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Today: 3 ways to get more out of health care data - The Advisory Board

Advisory Board Blog post of getting more value out of your healthcare data.


Today in health it and advisory board article healthcare, Dina blog series three ways to get more out of your healthcare data. My name is bill Russell. I'm a former CIO for a 16 hospital system and creator of this week health. Instead of channels dedicated to keeping health it staff current and engaged.

We want to thank our show sponsors who are investing in developing the next generation of health leaders, Gordian dynamics, Quill health. Tau site nuance, Canon medical, and current health. Check them out at this week. All right, every now and then I come across an advisory board blog or article.

That I think would be relevant. , Actually, this is part of a series and I think the whole thing's relevant. So they have a two installments of the series prior to this one. And they look at opportunities that lie in health data. As well as the challenges that preclude us from reaping greater value from the data.

Okay. So that's what they looked like. In the first two, and this one,

what they're going to do is propose ideas on how cross industry leaders can use their data. Better to create shared opportunities for their organizations and patients. All right. So, , here are some of the first couple of things. Let's first contextualize. The hypotheses and the constraints, the industry faces. All right. So these are good constraints.

Number one leaders don't want to place additional burden on the clinician. Absolutely agree. A thousand percent, many, many data access and quality challenges could be solved by something requiring clinicians to follow more stringent rules on what the day data they must capture. And in what format, industry leaders often avoid doing this and rightly so.

It's no secret that provider burnout is among the industry's greatest challenges. Then the last standardizing, the expanding data collection without adding new burdens to those collecting the data necessarily will add complexity. To potential solutions. All right. So we don't want to add additional burden to the clinician, but there's a challenge here. We need to collect really good quality data.

All right. So number two, liters opt for limited, but attainable workarounds rather than transformative, but difficult solutions. To root causes. Okay. So in part, because leaders avoid further burdening clinicians, they tend to tackle healthcare data challenges by building work arounds to challenges rather than solving their root causes. For example, NLP is commonly viewed as a way to standardize clinical notes so they can be analyzed at scale.

Yet NLP while an important and worthy tool does nothing to solve the root cause. Problem of unstandardized clinical terminology. The reality is that the industry's model for data collection is so deeply ingrained. And legacy billing model that there is little incentive to tackle root causes head on because they would require uprooting the billing process altogether.

Yeah, absolutely. No offense to any of the EHR providers out there, but at the end of the day, , fundamentally you need to generate a bill or you don't get paid and the health system doesn't get paid. So we can do the run around as much as you want here, but at the end of the day,

That is the underlying process for everything that goes on in the EHR. And obviously quality clinical care. And all of the things are associated with that are deeply important as well. But in order to do that, you have to tackle a very, , naughty problem. Let's just say it that way. So given those two things, right? So given that leaders don't want to place additional burden on the clinicians and leaders opt for limited.

But attainable a work arounds. Here's the three ways to get more out of healthcare data, healthcare needs new approaches for how to draw more value out of its data,

our hypothesis for how to do so are far from fully baked. Rather, they are meant to be a starting point for future conversations and progress to that end. We have included a few of our own open questions on each. Here they are. Focus on data terminology, not just data interoperability data interoperability will indeed unlock greater value from healthcare data, but as leaders pursue interoperability.

They must also work further upstream by tackling one root cause or poor interoperability on standardized clinical terminology. Creating standardized terminology can help ensure that clinical data is usable to researchers and administrators. And AI models by the way, as it is to frontline clinicians.

All right. So this is, this is fundamental. If you're going to do a data governance. , program, you've got to address terminology

and over the years, this has been a pretty gnarly problem in and of itself. This is why you need to understand what us CDI is. Right? So all the interoperability stuff that's going on, it's kind of neat. We're able to move the data from one point to another. But at the end of the day, they have a very strong.

A team working on standardizing the terminology, standardizing the data elements within the model. And that's what us CDI. Is to the whole TEFRA framework and the 21st century cures data interoperability. , and so again, , it is a very hard problem to solve. You have to have strong leadership, you have to bring together clinical leadership and, , data scientists, data experts.

