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How data-driven technologies are enabling a hive mind in healthcare

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Data and analytics have been invaluable in tracking the supply chain of PPE

Since early 2020, the world has witnessed radical changes to the policy, economic welfare, industry development and life itself. With the number of coronavirus infections recently surpassing over 43 million cases worldwide, we’re sailing uncharted waters. Naturally, healthcare has been heavily relied on to mitigate the pandemic’s burden on a macro level, with data-driven technologies adopted to make smart yet critical decisions more efficient. But what exactly has data rich Artificial Intelligence (AI) been doing behind the scenes to keep services afloat?

COVID-19 has accelerated healthcare’s already growing dependence on data-driven technologies. Data and analytics have been invaluable in instructing and tracking the supply chain distribution of PPE equipment, which has been in shortage throughout the pandemic. The financial crisis occurring as a consequence of the loss of elective procedures has impacted revenues and margins, whilst creating unprecedented risks for the health and safety of caregivers.

Perhaps the most significant area of transformation is the underlying clinical processes for patient engagement, diagnosis and post-discharge care, which are likewise, driven by AI and analytics. These factors have spurred the industry to pursue swift digital transformation and to innovate new kinds of patient engagement.

How healthcare providers already utilise and depend on data and analytics

Many healthcare organisations are looking to harness the vast potential of AI and its four components – machine learning (ML), natural language processing (NLP), deep learning and robotics, to transform their clinical and business processes. They seek to make sense of an ever-expanding wave of structured and unstructured data and to automate iterative operations that previously required manual processing. There is tremendous potential for analytics to deliver on the promise of better-quality care, at lower costs, by empowering staff to harness the power of predictive and prescriptive analytics.

Analytic Process Automation (APA) systems have optimised healthcare services in use cases such as transforming waiting list scheduling. Online waiting lists can automate the analysis of recent data across a hospital to visualise the number of referrals and median waiting lists for new appointments.

Regionally, the Saudi Arabia Ministry of Health manages local government hospital and medical activities with the assistance of automation platforms, allowing various administrative tasks such as collecting COVID-19 test results to determining quarantine durations automatically.

This network analysis has allowed the government to understand the virus reproductions rate and track its spread.

Crucially, this information has informed decisions, which have assisted in reducing the spread of the virus and ultimately, saving lives. Through insight analysis, the Saudi Arabia Health Ministry could target where, who and when they were required to respond to COVID-19 cases and inform quarantining regulation.

Democratising data and analytics in healthcare

Data-driven technologies are beginning to empower every human decision, thus liberating workers from the monotony of basic tasks, such as temperature checks when entering a hospital. As more organisations evolve towards a data-led culture, the rate at which smart systems can be scaled across all parts of a business has emerged as the true measurement of success.

However, there remains a data-literacy gap across the healthcare sector globally. As the amount of data collected surges exponentially, the sheer quantity can overwhelm businesses. Consequently, many organisations have little choice but to focus on narrow portions of data – an incomplete fraction when solutions demand a greater percentage of the whole.

The emergent category of APA could be the key to capturing the best of man and machine at scale. APA automates business processes and grants even novice-level knowledge workers direct self-service access to business-critical data insights at speed. In practice, this means more employees can adopt and benefit from data with minimal training – reducing reliance on data specialists and democratising data analytics.

On the horizon

There is little doubt, that 2020 has proved to be the most pivotal landmark across all industries, and particularly for the healthcare world. As the industry painfully realised the importance of data and analytics in harsh circumstances, this allows the opportunity for technology to spearhead a new frontier for healthcare. As the complexity of data increases, it is imperative to adopt both descriptive and predictive analysis, while embracing ML and NLP to obtain the necessary information. Nonetheless, the healthcare sector must remain inquisitive and progressive. By asking the right questions and applying the right technologies, data and analytics can help solve some of our toughest, shared challenges.

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