Navigating the Complexity of Patient Data: Why Pharma Needs Specialized Intelligence Platforms

Generic analytics tools often fail to address the complexity of patient data in pharma. Learn why putting specialized intelligence platforms in the hands of analytics as well as commercial teams is essential for generating actionable insights that drive better patient outcomes and commercial success.

The pharmaceutical industry has long recognized the value of real-world data (RWD) in driving decision-making and improving patient outcomes. However, the complexity of patient-level data presents unique challenges that generic analytics tools are ill-equipped to handle. To fully harness the power of patient insights, pharmaceutical companies must embrace specialized intelligence platforms designed specifically for their needs.

The Intricacies of Patient Data in Pharma

Patient data in the pharmaceutical landscape is characterized by its diversity, fragmentation, and inherent complexity. Electronic health records (EHRs), claims databases, patient registries, and other sources provide a wealth of information, but each comes with its own set of idiosyncrasies and limitations. Additionally, the data often lacks standardization, with inconsistent terminologies, coding systems, and data structures across different sources.

It is crucial to understand that RWD alone does not equate to patient-centric intelligence (PCI). PCI goes beyond the raw data, encompassing the advanced analytics, domain expertise, and contextual understanding necessary to derive meaningful insights. Generic analytics tools, while powerful in their own right, often fall short in addressing the nuances and complexities specific to the pharmaceutical industry.

The Limitations of Generic Analytics Tools

One of the primary limitations of generic analytics tools is their lack of built-in domain knowledge. These tools are designed to be broadly applicable across industries, which means they may not have the necessary context to interpret and analyze patient data effectively. Without a deep understanding of medical terminologies, disease states, treatment pathways, lines of therapy, and regimens, generic analytics tools struggle to provide the level of insight required for informed decision-making in pharma.

Furthermore, generic tools often lack the ability to handle the scale and variety of patient data encountered in the pharmaceutical setting. The sheer volume of data generated by EHRs, claims, and other sources can overwhelm traditional analytics platforms, leading to performance issues and delays in insight generation. On top of that, the need to integrate and harmonize data from disparate sources poses significant challenges, requiring extensive data preprocessing and transformation efforts.

Without a deep understanding of medical terminologies, disease states, treatment pathways, lines of therapy, and regimens, generic analytics tools struggle to provide the level of insight required for informed decision-making in pharma.

The Downstream Challenges for Analytics Teams

The limitations of generic analytics tools create a cascade of problems for analytics teams within pharmaceutical companies. Without the right tools in place, these teams often find themselves bogged down in time-consuming tasks from data preparation and cleaning to dashboard customizations and logic updates. Instead of focusing on generating valuable insights, they spend a significant portion of their time getting the data suitable for analysis and getting the visualizations suitable for decision making.

Adding to the challenges, the lack of built-in domain knowledge in generic tools means that analytics teams must invest additional effort in translating medical concepts and business rules into the language of the tool. This not only lengthens the insight generation process but also increases the risk of errors and inconsistencies in the analysis.

As a result of these challenges, analytics teams often find themselves stretched thin, struggling to keep up with the demands of the business. They may become focused on addressing narrow, specific requests rather than leveraging their expertise to uncover broader, more impactful insights. This reactive approach hinders their ability to provide strategic guidance and proactively identify opportunities for improvement in patient care and commercial performance.

The Value of Specialized Intelligence Platforms

To overcome these limitations, pharmaceutical companies are turning to specialized intelligence platforms purpose-built for their industry and use cases. These platforms offer a range of features and capabilities that address the unique challenges of patient data analysis in pharma.

First and foremost, specialized platforms are equipped with built-in domain knowledge and data models tailored to the pharmaceutical context. They understand the intricacies of medical data, including disease ontologies, treatment protocols, and patient journey dynamics. This enables them to process and interpret patient data more accurately and efficiently, reducing the need for manual intervention and data preprocessing.

These platforms are also designed to handle the scale and complexity of patient data inherent in the pharma landscape. They employ advanced data integration techniques, such as natural language processing (NLP) and machine learning, to automate the process of harmonizing and structuring data from diverse sources and enriching the data with additional clinical and drug context, such as lines of therapy and regimens. This not only saves time and resources but also ensures a more comprehensive and reliable foundation for analysis.

Specialized intelligence platforms offer advanced analytics capabilities specifically geared towards pharmaceutical use cases. These may include patient cohort analysis, treatment pattern mining, outcomes prediction, and more. By leveraging techniques such as machine learning and artificial intelligence, these platforms can uncover hidden patterns, identify risk factors, and generate predictive insights that inform strategic decision-making.

Adopting Prospection AI for Patient-Centric Intelligence

Nicholas (Nic) Phillips, who leads Business Insights in Strategy and Business Excellence for Ipsen ANZ, was recently featured in an article where he shared his experience with the specialized intelligence platform, Prospection AI.

In selecting Prospection AI as their patient-centric intelligence (PCI) platform, Ipsen valued its ease of use and the ability to put insights directly in front of brand teams. Nic emphasized the importance of understanding the “why” behind sales numbers, stating, “Importantly, we care most about patient outcomes – and you just can’t get a clear picture of the population-level impact on patients, on their journey, and on their care, without using a tool like Prospection AI.”

Nic spoke about his belief that better patient outcomes go hand-in-hand with better commercial outcomes, and AI in conjunction with patient-level analysis will be a major factor in achieving these improvements. Nic’s advice to other commercial leaders considering the adoption of AI-driven tools is to take the first step as soon as possible, emphasizing the importance of equipping brand teams with data-analysis tools that provide objective, quantitative insights into the behavior of clinicians and patients being treated.

“Importantly, we care most about patient outcomes – and you just can’t get a clear picture of the population-level impact on patients, on their journey, and on their care, without using a tool like Prospection AI.”
Nicholas Phillips | Ipsen

Embracing Tailored Solutions for Optimal Results

The complexity of patient data in the pharmaceutical industry demands specialized intelligence platforms that can navigate the intricacies and deliver actionable insights. Generic analytics tools often fall short in addressing the unique challenges faced by pharma, leading to inefficiencies and missed opportunities.

Ipsen’s journey with Prospection AI highlights the transformative potential of adopting a patient-centric intelligence platform. By leveraging AI and advanced analytics, Ipsen empowers its brand teams to make data-driven decisions, optimize commercial strategies, and ultimately improve patient outcomes.

As the pharmaceutical landscape continues to grow in complexity, the adoption of specialized intelligence platforms will become increasingly critical. Companies that embrace these tailored solutions will be well-positioned to harness the power of patient data, drive better decision-making, and achieve a competitive edge in an increasingly data-driven industry.

At Prospection, we understand the unique challenges faced by pharmaceutical companies in their quest to derive meaningful insights from patient data. Our patient-centric intelligence platform is purpose-built to address these challenges head-on, providing a comprehensive solution that combines domain expertise, advanced analytics, and intuitive visualizations.

The time to act is now – the future of pharma analytics lies in specialized platforms designed to meet the industry’s specific needs. By partnering with Prospection, pharmaceutical companies can unlock the full potential of their patient data, drive commercial success, and ultimately improve the lives of the patients they serve.


Contact us to learn more about how patient-centric intelligence platforms help pharma commercial and analytics teams produce precision insights that improve outcomes for patients, leading to better brand performance.