What is data analytics in healthcare?
Healthcare doesn’t function without data. Across the industry every day, large organizations and individual professionals leverage healthcare data analytics to support everything from patient care to drug development. So, what is data analytics in healthcare, and how is it helping the industry progress?
A brief history of healthcare data analytics
Healthcare data analytics can be traced back to the 1950s1, with journals and publications of the time sharing analyses to support physician diagnosis and the concept of modern biostatistics. The scientific community at the time had concepts similar to what we know today as machine learning and artificial intelligence, but lacked the tools and technology to realize those ideas.
It’s not until the 1980s that artificial intelligence started to gain momentum in healthcare, and in the 1990s, a study found that an artificial neural network performed better than a group of medical physicians in diagnosing heart attacks with data from EKGs.1
Enter the dawn of the digital age in the late 90s and early 2000s, and the technology we have today makes the 1950s feel further away than 70 years ago. Computers, machine learning, and artificial intelligence are now mixed into many aspects of care, informing unique patient-provider interactions, supporting clinical trial design and population health management, and more – and there’s still much to learn about what data can help us achieve together.
What is data analytics in healthcare, and how is it supporting better health outcomes and economics?
Data analytics inform practically every piece of healthcare.
There are many examples to show the power of healthcare data analytics, including:
Informing population management and advancing health equity
Population health management analytics is extensive data collection, aggregation, and analysis that informs health plans, provider networks, ACOs, and health systems on at-risk members, care delivery gaps, and performance benchmarks.
Data analytics allow healthcare companies to see where they’re missing the mark and adjust accordingly, at greater speed with the power of data. Having population-level insights in a matter of minutes vs weeks or months enables corrective action. It improves operational efficiency within health plans and provider groups, and more importantly, enhances the quality of care that all patients receive – especially those who need it most.
With the ability to drill into subpopulations based on social determinants of health or identify geographic areas that have more at-risk members than others, data can help key healthcare leaders see the big picture. It also helps them focus on specific regions in need by identifying hot spots of areas with lower health outcomes than their neighboring regions.
Understanding member populations is the first step towards improving the care patients receive, and when SDOH data is combined with organizational data or claims data, the opportunities to advance care outcomes are practically endless.
Identifying opportunities for workflow efficiency
The data within the technology that supports healthcare delivery can help inform how organizations operate.
Health plans can use their quality management and risk score accuracy solutions to understand internal coding gaps and opportunities for quality and risk collaboration. Providers can dig into their claims denials and the reason codes within them to see if there is a workflow pattern that can be addressed to drive better revenue outcomes. Pharmacies can leverage real-time data exchange and electronic decision support to improve their operations and clinical performance.
Unlocking new data capabilities
As healthcare data analytics capabilities and insights continue to advance, new standards such as Fast Healthcare Interoperability Resources (FHIR®) APIs and Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) are further improving industry-wide data sharing and transparency – while maintaining patient privacy and data security.
Regulations now require health plans to provide their members real-time access to their health data via FHIR-based APIs, which allow for patient-level data sharing at the point of care. They eliminate dark spots in the patient health history that EHR data alone can’t fill and do so with the speed and accuracy that providers and patients need.
This is great for consumer transparency and empowers patients to be better engaged in their healthcare, without having to worry about keeping track of all their care details. The APIs do the heavy lifting for them, improving visibility for providers and the care experience for patients.
For pharmaceutical companies and research organizations, OMOP CDM is essentially getting everyone on the same page. It’s bringing a newfound level of access and consistency to massive healthcare datasets, enabling authorized parties to better supplement and make sense of all the data available to them. The potential for impact is extensive, with clinical trials, drug efficacy and speed of development, and health equity research all having something to gain.
Another way pharmaceutical companies benefit from healthcare data analytics is in analyzing their product’s performance in the market, such as post-market drug safety monitoring. This is the fifth and final stage of FDA drug development, with a primary focus on monitoring drug efficacy and performance in the market, but it has an indefinite timeline.
Post-market surveillance can go on for months or even years – and that entire time, pharmaceutical companies need the support of advanced data analytics to understand their product’s performance.
Harness and apply the power of data analytics with Inovalon
The role of data analytics in healthcare continues to expand. It has become an integral part of the massive, complex system we’re all part of and working to improve every day.
To discover what’s next for healthcare data analytics, check out this blog on the future of healthcare data featuring insights from industry experts.
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