Ever since the rise in big data analytics, several industries, like the healthcare industry, entertainment industry, sports industry, and more, are going ahead to utilize the benefits of data analytics.
The healthcare industry, in general, generates an enormous amount of data regarding patients’ health, and these analytics in healthcare help providers get actionable insights from the raw data. Healthcare professionals can use the information strategically to improve operational efficiencies, staffing requirements, and patient care.
Healthcare data analytics can be applied to patient care, improving clinical operations, measuring treatment effectiveness, and enhancing disease prevention. This guide will help healthcare professionals understand the need for big data tools in a healthcare organization and the best examples of data analytics in the healthcare industry.
ALSO READ: Web3 Tutorials: Resources to Master Web3 Development in 2023
Top 10 Healthcare Big Data Analytics Examples
Healthcare is one of the largest sectors leveraging the power of data analytics tools to manage in-house operations and predict future trends. By the end of 2025, the big data market in the healthcare industry will cross $70 billion with a growth rate of 568%. The complex and large volumes of information collected by digital technologies are converted into actionable insights using data analytics solutions. Some real-world applications and examples that enhance patient care and improve operational processes include:
- Healthcare analytics allows hospitals to use data visualization techniques to forecast patient loads and plan resource allocation.
- Electronic Health Records (EHRs) help doctors store patients’ demographics, allergies, medical history, lab test results, etc., in a digital format that allows patients to follow doctor instructions and improve their health.
- Real-time alerting devices collect patients’ health data and send live alerts to the doctor for better and quick care. Digital tools enable care managers to respond quickly if the results get disturbed.
- People in the digital world are interested in using smart devices that record their every step. Wearables like smartwatches monitor patients sleeping habits, heart rates, etc., to help them lead healthy lives and take measures to improve their health.
- Predictive analytics, a major Business Intelligence trend, helps doctors make data-driven decisions based on the complex health histories of patients without human intervention.
- Analytics in healthcare help organizations secure personal and valuable patient data from fraud or breach. Advanced security solutions like firewalls, anti-virus software, encryption technology, etc., add layers of security to the healthcare system.
- Big data tools in healthcare improve intelligent staffing and personnel management by forecasting demands and streamlining patient care.
- Medical analytics help in risk and disease control, allowing healthcare providers to understand symptoms, medication type, and frequency of hospital visits to suggest better care.
- Long-term finances and patient care depend highly on supply chain management. Data analytics in the healthcare sector help professionals track supply chain performance metrics, save resources, and make strategic decisions.
- Human errors while prescribing medicines, testing, or billing can risk patients’ lives and harm the hospital’s reputation. Big data and forecasting tools can help prevent prescription and billing errors to improve care and reduce expenses.
The benefits of adopting data analytics tools are endless, and many healthcare organizations are taking steps to leverage the power of technology. Inferenz’s data analysts team helps businesses implement data analytics solutions efficiently to handle the datasets and predict diseases before their onset.
The team of Inferenz has recently worked with a leading Pharmaceutical company from Germany to incorporate data analytics solutions, understand vital health parameters, and save patients from emergencies. Read the complete case study here.
ALSO READ: Predictive Analysis Tutorial: Ultimate Guide To Implement Predictive Model
Why Use Big Data Analytics In Healthcare?
Big data analytics tools can positively benefit the healthcare industry, amongst which reducing costs and improving patient care are the top advantages. Below are the three main benefits of integrating the right BI software and analytics in healthcare.
-
Big Data Analytics To Reduce Costs
Analytics in healthcare is a strategic approach to reducing unwanted costs, which is the product of inefficient planning or resource management. Advanced technology is a powerful tool to improve the operations in every size – big or small – health institution. Early identification of health ailments in patients with tools will help doctors provide immediate support and reduce high expenses.
-
Resource Optimization
Big data analytics tools allow professionals to gather and analyze real-time information about an organization’s performance. Predictive data analytics in healthcare can help experts determine staffing schedules, check the medical supply inventory, and improve resources and personnel management.
-
Reduce Medical Errors
Using big data and tools helps doctors eliminate the risk of medical errors, reduce manual intervention, and improve the hospital’s reputation. Data gathering and management tools help professionals handle massive and complex data sets to streamline the decision-making process in real time.
The Future Of Analytics In Healthcare
As one can see, data analytics solutions play a huge role in helping doctors and healthcare experts revamp how they provide patient care and manage diseases. . Due to the positive aspects of advanced technology like analytics in healthcare, there is an upsurge in adopting data analysis tools in the industry.
At Inferenz, we have a team of certified data analysts who help healthcare businesses tackle challenges by integrating data analytics solutions. Incorporating analytics in healthcare helps small and large health institutions reduce medical errors, improve faculty management, and enhance productivity.
