Applications of Big Data Analytics

Applications of Big Data Analytics

Big Data could be defined as the latest form of technologies, which has the major form of potential for the fast pace of change within the working of organizations. This new kind of technology is also used within the healthcare industry for enhancing the experience of the customers and thus would be able transform the models of business. The industry of healthcare handles huge amounts of data and is majorly driven by various form of regulatory based requirements, compliance, maintenance of records and various kinds of similar based aspects related to the care of patients (Raghupathi and Raghupathi 2014). The primary goal behind the implementation of Big Data analytics within the healthcare industry is for the introduction of medical practitioners and analysts within Healthcare. They would be highly required for analyzing of the improved form of advancements within the computing field, which would be highly used for handling data and thus be able to make several kind of inferences based on heterogeneous and large forms of data within the healthcare industry (Groves et al. 2013).

Improved Prediction of the Health of Patient – Big Data could be used for the predicting the health of the patient based on various forms of analytics. The data collected from various forms of the healthcare queries based on the data of the patients would be collected from the hospital records. This data would be highly required for improving the mode of improvement of healthcare and the medication facilities for the patients. Predictive analytics could be defined as the biggest form of trend within the business (Murdoch and Detsky 2013). The primary goal of intelligence within healthcare industry would be helpful to doctors in order to make data-driven form of decisions within instant timeframe. These improved form of decisions would be useful in such cases where the patients would have various forms of complicated medical based histories and who would be suffering from different vital medical conditions.

Electronic Health Records (EHR) – This is regarded as the most widespread form of adoption of the technology within healthcare. Each patient would have their own digital health records that would include the medical history of the patient, results based on laboratory tests conducted and demographics. These records would be shared with the help of secure form of information systems, which would be available from both the private and the public sector (Bates et al. 2014).
Alerting on Real-Time – Big Data analytics could be useful for alerting the patients and doctors about the various forms of information related to their healthcare. The various healthcare institutions and healthcare experts would make proper use of different sophisticated tools in order to keep a track of the vast streams of data (Kayyali, Knott and Van Kuiken 2013).

Informed Planning of Strategy – The use of big data within healthcare would be helpful for improve the various forms of planning of strategy in order to gain better form of insights. The data collected could be useful for preparing heat maps that would be targeted for addressing several forms of issues that would include chronic diseases and growth of population (Auffray et al. 2016).

Improved Prediction of the Health of Patient

Potential Benefits and Impacts of using Big Data analytics in Epworth Healthcare

The potential benefits and the various impacts within the healthcare industry are:

Reduction of Errors – There have been several forms of errors based on humans. Wrong form of medications have been prescribed or might have been dispatched to the patients. The use of big data analytics within healthcare would be helpful for keeping the track of the records of the patients. The use of big data analytics could be useful for collaborating with the data, which would be helpful for reducing the errors within the internal systems (O’Driscoll, Daugelaite and Sleator 2013).
Personalized form of Medicines – Big Data analytics could be helpful for generating the genetic blueprint of a person and would be able to gain the information of the lifestyle of the patient. This would be helpful for prediction of the ailment within the patients and thus would help in identifying the best form of medical treatment. Big Data could be also used for tracking the movement of population (Costa 2014). The actionable insights that would be acquired from the use of big data would be able to gain a fair form of idea as to determine where the treatment centers could be placed for the betterment of the patient.
Real-Time Care – The use of big data analytics would be able to provide proactive form of care to the patients. The patients would be able to get constant suggestions from the doctors. Various algorithms based on machine learning could be used for triggering real-time alerts based on their devices. The digital generated reports would be submitted to the mobile based applications from where the patients would be able to check their records and thus submit the details to the doctors. This would be impactful for ensuring a persistent and convenient mode of healthcare facility (Roski, Bo-Linn and Andrews 2014).
Savings of Costs – One of the major methods within the facility of medication within healthcare is the problem based on staff engagement. The Epworth Healthcare organizations requires a wide engagement of doctors and nurses in order to take care of the patients in an efficient manner. However with the impact of big data within healthcare sector, it would be very much helpful for putting the responsibility on the big data analytics (Kellermann and Jones 2013). The implementation of predictive mode of analytics with Big Data could be regarded as an important tool. The Rate of Investment would be reduced to drastic amount and it could be utilized to a maximum rate.

Supply Expenditure – The increasing number of patients and hospitals have also raised the issue for supplying tools, which would be highly needed for the purpose of medication. There is a proper form of budget within every aspect of the healthcare industry. Over-stocking is one of the common form of occurrence in which the supplies and tools are purchased in excess amount. With the impact of Big Data analytics, predictions could be made based on the records of the past and several form of amendments could be made based on the records (Hilbert 2016). The predictive form of analytics could be helpful for enabling the hospitals in order to save lots of money as they would be able to forecast the demand for supplying the proper medicines in an accurate manner.

