Leveraging Data Analytics in Pharma: 7 Transformative Use Cases

Data analytics in pharma

On the path from lab to patient, data analytics serves as the pharmaceutical industry's compass that guides decision-making processes and clinical trials with valuable insights. They, in turn, promise to bring more effective therapies to the market faster.

In the third quarter of 2023, the pharmaceutical sector witnessed a significant surge in big data engagements, marking a 25% year-over-year growth compared to the same quarter in 2022. Remarkably, when comparing consecutive quarters, the industry saw an 82% leap in deal activities during Q3 2023 versus the preceding quarter.

Pharmaceutical giants including Johnson & Johnson, Bristol-Myers Squibb, Merck, AstraZeneca, and Pfizer are at the forefront of embracing big data initiatives.

Let’s explore how these leaders and many other pharma companies are leveraging big data analytics, highlighting diverse use cases that reshape the pharmaceutical industry.

7 most impactful pharma analytics use cases

Top 7 pharma analytics use cases

There are numerous use cases for data analytics in the pharmaceutical industry. Let’s explore the seven most popular ones and look at real companies that are already utilizing these technologies.

1. Drug discovery and development

Pharmaceutical analytics can improve the development, effectiveness, and safety of new drugs.

In the discovery phase, data analytics helps find promising drugs from huge collections of chemicals by focusing on those that precisely target certain diseases or biological paths.

Advanced algorithms, including machine learning and artificial intelligence, analyze patterns and relationships within biological data. This accelerates the identification of compounds that are most likely to succeed in preclinical tests. This process cuts down the time and cost needed to go from a concept to clinical trials.

As for the development phase, predictive data analytics tools analyze patient data like genetic information, clinical outcomes, and biomarker data, enabling researchers to design more targeted and effective clinical trials in the future.

Furthermore, real-world data analytics facilitates drug performance monitoring post-market. It can then identify potential adverse reactions or additional therapeutic benefits not observed during controlled trials.

Insilico Medicine, for example, used artificial intelligence in drug discovery, advancing the first AI-designed drug, INS018_055, into Phase II clinical trials for Idiopathic Pulmonary Fibrosis (IPF), a disease affecting around five million people globally. The organization accomplished the full discovery process from target identification to the nomination of a preclinical candidate in 18 months, utilizing a budget of only $2.6 million.

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2. Clinical trials

In pharmaceutical clinical trials, data analysts focus on patient recruitment and trial optimization to ultimately streamline the drug development process. They analyze historical data and current trial metrics to identify the most effective recruitment channels and criteria for participant selection. This reduces the time and cost associated with enrolling suitable candidates.

In addition to this, pharmaceutical analytics enable companies to closely monitor and analyze the side effects reported by trial participants in real-time. They employ sophisticated data analysis techniques to quickly find patterns or trends in adverse reactions, allowing for immediate adjustments to dosages or protocols if necessary.

Unsurprisingly, Pfizer, a leading name in the industry, successfully utilizes data analytics in clinical trials and drug development. Mohanish Anand, the company’s Executive Director and Head of Study Optimization, emphasizes how they use predictive modeling to estimate the time needed to reach important goals and finish clinical trials.

Anand’s team is tasked with integrating diverse data sets, crafting detailed project plans and forecasts, and communicating these plans to the broader team for informed decision-making. Leveraging data from previous trials, anonymized medical records, and insurance claims, while prioritizing patient privacy, enhances their ability to forecast drug development timelines much more accurately.

3. Insights into patient behavior and improved outcomes

Pharma analytics can aid companies in understanding patient behavior — a key aspect that significantly impacts the success of all pharmaceutical interventions. By gathering and analyzing patient data and real-world evidence, pharma firms can gain deep insights into how patients interact with medications, including adherence patterns, lifestyle impacts, and medication usage. This allows for the customization of treatment plans to better suit individual patient needs.

Pharmaceutical analytics also facilitate a more patient-centric approach to drug development and marketing. If the company truly understands patient behaviors and preferences, it can design more effective patient support programs, educational materials, and treatment strategies.

Oncora Medical, for instance, is revolutionizing oncology care in top-tier cancer centers across the US with its Oncora Patient Care and Oncora Analytics software. It leverages historical treatment data to enhance decision-making for oncologists, offering personalized, evidence-based treatment plans that improve patient outcomes.

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Another notable example is the studies supported by the Michael J. Fox Foundation for Parkinson’s Research (MJFF). The foundation launched the Parkinson’s Progression Markers Initiative (PPMI). This initiative works across the United States and Europe and is pivotal in assembling an extensive dataset and biosample library, unparalleled in Parkinson’s research. PPMI’s mission is to facilitate important breakthroughs by sharing its findings in real time with the global research community, which also improves the speed and scope of discovery.

To date, PPMI has enrolled nearly 2,000 participants, spanning Parkinson’s patients, at-risk individuals, and control volunteers, across 51 clinical sites worldwide. The initiative’s data are accessed by researchers around the globe over 2,200 times daily. Through PPMI, MJFF demonstrates the power of data-driven research and its ability to accelerate the journey towards more personalized treatments and, ultimately, a cure for Parkinson’s disease.

