Claims, Referral and Payor Data
Successful engagement of external experts ranks very high on the list of challenges medical affairs professionals face. No surprise here, building relationships that support long-term engagement is hard under the best of circumstances – and the circumstances are hardly perfect since the pandemic made in person meetings impossible.
Another task that routinely makes it into the top three challenges: identification of the best healthcare providers (HCPs) or key opinion leaders (KOLs) for a specific activity, whether that’s a speaking engagement, a clinical trial or a chance to gather real-world data and insights from physicians actively treating patients.
The more medical affairs know about these external stakeholders, the better they are able to identify and engage them. While academic achievements such as publications or speaking engagements paint an important part of the picture, these standard KOL metrics do not capture an HCP’s work and experience related to all aspects of treating patients. This medical view, in addition to the more academically skewed view that e.g. publications provide, is important for developing a holistic view of HCPs.
Over the last months, H1 has added several new data streams consisting of many billions of data points to our database. These claims, referral and payor data allow medical affairs professionals to obtain a more complete and balanced view of HCPs.
Here is an overview of the type of data that is now part of our database and as well as examples of how medical affairs can use that information.
Claims Data – Find the Most Elusive KOLs
If you are looking for an internationally recognized researcher, their publication and speaker list are excellent indicators of their influence and clout. The same metrics are all but useless if you are looking to identify a local or regional practitioner with extensive experience treating patients. Social media, might be able to point you in the right direction, e.g. a physician actively tweeting out patient-focused information is likely involved in treating them on an ongoing basis. As cutting-edge as this approach may sound, it is still like looking for the proverbial needle in the Twitter haystack. A much better way to go about it is using claims data which can tell you exactly which physician is performing which diagnoses and procedures and how often, making it easy to identify physicians with significant treatment experience.
This knowledge allows your medical science liaisons to engage these physicians and support them with information custom-tailored to the specific needs and characteristics of their patient population, e.g. their co-morbidities.
Patient Journey – Learning from the Patients’ Path Through the Healthcare System
There is a second way of looking at claims data: on a per patient basis. De-identified patient data can be used to track patients’ journeys through the healthcare system on an individual rather than an aggregate basis. This data can show which combinations of diagnoses and procedures patients often undergo together, how individual patients advance through the treatment, what first and second line treatments patients receive and how treatments and outcomes vary based on patient-level risk factors, such as co-morbidities, gender, age or geographic location.
For medical affairs this information can highlight unmet medical needs their product might address, identify patient populations that otherwise might have been missed and pinpoint physicians and other healthcare professionals that medical science liaisons (MSLs) can better support with custom-tailored information.
Data about individual patient journeys also provides increased opportunities for MSLs to generate medical insights during conversations with the physician that can inform the broader medical strategy of the organization.
Physician Referral Networks
Physician referral networks are of particular interest to clinical development teams during clinical trial site selection as well as the medical affairs team as they support their colleagues with this task. Detailed information about a physician’s referral network is critical to two main stages of the process. The data can help:
- Identifying principal investigators (PIs) with a sizeable network of physician that they can tap into to reach their enrollment goals.
- Identifying members of the physician network who might benefit from more information about the clinical trial, e.g. which unmet need the trial addresses. The more aware and better informed the referring physicians are, the more likely they are to refer eligible patients to the clinical trial.
In addition, MSLs can use referral data to identify local and regional thought-leaders. While claims data provide information on who is actively treating patients, referral data reveal which colleagues a treating physicians trust to take care of patients with more specialized medical needs. These local leaders are excellent candidates for engagement, e.g. as candidates for speaker training.
Payor Type and Mix
Payor mix refers to the percentage of revenues a physician or healthcare organization obtains from governmental programs such as Medicaid and Medicare and private insurance companies. Payor mix information is highly relevant for field medical teams that focus on engaging payors. The information helps them to prioritize payors in their territory based on the number of patients, claims and physicians a payor covers. Using detailed payor data allows MSLs to be more efficient and to meet the information needs of the most important payors.
Understanding the Healthcare Ecosystem
Claims data, referral networks, patient journey and payor mix provide valuable information and insights into the trends and dynamics of the interconnected healthcare ecosystem. They connect the dots between patients, physicians and payors by telling what the patient suffers from, which physician they saw, what actions the physician took and who is covering the patient’s care.
On a strategic level, this information can inform where MSLs are deployed, how territories can be optimized, and allow medical affairs to identify opportunities for engagement with healthcare providers and patients.
The H1 Solution
HCP Universe is monitoring more than 4 million HCPs and tracking over 3-billion diagnosis codes (ICD) along with 2-billion procedure codes per year covering 200 million patients in the US alone. Our database contains claims data going back more than four years making it possible to detect trends and developments. To learn more about this wealth of claims, referral and payor data in the H1 database and to discuss how that information can be used to make your work more efficient please get in touch with us here.