Price Transparency: Pharmacy & Medical. What's Next?

08/21/20 • 35 min

Carm Huntress, CEO of RxRevu, joins hosts, Gary Austin and Pooja Babbrah to discuss price transparency.

Gary asks Pooja to start the discussion with a primer for price transparency. Pooja states that the topic itself is quite complex and, unfortunately, not very transparent. When receiving a prescription from your doctor, you never really think to ask if you should go to a different pharmacy or if there are coupons available for the medication. You simply pick up the prescription and pay the price that is due. Additionally, since the price of medications are going up, many patients do not pick up prescriptions because they are too expensive. Patients cannot afford to pay for them. Pooja explains that when we think about price transparency, it is all about being transparent with the consumer, letting them know what prescriptions and procedures are actually going to cost them out-of-pocket.

Gary then asks Carm to briefly go over RxRevu and to give some opening comments about price transparency. Carm shares that RxRevu is a Denver based company focused on price transparency around drugs. The company helps providers at the point of care make cost-effective decisions by providing a large amount of information. Carm says on a monthly basis, RxRevu is helping millions of patients around affordability issues with their prescription drugs.

There is a recent CMS ruling requiring hospitals and payers to post their charges online. Gary asks Carm what all of this means for consumers. Carm says the big idea of this ruling is directionally right. Healthcare is really the only industry where consumers don’t know the price of products and services before they buy them. Carm explains that the problem with price transparency is that there’s so much complexity in actually pricing medications and procedures, especially since negotiations are affecting these prices.

Pooja says this ruling was an important step forward. There’s a list of 300 items hospitals and payers are required to give pricing on. She notes that it may not be easy for them to offer this information and the timing for when they actually provide a complete list may be far into the future.

Gary points out that price transparency sounds like a good thing for consumers, but what about the physicians? Gary asks Carm if the availability of this information will really change physicians’ prescribing and referral habits. Carm explains that price transparency thoughtfully puts the right type of information into workflow, including what the patient will pay at their preferred pharmacy, types of lower cost therapeutic alternatives and insurance coverage. Carm states early findings show that one in every five doctors who are presented with this information make a cost-dynamic change.

Prescription price transparency is moving along faster than medical procedure transparency, is that a fair observation and if so, why? Carm agrees with Gary and believes this is the case because the need for price transparency in pharmacy has been driven harder given the longevity of the idea. He says the claims system and the engines that we talk to in terms of getting the data out are a little less complex than on the medical side. The PBMs and the systems behind the PBMs were more ready to do real time benefit and that’s why it came to market and matured faster.

Gary asks Pooja what’s going to be the tipping point for price transparency across the industry. Is there some event or some thing that will be a tipping point as we look out a year? Pooja thinks that from the PBM to the physician standpoint, we’re almost there, but in order to progress further, we need to incorporate the consumer side. Doctors can’t be price shopping for consumers all the time, so that’s where we bring in the second piece of consumer-facing price transparency.

RxRevu is growing exponentially. What is your company doing better, faster, or cheaper than the next guy with price transparency? Carm explains price transparency is the company’s singular focus, which allows them to execute at a high level. RxRevu works alongside clinicians to finetune those transactions to make sure they go through clean and with the best experience possible. Secondly, Carm notes the company is a startup. There is no legacy business or bureaucracy in their organization. There’s a real mission in delivering more value through better prescribing decisions. Lastly, Carm states that RxRevu is a startup. They have taken venture money, but also have several health systems as major investors. This has allowed the company to have a much more intimate relationships ...

Carm Huntress, CEO of RxRevu, joins hosts, Gary Austin and Pooja Babbrah to discuss price transparency.

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APIs, FHIR Accelerators & Infrastructure

Guest, Patrick Murta, Solution Architecture Fellow with Humana and Chief Architect for the ONC FHIR at Scale Taskforce (FAST), joins hosts Gary Austin, Jocelyn Keegan and Ken Kleinberg to discuss how Fast Healthcare Interoperability Resources (FHIR) is transforming health data exchange and what HL7 FHIR Accelerators are doing to expedite progress and FHIR adoption.

Gary Austin kicks off the discussion by asking Murta a couple of level setting questions. Why are FHIR APIs so different from legacy EDI transactions? Is this just an IT thing or is it a generational technology and business change? Murta explains that FHIR allows integration from not only an administrative perspective but also a clinical perspective. Information can now be served directly into the workflow at the exact time it’s needed. FHIR enables business and clinical processes that were not yet possible with previous generations of technology.

Gary shifts focus to FHIR API Accelerators. He mentions POCP is a program manager for the Da Vinci FHIR Accelerator, has representation on CARIN (consumer-facing accelerator) as well as Gravity (social determinants of health accelerator). How do these accelerators “accelerate” the adoption of FHIR APIs? There are two important factors contributing to this acceleration: the technology (FHIR) and the cross-industry agreement and willingness to collaborate. Put these two things together and you can achieve much more, much faster than if operating ad hoc across the industry.

On top of the FHIR API accelerators, there is FAST (FHIR at Scale Taskforce). Gary asks both Murta and Jocelyn to weigh in on the subject, including an overview, its origins and why it’s needed. Murta notes the reason FAST exists is due to the development of “functional consortiums” like Da Vinci, CARIN and Gravity. Those groups focus on functionality whereas FAST focuses on the infrastructure capabilities needed to run those functional use cases at scale. FAST was created to identify any barriers to scalability of FHIR and create the appropriate tiger teams to start coming up with solution frameworks to address those problems.

