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Future Data Trends In Pharma And Healthcare

What does the future hold for data use in pharma and healthcare?

26Apr

The future of pharma and healthcare is exciting, with new technologies and techniques constantly improving how the industry services its clients. There are some significant developments in the area regarding the use of data too, which is likely to have as much impact in the area as any other kind of development. So, what data developments are we most excited about in pharma and healthcare?

Use In Clinical Trials

Recruiting for clinical trials is always one of the most expensive and time consuming elements for a pharma company, with trials often being under filled or inadequately sourced. Big data is increasingly being used to find suitable test subjects, especially for drugs being used to treat rarer conditions or aimed at treating a particular subsection of patients.

Through the use of social media or data aggregation software, it is possible to pinpoint those who are more likely to be willing to participate and who also fit the criteria for the trial. It is something that many companies are already undertaking, but is likely to increase as people share more data and the necessity for newer and more effective drugs becomes more important.

Open Data

Open data is a movement that has been gaining pace in many areas, but one in which pharma and healthcare companies have traditionally been sceptical of simply because of the highly confidential nature of the data they hold. However, this is changing, with work being done by collaborations like those seen between DeepMind and Moorfields Eye Hospital in London, which has collected thousands of different images of eye diseases to help with diagnosis of issues in the future.

At present the success of this work is yet to be seen, but given the hype around it and the power that DeepMind has had in the past, it is likely that we will see this approach by more and more companies. It is unlikely that this will actually be undertaken by pharma companies in general at present, but the information that comes from other companies is going to play a big part in it.

If it can identify the most common illnesses or precursors, it gives pharma companies the opportunity to create products specifically for early signs, potentially creating new opportunities for them.

Collective Knowledge

One of the potential impacts that big data will have is on how healthcare companies treat specific patients. People are individuals in every area of life and unique backgrounds, genetic makeup, and environmental situations, means that this is particularly the case in disease and medicine. Two people who have the same condition may react completely differently to the same treatment because of this.

Through the collection of data on millions of patients, with billions of different unique characteristics, it will be possible to identify the most effective course of treatment for patients based on the collective knowledge of what’s worked for similar people in the past. This is something that is not widely available due to both the relatively recent digitization of health records and concerns about anonymity within them. However, as it is introduced and increasingly used, we are likely to see considerable improvements in how people are treated.

Modeling

The ability to predict how a drug is likely to perform on different people and different diseases, plus potential side effects, is of huge benefits for pharma and healthcare companies. Through the use of the huge amounts of data and computing power available, it is possible to do this at speeds that are accelerating every day.

It allows pharma companies to cut the time to market considerably, whilst also not wasting resources on products that won’t work. It will allow disasters like the Biotrial study from January 2016 which left one person dead and 5 with severe brain damage or the TGN1412 trial from 2006 which saw 5 people permanently disfigured and come close to death, to be a thing of the past. Looking at how the drug will work and combining it with data from patients will create a powerful tool that will help predict likely outcomes from its use. 

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