pharma digital adoption

One can trace back the adoption of data analytics in the global pharmaceutical industry to the early 2000s, when data storage capabilities and advancements in computer technology enabled pharma companies to collect and analyse large amounts of data efficiently.  

Optimised data at every stage of a clinical trial  

One of the earliest applications can be found in using data to analyse clinical trials. Here, data helps companies identify probable patients for particular treatments, and evaluate the effectiveness and safety of the new drugs before pushing them into the market.  

For example, let’s say a pharma company is running an adaptive clinical trial to test the efficacy of a new diabetes drug. In an adaptive clinical trial, companies can modify their trial protocol on the go, based on the results from the analysis at every stage of the trial.  

In the first step, the trial randomly assigns the new drug and the old drug to the selected patient population. Once both sets of patients have been administered the drug for a certain period of time, the company collects the initial data and analyses to find various parameters, like which drug performed better, or whether the patient population needs to be increased or decreased based on how the drug is reacting to them.  

In other words, data is tightly integrated into the trial from start to finish. The advantage being that it allows pharma companies to push new drugs into the market quicker, and at a much lesser cost. 

The Evolution of Pharma Data Use  

Today, data usage in pharma has grown beyond clinical trials and drug discovery to integrating data with AI capabilities to bring innovation into the industry, and improving patient outcomes.  

Particularly in the UK, the adoption can be seen in the areas mentioned above, and in supply chain, telemedicine, and data collaboration. Let’s look at each of these in more detail. 

  • On the drug discovery front, many pharma companies in the UK have been using AI and ML technologies to find new drug candidates, and quicken the pace of drug discovery. For example, in 2019, AstraZeneca partnered with BenevolentAI to use AI to find new drugs to treat chronic kidney disease. 
  • When it comes to clinical trials, as we saw in the earlier example, data has been used to streamline the trial process, patient management, and accelerate new drug releases in the market. For example, UK-based clinical trials company, Synexus, created a digital platform where patients can participate in clinical trials remotely. 
  • On the manufacturing and supply chain front, digital technologies are being used to improve processes and increase efficiency of outcomes. For example, GSK incorporated data quality management protocols to make the manufacturing process more efficient and compliant. 
  • With the support of initiatives launched by the UK Government, many pharma companies have engaged in data sharing and collaboration. For example, under the Accelerated Access Collaborative scheme, NHS England and industry stakeholders have partnered to adopt and accelerate innovative technologies. 

The Flip Side of Digital Adoption 

Having said that, experts in the industry still believe that its digital adoption has still been slow, compared to other industries like retail, telecom, or even automobile.  

Last year, McKinsey spoke to 100 DnA (digital and analytics) leaders in the pharma industry who said that adoption of digital technologies over a period of five years improved their bottom line by 5-15% in specific areas. It resulted in an annual global impact of USD 6 billion to USD 9 billion. The report suggests that had they used the end-to-end digital capabilities, it could have resulted in an annual global impact of USD 130 billion to USD 190 billion. 

Which brings us to the question; why is pharma reluctant to go digital? There are a number of reasons; 

  • The global pharmaceutical industry is highly regulated, and requires stringent security and privacy measures in place, which can make it difficult for companies to easily adopt and integrate new technologies that require them to share and exchange data with external partners. 
  • The first reason also makes the industry more conservative in its openness to adapt new technologies. In other words, they are risk-adverse to change. 
  • Many of these companies operate on legacy enterprise planning and data warehouse systems which they would have invested heavily in. And, invariably these systems may not be compatible with newer digital technologies, which can make it challenging to integrate and customise data usage and application. 
  • Integrating and adopting new technologies requires a top down approach. The management needs to understand how it works, design objectives, and translate this to every rung of the organisation. Secondly, it also requires companies to invest in training its resources in specialised skills to get the best of these technologies. 
  • The industry is also highly competitive. So, companies tend to be cautious especially when it comes to making heavy investments in new technologies. Unless they see a definite ROI, they may be averse to a drastic digital change. 

The Solution to Greater Digital Adoption in Global Pharma 

A Merit expert says, “Pharma companies will continue to play it safe and invest in digital technologies in areas where they have seen proven results in the past – in drug discovery, clinical trials, and end-to-end supply chain management, and shared services to name a few.” 

To reap the full benefits of going digital, companies need to solve three key challenges; expanding their data sources to make the AI models more effective, make significant investments in hiring and training talent in using digital technologies, and moving beyond the proof of concept stage to adopting the technology at full scale. 

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Our data scientists understand your data needs and create customised tools to deliver the right data in the format you need. They scale up and scale down the data collection process based on specific needs and validate data quality before it is used for analytics and decision-making.  

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