When Jurgita Kaubryte spotted a job bringing together her passion for healthcare and mathematics, she seized the opportunity. Now she’s helping Oracle Cerner grow their NHS client services within the burgeoning field of data science.
Jurgita Kaubryte’s future career path was set early in life, but she didn’t know it immediately. Coming from a family of doctors, with a pharmacist mother and a dentist father, she was passionate about healthcare.
But she was also interested in finding quantitative solutions to problems.
“My favourite subject was maths at school, and I chose economics to start my career,” she explains.
As an adult, it’s no surprise that she found her way into what Harvard Business Review once termed ‘The Sexiest Job of the 21st Century’. Working at Oracle Cerner as a senior data scientist, she is leading the expansion of their data science services to NHS clients.
Her job, which she combines with studying for a data science PhD in cancer genomics, involves developing machine learning models to support population health management by, for example, helping predict falls in the elderly and identifying people at risk of high blood pressure.
Data Science in Demand
Demand for data science has grown dramatically in the last few years, with a Royal Society 2019 report claiming that the need for workers with data science skills has tripled in five years. Oracle Cerner is no exception, says Kaubryte, who will be taking on three data science interns in September.
“Health data science is still quite new so we’re a small team currently,” she says.
“But we’re experiencing increasing demand for data science services from our NHS clients and are planning to hire more people this year.”
As well as creating predictive risk models to help NHS Integrated Care Systems (ICSs) with population health management, Kaubryte also designs new internal processes for Oracle Cerner’s data science business in the UK.
She also set up the company’s data science internship scheme in the UK, which will run for the first time this year. She says it will allow recent graduates to spend four months working on a data science project and collaborating with NHS clients.
“We are excited to open this learning opportunity to recent graduates and grow our data science team,” she says.
Four Key Competencies
When hiring data scientists, Kaubryte looks for four key competencies. These are quantitative skills, such as statistics and machine learning, and knowledge of programming languages.
Other skills, she says, include communication and visualisation.
“You need to be a storyteller because, if you can’t explain your model to clinicians and other users, it won’t be used,” Kaubryte adds.
The final competency is domain knowledge, which she sees as important to Oracle Cerner’s business. Data scientists need strong healthcare data knowledge, she says, and understanding of how NHS healthcare works.
Developing Future Skills
Kaubryte works ten-hour days, four days a week at Oracle Cerner, leaving Fridays to work on a PhD at University College London, where she is developing machine learning models on linked electronic health record and genomic data to predict and improve cancer outcomes.
“It’s very challenging because I do longer hours than most people, but it’s also rewarding. I enjoy having a foot in both academia and industry,” she says.
“Through my work at Oracle Cerner, I help solve challenges in the NHS with immediate impact on current services, while through my PhD, I get a chance to contribute to long-term advances in healthcare, such as genomics research.”
Kaubryte believes having a PhD is becoming important for career development.
“A Masters [degree] is no longer enough for some jobs in data science. And my feeling is this [requirement] will become stronger [over time],” she states.
Preventing ill Health
Kaubryte was working at IBM Watson Health when she was alerted to the job at Oracle Cerner by a colleague she’d previously worked with at Telstra Health UK.
“What attracted me was that Oracle Cerner, through NHS partnerships, have access to a wealth of rich data, allowing us to make a difference in population health management,” she says.
“Preventing healthy people getting ill is the most powerful thing you can do in healthcare.”
“And now, as part of Oracle, we have access to world-class data science platforms and tools,” Kaubryte adds.
Among the projects she’s completed so far is a predictive model to help GPs identify people who will develop hypertension within the next five years. The model, developed alongside Hampshire and Isle of Wight ICS, is due to go live within the next few weeks and will support preventative services for long-term conditions.
In addition, she’s recently developed a model for predicting falls in older adults.
Forward to the Future
Later this year, Kaubryte plans to develop a predictive model to help prioritise patients waiting for elective treatment.
“It’s an exciting project because NHS waiting lists are a massive problem,” she says, quoting the statistic that a record 6.6 million people are currently awaiting treatment.
The model will use health, lifestyle, and demographic data to predict the risk of, for example, emergency admission, and rank each patient by their likelihood of future deterioration. They can then be reprioritised, if necessary, on the waiting list to get treatment faster.
Kaubryte will also be developing new techniques for visualising and explaining models, mitigating healthcare inequalities, and monitoring models to ensure their performance doesn’t decline over time.
“Even in the data science world, the importance of model monitoring is not fully understood by everyone,” she says.
“Anyone can develop a model, but ensuring it works well over time is more complex and difficult to do.”
In the future, Kaubryte believes healthcare providers will use data science to predict and prevent health deterioration and tailor treatment to patients by analysing their genetic, lifestyle and health data.
“Precision medicine is a future focus for the NHS and, through the advances in technology and emerging new data sources such as genomics, will become important for helping detect diseases earlier and providing personalised treatments,” she concludes.
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