What's the Code for Scalable Artificial Intelligence?

What's the Code for Scalable Artificial Intelligence?

What's the Code for Scalable Artificial Intelligence?

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. The 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 implementation, and 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. Frame-storming 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.

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Digital Health Entrepreneurship is available on Amazon and Springer.

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

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  • Daniel Russell

    Brilliant

  • Gareth Haynes

    Interesting stuff !!!

  • Daniel Russell

    When A.I is fully developed then it would be the last invention.

  • Scott Andrews

    Great article

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Arlen Meyers, MD, MBA

Former Contributor

Arlen Meyers, MD, MBA is a professor emeritus of otolaryngology, dentistry, and engineering at the University of Colorado School of Medicine and the Colorado School of Public Health and President and CEO of the Society of Physician Entrepreneurs at www.sopenet.org. He has created several medical device and digital health companies. His primary research centers around biomedical and health innovation and entrepreneurship and life science technology commercialization. He consults for and speaks to companies, governments, colleges and universities around the world who need his expertise and contacts in the areas of bio entrepreneurship, bioscience, healthcare, healthcare IT, medical tourism -- nationally and internationally, new product development, product design, and financing new ventures. He is a former Harvard-Macy fellow and In 2010, he completed a Fulbright at Kings Business, the commercialization office of technology transfer at Kings College in London. He recently published "Building the Case for Biotechnology." "Optical Detection of Cancer", and " The Life Science Innovation Roadmap". He is also an associate editor of the Journal of Commercial Biotechnology and Technology Transfer and Entrepreneurship and Editor-in-Chief of Medscape. In addition, He is a faculty member at the University of Colorado Denver Graduate School where he teaches Biomedical Entrepreneurship and is an iCorps participant, trainer and industry mentor. He is the Chief Medical Officer at www.bridgehealth.com and www.cliexa.com and Chairman of the Board at GlobalMindED at www.globalminded.org, a non-profit at risk student success network. He is honored to be named by Modern Healthcare as one of the 50 Most Influential Physician Executives of 2011 and nominated in 2012 and Best Doctors 2013.

   

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