Jocelyn points out that implementations happening today are targeted toward point-to-point traditional connections, but everyone that is building is asking themselves how I can reuse and leverage this API over and over again with all of my business partners. Although those questions are not being answered right now, implementors are evaluating how and where they can take advantage out of the recommendations and tools coming out of the FHIR community and specifically FAST .

Gary asks Murta how he balances the details of specification and implementation guides with the galactic vision of accelerating the entire industry? Murta says it is rather easy to separate the different parts of the technology stack. With Da Vinci, the focus is on solving something for a stakeholder in healthcare (use cases). How would the provider, clinician or member interact with this data? Where do they need it in the workflow? What information do they need? On the other hand, with FAST, there is an emphasis on core capabilities and infrastructure. What do these transactions look like when flowing through the technical layers? What identities are going to be used?

Jocelyn goes on to explain the temporal dimensions of the matter. What happens in 2020 with respect to these accelerators? Where do you see 2021 going? Jocelyn states that the current pandemic reveals why these workflows are critically important and how we need to move forward as an industry. In spite of the world we are living in now, Jocelyn is excited that we will exit 2020 with some good production implementations of Da Vinci implementation guides and start to see real recommendations coming out of the FAST community. She believes 2021 will be about continuing to finetune and support the wave of people needing to implement.

The discussion pivots to Ken as Gary addresses a significant problem plaguing the industry: identification at the human level. How do we take the intricacies of TEFCA (Trusted Exchange Framework and Common Agreement) and deliver that at scale? Ken explains that TEFCA is the federal government’s latest attempt to connect the network of networks. It is voluntary and still in the development phase. TEFCA is being designed to address three modes...

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Health Analytics and Improved Interoperability

Guest Dale Sanders, CTO of Health Catalyst, joins hosts Gary Austin and Ken Kleinberg to discuss the future of interoperability and health analytics and how healthcare can be positively impacted.

Gary begins the discussion with patient identification. He asks Dale why patients do not yet have a single healthcare identifier for their healthcare records. What is going to change this situation? Dale explains that the answer is a combination of a voluntary system for those in the commercial healthcare space combined with a mandatory system for those that are benefitting from Medicaid and Medicare. We need to commercialize the management of those patient identifiers, just like we’ve done with internet domains.

Ken states that TEFCAis looking at an infrastructure for a nation-wide health information exchange with a lot of these interoperability initiatives. He thinks we could do the same with patient identifiers if we could agree upon a dozen or so fields to try to do a better job capturing core data that could aid with patient identification.

Gary asks Dale why we can’t have data normalized across the board. Why is this such a difficult process? Dale explains that it boils down to economics. He suspects we’ve normalized enough to enable reimbursements but that’s it. If you think about it, does it matter to a single healthcare system to normalize to a national or international standard? Dale explains that it is probably not that valuable for individual institutions. They are normalizing to their own vocabulary, which works for them. If we’re interested in analyzing data beyond the boundaries of healthcare systems – as we need nationally for public and population health – we need to incentivize or mandate broader normalization of vocabulary and go way beyond LOINC codes.

Gary asks Dale if he sees the CDC mandating some of this for public health purposes post-COVID. Dale strongly believes it is time to mandate normalization. Ken notes that lab mapping is strikingly ineffective. There are physicians ordering tests by names that they know, but they don’t really know what tests they are actually asking for because what’s behind the scenes isn’t visible to them. There is no convention for how those things are named, which can be a problematic situation.

Gary asks Dale why there is not a standardized vocabulary. Why can’t the power of the computer be used to translate all of this in a normalized fashion? Is the power just not there?

Dale points out that human language alone is a very difficult thing to make sense of, especially the English language. If you layer on the complexities of clinical vocabulary, then it gets even more complex to turn that into computable, discrete elements. We can progress toward passive dictation, but there will always be a need for humans to make edits.

The discussion moved on to cover combinatorial data. Gary asks “How do you look at pulling this data together and rationalizing it across the two massive domains (administrative and clinical data) that are really driving healthcare in this country?” Dale explains the Health Catalyst data model. He says what you would see are domain and vocabulary-oriented data models that sit in between late binding and enterprise data modeling. There is this middle ground of curated data that is a manageable thing to keep up with and execute.

Gary asks Ken who he thinks is going to win by pulling all this payer and clinical data together. Is it the analytics companies, the EMR companies, big tech or who? Ken says there is this concept of a converged platform between payers and providers. Registries are an example of how pulling information together that a lot of people can use. The EHR vendors have taken a run at this, the analytics companies have taken a run at this. We’ve got the population health management vendors, some of which have been more on the business side of value based care, but there are also some that are on the tech side. It’s frankly a huge opportunity. The payers don’t have the same reliance on vendors that the provider side has. We have a very defined market of electronic health records vendors for providers, but how do you identify who the vendors are for payers? They are, in a way, a fortress to penetrate. I think Health Catalyst has a huge opportunity to sit in the middle and bring these two worlds together.

Dale suggests that in the future, payers will need to become providers and providers need to be...