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How Technology Has Aided The Life Science Industry

ByMedia Admin

Dec 7, 2022 #technology
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From better data management to training opportunities, technology and the growth of new systems can help facilitate further efficiencies in the pharmaceutical and life science industries.

Martin Bate, the Head of Permanent Recruitment at Orion Group commented: “As technology continues to expand, so can our expectations of life science development. Through the upgrades in technology, from data management and patient communication to virtual reality, the possibilities for advancement are many.

New job opportunities and further diversified clinical trials, all seem more likely. Patients are at the forefront of life science, and as technology advances, we can expect to see better medications, faster prescriptions, and more.”

Life science is booming in the Irish market, with it have an international reputation of strength. In fact, as of 2021, the life science sector in Ireland accounted for 32% of GDP. And with some of the strongest pharmaceutical and medical technology companies operating in the country, including 9 of the top 10 US technologies companies, Ireland’s life science industry is progressively promoting growth.

With such expected improvements and growth in the life science industry, we will see an increase in the use of technology across the sector. Here, we will explore how technology is supporting this development.

Big data for predictive modelling aids recruitment

Big data is one technological advancement which is helping to support and advance the life sciences industry. Through data management systems and predictive modelling, we can expect to see better processes across the industry. Life science recruiters can use big data to monitor job seeker applications. Recruiters can also use this to examine recruits more thoroughly, with access to information such as internet usage giving a better insight into jobseekers’ interests and persona outside of the workplace. As the role involves sensitive data and situations, it is important that each recruit is specifically vetted.

Data improves clinical trials

Big data can also assist in clinical trials, so results collection can be more efficient. This will also be useful in conjunction with predictive models as hypotheses can be predicted using these systems. This can also assist clinical trial specialists in understanding the variables affecting their studies. As clinical trials become more efficient, higher-quality results are found. A more accurate clinical trial will help promote more effective medications and treatments.

Synthetic data can also be used to improve the efficiency of clinical trials – making them faster and cheaper. As synthetic data uses real-world examples and historical data to make predictions, trials can be conducted without using real-world patients and so, personal data isn’t put at risk of exposure. As predictive modelling and big data are used in conjunction, processes, and systems to streamline clinical trials will also be put in place. Variants which may have caused an issue in the past, such as patients who are likely to be unresponsive during clinical trials can be filtered out before the event.

A Metaverse expanding clinical trials

The rise of the metaverse is opening the world of life sciences up to new horizons. Now, clinical trials can be held remotely through virtual, or even augmented reality. Decentralising clinical trials means that a more diverse range of candidates can be reached – making them more inclusive and paving the way for more representative results.

Trials can help educate participants on healthier habits and can help reduce the risk of noncommunicable diseases through stress management, healthy nutrition and physical activity education. VR can also be used for hospital appointments and exposure therapy, as patients and clinicians dial into the same virtual room. However, there are limitations to the studies as participants with pre-existing conditions such as vertigo being unable to partake. These trials are exploring the success of technology as a mediator between patients and clinicians.

Equally, the metaverse could even help life science recruiters as interviews can be held over virtual reality. With the necessity for clinical trials to be conducted in one location, job opportunities can be expanded. Clinical trial technicians will have the opportunity to work remotely. This means highly specialised doctors can reach patients, no matter their location. This could help, for example, during the diagnosis and regular monitoring of rare cases such as neurological disorders, as specialised doctors can routinely check-in with their patients virtually – with full access to all medical records and updates during their conversations.

Direct to patients

Applications are being used to better connect doctors and patients. This also gives the patients direct access to their medical history and personal information stored by GPs.

Prescriptions are also improved through the use of technology as they provide faster transfers to a number of locations, meaning that patients are able to get their prescriptions no matter where they are. Face-to-face contact with doctors and nurses is no longer required as patients can speak to a medical specialist and receive their medication online, all tracked through medical applications.

Even better, customers and patients might find medicine tailored specifically to them as technology advances. Using genetic information of each patient, pharmaceutical companies might be able to provide medicines which works better with an individual patient. Rather than a one-size-fits-all approach, genetic information can be used to determine the most effective medical interventions, likelihood of side effects, and the benefits of specific medications for individuals, making treating conditions more efficient.

Whether it is advancements in providing medicine or it is making medication more personalised and efficient, technology is progressing the future of life science. Not only will we see more diverse clinical trials, but prediction modelling systems and big data might be used to help increase the efficiency and success of trials and their outcomes.