In health informatics, Big data is required for understanding the outcome of any diseases which is epidemic in nature (Andreu-Perez et al., 2015). It ultimately helps in improving the method of treatment and quality of life of many people. This particular technology is helpful in prevention of premature deaths and the development of diseases. Big data also aim to provide various kind of information which is all related to disease and warning sign for its treatment. It will help in preventing co-morbidities which will help in assisting the government for saving cost related to medical treatment (Raghupathi & Raghupathi, 2014). The as big data technology aims to provide huge amount of data so it can be beneficial for detection and diagnosis of diseases. Big data have exceeded both the capacity and capabilities of storage and analytical system. This particular technology demand for some of new problem-solving based approaches (Agarwal & Dhar, 2014). With the help of power of computing and development of database technologies, wireless data and mobility. It is possible for bringing together both big data in some of most profitable way. Big data solution is considered to be cost effective which can easily overcome large number of challenges which is encountered due to fast growing volume of data (Zhang et al., 2017). Apart from this, it emphasizes on the potential analytical value. Both innovation and opportunity can be easily applied to for identifying various kind of opportunities along with improving the future.
In the coming pages of the report, an idea has been provided regarding importance of big data in healthcare domain. After that, an overview has been provided on research problem. Various application of big data technology in healthcare has been discussed in details. An idea has been provided regarding issues in research and various challenges associated with it. All the related work with respect to big data technology has been discussed in details. The last section of the report provides a list of recommendation for big data technology in healthcare.
Big data technology is shaping the whole healthcare industry which ranges from medical research to patient wellness (Wang, Kung & Byrd, 2018). The new era of data from electronic medical records (EMR), wearable offers comes up with chance to make various patient more preventive and predictive in nature. Free data from silos is considered as one of the one biggest issues is free data which tends to remain. An issue which mainly tends to affect both patient care and healthcare (Andreu-Perez et al., 2015). The first step which should be taken is EMR interoperability so that various patient can easily share information with various providers. With the help of portable records patient will not only lead to major kind of improvement which is seen as efficiency of whole healthcare system.
The second challenge which is faced is security of data. Various hackers around the globe have made healthcare as one of the major targets (Archenaa & Anita, 2015). It is mainly seen for the development of some sensitive information. In order for individual to feel comfortable which is related to sharing of data (Archenaa & Anita, 2015). Almost every person in the healthcare ecosystem should work for protecting the data and keeping various information private. More investment is required in some of the back end structure but it is all about question of power of computing.
The third issue which is encountered is the method of data sharing which is used for the purpose of research. The new ideas in big data technology are changing the whole healthcare industry (Jagadish et al. 2014). Various institutes around the globe are lacking some of the technological infrastructures. Scientist in both academic and industry should change the attitudes of various data (Belle et al., 2015). Various research and industry should change some of the attitudes with respect to data.
Big data can easily reduce the bias or regency effect bias. Regency bias can take place when various events weigh more heavily than earlier events (Belle et al. 2015). It can easily lead to much earlier kind of events which is needed for improving the overall health of the organization. Any kind of error or troubleshoot in the organization can be easily identified by the help of immediate step (Bello-Orgaz, Jung & Camacho, 2016). Various operational problem needs to be tackled which may lead to some kind of incorrect decisions.
With the help of big data technology real-time information can be easily incorporated in big data. Real-time of big data comes up with various kind of benefits. Any kind of error can be easily incorporated and various kind of problems related to operation can be easily tackled (Lazer et al., 2014). It can be considered to be time-saving, cost and increasing the overall productivity. The given service can be easily tackled as it aims to provide various kind of real-time services. It ultimately aims to provide various kind of information on the patients and at the similar instance they can easily tackle any kind of medical issues without any kind of intervention (Chen et al., 2016).
In the domain of healthcare, big data technology is used for carrying out some kind of predictive analysis (Manogaran et al., 2018). It is mainly used for identifying various kind of medical problems before it is becoming a problem which cannot be managed. Various healthcare professionals are able to reduce risk and overcome the problem with various information which has been derived from big data technology. Apart from all this, big data technology is considered to be helpful in analyzing various fraud in the domain of healthcare mainly the insurance claims. Fraudulent and inconsistency in the firm can be easily tackled. With the help of big data technology inconsistency and false claims can be easily tackled (Chen, Mao & Liu, 2014). It will ultimately help the insurance company to prevent and tackle any kind of loss.
Healthcare can be also benefitted with the help of big data technology. It can be easily done with the help of data management, EMR and lastly data analysis (Viceconti, Hunter & Hose, 2015). Big data is considered to be helpful in analyzing the right amount of population and target group. Big data technology comes up with diverse group of population and certain group can be easily identified for risk assessment and its screening. The current big data technology will ultimately lead to development or any kind of modification of the given program and its intervention of the target group for large number health issues. It will ultimately help in carrying out large number of clinical trials which needs to initiated on regular basis. Big data technology is very much helpful in providing a clear picture regarding the type of population along with large number of medical problem (Costa, 2014). The distributed pattern or information of diseases is very much helpful in analyzing quick development of program along with affected programs as soon as possible.