Electronic Health Records (EHR)

Potential Risks associated with Big Data Analytics in Epworth Healthcare

Although Big Data analytics has a huge amount of potential for creating various forms of improvements within the sector of Epworth healthcare, yet they are faced with various forms of challenges and risks. These risks are an important factor, which should be taken into high form of consideration in order to mitigate them and thus ensure a healthy mode of communication and treatment for the patients. The nature of big data is a bit complex in its form. This would require high level of vigilance into the various prospects of the risks that would be identified within the use of Big Data analytics within the healthcare sector (Patil and Seshadri 2014). The various aspects, which should be highly considered are the factors based on collection of raw data, storing them into high level of secure platforms, analyze them properly and thus finally present them to the staff members, business partners of healthcare and patients that would be able to increase the level of satisfaction to the patients. Some of the major forms of risks that are mainly associated within the healthcare sector are:

Capturing of Data – The data that are captured by the healthcare organizations comes from various kinds of sources. The data should be clean, accurate and precise as this would help in the ease of processing of the data by the big data analysts. The healthcare providers who mainly support these form of facilities should be able to prioritize the valuable types of data based on the specific requirements of the patients (Agarwal and Dhar 2014).

Storing of Data – The clinicians within the Epworth healthcare industry store their data within the computers. This has raised various forms of concerns in relation with the security of the data within the devices, performance issues and costs of maintenance of the devices. As the huge volume of data based on healthcare is growing exponentially, some of the providers of these services would not be able to manage the various incurred costs to the organization and the potential impacts to the data centers. Cloud based storage is a growing sector within the aspect of Big Data. The collected data should be stored in a secure platform in order to reduce the various kinds of costs and reliability (Hashem 2015).

Security of Data – The high form of security within the data stored in Big Data platforms is a major form of concern. There are a wide number of security aspects, which should be highly considered. There are various instances of attacks within the system in the recent past. High level of breach in the data of the organization, hacking and other kinds of attacks majorly threaten the healthcare industry. In order to safeguard the use of big data within the organization, there should be proper form of measures, which would be helpful for protecting the data of the patients. The healthcare organizations should constantly ensure the update of protocols based on security of data. They should be able to constantly review the high-value assets of data in order to prevent the data from getting hacked from malicious parties (Gandomi and Haider 2015).

Alerting on Real-Time

Conclusion

Based on the discussion from the above report, it could be concluded that the use of Big Data analytics within the Epworth healthcare sector would be a major help for the improvement of processes within the system. Big Data has a huge form of potential for transforming the sector of healthcare by improvising the traditional performance within the sector into a more modern form. The impact of Big Data within healthcare would be able to remove the traditional methods that were based on people who would update the works within the sector. This report discusses about the various major impacts that would occur with the implementation of Big Data within the sector. There are a wide range of applications of Big Data within the concerned industry of Epworth healthcare. These aspects should be highly considered by the industry in order to improve the operational functions within the industry and thus help the doctors and patients with the improved methods of medication. Epworth healthcare should also considered the various kinds of risks that might get incurred within the systems with the implementation of Big Data. Hence they should be proactive in considering the impacts within the system in order to provide better form of efficiency within the systems.

References

Agarwal, R. and Dhar, V., 2014. Big data, data science, and analytics: The opportunity and challenge for IS research.

Auffray, C., Balling, R., Barroso, I., Bencze, L., Benson, M., Bergeron, J., Bernal-Delgado, E., Blomberg, N., Bock, C., Conesa, A. and Del Signore, S., 2016. Making sense of big data in health research: towards an EU action plan. Genome medicine, 8(1), p.71.

Bates, D.W., Saria, S., Ohno-Machado, L., Shah, A. and Escobar, G., 2014. Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), pp.1123-1131.

Costa, F.F., 2014. Big data in biomedicine. Drug discovery today, 19(4), pp.433-440.

Gandomi, A. and Haider, M., 2015. Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), pp.137-144.

Groves, P., Kayyali, B., Knott, D. and Van Kuiken, S., 2013. The ‘big data’revolution in healthcare. McKinsey Quarterly, 2(3).

Hashem, I.A.T., Yaqoob, I., Anuar, N.B., Mokhtar, S., Gani, A. and Khan, S.U., 2015. The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, pp.98-115.

Hilbert, M., 2016. Big data for development: A review of promises and challenges. Development Policy Review, 34(1), pp.135-174.

Kayyali, B., Knott, D. and Van Kuiken, S., 2013. The big-data revolution in US health care: Accelerating value and innovation. Mc Kinsey & Company, 2(8), pp.1-13.
Kellermann, A.L. and Jones, S.S., 2013. What it will take to achieve the as-yet-unfulfilled promises of health information technology. Health affairs, 32(1), pp.63-68.

Murdoch, T.B. and Detsky, A.S., 2013. The inevitable application of big data to health care. Jama, 309(13), pp.1351-1352.

O’Driscoll, A., Daugelaite, J. and Sleator, R.D., 2013. ‘Big data’, Hadoop and cloud computing in genomics. Journal of biomedical informatics, 46(5), pp.774-781.
Patil, H.K. and Seshadri, R., 2014, June. Big data security and privacy issues in healthcare. In Big Data (BigData Congress), 2014 IEEE International Congress on (pp. 762-765). IEEE.

Raghupathi, W. and Raghupathi, V., 2014. Big data analytics in healthcare: promise and potential. Health information science and systems, 2(1), p.3.

Roski, J., Bo-Linn, G.W. and Andrews, T.A., 2014. Creating value in health care through big data: opportunities and policy implications. Health affairs, 33(7), pp.1115-1122.