If you’re a data analyst or cybersecurity specialist in search of a secure industry on which to hang your hat, rather than heading to Silicon Valley to join (or begin) a startup, you might consider pursuing work with a local hospital or public health clinic. That’s because the IoT and wearable technology bubble is about to burst, considering the lack of hospital security measures being taken at the network and administrative levels.
There are some major weak points that these facilities will need to address in the near future concerning their electronic medical record systems as well as the current adoption of wearable technology for patients and clients. Here are a few examples of ways you can put your skills and experience with big data to use within the healthcare industry.
Data Breaches Are on the Rise
Data breaches are one of the major healthcare management changes to watch in the near future. In fact, data breaches of healthcare systems have increased by about 23 percent since 2015. The majority of these breaches targeted small and medium-sized facilities because they haven’t yet adopted basic security practices. Cybercriminals can use this medical data to sell fake identities, or they can use ransomware to extort money from healthcare organizations in order to regain access to their systems and data.
To prevent these breaches, cybersecurity and data specialists can more closely monitor networks for security breaches, investigate and document intrusions, and incorporate firewalls and data encryption software within record keeping systems. Cybersecurity specialists can also perform penetration testing, which involves simulating attacks in order to discover and respond to any vulnerabilities within their systems.
Big Data Can Combat Healthcare Shortages
The need for data analysts isn’t limited to securing records systems within traditional healthcare facilities. For example, data analysts’ ability to interpret statistics related to healthcare shortages can help social workers to meet the needs of clients in rural areas. This can be especially effective in ensuring that areas with high concentrations of issues related to mental health and addiction are identified and that these groups can get the help they need.
In addition, because patients in rural areas are often spread out farther from major hospitals and other facilities, patients may not schedule appointments as often, and there are fewer healthcare professionals to meet a patient’s needs when they do seek help. This is where remote healthcare options such as apps and wearable technologies can help doctors and nurses to monitor patients remotely. By tapping into the information collected by these devices, data analysts can watch for indicators to potential health events within individuals and larger populations and assist medical professionals in preventing conditions or responding in a timely way.
Data Analysts Can Reduce Healthcare Costs
Obviously, the potential for IoT technologies to prevent or catch conditions early rather than simply treating them once they’ve developed will lower the cost of patient care. Properly utilizing big data can encourage healthier patients and cut down on emergency room visits, unnecessary tests, and expensive reactive treatments.
Big data can also save healthcare facilities money by illuminating trends in scheduling. For example, one facility that was frequently filled to capacity found that they could save about $435,000 annually by discharging patients sooner by at least half a day. By making more beds available to incoming patients, the hospital also improved the patient experience.
Similarly, some hospitals are already using big data from patient records to predict daily and hourly patterns about when patients will be admitted to their facilities. This allows hospitals to save money by keeping fewer staff members on the floor during slow periods. At the same time, this helps to ensure hospitals are fully prepared during peak times so patients can receive proper care in a timely fashion.
Challenges for Big Data in Healthcare
The greatest obstacle for big data’s use across healthcare facilities is a lack of compatible data systems. To best utilize the information hospitals and other healthcare facilities collect, the data needs to be centralized and visible to a variety of institutions. This would allow medical professionals to view not only their internal data, but other big data derived from different regions and patient circumstances that could contribute to their treatment strategies.
Aside from technical issues, centralizing this data is challenging in part due to issues with patient confidentiality. There are differing laws between different local and national governments which restrict what patient information can be released with or without consent. Also, institutions that have directed resources toward creating data sets based on their own records may not be willing to share their research with other healthcare facilities.
IoT technology is becoming more widely used in healthcare, which allows medical professionals to monitor patients more closely within and outside of the facility, providing better care at a lower cost. As both large and small facilities see the tangible benefits of examining this patient data along with trends in their scheduling and other practices, the role of big data and the professionals who analyze and interpret the information for healthcare facilities will only continue to grow.
About the Author
Avery Phillips is a freelance human based out of the beautiful Treasure Valley. She loves all things in nature, especially humans. Leave a comment down below or tweet her @a_taylorian with any questions or comments.
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