Artificial intelligence or AI is fast becoming a key technology for the life sciences field. Combined with advanced analytics, it has almost limitless potential to deliver superior data, leading to better and more efficient drug development, reduced costs and increased profits.
But the question is how best to integrate artificial intelligence and analytics software into individual businesses. As competition increases and there are further pressures on pharmaceutical companies to reduce prices and deliver value for money, the role of data becomes ever more important in demonstrating value. AI has key benefits to offer, especially in artificial intelligence-driven data analytics projects where integrating data from the real world–such as medical insurance claims–with genomic research and clinical trials can uncover new discoveries.
AI-powered analytics and the Big Data challenge in Life Sciences
The life sciences sector has always created a vast amount of data driven by scientific research, clinical trials, patient profiling, compliance and regulatory requirements. The volume of data – both structured and unstructured data – grows enormously and companies are looking to AI-powered analytics–sometimes known as cognitive computing–to help.
By combining all data from all available sources – such as real-world outcomes, clinical data, genetic data, demographic data, and patient sentiment surveys – organizations can gain actionable insight. Life sciences and pharmaceutical firms can begin to apply artificial intelligence and predictive analytics to deliver more effective business in areas such as streamlined clinical trials, accelerated discovery and approval of new medicines, improved production and supply chain operations and more targeted and personalized sales and marketing.
Traditional business intelligence (BI) analytics and data analytics tools are being augmented by a technology that can quickly and effectively handle Life Sciences data. The development of AI gives the ability to combine capabilities such as machine learning – the ability for a computer to learn by itself without being programmed – and natural language processing (NLP) – the ability for a computer to synthesize and understand natural language – with advanced, automated analytics to revolutionize almost every aspect of Life Sciences.
The potential is certainly huge. A report by Accenture estimates that by 2026, Big Data analytics in medicine and the pharmaceutical industry, using machine learning algorithms, will be generating value of around $150 billion per year.
The message is clear: those organizations which find the best ways to use AI-powered analytics technologies have much to gain over the next few years. But before jumping on the bandwagon, it’s important to make sure you’re not getting caught up in the hype surrounding this type of software. You need to understand what AI-powered analytics solutions offers your business.
When choosing your AI-powered analytics tool for Life Sciences firms, there are a few things to consider:
Establish your requirements
Artificial Intelligence isn’t a cure for all ailments, so it’s important to first establish what you want it to achieve. The best AI-powered analytics tools can help to deliver efficiencies in the supply chain, improve clinical trials, accelerate new product development, help implement anti-counterfeiting measures or enhance patient diagnosis and treatment. There are different artificial intelligence technologies – each with different capabilities – and you need to know how best to combine AI with advanced analytics for your business application. For example, if you want your AI-powered analytics software to deliver actionable insight for a more effective decision making model then you are likely to look for something with machine learning algorithms that can power prescriptive and predictive analytics.
Data rationalization
Successful analytics capabilities starts with the data. There are so many sources of both structured and unstructured data that can be used to help improve performance. But many large organizations still don’t have full visibility of all their data. It is siloed in different sectors of the business, retained in a variety of formats, and not given the chance to work for the benefit of the business as a whole. Big Data analytics begins by rationalizing that data. This will usually happen as part of a wider digital transformation process that pulls together a corporation’s data into a single repository where it can be categorized and used to feed your artificial intelligence and automated analytics systems.
Simplicity of integration
Implementing AI-powered analytics tools into any aspect of your organization is likely to require significant investment, though the ROI is likely to be relatively swift. However, it often makes sense to implement it by degrees, identifying the ‘low-hanging fruit’ that can be improved by the application of AI and analytics technologies. This might involve an aspect of the production line – such as detecting contamination within batches – or it could be identification and recruitment of subjects for clinical trials.
Scalability
Once artificial intelligence technologies have been applied to one aspect of your business, it’s likely that potential will appear for further implementations. It’s important that the AI-powered analytics software you adopt should be scalable, both in terms of expanding its ability in the area where it was first introduced (the supply chain, say), but also across other aspects of the business wherever possible.
Open source machine learning
The concept of open source has been around the software industry for many years. In effect, the source code of a particular technology or solution is open for everyone to add to and improve. This approach has been proven to speed product innovation and improve product quality through communities of developers working together to address bugs and speed product development.
Neither the medical or scientific communities are novices when it comes to open source. Both the concept of open source and the Life Sciences communities using it are mature enough to be able to use the approach to gain the best results. Within the field of artificial intelligence technologies, communities are already coming together – such as healthcare.ai to pioneer open source AI developments in healthcare.
Key area of Life Sciences that can benefit from artificial intelligence software
As Artificial Intelligence becomes more widespread in Life Science, a number of real world use cases are appearing. These include:
Disease identification and diagnosis
The correct diagnosis and identification of diseases is perhaps the biggest challenge in healthcare. It is also an area where AI-powered analytics software can excel. There is simply so much data available and applying machine learning algorithms can bring this all together and allow automated analytics to very precisely identify diseases. Open source machine learning is allowing clinicians, researchers and manufacturers to work together to diagnosis the disease and use predictive analytics to set out the best route for treatment.
Personalized Medicine
There is much research going on regarding the use of machine learning and predictive analytics in customizing treatment to a person’s unique health history. This can lead to better preventive, individualized health programs based around mobile health apps combined with other patient data. This use of AI-powered analytics technologies has two tangible benefits. First, it allows personalized health plans, designed for prevention before cure, that better target treatments, lower costs and improve patient outcomes. In addition, sel
f-managed health data is giving a new source of valuable information that artificial intelligence data analytics tools can employ for the research and development of new drugs and treatment protocols.
Clinical Trials
Clinical trial research is a long and arduous progress. Machine learning algorithms can help make it easier in a number of ways. One is by using advanced predictive analytics on a wide range of data to identify candidates for clinical trials for target populations much more quickly. Clinical trials are expensive and can often lead to contradictory results compared with other similar trials. AI-powered analytics software is now being used to improve trial outcomes by looking at many elements of the trial.
For example, AI-powered analytics tools can analyze operational data from historical cases, measure responses to drugs and, predict the performance of different sites within the trial. In addition, predictive analytics can help and use predictive criteria to determine whether taking a drug will result in a positive or negative outcome, whether an individual patient is likely to drop out, and, even, whether a trial will be successful.
Accountable care
The basic idea behind accountable care is that different organisations from the health care system work together to improve services and health outcomes with the local community. It’s designed to increase collaboration and reduce costs. AI-powered analytics allows for all the accountable care organizations to have a better understanding of what occurs with patients. In addition, it helps quickly identify error and waste within the system. Using machine learning algorithms, AI powered analytics solutions can tackle fraud, reduce cost and enable the creation of more effective reimbursement models between the accountable care organizations.
Why choose OpenText for AI-powered analytics?
Already a renowned leader in enterprise information management (EIM), OpenText™ is also at the forefront of practical applications for AI-powered analytics. OpenText™ Magellan™ can deliver improvements based on the data and systems you already have, utilizing the power of open source machine learning to pull together all your data, both structured and unstructured, and analyzing it to identify patterns.