Added: 21st June 2018 by DXC Technology
The life sciences business model is changing. In the past, companies typically worked with fewer partners, most often collaborating only with the physician, or indirectly, with patients. That later expanded to liaising with academia and the contract research organizations (CROs) directly affiliated with them.
Today, those walls have come down and pharmaceutical companies are collaborating with businesses adjacent to their market, including technology, social media and healthcare. This expanded collaboration has led to a greater need for interoperability across the ecosystem of different partners. Yes, these partnerships are important and valuable, but why should you bring all that content and all those applications from different platforms together? In a nutshell, there are greater efficiencies to be achieved, newer innovation pathways to be discovered and movements toward better outcomes to be accelerated.
Another consideration is that the industry is also grappling with the changing needs of big data. That term is so misused, but pharma companies are drowning in a sea of data: data from trials, data from discovery; data from research partners; data from health records and data from devices, as more and more consumers use apps to track their health. With all of this data, it is not hard to see that we should take advantage of it and apply precision medicine and personalized healthcare to ensure that patients are taking the right medicine or the right therapies at the right time.
All of these factors are creating a need for a connected life sciences platform, one that enables collaboration and interoperability across the entire digital value chain of pharma companies and beyond. In this environment, having a solution that focuses on one business area, such as research and development, is no longer viable. Instead, a connected platform is needed; one that begins at the clinical stage, where it’s important to ensure that all the right patient data is captured, through to making sure the drug is taken at the right time and any adverse events are picked up, and managing the entire life cycle of the drug.
This provides a macro view that opens the way to gathering digital insights, analysing those insights and providing them to the business so it can make good strategic decisions. New capabilities such as automation and machine learning make it possible to combine data from internal applications and platforms and combine those with information and data from outside the company, e.g., their partner’s application ecosystem. Those insights can then be fed back to the company.
With that information at their fingertips, companies can make better decisions about how to use the data. It also can lead to advantages, including greater efficiencies and better productivity that can help speed drugs to market; and they can save money and redirect those funds into new clinical trials, new ways to innovate with compounds in the pipeline, or new therapeutic uses for a marketed product.
These capabilities are now becoming available to companies thanks to technological innovations and improvements, and the greater affordability of some key developments. For example, in genomics, the cost of sequencing genomic data was once prohibitively expensive. Now, those costs have fallen exponentially, making it possible for more innovative companies to explore how the science can be adapted for new therapies.
In other areas, new digital technologies have become embedded in provider solutions to enable automation and artificial intelligence (AI) capabilities, such as voice technology. It’s become common in everyday life to access information simply by speaking a phrase to a device and having it interpret what’s being said to provide an answer, for example, in a document search. There’s no reason why those same capabilities can’t be extended into an enterprise setting, and leading-edge technology companies are experimenting with prototypes.
The 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 broader healthcare ecosystem to enable better insights on how best to treat a patient. For providers and patients, it means better care; for life sciences companies, it opens the way to a more seamless regulatory process. By having better data, it becomes possible to segment cohorts of patients for a particular drug, which in turn means clinical trials will be better planned, have more predictable outcomes and be more successful. That means reducing the risk in getting a drug to market or increasing the speed of moving that drug to market.
By feeding all of this data back into a connected life sciences platform, it becomes easier to bridge the gap between clinical, regulatory, and pharmacovigilance approaches; between drug developers and healthcare providers; and between clinicians and patients. And at the end of the day, breaking down those barriers and improving how all stakeholders collaborate will deliver better outcomes to companies, providers and patients alike.