Table of Contents
Big data characteristics healthcare
Support of big data analytic in health care databases
Big data algorithmic techniques
Abstract
The concern directed to the healthcare is increasing on a daily basis because of the immense rise in population. The development technology and the strategies of minimizing the costs has facilitated the healthcare systems and firms to acquire advance equipment and technologies. The healthcare facilities can gather and store enormous quantities of data which is related with the patients such as medicine, diagnosis and diseases. Moreover, there is a frequent growth of the databases. The big data is a progressive sector which has the ability of handling enormous databases to avail the needed knowledge to the appropriate user. Big data analytics and approaches aids in storage and evaluation of the healthcare data which is presented in multiple formats. This research paper is a systematic review of significance of big data on the healthcare sector.
Introduction
The healthcare system is one of the largest industries in the global economy. The sector is one of the most complex sectors since it is characterized by patients who constantly demand improved care management services from the practitioners. There is a need for a quick response which dictates the specialists to seek more efficient solutions and technologies which are constantly improvising the service dispensation and research (Ebeling, 2016). The bid data analytics in conjunction with industry analytics have made a significant mark in the healthcare sector.
Get Help With Your Essay
If you need assistance with writing your essay, our professional essay writing service is here to help!
The next advances in information technology have led to a smooth creation of data. The healthcare industry produces large amounts of data by maintaining the patient’s records. As opposes to storing the data in a printed method, the industry has adopted the modernized way of digitizing the data which is limitless. The digitized data is utilized to improve the delivery quality and minimize the time and costs to achieve the objective of quality delivery of products and services. Moreover, big data enables the industry to provide advanced personalized care which improves the outcomes and minimize unnecessary costs. Therefore, the description of big data in healthcare is concerned with enormous electronic health datasets which are complex and hard to interpreted using the traditional data management systems and frameworks.
Discussion
The big data in the sector is comprised of the clinical data, the image scans, laboratory inventories, social media posts such as Facebook and twitter, medical journals and the medical journals (Langkafel, 2015). Thus, there are multiple datasets which are available for the data scientists to discover the hidden patterns within the industry. Multiple data analytics approaches such as data mining and artificial intelligence can be applied to evaluate and analyze the data. The approaches can be utilized to understand the anomalies which are present through the integration of multiple amounts of data from various datasets.
High-risk patient health care
There are various cases of healthcare costs and complications which frequently arise when various patients seek emergency care. Even though there is an emergence of higher costs, the patients sometimes don’t benefit from better outcomes. Thus, there is a necessity to implement a change in the sector to revolutionize the manner through which the hospitals work. When each record is digitized, the patient patterns will be easily identified effectively with simplicity. Emergency Visits (ER) have been minimized in the healthcare sector due to the help of predictive analytics. It has been attained through the identification of the patients who have resorted to hospital support to crisis situations frequently through the identification of their chronic health issues through availing a corrective treatment schedule. Moreover, big data analytics aid in minimizing emergency scenarios.
Cost reduction
Multiple clinics and medical organizations are faced with high levels of financial wastes. The wastages arise because of ineffective financial management the major cause of the in-house losses is because of under or overbooking by the staff. Predictive analysis solves financial management problems through effective staff allocation with easier admission rate prediction.
Hospital investments will be optimized through the utilization of big data to minimize the investment rate where possible. Moreover, the insurance companies will gain a competitive advantage through backing the health trackers and wearable to ensure that the patients don’t overstay in the hospital premises. Also, the patients will gain through reduced waiting times by gaining immediate access to the staff beds. Therefore, big data analysis will minimize the issues related to staffing requirements and bed shortages.
Patient health tracking
It is recommended that potential health problems must be identified earlier before developing into aggravating issues. Inadequate data will make the system to identify and detect the scenarios which might have been prevented easily if huge amounts of data were present. Patient health is a strong utility which has emanated from big data and the internet of things
Find Out How UKEssays.com Can Help You!
Our academic experts are ready and waiting to assist with any writing project you may have. From simple essay plans, through to full dissertations, you can guarantee we have a service perfectly matched to your needs.
View our academic writing services
Big data enables various methods of analysis to track the patient’s details and statistics. These statistics are comprised of heart rates, sleep habits, glucose levels, and pressure. Modern medical innovations permit medical specialists to monitor various aspects of the patient’s body. The monitored results facilitate the healthcare equipment to try and minimize the influx of the patients to the hospitals.
Further, tracking the patients’ health status earlier will curb the development of particular conditions and ailments. This will enhance optimal care within the appropriate time. There are multiple unpleasant scenarios which can be prevented which forms one of the objectives of the entire healthcare sector (Marconi & Lehmann, 2014) . Therefore, it is crucial to revolutionizing the way tasks are performed in the system to facilitate and improve healthcare through the utilization of big data to discover hidden patterns.
Justification
Advancement in the system
Analysis of big data will propel significant advancements to the system through the aid of technological innovations. The ability to access multiple data points within seconds will facilitate a swift discovery of effective solutions. The conditions and diseases will be easily treatable through achieving personalized solutions for the lesser complicated health issues in the world. It is crucial for the sector to attain major advancements which are worth being evaluated and design frameworks to ensure that further studies have been achieved.
