The Future of Big Data in Healthcare

The Future of Big Data in Healthcare

Over the past few years, big data has been a significant buzzword because most business organizations were unaware of how to tackle it. However, it is notable that numerous sectors including healthcare have increased its application to gather new insights that boost the industry through the guarantee of actionable changes (Dinov, 2016). Particularly in healthcare, there is an unprecedented amount of information on patient care within a decade. It implies that today, data is being streamed from almost all the aspects of an individual’s daily life.

1.     Big Data

Big data refers to a complex and large data sets that render traditional data processing methods and applications inadequate or obsolete. Big data is the use of advanced methods of predictive analytics to retrieve the value from data, often to a specific data set size (Milicchio et al., 2016). Big data accuracy yields confidence in decision making, which in turn results in minimized risk and cost.

Health monitors, mobile applications, fitness tracker, electronic medical records, and the social media are sources of useful data and information. Notably, significant efforts have been made in healthcare to digitize the records of patients, especially in the developed countries. In the United States, the government is advancing the big data agenda by providing a massive amount of healthcare data to the public through government websites. As technology advances, new platforms and tools emerge daily to help healthcare in leveraging the ever-expanding data sets in new creative ways. Furthermore, as these tools evolve, healthcare professionals will understand the current and future state of the sector from a wider perspective (Sercar, 2013). Eventually, the move (together with a rapid feedback) will potentially alter healthcare experience of patients and medical professionals.

If big data is leveraged properly, doctors will easily determine the level of risks for chronic diseases such as cancer, diabetes, or obesity to provide preventive healthcare measures. Additionally, big data has a potential to change insurance reimbursement models, especially the best ways to reimburse doctors (Selanikio, 2015). Big data can also radically alter patient satisfaction metrics, planning, staffing, risk mitigation and identification, and drug development.

Recent statistics indicate that healthcare is traditionally lagging behind other sectors because of the concerns about patients’ privacy and confidentiality. However, if positive changes regarding big data in healthcare are observed in the long run, it will result in better options to anticipate, treat, and prevent illnesses. Moreover, physicians can make better decisions if data mining tools are embedded within their workflow to facilitate the process (McCandless, 2012).

2.     Data Mining

Data mining is the extraction of hidden but predictive information from significantly large databases. It is the advanced technique that helps corporations to highlight the most useful and important data in their information warehouses. In essence, data mining tools have a tendency to predict future behaviors and trends hence are handy in healthcare provision. A data mining tools answer business queries traditionally considered as too time-consuming and complex to resolve. In the modern times, doctors can implement data mining techniques rapidly on existing hardware and hardware platforms to improve the value of information resources (Provost, 2013). Besides, the system can be integrated with the latest healthcare products found online.

A data warehouse, on the other hand, is a platform that harbors all the corporate data in a normalized and centralized form. The data is deployable at any time to users for decision support, archiving, or executive reporting. In a physical sense, a data warehouse is an important information repository required by the healthcare facilities to thrive especially in an information age. Data mining technology allows the management of medical facilities to focus on the most vital information in the data gathered on patients’ behavior (Estape et al., 2016). In fact, this technology discovers important information within the data that reports and queries cannot reveal effectively.

3.     Uses of Big Data

It is true that big data has advanced rapidly since 2000. People routinely collect and generate a timeline of their activities by using technology to understand and analyze information. The intersection of these modern trends (also referred to as Big Data), helps businesses in all sectors to become more productive and efficient. In specific, big data in healthcare is applied in the prediction of epidemics, improvement of the quality of life, and provision of a cure for diseases (Doshi et al., 2016). Undeniably, the global population is expanding remarkably because people are living longer. In the West, treatment delivery models are changing at a rapid pace because data drive most of the decisions behind these developments.

