In this edition you might want to check out:

• The latest Marilyn’s Musing on The Great Resignation
• An interview with Michelle Gallaher on Artificial Intelligence in Healthcare
• The Global Talent Visa Program – fast-tracking highly-skilled talent
• AusBiotech’s Decadel Biotech Blueprint open for consultation
• Upcoming events including the ARCS Australia Workforce and Capacity Summit

Early days, we know, but we’d also like to take the opportunity to thank everyone for their support in what was once again a tumultuous year and wish you all health and happiness over the coming festive season.

It’s in the news and in my inbox every day. News about the Great Resignation, the Big Resignation or what some people are calling the Big Quit. But while many people in the US or elsewhere might be changing jobs leading to this so-called phenomenon, we’re not actually seeing it here. Australians are in fact changing jobs LESS than before.

The brains trust at the Australian Bureau of Statistics will even back me up on this one. Their data on job mobility shows that only 7.5% of employed people changed jobs, the lowest annual job mobility rate on record. In the professional, scientific and technical industry the ABS reported a reduction in the number of people changing jobs from 9.2% to 8.3%.

Of course, this could change, but there are a few things worth considering about the ‘Great Resignation’ and then…I don’t want to talk about it anymore!

To read the full article including questions to ask yourself, click here.

• Opin – a clinical trial recruitment platform which matches patients to clinical trials anywhere in the world
• Trial Key – uses big data and predictive analytics to model trial protocols, assigning a probability of success and providing insight into how to adapt trial protocols to improve outcomes
• Social Media Insights – a listening tool that gathers data and provides evidence-based insights from healthcare professionals, patients and carers who share their experience and opinions publicly to social media

The role of AI is really to free up people from those mundane “middle-office” tasks, enabling them to focus on human-centred, high value activities. AI is about speed, accuracy, and accessibility as it can identify patterns in data that humans can’t, or at least at speed. Good quality AI requires good quality data, plus quality decisions and training from humans to ensure reliable results. You can’t have one without the other.

AI is changing the industry in terms of early diagnosis, drug discovery, treatment planning and care efficiencies, decision support and augmentation, rehabilitation and therapeutic optimisation and personalisation. Some therapeutic and discovery areas are more aligned with emerging AI technologies than others, but every area of the healthcare and life sciences sector is now coming into contact with AI technologies.

I’ve seen AI used in various ways across the industry. If you take drug development for example, the consequence of a delayed a clinical trial because of a poorly designed protocol or slow recruitment could mean significant erosion of the value of the product through lost revenue, reduced patent life, competitor advantage and most importantly the inability of a patient to access therapies or diagnostics. 

With 86% of clinical trials failing to recruit on time or on budget, AI has a valuable role to play in improving access for patients, as well as trial efficiencies and cost and risk reduction.

One that immediately comes to mind is a company that was developing a product for a neurodegenerative disorder. They wanted to identify key opinion leaders in the digital space and understand what these KOLs were talking about publicly in terms of treatment options, use of a competitor product, and to understand their influence relationships with each other. 

Through our social insights offering, we were able to gather business intelligence to identify digital KOLs (DOLs), map influence across their global networks, identify the specific language and terms they were using (which was unusual) and at the same time uncover unmet clinical needs and market messaging and adoption opportunities. 

We use natural language processing and machine learning, types of AI, to look at patterns, clusters and groups within text, filtering the noise from the value. We can also stack and analyse data from other sources with social data, to validate and expand information.

The life sciences industry is notorious for using jargon but the solution that we have is industry-specific and able to put these words into context. For example, the term “cell death” is generally a positive in life sciences, but if you were to just pick up the word “death” with an ordinary monitoring platform it would appear as a negative sentiment.