Enhanced patient engagement
Through big data and analytics, there has been an improved patient engagement. Creation of mass awareness for the consumers to wear wearable and other forms of tracking devices will definitely present positive alteration in the healthcare sector. It will lead to a decline in emergency cases within society. The physician responsibilities will reduce since the patients have a deeper understanding of the importance of the devices and the engagement will be facilitated through big data initiatives
Big data characteristics healthcare
Recently, there has been a tremendous growth in the data produced, gathered and shared by various organizations. Enormous data cannot be managed and processed using conventional techniques. The big data is characterized by 4 V’s. These are velocity, volume, veracity, and variety. These characteristics are relevant to the health sector as explained below:
Velocity
The term describes the pace through which the large amounts of data are formulated, gathered and stored for analysis. The source of such kind of data include photos, social media, videos, and emails. Moreover, evaluation is a critical element when it comes to the velocity characteristic. Instant updating on the websites and information such as booking, credit cards must be conducted with the same speed as the ones which are generated in the real world.
As explained above, healthcare also generates large amounts of data in an electronic format through multiple sources such as patient records, social media handles for the health organizations and medical images. This kind of data is collected is in various formats such as the structured, unstructured and semi-structured which presents a difficult task for the traditional database management tools to analyses. Therefore, big data is an essential factor to aid in enhancing the clinical support system.
Volume
It is concerned with the significant quantity of the data which is generated every second through the data sources. The traditional database technology and approaches can’t store or evaluate such continuous churned data volume. Therefore, other techniques will be utilized to divide the voluminous data into various to store in different locations, perform various arithmetic functions and to evaluate it through a particular software. Just like the growth in volume, the volume of health information increases every second.
Variety
The ever-changing technology has encouraged the users to come up with various kinds of data. The data is in the following forms: structured, semi-structured and unstructured types. Big data is the emerging technology which permits both structured and unstructured data which is collected by various devices within the internet of things. In the healthcare sector, most of the data is in the structured form like the prescription and the names of the tablets. There are other forms of unstructured data such as pictures from the scanners, report and the graphical images. This insinuates that there is a variety of data from multiple sources. Such data is gathered and maintained in computer systems to aid in further processing and assessment. The big data analytics avails a swift process which aids the scholars and experts with the necessary information for comparison with the past information to discover the changes in the field (Natarajan, Frenzel, & Smaltz, 2017).
Veracity
Gathering of data from multiple sources isn’t useful unless it is true. A higher percentage of the data which s gathered from the internet might not be true. The false information might lead to wrong findings after the analysis process. Moreover, the results will mislead society. Veracity is concerned with the quality and faith in the information which is gathered, stored and published. Generally, the information posted on social media might not true in accordance with the content and its respective accuracy. The data must be true and accurate in conjunction with frequent updating with reference to time since the user need the most recent information to utilize in the process of decision making.
Support of big data analytic in health care databases
There are multiple advantages in the implementation of big data analytics in the healthcare database. Information the healthcare system sources its information from multiple sources which is comprised of various types of data assessment of the diverse and complex data is difficult during the process of database management and tools and approaches. Since there is a need for fast development in computing technology, big data presents the optimum solution to efficiently use the significant value of the accumulated data. The big data is the most appropriate answer to the healthcare industry and facilitates increased quality of human life. The major objective of big data is not only to gain more profits but also minimize the time and wastages accruing from time and other wastages and predicting of the disease outbreaks and their cure. This, in turn, aids the society to lead a quality life.
Figure 1the conceptual; model to utilize big data analytics in healthcare system
Since the population is increasing each day, there is a need for a timely medical treatment which is significant and forms the major source to attain these requirements of data. The data obtained collected from various medical institutions is essential to aid the patients by presenting an indication as a warning message in case the patient has a disease in the early stages of the disease. Moreover, it will inform the patient on the severity of the disease to take preventive measures will be cheaper as compared to the diagnosis at the last stage which will require higher costs in the treatment
Due to development of mobile phones and advanced quality with useful applications, the users can access the information through queries regarding their personal health, minor health tips, and specialized firms for a specific type of ailments. Such kind of applications will aid the public to improve their health and ways of living to attain the optimum health standards. It is not only the queries that the public will gain but also they will share and post their health experiences through the online platforms. Nowadays, submission of online data to any given health firm is readily available with appealing and interactive formats which are easily predicted. Access to enormous amounts of information on the internet aids to create mass awareness to the public through the identification of their health problems through online discussions or perusing the genera; preventive actions and the disease causes. The wide range of information which is gathered from multiple sources of the healthcare systems must be compiled and designed in an all-inclusive way such that it can be elaborated as a complete package of healthcare to the novice users of the system. Therefore, the big data has the potential of presenting and visualizing meaningful patterns to the huge healthcare data.
