Big data. Today, it’s more than a buzzword. It’s a reality for industries, businesses, and individuals alike. The healthcare industry is no stranger to this immense data collection, especially with the growth of the Internet of Things (IoT), wearables, and smartphone apps, in addition to more traditional data collection methods.
The more healthcare data generated, the more providers, health systems, and employee benefits sponsors can focus on patient accessibility, outcomes, and affordability. Big data can reduce healthcare costs, increase the number of successful treatments, and improve the overall quality of life, to name a few benefits. According to Tech Jury, the big data analytics market in healthcare alone is expected to reach over $67 billion by 2025.
This wealth of healthcare data enables the industry to leap forward, making better decisions while increasing the quality of patient care. However, as with all big data, the management of this information is overwhelming. Interpreting unorganized data related to, for instance, clinical decisions, drug procurement, or population monitoring can be convoluted without the right tools.
With the proper tools and resources, however, healthcare stakeholders can reduce overwhelm instead of focusing on accessing and utilizing information, often from disparate sources, optimizing healthcare at all levels while decreasing care costs. In this article, we’ll explore four real-world applications of big data analytics in healthcare.
What is Big Data?
Before we jump into the world of healthcare, let’s first look at big data generally. Originally coined in 1987, “big data” referred to quantifying large volumes of information. Since then, the concept of big data continues to evolve.
Put simply, according to Oracle, “big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.”
First, we had the three Vs of big data, including “greater variety, arriving in increasing volumes and with more velocity.” In recent years, we’ve added two more Vs—veracity (identifying data’s inconsistencies) and value (garnering actionable insights)—to big data’s concept, making “big data a huge business.”
By using predictive analytics, big data stakeholders can uncover relationships and patterns, forecasting outcomes. In healthcare, for instance, some distinct trends have emerged from big data, including creating patient-centric care, monitoring patients in real-time, and reducing fraud and waste in the industry.
What is the Role of Big Data Analytics in Healthcare?
Generally, big data analytics refers to using analytical tools and techniques to understand trends and patterns in large, unwieldy data sets. Think about analyzing the information from office visits, lab results, prescriptions, hospital stays, diagnoses, electronic health records (EHRs), and more for each person in a city, state, or country. That’s a lot of data.
With the right tools, big data analytics can break this information down, categorizing it, identifying trends, and making the data useful and actionable. Connecting the dots in care can help stakeholders predict which treatment strategies work best, eliminate unnecessary or expensive testing, improve drug utilization, and, ultimately, boost health outcomes while reducing excess cost.
Data analytics makes sense of something that—in its raw form—is nonsensical. However, with analytical tools, you can make sense of the past while more accurately predicting the future while improving care procedures and outcomes, enhancing the patient experience, researching new drugs and treatments, and reducing costs.
What Are Some Real-World Applications of Big Data Analytics in Healthcare?
Next, let’s look at four real-world applications of big data analytics in healthcare.
1. Managing Electronic Medical Records
Finding their roots in the 1960s, electronic medical records (EMRs) have continued to evolve and proliferate over the years. However, as many healthcare professionals know, EMRs have experienced a rocky growth pattern, creating silos of data across numerous systems.
Because of this, EMRs are a primary source of data for healthcare analytics. For example, healthcare professionals can generate statistical patient reports on demographics, age, medical tests, prescriptions, and the like through big data analysis, spotting similarities or differences across various patient populations.
Further, the efficient use of EMRs allows healthcare professionals to coordinate care, reducing inefficiencies, costs, and frustration for both the provider and the patient. Additionally, through EMR analytics, medical providers can predict targeted therapies off similar health conditions, lifestyles, age, or genetic factors.
2. Predicting Patient Numbers and Need
The global pandemic exposed numerous healthcare industry fissures, including running out of beds or not having enough staff to care for infected patients’ stressful ebbs and flows. While the incredible ebbs and flows of late may be once in a lifetime with a global pandemic, fluctuations in the number of patients needing care typically exist, often challenging healthcare providers and facilities.
Enter predictive data analytics, once again. Analyzing big data can provide answers to future trends on patient numbers, allowing healthcare facilities to staff shifts appropriately and making sure that patients in need are seen. Additionally, you can shorten wait times with predictive analytics while also allowing medical providers to serve their patient population effectively.
3. Continuing the Growth of Telemedicine
Another healthcare delivery technique coming to the forefront is that of telemedicine. Again, thanks to COVID, telehealth usage “surged as consumers and providers sought ways to safely access and deliver healthcare. In April 2020, overall telehealth utilization for office visits and outpatient care was 78 times higher than in February 2020,” according to McKinsey. In fact, telehealth utilization has now stabilized “at levels 38X higher than before the pandemic.”
And patients aren’t the only ones contributing to the growth of telemedicine. Investment in “virtual care and digital health more broadly has skyrocketed fueling further innovation, with 3X the level of venture capitalist digital health investment in 2020 than it had in 2017.”
With the substantial growth in telemedicine, with high levels of investment spurring it on, this technology will continue to contribute to healthcare’s big data, requiring stakeholders to extract meaningful and useful information.
4. Identifying Risk and Trends in Claims Data
Claims data is often seen as a black box of information—seemingly impossible to pull out anything meaningful or actionable. Employers, pharmacy benefit managers (PBMs), and healthcare advisors can more succinctly and effectively identify risk, errors, utilization, and trends in health plan claims data through AI, automation, and data modeling.
Let’s specifically look at pharmaceutical claims data.
Healthcare data analytics give you objective visibility and insight into pharmaceutical spending. Armed with this information, employers, PBMs, and advisors can make more informed decisions about pharmacy benefits while reducing costs.
Additionally, if healthcare data analytics is automated through AI, then the visibility and insight can come in real-time, opening the doors for faster, more accurate, and more effective decision-making, proactively identifying opportunities instead of reacting to events occurring in the past.
What Are the Challenges of Healthcare Data Analytics Implementation?
Throughout this article, we’ve hit on numerous benefits of healthcare big data analytics for various industry participants; however, challenges do exist.
Still, as a developing field, healthcare big data is still evolving—from technology to implementation. As a result, many entities struggle to know where and how to start, integrating meaningful and actionable results into their workflows.
Additionally, with so much available data, healthcare entities need to ensure that their security and privacy measures are up to date, protecting their information from hackers. Along with privacy and security measures, entities must also upgrade their systems and databases, ensuring they can handle this high level of data.
Ownership of the data is yet another hurdle, understanding and acknowledging who owns the data and who manages it. Legally, this makes a difference, as healthcare entities need to understand their obligations and responsibilities when it comes to the oversight and management of big data.
What’s Next for Big Data Analytics in Healthcare?
As more data is generated and technology continues evolving, so will the future of healthcare data analytics. As a result, industry experts will need to persist in navigating this digital landscape.
But just like any highly-technical, quickly evolving area, it’s essential to surround yourself with subject matter experts, taking some of the load for themselves. To make big data analytics successful, PBMs, employers, and other healthcare stakeholders must have understandable and actionable data, allowing these entities to extract meaningful outcomes from vast amounts of information.
Through advanced pharmacy claims analysis, Xevant can help you manage big data, helping you to identify trends, mistakes, and outcomes in your pharmaceutical information. Whether you’re a PBM, TPA, or benefits manager, Xevant provides the data you need before you need it.
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