Big data can be described as a large and complex set of data that requires specialized approaches to use effectively to uncover hidden insights and help solve problems that may arise in healthcare. Big data in healthcare focuses on the patient’s data to improve their clinical experience, use operational data to maximize workflow, and use financial data to streamline revenue (Agarwal, Gao, DesRoches & Jha, 2010). Nurse executives in the healthcare system need to be diverse thinkers on manipulating vast amounts of data to shape and reshape the system (Gefen & Ragowsky, 2005). Critical analysis and synthesis of the data are done to develop conclusions on how to plan and run the organization. Although the assessment of big data is vital, it bears benefits as well as detriments.
The main challenges that big data has on healthcare are increased healthcare data and growing costs. Health records moved from manual to digital, which increased the volume of the data made available. Health care data is not limited to what the healthcare providers present but includes data from the internet-of-things devices such as wearable devices, smartphone applications, and increased social media traffic. This extensive collection of data from these sources means a large data deposit that needs to be analyzed in the healthcare system (Bryant, Katz & Lazowska, 2008). Large amounts of input suggest that processing this data to give meaningful output requires high healthcare investments to allow for such analysis to be made by trained clinical staff. Big data needs to be sorted to develop conclusions on how to run the organization to achieve quality and enhanced information.
On the other hand, big data ensures effectiveness and personalized care with complete records of the patients. The data collected from the various health care platforms brings about more detailed information about a patient’s health. Although the information contained might seem bulky, once it has been sorted and organized, it provides a perspective on efficiency in healthcare services and neat patient information storage. Big data makes it possible for clinical systems to provide quality care to patients (Goh, Gao & Agarwal, 2011). Some patient records bring to focus those who have a risk of chronic illnesses. Hospitals need to identify such cases early and develop treatment, ensuring that the patient’s condition is mitigated before getting worse.
Challenges experienced in healthcare systems need to be solved for big data to become a valuable healthcare system tool. Getting rid of medication errors is one way to begin. A significant cause of health problems often leading to death is giving wrong medications to patients. Implementing high-end computing tools in the healthcare system is essential to access and process the collected data (Goh et al., 2011). As much as a vast amount of data is available, it is imperative to avoid making errors. The records compiled should be analyzed for proper medication to be administered. In the case of an already made error, the system should highlight this to the health personnel.
Digitized data means that a significant concern is it being compromised through security breaches. Healthcare systems need to be safe and secure from hacking, malicious software, and data theft. Healthcare stores sensitive and confidential data about the patients and clinical staff in the records, making these systems vulnerable and at risk of attacks. Therefore, safeguarding healthcare systems need to be prioritized to protect the stored data.
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