There have been remarkable shifts in what we thought possible in the realm of healthcare over the past quarter century. Whether that’s the shift from the digitization of patient files with the advent of electronic medical records (EMRs) or, more recently, the transformative potential of AI powered healthcare impacting clinical outcomes, the technological revolution has imbued the healthcare space with unprecedented levels of advancement.
In this blog post, we’re going to dive into some of the aspects of AI powered healthcare, but with a unique wrinkle: the scalable and long-lasting strategic AI approach known as the AI platform.
In 2019, author Eric Topol, MD, wrote “Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again,” casting an optimistic light on the ways in which AI could transform everything physicians do – from notetaking and image analysis to diagnosis and treatment- while reducing the costs associated with care and allowing for better connections between patients and clinicians.
Though we are still in the earlier stages of AI in healthcare, we have certainly seen a laundry list of benefits that support the optimistic vision of Dr. Eric Topol.
While the power of AI in healthcare has often manifested through single point solutions, the marketplace has evolved. Health systems are no longer content with a one off AI solution for one pathology. In fact, many systems are opting to include stakeholders across the organization when setting out on their AI journeys.
In order to include the voices of the variety of AI benefactors in a health system, leaders are finding the AI platform to be the best solution. In short, an AI platform offers a level of scalability not offered by single use case models because it accounts for the many stakeholders throughout the health system whose workflows are being impacted by AI.
The infographic below provides insight and ideas as to which stakeholders should have a seat at your facility’s AI strategy table:
While we alluded to it in the last section, there are a variety of ways to integrate AI into your health system:
Like healthcare itself, paths to clinical AI integration haven’t and will not be one-size-fits-all. The three approaches outlined above, however, have gained traction, each presenting its own unique set of advantages and challenges as we pursue a total AI powered healthcare system.
Ultimately, health systems looking to reap the benefits of AI would behoove themselves to start with a strategy, not an algorithm. Is it the chicken before the egg? The cart before the horse? Running before you can walk? There are many idioms that speak to the same idea: a desire for fast results. Implementing clinical AI is the same. Many healthcare providers become obsessed with finding the golden algorithm. The one that promises immediate impact. This, however, is like purchasing a tool without knowing what you are going to build with it.
It all begins with a comprehensive AI strategy. Click here to view our strategy page and resources.
It’s nearly impossible to see a major shift in an industry such as healthcare that doesn’t have its share of challenges. Below are a few of the challenges experienced as AI in healthcare is becoming less of an outlier and more of a standard practice:
Imagine a vast ocean of healthcare data. It spans as far as the eye can see and promises limitless potential for improved outcomes. But this ocean isn’t pristine – it’s contaminated by unstructured notes, inconsistent formats and ever-shifting protocols. To some, it may also not be accessible. Navigating this ocean is treacherous, and the reality for AI implementation.
Unreliable and Unstandardized Data
A notable portion of healthcare data isn’t structured, meaning it lacks a predefined format or organization. This includes narrative clinical notes, radiology reports and patient-generated data. It’s challenging to process and analyze using traditional methods.
Data “Noise”
Creating an AI powered healthcare system involves countless data challenges. For instance, correctly matching an algorithm or combination of algorithms to the appropriate study while avoiding exposure to data not relevant to a given algorithm may seem straightforward, but it’s a complex process and often is underestimated. It’s important to monitor the “yield,” defined as the percentage of exams correctly analyzed by the appropriate algorithm. Relying on manual methods to match protocols to AI algorithms means a significant portion of exams that should be analyzed will be missed, thus not equally applying the benefits of AI to all exams.
Picture a hospital unit, not filled with patients in beds, but with AI point solutions occupying individual rooms – each functioning as its own silo of data and functionality. Radiologists are forced to go from room to room to piece together insights from each algorithm. Physicians are bombarded with calls from each room. AI is becoming more of a burden than a sidekick. You get the picture.
Limited Resources to Manage Solutions
In the face of limited resources and the ever-growing complexity of AI solutions, health systems need a streamlined approach to manage and deploy these technologies. Without it, many problems can manifest, including unique hardware requirements for each solution, inefficient integration processes and scattered governance approaches.
In actualizing the power of AI in your healthcare facility and reaping its benefits, there are several steps you should take:
As the market of available AI options continues to grow, it’s important to ensure the platform you select expands AI strategy beyond individual algorithms. You need a partner that enables growth with evolving patient care needs, facilitates change management and establishes a robust governance framework.
A great deal of scrutiny is required when assessing your AI partnerships. Here are a few sample questions you should consider asking when evaluating your options:
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