Artificial intelligence is growing rapidly in healthcare, with many large and small applications gradually entering the workflow of the entire industry.
Whether it is assisting clinicians during telemedicine sessions, recording entire conversations between doctors and patients, writing notes for nurses to answer questions on patient portals, helping patients sort out their problems through chatbots or any other application, AI is proving its usefulness to many stakeholders in the healthcare sector.
Narinder Singh has been working with AI for many years. He is the CEO and co-founder of LookDeep Health, a virtual care, virtual nursing, and virtual care company. Previous roles include working in Accenture’s Strategic Technology Center, a corporate strategy position in the office of the CEO at SAP, co-founder of Appirio, president of Topcoder, and vice president of engineering at webMethods.
Healthcare IT News sat down with Singh to discuss how AI can help increase capacity in telemedicine, the risks AI poses to hospitals and health systems, how provider organizations can mitigate these risks, and the role of AI in scribe technology.
Q. You note that telemedicine will of course remove the burden of distance in healthcare interactions. But you say it does not increase their capabilities. How do you see AI helping here?
ONE. Let me start with some context for why this is an important question, and perhaps the question for the future of hospital care. Every week we talk to hospitals who note that they are either experiencing increased patient load or staffing constraints, and most cite both reasons.
US population over 65 years old increased five times faster than the total population between 2010 and 2020 – the fastest rate in more than a hundred years. This is part of a long-term trend and highlights the increasing age and associated severity of patients that hospitals will care for in the future.
At the same time, we have seen repeated predictions from worrisome people too damaging for nursing and other hospital roles – and that’s not to mention the financial pressures that make expanding staffing levels nearly impossible.
We’ve now had a generation of telemedicine in the hospital – from eICU to teleconsultation and now virtual sitting and virtual nursing. There have been many successes on a project level, but on a macro level, telemedicine in the hospital has generally had a very limited impact on care, with one major exception – COVID.
During the pandemic, we learned that seamless access through telemedicine creates flexibility that allows the system to adapt. However, it does not expand our resource capacity. Telemedicine may bridge large gaps, but it does not change the basic units of work required to deliver care.
AI can mean many things today, but let’s start with what it means for telemedicine—the ability to extend our observational capabilities (rather than how it impacts decision-making). Today, a nurse caring for six patients will be in any one patient’s room for an hour or two. Doctors will generally be in each patient’s room for just a few minutes a day.
As a result, most of the time, patients are not monitored by a physician, despite the fact that much of what is happening to the patient can only be assessed and understood at the bedside.
Are they less active; struggling to get out of bed; does their breathing seem more labored; does the alarm go off because the sensor slips off their finger or the breathing tube slips off their neck; etc?
One branch of AI, computer vision, could allow us to monitor every patient at all times. This could help allocate the most scarce resource in hospitals – the clinical attention of nurses and doctors – more appropriately.
We have decades of evidence that increasing clinical bandwidth has a positive impact on patients. Video alone – even in exciting areas like virtual nursing – will only repeat the frustrations of the past. With AI, we can make better use of our most important constraint, time and expertise.
Imagine a world where AI acts as a guardian angel for patients and their caregivers, identifying potential problems and alerting healthcare professionals before a small issue becomes a major one. This isn’t just about efficiency; it’s about fundamentally changing the way we deliver care.
AI can provide that extra layer of support, ensuring no patient is left unattended, even for a moment. Not to replace the human touch, but to enhance it, making our healthcare system more responsive, more resilient, and ultimately more humane.
Q. You warn that AI poses real risks to hospitals and health systems. What are they?
ONE. Artificial intelligence can streamline prior authorizations, patient coding, and the complex interactions between insurance and health care providers. But it could also spark an epic civil war between them.
This efficiency could lead to a faster but more complex litigation landscape, ultimately requiring more human adjudicators to resolve disputes. Rather than reducing administrative work, it could actually increase it. Generative AI could infinitely scale up the most skeptical stereotypes about overuse and aggressively deny claims.
