Expert Ecosystem — Peer Learning Insights Module Integration
Scale: Enterprise-scale platform used by global pharmaceutical commercial and medical affairs teams.
Product/System impacted: Expert Ecosystem — the Client’s existing application for KOL identification and management, extended with a new Peer Learning Insights module.
Challenge
Main problem: the Client’s Expert Ecosystem lacked visibility into peer influence dynamics — specifically, which healthcare providers are recognized by their peers for clinical experience and scientific leadership. Commercial clients needed this intelligence to identify and engage influential HCPs beyond traditional KOL metrics.
Why it existed: The Expert Ecosystem was built primarily around structured data (publications, trial participation, etc.). Peer-recognized influence — a key commercial differentiator — required integration of a new third-party commercial data source not previously available in the platform.
Impact before project: Pharmaceutical teams could not distinguish peer-recognized clinical leaders from the broader HCP population within the platform, limiting targeting precision for medical education and engagement programs.
Goals / Success Criteria
Top goals:
- Integrate third-party Peer Learning Insights data into the Expert Ecosystem
- Build a new ‘Peer’ module within the application featuring a filterable, sortable HCP list
- Develop interactive visualizations of peer network connections
Key metrics: Successful third-party data integration; Peer module functional delivery (filtering, sorting, search); score and rank alignment with existing application logic; MVP phase completion.
Constraints: Must align peer scores and ranking logic with existing Expert Ecosystem data models; MVP scope defined and delivered first; interactive visualizations must render accurately at scale.
Our Role
FLS responsibility: Integrated within the Client’s team, contributing full module development — third-party data ingestion, new Peer module UI/UX, interactive network visualizations, and alignment with existing application logic.
Scope & timeline: MVP phase delivered first; phased rollout approach.
Teams involved: Project Manager, Business Analyst, 2 Frontend Developers, 2 Backend Developers, Big Data Engineer, QA Analyst.
Approach
Key delivery phases: Data integration & mapping → Peer module design → Core feature build (list, filters, search) → Visualization development → Score alignment & QA → MVP release.
Risk-reducing decisions: MVP-first delivery approach de-risked the integration — core functionality was validated before interactive visualizations were built. A Big Data Engineer was added to the team to handle the third-party data pipeline requirements.
Quality assurance: Score and rank alignment was explicitly validated against existing application logic; QA embedded throughout; phased release with MVP gate before full feature rollout.
Solution
As part of the Client’s extended team, we co-delivered the Peer Learning Insights module within the Expert Ecosystem — a fully integrated, data-rich feature set enabling pharmaceutical teams to identify and engage peer-recognized clinical leaders.
Core components:
- Third-party Peer Learning Insights data integrated into the Expert Ecosystem platform
- New ‘Peer’ module: filterable and sortable list of peer-recognized HCPs
- Sorting, filtering, and search functionality for the HCP list
- Score and rank alignment with existing Expert Ecosystem logic
- Interactive visualizations of peer network connections between HCPs
Differentiator: The combination of third-party data integration with interactive peer network visualizations created a genuinely new intelligence layer in the platform — moving beyond static HCP profiles to dynamic relationship mapping. The addition of a Big Data Engineer to the team ensured the data pipeline was built for scale, not just MVP.
Technology
Stack: Frontend (new Peer module with advanced table and visualization libraries); Backend (third-party API/data integration); Big Data pipeline for peer data ingestion and processing.
Tooling: Advanced frontend table library with rich filtering and UX; network visualization library for interactive peer connection graphs; data pipeline tooling for third-party integration.
Accelerators: Existing Expert Ecosystem data models reused for score alignment; shared component patterns from previous SOWs.
Results
Measurable improvements:
- MVP phase successfully delivered
- Third-party Peer Learning data successfully integrated — a net-new data source for the platform
- New ‘Peer’ module live within Expert Ecosystem with full filtering, sorting, and search
- Interactive peer network visualizations delivered — a first for the Client platform
Strongest impact statements:
- Pharmaceutical teams gained access to peer-influence intelligence for the first time — the result of close collaboration between our team and the Client’s, enabling more precise KOL identification and engagement strategy
- Interactive network visualizations — built together with the Client — transformed peer data from a table into a relationship intelligence tool, increasing the platform’s strategic value for medical affairs teams
Before / After Snapshot
| Metric | Before | After |
| Peer influence data | Not available in platform | Fully integrated third-party Peer Learning Insights |
| HCP targeting precision | Structured metrics only (publications, trials) | Peer-recognition layer added for nuanced targeting |
| Platform modules | Expert Ecosystem only | + New ‘Peer’ module with filtering, sorting, search |
| Data visualization | Static HCP profiles | Interactive peer network connection graphs |
| Team capability | Standard frontend/backend | + Big Data Engineer for pipeline scalability |
AI Enablement
AI was not a primary component of SOW 4–5. The focus was on third-party data integration, module development, and interactive visualization. The peer influence scoring logic used algorithmic ranking based on Peer Learning Insights data rather than ML/AI models. AI capabilities explored in SOW 3 remain on the roadmap for future platform phases