It is imperative to expand value frameworks to catalyze more impactful evidence generation strategies, craft value narratives, and ultimately optimize patient outcomes. Life science organizations must shift from looking at one-time costs and immediate clinical outcomes to a broader set of value drivers to help payers approve products and create the optimal case for market adoption by appealing to endpoints that create value for both patients and providers. A broader set of value drivers includes the opportunity cost of not treating a patient, the overall impact on total cost of care, and patient preferences or quality of life. As critical as it is to get patients to the care they need, it’s even more important to ensure that the entire care pathway is accounted for in a holistic manner to determine the true benefit of a new clinical product.

Life science leaders should be aiming to ensure data is transformed into evidence that meet both patient and population needs.

Questions for life sciences leaders to consider:

• What kinds of real-world evidence meaningfully inflect choices about product access and use?
• How do different “types” of payers weigh health economic data? What metrics beyond safety and efficacy have the greatest salience with different kinds of payers?
• What partnership models are payers willing to engage in to expand the time horizons they consider for value?
• When in the product lifecycle can changes in data collection and evidence generation create meaningful impacts on payer and provider perception of value?

Expanding the definition of an influencer

Because value means something different to each stakeholder, it is critical for life science organizations to understand a multitude of viewpoints so that they in turn can support the rest of the healthcare ecosystem in achieving medical value.

Life science leaders need to think beyond one source or channel of evidence dissemination and understand how different sources of information and influence interact. In the past, most targets of medical strategy have focused on key opinion leaders(KOLs) as “key influencers.” While KOLs remain an important part of information dissemination and clinical consensus, other influencers such as DOLs are increasingly able to change perceptions of products and companies. Some examples include:

Because of this, non-traditional perspectives should be leveraged as these groups can be beneficial partners to life sciences organizations in the education and marketing for newer products, as well as data tracking.

Additionally, consulting a broader range of external stakeholders creates diverse insight generation. A parallel shift that is occurring is the shift from a model of evidence dissemination to one where it is vital to understand how evidence circulates to create medical consensus. Life science leaders generally should be taking an “ecosystem approach” to thinking about evidence circulation and influence by looking at social data, journals, blogs, conferences, etc. and mapping out the interplay of patients, physicians, and other influencers.

Questions for life sciences leaders to consider:

• How can life science leaders overcome the internal siloes with HEOR, regulatory and market access that limit the effectiveness of evidence generation to meet all customers’ demands?
• What communication tactics are most effective to translate insights from the field into product/TA strategy?
• What is the most effective way to disseminate field-based value stories throughout the entire organization?
• Which strategies for evidence dissemination (e.g. publications, conferences, office visits) are most effective to reach different providers archetypes (e.g., physicians, KOLs, risk-bearing providers?)
• How can you use conversations from online clinician communities to better understand HCPs’ uses and perceptions of your products as well as current evidence needs?

Designate teams to alleviate administrative burdens

Many stakeholder groups struggle to identify who is (or should be) responsible for capturing and tracking outcomes data. This challenge is made more complex as physicians are already burnt out and not reimbursed for the additional tasks of data collection, tracking patients, and providing back data. As health care leaders struggle with these challenges, they are increasingly looking to adopt risk-sharing finance models to create accountability for cohesive, patient-centered care delivery through value-based care.

Life science organizations must identify practical ways to partner externally, aggregate data, and incentivize its collection through designated teams. An example of this is Optum’s approach to alleviate payers’ data adjudication burden by outcomes-based programs. Third parties are responsible for verifying payers and providers, assessing patient health status across the care continuum, and identifying outcome events. Most notably, this program leverages claims and other clinical data to ensure understanding of outcomes and patient segments to create value for all stakeholders.

Questions for life sciences leaders to consider:

• How can we streamline data management, analysis and use?
• How do we coordinate data requests and uses between internal organizations?
• How will we determine what the source of outcome data is, and in turn, which metrics to choose?
• How do we get stakeholders to not only accept outcomes-based contracting, but to accept the viability that comes with it?
• How should our data collection strategy adapt for market dynamics like site-of care shifts to gather the data necessary to provide localized insights?

Payers, regulators, patients, and providers have different incentives and perspectives when it comes to measuring meaningful impact. For example, payers may argue that if a drug or therapeutic does not provide meaningful clinical improvement and reduction in health costs, that treatment is not beneficial while providers may feel that any change in a patient’s clinical pathway may be sufficient and justified, regardless of reduction in cost.

Life science leaders have a responsibility- and opportunity- to step in and incorporate alternative perspectives and directly have an impact on the availability and use of new medications by other stakeholders in the ecosystem.

Real world evidence (RWE), any evidence about a medical product or intervention that comes from outside of a clinical trial, provides insight into real-life treatment outcomes and can offer a more accurate representation about how medical products and interventions work for different patient populations. RWE will be vital to life science leaders' success in demonstrating product value and aligning on time horizons. Because many new products pose a challenge to randomized controlled trials (often due to niche populations, small trial pools/limited trial participants, and ethical concerns over the use of placebos), life science organizations should continuously evaluate clinical products post-trial, and use RWE to fill in evidential gaps to make the case for value.

Additionally, an alternative approach to appeal to payers and gain buy-in is to determine impact and have stakeholders align value end points to a meaningful time-to-impact horizon in order to determine true value.

Because some disruptive therapeutics and diagnostics are durable and/or curative in nature, it is critical that longer periods of monitoring in clinical settings beyond the typical health plan focus are incorporated to prove value for each patient. 12-18 months does not have to be the default for medical value, and increasingly, has proven to fall short of the time it takes to see impactful clinical measures for patients with more complex and severe disease states.

Life science organizations partnering with payer groups should increasingly differentiate financial and clinical endpoints, and determine which endpoints are most beneficial to the individual and population-level total cost of care, compared to endpoints that only align with annual budgets and contracts.

Questions for life sciences leaders to consider:

• How do payers evaluate a treatments’ long-term value when most of their covered patients only remain in-network for a limited time horizon?
• What metrics do payers use to evaluate whether a treatment will impact the downstream utilization of medical services?
• How does the integration of pharmaceutical and medical treatments (e.g., CarT therapy) impact how payers make coverage choices?
• How do leaders leverage real-world data and convince regulators to be more comfortable with less-traditional sources of data?
• How can RWE help us overcome the mismatched time horizons of interest for us and other stakeholders?
• What new metrics do payer and provider stakeholders value for which evidence is scarce?
• What organizational model allows us to get the most value from our RWD assets?