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What’s the CODE for Scalable AI?

Everyone seems interested in getting the most impact out of their innovation efforts. Collaborative Innovation Networks (COINs) have mushroomed since the invention of the Internet and cheap mobile communications. Community based innovation and biomedical and health online innovation networks have the potential to speed new product development, offer alternative financing platforms for early stage ventures, provide education, information and support to those with particular diseases and help to lower the costs and speed of clinical trials. A recently announced collaboration between the FDA and PatientsLIkeMe will make it easier to do post marketing surveillance for drugs.

However, questions remain about their effectiveness in creating value, security, commercial and clinical validity, legal status and sustainability.

Despite advances in computer technology and other parts of the 4th industrial revolution, there are many barriers to overcome before machine learning crosses the chasm. Here are some things you should know about dissemination and implementationand innovation diffusion basics.

There are four basic categories of barriers: 1) technical, 2) human factors, 3) environmental, including legal, regulatory, ethical, political, societal and economic determinants and 4) business model barriers to entry.

Advances in data science and artificial intelligence have challenged organizations, both large and small, local and multinational, in all industries, including sick care, to create the structure, processes and outcomes at scale.

So, what are the best practices for building a Collaborative Online Data Enterprise (CODE) at scale?

  1. Identify the problems within the organization’s data and end user community. Framestorming before brainstorming might help.
  2. Create a centralized system to structure the process
  3. Find the right leadership. Lead innovators, don’t manage innovation.
  4. Build the right organizational processes
  5. Budget appropriately
  6. Provide education and resources to stakeholders
  7. Build internal networks
  8. Assign mentors
  9. Create peer support platforms like office hours, monthly problem sharing meetings
  10. Test solutions and share
  11. Create a centralized searchable database of analytics solutions that are reproduceable
  12. Allow for data science innovation
  13. Break down innovation silos
  14. Create high performance virtual teams
  15. Don’t forget about the people part of AI

Data science at scale is about building the right culture. Inspirational leadership usually works better than motivational leadership. There should be no secret CODE to get into the club.

Arlen Meyers, MD, MBA is the President and CEO of the Society of Physician Entrepreneurs

Dr. Anthony Chang’s book is available on Amazon now! 

Intelligence-Based Cardiology and Cardiac Surgery Intelligence-Based Cardiology and Cardiac Surgery: Artificial Intelligence and Human Cognition in Cardiovascular Medicine (Intelligence-Based Medicine: Subspecialty Series) Data Science, Artificial

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