What do nursing and baseball have in common? More than you think

Data. Big data. Using data. Gathering data. Protecting data. Modern life is data obsessed; data surrounds us and rules our lives in ways we mostly don’t see.

Facebook and Google track what we engage with on the internet and target us with personalised ads and shopping malls use our phones to track our movements in stores to tailor how they lay  out the floor plan.

Data can, of course, be used for good.

Universities are increasingly using data to analyse student retention and teacher effectiveness, and elsewhere governments are analysing patterns to improve traffic flow, environmental protection and detecting fraud, to name a few uses.

Experts argue that one reason that data is becoming so important is that humans are unreliable.

Data analytics became famous outside of data science circles with Michael Lewis’ book, and latterly the movie starring Brad Pitt, Moneyball: the art of winning an unfair game.

In the book, Lewis details how Billy Beane – played by Pitt – took an unfancied baseball team with the lowest team payroll in baseball to regular success by using data to recruit similarly unfancied players on the cheap.

Beane, himself an ex-professional, had an issue with the way baseball recruited players. He believed that the old methods relied too much on human intuition with little attention paid to on-paper performance and cold hard stats.

People often rely too heavily on their own beliefs, or as psychologists Daniel Kahneman and Amos Tversky put it, people tend to use the “availability heuristic”. This theory states that, unconsciously, we operate under the principle that the more available and relevant information there is, the more likely the event is judged to be.

For example, reading lots of news stories about people losing their jobs might lead you to believe this is common, and you are in danger of getting fired. Or we might fancy a baseball player to be a good acquisition because they look big and strong and because we know of other successful players who are just that.

This is a problem because the human mind plays tricks and, as Lewis wrote in Moneyball, there is “a lot you couldn’t see when you watched a baseball game”. In any industry there are small details, everyday details, that we may miss.

This is certainly true of healthcare, but where Beane wanted to use data rather than professional intuition, the University of New South Wales wants to go further and use both. UNSW is in the second year of offering its Masters in Health Data Science. And it is targeting nurses in what it describes as a “big picture approach to improving patient care”.

It has been estimated that up to 30 per cent of the entire world's stored data is health-related.

Louisa Jorm, foundation director of the Centre for Big Data Research in Health at UNSW, says that the health system is generating huge amounts of data that are currently unused and the future of data analytics within the health sphere will be led by people with clinical experience.

“There's a huge amount of work that has gone on by people from computer science backgrounds that is useless for implementation in health settings because they don't understand basic issues of workflow of how patients and clinicians interact with one another, or what may be acceptable or may work in a clinical setting,” says Jorm.

“I think this masters puts [nurses] in the driving seat to be health data scientists, which is an emerging profession, [and] there's massive workforce demand and their clinical skills will mean that they're very effective practitioners of health data science.”

The stats seem to back Jorm up. LinkedIn has reported that data scientist roles have grown over 650 per cent since 2012 and, under the Obama administration, the US even appointed a chief data Scientist, DJ Patil. Google Chief economist Hal Varian has called data scientists "sexy".

“The sexy job in the next 10 years will be statisticians. People think I’m joking, but who would’ve guessed that computer engineers would’ve been the sexy job of the 1990s?”

So, what does this mean for healthcare?

Jorm sees Health data scientists having real influence in medical research going forward as well as policy issues, quality and safety issues, health outcomes and health economics of the system, all of which require analysis.

“Nurses would be ideally placed to do that sort of unit or ward level analysis. And then obviously you can then move up to the hospital level or the local health district where you're aggregating a larger amount of data and you may be looking at things like trying to decrease waste and duplication in things like pathology testing or imaging. And again, that's something that really requires people who are sitting within a health service or a hospital,” she said.

Victoria Blake is an ex-ICU nurse and current masters student who now works as a clinical data manager and a data analyst for a private company that performs coronary angiograms and stenting procedures in Sydney’s Prince of Wales hospital. She likes the idea that with data analysis she is having an effect on a greater scale.

“I love being able to help people on a larger scale… I think that in this way I can kind of really make a change to help care and how we provide care to patients,” she said. And she agrees with Jorm that nurses like her are well placed to influence change.

“I didn't really know how much I could contribute as a nurse as opposed to anyone else in this kind of field,” she says.

“But I think as I've gone along, I've really realised that nurses have this understanding of how the data is generated and the little eccentricities and the quirks the job does have, and can also see where the problems are and where the help is really needed to fix those problems. So I think that nurses are great for this kind of work.”

Blake thinks that with this new emphasis on data analytics, the job of the clinician will be made much easier and ultimately the patient will benefit.

“I just think that's got so much potential because we're only human and we can only do so much and remember so much stuff. Having computers to assist us doing those kinds of things could be huge.”

And much like the impact data had on baseball and the assumed knowledge of baseball scouts, it has the power to impact the performance of hospitals who may believe that they are doing the right thing. Studies have found that public reporting and the release of hospital performance data encourages hospitals to identify problem areas that need improvement.

“I think in particular the public reporting and benchmarking that data allows you to do has the potential to drive improvements in care. And sometimes people don’t really think about what they are doing and may have a false impression about what their patterns of practice are. So, they can objectively look at that using data,” Jorm said.

The Bill and Melinda Gates foundation focuses on health problems throughout the world and relies on data to point them to the issues and then, using innovation, they set about solving the problems. For them, data is key.

"This one of the benefits of measurement – the ability it gives government leaders to make comparisons across countries, find who's doing well and then learn from the best," Gates has said.

"Data and innovation are like the proverbial chicken and egg – one cannot exist without the other and each produces a new iteration of the former."

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