How digitization makes it easier to leverage real-world evidence across the life sciences value chain

by Gireesh Gaonkar

The life sciences value chain — from R&D to manufacturing to marketing and sales — has long battled with addressing the challenges presented by silos, a complex network of global partners and affiliates, and as a result, inconsistent and disconnected ways to manage the large pool of data generated. But with the rise of digital tools and technologies, an opportunity exists to better leverage the data from those data silos.

This issue of data silos is particularly relevant when it comes to providing real-world evidence, which has become core to every life sciences company’s needs and objectives. There are many ways in which real-world evidence is integral to the life sciences value chain, but to understand the advantages that digital tools and technology can bring to bear, I’d like to focus on three key examples.

Exploring the case for real-world evidence

The first example is at the human recruitment clinical trial phase, or post-Investigational New Drug (IND) approval, where pharmaceutical companies and their contract research organization partners begin the search for patients with suitable profiles and conditions to test their new drugs. In this scenario, the company needs to understand which demographic is most likely to have patients with that set of symptoms. The quicker the company can access this data, the faster it can recruit and enroll patients in that community.

Second, when a pharmaceutical company launches a drug or is reviewing the performance of a recent geographic launch, it is beneficial to gather evidence on how that drug has performed and take those findings to stakeholders in other markets to gain better leverage when negotiating pricing and reimbursement in those markets. During these negotiations, real-world evidence can help to provide insights on effectiveness and efficacy, on long-term benefits and harms, on use in diverse populations, on dosage and adherence, as well as on a broader range of outcomes.

The third example takes place in the medical context, once the drug is on the market. In the United Kingdom, there is a huge breakdown in data sharing among the various silos and systems of engagement in the National Health Service (NHS). For example, a patient with chronic obstructive pulmonary disease (COPD) has severe breathing difficulties and is rushed to the accident and emergency (A&E) department of a hospital, where a doctor who does not have complete knowledge about that patient’s history attempts to evaluate that person’s condition and treat him or her.

The problem with data silos across the various systems of engagement — the doctor’s office, A&E, patient administration, pathology, specialists and so on — is that it’s impossible to get a real-time connected view of the patient’s data, and this can potentially exacerbate the patient’s condition. There have, in fact, been examples where being unable to get the data in a collated way in an electronic health record has proved fatal.

Nonexistent, or at best poor, integration across these multiple systems of engagement in the NHS tends to reduce the quality and efficiency of care, and that is a severe impediment to effective care, follow-up and monitoring of the patient.

The ability, on the other hand, to have integrated data across the systems creates opportunities for life sciences companies to leverage real-world evidence to demonstrate the value of a particular drug or therapy, on the one hand, and for the NHS to improve care, follow-up and monitoring of patients on the other.

That’s not to say the answer is simple; far from it. Data integration has been the Holy Grail across the NHS and with other providers for many years. However, digital capabilities make it easier to create greater synergies with the consent of the appropriate parties. Healthcare practitioners can’t use a patient’s secondary data without his or her consent. If the NHS can persuade patients that by effectively integrating their EHR record across the United Kingdom and allowing it to be shared with different systems of engagement in a secure manner, their condition can be effectively monitored and diagnosed, it seems likely that most patients would give their consent.

A complete health picture

Leveraging digital solutions, data can be grouped into specific dimensions — therapeutic areas, cohort profiles, demographics and so on — and used to provide insights into more targeted segments for either postlaunch follow-up, as in the third example, or, as in the first example, for patient recruitment in a clinical trial.

Having actionable real-world evidence through connected data across systems of engagement provides useful insights into levels of drug efficacy and associated patient outcomes — a priority for both the pharma industry and the NHS.

And, an added benefit for patients is that having an integrated data record of their health profile creates greater opportunities for precision medicine.

These new datasets can raise the bar in terms of providing real-world evidence toward more-targeted profiling of the patient, toward more-effective postlaunch follow-up, as well as using efficacy data on a drug to approach payers — be they governments or insurers — with better bargaining power.

Visit DXC at Station 1 in the Re-charging Lounge at DIA Europe in Basel.

And please attend our session, “Driving the change to next-generation connected platforms and services in Life Sciences,” in the Innovation Theater.


Gireesh Gaonkar is the account general manager for DXC Life Sciences in the United Kingdom and Ireland. He is an advocate and keen proponent of the convergence of pharma with payers and providers in the health outcomes marketplace. He has over 20 years of experience in the life sciences industry.

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  1. […] pathways and uses for such technologies are limitless. For example, real-world evidence is now a crucial element of the drug life cycle, and that information needs to be fed into the […]

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  2. […] of life sciences 3.0 is the objective defined by regulators, providers, insurers and consumers: real-world evidence or real-world data. This real-world data extends well beyond the walls of the pharma company or […]

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