Data Analytics: The New Gold Standard In Drug Value

Data analytics is moving from a nice-to-have add-on to a foundation of strategic thinking



Today’s drug developers operate in something of a perfect storm. Gone are the days of ‘blockbuster’ therapy area products, replaced with increasingly sophisticated personalized, niche and orphan bio-pharmaceuticals, with or without companion diagnostics.

Simultaneously, the increasing success of modern treatments has fueled societal expectation of healthcare systems, driving the demand for treatments for previously incurable or intractable conditions, and with it driving the political pressure to fund such treatments. In turn, payers have responded through intense price scrutiny and access barriers.

This perfect storm of high development costs, price scrutiny and access pressures have turned health economics, pricing and market access disciplines into business-critical functions.

Squeezed from all sides, todays leading companies are evolving these disciplines by establishing dedicated ‘enterprise analytics’ divisions, relying more and more on data to deliver the insights they need to understand therapy areas, navigate treatment pathways, identify sub-populations, appreciate value, formulate competitor strategy, maximize launch sequencing and negotiate pricing.

A fundamental shift
Successful decision-making throughout development and into marketing phases depends not merely on data but the accuracy and appropriateness of that data, and the ability to interpret it in the context of complex markets.

However, the fundamental shift underway is in the use of data analytics as a strategic foundational layer rather than a nice-to-have afterthought. Companies are asking what insights are needed to assist in more informed decisions before actively seeking those answers during the development of new treatments.

The promise of data-driven insights is complicated by the increase in development partnerships. With more companies joining forces to share risk, resources and, hopefully, the spoils of development, they face difficulties in both sharing sensitive data, and then agreeing on its interpretation, a picture that has increased complexity with the introduction of new European GDPR rules.

Despite the complexities, data-driven insights have the power to revolutionize economic modelling and to deliver real therapy-appropriate value measures that both developer and payer can trust, a new gold standard in drug value assessment.

Payer interviews: Gold standard or outdated?
This shift can be demonstrated in how companies are changing their approach to market access.

Payer and KOL panel interviews have long been the gold standard in establishing attitudes towards data-based value arguments for new products entering treatment pathways. However, such interviews typically take a long time to organize and conduct, are often expensive, and still rely on the subjective opinion of a relatively few individuals.

While these meetings are invaluable in confirming value assumptions, treatment pathways and comorbidities, KOL opinions will inevitably vary widely from region to region. In a climate of budgetary scrutiny, the risk of over-reliance on this source is in reducing the outcome of the interview to binary, value vs. price arguments, which carry far less weight with HTAs and clinical guideline bodies than those employing hard evidence derived from randomized trials, non-randomized trials and meta-analysis.

Such meetings need to be underpinned with enterprise analytic-derived insights, allowing interviews with KOLs to focus on patient value, leveraging data-driven insights to creatively explore new and more collaborative ways of demonstrating value and new pricing deals that are mutually beneficial to developer, patient and payer.

This data-driven analytics revolution is already underway; from the simple analysis of previous launches in a therapy area and/or region to identify additional evidence requirements, to the more complex launch sequence optimization, or the benefits of developing a companion diagnostic in securing market access for a niche indication.

This is underlined by the trend towards a more consumer-facing industry, where consumers themselves are seeking a greater role in their own care. The resulting shift towards paying for value rather than volume will drive a fundamentally different approach to the use of data as decisions about approval, prescribing, and marketing of drugs are more closely tied to patient results and needs.

Source, quality, appropriateness, interpretation and cost
As companies seek to navigate complex market access and pricing negotiations, the cumulative impact of inaccurate, misaligned, out-of-date or inappropriate data soon begins to tell – cumulative because, once poor data enters complex calculations, it becomes increasingly difficult to see how assumptions and calculations are impacted over time. Great care must, therefore, be taken in order to ensure reliability of conclusions.

Such errors range from the complex to the careless, such as inadvertently entering pricing negotiations with rebated price lists or entering payer and regulator discussions using a mix of transactional data and list prices.

To minimize such occurrences, it is helpful to consider the following four factors each time a new data enters the fray: source, quality, appropriateness, interpretation and cost.

  • Source – Does the data originate from reliable sources, systematically gathered in repeatable and robust methods? Does the data represent the real-world construct to which it refers?
  • Quality – Is the condition of the data set fit for its intended use? Is the data consistent, especially across increasingly large data sets? Should the data be cleansed before use?
  • Appropriateness – Is the use of a data set appropriate for the targeted insight?
  • Interpretation – Has adequate care been taken to understand the context of data interpretation?
  • Cost – Does the cost of collecting, cleaning and manipulating the data justify the value its insights are designed to bring?

When these factors combine, they permit the rapid integration of new data sources, especially valuable in fast moving markets. Leading companies are increasingly establishing analytic disciplines based on such processes to integrate and interpret new data as it arrives to stay ahead of competition.

The (false) promise of Big Data and AI
The volume and types of information available in healthcare systems has expanded significantly in the last 40 years with the uptake of electronic medical records, digitized medical imaging and genomic insights. The sheer volume of data is a rich resource for developers and healthcare providers alike, yet it demands equally large resources to mine it and deliver beneficial insights.

The emergence of artificial intelligence computing appears as a panacea to mine Big Data – plug all your data into a Watson-type computing platform and wait. Undoubtedly, such computing power can be programmed to highlight previously unseen correlations buried within vast data sets, but this approach might be seen as akin to the old days of blindly mixing compounds together to seek a therapeutic use.

Instead, like modern drug development techniques, we would advise starting with very specific target questions for enterprise analytics to answer.

Conclusion
The fundamental shift underway is in the use of data analytics as a strategic foundational layer, asking what insights are needed to assist in more informed decision making, driving data points to include during the development of new treatments.

The following four elements are essential for drug developers to realize the benefits of this new approach:

  1. Put data analytics at the center of strategic thinking and augment conclusions with KOL input, rather than the other way around. Empower development around patient insights derived from carefully designed data-analytics.
  2. Focus on discovering patient value rather than on maximizing price, especially for those patients whose characteristics mean that they are likely to derive the greatest benefit from a new innovation or treatment approach.
  3. Establish processes, or work with a skilled data analytics company, to clarify source, quality, appropriateness, interpretation, and cost considerations to enable quick assimilation of data into strategic thinking without risking that data corrupting decision-making.
  4. Where multiple parties are involved, outsource analytics to a trusted third party who can act as agent in assimilating data and insights.

Preeti Patel is Chief Executive Officer at Global Pricing Innovations.
 


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