Embedding Patient-Centricity by Collaborating with Patients to Transform the Rare Disease Ecosystem
Bibliographic record
Abstract
What is patient-centricity? In some contexts, it has been associated with targeting therapies based on biomarkers or enabling healthcare access. There has been a surge in patient-centricity publications, and in many cases for the biopharmaceutical industry, patient engagement is used to endorse pre-held assumptions at a specific moment in time. Rarely is patient engagement used to drive business decisions. Here we describe an innovative partnership between Alexion, AstraZeneca Rare Disease and patients that allowed a deeper understanding of the biopharmaceutical stakeholder ecosystem and an empathic understanding of each patient's and caregiver's lived experience. Alexion's decision to build patient-centricity frameworks resulted in the formation of two unique organisation design platforms: STAR (Solutions To Accelerate Results for patients) and LEAP (Learn, Evolve, Activate and deliver for Patients) Immersive Simulations. These interconnected programmes required cultural, global, and organisational shifts. STAR generates global patient insights that are embedded in drug candidate and product strategies while helping to establish enterprise foundational alignment and external stakeholder engagement plans. LEAP Immersive Simulations produce detailed country-level patient and stakeholder insights that contribute to an empathetic understanding of each patient's lived experience, support country medicine launches and provide ideas to have a positive impact along the patient journey. Combined, they deliver integrated, cross-functional insights, patient-centric decision making, an aligned patient journey, and 360° stakeholder activation. Throughout these processes, the patient is empowered to dictate their needs and validate the proposed solutions. This is not a patient engagement survey. This is a partnership where the patient co-authors strategies and solutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.028 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".