Gaining a Competitive Advantage with Data: What Medical Affairs Teams Need to Know
Over the past ten years, converging trends in healthcare, technology, and medicine have transformed the role of the Medical Affairs (MA) teams in life sciences organizations. These teams have grown in size and scope, becoming largely independent of commercial organizations, and are considered a trusted resource for unbiased, scientific information.
While the influence of MA has grown, the ongoing evolution in the industry has challenged medical affairs professionals to effectively direct their resources and measure how they add value to their companies — and ultimately to patients who are battling complex diseases.
A little history
Traditionally, attempts to evaluate MA professionals consisted of counting the volume of interactions, or issuing surveys to physician key opinion leaders to obtain qualitative feedback. The volume of activity served as a proxy for the impact or effectiveness of interactions.
Not bad, but not an accurate measurement of the impact MA teams have on patients’ lives through their direct engagement with healthcare providers.
Amidst an increased focus on value-based care — for patients, providers and payers — MA teams have been in search of tools to better demonstrate a measurable impact from their work on their organizations and, more importantly, on patients’ lives.
The answer can be found in patient-level data that enables insights on disease burden.
A new approach to data
Today, MA teams can access systems that provide insights from deep and broad patient-level data, enabling them to quantify which physicians are treating subgroups of patients and which are the most important providers to reach. Not just based on their specialization or industry influence, but based on the volume of relevant patients they treat.
Having the right historical data can enable MA teams to provide disease-state education tailored to the needs of different stakeholders — from academic medical specialists to the community physicians who treat high volumes of patients — and draw insights from both. MA can use patient-level data to help physicians better understand the needs of patients as they journey through the healthcare system.
“You want to have a confirmed set of providers who are actually seeing the patients you’re interested in impacting,” says Paul Zakas, senior customer success manager, Komodo Health. “And that’s what patient-level data allows you to do—to get a clear picture of who’s currently out there treating patients, to know who is the best to communicate with, and to educate on a new therapy, or new standard of care that’s emerging in a certain disease.”
Later, post-visit data helps MA teams measure how their engagement impacted patients by looking at a variety of actions such as lab visits, results, procedures assigned, referrals, and outcomes. The goal? To collect as much post-engagement information as possible to measure the impact of their influence. This data can help drive future patient decisions, potentially speeding up future diagnoses and better outcomes.
Educate those who matter most
Access to patient-level data can assist MA in determining what kind of education would make the most impact for health care providers (HCPs).
For instance, CAR-T therapy — a treatment in which a patient’s T cells are changed in the laboratory to bind to cancer cells and kill them — has shown remarkable success, but is only available at a limited number of cancer centers with specialized expertise in cellular therapies. Not all providers, even oncologists, know how and when to direct patients for treatment. Yet, as patients navigate the system, delays can mean the difference between life and death.
Technologies that are built on deep, broad and timely data can help uncover referral patterns to understand where CAR-T patients originated and their journey to treatment, highlighting opportunities for improvement in efficiency. AI can map encounters for HCPs to identify patient journey pathways, and pinpoint which physicians may need more information to get their patient on the right path to CAR-T faster.
More than 7,000 orphan diseases afflict fewer than 200,000 individuals for any single disease type in the United States. Yet, all together, more than 25 million people in the United States suffer from these rare, difficult to diagnose diseases. In the past, those patient journeys – so unusual and seemingly disparate – couldn’t have been linked or analyzed.
Here’s where data and AI can help. Up-to-date, longitudinal data can provide a better understanding of entire patient journeys, linking those rare disease signals through AI to identify patients whose disparate symptoms are actually indicators of a potential disease. Connecting the dots from across the patient journey can lead to earlier diagnosis and a faster path to effective treatment. Those welcome insights on rare diseases can be communicated to HCPs, whether in large, academic environments or smaller, more rural settings.
“Many different types of providers have touchpoints with patients and need to be educated about the disease that they’re treating,” says Pamela Morris, area VP, account strategy, Komodo Health. “You cannot just focus on providing disease state education to top-tier academics because patients have so many other touchpoints along their journey. That final touchpoint or diagnosis might be at an academic center, but what if you could help trigger the right diagnosis much sooner?”
This approach puts patients first, contributing to changes in referral pathway behavior around the country by speeding up treatment and reducing overall costs. MSLs, the subset of MA teams that establish and maintain peer-to-peer clinical relationships with physicians, can more effectively focus their time where it will have the greatest impact on improving quality of care in the system, be it at an academic center or a rural clinic.
Follow the journey
An effective data system must provide users with the ability to see the impact of field visits on patients and to view disease burden for an area of interest. According to Zakas, one way to measure impact, particularly in the case of diseases with unmet needs, is to follow patient diagnoses, and determine whether the information provided to a clinician enabled a more accurate diagnosis over time.
“That’s where the medical affairs team can prove their value in that community,” says Zakas. “When you reach a provider who treats a certain number of patients, that provider’s baseline number of patients may not have changed, but the health of those accurately diagnosed with a certain disease based on that medical education has improved.”
These new, analytical technologies help enable MA teams to better target their resources — and objectively evaluate their own work — by using metrics that quantify both the business and clinical value of their activities. These same insights also enable a more personalized and nuanced approach in order to reach the populations of patients and providers who matter most.
Ultimately, this is the goal of health tech: to bring healthcare insight into sharper focus to help improve patient outcomes and reduce the burden of disease.