AI tools are making strides in reducing the time doctors spend on paperwork, especially outside of the hospital setting. But in a hospital setting, the complexity of care and the lack of a defined “visit” mean these tools are still ineffective.
We’ve had years to learn how difficult and specific it is to develop and implement machine learning algorithms in hospitals. The allure of a magical approach that can eliminate that tedious work and integrate it into clinical workflows is appealing but naive.
“Generative” models are relevant to many parts of healthcare, but they are not a golden ticket. They do not address the need to synthesize defined sets of information and repeatedly draw the same conclusions from them. The ability to predict inputs and outputs is critical to assessment and certainty in clinical decision making.
Q. How can hospitals and health systems address the risks posed by AI?
ONE. On the first point regarding the battle between insurers and providers, I don’t see an immediate solution. You can’t have humans trying to handle the volume of claims or responses generated by AI, so getting into this arms race is inevitable.
However, engaging in ways that lay the groundwork for evaluating and incorporating generative models into your workflow will create leverage for the future. Key steps include securing PHI, ensuring checks and balances on output, evaluating models within and outside of their scope, and not alienating your workforce with hasty announcements about replacing their roles just to make a few dollars an hour.
This is just the beginning.
We’ve seen insiders like Sequoia and Goldman question the hype and benefits of generative AI. We’ll go through a valley of despair; but focusing on the practical and not falling in love with the big picture will help many innovation teams avoid being cut. Hospitals need two opposing mindsets.
First, testing is essential. Creating non-clinical content (emails, communications), reviewing EHR context summaries, improving language translation and transcription – these are all areas where AI can be safely tailored and targeted for improvement. These applications can free up valuable time for healthcare professionals to focus on more important tasks.
Second, hospitals should enforce rigorous evaluation and repeatability requirements. For clinical scenarios, you should expect evidence of any claims of capability. Better yet, have an approach to continually evaluate the AI capabilities of the solution. Specific claims should ensure that the same set of inputs produce the same results, maintaining consistency and reliability in clinical decision making.
In other industries, technologists, as Norman Vincent Peale once joked, “aim for the moon and settle for landing in the stars.” In healthcare, we’ve seen the disastrous effects of such strategies that set industries back a decade or more (Theranos in blood testing, Watson in AI for cancer treatment).
You can be pragmatic without being slow – the right leaders will strike that balance.
Q. You’ve seen more than half a dozen transcription companies raise over $30 million in the past few years. Why is that? And what role does AI play in these transcription technologies?
ONE. There are over a million physicians in the United States. Their time is precious, and a generation being treated like both junior experts and data analysts has led to a terrible burnout.
The math is simple, and technology is more accessible than ever. The story goes that “The time is now” is not a new concept, but it may finally be a reality. It is a great use of technological advances.
AI is playing a pivotal role in these transcription technologies by significantly improving the accuracy and efficiency of transcription. With AI, transcription can be done in real-time, with greater accuracy, and at a fraction of the cost.
The challenge is that in recent months, advances in AI have continued at a breakneck pace – redefining the starting point for building such solutions. It is clear that transcription solutions are not basic AI models; they are solutions built on top of basic AI models.
The cost of developing competing solutions has likely dropped by 95%. Better integration with clinical workflows, unique go-to-market models, and innovative derivative solutions remain critically important differentiators. However, the quality of differentiation between leading solutions in the AI aspects of replication will be essentially zero.
As a result, in this future, only inertia will prevent prices from plummeting, which will be great for healthcare providers. Lower costs will make these advanced transcription solutions more accessible to more clinics, further reducing the administrative burden on physicians and allowing them to focus more on patient care.
The surge in investment in transcription companies is a testament to the transformative potential of AI in healthcare – the risk is that commercializing this category will result in over-promising to keep up with investor expectations.
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