Introduction
Like other industries, the healthcare sector has adopted the use of big data, which presents both benefits and challenges. It is not just the amount of data that matters; instead, it is what organizations do with the information that is important. Big data allows a large amount of data to be consolidated, standardized, digitized and analyzed using cloud-based solutions. This helps transform the healthcare industry since the data may be used to derive meaningful insights to improve decision-making and enhance patients’ experience and outcomes. In addition, data mining can also drive innovation by creating new prescriptive and predictive treatment models at a scale not experienced in the past. The big data analytics in healthcare has life-saving results and enormous benefits that can help reduce operation costs.
Possible New Uses of Big Data in Healthcare in the Next 5 Years
Since healthcare is producing a large volume of data, it will become easier not only to collect such information but also to create comprehensive reports and convert them into relevant insights through big data analytics. The data may then be utilized to invigorate the healthcare ecosystem and improve the quality of healthcare services. Big data can also be used for predictive analytics and assist clinicians to quickly and accurately make data-driven decisions and improve patients’ treatment. Prescriptive analytics is another area where big data may be used to enhance healthcare operations. Health experts can retrieve patients’ data and feed it into an algorithm that will suggest the most likely diagnoses and prevent medical errors (Dash et al., 2019). Therefore, with most hospitals admitting many patients due to Covid-19 and other illnesses, such software will be significant for clinicians who attend to several patients.
Big data analytics will also be used to improve patients’ experiences. The digitization of healthcare records will increase the understanding of patients’ medical history and provide more information on social determinants, as well as socioeconomic patterns to inform public health. In this case, data will be essential in tracking patients who have the highest co-morbidities, the risk for readmission, and need additional care. Diverse data can be used to predict the number of patients expected in a facility on a daily and hourly basis. This can help save costs for either under-staffed or over-staffed organizations by determining admission rates and assisting with staff allocation (Abdalkareem et al., 2021). Post Covid-19 is also expected to reshape the policy and culture regarding the use of emerging technologies to introduce alternative ways of providing care, such as remote monitoring, telehealth, and home-based care (Jia et al., 2020). These changes will also facilitate the utilization of the Internet of Things (IoT) devices, machine learning, and leverage of 5G, alongside big data analytics to manage these alternative forms of care.
The Main Sources of Big Data Used in Healthcare
There are several sources of big data in the healthcare system. A vast amount of information can be obtained from hospital records, medical examination results, and patient’s medical records (Dash et al., 2019). Healthcare professionals gather critical information about various diseases as they provide primary, secondary, and tertiary care to patients on a daily basis. This data is later stored in hospital records and the individual patient’s medical records. Information for big data can also be sourced from digital healthcare databases, including the Electronic Medical Records (EMRs), Electronic Health Records (EHRs), and Personal Health Records (PHRs). Big data can access millions of patients’ information through these platforms, including environmental, medical imaging, biometrics, and socio-behavioral data.
Biomedical research provides a considerable amount of big data critical for public health. Through research and experimentation, epidemiological, pharmacology, cellular, genetics, cancer, and molecular data can be obtained, which may then be used to improve the current healthcare system. Likewise, the IoT is another vital source of big data. The healthcare sector has adopted a system of integrating sensors and computer chips in various equipment. Some of the technological devices that utilize IoT in healthcare include the Radio Frequency Identification readers and tags (Dash et al., 2019). These technologies collect data from patients and transmit it over the internet. For instance, information channeled to big data may originate from healthcare testing equipment like those utilized in performing electrocardiography.
Type of Data Security That Should Be Included in Big Data
Privacy and security are significant concerns because of the type of data collected and stored by healthcare organizations. These may include patients’ names, social security numbers, addresses, and payment account information, which can be stolen. However, various techniques can be employed to ensure the privacy and security of big data in the healthcare sector. One of the most widely used tools is data encryption. This is essential when transferring sensitive data from workstations (including electronic devices used by administrators and clinicians) to cloud-based systems. The technique ensures that the data in transit is unreadable to intruders except for health experts and other authorized recipients. Therefore, in case of a breach, encrypted files will be unusable to cyberattackers since they need decryption keys.
