Healthcare Data Analytics: A Summary of How Data Is Improving Delivery and Outcomes Across the Care Continuum
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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.
This blog explores the value of applied data analytics in healthcare. We cover:
- A brief history of data analytics in healthcare
- Examples of healthcare data analytics in action
- The impact of healthcare data analytics in improving quality care
Healthcare data analytics turns complex clinical, claims, and operational data into actionable insights. In this guide, we explain what healthcare data analytics is, where it is used across the care continuum, and how it improves patient care through better quality, efficiency, and decision-making. We also cover key use cases like population health, revenue workflows, interoperability standards, and SDOH-driven outreach.
What is healthcare data analytics?
Healthcare data analytics is the process of examining vast amounts of medical data to uncover meaningful insights. These insights help improve patient outcomes, optimize service delivery, enhance operational efficiency, inform the product life cycle, and much more throughout the care continuum. Sophisticated analytics, informed by authorized data from various sources, help healthcare professionals turn complex datasets – often stored in different places – to a clear story to make an evidence-based decision.
Data analytics in healthcare can take many forms, including predictive analytics, retrospective analytics, commercial analytics, prescriptive analytics, and utilization and treatment analytics. Predictive analytics use historical data to forecast future outcomes, empowering health plans and providers to anticipate patient needs and potential health risks. Prescriptive analytics suggests actions to achieve desired outcomes, such as personalized treatment plans that cater to individual patient needs. Commercial analytics help life sciences organizations understand how to best bring a new treatment to market.
In these various examples, one thing is consistent: healthcare data analytics help create a more holistic view of large, complex data. This in turn helps improve patient health, care quality, and healthcare cost and resource management. Healthcare data analytics transform raw data into valuable information, delivering the right data at the right time to support clinical decisions, improve organizational performance in various settings, and drive innovation across the healthcare continuum. As healthcare data continues to grow in volume and complexity, the role of data analytics is increasingly crucial in shaping the future of healthcare delivery.
The history of healthcare data analytics
Healthcare data analytics can be traced back to the 1950s, 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.
Examples of healthcare data analytics in action
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
- Use case: Healthcare data analytics for population health and 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 efficiencies
- Use case: Data analytics in healthcare to reduce denials and improve workflows.
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.
Commercial healthcare data analytics
- Use case: Healthcare data analytics for commercial and life sciences performance.
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.
Unlocking new data capabilities
- Use case: How data analytics help healthcare organizations provide their members with real-time access to data.
As healthcare data analytics capabilities and insights continue to advance, standards and models such as the HL7® FHIR® (Fast Healthcare Interoperability Resources) standard and the 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 through APIs that support the FHIR standard, 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.
How do healthcare data analytics improve patient care?
In today’s increasingly data-driven healthcare atmosphere, where organizations seek to improve both quality and economics, merely having data analytics is not enough to improve healthcare delivery.
Even with analytics ran to inform and guide decision making, there is still more needed to drive impactful action. What truly sets top tier organizations apart from their peers is having a platform in place to ingest, normalize, and continue to enrich their data – helping establish a genuinely data-driven healthcare organization.
The idea that rapid access to the right data will drive improvements in quality outcomes, economics, and analytic capabilities has never been more true than right now. But too often, that data isn’t easily located, with different types of data housed in silos – sometimes in different departments or even in different buildings or geographic locations. We need systems that coordinate and collaborate to enable us to maximize the impact of the data we have.
Without the power of analysis, we just have numbers. How much data we have matters much less than what kind of data we have. It’s important to consider:
Where did the data come from?
Is it accurate?
What is the story that it tells, and do you have the right tools to interpret that story?
The relationship between data analytics and quality requirements
For the past decade, the economic and regulatory pressures on payers, hospitals, and providers to demonstrate improved quality outcomes has driven a significant effort to improve healthcare data collection and integration. Many organizations are now heavily focused on combining provider clinical data with non-clinical or administrative payer data, as well as patient data from disparate sources. Organizations are making significant investments in underlying data technologies and partnerships with companies that can acquire and cleanly integrate that clinical data to meet this need and drive greater transparency, care continuity and clinical quality outcomes for their members.
