Big data in healthcare could be an effective method of helping providers find information in large amounts of data that would otherwise remain hidden. Applying the technology to the health field brings numerous opportunities, but some challenges, too. Here are four of the potential obstacles:
1. The Risk of Eroded Privacy
The leading big data platforms used today can find patterns and trends in data and do so much faster than humans could without the help of computers. But, some people understandably worry that the increasing use of big data within the medical field could give people less control over what happens to their data and even promote privacy concerns about sensitive health issues.
In 2016, news broke that an agreement between the United Kingdom’s National Health Service (NHS) and Google gave the latter company access to the health data from 1.6 million patients so that the company could develop its artificial intelligence (AI) health tool. Some critics wondered if Google should have so much information even though the company has a relatively good record of data privacy.
It’s easy to imagine how other companies might share health data in ways people wouldn’t like. For example, many mobile apps track productivity and convert the data into metrics that let people see how they spend their time.
Health apps also track a person’s step count, heart rate and other aspects. What if that data eventually got sold to an insurance company that fed the information into a big data platform to determine someone’s premium rate?
People often have to agree to terms and conditions before they start using apps, and many of them don’t read the fine print before indicating acceptance.
2. A Limited Pool of Data Scientists
Big data gives businesses a competitive edge and promotes more informed decision-making. So, it’s not surprising that various entities in the health care sector use big data in numerous ways.
For example, a hospital administrator might depend on the technology to reduce costs or identify the factors that impact the quality of patient care the most, while insurance companies frequently use big data platforms to spot instances of health care fraud.
An overall shortage of data scientist talent already exists, and a recent study confirms that health brands consistently seek data scientists to help them meet well-defined priorities. As entities from within health care as well as other industries keep trying to recruit data scientists during a known shortage, it’ll naturally become even harder for companies to meet their hiring needs.
3. A Lack of Compatibility Between Systems
Medical organizations collect data from various sources. As such, researchers found that technology incorporation is one of the challenges of big data integration in health care. For example, particular departments in a hospital may use certain platforms to gather data that other segments of the organization don’t need. Moving all the data from those sources to a big data platform may prove impossible due to incompatibility.
And, there’s the likely case that various stakeholders house or manage the data. Silos within a business can hinder communications between the parties handling the data, too.
In any of these instances, getting all the data compiled and in a usable state can be prohibitively time-consuming and expensive. The more systems a healthcare facility uses, the harder it could be to capture data consistently or ensure that the systems currently used within an organization are capable of importing information onto a big data platform.
A similar issue is that healthcare organizations may not be willing to make the investments required to make compatibility issues less problematic. Although many facility executives are getting on board with big data, some feel reluctant to give the go-ahead to transition the necessary systems for big data-related success.
4. The Potential for Data Breaches
Besides the possibility of people’s health data getting evaluated by big data platforms in ways they don’t like, there’s a related issue whereby hackers know that medical data is even more valuable to them than credit card data. Concerned consumers can regularly check their credit card statements for unauthorized transactions, but it’s not as easy to do that with health information.
Another one of the challenges associated with big data in healthcare is that the number of parties handling the data could increase. Then, there’s the risk that some of them could fail to adhere to cybersecurity best practices and leave the information more available to hackers.
Or, if cybercriminals know that healthcare brands are using big data, they may get the impression that an attack on those organizations would be exceptionally lucrative. These realities mean that any organization or party dealing with medical information must take data governance and stewardship exceptionally seriously to protect information as much as possible.
Plenty of Potential, But Precautions Are Essential
Big data in healthcare has vast potential that ranges from reducing hospital readmissions to monitoring the spread of diseases. But, this list highlights some of the challenges for the sector to overcome if choosing to use it.
Hospitals and other entities interested in using big data should not necessarily avoid the technology due to these difficulties, but they must show the willingness to understand the obstacles and work to reduce them.
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