(Newswire.net — February 13, 2023) — The coronavirus pandemic has caused significant disruptions in the medical sector. High hospitalization rates, increased expenses, and a surge in mental health issues because of strict lockdowns have put hospitals in dire need of help. Big data in healthcare offers a solution by providing real-time relevant information that can help hospitals tackle current challenges and plan for the future.
To fully benefit from the technology, medical facilities employ big data consulting firms, stimulating market growth. The global big data analytics in the healthcare market was valued at around $16.9 billion in 2021 and it is forecast to surpass $67.8 by 2030, rising at a CAGR of 19%.
So, how is big data used in healthcare? And what to do to prepare your organization for this initiative?
What is big data in healthcare?
Big data in the medical field stands for enormous information volumes generated within the healthcare sector. It comes from digital technologies that capture patient data, EHRs, public records, research studies, etc. In other words, big data is too complicated for traditional technologies to handle.
Medical facilities turn to analytics to draw conclusions from big data. This involves discovering patterns in huge volumes of data through various computer science and statistical, and economics-based techniques. McKinsey’s big data report highlights the main methods:
- Statistics. Gathers and processes data from experiments and surveys
- Machine learning (ML). A subtype of artificial intelligence, which relies on computer algorithms to analyze big data and produce assumptions
- Data mining. Identifies meaningful patterns in datasets using ML and statistics
- Data fusion. Aggregates and analyzes data from multiple sources
- Natural language processing. Draws insights from human language
- A/B testing. Compares test groups to a control group to determine which treatment is the most impactful
Top 5 big data use cases in healthcare
Here are five exciting ways to use big data in healthcare.
Managing population health
Research related to population health assists hospitals in identifying the weaknesses of certain patient groups and addressing problems before they become severe. An example of using big data in the healthcare sector is creating algorithms that forecast the likelihood of falls among seniors. To achieve accurate predictions, the solution aggregates different types of data, such as medical history, social factors, and living conditions.
One illustration of population health management comes from Linguamatics. The company is based in Massachusetts and it employs natural language processing and predictive analytics to manipulate unstructured medical data and pinpoint lifestyle factors that may cause health complications.
Improving patient and staff engagement
Patients can use smart devices that track their health information, such as heart rate and sleeping habits. All this healthcare big data can be monitored by physicians, who will address any deterioration before it escalates, reducing doctor visits for conditions like asthma and blood pressure. Simultaneously, as patients can see the devices’ readings, they will become actively involved in their own health management.
Highmark Health, based in Pittsburgh, teamed with Google Cloud to build a “data-driven care plan for every member.” The medical facility aims to employ big data to engage patients and provide personalized treatment options. Among the first application of this system was recommending joint replacement patients visit virtual physical therapy before their surgery.
Discovering new treatment options
Big data in healthcare can speed up the medicine discovery process. For instance, machine learning models can predict potential drug effects, reducing the need for time-consuming lab experiments. Moreover, AI and big data enable researchers to virtually test drugs on human simulations and help locate and recruit suitable participants for advanced phases of medical testing.
Deep Genomics utilized big data and artificial intelligence to analyze over two thousand diseases and a hundred thousand mutations to determine the exact mechanism behind Wilson disease mutation. This enabled the research team to produce the DG12P1 drug in just one year and a half.
Cutting down healthcare costs
A recent survey by the Society of Actuaries revealed that 39% of the participating executives, who employed predictive analytics and big data in healthcare actually witnessed cost savings.
One instance of excessive spending that could have been avoided is hospital-acquired infectious diseases (HAIs). Utilizing big data can support medical facilities in understanding how infections spread, and guide them through taking the necessary precautions.
Augusta Health in Virginia exemplifies the use of analytics in patient care. By combining Electronic Health Record (EHR) data with geocoded hospital floor plans, a visual map was created to show the spread of multi-drug resistant organisms. The information gained an improved understanding of the spread of C. diff and MRSA infections, helping prevent future HAIs.
Other instances of financial loss include appointment no-shows and failure to foresee patient deterioration. Big data-powered technologies can support hospitals in addressing all that.
Reducing hospitalization rates
Hospitals rely on medical software development companies to deploy big data and analytics in monitoring hospitalization risks for people with chronic conditions. By evaluating factors, like recent experiences, health conditions, medication use, and physician appointments, healthcare centers can offer personalized preventive arrangements to decrease hospitalization rates. At the same time, hospitals can predict how many people are likely to be admitted and arrange their capacities accordingly.
The Mayo Clinic aggregates patient data from monitoring devices to accelerate disease detection and speed up the diagnosing process. The Clinic is coupling big data analysis with artificial intelligence to detect “silent” health issues, like a thickened heart pump before they worsen and lead to hospitalization with a stroke.
Tips to proceed with big data initiatives
At this point, you’re probably convinced that big data in healthcare has the potential to benefit your organization greatly. How do you proceed?
Well, to switch to a data-driven organizational style, on the managerial level, commit to a vision, and adjust your internal processes if needed. Hire new employees to fill in new roles or outsource some of the responsibilities. Dedicate time for employee training. Also, ensure that the management is on board with big data initiatives to avoid any misunderstandings later on.
On the data level, determine which data storage option is the most suitable. You can opt for the cloud or store everything on the premises. The cloud is normally the preferred choice for most medical facilities. When selecting a cloud vendor, consider the one who is aware of the compliance and security requirements specific to the healthcare sector. Make sure your infrastructure is scalable and set up strong data governance practices. Define what data ownership means within your organization and designate owners for different datasets.