Australia is in an unprecedented period of technology innovation where data, analytics and artificial intelligence (AI) is impacting all sectors including the life sciences industry. 
AI is defined as the broad field of new technologies that enable software to sense, comprehend, act and learn. It is disrupting how life science organisations operate and compete, while also accelerating the delivery of business outcomes in the ‘as-a-service’ economy. 
Analyst firm IDC has predicted that the worldwide content analytics, discovery and cognitive systems software market will more than double from US$4.5 billion in 2014 to US$9.2 billion in 2019. 
And using AI on the commercial side of the business offers tremendous opportunities. 
In a recent Accenture survey, more than 90 per cent of life sciences’ executives recognised AI as important in driving innovation and achieving outcomes, such as hyper-personalised experiences, new sources of growth and new levels of efficiency. 
So, what kind of use cases might arise? Accenture’s experience in AI shows just how broad the possibilities are with examples in commercial, marketing and sales operations as well as patient engagement: 
Speeding up commercial operations
By including AI as part of commercial operations, companies can automate processes that are repetitive and time consuming for staff. This could result in an increase in the overall process efficiency of the organisation. 
Accenture’s experience shows that Robotic Process Automation (RPA) can increase work efficiency by 20 per cent and increase accuracy by 40 per cent. 
For example, Medical Legal Regulatory (MLR) Review Automation uses RPA and machine learning to screen all MLR submissions in the identification and alerts of compliance issues before they reach reviewers. 
This is made possible by the AI algorithm which can scan materials, identify possible gaps based on a checklist and make pre-approval recommendations based on precedents. 
The benefits of using this technology is significant, as it notably speeds up the time to market and improves consistency because the process minimises human error whilst increasing the MLR reviewer’s capacity. 
Optimising marketing campaigns  
Perhaps the most mature use case of machine learning is the ability to leverage data science to create more personalised interactions with customers.
Today, applications can predict the next best channel, message and timing for customer engagement. AI has the ability to bring together data from across business units using technologies that automate tasks associated with traditional marketing activities.
This allows marketers to concentrate on higher order tasks such as developing a creative campaign, resulting in direct benefits such as improved customer satisfaction, retention and more sales.
Life sciences organisations also invest a significant amount in marketing promotions. Through AI, organisations can generate useful customer insights to ensure these efforts are highly targeted and generate ROI.  
Helping sales reps become trusted advisors  
The key to a company's’ sales success is building trusted relationships with the right prescribing doctors; so, having access to the right data can make all the difference. 
Data such as the doctor’s prescribing habits, demographics of the area they serve, managed care impact on the drug they are selling and new treatments that are available is very valuable. 
Most importantly, AI enables the pharmaceutical sales representative to provide personalised recommendations to the doctor. AI could enable sales reps to have greater insights into the prescribing doctors’ profiles and provide the right drug information including benefits, safety and side effects. 
The technology could also make real-time content recommendations for a sales representative, tailored to where they are in the sales cycle, and greatly improve their chances of a successful outcome. 
Engaging more patients
One of the biggest challenges healthcare practitioners face today is in the area of patient compliance, particularly in the case of chronic conditions. 
Research has shown that during treatment of chronic illnesses, approximately 50 per cent of patients fail to comply with their doctors’ long-term therapy recommendations. Being able to improve patient engagement is an important area of focus for life sciences companies and one where technologies such as AI play a key role.
AI, when embedded in wearable devices, smartphones and tablets can be highly effective at keeping patients on track with their treatment pathway. Intuitive apps empower patients to self-manage their medication regimens and appointment schedules from their mobile or tablet devices. 
When connected to cloud-based platforms, these technologies allow doctors and pharmacists to communicate with these patients to clarify their understanding of conditions, complex drug regimens and potential side effects.
For example, medical device company, Medtronic recently launched Sugar.IQ, a personal diabetes assistant exclusively available to Guardian Connect CGM customers on insulin injections. 
The mobile app leverages the power of AI along with Medtronic’s diabetes knowledge. The app provides real-time information to the patient, helping them monitor their glucose levels and know when and how much insulin to use. 
Another example is HealthTap, a World Economic Forum Technology Pioneer. It launched Dr. A.I., a personal AI-powered chatbot which operates like a ‘doctor’, translating a person’s symptoms into personalised, doctor-recommended courses of action. 
Dr A.I. leverages HealthTap’s repository of data and doctor knowledge and, applies complex algorithms to shape clinical expertise and inform patients to the level of doctor recommended care. 
Key success factors 
The best way to capitalise on AI’s potential is to start small, with highly targeted use-cases. Here are some guiding principles to consider: 
  • Treat AI as a co-worker, collaborator, trusted advisor and enabler of rapid response to patients’ and the healthcare ecosystem’s needs
  • Ensure you have the appropriate budget and a task force to support AI development 
  • Accept that failure is an option
  • Have enough data to draw conclusions or generate recommendations
  • Know what questions you would like your computer model to answer and what the next steps are once you know the answers. 
If companies’ leverage the value of AI, they could propel themselves into a new level of efficiency, revenue making, customer personalisation and patient outcomes. Those that do not take on board these new technologies are at risk of being left behind, which may have profound effects on their business. 
Dhannu Daniel is Accenture’s ANZ Life Sciences Lead.  



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