Digital health definitions vary but the term is usually used to describe the use of information and communications technologies to exchange medical information. How it is used and for what purpose varies from one use case to the next.
Here's how I slice and dice the industy:
1. Remote sensing and wearables
3. Data analytics and intelligence, predictive modeling, AI, blockchain
4. Health and wellness behavior modification tools
5. Bioinformatics tools (-omics)
6. Medical social media
7. Digitized health record platforms
8. Patient-physician patient portals
9. DIY diagnostics, compliance and treatments
10. Decision support systems
Research published by MarketsandMarkets projected that the healthcare artificial intelligence market is expected to grow from $667.1 million in 2016 to more than $7.9 billion by 2022, a compound annual growth rate of 53 percent over the forecast period. This explains why companies such as IBM and Google are dominating advancements as they develop deep learning techniques that can revolutionize the way diseases are diagnosed, treated, and even prevented.
However, with AI’s success, comes its many challenges.
According to Niall Brennan, former chief data officer at Centers for Medicare and Medicaid Services (CMS), one of the key challenges related to whether or not artificial intelligence and machine learning gain traction is “translating it into something tangible that will resonate with payers and lead them to think about realigning financial incentives” to improve patient outcomes and reduce healthcare costs. In other words, while you need to demonstrate technical, commerical and clinical value, you also need to make the business case for translating data to value.
Artificial intelligence is being deployed in sick care in many different ways:
- Surf-n-turf. Patients can use AI driven symptom checkers to identify most likely diagnoses and then triaged (turfed) to the most appropriate care site or healthcare professional
- Patient track and trace. AI is used to track patients with chronic dieases to improve compliance and minimize waste
- Sickcare to healthcare. AI used for disease prevention and maintaining wellness
- Mobile learning. AI used to create personalized mobile learning platforms
- Patient engagement.
- Reducing doctor burnout
- Transforming clinical documentation and EMRs
- Patient reported outcomes (PROs)
- Drug discovery and development
- Supply chain management
- Improving patient safety and quality
- Improving eldercare
- Behavioral health
- Social determinants
- AI hype
- Disparate health outcomes and care inequality
- Precision medicine
- Pattern recognition in pathology, radiology, retinal scans and dermatology
If that doesn't make your head explode, how about combining AI with blockchain? Or using AI to pick AI investment winners? Or a platform that allows you to use AI to create AI?
The list of potential AI applications in biomedicine and clinical care will continue to expand. The challenges for AIntrepreneurs are substantial. Most AI digital health companies will fail (here are the reasons why) and they will have to deal with the social, economic and ethical issues.
We will all need to work together if patients and doctors are to win the 4th industral revolution.
Arlen Meyers, MD, MBA is the President and CEO of the Society of Physician Entrepreneurs.
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