Growth of data in pharmaceutical industry is mainly derived from retailers, patient and research and development (Luo et al. 2016). Big data technology helps the pharmaceutical industry which helps in analyzing large number of threats and effective drugs for it. It will be very much helpful in delivering quick access.
Big data is changing the whole industry especially the method in which various decision is taken into account (Elhoseny et al., 2018). Big data technology has already exceeded both the capacity and capability of given conventional storage and reporting of analytical system. It requires new kind of problem-solving approach (Chen et al. 2016). With the development of powerful computing and advanced database technology, mobility and social networking. It has now become very much easy for analyzing various process which can make use of big data technology in some of the convenient ways.
Big data technology comes up with proper kind attempt which is considered to be cost-effective in nature (Kambatla et al. 2014). It is needed for solving large number challenges for growth and fast growing volume of data. It aims in realizing the potential analytical values. The fact should be taken into account that trend in analytics have will be helpful in figuring out the large number of things which has happened. It mainly aims to analyzing the root cause and carrying out predictive analytics (Gudivada, Baeza-Yates & Raghavan, 2015). It mainly helps in analyzing what can happen in the near future. Apart from this, various opportunities and future improvement have been discussed in details.
Big data can be defined as a huge amount of data when it aims to meet three most vital criteria that are volume, variety and lastly velocity. Volume emphasizes on the point that a lot of that is terabyte or even more than that (Lee, Kao & Yang, 2014). It is considered to be most immediate kind of challenge for big data. It comes up with scalable support which is needed for providing support for complex and its distribution in various kind of data source. While various firms round the globe comes with proper kind of capacity which is needed for storing large volume of given data (Hashem et al., 2016). The challenge mainly emphasizes for identification, location and analyzing the various pieces of data.
Big data can be stated as the summation of various type of data which can be both structured and unstructured in nature. It is inclusive of both multimedia, social media, blog and lastly server and lastly various kind of financial transaction. Both the given technology that is GPS and RFID tracking information, audio and video streams and lastly web content (Chen, Mao & Liu, 2014). Some kind of standard and technology mainly tend to exists for dealing with data which comes up with large volume of data. It can be needed for dealing with large volume of structured data. It can easily become very much significant in nature and the given process can come up with large variable amount of data and can easily turn data into proper action form. It can easily analyze the potential effect of big data technology which is known to be an effective analytics. It will ultimately help in making better kind of decision (Hashem et al., 2015). It will help in providing large number of opportunities which cannot exists.
There are large number of challenges in big data which can be seen in the domain of data protection, collection and lastly sharing of both health and data usage (Hilbert, 2016). Big data analytics can easily make use of some of sophisticated kind of technology which comes up with potential to make use sophisticated technologies (Agarwal & Dhar, 2014). It is very much helpful in transforming the given data and make some of informed decision. Various kinds issues like privacy, security and lastly proper standard of governance which needs to be taken into account.
Information related to cancer treatment can be easily incorporated in the given big data technology (Jagadish et al., 2014). It ultimately helps in providing a proper kind of overview and best kind of treatment which is related to cancer especially the nanotechnology. It is mainly helpful in drug delivery which is seen in cancer treatment (Weber, Mandl & Kohane, 2014). Apart from all these, there can be adverse effect which can be used for analyzing large number of things.
Apart from all these, health informatics and personal information which is seen or encountered in various health industry (Kambatla et al., 2014). It can be stolen or hacked and another kind of big data which is seen in most commercial technologies like telecommunication and banking finance. Before the implementation of big data technology, it is important to ensure the administration, security and privacy of big data which needs to be protected. Protection in health informatics can be easily sent through transmission security, multilayer authentication. It can be done with the help of anti-virus software, data encryption, firewalls which is considered to be important (Costa, 2014). With the passage of time data is becoming both global and vital and apart from this is became very much complicated in nature. It can easily have serious kind of effect on various kind of standards like language and its terminology. Accessibility in healthcare needs to be properly reviewed and checked or monitored (Kim, Trimi & Chung, 2014).
Big data is considered to be a massive one which comes up with less structured and is heterogeneous in nature. There is continuous need for identification and classification of given data so that it can be effectively use (Bello-Orgaz, Jung & Camacho, 2016). In many cases, it is seen for laborious search and look for specific data in the given big data technology. Big data is needed in the contextualization and pulling up together so that it can easily become very much relevant to the given data (Lazer et al., 2014).
