The Evolution of Data Analytics in Healthcare


The Evolution of Data Analytics in Healthcare

The Evolution of Data Analytics in Healthcare

Data sharing has traditionally been something of a sticking point in the field of healthcare, given the need to balance the clear advantages of data analytics with justly-stringent privacy regulations and concerns.

But the COVID-19 pandemic forced unprecedented data sharing among governments, healthcare providers, and pharmaceutical companies, leading to the rapid development of vaccines in less than a year after the virus emerged.

It’s also been suggested that the pandemic – or, at least, the supply chain shortages of face masks and personal protective equipment that caused added problems in 2020 – could have been prevented or lessened by better data analysis. It is felt that the information was all there; it just wasn’t effectively translated into actionable insight.

So, while privacy and security concerns must remain high priorities, it seems clear that data analytics can and should play a more significant role in healthcare. Major changes will need to be decided on by industry and regulatory authorities, but there are already several ways data analytics can be leveraged to enrich the patient experience.

Anticipate needs and personalize care better with non-traditional data sources. The healthcare information ecosystem can be expanded to include patients and providers but pharmacies, life sciences and tech companies, employers, government, community organizations, and social media. Instagram, for instance, might be the only venue to reveal that an unconscious patient recently ate shellfish.

Coordinate care among practitioners. No patient likes having to fill up a basic information form every time they see a new specialist, yet this continues to be a requirement, even in this electronic age. Healthcare providers should consider making patient data available – with permission, of course – across the entire spectrum of relevant care, encompassing doctors, nurses, therapists, pharmacists, and more.

Engage patients more proactively with targeted intervention. Any healthcare practitioner can tell you that patients don’t always know when something’s wrong – or aren’t always willing to admit it. Data analytics can help. Repeated posts on social media, for instance, could prompt a gentle encouragement for the patient to get a mental wellness checkup. Community illness stats might call for a flu shot, and so on.

These are just ways in which more data-driven healthcare can make patient care more efficient and effective, potentially saving – and certainly improving – lives.