Software developers have created apps for use in smartphones. Owners of iPhones, for example, can install pedometers to measure how far they walk in a day, or calorie counters to assist them in planning their diet. Arguably, millions of people use mobile technology to improve their lifestyles and health. In addition, there is a steady stream of wearable devices designed by start-up tech firms like Samsung Gear Fit, Fitbit, and Jawbone that allow the users to track their health progress and upload the data for comparison to that of other device users. It is predictable that in the near future, patients can share this data with doctors for use as part of the physician’s diagnostic toolbox when visited by ailing individuals. In cases where the device user is not sick, their ability to access enormous, ever-growing information database on general public’s state of health ensures that the problems can be spotted early before symptoms can show. Eventually, the healthcare professionals can prepare and provide educational or medicinal remedies in advance (Kolb & Kolb 2013).

A new and essential use of big data is emerging, where data professionals and medical practitioners partner to look into the future and identify problems before they can happen. A classic example of this partnership is seen in Pittsburg Heath Data Alliance (PHDA) that aims to collect data from insurance and medical records, genetic data, social media, and wearable sensors (Milicchio et al., 2016). Thereafter, the group uses the information to draw a clearer comprehensive picture of a patient to provide a tailored package of healthcare.

Furthermore, IBM and Apple have collaborated to design a big data healthcare platform that enables Apple Watch and iPhone users to share data using Wayson Health IBM cloud healthcare service. This project aims at uncovering the latest medical insights from biomedical data and real-time activity of millions of technology users.

Further, telemedicine is gaining a wide acceptance, especially in urban areas. Patients are able to receive medical treatment and assistance remotely, usually in their own homes with the aid of internet connection and a computer. There are hundreds of websites with useful data for self-diagnosis at the moment. Besides, ailing individuals can receive online medical services through one-on-one sessions with qualified healthcare professionals. Essentially, interactions such as these leave a data trail that can be analyzed to improve future services. Then, the valuable information can be integrated into the general public health trends to boost the way people access healthcare.

Medicines treat various diseases affecting the patients. What many people do not know is that big data is used to design the portions and pills provided to the sick. Significant amounts of data and information on applicant enable medical researchers to select the best subjects. Commonly, data sharing arrangements between global pharmaceuticals lead to a breakthrough in healthcare. For example, with the help of big data and data sharing, it was discovered that desipramine, often used as an anti-depressant, can cure acute lung cancer and other chronic illnesses.

Medical data is personal, hence the need for extreme security and exclusive accessibility to a personal physician, and the patient. However, the internet and online platforms crawl with cyber thieves that target confidential medical records for malicious use. Many of them earn more from stolen health data than from hacked credit card information.

4.     Statistical Ways to Analyze Big Data

The data sets in big data are mostly too big and complex to process solely on database management tools. Medical records, large-scale e-commerce data, and military surveillance are examples big datasets that require more than one terabyte of storage. One of the statistical ways to analyze big data is the use of association rule learning. It is a statistical method used to discover intriguing correlations between large databases and variables. Major supermarket chains in the United States applied this method to discover interesting links among the products using the business data obtained from the POS supermarket systems. Particularly, association rule learning is beneficial in the analysis of biological data to discover new relationships, the extraction of information on internet web visitors from the log in information, and monitoring of internet logs to track malicious activities such as phishing or hacking.

            Classification tree analysis is a statistical method applied to the identification of specific categories for new observations. The historical data set is required to ensure consistency and accuracy of the statistical outcomes. In the modern times, statistical tree method is beneficial in the categorization of organisms into new groups, automatic assignment of documents to new classifications, and the development of the profiles of patients or students using online platforms.

            Genetic algorithms are inspired by the work of evolution through natural selection, inheritance, and mutation. Therefore, this mechanism is used in evolving constructive solutions to issues that demand statistical optimization. In this way, the technique allows for scheduling of physicians for emergency medical rooms.

Regression analysis, at a basic level, involves manipulation of independent variables to observe their influence on dependent variables. Essentially, this statistical method describes in detail how the dependent variable data value alters with the variation of the independent variable. Statisticians recommend the use of this technique for continuous quantitative big data such as patient’s age or weight.

