Over the past few years, the buzz in the healthcare industry has been the idea of utilizing artificial intelligence (AI) and machine learning to augment the quality of care for patients and the efficiency of operations for providers.
And for many thought leaders at the forefront, AI in healthcare means more than just algorithms—it means a fully digital experience for the patient and the provider built around trust, actionable insights, interoperability, privacy, accessibility, and transparency. It’s a long list, but if these factors can align over the next few years, it could spell a radically different future for healthcare.
In a recent Net Health webinar, “The Modern Healthcare CIO”, Dwight Raum, CIO of Johns Hopkins shared his thoughts on what it’s going to take to measurably improve healthcare with AI. Raum envisions a future where digital solutions become the initial point of entry that not only guides patients to easier access to clinical care, but that also routes patients to the most efficient path to getting well.
Here are a few suggestions to help improve healthcare analytics through AI and data.
Resist the Temptation to Swing for the Fences with Healthcare Analytics
The immense potential AI brings to healthcare analytics is nothing short of exciting. The employment of data through innovative tools and analytics is unleashing new insights—things that we just couldn’t see before. And while this new frontier brings the temptation to swing for the fences, there are inherent risks that advocate for a slower and one-base-at-a-time approach.
First, many AI models are extremely data-hungry. They’re potentially looking at thousands of different dimensions of information. If the model requires full ingestion of that data to run and execute every time a patient is served, it’s not always a resource-efficient or scalable process.
Additionally, how representative of your population is the data you’re using? Will the training data that feeds algorithms represent only a subset of a patient population? Or is a specific facility’s patient population representative of the training date? These are real risks that need to be addressed. But by taking a more calculated and measured approach to implementing AI, finding answers and addressing the potential bias becomes easier.
Earn the Buy-In for Predictive Analytics and Data
When it comes to effectively employing predictive analytics and data to drive the clinical decision-making process, buy-in is key. And this isn’t just buy-in at the provider level. It requires buy-in from patients and payers, as well.
What does it all come down to? Transparency and explainability. For providers, if we just have a black box that’s making decisions without insight into how those decisions are being made, it can be difficult to build trust. For patients, there is direct market evidence that we can actually influence their behavior through the digital experience. But without the ability to tangibly articulate the insights coming out of the tool, it may be a struggle to get patients to take action and intervene in their own health trajectory.
Build Interoperability
Another factor critical to securing buy-in with AI is the interoperability of systems. We know the healthcare ecosystem is going to rely on a collection of independent vendors. How are we going to make these systems work together?
Not only does this pertain to internal systems like EMR integration, but it pertains to outward-facing practice management and resource utilization tools that determine how we interact, engage, and sustain engagement with patients in the real-world. We need to build a robust environment for people to interact with their providers in a way that meet the demands of the patients by most efficiently leveraging the supply of services and practitioners available.
Learn Lessons from the Pandemic
COVID, for all the negatives that it’s had, and there have been a lot, has driven some positive outcomes. Because of how commonplace the use of technology became during the pandemic, the threshold for getting patients to buy in and try new things became lower. What this means going forward for providers is that the energy required to earn that buy-in is also lower, which can help to free up resources and add to efficiencies.
It’s often said that COVID-19 has been the largest driver of digital transformation in IT regardless of industry. And for the healthcare industry, the forced reliance on digital solutions throughout the pandemic has helped pave the way for new opportunities.
How You Can Learn More to Improve Healthcare Analytics
The discussion on how best to use AI, data, and machine learning to improve healthcare analytics is an important and ongoing one. If you’re interested in learning more about the future of AI in healthcare and how your organization may be able to position itself to capitalize, we’d encourage you to check out the full webinar here.
The Modern Healthcare CIO
Digital Transformation in a Post-COVID World
