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Healthcare: Leveraging tech and minimising data risks

Machine learning and AI can help revolutionise outcomes by freeing up time for healthcare practitioners to make more informed clinical decisions.

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By combining patient- generated data with academic evidence, healthcare practitioners are able to create personalised treatment options. AI could be an even bigger catalyst to provide such practitioners with real-time capability to positively impact patient outcomes.

AS SINGAPORE'S population ages, the country is putting greater focus on healthcare innovation and greater investments into digital health. The Smart Nation initiative is one such example that has greatly impacted the healthcare sector in expanding provider capabilities and ensuring the best care is available to Singaporeans. For the elderly and those with disabilities for example, developments in assistive technology, analytics and robotics are making a real difference to the way they complete tasks and activities.

However, increasing digitalisation of health records is producing more and more data, relating to patient health status and delivery of care. This has resulted in a new data dilemma: having too much data and not knowing how to unlock its full value. According to International Data Corporation (IDC), global healthcare data is predicted to grow from 153 Exabytes in 2013 to 2314 Extabytes in 2020 - the equivalent of storage volume on laptops stretching halfway to the moon.

Of the large volumes of information gathered, approximately 80 per cent exist as unstructured data that requires review and analysis. Moreover, navigating healthcare with its vast amounts of data and continuous advances in technology can be a complex task. From a clinical perspective, current health IT requires clinicians to use common database tools, such as the electronic health record (EHR) to retrieve structured data - that is, quantifiable, measurable data. This provides clinicians with patient information such as their lab results, blood sugar levels and cholesterol that they have to manually inspect, and which makes it hard to identify the critical information they should pay most attention to. Therefore, healthcare providers need to ensure they have the right tools to gather, organise and analyse this information.

WHY DATA MATTERS

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In today's environment, clinical evidence is developed based on scientific research, which is then used to develop clinical decision support (CDS) tools such as order sets, care plans, clinical pathways and other "structured content" to be implemented in care settings. Such tools are meant to help doctors and nurses make the best medical decisions at the point of care, addressing inconsistent care practices, which pose the greatest threat to the quality, cost efficiency and outcomes of care delivery.

However, while scientific research is an important evidence base that fuels CDS, this research data is not always available and it takes a very long time to find its way into practice. Research has found that it takes 17 years for only 14 per cent of new scientific discoveries to find their way to daily practice. This leads to a high degree of knowledge variability that directly impacts the quality and safety of patient care.

There is a new pool of evidence that the global healthcare industry is increasingly setting its sights on: "real-world data". Real-world data is signalling a new opportunity: when transformed into actionable knowledge, such data becomes real-world evidence that can offer unprecedented insights to healthcare professionals (HCPs), helping them make better and safer care decisions.

So how then can advanced technology such as Machine Learning and Artifical Intelligence expedite the route from evidence to practice, and revolutionise care outcomes?

THE PROMISE OF AI

The future of clinical decision-making can be made based on real-world evidence through the use of AI. If handled well, AI has the potential to reduce the time for new discoveries to find a way into practice.

Supported by advanced algorithm and machine-learning capabilities, AI can analyse existing patient data to study clinical data to draw new findings, findings of a smaller cohort of patients, and hypothesis oftentimes not covered in larger scientific research. For example, in Germany, Elsevier has partnered with a healthcare service provider to evaluate six million anonymised patient datasets along with data from 25 million medical publications.

However, real-world evidence data alone is not enough. By combining patient-generated data with academic evidence, HCPs are able to create personalised treatment options. AI could be an even bigger catalyst to provide HCPs with real-time capability to develop real-world evidence, which can positively impact patient outcomes. Just imagine if your doctor could tell you just how aggressive the disease you have is, and which treatments will work best for that particular strain.

Moreover, with deep analytics, data collected through structured content can be easily analysed to feedback into care practices.

Don't let cybersecurity risks threaten the adoption of technology

While digitalisation can reap benefits for the country's healthcare system, there is an additional dilemma. The huge quantities of patient data may become a major target of cyberattacks. For instance, SingHealth's data breach in July this year resulted in the health data records of 1.5 million people being stolen. However, in the words of Minister for Communications and Information, S Iswaran, such incidents should not "cow or deter us from pursuing this path, because it is a path that's going to create the opportunities for this and future generations of Singaporeans".

Resolving this dilemma necessitates a delicate balance between pursuits of the future that such sophisticated technology promises, and ensuring there are safeguards in place to protect the identity of patients. Healthcare systems around the world need to persist because using data and technology to create better systems and solutions is the way forward.

  • The writer is managing director for Asia-Pacific at Elsevier.