Social network analysis was adopted first in the telecommunications industry. Later, sociologists used the statistical technique to analyze interpersonal relations. Currently, the method has gained a wide acceptance, especially in the commercial and healthcare sector, where it is used to study the relationships between people and how it affects their well-being and health. While nodes represent persons within a given network, ties are representative of individual relationships.

5.     Critique of Big Data and Data Mining

            Even though big data effectively detects subtle correlations often missed by the analysis of smaller data sets, it cannot explain the reasons for the correlations or their meaningfulness. Secondly, big data are useful in scientific inquiries but are unsuccessful as a replacement of traditional techniques. For instance, molecular biologists are interested in the inference of 3D protein structure of underlying DNA sequences, which begs the need for the use of big data as one of the numerous available scientific tools. However, big data and data mining cannot be independently relied on to crunch complex genome data to solve the problem, irrespective of how effective these statistical methods are. In fact, there is always a need for basic knowledge and understanding of biochemistry and physics.

            Data mining is associated with the unauthorized collections of information on the behavior of people, hence infringement on personal privacy and ethics. Mined data can be applied in different contexts and instances that beg the questions of legality. In the past, governments through federal agencies such as NSA and CIA have gathered data for law enforcement or national security purposes, thus raising concerns and fears among the citizens. Furthermore, data aggregation (introduced by data mining) poses a threat to a subject’s privacy because the data miner has an access to new but previously anonymous sets of compiled data.

6.     Ideas for Future Applications of Big Data in Healthcare

In future, patients that battle multiple health problems will access care more frequently. However, the utilization of big data will lead to a dramatic slump in the cost of healthcare because the doctors will have the patient’s information in the EHR. The data systems, once fully operational and connected, will notify the providers of problematic trends and data errors for swift intervention. Eventually, savings on late interventions will be attained and the funds can be redirected towards boosting the innovation and system functionality. As information on patients is gathered over time, the system operators have an opportunity to learn and improve the provision of healthcare in future.

References

Dinov, I. D. (2016). Volume and value of big healthcare data. Journal of Medical Statistics and Informatics J Med Stat Inform, 4(1), 3. doi:10.7243/2053-7662-4-3

Doshi, J. A., Hendrick, F. B., Graff, J. S., & Stuart, B. C. (2016). Data, Data Everywhere, But Access Remains a Big Issue for Researchers: A Review of Access Policies for Publicly-Funded Patient-level Health Care Data in the United States. EGEMs (Generating Evidence & Methods to Improve Patient Outcomes), 4(2). doi:10.13063/2327-9214.1204

Estape, E. A., Mays, M. H., & Sternke, E. A. (2016). Translation in Data Mining to Advance Personalized Medicine for Health Equity. IIM Intelligent Information Management, 08(01), 9-16. doi:10.4236/iim.2016.81002

Kolb, J., & Kolb, J. (2013). The big data revolution: The world is changing, are you ready? Chicago, IL: Applied Data Labs.

McCandless, David. The beauty of data visualization. (n.d.). Retrieved May 28, 2016, from http://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization?language=en

Milicchio, F., Rose, R., Bian, J., Min, J., & Prosperi, M. (2016). Visual programming for next-generation sequencing data analytics. BioData Mining, 9(1). doi:10.1186/s13040-016-0095-3

Provost, F. (2013). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking.

Selanikio, J. (2015). The surprising seeds of a big-data revolution in healthcare. (n.d.). Retrieved May 28, 2016, fromhttp://www.ted.com/talks/joel_selanikio_the_surprising_seeds_of_a_big_data_revolution_in_healthcarehttps://www.ted.com/talks/joel_selanikio_the_surprising_seeds_of_a_big_data_revolution_in_healthcare.

Sercar, T. (2013). Viktor Mayer-Schnberger and Kenneth Cukier, Big Data: A Revolution That Will Transform How We Live, Work, and Think. OZ Organizacija Znanja, 18(1-4), 47-49. doi:10.3359/oz1314047