Big data analytics is essential for healthcare providers scaling and being massive in size. Since the big data market is incredibly prospective in general, such a growth tendency also refers to the healthcare industry.
When analyzing the nature of big data analytics, we should learn about data sources that healthcare professionals utilize to gain better insights. At the same time, their number is constantly increasing because of ongoing digital transformation. Thus, with adequate data analytics tools, healthcare vendors can extract and process necessary information from the following sources:
- Electronic Health Records (EHRs)
- Patient portals
- Clinical studies
- Internet of Things (IoT) devices
- Health databases
- Government agencies
- Billing records
- Staffing schedules
- Search engines
- Scholarly and scientific journals
Investing in big data analytics is the right decision, and healthcare organizations aiming to deal with the information efficiently should take that into account. Besides, the evidence shows that analytics instruments will continue to be in demand in the next few years.
But what are the key advantages of big data analytics that make this technology so critical for healthcare? Let’s briefly describe them below.
- Cost reduction. McKinsey reports that in the United States, healthcare expenses take 17.6% of the nation’s GDP, which is about $600 billion. And the issue is that such expenses are much higher than the expected benchmark. But fortunately, sophisticated and intelligent data-driven approaches can reshape the industry and cut costs. That is because big data tools give healthcare providers a direct incentive to share patient data.
- Eliminating medical errors. The scholarly research states that almost 100,000 patients die annually because of medical errors in hospitals and clinics. Also, these errors cost the industry about $20 billion each year. However, utilizing big data analytics eliminates human error’s influence. For example, the relevant tools can alert healthcare professionals in the case of prescribing wrong medications or faulty clinical tests.
- Optimal staff management. Today, we notice the rising rates of healthcare employees’ burnout because of the workload and inappropriate staffing procedures. So, they must work in a clinic with optimal schedules and management. Healthcare organizations can collect and process real-time information with predictive analytics to measure workers’ performance. Depending on such insights, they can change their staffing schedules and provide patients with the best care.
Do you want to learn about the top 20 big data analytics applications in healthcare? Check out the article.
