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Becker’s Healthcare Podcast
From Back Office to Bottom Line: Driving Revenue & Access in Specialty Pharmacy
00:14:35
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- [00:00]
A
Hello everyone. This is Erica Spicer Mason with Becker's Healthcare. Thank you so much for tuning into the Becker's Healthcare podcast series today. So today I'm thrilled to be joined again by Will Yin, the CEO of Mandolin, who will discuss driving revenue and access in specialty pharmacy. Will, thank you so much for being back with Beckers today. It's great to have you on the podcast again.
- [00:22]
B
Yeah, thanks for having me, Erica.
- [00:25]
A
We're really happy to have you. And I know the last time that we met we talked about specialty drug access and where pharmacy strategy are starting to intersect with AI and automation. So I'm excited to go a little bit deeper today on how you're viewing the evolving financial model for specialty therapies and health systems. So in that realm, what's changing in payer contracting, reimbursement and risk and how does that impact pharmacy operations right now?
- [00:53]
B
So of course payer requirements are changing faster and faster, and they're only getting more complex. At the same time, health systems continue to deal with ever thinning margins. Specialty is a big potential revenue driver, but recent changes make that potentially challenging. So a few examples. Starting in 2026, CMS is going to be reducing reimbursement rates for Drug Administration HOPD sites to align them more closely with rates paid for physician offices or non hospital sites. Of course, we also have the big beautiful bill, the IRA 340B rebate changes, generally a theme of site of care requirement changes and payer steering, making it more difficult for systems to get HOPD level reimbursements. While generally this is a challenging and potentially stressful time for health systems, there remains a significant opportunity for upside here as well. So to capitalize and convert that potential liability into upside, health systems need three things. One actual clarity into their own SOPs. So this seems like a small thing, but every time payers update policies, the rate of diffusion of that knowledge to a human team is heterogeneous, leading to wildly different performance and practices across teams. This was manageable in the old days, but now the cost of errors and the incentives by payers to drive errors means that health systems need clear, documented practices that everyone consistently follows. Number two consistent quality of labor. Once the appropriate SOPs and procedures are excavated, they're optimized, they're documented. And keep in mind that this is a recurring process that's redone nearly every quarter. In many cases, IDNs need to they need a labor force that consistently follows those practices and can be held accountable for costly missteps. Now that's obviously a challenging ordeal. And our contention is that automated work is a huge advantage here. But whether or not it's an agent doing the work, the work being done in back offices need to start looking a lot more like an assembly line of consistent practices updated to exacting requirements. And finally, data. These sophisticated systems, they can't persist without telemetry streaming out of the various systems that we're talking about. This takes a number of different shapes, so these could be QA and QC scores on workers for an accurate picture of where every workflow is in its process. But it also ripples upwards very quickly into getting into more strategic data system wide about reimbursements, patient out of pocket costs and cytokare optimizations.
- [03:50]
A
Well, thank you so much for outlining those three needs so clearly. So just to share those back with the listeners, sounds like health systems really need clarity into their SOPs, consistent quality of labor, and especially the third item, data. And so I want to pivot to talk a little bit more about how Mandolin is is approaching these needs. I understand Mandolin emphasizes reducing time to therapy and preventing revenue leakage, especially via process automation. So how are teams that you work with seeing real gains in both access and revenue cycle performance in using this tech?
- [04:28]
B
Great question. So let me take the time to therapy question first because it's easier for us to see and track those gains. So on time to therapy, our customers are seeing referral automation drop from several hours to around 30 minutes in most cases. In certain cases, especially with E scripts, we've seen an average complete intake time of around three minutes. Now, an important part of time to therapy or important contributors is verifying benefits. So calls typically take longer and these are typically done over the phone. And the calls take longer because an actual phone call needs to be made to a payer in most cases. Anecdotally our customers tell us that even though a call takes one to two hours, it can take several days before the call is even made because of staffing bottlenecks. So our system automates these calls. Even though our BVs are still phone calls, most are complete within two hours of hitting our desks, with rare outliers being still within a max of say 24 hours or so. Now, on the point of preventing revenue leakage, putting on my medication access hat here we want to make sure that when a prescription is written that the patient gets treated quickly, ideally within the four walls of our health system and that we can get cleanly reimbursed for them. Sounds simple, but the reality of how to get there right now is incredibly convoluted. We first need to do a thorough benefits investigation to optimize site of care Depending on what options the health system has Traditionally, sites of care and service lines are fairly siloed. If a drug isn't covered via hopd, it could easily flow out of the system to an independent AIC or a home infusion company. Even if the health system has the infrastructure in house to process it, running a cost or reimbursement analysis at the same time as identifying the most optimal site of care for the IDN patient, looking at patient out of pocket cost reimbursement to the IDN and ultimately access broadly is impractical if not impossible without the help of agentic AI. What's incredibly powerful about AI at this moment is health system operations leaders can get visibility into which scripts are being written and margin on all sites of care across the system. And just like a command center for specialty drugs, AI can determine rules for how these scripts get routed to optimize for margin patient financial clarity, and for keeping scripts within the system which would otherwise be subject to leakage.