Although, in cases, it is seen that big data technology can be considered to be vital for carrying out simulation and modeling (Elhoseny et al., 2018). It can be easily used for identifying and pooling the given structure and so it can be considered to be bit challenging one for analyzing and understanding the given output. It is also required for extraction some of the specific information or related data (Wamba et al. 2015).
Cloud storage can be used at times when there is need of some vital data. The whole system is generally designed in given cloud platform. In cloud storage, there is need for having proper kind of information which is required for uploading at the same time (Lee, Kao & Yang, 2014). The whole storage part is mainly inclusive of data upload at the given time. The storage part is mainly inclusive of graphics like X-ray, MRI and many other (Riggins & Wamba, 2015). The given system should be designed in such a way that it can easily generate proper kind of graphic presentation which is provided in the form of given data. Various kind of clinics are needed for visualization and having quick understanding with respect to data which is provided.
Miscommunication or any kind of gap is considered to be one biggest issue which encountered in big data technology (Lee et al., 2015). User can easily have an understanding with respect to data which is being provided. Health data from various clinics and hospital are required to be pooled up in together way (Hashem et al., 2015). With the help of big data technology various kind of forthcoming technology can be easily tackled. Big data can impose huge amount of risk and undermine various kind of doctors. Various patient round the globe will tend to rely on this particular technology instead of consulting to various healthcare practitioners (Lee et al., 2015).
Various hospitals around the globe have become unsuccessful with big data analytics project in the past. In many cases, it is seen that cost or delay can be one of the vital factors behind the major failure.
Using one size fit all data: With increase in drive there can be certain number of measures in the progress of any project budget (Hilbert, 2016). Analytics is being currently used by sales and marketing, C-suite and average worker. Each of given user needs to have an idea regarding the analytics which is applied to certain number of skills and needs.
Making use of tool which are incompatible: Due to varying role in the organization, it is seen that organization can end in making use of certain number of tool. It is mainly used for tailoring each and individual department (Lupton, 2014). But in many cases the organization as a whole may fail to integrate it with the whole given picture. Identification of various kinds of issues of integration in analytics is considered to be biggest mistake.
Non-governing access to data: Hospital should not leave staff members for the full access to given data. It is mainly available to use for large number of users(Gudivada, Baeza-Yates & Raghavan, 2015). Data analytics cannot be fully implemented without the help of IT department without the help of business office.
Various research is focusing on certain number of attempts of various kind of healthcare which is required for handling large volume of data. It aims in data source to find out certain number of patterns and trends with it (Hashem et al., 2016). The architectural framework for healthcare system by make use of big data mainly comprises of various things like data source layer, platform layer and lastly transformation layer. The last layer in this particular architecture analytical layer. Data source mainly comprises of both internal and external source in healthcare which is found in the form of various formats (Kim, Trimi & Chung, 2014). Transformation layer mainly deals with various kind of operation like extraction, loading of data and lastly transformation(Luo, Gopukumar & Zhao, 2016). The layer of big data platform comes up with large number of technology Hadoop tool which is required for preforming a large number of operation.
Conclusion
Big data technology comes up with great potential which is required for the overall look of the healthcare industry. It aims in delivering a large number of features like discovery of drugs, personalization of patient care, efficient treatment and improving clinical outcomes and lastly patient safety management. There is large number of choices with respect to big data technology which ranges from onsite to cloud technology. On-site comes up with two kinds of option which can affect the big data technology. Cloud-based software technology that is software as a service can be considered to be helpful in reducing large number of barriers in the current big data technology. Google and Amazon have already implemented Map reduced based solution which is required for processing huge amount of datasets. It is done by making use of large number of data which is available in the computers. Open-source Hadoop is a well-known framework which is required by various organization like high-performance, scalability and relative cost of operation which is required for dealing with big data technology. Both training and professional service can be considered to be effectively deploying Hadoop solution by making use of open source framework. SaaS is considered to be as one of the most vital technology for democratizing the output of the given big data technology. SaaS-based control solution along with proper healthcare helps in controlling the given datasets of data along with exposing the given access through various kind of service. It can be considered to be helpful for eliminating some of aggregation and integration of various kind of challenges. Various kind of additional service help in overcoming large number of problems. Various kind of additional service which helps in analyzing subsets of data which is exposed access to various kind of service which can eliminate some of issues related to integration.
For the successful identification and implementation of big data solution. One can easily have an idea regarding the various kind of benefits which can be collected from it. Various healthcare industry round the globe needs to put all their time and resources which is required for visioning and its proper planning. It helps in providing the foundation which is needed for string kind of execution. Without proper kind of preparation, organization cannot realize the overall benefits of big data. It will be helpful in analyzing the various kind of risk which are left behind the competitors.
A list of recommendation can be given to various healthcare organization which is planning to implement big data technology are:
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