As a hospital or health system, you’re doing everything you can to support clinicians and provide excellent patient care – so why are your finances suffering? The answer, more often than not, is poor data quality. Inaccurate or incomplete data can jeopardize revenue cycle management, leading to increased denials and lower reimbursement rates.
In a recent webinar, Leveraging Data for Smarter Financial Decisions, made in partnership with Modern Healthcare, leaders from Valleywise Health, IMO Health, and UW Health shared how they’re using analytics, AI, and automation to manage risk, boost reimbursement, and optimize revenue cycle workflows.
Short on time? Keep scrolling for 5 strategies to improve financial outcomes in healthcare.
1. Use data to prepare for policy changes – before they take effect
“Right now, with the potential federal legislative proposals for significant changes, these could affect healthcare reimbursement… so right now we’re going through the various different options and proposals that are there with as much knowledge as we can, using data to try to estimate what that impact will be.” –Claire Agnew, Chief Financial Officer, Valleywise Health
Agnew explained how Valleywise Health uses data to model different legislation scenarios. As a robust safety net system with Medicaid as its largest payer, even the smallest changes can have significant impacts, she said. By thinking ahead and leveraging data, the health system can alleviate much of the uncertainty that surrounds policy changes.
2. Prioritize price transparency for a competitive advantage
“[Price transparency] has really provided insight into payer rates with others that operate in the same markets that we do. Analysis and analytics of that information allows you to go into negotiations with an understanding of where your organization sits compared to other healthcare providers.” –Robert (Bob) Flannery, Chief Financial Officer, UW Health
Flannery emphasized the value of price transparency when negotiating with payers. Instead of showing up to meetings with vague numbers and estimates, his team can now enter such conversations with facts – making them more productive. Broom connected this to IMO Health, explaining how we equip businesses with these reports, allowing “CFOs like Claire [Agnew] and Bob [Flannery] to benchmark themselves against other organizations.”
3. Avoid unnecessary denials with data analytics
“We collaborated with customers, health systems, in order to do analyses on their denial data… and be able to track that all the way back to where that was occurring in the workflow. By doing that through data analytics, we were able to provide a very targeted and tailored solution that has allowed hospitals to see about a 10x annualized ROI.” –Megan Broom, Sr. Director, Product Sales, IMO Health
“It’s getting harder and harder for providers with all of the things that are thrown at providers from a payer perspective… [data analytics] really is impactful, I think, in terms of having that information to work with our teams to make sure that we can avoid those denials by doing it the way the payers want to on the front end.” –Flannery
Denials are often handled on the back end, retroactively, but they’re triggered by missteps that occur much earlier in the revenue cycle process. Broom shared how IMO Health has partnered with various health systems to analyze their denial data and provide specific, personalized solutions for higher ROI. Flannery echoed the value of data analytics in helping provider organizations align with payer expectations.
4. Supercharge your revenue cycle with AI-powered tools and automation
“We’ve made some significant changes inside of our rev cycle. I’ll give a couple of examples; predictive analytics for payment collection, whether it’s from the patient or from insurance, automated coding such as single-visit coding, claim scrubbing, and some error detection tools – things to help us with denials and appeals.” –Agnew
Agnew spoke about how Valleywise has integrated automation and other AI tools into their revenue cycle strategy to fortify it and reduce denials. From claim scrubbing to predictive analytics, these advanced tools help to prevent denials before they even occur.
5. Feed revenue solutions clean data – ‘garbage in, garbage out’
“What I’m looking to do is really drive as much data quality in the health system as possible so that when they do make investments in AI and machine learning solutions, that those are fed with the cleanest data so they can get the most accurate, cleanest result out of it.” –Broom
“One of the changes that we’re also going through right now is a refuel in Epic, which is our EMR system… we’re trying to bring it down to the foundation level, because the more that we are like the other Epic facilities, then the more we can use comparative data inside of Epic… it starts with good data.” –Agnew
If you feed poor-quality data into AI applications, you’ll get poor-quality results, said Broom. That’s why IMO Health focuses on ensuring that health organizations have precise, usable data from the start – and as a result – analytics, billing, and decisions they can trust.
