From Listening to Leading: Reimagining HEOR Through the Lens of Patient-Centred Evidence
Dimple Dang , Co-Founder & Director, MarksMan Healthcare
Dimple Dang, Co-Founder of MarksMan Healthcare, is a strategic growth architect shaping the firm’s global footprint. She forges high-impact collaborations, leads business expansion, and builds a talent ecosystem aligned with excellence. Her leadership elevates credibility, strengthens brand equity, and positions MarksMan as a trusted strategic partner delivering value across global healthcare markets.
Abstract:
Healthcare is entering an era where evidence alone is no longer sufficient unless it is also meaningful. For years, patient voice has been discussed, documented, and acknowledged—yet often positioned at the periphery of evidence generation. Today, the role of HEOR (Health Economics and Outcomes Research) must evolve. It must move from listening to leadership, from observing patient experience to shaping decisions around it.
This evolution demands more than intent. It requires structure, rigor, and the courage to integrate patient voice - across real-world data, digital ecosystems, and social sentiment - into the very architecture of evidence and value communication. When done responsibly, patient-centred evidence strengthens credibility, informs access, and ultimately leads to better healthcare outcomes.
“Patient voice becomes powerful in HEOR not when it is heard, but when it is structured, validated, and trusted enough to influence decisions.”
Q1. HEOR has traditionally gathered patient insights passively. What does “moving from listening to leading” truly look like in today’s evidence-generation ecosystem?
Moving from listening to leading requires HEOR to fundamentally reposition patient voice—from a retrospective input to a strategic driver of evidence design. Traditionally, patient insights were collected after pivotal decisions had already been made, often serving as supportive or contextual information. Leadership today means embedding patient relevance at the point where evidence questions are framed, outcomes are selected, and value narratives are structured. In practice, this shift transforms HEOR from an observational function into an architectural one. The patient's voice becomes a lens through which evidence priorities are defined, not merely interpreted. When HEOR leads in this way, patient insights influence regulatory dialogue, access strategy, and long-term value communication—without compromising scientific rigor.
Q2. How can real-world patient voice be systematically integrated into early-stage HEOR study design to avoid tokenism and ensure meaningful impact on outcome selection?
Tokenization is the “checkbox” version of patient engagement - where patients are consulted, but their input doesn’t materially shape the HEOR study design or decisions. Hence, it occurs when patient engagement is symbolic rather than consequential. To avoid this, patient voice must be integrated at the earliest stages of HEOR study conceptualization—before endpoints are finalized and methodologies are locked. Structured qualitative research, early patient advisory panels, and hypothesis-generating patient interviews allow HEOR teams to understand what outcomes truly matter in real-world settings. We emphasize traceability—clearly mapping patient-derived insights to specific outcome measures and economic assumptions. This ensures patient contributions are visible, auditable, and influential, reinforcing credibility while safeguarding against superficial engagement.
Q3. What frameworks or methodologies do you find most effective in transforming qualitative patient experiences into quantifiable, decision-relevant endpoints?
The most effective approach is a mixed-methods framework that respects the depth of qualitative insight while meeting quantitative decision requirements. This typically begins with structured qualitative exploration to identify themes, followed by psychometric validation and endpoint refinement aligned with regulatory and payer expectations. Advanced tools such as natural language processing and sentiment analysis—particularly from patient narratives and social media listening—enable scalable pattern recognition. However, these technologies must be governed by scientific oversight to preserve context and avoid misinterpretation. The goal is not to dilute patient experience into numbers, but to translate it responsibly into trusted evidence.
Q4. In what ways can patient-generated real-world data (PG-RWD) complement traditional real-world evidence (RWE) to strengthen economic models and comparative-effectiveness analyses?
Patient-generated RWD captures dimensions of care that traditional datasets often miss—treatment burden, daily functioning, symptom variability, and adherence behavior. When appropriately validated, PG-RWD enriches economic models by improving assumptions around persistence, resource utilisation, and quality-of-life impact. By triangulating PG-RWD with traditional RWE sources, HEOR can create more realistic and decision-relevant models. This integration strengthens comparative-effectiveness analyses by reflecting healthcare as experienced by patients, not just as recorded in systems.
Q5. How should HEOR teams collaborate with Medical Affairs, Market Access, and Clinical Development to ensure that patient voice is reflected consistently across the value communication journey?
HEOR must act as the connective tissue across functions. Patient insights should not be reinterpreted independently by each team; instead, they should flow through a unified evidence strategy. This requires early cross-functional alignment on how patient relevance will be defined, measured, and communicated. When HEOR leads this integration, patient voice remains consistent across clinical endpoints, value dossiers, publications, and payer conversations—enhancing both credibility and impact.