Access control is another data security technique that hospitals can employ. It restricts access to sensitive patient information through multi-factor authentication methods (Abouelmehdi et al., 2018). For additional protection, this technique requires users to provide their personal information like passwords, usernames, and extra verification details, such as a one-time passcode, which may be sent to their mobile phones and email accounts to validate their identities. Consequently, this ensures that only authorized persons access patients’ data.
System monitoring applications can check a wide array of various processes and operations. This can help log all access and information to enable the security team to monitor the data users and from what devices and locations they are accessing the system. In addition, the logs can also be vital for auditing purposes and assist hospitals in identifying areas of concern and implementing protective measures. When a breach occurs, security teams can launch an investigation to pinpoint precise entry points, determine the cause, and assess damages (Abouelmehdi et al., 2018). Lastly, it is also essential to use malware and spyware applications to ensure that the system is protected from viruses, which may destroy patients’ information.
Problems Encountered While Using Big Data in Healthcare
The benefits of big data in the healthcare industry are undeniable, but many problems confront its implementation. For instance, high cost presents a significant challenge for adopting big data in this field. The hospital will have to hire data scientists and train their employees to work with big data. The hardware and software applications will also need to be purchased to manage the data. The issue of fragmented data and data quality also presents another problem of using this technology. Payers, providers, social workers, public health specialists, and patients are all sources of information, but data interoperability is a perennial concern. Several healthcare organizations are still using the traditional approach like pen and paper to record information. Thus, data sharing with external partners may be hindered because if others digitalized their data, some might fail to do the same (Adibuzzaman et al., 2018). Even if data is entirely digitized, users can face semantic challenges as various systems are more likely to use different terminology to describe the same thing.
Healthcare information is ever-changing, and most elements need to be updated to remain relevant. The introduction of personalized care models, new drugs, and treatments can alter the service delivery and information gathered, making it challenging to keep healthcare data accurate and current. Healthcare professionals will also need to determine how to deal with the new information coming in real-time. For example, updates from some datasets, such as patients’ vital signs, can occur every few seconds. Therefore, since healthcare data is non-static, this may derail big data analytics adoption because most data cleaning processes are still carried out manually. However, some IT experts may offer automated cleaning based logic rules to correct, compare, and contrast huge datasets (Dash et al., 2019). This increases the cost and time needed to guarantee high levels of integrity and accuracy in healthcare data. Therefore, comprehending the volatility of big data poses a considerable challenge for hospitals that do not often monitor their data assets.
Conclusion
Healthcare data comes from different formats and sources, such as paper, digital, EHR, and PHR. A huge amount of information collected can promote public health surveillance, improve patient outcomes, and data-based decision-making. However, the healthcare industry is susceptible to cyberattacks due to the nature of the information collected. For this reason, hospitals need to employ data security measures, such as regular risk assessments, authentication, and encryption methods to protect patients’ information from unauthorized access. Still, organizations may face other challenges, such as high costs associated with big data implementation. Lastly, the healthcare system is also fragmented, making sharing data with other parties challenging. Addressing these problems can lead to rapid adoption of big data and improved engagement and patients’ experience in healthcare.
References
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Abouelmehdi, K., Beni-Hessane, A., & Khaloufi, H. (2018). Big healthcare data: Preserving security and privacy. Journal of Big Data, 5(1). Web.
Adibuzzaman, M., DeLaurentis, P., Hill, J., & Benneyworth, B. D. (2018). Big data in healthcare, the promises, challenges and opportunities from a research perspective: A case study with a model database. AMIA Symposium Proceedings, 2017, 384–392.
Dash, S., Shakyawar, S., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: Management, analysis and future prospects. Journal of Big Data, 6(1). Web.
Jia, Q., Guo, Y., Wang, G., & Barnes, S. J. (2020). Big data analytics in the fight against major public health incidents (including Covid-19): A conceptual framework. International Journal of Environmental Research and Public Health, 17(17), 6161. Web.