Why leveraging real-world data and patient analytics is essential for healthcare

8 Feb 2022
Why leveraging real-world data and patient analytics is essential for healthcare

Real-world data (RWD) is fundamental to delivering patient-centric care. A massive amount of health-related information is reported and collected in real-world medical settings every minute.

This data is collected, de-identified, and stored in a variety of sources. Synthesizing and aggregating these RWD sources with artificial intelligence (AI) and machine learning (ML) tools can offer pharma companies a longitudinal view of patients, giving them analytical insights into patient journeys and interactions, and helping them create informed commercial strategies with predictive, evidence-based data.

Diversity and quantity in patient data sources

Patient data includes information relating to patient treatment history, lifestyle choices, genetic data, medical interactions, and more - all stored physically or electronically in a wide range of medical sources, including electronic health records, pharmacy and payer databases, social media, wearables, implantables, home health monitoring devices, and patient-powered research networks.

The diagram below provides an understanding of a variety of patient data sources:

Types of patient data sources

1. Types of patient data sources

Pharma companies can leverage analytical solutions to explore and examine these data types and then transform their findings into descriptive, predictive, and prescriptive insights that ultimately help them optimize their product and patient journeys.

Leveraging patient analytics across the product life cycle

Most pharma companies recognize the value of patient analytics, but far too few extend their analytics programs across the entire product lifecycle. Here’s how patient analytics drives value across various product stages:

  • Late-stage pipeline

    Pharma companies can leverage data insights to understand patient history and behavior as they work toward identifying their target market for launch. This can also help them improve the accuracy of forecasts by analyzing diagnostic features, ruling out misdiagnosis, checking comorbidities, prescriptions, and the length of possible treatments.

  • Pre-launch and launch

    Data insights can help pharma companies evaluate market readiness by identifying early adopters among healthcare providers (HCPs), and optimizing their engagement on the right channels. In addition, it can also offer visibility into the prescription volumes recorded by competing brands.

  • Post-launch and active marketing

    Pharma companies can track and evaluate the product's performance, patient adherence, and response with ease, enabling them to adopt appropriate strategies at the right time and sustain brand performance as they pursue scalability.

  • Market maturity

    As pharma companies work towards brand expansion, data insights can solidify their maturity by enabling them to identify label expansion opportunities. It can also help them identify the HCPs who are loyal to the brand so as to drive targeted activities and keep them engaged.

Critical Success Factors

Patient data analytics often turn out to be one of the most challenging undertakings for the healthcare industry. Pharma companies often struggle with:

  • Identifying the appropriate RWD source for a specific use case
  • Implementing analytics across a variety of therapeutic areas
  • Generating patient insights from analytics quickly
  • Declining adoption of patient analytics across therapeutic areas

The coordinated focus on three major success factors can help pharma companies tackle these challenges and scale patient analytics to new heights.

Critical success factors to scaling patient analytics

2. Critical success factors to scaling patient analytics

  • People

    People are the key element behind accelerating insights through patient analytics. Pharma companies must build a highly-specialized team of Data Science experts who understand the complexity and granularity of RWD across therapeutic areas, and possess the skills required to create competent analytics processes across the product lifecycle.

  • Process

    Processes determine the success of any analytics journey. It sets the stage for effective management systems defined by best practices and governance framework. Having the right set of processes and standard operating procedures is crucial to support effective drug development and pricing, alignment of teams across therapeutic areas, and facilitation of proactive risk management.

  • Technology

    Technology is the key enabler for decision-making. By having the right tech stack, enterprise AI software, data catalog/governance software, standardized/re-usable code repositories, and more, pharma companies can accelerate improved patient outcomes while maintaining compliance in a rapidly changing global landscape.

Maximizing value through patient analytics

Leveraging patient analytics to let data paint a larger picture can do many things for pharma companies, helping them improve patient outcomes by delivering the most relevant information to clinicians, HCPs, and payers. Here are some examples:

  • Get accurate medical interventions

    Usually, analytical data is recorded and stored for reference when conducting tests. It helps practitioners check previous recommendations, analyze effectiveness, and develop accurate treatments. Consequently, specialists administer effective medications and care, breaking cycles of misdiagnosis and ineffective treatment. Patient analytics could be the perfect opportunity for discovering and developing new drugs.

  • Save time as you work towards developing drugs

    Developing drugs takes time. The more you understand your research, the less time it will take to develop a new drug. Applying AI and ML algorithms in your data processing efforts will help you analyze data more quickly and efficiently than ever before.

  • Improve healthcare

    Patient analytics can improve patient healthcare services by minimizing trauma resulting from treatments with a systematic approach. The result is reduced patient relapse and endless readmissions as you work on patient-centered solutions.

Conclusion

When it comes to modernizing processes to build a patient-centric model in an increasingly data-driven healthcare market, the longer the delay, the wider the gap. With a sound and well-equipped patient analytics foundation, pharma companies have an opportunity to make significant progress in improving patient health outcomes. Those that seize the opportunity will have the runway to grow, thrive, and be future-ready.