4. Symptom identification

Pharma analytics for symptom identification

Pharma analytics leverages vast datasets and advanced analytical techniques to uncover patterns and correlations between drug interactions and patient symptoms. It can use real-world data from electronic health records, clinical trials, and patient-reported outcomes to identify potential side effects and adverse reactions of medications that may not have been fully understood during the initial phases of drug development.

In addition to this, pharmaceutical analytics are crucial for the early detection of symptoms associated with specific diseases, which drives the development of targeted therapies. By employing machine learning algorithms to analyze complex datasets, researchers can identify biomarkers and genetic factors that may predispose individuals to certain conditions.

Take a look at Patientory, an innovative health-tech company that strives to revolutionize how individuals across the globe interact with their health data. We helped them develop an advanced platform that introduces a comprehensive health application. This app allows users to monitor their health status by integrating lifestyle information into a personalized medical dashboard.

First, users complete a questionnaire that delves into various aspects of their health and lifestyle, including habits, family health history, work-life balance, sleep quality, current symptoms, medications, and physical activity, among others. Then, Patientory processes the information and crafts a customized care plan aligned with the user’s specific health needs.

Additionally, in response to the changing healthcare landscape, we integrated a COVID-19 tracker into its app, allowing users to record vaccination data and monitor symptoms.

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5. Disease trends forecasting

Pharma analytics can predict disease trends, enabling healthcare providers and policymakers to prepare more effectively for future health challenges.

Various pharma data analytics tools can analyze patterns in healthcare data to pinpoint populations at risk and anticipate the spread of diseases. They can especially target those populations that may be more vulnerable due to genetic predispositions, environmental factors, or socio-economic conditions.

Plus, pharma analytics can make it possible to track and forecast pandemic hotspots, providing valuable insights that help in mobilizing resources, planning vaccination drives, and implementing targeted public health interventions.

Johnson & Johnson, another pharmaceutical giant, did just that. Through its Janssen Pharmaceutical Companies, they utilized advanced data analytics to track and forecast the spread of COVID-19.

These efforts led to the creation of a global surveillance dashboard, which provides real-time insights into virus movement at both local and global scales. By analyzing patient registries, real-world databases, and studies, Janssen could understand factors influencing disease outcomes and focus on enrolling populations at higher risk or exposure in vaccine trials.

6. Marketing and sales performance

Big data analytics in pharma can greatly enhance marketing and sales performance for pharmaceutical companies by providing deep insights into market trends, consumer behavior, and competitive landscapes.

For example, pharma analytics can process prescription patterns, demographics, and sales data to help companies identify high-potential market segments and tailor their marketing strategies to target specific physician groups or patient populations more effectively. Advanced analytics tools facilitate the prediction of product demand, optimization of supply chain decisions, and allocation of resources to areas with the highest return on investment.

Real-time pharmaceutical analytics can also monitor the success of marketing campaigns and sales initiatives, allowing for quick adjustments to strategies based on actual performance data.

7. Supply chain optimization

Pharma analytics for supply chain

Pharma analytics leverages data-driven insights to predict demand, streamline inventory management, and enhance medicine distribution processes. Companies can analyze historical sales data, patient demographics, and market trends to accurately forecast demand for various drugs and vaccines, which ultimately leads to much more efficient production planning and reduced inventory costs.

Advanced analytics also enable real-time tracking of shipments and inventory levels, helping to minimize the risk of either stockouts or overstock situations. By identifying bottlenecks and inefficiencies in the supply chain, pharma analytics can guide strategic decisions to improve logistics, reduce lead times, and ensure that crucial medications reach patients on time.

This is exactly what Sanofi utilizes data analytics for. In 2023, this pharmaceutical company launched plai — a cutting-edge application that uses AI to deliver real-time, responsive data interactions and provides a comprehensive 360° view of all Sanofi operations, including the supply chain.

This recent integration of plai into Sanofi’s biopharmaceutical supply chain has demonstrated its capability to forecast 80% of potential low inventory situations. This enables teams to implement preventive measures to ensure supply continuity more swiftly than previously possible.

Among other pharmaceutical companies that utilize data analytics to improve supply chain efficiency are Pfizer, Johnson & Johnson, AstraZeneca, and Merck & Co.

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So, is data analytics useful in pharmacy? Absolutely. It transforms patient care, optimizes operations, and drives the ever-necessary innovation in pharmaceutical practices. As they say, data is gold, and the pharmaceutical industry can’t afford to skip mining this invaluable resource for insights that drive smarter decisions and, most importantly, streamline patient care.

Data analytics truly plays a critical role in the pharmaceutical industry and other sectors. Just name it — healthcare, finance, retail, or even sports — every field benefits from the insights and efficiencies it brings.

We at PixelPlex understand it like no other, which is why we offer businesses to turn to our expert data analytics services. From predictive analytics and strategic planning to deep learning algorithms that personalize customer experiences, our solutions are tailored to meet the unique challenges and objectives of each industry we serve.

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Anastasiya Haritonova

Technical Writer

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