Dozens of venture capitalists see the most potential for applied artificial intelligence in healthcare. As noted, technology has already been used to incrementally improve patient medical records, care delivery, diagnostic accuracy, and drug development, but with A.I. we could achieve exponential breakthroughs.
In fact, sick care AI is already here. A new report from Accenture, titled “Artificial Intelligence: Healthcare’s New Nervous System,” provides a bit of clarity on why technology will be a growing force in the medical world.
Accenture predicts the health AI market will grow at an annual rate of 40 percent through 2021, reaching $6.6 billion by that year. In 2014, the market was only at $600 million.
The organization estimates each item will have the following value (or potential annual benefits) by 2026:
- Robot-assisted surgery — $40 billion
- Virtual nursing assistants — $20 billion
- Administrative workflow assistance — $18 billion
- Fraud detection — $17 billion
- Dosage error reduction — $16 billion
- Connected machines — $14 billion
- Clinical trial participant identifier — $13 billion
- Preliminary diagnosis — $5 billion
- Automated image diagnosis — $3 billion
- Cybersecurity — $2 billion
Artificial intelligence is defined as the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. Fundamentally, AI tries to understand the human and non-human processes of thinking, feeling and acting.
Investors think AI will cure sick care. But, doctors and patients are skeptical because they know there is as much art as science to taking care of patients and they doubt high tech, no matter how sophisticated, can replace high touch, intuition or clinical judgement.
However, like EMRs and telemedicine, there are significant barriers to widespread dissemination and implementation:
1. lack of curated data sets
3. risk aversion
4. security and confidentiality
5. workflow disruption
7. education and training
8. manpower shortage
9. the paradox of productivity
11. high tech – high touch conflict
14. ecosystem integration and 4th industrial revolution
15. creating valid business models
1.Dissemination is the intentional, active process of identifying target audiences and tailoring communication strategies to increase awareness and understanding of evidence, and to motivate its use in policy, practice, and individual choices.
2. Implementation is the deliberate, iterative process of integrating evidence into policy and practice through adapting evidence to different contexts and facilitating behavior change and decision making based on evidence across individuals, communities, and healthcare systems.
4. Dissemination and implementation is but one landmark along the digital health innovation roadmap
5. Context, engagement, and evaluation are key
8. Products need to be technically and clinically validated before they are commercially validated
9. There are significant digital health gaps that impede dissemination and implementation
10. The goal is to cover the AREA under the curve: awareness, relationships, engagement, advocacy
11. There are many factors that affect digital health intervention engagement and there is a lot about it that we still don’t know.
12. The research gaps, particularly in digital health adoption and what they achieve, are large. Endpoints can be clinical, experiential or business metrics.
As noted in a recent article, an inadequately prepared doctor may make mistakes one patient at a time, a faulty algorithm could affect an entire population of patients.
Once again, we need to align the elements of the sick care ecosystem to drive innovation and that starts with creating rules. Rules eats culture and culture eats strategy.
Arlen Meyers, MD, MBA is the President and CEO of the Society of Physician Entrepreneurs