Vishal Pratap Singh | Friday, 11 March 2022
As the healthcare industry across the globe has started focusing increasingly on outcomes, pharma companies are looking to sources beyond Randomized Clinical Trials (RCTs) to measure and demonstrate the value they bring.
Real World Evidence (RWE) has been in use for decades but the recent advancements in digital and advanced analytics allow it to be employed in new ways. Real World Evidence solutions market size was valued at USD 870.79 million in 2020 and is projected to reach USD 2463.14 million by 2028, growing at a CAGR of 13.92 per cent from 2021 to 2028.
“Real World Evidence can be helpful in understanding how patient characteristics and behaviours affect health outcomes, thereby helping to predict the progression of a disease, patient’s response to a therapy or the risk of adverse events while increasing the efficiency of research and development investments and accelerating time to market” says Dr Mansukh Mandaviya, Union Health Minister.
Provides Deep Insight of Patient Characteristics
In the past one decade, the introduction of advanced Real World Evidence analytics has made real world data an even more powerful resource for pharma companies. Unlike traditional RWE analytics which was dependent on descriptive analysis to characterize patients and established matching techniques to compare groups of patient with similar characteristics, advanced Real World Evidence analytics uses predictive models, machine learning, probabilistic causal models and unsupervised algorithms to extract deeper insights from rich data sets.
Industry experts say that it enables pharma companies to draw on thousands of patient characteristics to gain a better understanding of what drives outcomes, to uncover insights into drug performance and differentiation at subpopulation level to run accurate scenarios with predictive models and to generate hypotheses at scale across therapies, comparisons and end points.
Helps in Cost Saving
There is an estimate that an average top-20 pharma company across the country that adopted RWE analytics across its whole value chain for in-market and pipeline products could unlock more than $300 million a year over the next three to five years.
Apart from cost savings, the introduction of real world evidence can help pharma companies identify new targets for molecules, accelerate time to market, improve formulary position and payer negotiations, and generate stronger evidence of differentiation and benefit or risk balance for in-market products.
Usually, it is seen that for any company which considers deploying Real World Evidence analytics, success will depend on building the right framework and capabilities.
Unlike traditional clinical trials, where necessary data elements can be curated and collection mandated, the creation of Real World Evidence requires assessing, validating and aggregating various, often disparate, sources of data available through routine clinical practice.
Useful in Regulatory Requirements
Real World Evidence can help companies to expand an indication profile for a product. It may not be feasible to perform a full clinical trial for a product that is commonly used for off-label indications by collecting Real World Evidence from disease or product registries.
Companies may be able to study safety and outcomes data for their product or device which can then be used to supplement a submission to the medical agencies. For certain products, a promise of RWE analysis in the post market period can be used as part of a regulatory approval. For example, it might not be feasible to perform a clinical trial on a product that is to be implanted for a long period of time.
In such cases, post market RWE can help provide that missing long term data as suggested by experts. Regulatory authorities are increasingly using Real World Evidence as they require it to satisfy post market approvals.
Decision Support Tools
Through comparing outcomes for different treatment options, it might become evident that particular drugs or dosages are better optimized for different individuals and treatment guidelines can be refined and optimized accordingly.
Through advanced analytics, Real World Evidence can also provide individualized decision support tools that can facilitate shared decision making between a patient and physician. For example, the bariatric surgeons collaborative uses a tool that predicts, based on individual characteristics, how a patient might respond to different types of bariatric surgeries. Leading organisations are already capturing value from advanced RWE through a range of applications including predicting outcomes in type 2 diabetes, predicting findings of an on-going phase IV cardiovascular trial.
Expectations from Future
By taking full advantage of Real World Evidence and advanced analytics, pharma companies can accelerate their shift from product-focused to patient-focused organisations. A few leaders have drawn up a blueprint for execution. Now, it is time for the rest of the industry to set its sights on the next horizon of evidence-generation capabilities.
Companies that aspire to scale up their RWE analytics will need a sandbox for conducting basic experiments with use cases and delivery models. More advanced companies will use scaled-up cloud platforms to build automated pipelines, repositories of analytical assets and visualizations for use by multiple stakeholder groups.
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