The integrated solutions of Symphony Health, a new Aetion data partner, extract data from anywhere and link them using an anonymized, unique patient identifier, empowering clients to uncover deep insights into the factors that drive patient and physician activity across the patient journey.
We recently sat down with Ki Park, Practice Area Head and Vice President of Managed Markets Consulting at Symphony Health, to discuss how Symphony’s real-world data (RWD) can unlock insights for market access and commercial clients, and facilitate work with payers. Read on as Ki shares more about Symphony’s data set, and how he and his colleagues help clients identify cases in which real-world evidence (RWE) can help address critical questions for commercial teams.
Responses have been edited for clarity and length.
Q: What is the Symphony Health Integrated Dataverse, and how does it differ from other RWD sets?
A: Integrated Dataverse, or IDV®, our underlying data platform, is comprised of multiple data sets from different data providers. IDV’s differentiator is its ability to integrate across disparate data sets, data vendors, and data assets by one common factor—the patient.
In our data, the patient is truly unique, regardless of the data provider, payer, or year under review. IDV uses our proprietary Synoma® patient tokenization process, which assigns a unique ID for a single patient that is trackable over time, even if a patient switches employers or insurance types.
We link data across prescriptions and medical claims from both physicians and hospitals. We also link claims across different data assets, like electronic health records (EHRs) and lab results. Ultimately, our clients receive a streamlined data set or complex analytic underpinned by Synoma, which provides a true longitudinal patient view.
Q: What questions do market access and commercial clients hope to answer with your data? And how do they work with your team to tackle those questions?
A: We find that clients want to analyze the full spectrum of physician, patient, and payer behavior—from identifying physician targets whose patient populations have great access, to identifying physicians with populations that have poor access but are willing to fight through payer barriers such as prior authorizations. They also want to understand overall patient behavior by doing studies around compliance, persistency, their willingness to pay for certain therapies, and how copay card programs drive overall abandonment rate reduction and increase length of therapy. From a payer perspective, our clients want to understand how payers control access through either cost measures or utilization management controls. My team helps our clients strategize how best to answer these questions using data, tools, and custom analytic projects.
Typical analytic projects that my team supports for clients include understanding price sensitivity with copay card optimization studies, understanding Medicare Part D patient types and their behavior, and understanding payer controls on individual therapeutic areas, as well as specific products. Lately, we have been diving more into analytics for clients with medically administered products. For both physician office and hospital based products, we have a proprietary medical payer identification process that connects payers to patients using their medical benefit. We couple that data with remittance data to provide the full financial picture for the medical benefit side.
Q: How do you help biopharma partners maximize the impact of the data, and of the evidence they generate from it?
A: For every project or analysis, we provide an overview, conclusion, and recommendations. Even when providing straight data to our clients, we make sure the client’s analytical team is trained in analyzing the data and deriving maximum benefits from their insights.
For complex analytics, our consultants and principals work with clients to truly understand their business questions to develop a methodology and a project plan to address to those questions. After getting buy-in on the methodology, we build out the data set that drives the end deliverable and provide the end product to the clients with thorough consultation through the end of the project.
Q: Where have you seen RWE adoption increase most among your clients?
A: Our clients want to understand how their drug helps patients overall, and how their therapies may be more beneficial than a competitor’s from either an efficacy or a cost standpoint—and perhaps both. We do many payer studies that ultimately look at how a patient’s compliance may lead to better outcomes, and layer this against the underlying theme of cost containment.
Payers want to provide patients with product that is efficacious, however, financial stewardship is a primary driving factor. Frequently, our clients want to understand their product’s benefits beyond cost savings. They look at patient compliance, which hopefully leads to better outcomes. But ultimately they have to layer in cost components, like the number of drugs taken, number of hospital visits, and how their product may drive those numbers down. Direct financials such as out of pocket costs to the patients and cost to the payer are common in many studies. They also look at the number of physician visits or hospitalizations before and after their product is used by the patient.
Recently, we have begun to examine the cost from a medical benefit side, layering in overall cost for physician office visits, patient out of pocket costs, and payer cost for medically administered products and procedures. The benefit to our clients is they have real utilization data with financial metrics to support their discussions with the payer to drive policy change.
Q: What are some challenges you’ve observed as clients start to adopt RWE in their market access initiatives, and how have you helped them overcome these challenges?
A: One of the biggest challenges is the fact that IDV is an open data set. There are many pros to open data sets, and our strategy has always been to use this model as it provides us with a very robust data platform across different data types within the United States. This robustness provides a sample of data that is highly representative of the U.S. patient population as compared to the U.S. Census, from gender, age, and disease standpoints.
However, with an open data set, patients may fall in and out of sample depending on their activity. Consequently, we will “close out” our data set as required for analytic studies. A closed data set may be smaller in sample, but the representation for that individual patient cohort is more complete. We do this by restricting the patients that enter a study by putting activity panels in place. The panels become more rigid depending on the type of study, and ultimately, by putting these restrictions in place, we end up with a pool of patients that we are highly confident will address the business question at hand.
Q: In combining Symphony’s data with Aetion’s RWE analytics platform, what benefits do you envision for clients?
A: A key client benefit is gaining access to many different real-world data sets—claims, EHRs, labs, and so on—linked together at a patient level through Synoma. Overall, layering Symphony’s robust patient-linked data with Aetion’s analyses, validations, and reputation for transparency will be a huge benefit for clients.
We’re continuously evolving with the market landscape, and these partnerships are part of our evolution. We are excited to partner with Aetion and look forward to engaging clients together and making analytics more powerful for them.
Q: What future opportunities and applications for RWD in the market access or commercial space most excite you?
A: Depending on technology’s direction, data analytics can advance tremendously. Just think about the potential for advancements with machine learning, as an example. We are close; the potential to identify the most beneficial course of therapy for individual patients and their disease is there. We just need access to all health related records for a patient.
However, I’m concerned about ongoing state and federal legislation concerning patient privacy. We hope the public and the government realize the benefits that data analytics can provide to society and to individuals facing life-threatening or disability-causing diseases, or even just helping to alleviate pain and discomfort. Having access to most robust and granular data available is critical.
Apart from legislation, as machine learning and other types of artificial intelligence progress, the data assets will drive a lot of incremental value. The possibilities are endless, as long as we have access to the most robust data possible to facilitate forward progress.