, to really define what the. The terminology should be and how it should flow throughout the system, how it should be represented on reports, you name it. There's just a lot of conversations. And it's extremely important to get right. So they have a couple of open-ended questions here. So how can leaders standardize terminology without disrupting.

The workflows of clinicians capturing the data. And in so far as standardizing Turmo terminology requires placing more burden on clinicians. How can leaders ensure that clinicians share. And the benefit of their increased labor. Good questions to ask, , number two here again, they're postulating some things here. So number two, be judicious and targeted.

With the data you share with clinicians? Yes. You don't want to overwhelm them. And, , let's just read what they have here. So clinicians will likely need to play some role in standardizing clinical data. Meaning their buy-in is crucial yet. They are fatigued by vendor. Pitching them. Data-driven tools promise to reap outsize rewards.

On clinical and business success. And by how often they fail administrators, executives, and vendors. I like must be more judicious when choosing what data they share with frontline clinicians and in what format. They also must target their data, sharing towards metrics that are most useful and actionable for clinical decision-making. You know, I think before you do anything, you should gather the data. You should be able to, , represent the data and tell stories around the data.

I mean show the disparity within the data to the clinicians. They will understand. The risk that it creates to have non-standard data flowing around that medical record, they will understand it. They will buy into it. They want better health care. I just think we need to tell better stories and we need to be stopping so frivolous with, Hey, I got a new tool and this tool is going to solve this problem.

The number of times that someone says a tool is going to solve this problem. And it actually does is very rare, right? There's always work associated with that. There's always workflow changes.

And it requires time. In crunch time, effort, it requires training. And I change. And to be honest with you, I think one of the things we've overburdened. Clinicians with is the amount of change we throw at them. I mean, we're doing, how often are we doing cycles of updating the EHR? Which potentially changes some aspect of their workflow or some aspect of what they're doing.

So again, keep. Thinking about , the amount of burden you're putting on the clinicians, just with your, your daily operations and the change that you're implementing. Number three, design business models that incentivize data sharing across organizations, traditional businesses and payment models have tended to disincentivize data sharing.

Under fee for service payments. For example, providers are averse from aggregating claims data with other providers out of fear of exposing their negotiated reimbursement rates. While these competitive constraints are unlikely to dissipate leaders can still drive greater value sharing. By creating partnerships that incentivize member organizations to pool their data for shared benefit and they cite true Veta as one of the examples.

And by the way, I don't disagree that Truvada creates an awful lot of value for these health systems that have decided to share their data through Truvada. , anytime you've heard me speak anything at all negative about Truvada. It is about privacy. And, , the fact that my data is in Truvada's system and I had very little say over that.

And then the second thing was security. I want to make sure that that security model is overseen by a group of security architects that really understands security. I never liked when you aggregate that much data. Into it, it becomes such a rich target. And I want to make sure that that target is, , secured. So those are the only two things I want to make sure that the Truvada team really hears outside of that great business model makes perfect sense. They own the data today. They're operating within the law and the parameters.

, to utilize that data for the good of mankind, so forth and so on. So again, good model coming together, partnering with the data I think is going to lead. To a lot of benefits. What's my, so what here? My, so what, here is a great article. I'd go pick these up the advisory board. They've, , it's a it's in their blog, so it's, it's open to anybody.

And it's not necessarily solutions for you. It's just things to help you to think through what you're doing

In your data practices. All right. So leaders don't want to place additional burden on clinician leaders opt for limited, but attainable, workarounds. That's the environment they believe we're living in today. We used to get more value out of your data. Focus on data terminology, not just interoperability.

Amen. , be judicious and targeted with the data you share with clinicians. Don't overburden them with too much information, give them actionable data and design business models that incentivize data sharing across organizations. These are all wonderful points. Glad they made them. Happy to share it with you. That's all for today.

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