Apr 06 2023

Having a Clear Vision for Automation and AI Amid Economic Pressures

Healthcare organizations shouldn’t sacrifice innovation as they face tight budgets and a strained workforce.

An increase in automation will likely sweep across industries soon. According to a 2022 Gartner survey, 85 percent of infrastructure and operation leaders who don’t have full automation now expect to become more automated within two to three years. 

In healthcare, interest in automation and tools powered by artificial intelligence continues to grow amid a grim economic forecast and an overburdened workforce. Based on discussions during industry events and one-on-one meetings, it appears that healthcare leaders are also looking to leverage technological solutions such as automation to alleviate some pressures.

Financially, healthcare organizations are also at a crossroad: The omnibus bill approved by Congress in December has provided more solid ground around telehealth, some health systems may still have savings from pandemic-related funding, and vendor contracts that were signed during the public health emergency may be up for review.

As providers examine their financial health, they’re looking for key investments and emerging solutions to reduce burdens and add value to their organization. There are examples of success among their peers that can help inform those plans. Healthcare organizations shouldn’t sacrifice innovation and change, even at a time of economic challenge.   

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Where to Start with Healthcare Innovation if Budget Is a Concern

If your healthcare organization is on a tight budget, start looking internally and examine the investments you’ve already made.

Begin with an app rationalization effort before taking on a new solution. How many applications does your organization use that perform the same tasks? Where do solutions overlap? Might a single platform do the work of hundreds or thousands of apps? Are you fully using the solutions? Do you need to deploy everything from one vendor? Look inward first, because if you haven't done that work, then you're not ready to bring on something new.

Another consideration is to move away from individual projects and endpoint solutions to a platform solution that can solve multiple use cases. Start with the best use case for a clear return on investment.

DIVE DEEPER: How to create digital transformation and innovation in healthcare.

How to Make the Case for a New Solution 

Whether it’s a one-year or five-year plan, the sooner you can put it together and get the necessary people on board, the easier it will be to execute the solution implementation. Make sure the plan includes ROI, how the solution helps patients, how it would meet legal and regulatory compliance and any billing integrations, if possible — those are the boxes that, if checked in advance, make it much easier to get buy-in to move forward with the project because you've already vetted objections and continued to refine the case for the planned technology. That way, it’s clear that the solution is supported not because someone thinks it's shiny and new, but because there’s a clear need and backing for it by relevant stakeholders. 

Also, consider a strategic IT partner, because knowing the ins and outs of what a vendor offers is not generally among a health system’s core competencies. Such a partner can support an organization through an app rationalization effort because it knows the technology. A partner can also help with AI solutions if that skill set isn’t available in-house, providing guidance on ethical considerations and AI governance.

LEARN MORE: Why healthcare leaders must address AI with a sense of urgency.

The Buzz of the Latest AI-Powered Solutions in Healthcare

Generative AI tools such as ChatGPT have been both a blessing and a curse to the forward momentum of AI in healthcare. Healthcare leaders will need to sift through the noise to get to the solutions that have legitimate uses now and in the future.

Though ChatGPT has been dominating headlines recently, other AI-powered tools such as robotic clinical automation and machine vision will need different building blocks.

Generally, the biggest building block across solutions is data. What does your organization’s data look like? Do you have a strong data governance program? Have you gone through a data modernization effort? If you don't have a good data platform and data governance strategy and you’re trying to build automated workflows, then the solution likely won’t accomplish what you envisioned.

Fundamentally, you’re going to have to change the way you do business when you're designing an AI deployment. You can't simply replicate what you're already doing. Don’t shy away from starting at the very beginning, redesigning and going from there. That’s another building block: having the organizational culture to try, fail and try again.

This article is part of HealthTech’s MonITor blog series.


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