Scene from the Movie Interstellar — Set in 2067

What ‘Interstellar’ Can Teach Us About AI and Practical Innovation in Healthcare

Dr. Salim Afshar
Reveal AI in Healthcare
6 min readOct 19, 2024

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Reflections on my way to HLTH 2024 in Las Vegas

In the movie ‘Interstellar,’ set in the year 2067, we witness a scene that speaks volumes about both human advancement and our curious reliance on old tools. The astronauts in the film, amid the most sophisticated technology ever imagined, are placed into Hypersleep Pods — a futuristic invention designed to slow aging, conserve resources, and ensure survival over decades of space travel. Yet, in this hyper-advanced environment, Anne Hathaway’s character is shown using a sphygmomanometer a blood pressure cuff first developed in the 1890s — to prepare Matthew McConaughey for his long sleep. It’s a jarring juxtaposition: a piece of 19th-century technology operating alongside the wonders of interstellar exploration. It’s like using a rotary phone to send a text — adorable, but a little concerning.

As I am about to attend the HLTH conference in Las Vegas, I can’t help but feel that healthcare today is in a similar place.

HLTH is one of the largest gatherings in the health industry, expected to attract over 12,000 attendees, including more than 2,750 CEOs and 400+ speakers. Attendees will include investors, healthcare providers, payers, employers, startups, and representatives from government and consumer tech sectors. The conference will feature keynotes from notable figures such as Greg A. Adams (CEO of Kaiser Permanente) and Kimberly Powell (VP at Nvidia), covering a wide range of topics including AI and emerging technologies, health equity, mental and behavioral health, consumerism in healthcare, and more.

We have visions of space-age technology, artificial intelligence, and personalized medicine, but our everyday infrastructure often looks more like the blood pressure cuff than the Hypersleep Pod.

Many healthcare executives and innovators are still using systems that are rudimentary compared to the potential we now see on the horizon. Electronic Health Records (EHRs), for instance, have become the metaphorical blood pressure cuffs of our era — ubiquitous, somewhat functional, but hardly transformative. Because medical record keeping software is often proprietary and won’t communicate with software from other hospitals, we still use faxes as a simple workaround. So talking about AI in healthcare sometimes feels like a person who is driving a horse and buggy talking about how they can us AI to create and autopilot navigation system — great ambition, but we need to sort out a few things first.

Over the past decade, through my work at Boston Children’s Hospital and beyond, I’ve observed a pattern of stages that systems go through who successfully incorporate sustained innovation and change. They are like baby steps towards getting to a point of true AI enablement in hospitals. These stages are not only crucial for large institutions but are also adaptable for smaller community health systems, offering a pathway to shift from legacy systems to an AI-empowered future.

The first stage is developing a common purpose — a unified understanding of why AI matters and how it could serve a shared vision. And before we go too far, we need to define for the community: what exactly is AI? What are its capabilities, and how can we use it in our current roles and systems to make things better or fundamentally rethink our operations? We need to understand what’s possible today and not get distracted by Elon’s robots and AGI predictions. AGI, or Artificial General Intelligence, refers to a level of AI that can understand, learn, and apply knowledge across a wide range of tasks — essentially, AI that is as versatile as a human. While it’s an exciting concept, we need to focus on what’s achievable today rather than getting lost in futurism debates.

This phase is more about building culture than technology. It’s about learning, dreaming, and, most importantly, engaging the entire healthcare community in a discourse on innovation. I mean everyone — nurses, doctors, staff, and even the community you serve. Establishing trust and a shared purpose is the foundation for all future advances. Universal participation is key; every voice matters. And while we’re at it, the flow of resources, knowledge, and information must be evaluated continuously to ensure we’re genuinely building a community based on trust. It’s basically like trying to get everyone on the same karaoke song — if one person is singing ‘Bohemian Rhapsody’ and someone else is belting out ‘Total Eclipse of the Heart,’ it’s going to be chaos.

The second stage involves translating that vision into actionable thoughts. This is when the organization starts to identify low-risk, high-impact areas where AI could make a difference. The infrastructure needs begin to crystallize, and partnerships are formed. Executive leadership must visibly commit to this process, ensuring transparency and the allocation of resources to create a clear roadmap. This is where dreams meet spreadsheets, and everyone tries to pretend they know what “machine learning ops” actually means. And worse, many organizations have no clear roadmap to advance beyond them.

The third stage is about gaining practical experience. This means taking action — implementing pilot projects, reflecting on the outcomes, and allowing those lessons to shape further endeavors. It’s all about trying, sometimes failing, and always reflecting with a spirit of learning, not killing. This action, relefection and learning, need to become ingrained in the culture of the organization. It’s also a moment for organizational alignment — adjusting structures and processes to accommodate innovation sustainably. This might mean tweaking roles, reshuffling priorities to better support the changes underway. This stage is like rearranging furniture to make room for something new.

I have seen people approach these stages in many different ways. In my current role as Chief Medical and Innovation Officer at Reveal HealthTech, I have seen large enterprises engage us to build their MLOps platform so that they can fully enable the Data Science teams they are heavily investing in, to hospital systems that are asking us to help them design a practical roadmap that goes beyond the implementation of AI scribes. The noise is significant, but despite many organizations being in vastly different places, we won’t move forward unless we engage honestly, ensure universal participation, and clearly agree on our shared purpose. The flow of resources, knowledge, and information must be evaluated to make sure we’re genuinely building a community based on trust. We have to put aside games that are too often played within organizations and work towards building unity. Only through collaboration can we overcome the barriers and ensure a meaningful transformation in healthcare. It’s like trying to assemble IKEA furniture — if we’re not all following the same instruction manual to build a chair, we’re going to end up with modern art instead.

The fourth stage is the systematic integration of external AI products, coupled with capability-building for frontline staff. Trust in AI isn’t created overnight — it’s earned through education, transparency, and hands-on experience. Nurses, doctors, and other healthcare workers need the support to grow their confidence and understanding of these new tools. You can’t just drop a chatbot into a hospital and expect everyone to be best friends with it. Trust is earned, much like convincing your cat to use the new, supposedly “better” litter box.

Finally, the fifth stage is restructuring current business lines to maximize patient benefit. AI isn’t just about making existing processes faster or more efficient; it’s about reimagining them to prioritize patient experience, outcomes, and equity. True transformation requires reshaping how we deliver care, guided by data and by a relentless focus on those we serve. In other words, it’s about rethinking the entire system so it works for the people it’s meant to serve — kind of like turning a Rube Goldberg machine into something that actually makes coffee instead of just looking impressive.

The scene in “Interstellar” with the old blood pressure cuff reminds us that even as technology advances, we often hold onto familiar tools — sometimes out of necessity, sometimes out of habit. The journey from where we are now to where we could be requires letting go of some of these old habits and taking intentional steps toward a more integrated, innovative future. The road isn’t simple, but as I prepare to attend HLTH in Las Vegas, there’s a growing recognition that these baby stages are no longer optional — they are essential for the future of healthcare. Besides, if we can figure out how to put people to sleep for 35 years in a metal box, surely we can figure out how to make healthcare work just a little better for all of us.

Salim Afshar MD

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Reveal AI in Healthcare
Reveal AI in Healthcare

Published in Reveal AI in Healthcare

the “Reveal AI in Healthcare ” publication explores questions, concerns, and innovations related to the emerging AI landscape in healthcare and all things related

Dr. Salim Afshar
Dr. Salim Afshar

Written by Dr. Salim Afshar

Chief Medical & Innovation Officer at Reveal HealthTech | Faculty at Harvard. https://www.revealhealthtech.com/

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