10 Lessons from 30 Recent Digital Health Failures. Some May Surprise You.

I’ve spent years scrutinizing the digital health and AI health sectors. I’ve meticulously examined the 30 most recent digital health failures.

I’ve spent years scrutinizing the digital health and AI health sectors. This journey has been an enlightening one, filled with valuable insights. However, it has also been both shocking and alarming to witness a myriad of digital health failures. I’ve meticulously examined the 30 most recent digital health failures, treating each one as an individual case study. Rather than detailing each failure separately, I have focused on uncovering common themes that led to these failures. More importantly, I aim to provide solutions and a strategic plan to overcome these challenges.

My aspiration is that by understanding these lessons, we can contribute to rebuilding trust and credibility in digital health and AI enterprises within the medical community. Despite initial hype, these 30 digital health enterprises faltered and eventually failed.

Learning from these failures is vital if we are to restore the industry's reputation and rebuild the faith that clinicians and patients once had in AI, machine learning, and technology in the healthcare sector.

Mark Roberge of Harvard Business School famously said, “Whether a company is valued at $1 million, $10 million, or $100 million, the failure rate is the same.” In the context of the digital health industry, the failure rate is alarmingly close to 100% if one considers the mere handful of profitable companies among thousands in the sector. What's causing this trend? What are the underlying reasons, and how can we reverse this damaging trajectory for the benefit of digital and AI tool creators, physicians, nurses, and patients alike?

The failure rate in digital health is indeed devastating. However, I firmly believe that giving up is not an option. AI represents the future of healthcare, and despite the challenges it faces currently, we must persevere to realize its immense potential.

Here is my overview.

What Are the Common Reasons That the Digital Health Enterprises Have Failed?

The recent failures of numerous digital health startups, as detailed above, provide key insights into the common challenges and pitfalls in this rapidly evolving sector. The primary reasons for these failures include:

1. Technology and Technical Problems

Technical challenges in AI and software development have led to customer dissatisfaction and loss of trust. 

1.1. AI and Machine Learning Challenges

• Babylon Health: This startup relied on AI to diagnose diseases and claimed that its technology could outperform doctors. The claim, however, led to intense scrutiny, and the bankruptcy filing indicates that the technology may not have lived up to its promise. The technical problems might have ranged from inaccuracies in diagnosis to difficulties in integrating the AI with existing healthcare systems.

• IBM’s Watson Health: Watson Health was an ambitious attempt to leverage AI in healthcare, but it faced significant technical difficulties. Major customer partnerships deteriorated due to underlying technical issues, forcing IBM to sell the division at a loss. The precise nature of these problems isn't detailed, but they could have involved integration, scalability, and reliability challenges.

• DeepScribe: This startup aimed to use AI to transcribe doctor-patient conversations into usable medical records. However, technical problems led to errors in reports, such as incorrect medical terminology and inaccurate medication records. Such flaws would have significant implications in a healthcare setting.