Current Big Data Challenges in Hospitals and Healthcare
Many businesses use data analysis to identify waste, improve spending, and increase profits. While healthcare organizations can reap these same operational benefits, tools like artificial intelligence (AI), cloud storage, data mining, and data visualization also can help hospitals and other providers improve care outcomes and save lives. Data challenges in healthcare settings can cost more than just dollars.
The healthcare sector’s data generation growth rate is expected to exceed that of any other industry sector through 2025, according to a study conducted by the International Data Corporation (IDC). Keeping up with the volume of information produced from imaging, telemedicine, electronic health records, and other sources of data will inevitably lead to data challenges in healthcare.
If hospitals and other providers are to meet and conquer those challenges, they’ll need data experts with the skills to assess and glean insights from the data as well as effectively communicate those findings to various stakeholders. Programs such as an online Master of Science in Business Analytics can prepare professionals to address data challenges in healthcare and deliver results.
How Big Data Improves Healthcare
Big data can help healthcare transition from a reactive, treatment-based approach to a more integrated, preventive model. Intelligent use of data also can speed the development of tailored approaches for greater patient engagement, which could lead to better compliance. Data can shed light on health drivers for different segments of the population. Analysis of that data helps providers develop best practices based on solid evidence. Proper use of healthcare data can negate any gender, racial, or cultural biases, inherent or intentional. The following approaches to storing and analyzing big data can be particularly helpful in healthcare settings.
Data mining, also known as knowledge discovery from data, is the process of finding patterns and anomalies within large datasets. Because vast quantities of data often are generated in healthcare settings, data mining is essential to find needed information and present it in an accessible format. If done correctly, data mining enables users to assimilate information from multiple databases and collection systems so it can be examined, analyzed, and used to plan and create more effective healthcare solutions.
Cloud computing—off-site storage of data and computer resources—is essential in addressing data challenges in healthcare. In addition to providing scalable storage solutions, cloud computing lowers costs through universal access to information across formats and geographies. Further, cloud storage allows for customizable security and credentialed data sharing. In healthcare settings where up-to-the-second information can save lives, cloud computing ensures uniform, on-demand connections to data, whether accessed from a doctor’s office, imaging center, hospital intensive care unit, or the scene of an accident.
AI finds repetitions or anomalies in mountains of data as it seeks to mimic human intelligence without bias or preconceived notions. AI can catalog results of particular treatments, survival rates, and recovery times in large numbers of patients in a variety of locations. It also can uncover connected health conditions that may be missed by busy clinicians. This kind of machine intelligence allows for data-based decision-making unavailable to humans because they simply can’t analyze that much data. Examples of AI use in healthcare range from flagging heart arrhythmias shown on electrocardiograms with more accuracy than trained experts in identifying microscopic cancer cells with a better than 95% success rate.
Big Data and COVID-19
Big data analytics is on full display as the world’s medical and scientific communities use AI, data mining, and other tools in response to COVID-19. Scientists, aided by technology, are scanning reams of data to understand the effects of the virus in an effort to predict what it’ll do next. Projects use AI and data mining to search for possible treatments by comparing virus details with those of existing drugs. Similar efforts are underway to evaluate different treatments and their effectiveness and compare the success of various social distancing measures implemented in different places.
The Challenges of Data in Healthcare
The data challenges in healthcare are myriad. Researchers can’t always access data on hospital outcomes. For example, privacy restrictions imposed by federal law regulate the release of medical information. Data fragmentation and the lack of uniform digitization impede efficiency, with some data overlooked because it’s stuck in silos. Some healthcare systems may want to improve their use of big data tools but lack staff members with the necessary training or information.
Another data challenge in healthcare relates to the decentralized nature of the industry in the U.S. Major systems may be digital, but many organizations and smaller providers are still paper-based. AI and many other technologies can only operate using digital information. This fragmentation of datasets makes it difficult to develop a complete picture of healthcare scenarios.
The possible problems created by data challenges in healthcare are balanced by the potential for exponentially better healthcare outcomes by meeting those challenges. If collecting data violates patients’ expectations for privacy, however, any gains are lost due to noncompliance. Furthermore, if data collection isn’t transparent, individuals are less likely to share the kind of information necessary for protecting society during the next pandemic.
The Skills of Effective Data Analysis in Healthcare
To meet the big data challenges in healthcare, hospitals and other health care organizations need skilled data analysts who can use information technology (IT) tools to solve problems. These data experts require an understanding of the healthcare industry and its policies. Like analysts in other industries, they’ll also need strong analytical, technical, critical thinking, and communication skills. Successful data analysts must have the capabilities to uncover data from a wide set of sources and databases, analyze it for relevant facts, and use it to showcase the next steps. Communication skills also are important since data analysis is useless if it’s not presented clearly and effectively to the relevant stakeholders and decision-makers.
Norwich University’s online Master of Science in Business Analytics program gives students the tools needed to conquer big data challenges in healthcare and help healthcare providers reap potentially big rewards. The program’s comprehensive curriculum includes courses such as Applied Regression with Research Methods; Information Visualization and Communication; and Big Data, Business Process, and Enterprise Analytics. The program trains professionals to use quantitative, statistical, and analytical research as well as understand and use unstructured data and develop the skills necessary to effectively communicate about data in written, oral, and visual formats.
Learn to Meet the Big Data Challenges in Healthcare by Earning an MS in Business Analytics
As healthcare data grows exponentially, the need for data professionals increases with it. Those who can conquer the mountains of data and deliver actionable insights will be in great demand. Predictive and prescriptive analytics are among the top 10 trends for business intelligence in 2020, according to a 2020 business intelligence report from Datapine. Other trends include data quality management, data discovery, data automation, and embedded analytics.
Discover how an online Master of Science in Business Analytics from Norwich University can equip aspiring business analysts to pursue rewarding careers in addressing the data challenges in healthcare.
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