The combination of this disparate data, including data originating from the patient, requires organizations to employ “smart” data management practices. The desire to extract important clinical insights that could be highly valuable for calculating quality and attaining desired outcomes is driving investments in natural language processing and machine learning.
Leveraging social determinants of health data to close care gaps
The more data you have on your members, the better you understand them and the better you are at predicting how – and why – they respond to circumstances and information. As the healthcare industry continues to improve care quality, the focus on providing patient-centered care has significantly increased, and part of providing the most effective care is understanding the effect social determinants of health – where individuals live, work and play – have on overall well-being.
Developing a 360-degree view of the patient remains the gold standard in healthcare data analytics, and understanding more about patients’ social needs is a valuable piece of that equation. SDOH are increasingly recognized as a key component to overall health and wellness of individual patients, as well as entire communities. Likewise, collecting and using social determinants of health data has become a fundamental aspect of improving care and lowering the skyrocketing costs of healthcare.
Data on the social needs of patients has long been a valuable tool for healthcare providers; however, establishing a better understanding of how this information impacts each patient is key to providing the right care to the right patient. SDOH significantly impact 80% of patient health outcomes2 – that’s more than other frequently used determinants, such as a patient’s health history.
Connecting SDOH data analytics and member outreach planning
Advanced analytics facilitate deeper patient-level insights and inform effective, targeted outreach to the right patient, through the right venue and at the right time. When you layer in socioeconomic data on top of the electronic health records you already have, you create a more accurate account of your current and future patient population. Predictive analytics allow for more informed decision making about suspected conditions that may impact a specific patient’s care plan, producing better individual health outcomes while driving efficiencies and improving economic performance.
To provide targeted interventions for patients, both providers and health plans need information about the social determinants of health – data that is increasingly being identified through artificial intelligence and other technologies.
Measuring performance with predictive analytics to remain competitive
Health plans are continually striving to effectively manage their performance and improve efficiencies – and doing so with only their own data is like operating in the dark.
If you are armed with broad and timely industry comparative data insights, then you know how you need to improve. Healthcare data analytics play an integral role in informing the development of effective strategies to navigate quality care and improve member outcomes.
Healthcare payment models and quality incentive programs are based on relative performance – graded on a dynamic curve. Having data-driven insight into not only your own past, current, and predicted future performance, but also how you are performing compared with others in the market, enables you to better target your performance goals and tailor the right strategies for success.
Encounter data is essential to gaining a better understanding of both the care we are providing and the care that should be provided. The ability to identify care gaps and more accurately monitor the disease status of patients is invaluable in today’s data-driven, value-based environment. This data can be used to effectively identify patients who are at risk or are overdue for an encounter.
Frequently asked questions about healthcare data analytics
What is data analytics in healthcare?
Data analytics in healthcare is the process of analyzing clinical, claims, operational, and patient data to uncover insights that improve decision-making, outcomes, and efficiency across the care continuum.
How is data analytics used in healthcare?
Organizations use data analytics in healthcare to identify care gaps, improve quality measures, reduce denials, optimize staffing and workflows, guide outreach, and support research and product development.
How do data analytics help healthcare organizations improve outcomes?
Healthcare data analytics helps teams act earlier on risk, personalize interventions, measure performance against benchmarks, and close gaps using more complete views of patient and population needs.
What is the impact of data analytics in healthcare?
The impact includes better quality and safety, lower administrative waste, faster corrective action, and more targeted member or patient engagement, especially when combined with SDOH and interoperability data.
Discover Inovalon’s healthcare data analytics solutions
Data is telling an important story about specific patients, entire populations, and organizational performance across healthcare – and the role of data analytics in healthcare will only continue to expand.
For organizations throughout the care continuum, now is the time to put your data to work in the most effective manner. Whether for closing care gaps, understanding disparities in healthcare, driving innovative research insights, or simply making it easier to access the right data at the right time, there’s practically no limit to the value of data analytics in advancing what healthcare can be.
The question is, what can healthcare data analytics solutions do within your organization to make a significant impact?
Contact us to learn how Inovalon can support your next big initiative.
Inovalon and design®, Inovalon®, and the Inovalon ONE® Platform are trademarks of Inovalon, Inc.
HL7® and FHIR® are registered trademarks of Health Level Seven International. Use of these marks does not imply endorsement by HL7.