Q6. What are the emerging challenges in validating patient-reported insights collected from decentralised trials, wearables, and digital health platforms?
Digital and decentralised data sources introduce challenges related to data variability, engagement bias, and signal noise. Wearables and digital platforms generate vast volumes of data, but volume does not automatically translate into validity. Social media listening and digital sentiment analysis offer valuable real-time insights into patient experience, yet they must be contextualised carefully. Robust validation frameworks, triangulation with traditional evidence, and transparent methodologies are essential to ensure these insights meet scientific and regulatory expectations.
Q7. How do you distinguish between patient “needs,” “preferences,” and “value drivers,” and how should each be embedded into HEOR narratives?
Patient needs represent fundamental gaps in care - symptom burden, functional limitations, access barriers, or quality-of-life impact and should anchor unmet-need justification and endpoint selection. Preferences capture the trade-offs patients are willing to make, such as convenience versus efficacy, influencing adherence, persistence, and real-world treatment choice. Value drivers emerge when needs and preferences intersect with system-level priorities like reduced hospitalisations, caregiver burden, or long-term outcomes. In HEOR narratives, separating these elements while linking them strategically ensures patient voice enhances relevance without diluting scientific or economic rigor.
Q8. How can HEOR proactively shape regulatory guidance rather than merely comply with it?
HEOR can move from compliance to leadership by demonstrating methodological excellence in how patient-experience data is collected, validated, and translated into decisions. Regulators increasingly seek clarity on governance, reproducibility, and analytical transparency rather than volume of patient input. By publishing patient-centric HEOR methodologies, engaging early with agencies, and contributing to scientific consultations, HEOR teams can help shape evolving standards and ensure patient voice is integrated meaningfully across regulatory frameworks.
Q9. What role do AI, NLP, and machine learning play in extracting patient insights from unstructured data?
Advanced analytics allow HEOR to capture patient insights at scale from unstructured sources such as social media listening, online patient communities, digital platforms, and wearable-generated narratives. NLP and machine learning help identify patterns in sentiment, unmet needs, and real-world experiences that traditional datasets often overlook. However, technology must remain an enabler, not a substitute for scientific judgment. Robust governance, bias detection, and human validation are essential to ensure insights remain credible, contextual, and decision-relevant.
Q10. How can patient-voice-informed evidence remain credible and reproducible across geographies?
Credibility across geographies requires standardized methodologies balanced with local contextual understanding. While patient experience is universal, healthcare access, cultural perceptions, and treatment pathways vary significantly. HEOR must apply validated instruments, transparent analytics, and clear documentation while triangulating patient insights with clinical and utilisation data. This approach ensures evidence remains robust, comparable, and trusted across diverse healthcare systems.
Q11. How should patient-centred value stories be communicated to payers?
Payers engage when patient voice is translated into tangible system-level outcomes. HEOR should explicitly link experiential insights to adherence, persistence, healthcare utilisation, and long-term economic impact. Positioning patient-centred evidence as a driver of efficiency and sustainability - not as an emotional overlay - strengthens payer confidence and supports balanced reimbursement discussions.
Q12. How can patient advocacy groups be engaged without compromising scientific independence?
Patient advocacy groups bring essential insight into lived experience and unmet needs, but engagement must be structured and governed. Clear objectives, defined roles, transparency, and independent validation are non-negotiable. When managed appropriately, advocacy engagement enhances relevance while preserving the scientific integrity required for regulatory, payer, and HTA acceptance.
Q13. What skills and cultural shifts are required for HEOR teams to lead patient-centred strategy?
Modern HEOR teams need capabilities beyond analytics—qualitative synthesis, digital insight interpretation, cross-functional collaboration, and ethical governance are equally critical. Communication skills that translate evidence into value narratives are increasingly essential. Culturally, HEOR must shift from executing analyses to owning evidence impact - recognising its role in shaping access, policy, and real-world patient outcomes.
Q14. What does the future HEOR model look like when a patient voice becomes a true driver?
The future HEOR model is integrated, proactive, and patient-centred by design. Patient voice will influence not only what evidence is generated, but how value is defined across clinical relevance, economic sustainability, and access decision-making. With digital listening, advanced analytics, and rigorous governance, HEOR will evolve into a strategic orchestrator—ensuring patient insight is trusted, structured, and powerful enough to shape reimbursement pathways.
