Big Data is dead. Long live … data.
The sheer volume of healthcare data now being collected dwarfs that of the largest online retailer (Amazon) and the largest social media platform (Facebook). Big Data has become just “data,” and wise corporate leaders are adjusting their business models to account for it—including the establishment of new standards for data science and the development of data centric teams.
It took a global health event to highlight the need not just for data but for actionable insight. And not just its impact on healthcare, but also its impact on education (when should schools go back to in-person?), business (do we co-locate when working?), and transportation (mass transit vs cycling or walking).
What’s more, there’s also been an evolution in public awareness and discourse with regards to race, gender, class, and related disparities in healthcare delivery and outcomes. The pandemic brought to the fore a very real debate about the role of the healthcare industry in serving the public welfare. And how do we do so while treating both patients and their data ethically.
Big data makes a big shift
The benefits of mining and analyzing patient data have long been known to leaders in the industry, and even longer to academics. Data science can improve business growth by identifying gaps in healthcare. Artificial intelligence can help improve patient outcomes by closing those gaps. Analytics can improve care and increase profits by identifying operational inefficiencies. The internet of things (IoT) can make our marketing more effective by telling us—in real time—precisely how patients seek and use healthcare products.
What’s been missing on a large scale, have been the data scientists. Part of the reason has been HIPAA, which makes hospitals and insurers wary. Another reason, at least in the US, is our competitive, private healthcare system, which discourages the sharing of customer data. But the pandemic changed that.
What COVID-19 did, essentially, was reveal a massive need for data scientists. It was this nascent field that gave us the charts and graphs that informed (or, failed to inform, or even misinformed) public health decision-making.
In 2019, the state of data science in healthcare might have best been summed up as an elephant in the room. In 2022, it’s all about ducks in a row.
Inputs and outcomes
By the time COVID-19 arrived hospitals had just come out of a transitional phase, at least in the U.S., from paper charts to electronic medical records. Every patient’s history is now digital.
In pharmaceuticals, research and testing consume vast datasets: of patents, publications, and past trials, as well as niche population sourcing for trial. The costly and time-consuming process of development and testing is being revolutionized by machine learning algorithms and highly accurate predictive models.
The insurance industry has long collected and used patient data to develop mathematical models to predict outcomes and inform customer interaction. But with the advent of “big data” insurers are employing machine learning to integrate external datasets (about advances in healthcare, for example) with internal patient and employer information.
There’s also been a concurrent shift in consumer culture, with smartphones and wearable devices that now track millions of people’s exercise, eating, sleep, and hygiene habits. Which insurers also now track and can act on to improve policies, outcomes, and customer experience.
Medical imaging is now largely digital, with every scan becoming a constellation of data points that can be fed to a deep learning algorithm to detect microscopic deformities invisible to even the best-trained radiologist.
And then there’s the explosion in the field of genomics, with DNA sequencing taking less time and money. The volume of genetic data alone is staggering. And this data can then be used by drug makers to tailor research and by care providers to tailor therapies. Personalized medicine is no longer a buzzword—it has increasingly become an expectation, with data guiding the way.
Specialized problems need specialized problem solvers
Where previously hospitals and other healthcare companies might have ignored their data, or turned to more traditional business analytics, there’s now widespread acknowledgement of the need: not only to manage the data, but to analyze and apply it.
The kinds of insights that proper data analysis can provide become almost limitless:
- How might we reduce inefficiency in clinical operations and telehealth?
- How can AI improve modeling in pharmaceutical research and development?
- What about improving or accelerating patient care, from diagnostics to outcomes?
- Might machine learning help clinicians better communicate risk?
- How could we improve insurance models for better risk management and decisioning?
These questions—and countless others—are the bread and butter of data scientists, all of whom are highly specialized. Many boast independent data science certifications. And until the pandemic, most sought employment either with enterprise-level tech companies, consulting firms, and healthcare giants—particularly in insurance and pharma.
Except those aren’t the only places they’re needed. And with a sudden swell in the ranks of remote workers, more and more companies are hiring their own data scientists as part of their staff. Hospitals are now hiring C-suite practitioners with data science certifications. (Even the United States has a Chief Data Scientist.)
The quality of data is another issue that data scientists are best equipped to address. Poor data quality—that is, data that doesn’t meet a user’s needs—yields poor outcomes. But data quality is largely defined by contexts, which vary by organization, time, and need. One of the best strategies for ensuring good data quality, and therefore good analysis of data, is to bring data experts into your context.
Which is to say: staff your organization with highly trained individuals with data science certification. And in 2022, that’s precisely what healthcare companies are now doing.
And if you haven’t yet? It’s not too late, until it is.
Depending on the size and function of your organization, you may find yourself in need of an entire cross-functional team of data scientists, analysts, and developers. Or you may need to appoint a single, multi-purpose Chief Data Officer to perform the varied functions, from developing (or contracting with) an appropriate database, writing search queries, analyzing results, formatting reports, and synthesizing strategies.
Even a smaller organization nowadays would benefit from the purposeful shaping and analysis of its data.
At the very least, it behooves smaller healthcare companies to include in their professional development budgets incentives for marketing, finance, and operations staff to pursue data science certification programs. These programs—such as the ones offered by CertNexus—ensure a high level of expertise in a skillset that is rapidly becoming standardized.
Data has changed both outcomes and the way healthcare is delivered in a very short period of time. The clearest way to catch up and remain on top of these changes—and to drive positive outcomes and innovation in policy—is to equip your team with true data scientists.
CertNexus is a vendor-neutral certification body, providing emerging technology certifications and micro-credentials for Business, Data, Development, IT, and Security professionals. CertNexus’ mission is to assist in closing the emerging tech global skills gap while providing individuals with a path towards establishing rewarding careers in Artificial Intelligence (AI)/Machine Learning, Data Science, Data Ethics, Internet of Things, and Cybersecurity.
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