Case Study: Lysosomal Storage Disorder

Beyond Coding Data: Identifying Patients with a Rare Lysosomal Storage Disorder

Client

A biopharmaceutical organisation working in rare neurodegenerative diseases engaged GDS for a project in an ultra-rare lysosomal storage disorder for Europe.

The objective

The objective was to identify where expertise resides and which clinicians not only diagnose but also manage patients, while also supporting the integration of these insights into its CRM system. The aim was to improve visibility across the care landscape and ultimately support better access to new therapies for patients in Europe affected by this rare progressive disease.

The challenge

In ultra-rare diseases, identifying the clinicians and centres involved in patient care can be complex due to several structural factors:

  • Traditional scientific output capture only part of the clinical landscape: The rarity of the disease results in limited scientific activity, meaning it does not capture all clinicians involved in diagnosing or managing the diseas.
  • Multi-specialty patient pathways and misdiagnosis; Many rare diseases present with non-specific symptoms, meaning that patients may initially receive alternative diagnoses before the correct condition is identified. Consequently, patients may enter the healthcare system through clinicians treating related or overlapping conditions. Although these clinicians may not ultimately diagnose or treat the rare disease, they can play an important role in the early stages of the patient pathway.
  • Limitations of healthcare coding systems; Rare diseases are often grouped within broader diagnostic categories in healthcare coding systems (e.g. the ICD-10 classification system). As a result, datasets may miss centres actively diagnosing and treating patients while including others not involved in the specific disease area.

GDS’s Approach

To overcome these challenges, GDS applied a broad analytical framework rather than relying solely on scientific visibility or healthcare coding systems.

Clinician analysis included:

  • Evaluation of scientific activity related to the disease.
  • Analysis of the most correlated conditions to identify additional clinicians involved in the disease through related scientific activity.
  • Assessment of broader indicators of expertise, including society roles, departmental affiliations, leadership roles, and other institutional signals

These multidimensional signals were integrated to build a comprehensive understanding of each clinician’s relevance within the disease landscape

This approach revealed clinicians and centres actively involved in patient care that were not visible in traditional datasets.

Outcome and Impact

The analysis provided a precise view of the clinical landscape for this rare lysosomal storage disorder where healthcare coding data would have been misleading for finding patients. An approach based solely on coding data would have identified only 4 centres of potential relevance. Through deeper analysis, GDS helped identify more than 20 centres with meaningful involvement in the disease area. At the clinician level, analysing signals beyond scientific activity also proved essential. Around 30% of relevant clinicians would not have been identified through scientific evidence alone. By revealing where expertise truly lies, this work brings clarity of the care landscape and helps ensure that patients affected by this rare disease can reach the clinicians and centres best equipped to treat them.