Enhancing Biopharma Strategy with Data Analytics Insights

Analytics data has become a commodity to biopharma.

Data has become a commodity within biopharma.

Multiple companies have made significant strides in procuring, integrating and managing datasets for biopharma. However, one fundamental challenge remains — generating actionable insights from datasets that drive strategy and execution. In some cases, the “data lakes” that have been built serve as functional “data moats,” with vendors, data science and Information Technology (IT) ensconced at the center.

At 81qd, we work to democratize and drive action from data — to bring life to data by leveraging client datasets or our internal open and closed patient-level claims, electronic health records (EHR) and social determinants of health data to inform strategy and execution.

Three principles define how to build bridges that enable commercial and medical action, personalize engagement and improve patients’ outcomes:

Improving biopharma engagement strategiess using patient centered data

There is art in choosing what data to leverage. For example, open claims data are valuable for market sizing and targeting due to volume and can provide a less biased, broad sample for patient journey analyses. However, not all patients will have their complete journey within the dataset. EHR data, while providing clinical depth through SNOMED codes and test results, will similarly not always have the complete journey. Integrating a smaller matched closed claims with open claims and EHR makes for a more robust analysis of outcomes and treatment approaches. We made these decisions recently to estimate the size of the patient population and support clinical trial recruitment for a client developing a product treating an ultra-rare disease without an ICD-10 code. Analysis of 81qd’s EHR SNOMED codes
was used to assess the patient journey and support market sizing. Insights from this assessment drove predictive analytics on 81qd’s linked open claims data to identify undiagnosed patients and the HCPs currently managing them, who could then be engaged for clinical trial recruitment.

Leveraging multiple data sources for improved hcp targeting insights

Currently, many biopharma companies and data science vendors work in silos to execute initiatives, or limit ownership of analytics to select functions. Citizen data users are data-savvy individuals who may not have data or analytics in their title but possess skills to leverage data for insights. They often serve as a bridge between the data experts (data science and IT teams) and the commercial and medical business. They need the right tools to be effective, such as self-service platforms and
easy-to-manipulate Excel workbooks to interrogate data. Too often, analytics partners develop black box platforms that are targeted at the data naive or complex CSV files for data scientists and IT. We have found citizen data users across medical affairs, brand marketing, commercial operations and thought leader liaison teams that are searching for tools to support strategy and execution. The outputs of any analytics initiative should empower citizen data users to actively and independently manipulate data to define insights.

integrating data insights for improved biopharma engagement strategy

An equal focus should be put on effectively integrating insights across analytics workstreams to integrating baseline datasets. For example, we partnered with a digital engagement team to identify clinical leaders through predictive analytics on patient-level claims data. This output was then integrated with a digital affinity analysis from another team to uncover that clinical leaders engaged online at double the rate of average targets. Integrating these outputs was critical in shaping segmentation and engagement strategy.

Data analytics today needs to move from the ivory tower castles of analytics anchallenges icond data science teams to the community of citizen data users. Crossing data moats will be driven by human intelligence in defining the datasets that should be leveraged, empowering citizen data users with data access tools and integrating relevant outputs to accelerate strategy development and execution. It is time to lay down the drawbridge and move forward, visit 81qd.com for more information.

To see how we use AI to assess key market stakeholders, go to

To see how we identify clinical leaders and map their networks, go to

To see how we use AI and RWD to find undiagnosed patients and their HCPs, go to

To see how we answer your business challenges leveraging Real-World Data

To see how to identify and manage interactions with influential Thought Leaders