Can AI Reinvent Healthcare Admin? [Newsletter #62]

Hello, AI enthusiasts from around the world.

Welcome to this week's newsletter for the AI and the Future of Work podcast.

Healthcare systems around the world are facing a shortage of personnel. On top of that, hospital staff are often overwhelmed with administrative tasks in addition to their already demanding clinical work. The result is costly mistakes and billions of dollars in wasted resources each year.

AI can help change this, but it must be applied responsibly.

This week’s episode explores how AI can improve the healthcare industry by freeing professionals to spend more time with patients and focus on what they do best.

Let’s dive into this week’s highlights! 🚀

🎙️ New Podcast Episode With William Chan, Iodine Software CEO

Hospitals save lives. Doctors, nurses, and clinical staff are essential to society. Yet our healthcare systems are struggling to survive.

The biggest obstacle is complexity. Tracking one patient means keeping up with treatments, diagnoses, prescriptions, prognoses, and billing. Multiply that by thousands and you have one of the most challenging systems in the world.

This challenge becomes even more urgent when we acknowledge that without funding, there is no mission. Hospitals may have the most noble, health-driven goals, but they need financial stability to operate. Unfortunately, the administrative work needed to get paid often falls on exhausted medical staff, leading to errors and delayed reimbursements.

Personnel shortages make the situation worse, as many regions now lack the headcount they need to keep their hospitals running. William Chan knows this reality well. As co-founder and CEO of Iodine Software, he has spent over a decade helping healthcare providers get paid.

Founded in 2010, Iodine Software pioneered the use of AI to improve hospital revenue cycles. 

PeopleReign CEO Dan Turchin sat down with William to discuss the many challenges affecting the healthcare system and the crucial role AI plays in addressing them.

William’s perspective is grounded and pragmatic. Not every healthcare problem has a perfect solution. Given the complexity of a hospital’s revenue cycle, there is rarely a clear-cut fix. The best strategy is to optimize each critical variable, and that is where AI makes the difference.

In this conversation, Dan and William discuss:

• The nurse and clinician shortage is making manual chart review nearly unsustainable. AI can help hospitals process vast amounts of patient data, serving as a force multiplier.

• Responsible medical AI does not replace humans in the medical field. Instead, it identifies issues and provides recommendations, with the final decision always in human hands.

• Hospitals serve diverse populations that vary by location. AI in healthcare must use diverse and representative data to reduce bias.

• The idea of AI recommending a specific treatment can sound unsettling. William stresses that AI must explain the reasoning behind every recommendation to build trust.

• Completely eliminating administrative work is unlikely, but AI can still help reduce billions in wasted resources.

🎧 This week's episode of AI and the Future of Work, featuring William Chan, inspired this issue.

Listen to the full episode to hear more of William’s views on how we can improve the future of healthcare and the human experience.  

📖 AI Fun Fact

AI in healthcare holds incredible promise, but it also comes with significant risks. Cassie McGrath writes in Healthcare Brew about how to guard against its biggest downside. According to the Deloitte Center for Health Solutions, 75% of large healthcare companies are “experimenting with or planning to scale generative AI across the enterprise”.

To build these models, AI is trained using existing data. For them to work effectively, they must have access to accurate information, which is even more critical in the medical field than in other verticals.

In our newsletters and podcasts, we have covered how concerns around bias and equity continue to plague AI in general. 

In healthcare, these issues take on greater weight because they can directly compromise the quality of care—especially for higher-risk patients. AI can also make it easier to overlook errors in a patient’s chart. Corrupt or biased training data can lead to poor automated decisions, and those mistakes could erode trust between clinicians and patients. 

PeopleReign CEO Dan Turchin reminds us that any bias in AI reflects human bias. AI only knows what we teach it. Whether the decision is life-changing in healthcare or impacting a different domain, we must take responsibility for both the integrity of the data and the outcome for the human.

For AI to be trustworthy, it must be transparent, predictable, and configurable. The answer is not to slow the pace of innovation but to reflect on human bias and make sure we're eliminating it as much as possible from the system, from pre-training to inference.

Worth A Read 📚

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Until next time, stay curious! 🤔

That's a Wrap for This Week!

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