How Can We Reduce Unnecessary Lab Testing?

[00:00:03] Dear listeners, welcome to Faces of Digital Health, a podcast about digital health and how healthcare systems around the world adopt technology with me, Tjasa Zajc. Laboratory tests provide doctors with crucial information for diagnosis, monitoring disease progression, evaluating organ function, assessing risk factors for diseases and more.

[00:00:32] Lab tests can be reassuring, however they're often deemed unnecessary. So in this short episode recorded at health we're going to talk about laboratory test optimization. How can it be done without undermining clinical autonomy? What kind of real-time insights can be offered to doctors with tech and more?

[00:00:56] You will hear from Pam Stoll, president of Avalon Healthcare Solutions, an American lab insights company that helps payers and providers optimize treatments, improve outcomes and drive down overall costs.

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[00:01:42] The newsletter is published roughly once a month and offers an in-depth insight into a specific topic. Now let's dive in. Pamela Avalon Healthcare Solutions is known for generating actionable real-time insights from lab results.

[00:02:11] Can you clarify that a little bit? So what exactly does that mean? How does it add value to the table of clinicians that know how to interpret lab results? So I guess I should start with what the big picture problem.

[00:02:25] So today there are 14 billion lab tests that are done every year and of those 14 billion lab tests, those 14 billion lab tests drive 70% of clinical decision making or at least are part of clinical decision making. And yet we know that of those tests, 30% of them are unnecessary.

[00:02:44] We know that one in three genetic tests that are ordered are inappropriate. We know and so there's opportunity for improvement in specificity for ordering of tests and then utilization of tests for the right diagnosis.

[00:02:57] So what, Avalon is the only lab benefits management company in the country that does end to end from the development of clinical policies with leading lab experts in the field to using those lab policies to bump against claims that we get from our health plan customers

[00:03:14] and then to ensure that the tests that are ordered are the right tests and then that the interpretation of those tests are used in the right way based on the health plans policies as well as the needs of the patient.

[00:03:26] So what in essence happens when an unnecessary test is ordered? Like does the claim or the cost for that test get denied?

[00:03:36] How do you manage basically the information gap between what is supposed to be ordered and what the doctor decides to order and who is there to determine if the doctor is right or wrong when we're talking about ordering tests. Yeah, so Avalon doesn't do claims adjudication.

[00:03:52] We get the claims from the payer. We then bump those against the medical policies, the clinical policies and we provide the guidance both to the health plan and to the providers to say look based on the appropriate science and evidence-based medicine.

[00:04:07] This is the test that should have been ordered. This is the way to interpret that lab data. And so we provide the guidance that then can be used by the health plans and also by the providers as well. We don't make those decisions for them.

[00:04:20] Does that mean that basically healthcare providers also reconsider which tests should be ordered if they potentially weren't based on the guidelines that you present to them? What we see over time is that providers start to learn, right?

[00:04:36] As they get this data, it's very specific data for them that they didn't have before to help them think about the right tests that can be ordered for the right conditions.

[00:04:46] By the way, we also know that 75% of the time tests that come back as negative or routine or without issues get reordered again too. So there's misuse, there's overuse, but there's also underuse. 55% of the time there's underuse of lab testing. So I want to make it clear.

[00:05:02] It's not just that we're looking for too many lab tests being ordered, but we also see times when there should have been a lab test ordered that wasn't. Because we now are collecting all this data and have the clinical policies, we can see that and provide that guidance.

[00:05:16] And where do you see that lab tests are often not ordered when they should be? Cancer is one place where we see that for example, where it might be helpful to run certain tests to identify the right specialty medications that could be delivered. But it's hard.

[00:05:29] There's so many lab tests out there. It's hard for all physicians to know with specificity exactly which tests need to be ordered at which time. Are there specific areas that you see have the most potential basically for optimization in lab results apart from cancer which you already mentioned?

[00:05:45] Yeah. So over time, one of the things that we're super excited about is leveraging of lab data, not just claims data, but actual lab values in a predictive way. For example, chronic kidney disease.

[00:05:57] We've done some pilot tests with some of our customers now where we're able to see at an earlier stage individuals that are likely to move into chronic kidney disease.

[00:06:06] And so because we see these lab values for individuals over time, that's something that can be caught and then get those patients, those members into programs or with the right kind of guidance to help them from progressing into that disease.

[00:06:20] And how do you see that prediction and that patient specific picture? Do you also basically create patient profiles based on the lab results that you get? Or how do you consolidate data to be able to predict for a specific patient what can be expected?

[00:06:38] So we provide that data back to the health plans that then they can use to aggregate and identify patients and encourage those patients to get into the right care management programs. And what's the most difficult thing for you when you are further developing the product and the models?

[00:06:56] You know, I think the most difficult thing for us is the fact that there's a... we are trying to change the use of lab tests. And there's a status quo out there. We're the only company that's doing this right now.

[00:07:09] And yet there's such a huge opportunity for every million members that we have in our programs. We save $20 million. So the impact in saving dollars as well as getting patients to the right, getting them the right tests and therefore ensuring that the care that they're getting is correct,

[00:07:25] the opportunity is huge, but there aren't that many out there doing it other than that. So I would say the biggest challenge is continuing to get the word out about what we do and how we do it. The members are considered the patients or? Yes, patients.

