OPINION: As we approach the edge of the fiscal cliff, highlighted this week by the release of the NSW Intergenerational Report – a $17 billion gap between revenue and what we’ll need to spend, mainly on health, in 50 years’ time - we need to get smarter about how we tackle ageing and disease. And the way to do that is sitting right there in your pocket.
Smartphones have revolutionised our ability to understand human behaviour in all its complexity. According to computer giant IBM, 2.5 quintillion bytes of data are generated daily around the world, from smartphones, fitbits, browsers, PCs, electronic records and GPS. 90% of the world’s data has been generated in the last two years alone. It paints a picture of where we go, who we talk to, the information we’re exposed to, even what we eat and how much we exercise.
These huge datasets of de-identified information are offering an unprecedented opportunity to turn around health care in a way previous generations would never have dreamed of.
In 50 years’ time, electronic medical records will be fully integrated with data about lifestyle factors and our underlying biology that combine to drive our risks of disease. Health departments will be able to drill down to the behaviours that are causing the upsurge in diseases like diabetes, and to tailor precision, integrative approaches to tackling them.
We know that one size doesn’t fit all – it’s hopeless to get people to exercise more, for example, if they live in an area where there are no open spaces, or if they have other conditions while affect their mobility. Big data can enable us to tailor packages of lifestyle modification, allied health care and medication to the needs and preferences of individual people, thereby maximising effectiveness and value for money.
This is a key point in history, where data that’s already being collected is really going to become the dominant driver in what happens in healthcare.
As Philip Bourne, the Director of Data Science at the US National Institutes of Health, points out “This is a key point in history, where data that’s already being collected is really going to become the dominant driver in what happens in healthcare.”
The last 12 months have seen a convergence of policy interest in Australia around the use of public data in a more targeted and effective way to tackle health challenges, increase the sustainability of the health system and save costs.
Treasurer Scott Morrison recently announced a 12-month public inquiry by the Productivity Commission to investigate ways to improve the availability and use of public and private sector data. That followed from Senate Select Committee on Health inquiries late last year that heard how linking administrative health datasets covering prescriptions, doctor services and hospital care could allow for research on effectiveness, safety, efficiency and value for money to be conducted at a population level.
Then there was the recent Public Sector Data Management Project, commissioned by the Department of the Prime Minister and Cabinet, which has delivered a roadmap to unlock the potential of public sector data to drive innovation, efficiency, productivity and economic growth, including in health.
Australia already has one of the most comprehensive collections of population-based health data in the world, thanks to our universal healthcare system.
Australia already has one of the most comprehensive collections of population-based health data in the world, thanks to our universal healthcare system. We have near complete data on services funded through Medicare, public and private hospitals, emergency departments, mental health services and residential aged care. To support precision health care, we need to start making this data even richer by integrating information from patients about lifestyle factors and what happens as a result of their interactions with the health system.
Our current Australia population-based data contain only very limited details of lifestyle factors that predict diabetes risk – for example, whether or not a patient is obese, or a smoker, is not captured routinely in hospital data. This can be addressed by building better connections between the electronic medical records held by GPs and hospitals, and through leveraging new technologies, such as smartphone apps, to capture lifestyle information, biosensor data and blood glucose monitoring data, from patients between visits.
Further, our current data do not tell us much about whether health care actually works, from the patient’s point of view. We don’t know whether a health service has addressed the patient’s problem, whether there were side effects or complications, and whether their quality of life has improved. Such patient-reported outcomes are collected routinely in some health systems, such as the UK NHS, and there is a growing international movement to standardise its collection.
Health systems internationally are leading the way. For example, a proprietary big data analytic platform in the US using demographic, medical claim, pharmacy claim, laboratory test, and biometric screening results was able to predict subsequent risk of pre-diabetes and to generate targeted cost-effective care management programs for individuals with or at risk of pre-diabetes.
Integrated big data also offers promise for evaluating new policies to tackle diabetes and other chronic conditions, such as the Turnbull government’s ‘Health Care Homes’ initiative, which will co-ordinate medical, allied health and out-of-hospital services for these patients. If we can build risk prediction models and collection of patient-reported outcomes into this program, we will be better able to personalise care, and to optimise the program’s effectiveness both in terms of the costs of health care and the quality of patients’ lives.
As the Australian Government seeks to boost innovation, reduce waste and ensure health care sustainability, the effective use of big data to drive precision health care is increasingly vital.
Professor Louisa Jorm is the Foundation Director of the Centre for Big Data Research in Health at UNSW. She is an Australian leader in research using large-scale health data, including hospital inpatient, mortality and Medicare data.