A big data project spearheaded by the Murdoch Children’s Research Institute (MCRI) in Melbourne to bring together multiple data sources could enable doctors to intervene earlier in childhood conditions.

Called Generation Victoria (GenV), the project is looking into several conditions, such as asthma, autism, allergies and obesity, to understand how those affected people as they became older.

But part of the challenge in projects such as GenV is the way research is typically done. According to Melissa Wake, GenV’s scientific director, researchers usually conduct their own research and collect their own data, which slows down the research process.

She likened it to taking a long train journey but having to build a new station and trains for each trip rather than leveraging an existing network. “We know that healthy children create healthy adults,” she said. “By 2035, we’re aiming to solve complex problems facing children and the adults they will become.”

GenV securely links data from a variety of national and Victorian data sources and will, with consent, use data from about 160,000 newborn children. This includes clinical information, data from wearables, and other sources from before birth through to old age. This data was never designed to be used together.

A jigsaw of unmatched pieces
Michael Stringer, GenV’s big data project manager at MCRI, said acquiring research data is the hard part.

“This is where a lot of effort goes. You can get data from participants through questionnaires and assessments. But with GenV we’re trying to get the data from all the ways they interact with existing services,” he said.

Data can come from hospital visits, immunisation records and records from local doctors, captured in a variety of formats including databases and images. Adding to the challenge is that there is no off-the-shelf data management tool for researchers.

“There’s no SAP for research processes,” Stringer said. “The best you can do is buy a collection of packages that do different parts of it and integrate it together as a coherent whole.”

The framework of GenV is designed so researchers can get a one-stop shop for research where they can leverage existing data securely.

A data model is vital
At the centre of GenV is the LifeCourse data repository, where data from a variety of sources can be accessed by researchers and other users. One of the keys here is having an effective data model.

Stringer said: “A well-curated data model is essential to having that database maintaining its value. It’s an effective way of transferring knowledge over the life of the database. Without it, the data is fragmented, and you end up solving the same problems multiple times”.

The model also ensures that when new data sources are used in future, they can be properly integrated. There is also a significant focus on metadata, which makes up about half of the data in the GenV system.

“Without that metadata – how it’s classified, what each particular variable means, what its quality level is – no one can actually use the information,” Stringer said.

Balancing quality and quantity
Unlike many other data warehousing projects, Stringer said the focus is not just on collecting and using data if it has a specific quality level. Instead, when data is added to LifeCourse, its quality level is noted so researchers can decide for themselves if the data should or should not be used in their research.

The GenV initiative relies on different technologies, but the two core pieces are the Informatica big data management platform and Zetaris.

Informatica is used where traditional extract, transform and load (ETL) processes are needed because of its strong focus on usability. Stringer said this criterion was heavily weighted in the product selection process. Usability, he said, is a strong analogue for productivity.

But with a dependence on external data sources and a need to integrate more data sources over the coming decades, Stringer said there needed to be a way to use new datasets wherever they resided.

That was why Zetaris was chosen. Rather than rely on ETL processes, Stringer said the Zetaris platform lets GenV integrate data from sources where ETL is not viable.

For example, many government data sources cannot be copied, but Zetaris allows the data to be integrated – through a data fabric – into queries run by researchers without extracting the data into a data warehouse.

Although the problems being solved through GenV by the MCRI are significant, the underlying challenges faced are the same as those of many organisations. Businesses today are dealing with large volumes of data from multiple sources, all structured in different ways.

Be it customer surveys, social media comments, website traffic or information from point-of-sale or finance systems, businesses need to be able to quickly and easily bring together different sorts of data in order to make good decisions.

The lessons from the GenV project are clear. Businesses must understand the problems they are trying to solve, invest time to create a strong data model, understand the sources and quality of data, and avoid creating a system that is limited to what they know today.

The post How big data is solving future health challenges appeared first on Artificial Intelligence.

Providing citizens with good healthcare is one of the greatest challenges facing governments today.

Changing population demographics and the prevalence of chronic disease in developing countries are putting tremendous pressure on healthcare systems around the world.

In Malaysia, the population over 65 years of age is expected to double to nearly 15% of the overall population by 2040.

This, combined with an expanding middle class, rising incomes and an increase in chronic diseases due to lifestyle changes, will drive demand and spending for healthcare services.

This trend is in step with global spending in the sector, which is projected to increase by 4.1% annually between 2017-2021.

