Big data? It’s being collected everywhere in healthcare settings; constantly generated by smartwatches and other consumer wearables, health apps, electronic health records (EHRs), and through genomics.
We already know data is gold. And, at this particular point in time, we’re essentially sitting on a gold mine. The question is, what are we doing with it? And how can we better leverage it to improve patient outcomes and establish more streamlined healthcare operations?
Using data and analytics in the healthcare industry
I’ve seen for myself how the recent explosion of data and analytics can be used to completely alter the landscape of healthcare for the better. If used correctly, big data allows us to not only improve patient outcomes through personalized medicine, but to also lower costs and create a more efficient healthcare system at large.
Smartwatches, genomics, medical imaging, and social media are all perfect examples of where big data is stemming from on a daily basis. However, without the proper tools and strategies in place, this wealth of raw data is overwhelming (if not impossible) for humans to make sense out of. Que data analytics.
In order to consistently transform data points into meaningful insights, healthcare organizations need the proper data analytic tools. Through statistical and machine learning techniques, analytics are able to rapidly detect patterns in the data that would easily go unnoticed otherwise. By using data analytics in a healthcare setting, we have the potential to provide more accurate diagnoses, more individualized treatment, and overall greater patient outcomes.
Because most patients have access to smartwatches and wearable devices, these types of improvements in healthcare are also widely accessible. With the simple use of a smartwatch and a scientifically-validated health app, a patient’s health can be monitored 24/7, with vital information being shared directly with physicians. Analytics will translate the data to let physicians and patients know when a complication has been detected, or when risk levels are heightened, and even recommend corrective action to help alter the trajectory. All of which offers a more targeted and individualized approach to the way we provide healthcare.
How to use big data to advance personalized healthcare
Health is deeply personal by nature. And big data is critical to the success of implementing more personalized medicine. With the proper use of data, healthcare providers can make more accurate and informed decisions regarding tailored treatment options. We have the ability to use an amalgamation of genetic, environmental, and lifestyle data and (through analytics) apply it to how we move forward with each individual patient.
For instance, we’re now able to take a patient’s complex data, such as their genomics, metabolomics, and proteomics, connected with their medical history and constant stream of heart rate and other data, to determine their specific risk levels for developing a particular condition. Based on their risk, it can be determined whether or not certain diagnostic measures and testing should be taken, or if it can be ruled out. We can also use biomarkers and predictive technology to anticipate how a patient will respond to certain treatments in order to recommend the most effective medications and healthcare plans.
As complex as the sciences and technology is behind personalized health, it can actually be incredibly simple to implement. There are multiple health apps with powerful data analytics that can be used by patients at any given time.
Of course, data analytics isn’t simply for the use of improving the quality of patient care. It can also be used to elevate the management of large-scale healthcare operations. In fact, analytics can be used to evaluate systems at an operational level, such as staffing levels, patient flow, and resource utilization. This data can then be used to determine bottlenecks and to offer the best course of action to remedy them. All of which can result in shorter wait times, lower costs, and overall greater patient satisfaction.
How can big data be used for healthcare management?
Healthcare organizations are complex entities. They’re made up of various layers of patients, healthcare providers, administrators, and policymakers. Keeping things running smoothly in such an intricate system can be challenging, to say the least. However, big data can offer structured solutions.
To start, remote patient monitoring is a developing field by which patients wear smartwatches or other devices connected to a health app that allow doctors to monitor their health status from a distance. With the constant influx of data and analytics rolling in digitally, healthcare providers are continuously aware of which patients are in need of the most immediate medical attention. And with predictive tech, they’re alerted as to which patients are most likely to need hospitalization, or are in need of diagnosis or medication adjustments. This allows physicians to prioritize and allocate their time and resources accordingly without the constant need for in-person visits, increasing efficiency and reducing costs.
Patient safety can also be monitored and improved upon with the use of big data. Data analytics can be used to better understand the cause of an adverse situation, such as medication errors. Through machine learning, data analytics can identify trends that cause a particular medical complication. Healthcare providers can then use this knowledge to prevent similar incidents from occurring in the future, lowering liability costs, and resulting in fewer patient injuries and greater patient trust.
Finally, data analytics can aid healthcare systems in ensuring that they’re continuously complying with the latest regulations. By feeding data analytic tools with pertinent information, such as readmission rates and hospital-acquired infections, organizations can spot which areas are in need of the most attention in order to make necessary improvements and meet regulatory standards.
Using big data to support clinical trials
The way we’re applying big data to create more personalized healthcare at this point in time is only the tip of the iceberg. Data can also be used to help support clinical trials, including the development of new drugs and therapies, and the prediction of how particular patient populations will respond to them.
Big data analytics is truly revolutionizing healthcare as we know it. We already have the data. It’s now about getting innovative in how we use it to create a more custom approach to healthcare that benefits patients, and creates more streamlined and reliable healthcare systems as a whole.