Electronic health records are very good at being repositories for valuable patient data. But they need help when it comes to putting that data to work for more innovative care delivery.
The ever-expanding volume and variety of clinical and social-determinant factors will require more advanced technologies to be optimally harnessed for precision medicine.
Enter AI and machine learning, which "will play a growing role in healthcare, under two main categories – generating knowledge and processing data," according to Auckland, New Zealand-based Kevin Ross, who will speak next month at HIMSS19.
Ross is General Manager at Precision Driven Health, launched as a partnership between Orion Health (where he is Director of Research) and government agencies and academic organisations in New Zealand to explore and promote precision medicine. He sees machine learning as a key enabler in the years ahead as health systems look to unlock the data and in their EHRs and put it to work for more personalised care.
"Health records have been electronic – and therefore accessible for analysis – for a relatively short period of time, but we are now seeing huge volumes of data being generated from different sources," he explained. "We've had insufficient computational power to process the volume of data in a genome, let alone a microbiome, etc. until fairly recently."
The advent of AI and machine learning opens new avenues for healthcare wisdom to be accrued. Medical research has traditionally come through "targeted studies on narrow subsets of the population," he said, "now we can analyse over large populations in relative real time, because the data is being collected digitally. New knowledge will come about by applying machine learning to these increased data sets to uncover patterns that are occurring today without being noticed."
In Orlando, Ross will explain how he and other researchers are making the most of some unique aspects of New Zealand's healthcare landscape – connected electronic healthcare data across the population, leading-edge research organisations – to enable the development of new technologies and data strategies for precision medicine.
"New Zealand has some unique benefits, including a long history of digital health records with well managed health ID numbers, so it is a lot easier to link different data sets together," he said. Add to that:
- Linked data between social services (health, education, justice, welfare, tax) available for research purposes;
- A single payer system whereby the incentive of patient, provider, and system are typically well aligned (e.g. early intervention benefits all)
- Willing collaboration between commercial and public provider organisations as well as between clinical and data science researchers
- A unique ethnic diversity (74 per cent European, 15 per cent Maori, 12 per cent Asian, seven per cent Pacific Islander – including those identifying multiple)
- A strong data science research community
- A population relatively comfortable with technology and with broad access