In the Life Sciences commercial space, there is an acute need to better understand the customer, broadly defined, as well as product usage at ever deeper levels for a whole host of commercial applications. Acknowledging the ongoing need to ensure security and patient privacy, the volume and variety of data now available in the healthcare market offers an unprecedented opportunity to leverage these rich assets to better answer complex questions through analytics. Additionally, realtime evidence of product effectiveness, safety, and value is increasingly becoming a prerequisite for ongoing market access. The application of analytics is therefore moving beyond descriptions and interpretations of historical events (i.e., traditional analytics) and entering a realm whereby a new breed of predictive and prescriptive analytics (i.e., advanced analytics) is able to extract insight to provide a much better understanding of issues, outcomes and the likely results of potential courses of action, resulting in better commercial decision-making.

In this era of ‘big data’, advanced analytics is set apart from traditional analytics given the ability to leverage incredibly large data sets in unprecedented, rigorous ways. For example, rather than conducting basic analyses around mean and variance or regression based analyses as is the case with traditional analytics, advanced analytics employs a variety of methods such as machine learning techniques like random forest modeling or support vector machines that are coupled with ensemble approaches to maximize predictive performance. Other techniques such as neural networks and natural language processing also belong to this realm of advanced analytics. See Table 1 for a brief comparison of the differences between traditional and advanced analytics.

It is important to note that this paradigm shift is not one in which advanced analytics will completely replace traditional analytic approaches. While innovation in advanced analytics will certainly render some historic approaches obsolete, many traditional analytics methods will likely remain appropriate for specific applications. In this manner, the innovative approaches afforded by advanced analytics will augment the remaining traditional methods to power an enormous range of new applications within healthcare. The challenge for Life Sciences companies is to leverage this dual opportunity by incorporating advanced analytics into their existing processes, to eventually become a part of routine business practice. This white paper presents an overview of the opportunities afforded by advanced analytics, examples for consideration of anonymized real-world applications that IQVIA has executed, as well as a diagnostic that Life Sciences companies can use to assess their readiness for achieving the promise of advanced analytics

THE NEED FOR MORE INFORMED DECISIONS ACROSS HEALTHCARE

Like most businesses, Life Sciences companies have a keen and ongoing need to accurately understand, anticipate and address what is happening in the constantly changing marketplace in which they operate. The complexity of the market and the wide range and variety of influences on decision-making dramatically increases the demand for timely information, insights and actions.

For many years, traditional data analytics has enabled Life Sciences companies to make crucial business decisions across the value chain spanning research and development, manufacturing, regulatory affairs, pricing/reimbursement, as well as sales and marketing. Companies continue to do so, mapping the who, what, where, and why of health care systems (e.g., practitioners’ prescribing habits, sales and reimbursement trends, patient characteristics, stakeholder influences, and competitor activity), drawing on both secondary data and primary market research to obtain detailed descriptive and diagnostic insights.

But there is a need to do more, with the crucial aspect of applying advanced analytics to inform commercial decisions so that outcomes delivered are better across the network of healthcare stakeholders, resulting in more mutually beneficial wins for Life Sciences companies, healthcare providers, payers, regulators and patients . Through more informed decisions, healthcare delivery can be made in a more effective and timely manner that benefits all stakeholders as follows:

Knowing more about disease characteristics, patient responses to therapy, patterns of product usage, and the ripple effect of treatment outcomes throughout healthcare systems is vital for Life Sciences companies seeking commercial advantage via a higher return on research and promotional investments. Effective communication and translation of clinical benefits into real-world value for new medicines can only be achieved by getting the appropriate message delivered to the right stakeholders, leading to more timely and beneficial intervention in disease management. One example of the work that IQVIA has conducted in this area is in the realm of digital healthcare advertising real-time optimization wherein the key channels and messages driving product growth are interpreted and understood on an ongoing basis, allowing dynamic adjustments to promotional campaigns.

For regulators, the application of advanced analytics by Life Sciences companies can have a two-fold impact. Firstly, advanced analytics can help Life Sciences companies to better build patient cohorts for clinical trial recruitment, enhancing the clarity of clinical trial outcomes. Drug regulatory agencies are therefore able to make decisions on a better built clinical trial, and have greater confidence regarding the results of the trial and how the product is likely to perform in the real-world setting. Secondly, once in the real-world setting, the application of advanced analytics on large anonymized longitudinal patient data sets can provide regulators with quicker insight into the way in which various products are performing in the healthcare environment.

Providers and payers, faced with increasing patient volumes as well as escalating pressures on costs, can immediately benefit from some of the current applications made possible by advanced analytics. For example, the use of precision medicine made possible by advanced analytics reduces uncertainty and the costs of non-responding patients and/or adverse reactions related to various treatment paths, while increasing visibility around product value. Similarly, advanced analytics can identify undiagnosed diseases at an early stage of progression, improve patient pathways or potentially avert the need for more expensive treatment, hospitalization or long-term rehabilitation by enabling early therapeutic intervention. As such, these new approaches can drive stability and sustainability in healthcare systems by ensuring that treatment decisions are better informed and more tailored to individual patient needs, while also being more timely and cost-effective. An example of the work that IQVIA has conducted in this area is in rare disease detection where otherwise undiagnosed patients receive incorrect or delayed treatment. Another example is advanced targeting wherein a deeper understanding of providers and payers is facilitated, including a better understanding of a provider/payer’s patient population, product preferences and drivers of behavioral change.

The ultimate benefit is in the form of a better outcome for patients. Through appropriately applied advanced analytic methods, patients (as well as their physicians) are more likely to receive the necessary information appropriate for their condition. As a consequence, there is a much better chance of identifying health risks and addressing these risks early enough to either ward off disease or manage it well enough to allow patients to continue living fulfilling and productive lives. Decision support tools informed by advanced analytics can better match a given patient to the most appropriate course of therapy to achieve enhanced outcomes at an overall lower cost of treatment. Analysis of behavioral patterns can also help patients with drug adherence/ persistency and responsible use of medicines, reducing wastage, treatment failures, sub-optimal disease management, and time-consuming or debilitating reabsorption into dependent medical care. At the same time, the approach eliminates a significant component of avoidable healthcare costs. In addition to the rare disease detection example described earlier, another real-life example of this is any one of the patient risk stratification studies that IQVIA has conducted that uses machine learning techniques to predict patients most likely to respond to treatment, thus allowing delivery of more accurate messaging, better patient outcomes and greater product value.

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