Leveraging Data Analytics to Transform Medical Affairs Strategy

CHALLENGE

A global pharmaceutical company struggled to accurately measure the impact of medical engagement in severe asthma and to identify patient populations in oncology indications such as renal cell carcinoma (RCC) and small cell lung cancer (SCLC). Existing coding structures and predictive algorithms were incomplete, resulting in limited visibility into patient identification, provider behavior, and overall strategic effectiveness.

SOLUTION

CMK Select partnered with the medical team to strengthen data and analytics capabilities across these areas. For severe asthma, CMK developed a comprehensive measurement plan, defined engagement parameters, and revised automated coding logic to improve accuracy, ensuring a smooth transition of ownership to a new manager. In RCC, we evaluated the existing algorithm across 40 million patient records, uncovered performance limitations, and proposed a tailored machine-learning model trained on known cases to improve detection. Building on these insights, we extended the methodology to SCLC by leveraging electronic medical record (EMR) fields to identify patients without distinct claims codes – an approach that also enabled earlier provider targeting for clinical trial recruitment and medical resourcing.

RESULTS

CMK Select’s analytics-driven approach delivered measurable impact across therapeutic areas. Over 14 months of severe asthma engagement data were integrated into strategic decision-making, improving insight and performance tracking. In oncology, a predictive machine-learning model replaced static RCC algorithms to enhance patient identification, while EMR-based analytics in SCLC enabled earlier provider targeting and faster clinical trial recruitment. Together, these advances strengthened medical strategy, optimized resources, and improved provider engagement.