Analyze Treatment Choices Along a Patient’s Journey

Vishnu Prashanth

Principal Consultant, Data Analytics

Alankar Khare

Senior Business Analyst

Client: BioPharma
Year: 2018

Once the drugs are launched in the market, there is a need to understand their performance as well as track the effect of any marketing initiatives being taken to increase the share. The conventional data sources (e.g. chart audit, sales data) present only a small part of the very big picture where a lot of stakeholders and indirect factors affect the share of the drug, thus limiting the insights and scope of the analysis. Analyses like Source of Business Analysis and Line of Therapy Analysis help go deeper by analyzing patient’s initial treatment choices and how they progress from one therapy to another. These analyses require metrices that are captured only in the patient level data sources. However, working with these data sources is not as simple and straightforward as the conventional sales data.

Numerous patient level data sources present in the market differ based on their source, (EHR, claims, etc.), types of metrices captured, time lag, etc. Even after finalizing the data source, creation of robust business rules around the patient pool poses a challenge as data in its raw format has issues related to continuity, richness on one side and a lot of additional information which can be overwhelming and hide the actual insights behind the noise. We leveraged our proprietary Cohort Builder tool to quickly analyze, compare and decide on the best data source for the analysis. Our expertise and experience with patient level data sources helped create business rules to build a robust patient pool and key patient categories specific to the analysis. This assisted in generating some interesting insights around patient treatment pattern which was consumed by different teams within the client’s organization:

  1. Insights were provided around the source of patients who are new on therapy vs those who have switched therapies and their variation across time post drug launch
  2. For the target drug, the analysis helped understand the potential patients with therapies which are likely to make switch to the target therapy thus helping in devising efficient marketing and messaging plan
  3. Validated the effectiveness of the current initiatives based on the overall uptake of the drug
  4. Insights around specialties initiating the patients and the ones who prefer the target drug helped in better targeting of prescribers

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D Cube Analytics is an Integrated Data Sciences company focused on extracting transformational insights from syndicated, real world and digital data to increase revenue realization, avert revenue loss, enhance internal productivity and improve end user experience for global Pharmaceutical organizations.

D Cube is pioneering a Digital Transformation wave within BioPharma by leveraging new age tools and methodologies like Artificial Intelligence, Machine Learning and Robotic Process Automation to greatly improve the productivity of workforce and significantly enhancing speed to insight. Through this new age product-based approach to delivering analytics, we greatly reduce the cost and complexity of deployments and provide measurable value across multiple business functions.

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