Case Study: Top-20 Life Sciences Manufacturing Company achieves improved patient intervention effectiveness and reduced therapy discontinuation with IntegriChain's machine learning solution

Unleashing Machine Learning to Improve Patient Intervention Effectiveness

Top-20 Life Sciences Manufacturing Company faced a manual, ad hoc intervention reporting process—largely spreadsheets and basic BI—while trying to improve patient intervention effectiveness across its specialty portfolio. The manufacturer engaged IntegriChain (using its ICyte capabilities and advanced analytics expertise) to replace the reactive process with an automated, repeatable solution focused on predicting patient opt-outs and therapy gap days so Market Access, Trade and Channel, and Patient Services teams could proactively target high‑risk cases.

IntegriChain applied supervised machine learning to integrated patient case, engagement, clinical and derived data, then delivered dashboards and reports (including an Intervention Quality Dashboard and ICyte Analytics) that accurately predicted gap days and opt-outs and exposed duplicate-intervention costs and data quality issues. The insights let teams prioritize interventions and adjust messaging—showing large differences between engaged and disengaged cohorts (PDC 92.8% vs 77.2%, ToT 471.6 vs 237.1, gap days 13.2 vs 26.4, discontinuations 29.4 vs 53.7)—and provided automated, on‑demand visibility for ongoing optimization.