- [07:06]
A
Hmm, fascinating. And will I know throughout our discussion already, you've noted complexity in these processes several times, and that certainly applies to areas like buy and bill and infusion operations for hospital and health system pharmacy. So in your view, how do you think health system pharmacy leaders should evaluate whether to build, buy or partner for their specialty drug access infrastructure? And to you, what criteria should matter most?
- [07:39]
B
Well, I don't want to speak for pharmacy leaders here, but I would imagine that the first step would be a really frank and honest appraisal of their ability to build out the underlying technology, process training and change management for a transition at this scale. And then assuming that there are pharmacy leaders that believe that they have the technical teams and know how to run this, then deciding whether this project is the highest and best use of their time. You very likely have seen the study out of MIT a few months back that 95% of all implementations of AI at the enterprise are failing to deliver value. I think that that study has been cited as an indictment of AI as a technology, but when you read the study, a few more nuanced themes emerge. The first is that businesses that buy AI from vendors are three times more likely to be successful with their implementation than those that choose to build in house. The second is an understanding that the technologies that are being deployed here in building out these automations is, generally speaking, not proprietary. Every AI business is ultimately buying its tools from OpenAI, Anthropic and other large language model vendors, and I'm speaking generally here, there's an obvious nuance and domain expertise that's relevant and we at Mandolin are particularly proud of in the specialty space. But it's certainly not like we invented AI and large language models. What ends up differentiating whether using a third party will be successful is not how beautiful ultimately the demo or the UI of the product is, but it's also in the plan that the vendor has for handling the change management, the deployment, the understanding of payer requirements, buy and bill infusion, changing set of care requirements, and ultimately providing analytics and visibility throughout. So my own forecast, and you can say that I'm pitching Mandolin here, but the truth is that I believe in this bet so much that I've built Mandolin's entire market approach around it is that health systems are going to go with technology providers rather than trying to run this in house, and that the technology leaders that win the market are going to be domain obsessed with specialty drugs and have incredibly sophisticated and performant teams who excel in complex implementations at health systems specifically.
- [10:08]
A
Yeah, it's a powerful prediction, Will, and I appreciate you bringing us to this kind of note of forecasting here because I wanted to use our last few minutes together to talk a little bit more about how leaders can use some of these tools to prepare for the years ahead and what's coming. So we know that AI and automation can unlock information from manual workflows or workflows that were previously siloed. How can leaders use this data not just to operationalize tasks, but also to guide their strategy, their forecasting and even contract negotiation?
- [10:44]
B
Yeah, I love this question because what you're getting at here is a very fundamental shifting of the data and power asymmetry, frankly back towards health systems and away from payers that's enabled with agentic AI. I can't speak for other vendors in the space, but when Mandolin performs work on behalf of customers, we record every aspect of it in a way that can be easily summarized, charted and tracked over time. This means that our customers can easily run analyses like Reasons for Denials, reimbursement rates by therapy, reimbursement rates across payers across regions, so on and so forth. All of this leads to a very powerful data driven tool that can be leveraged to negotiate better payer contracts for reimbursement enforcement. But it's also fair to recognize that it's to an extent running just to stay in place. The 340B rebates that are kicking in next year are going to require all health systems to be even more sophisticated and data driven, to be able to properly ensure that they're prepared for those stricter audit processes and ultimately to earn those reimbursements.
- [11:56]
A
Well, thank you so much. You've given us so many great things to chew on, especially listeners as they listen to this episode. It and and take this information and apply it at their own organizations. And kind of in that same vein, I wondered if you had any particular advice for health system pharmacy leaders who might be exploring automation for specialty therapies, but they haven't started yet. You know, is there one actionable step that they can take to get started this year?
- [12:27]
B
Well, I think the first thing to mention is that all things considered, we're still in the early innings of AI automation. And of course there's a lot of promise here, but there's also a lot of hype. So I'm deeply sympathetic to leaders who don't want to jump in into nascent technologies this early. At the same time we have this sort of a frog in the pot situation where the water is just going to keep getting hotter. Kodiak reports that write offs as a percentage of net patient service revenue are on track to increase something along the lines of 33% this year alone. Overall bad debt jumped 17% in the past two years across health systems nationwide. So you can't just sit on the sidelines either. So what's the safe way to decide what to work on? Well, first off, I'm happy to chat through Overall Strategy with any of our listeners by the way, so any pharmacy leaders, please hit me up on LinkedIn if you want some frank talk about this stuff. But the general advice is going to be know what you want to accomplish two have a frank self appraisal of your system's strengths, weaknesses and ambitions 3 find an automation vendor with deep domain expertise in the complexity of a specialty pharmacy space and four evaluate them hard. That's the way ultimately that you can actually separate the signal from noise in this moment. Wow.
- [13:54]
A
Fantastic advice Will. And I appreciate you opening up to listeners and letting them know if they'd like to connect on LinkedIn to reach out. I think it's good to keep this dialogue going. So I want to thank you again for spending time with Beckers today and all of your insights.
- [14:09]
B
Thanks for having me Erica.
- [14:11]
A
It's been great having you and listeners. If you missed our first episode on pharmacy strategy meets AI automation, be sure to find that linked in the podcast description below. And a special thank you to Mandolin for sponsoring today's discussion hope you all have a great day, and we look forward to having you join us for future episodes of the Becker's Healthcare podcast series.