The ability of healthcare providers to collect patient data has traditionally far outpaced their ability to translate that data into meaningful insights to improve patient care. Despite understanding that there are clear benefits of big data in healthcare, clinicians and healthcare administrators wonder how to optimize those benefits.
Advances in data science and computing algorithms allow us to better leverage the benefits of big data in healthcare. Now providers can begin to realize the full potential of their data systems to revolutionize patient care.
Demystifying Big Data: What is Big Data?
Simply put, data analytics in healthcare is about collecting and parsing healthcare data too voluminous and complex to be managed by traditional data processing. It means translating massive quantities of healthcare data into useful, actionable insights for patients, caregivers, and medical providers.
Data scientists define and evaluate Big Data using the 5 V’s:
- Volume: How much data is there?
- Velocity: How quickly is this data generated and transferred?
- Variety: How many sources is this data coming from, and how many types of data are incorporated? Today, we can collect data from electronic health records (EHRs), medical sensors, and wearable devices like Apple Smartwatches, health and wellness apps, genetic testing, and telemedicine, to name a few.
- Veracity: How accurate and trustworthy is this information? Acting from incomplete or faulty data can nullify any potential benefits and even cause harm.
- Value: How useful are the insights available from this data, and how readily can those insights be applied?
Benefits of Big Data Analytics in Healthcare
To understand the benefits big data analytics has to offer, it’s useful to examine its applications to improve patient care and drive down healthcare costs.
Improving Patient Outcomes and Satisfaction
By analyzing patient data over time from multiple sources, healthcare analytics systems may spot patterns and trends in patient data, even before a trained medical professional. This supplements a doctor’s expertise, serving as a second pair of eyes on all their workups.
For example, one mildly abnormal blood test may not initially pique a physician’s alarm. However, a finely-tuned algorithm may compare that blood test with the patient’s previous five. Based on the comparison, the algorithm may flag a possible early-stage malignancy. Of course, the earlier an illness is detected, the higher the likelihood of a favorable patient outcome.
Beyond spotting patterns in an individual’s medical history, big data analytics can also perform meta-analyses on existing clinical studies and trials. Meta-analysis studies are a low-cost way to glean powerful insights from patterns in previous studies that may have otherwise gone unnoticed.
Aggregating, organizing, and interpreting patient data from multiple sources allows physicians to review and compare all the patient records they need in one place. This empowers medical professionals to make fully-informed decisions about their patient’s care, without waiting days or weeks to receive medical records.
Personalized Preventive Patient Care
Using data gleaned from the patient’s clinical records, family history, and medical devices, data algorithms can create custom healthcare screening schedules and nutrition recommendations for each individual patient’s needs.
Such recommendations should not supplant the advice of a trained medical professional. Instead, the technology would reduce the burden on physicians to follow up with patients about the tests they may need.
Lowering Supply, Testing, and Administrative Costs
Reductions in Unnecessary Testing
Every healthcare facility struggles with cutting unnecessary testing, whether because of physician habits or inaccessible medical records of previous test results. In fact, unnecessary tests cost upwards of $200 billion per year. Big data analytics addresses this concern in two ways:
Firstly, by making test results available across provider networks, you reduce the need to run expensive tests twice because the results from the first test are inaccessible to the current medical provider. Algorithms can then compare these testing results to flag abnormalities or inconsistencies for physician review.
Secondly, by comparing prescribed tests against Choosing Wisely recommendations designed to cut unnecessary medical testing.
Optimized Supply Chain & Inventory Replenishment
Predictive inventory models can help administrators fine-tune inventory management, reducing manual miscounts.
Data analytics also helps administrators closely monitor trends in supply chain costs and the utilization of equipment and pharmaceuticals. These insights are invaluable when it comes time to negotiate advantageous supply replenishment contracts to meet budget targets.
Staffing medical facilities has always been a challenge. But the COVID-19 pandemic escalated this challenge into a crisis for hospitals and medical offices across the country.
Big data analytics offer the ability to alleviate this crisis by more accurately forecasting staffing needs. This prevents understaffing, which risks inadequate patient care and accusations of neglect. It also helps avoid the other extreme: costly overstaffing. Predictive staffing powered by big data analytics helps administrators find the ideal middle ground.
WillDom partners with healthcare organizations to build and optimize big data analytics solutions. For more information on the benefits of big data in healthcare systems, connect with us through LinkedIn or visit our website where you can also explore our services.