Security
Security is a crucial factor in big data. This is because it contains millions of personal information about people and their respective history. Thus, it is crucial to ensure that proper measures have been implemented on the database containing the data. The databases must be protected from any incidence of cyber theft, phishing and data breaches where data is stolen and sold afterward for huge sums of money.
The big data in the healthcare sector is faced with a myriad of challenges. There are incidences of the potential presence of untrusted mappers. After data collection, the data is subjected to parallel processing. One of the main processes utilized here is the Map-Reduce framework. During the data splitting process into various bulks, the mapper is utilized to process and allocate the splits into specific storage options. If unauthorized party access the mappers’ code, then they can alter the settings of the existing mapper to attach malicious codes. Through such occurrence, the data processing process might be effectively ruined as the cybercriminals will utilize the mappers to produce inadequate value pairs for malicious gains. The major problem is that attaining such access won’t be hard because the big data technologies don’t avail an extra layer which is designed to curb any incidences of data breaches. It tends to depend on the perimeter security systems.
Even though encryption is a better-known technique for protecting confidential data, there are some issues with big data security. Despite the efforts made to encrypt the big data, this type of security measure is normally ignored. The sensitive data is basically stored in the cloud without any form of encryption. The major reason for this vulnerability is due to constant encryptions and decryptions of the enormous data chunks which reduce the speed of the processes on the big data.
Further, the big data in the health sector is affected by the possibility of sensitive data mining. Perimeter-based security is generally utilized for big data protection. This insinuates that each point within the data is strongly secured. Lack of control in the sector may allow corrupt information technology officials and specialists to mine data which is unprotected and sell it for malicious benefits. Such occurrences will result in enormous losses in case such kind of information is connected with a newer product and service launch. Therefore, it is crucial that the healthcare sector to ensure that the data is appropriately protected through additions of extra perimeter and the security of the system will greatly improve from anonymization.
Big data algorithmic techniques
The big data is an approach of winnowing the hidden information which isn’t known but crucial from huge amounts of data. The information attained from the patterns and visualizations can be utilized to predict the future scenarios as an aid to the policy formulation process. Significant knowledge can be attained through the application of data mining applications in healthcare applications such as decision support systems. The data mining procedures permits various procedures to transform the bundles of data into valuable data to be utilized as decision support.
The big data mining processes in healthcare are concerned with learning models which are applied to predict the diseases of the patients. For instance, data mining will aid the healthcare insurance firms to identify hypocrisy and misuse, aid the doctors to gain insight on the efficient treatment and appropriate practices, the patients gain improved and more economical healthcare services and health institutions can formulate decisions through the Customer Relationship Management (Wang, 2014)
Figure 2: big data in healthcare analysis process
There are multiple data mining algorithms. They are comprised of clustering, regression, statistical learning and classification. Big data analytics involves multiple approaches. One of the most significant categories is the predictive analytics which is comprised of the multiple statistical approaches from modeling, machine learning and data mining which evaluate the emergent and historical data to present a prediction concerning the future. In the healthcare sector, there are various predictive approaches which are utilized to ascertain whether a person is at risk for readmission or on a strict recession. Therefore, it is significant to understand machine learning since it is widely utilized in the predictive assessment.
Conclusion
The healthcare sector is one of the most complex sectors since it is characterized by patients who constantly demand improved care management services from the practitioners. The healthcare industry produces large amounts of data by maintaining the patient’s records. Big data enables the industry to provide advanced personalized care which improves the outcomes and minimize unnecessary costs. Multiple data analytics approaches such as data mining and artificial intelligence can be applied to evaluate and analyze the data. There is a necessity to implement a change in the sector to revolutionize the manner through which the hospitals work. When each record is digitized, the patient patterns will be easily identified effectively with simplicity.
It is recommended that potential health problems must be identified earlier before developing into aggravating issues. Big data enables various methods of analysis to track the patient’s details and statistics. Tracking the patients’ health status earlier will curb the development of particular conditions and ailments. Analysis of big data will propel significant advancements to the system through the aid of technological innovations. Creation of mass awareness for the consumers to wear wearable and other forms of tracking devices will definitely present positive alteration in the healthcare sector. Security is a crucial factor in big data because it contains millions of personal information about people and their respective history.
References
- Ebeling, M. F. (2016). Healthcare and Big Data: Digital Specters and Phantom Objects. Basingstoke, England: Springer.
- Langkafel, P. (2015). Big Data in Medical Science and Healthcare Management: Diagnosis, Therapy, Side Effects. Berlin, Germany: Walter de Gruyter GmbH & Co KG.
- Marconi, K., & Lehmann, H. (2014). Big Data and Health Analytics. Auerbach Publications.
- Natarajan, P., Frenzel, J. C., & Smaltz, D. H. (2017). Demystifying Big Data and Machine Learning for Healthcare. Boca Raton, FL: CRC Press.
- Wang, B. (2014). Big Data Analytics in Bioinformatics and Healthcare. Hershey, PA: IGI Global.
Cite This Work
To export a reference to this article please select a referencing style below:
