perspective Fred Hersch MBBS, MPH

 

10.15─10.35 Collecting and Wrangling Big Data from Air Quality Sensors Peggy Lai MD MPH

 

10.3511.00 Coffee break
11.00─13.00 Parallel Workshops:

 

1.       Open Source Data Science Tools

 

Shion Seino

Euma Ishii

Kenneth Paik MD MS MBA

2.       Digital phenotyping to track infectious disease outbreaks Olivia Waring

Tiffany Wang RN

3.       Data Analysis Using a Registry Database David Pilcher MBBS
13.0014.00 Lunch break
14.00─14.20 Where there is No Data:
Using Complex Systems Modeling to Evaluate Health Policy in Low-Resource Settings
Mark Shrime MD PhD

 

14.20─14.40 Artificial Intelligence in Ophthalmology Michael Morley MD

 

14.40 – 16.40 Parallel Workshops:

 

Automated Image Classification Ngiam Kee Yuan

Mengling Feng PhD

 

Machine Learning in Healthcare Joseph Byers RRT

Wei-Hung Weng PhD

Transform your idea into a data science research project Michael Morley MD

Katharine Morley MD

15.3016.00 Coffee break
16.00─17.00 Wrap up and Next Steps Facilitator and Participants