I recently had the privilege to attend the Data and Analytics in Healthcare conference hosted by Corinium Intelligence in Australia. Following this, I wanted to use my blog piece this month to discuss some of the key lessons and insights shared during the event. I’ll jump straight in.
Data governance and ethics
Of course, it is easy to get carried away by the hype around generative AI, machine learning (ML), large language models (LLMS), and so on. Even so, it was sobering to see several presentations and panel discussions still focusing on data governance and ethics related to these topics. But given how we are talking about healthcare data this should not come as too much of a surprise.
A useful framework in this area is the Australian Digital Health Capability Framework and Quality in Data. This looks to align with existing industry-specific frameworks, ensuring that all health and care workers are empowered with digital capabilities. Concern was expressed that as much as 60% of AI and ML tools, especially cloud-based ones, share healthcare data with third parties without consent.
Additionally, delegates heard that data governance and adherence to ethics do slow the adoption of insights. There were examples where research results were not implemented for years due to the number of frameworks, data governance, privacy, and other controls that had to be followed. It was mentioned that legislation is often a step behind ethics, resulting in the need to reword policies down the line.
On the positive side, the sharing of ‘de-identified’ health data for research and outcome improvement was unanimously supported. One of the presenters compared this to sharing an organ for transplant. Why would anyone not want their de-identified data to be shared if it can improve the health outcomes of others facing similar circumstances?
Data management
Another area that was covered, which is close to my heart, was that of data sourcing and data management. It was stressed that to obtain advanced insights from data, it must be the right data, of high quality, and available in a processable format. The amount of unstructured data that is hard to mine and interpret in healthcare is staggering. In short, you need a solid data foundation if you want to use AI and ML effectively. This comes down to having data that is scalable, understandable, accessible, and fit-for-purpose.
One of the biggest challenges in healthcare data remains data linkage – joining the dots between related data in different datasets, originating from different systems often managed by different organisations. An interesting observation made was around the bias in healthcare – where we mostly collect data about sick or ill people. In fact, hardly any data is collected about healthy people within this context. This makes it difficult to identify what target populations for treatments, or comparative control groups’ variables, should look like. Making this more difficult is the fact that a lot of data is collected and stored but not used to its full potential.
Process and methodology
There were several good sessions centred on the processes and methodology to follow when adopting analytics, especially AI, ML, and LLMs in healthcare. While these were too detailed to cover here, the general sentiment was that one needs to be more careful and thorough about the design, evaluation, and interpretation of results. Especially in rare cases, do we have enough volumes of training data of sufficiently high enough quality for advanced models? Is the technology mature enough? And do we have proper processes for ongoing monitoring and improvement?
In other industries, people may get annoyed or even switch providers when an incorrect marketing campaign is fired off at them, or an inappropriate product is recommended. In healthcare, the implications of these ‘mistakes’ can have more serious implications, even life-threatening ones.
Building capacity and capability
Other sessions had interesting discussions on developments around capability and capacity building. My impression was that healthcare organisations in other countries are also scrambling for resources and funding. Key approaches to help overcome this include partnering, collaboration, and innovation across organisations and teams. The adage of start small and build on RIO shown came through as well.
Additionally, culture is key. It was mentioned that literacy and education take as much as 70% of the effort of adopting new technology and insights. The mind shift that must happen at the decision-making levels was also covered. Insights and data have to have a seat at the table.
An interesting study showed that both text analytics and AI only did an okay job of coding and classifying electronic medical records, with not a huge difference between the two. So, while it takes coding and classification experts hours to apply coding and classification to cases and diagnoses, there is a massive risk in replacing that expertise, insight, and interpretation with an automated process. You simply cannot automate the acquisition of health knowledge and interpretation.
The general message that came across was that AL and ML were efficient in reducing the administrative burden of clinicians and allied health staff. But despite some amazing (isolated) research outcomes, it was too risky and unethical to have technology make or influence diagnosis and treatment. However, healthcare is overloaded with administrative processes and many redundant data capture processes that can be automated to free up the clinicians and allied health staff to focus on what they are trained for and do best.
Conclusion
In closing, I didn’t review specific AI, ML or LLP case studies – they were very interesting and relevant and well presented, with lessons learnt, but it’s just too much detail to cover in this post.
It was another great and relevant event put on by the Corinium team. I walked away with many notes and some key aspects to incorporate in my strategic and operational plans going forward. I learnt about a few new concepts and made a few new connections too. I hope you find the above brief insights shared of value.
Of course, a nice venue and having proper barista-made coffee and wholesome food, together with networking drinks afterwards, rounded it off to make it an enjoyable experience. All in all it was a great and insightful day!