As a result, the Health Ministry is working to widen access to health services at an affordable cost.

The current healthcare protection scheme Peka B40, which began in April 2019, is expected to initially benefit 800,000 recipients of the Bantuan Sara Hidup (BSH) programme who are over the age of 50.

One highly promising solution to these challenges is the application of data analy-tics.

Healthcare systems generate extraordinary volumes of data and it is clear that if this mountain of information can be efficiently mined, the insights revealed could transform the industry, from pharmaceutical research and development to the operational management of healthcare providers and patient outcomes.

Technology transformation

The region is seeing an extraordinary surge in the spread of technology in both the corporate and personal spheres.

In 2017, 17 million Malaysians accessed the internet from a mobile phone and that number is expected to increase to nearly 22 million by 2023.

Digital transformation and Industry 4.0 initiatives – including the Internet of Things (IoT) and artificial intelligence (AI) – are also gaining momentum in enterprises across the region.

Technology is already impacting personal attitudes to wellness – people are increasingly taking individual responsibility.

Wearable devices like fitness trackers and smart watch health apps are burgeoning; in fact, just over 170 million units of wearable wristwear devices are forecast to be shipped in 2020, more than any other wearable category.

What this means for the Asian healthcare sector is that stakeholders – patients, providers and insurers – are now at the intersection of a system that finds itself burdened with ageing populations and rising costs, as well as the digital revolution that is reshaping our world.

As the demand for healthcare services increases, so do expectations of what they could and should deliver.

The opportunity is clearly to bring the transformative power of digital technology, including AI and data analytics, to bear on the traditionally labour-intensive and documentation-swamped healthcare industry.

It must be acknowledged that healthcare is still not an industry known for technological innovations – hospitals and healthcare centres typically record charts by hand, use minimal analytics and keep data in silos.

This is at least partly due to factors such as regulations, liability issues and complexity, as well as point-to-point integration interfaces.

However, healthcare providers need to recognise that the benefits of a developed digital health market are profound in the ability to empower patients, improve engagement, make care safer and optimise operations.

The judicious application of digital healthcare also has a significant potential impact on capital expenditures by helping to prevent illness and support the provision of care through alternative locations such as clinics.

As a result, fewer hospitals may need to be built and fewer professionals trained, which would help to relieve the burden on healthcare investment.

Analytic insights

The accumulation of data is just the beginning.

What will really bring about the transformation of healthcare organisations is robust and meaningful analytics at the individual and population levels, aided by advanced AI technologies that scour diverse data sets and reveal actionable insights.

Much of this kind of data will be provided by wearables, as they move from the wellness sector to real-time patient monitoring.

Some examples of such wearable diagnostic and assistive devices include patches that monitor for cancer, ultraviolent (UV) exposure sensors and baby monitoring socks that track oxygen levels, heart rate and sleep.

If healthcare organisations are able to grasp the full potential of the available data, the quality and affordability of care will be significantly impacted.

Analytics can help advances in medical research, as well as improve overall patient outcomes and population health.

Making the resulting insights available on-demand to decision-makers across the organisation can be the key to unleashing an insight-driven healthcare organisation.

It would be wrong, however, to imagine that AI is poised to replace human healthcare professionals.

AI delivers its real value when human decision-making combines with computer-generated analytics, and when algorithms benefit from the input of medical experts.

Confidence that the data is clean, properly indexed and de-fragmented is essential if leaders are to use the resulting insights to make better decisions.

But digital healthcare is really not so much about the technologies.

It’s a question of how the industry as a whole will use these advances to solve healthcare problems, to improve the patient experience and to secure the growth of healthcare providers.

In the coming digital health era, there is no doubt that digitally-enabled care is a fundamental business imperative.

Apart from the benefits to patients of data-driven diagnoses and treatments, all elements of the industry – providers, insurers, medical technology firms and pharmaceutical companies – are seeing major shifts in how care is being delivered.

Over the longer term, the application of data analytics will help system-wide operations and organisations to deliver more powerful health care.

It is certainly the case that technological innovations such as centralised data, advanced analytics and application programme interfaces (APIs) are opening doors for the healthcare industry to help provide better care for all.

Whether it is preventing infection or to get a better sense of the patient as a whole, data-driven solutions are the way of the future for healthcare.

A better healthcare system that puts the patient first and fosters a healthier nation is an objective that all can agree on.

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