Why Is Big Data Analytics So Indispensable In Modern Healthcare System ?

Big Data Analytics in Healthcare

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The health care system is increasingly adopting the use of electronic health records. This has led to an increase in the quantity of clinical data that is available. As a result, big data has been adopted as a way of analyzing these large quantities of data. The main reason why big data technology has gained popularity is because it can be able to handle large volumes of data compared to the traditional methods(Wang et al., 2018). It also supports all kinds of data including the structured, semi-structured and unstructured. It also provides predictive model design and data mining tools and this makes the decision making process to be better. Big data framework allows for batch processing as well as stream processing of information. Batch processing makes the analysis of data within a specific period of time possible (Wang et al., 2018). On the other hand, stream processing is used for applications which need real-time feedback. Applications of big data analytics in health care leads to an improvement in the patient-based services as well as detection and control of spread of diseases. It also leads to new knowledge and intelligence as a result of the integration and analysis of data with different nature. Therefore, the use of big data analytics in the health sector has increased due to the need for improved medical services, faster analysis of information and accuracy, and cost reduction.

The main role of the health care sector is to ensure that the population remains healthy. Therefore, there is need for better service delivery at all times. Big data analytics have enhanced the ability to provide the services to the patients in a number of ways. First of all, it has positively resulted to better image processing (Wang &Hajli, 2017). This has enhanced the processes of diagnosis, therapy assessment and planning. Medical images present the data that is used in all these processes. As such, big data analytics provides for an efficient way of storing the information because it requires large storage capacities in the long run. The demand for accuracy also makes big data analytics an efficient tool to use in the analysis of information related to image processing.

Signal processing is another area in medicine that requires the use of big data analytics. This is because it results to production of large volumes of data which require being stored in high speeds from several monitors and different patients(Wang &Hajli, 2017). On the other hand, physiological signals also have a problem because of the spatiotemporal nature. This makes the analysis of such signals to be more meaningful when they are analyzed alongside the situational context. The current methods used in the analysis of disparate and continuous monitoring devices are unreliable(Wang &Hajli, 2017). Therefore, the use of data analytics helps in the improvement of the alarm systems because they provide true patient psychological condition from a broader and a comprehensive point of view.

Big data analytics has also led to faster processing of information. The use of EHR leads to production of high dimensional data which requires both long time to compute as well as accuracy. The use of simple tools leads to reduced accuracy of the overall data (Manogaran et al., 2017). Therefore, big data analytics allows for effective analysis of the information including classification of the right information to patient history as well as ensuring an uncorrupted medical record and hence resulting to effective treatment.

Available techniques to use in big data analytics such as filtering and wrapping also make analysis of data easier. The use of filtering methods leads to limiting of the number of features that are included in the analysis. Wrapper methods allow for the selection of the selection of features by evaluating the metrics such as cross-validation accuracy(Manogaran et al., 2017). Therefore, big data analytics make data analysis to be simpler and more accurate. Medication errors are caused by the human factors such as mixing of patient names. Therefore these errors may lead to negative impacts on the patients. As a way of improving on medication accuracy, the health sector uses big data analytics(Manogaran et al., 2017). This is because it aims at eliminating the medical mistakes which the employees are not able to avoid on their own. Big data makes it possible to avoid giving patients the wrong medication and hence treat them better.

Cost is one of the major problems experienced in health care. Costs range from storage as well as information finding. The cost of human genome sequencing has been reducing because of the adoption of big data analytics. This is because it has made it possible for the development of high throughput sequencing technology (Wang, Kung & Byrd, 2018). Adopting big data analytics also helps to decrease the hospital costs and wait times. This is because using the available information; it is possible to approximate the number of patients who are expected to visit the hospital at a particular time. It helps monitor patients at all times and hence avoid hospitalization. This can be done through the use of sensor devices which are applied on the patients so that they are monitored concerning their state and be able to help them.

To be able to reduce the costs in health care, it is important to identify the high cost patients. This can be done through identifying the case managers to give them effective care. However, the process of identifying the high-cost patients is expensive(Wang, Kung & Byrd, 2018). Therefore, it becomes necessary to identify the issues that affect the process. The first one is the determination of the approach that is used in predicting the high-cost patients. The second issue is the measurement sources which can be adopted and ensure that there is an improvement on predictions. These are aspects such as behavioral health and the socio-economic factors. The third issue is the determination of how the predictions can be made actionable. Lastly is to account for the cases of outcomes in predictive models which often come from low-risk groups. Therefore, all the information that is required as well as the analysis that needs to be conducted can be most effective using tools such as those found in big data analytics. As such, health care requires adapting to the new developments of this technology to lower the costs.

The cost of healthcare can also be reduced by predicting the frequency of high cost hospital readmissions. It is necessary that health care facilities use algorithms that can predict the likelihood of readmission. However, the values produced by predictive algorithms tend to be much similar. As such, there are four areas of the predictive algorithms that may require differentiators; the patient tailored intervention, patient monitoring after discharge, precise interventions to the patients, and monitoring some specific patterns after discharge (Groves et al., 2016). In this way, it is possible to identify whether the patients have complications after discharge and hence be able to reduce the false rate of readmissions.

The cost medical services can also be reduced by estimating the risks of complications that are experienced when the patient is presented to the hospital for the first time. This is a useful area because it leads to proper management of resources in the hospital. The number of patients in a hospital requires that right information is fed to the system. However, most importantly is to have a clear calculation of the risks that are probable as well as the data on the number of patients in and out of the hospital (Kruse et al., 2016). In most cases, this information is complicated because it involves data from various sources. As a result, applying big data analytics can ensure that this information is well classified to avoid complications with admissions and management of the hospital activities.

In conclusion, developments and adoption of big data analytics in the health care sector is due to the need to improve medical services, faster analysis as well as reducing the cost. Faster analysis is enabled by the use of tools which support analysis of different forms of data. It is also because big data analytics enables data of high capacity to be stored. Accurate information is obtained from accurate analysis hence reducing medical errors. It also helps in providing the right information hence the management of aspects such as readmissions and costs.