[00:07:39] Members of the health plans that we serve. Okay, yeah. Good. Yeah, so you definitely have a lot of space to scale. We do. We do. Today we have 33 million members, contracted members through the health plan customers that we have and we're growing.

[00:07:55] We're really excited about where we are in the uptake that we're getting. We mentioned basically the provider or the clinician side of ordering tests, of not being aware of what needs to be ordered or what not or how to interpret the values.

[00:08:09] What about the patients? Do they also get any specific feedback from you about new insights in the lab results or is everything done directly to through the healthcare provider? We don't communicate directly with the patients or with the members.

[00:08:24] So communications would either come via the health plan and are through their provider. And do you see any opportunities in also maybe engaging the patients a little bit more in terms of just the awareness about lab tests and their specific conditions? How do you see that developing?

[00:08:41] Yeah, it's a good question. Our role right now is to support the health plans and the providers that we work with, right? So to give them the information, the interpretation, the analytics that they haven't had before.

[00:08:53] And I think we always have to keep at the forefront, not creating confusion for patients and members and adding a new communication channel that they're not used to.

[00:09:01] So in my opinion, I think working directly through the health plans and the providers is the best path so they don't get confusing messages. Yeah, absolutely. It's like Googling your own condition. It's rarely good. I know we all do that.

[00:09:15] We briefly mentioned earlier how clinicians can't really know about all the tests and also the field of biomarkers and everything is developing very fast. If we just look at the genetic tests, there's more than 175,000 of them available. And yeah, your statistics say that there's 10 new tests a day.

[00:09:35] Yeah. So how do you manage that? And how do you see that could be better managed?

[00:09:39] Yeah. So because of our data engine and our predictive analytics that we have built in, we're able to in real time capture the tests that are coming in via claims and then match those up against the policies.

[00:09:52] So yeah, it's a challenge to keep up with all of it. But because of the data and analytics engine that we've built, we're able to do that and provide that feedback back to the clinicians in real time. But it's hard.

[00:10:04] Do you think there's a danger that basically if the clinician gets an information about a wrong or unnecessary test that's being ordered, that basically they might skip a test that's necessary next time?

[00:10:21] Next time. When we talk about 70 or 30% of tests being unnecessary and when you try to somehow systemize or quantify that and put engines on that, it can quickly happen that basically a test that is there for a clinical reason, the clinician will say that it wasn't necessary.

[00:10:41] So do you see any friction in that sense?

[00:10:44] To date, we haven't had a lot of provider friction. I think there's always concern that there might be. But the approach that we take with our health plans is about providing guidance and information and certainly sometimes denial of some claims.

[00:10:57] But when you're doing this based on science and clinical evidence, you find that there's a learning curve over time. So a provider might not agree in the beginning, but over time they learn and you just... We haven't seen a lot of disruption. We just haven't.

[00:11:13] Is there any feedback from the end users that kind of stuck with you, that's in your mind that maybe you could share? What kind of feedback are you getting from clinicians that are the ones that are ordering tests?

[00:11:28] The feedback that we get... Let me put it this way. The feedback that we are getting from our clinical advisory board, which is the direct feedback that we get, is that we are doing absolutely the right thing and that there's such an opportunity to improve the specificity of the tests that people get to ensure that they get the right care.

[00:11:46] The end of the day, it also saves money if unnecessary tests aren't ordered. I guess sustainability is a big thing that needs to be addressed in healthcare.

[00:11:55] If we can save money through the delivery of better medicine and better health, and oh by the way now as we move into the next phase of our company into leveraging the data to actually predict and therefore change outcomes, that's what everybody is working to do and we feel like we're in a good path to do that.

[00:12:12] Perhaps just one more question since you mentioned the predictions. Can you talk a bit more about that? So how do you do then... Yeah, how do you basically work on the predictions and developing them?

[00:12:21] So because we have lab data, real lab data with lab values from the individuals that we are providing information about, we can see progression over time in various diseases.

[00:12:34] We can also see connection of that lab data to drugs that people have been prescribed to say, okay based on what we see in this lab data is the drug that you've been prescribed producing the right results from a lab values perspective, not from a reported outcomes perspective and actual lab values.

[00:12:50] And so our analytics engine is able to match those things together to again provide guidance to health plans and providers about this is what we're seeing. Here's an individual that has lab values that looks like this, and they probably are a stage two relative to chronic kidney disease.

[00:13:07] Perhaps you can get them into some type of program. So it's the matching of the lab values to the care pass that they should be on and what's been prescribed for them.

[00:13:18] And how are you expanding your customer base? How are you basically getting new customers to work with you?

[00:13:25] Yeah, so right now we're really excited about our client basis. We have over 20 clients. We have representation from national, marquee national health plan accounts as well as regional blues plans and other independent blues plans.

[00:13:41] And then we continue to add. We're growing quickly. I think if anything you asked before about a big challenge that we have, one challenge we have is just supporting the scale with our growth and that's typical of a company of our size.

[00:13:52] You've got about 250 employees now and actively working to continue to keep up with the demand. And if you're interested in the future of our company, please visit us at www.fodh.substack.com or follow us on LinkedIn.

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