Let’s face it: Data is everywhere, especially in healthcare. From electronic health records to patient-reported outcomes, data piles up and can feel overwhelming without the tools and resources to make sense of it.
Applying big data analytics in healthcare is the solution. Read on for an introduction to healthcare data analytics, including the types, uses, value, and potential future of data analytics in healthcare. Specifically, we will cover:
In general, big data analytics is the practice of using analytical techniques to make sense of large sets of diverse data.1 In healthcare, unwieldy data sets are the norm. A comprehensive data entry for a single patient could include everything from electronic health record data and physician notes to imaging, prescriptions, lab results, insurance, monitoring equipment output, and even social media posts.
Data sets like these are often impossible to analyze on more general, commonly used software platforms and hardware systems.1 This is where the application of big data analytics in healthcare becomes a must-have instead of just a nice-to-have.
Advancing healthcare and improving patients’ lives requires both measurement and extensive analysis of these large sets of healthcare data to glean valuable, actionable insights.1 Physicians, researchers, medical specialty societies, pharmaceutical companies, and every other healthcare stakeholder can then use these insights as jumping-off points for improvement.
The role of data analytics in healthcare is clear: Pull the signal from the noise.
Whether you need to improve quality, advance research, manage risk, or anything in between, you have access to mountains of data. However, just having the data won’t do you much good if you don’t have a systematized way to organize, analyze, and interpret it.
This is one of the reasons why data analytics is so important in healthcare. By using analytical techniques, you can not only make sense of the past, but also chart a course for the future, for the benefit of the entire healthcare community.
One of the benefits of data analytics in healthcare is that there are many types you can use to answer many questions.
Let’s say you’re interested in readmission rates after surgical procedures. You’ll want to answer questions like: How did hospital readmission rates change over the past five years? What surgical procedures were associated with the highest readmission rates? What will readmission rates look like over the next year without any interventions? How could a potential intervention affect readmissions?
Healthcare analytics can help answer all of these questions. Let’s dive deeper into the four main types of healthcare analytics, their definitions, what questions they answer, and the potential limitations of each.
Data analytics can help improve healthcare for all industry stakeholders, from health systems and physicians to patients, pharmaceutical and medical device companies, and specialty societies.
The many uses of big data analytics in healthcare can be organized into four main buckets. Healthcare analytics can help your organization enhance its competitive position, improve quality, advance research initiatives, and manage risk and reporting.
To tap into these key uses of healthcare analytics, you need cutting-edge software and a trusted process to take you from an unmanageable jumble of data to actionable, real-world evidence.
That process involves:
In addition, you’ll want to make sure your data analytics software solution is flexible enough to evolve with your needs, and that it’s built with high-level security features in place. That way, you’ll have everything you need to unlock the greatest possible value from your data.
Beyond its many uses, healthcare analytics provides real-world value to those who act on analytical insights. Below, you’ll find three key examples of health industry players that have reaped the benefits of using big data analytics in healthcare.
Sound Physicians is the largest hospitalist and critical care group in the United States and a leader in physician performance and analytics. Sound relies on an advanced analytic and IT infrastructure and workflow to improve care, manage performance, and monitor trends.
Notably, Sound focuses its clinical strategy and resources on value-based payment models such as the Bundled Payment for Care Improvement–Advanced (BPCI-A) program.
Sound’s partnership with ArborMetrix helps them leverage technology and analytics to drive real-time actions and accountability. We integrate clinical and financial performance data from Sound’s service lines and the organization’s 3,500+ clinicians onto a single healthcare analytics platform. This solution puts rich, clinically-relevant data into the hands of Sound’s regional operators and physicians to drive performance improvement and operational excellence.
Subsequently, acting on these insights and relying on advanced data science allows Sound Physicians to support physician-led decision making, the right clinical interventions, and an efficient management process.
MBSC, a quality improvement collaborative funded by Blue Cross Blue Shield, leverages a powerful patient registry to improve bariatric surgery in Michigan. MBSC used the ArborMetrix platform to power outcomes calculators, patient-reported outcomes, and video surgical analysis, resulting in a 67% decrease in post-surgical death rates and $35 million in statewide savings, among other impressive results.
ELSO is an international non-profit consortium of healthcare centers and physicians dedicated to the development, evaluation, and improvement of ECMO (extracorporeal membrane oxygenation) and other innovative therapies for the support of failing organ systems. ELSO uses a comprehensive patient registry to advance ECMO research through real-world evidence and track the safety and effectiveness of devices and procedures in new patient populations.
To help ECMO device manufacturers fill their knowledge gap, ELSO leveraged this clinical registry to share data through a medical device registry. The device registry takes real-time data from more than 700 centers worldwide and transforms it to help manufacturers know how clinicians are actually using their devices, what the average patient looks like, and how their device performs in the real-world.
Recently, ELSO has also used its registry to track the usage and outcomes of ECMO for COVID-19 patients.
All three of these diverse examples show how motivated stakeholders can improve healthcare using big data analytics.
New technologies are continuing to emerge that push the boundaries of how healthcare analytics can be used. From artificial intelligence to machine learning to natural language processing, the true future of healthcare is in wielding these technologies for greater impact.
Artificial intelligence (AI) refers to a collection of technologies that can autonomously think and adapt with intention.4,5 In healthcare, AI can use input data to diagnose disease, structure clinical trial cohorts, and identify malignant tumors, among many other uses.
Machine learning and natural language processing are subsets of AI. Machine learning involves generating models to describe data. As more data is introduced, the algorithms adapt to the new information to create models that fit the data as accurately as possible.4 Machine learning is often used in precision medicine in a predictive capacity—given a patient’s unique medical history, machine learning techniques can predict what treatment is most appropriate.
Natural language processing (NLP) uses various techniques to make sense of human-generated speech or writing.4 NLP is especially useful for extracting information from patient records and classifying clinical documents.
This is just a taste of what’s in store for healthcare data analytics. With technology advancing at a rapid clip, new discoveries that can create even greater impact are inevitable.
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