Big Data Analytics In Healthcare Promise And Potential Pdf
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- Big Data Analytics in Healthcare: Investigating the Diffusion of Innovation
- Big Data Analytics in Healthcare: Investigating the Diffusion of Innovation
- Challenges and Opportunities of Big Data in Health Care: A Systematic Review
The concept of Big Data is popular in a variety of domains.
Big Data Analytics in Healthcare: Investigating the Diffusion of Innovation
To describe the promise and potential of big data analytics in healthcare. The paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the challenges, and offers conclusions. The paper provides a broad overview of big data analytics for healthcare researchers and practitioners. Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs.
Its potential is great; however there remain challenges to overcome. While most data is stored in hard copy form, the current trend is toward rapid digitization of these large amounts of data.
Reports say data from the U. At this rate of growth, big data for U. Kaiser Permanente, the California-based health network, which has more than 9 million members, is believed to have between Big data in healthcare is overwhelming not only because of its volume but also because of the diversity of data types and the speed at which it must be managed [ 7 ]. For the big data scientist, there is, amongst this vast amount and array of data, opportunity. By discovering associations and understanding patterns and trends within the data, big data analytics has the potential to improve care, save lives and lower costs.
Thus, big data analytics applications in healthcare take advantage of the explosion in data to extract insights for making better informed decisions [ 10 — 12 ], and as a research category are referred to as, no surprise here, big data analytics in healthcare [ 13 — 15 ]. When big data is synthesized and analyzed—and those aforementioned associations, patterns and trends revealed—healthcare providers and other stakeholders in the healthcare delivery system can develop more thorough and insightful diagnoses and treatments, resulting, one would expect, in higher quality care at lower costs and in better outcomes overall [ 12 ].
The potential for big data analytics in healthcare to lead to better outcomes exists across many scenarios, for example: by analyzing patient characteristics and the cost and outcomes of care to identify the most clinically and cost effective treatments and offer analysis and tools, thereby influencing provider behavior; applying advanced analytics to patient profiles e.
Many payers are developing and deploying mobile apps that help patients manage their care, locate providers and improve their health.
Via analytics, payers are able to monitor adherence to drug and treatment regimens and detect trends that lead to individual and population wellness benefits [ 12 , 16 — 18 ]. This article provides an overview of big data analytics in healthcare as it is emerging as a discipline. First, we define and discuss the various advantages and characteristics of big data analytics in healthcare.
Then we describe the architectural framework of big data analytics in healthcare. Third, the big data analytics application development methodology is described. Fourth, we provide examples of big data analytics in healthcare reported in the literature. Fifth, the challenges are identified. Lastly, we offer conclusions and future directions. Health data volume is expected to grow dramatically in the years ahead [ 6 ]. Although profit is not and should not be a primary motivator, it is vitally important for healthcare organizations to acquire the available tools, infrastructure, and techniques to leverage big data effectively or else risk losing potentially millions of dollars in revenue and profits [ 19 ].
What exactly is big data? A report delivered to the U. Big data encompasses such characteristics as variety, velocity and, with respect specifically to healthcare, veracity [ 20 — 23 ].
Existing analytical techniques can be applied to the vast amount of existing but currently unanalyzed patient-related health and medical data to reach a deeper understanding of outcomes, which then can be applied at the point of care. Ideally, individual and population data would inform each physician and her patient during the decision-making process and help determine the most appropriate treatment option for that particular patient.
By digitizing, combining and effectively using big data, healthcare organizations ranging from single-physician offices and multi-provider groups to large hospital networks and accountable care organizations stand to realize significant benefits [ 2 ]. Potential benefits include detecting diseases at earlier stages when they can be treated more easily and effectively; managing specific individual and population health and detecting health care fraud more quickly and efficiently.
Numerous questions can be addressed with big data analytics. McKinsey believes big data could help reduce waste and inefficiency in the following three areas:. Clinical operations : Comparative effectiveness research to determine more clinically relevant and cost-effective ways to diagnose and treat patients.
Public health : 1 analyzing disease patterns and tracking disease outbreaks and transmission to improve public health surveillance and speed response; 2 faster development of more accurately targeted vaccines, e.
In addition, [ 14 ] suggests big data analytics in healthcare can contribute to. Evidence-based medicine : Combine and analyze a variety of structured and unstructured data-EMRs, financial and operational data, clinical data, and genomic data to match treatments with outcomes, predict patients at risk for disease or readmission and provide more efficient care;.
Genomic analytics : Execute gene sequencing more efficiently and cost effectively and make genomic analysis a part of the regular medical care decision process and the growing patient medical record [ 25 ];.
Pre-adjudication fraud analysis : Rapidly analyze large numbers of claim requests to reduce fraud, waste and abuse;. Patient profile analytics : Apply advanced analytics to patient profiles e. According to [ 16 ], areas in which enhanced data and analytics yield the greatest results include: pinpointing patients who are the greatest consumers of health resources or at the greatest risk for adverse outcomes; providing individuals with the information they need to make informed decisions and more effectively manage their own health as well as more easily adopt and track healthier behaviors; identifying treatments, programs and processes that do not deliver demonstrable benefits or cost too much; reducing readmissions by identifying environmental or lifestyle factors that increase risk or trigger adverse events [ 26 ] and adjusting treatment plans accordingly; improving outcomes by examining vitals from at-home health monitors; managing population health by detecting vulnerabilities within patient populations during disease outbreaks or disasters; and bringing clinical, financial and operational data together to analyze resource utilization productively and in real time [ 16 ].
Over time, health-related data will be created and accumulated continuously, resulting in an incredible volume of data. The already daunting volume of existing healthcare data includes personal medical records, radiology images, clinical trial data FDA submissions, human genetics and population data genomic sequences, etc. Newer forms of big data, such as 3D imaging, genomics and biometric sensor readings, are also fueling this exponential growth.
Fortunately, advances in data management, particularly virtualization and cloud computing, are facilitating the development of platforms for more effective capture, storage and manipulation of large volumes of data [ 4 ]. Data is accumulated in real-time and at a rapid pace, or velocity. The constant flow of new data accumulating at unprecedented rates presents new challenges.
Just as the volume and variety of data that is collected and stored has changed, so too has the velocity at which it is generated and that is necessary for retrieving, analyzing, comparing and making decisions based on the output. Most healthcare data has been traditionally static—paper files, x-ray films, and scripts. Velocity of mounting data increases with data that represents regular monitoring, such as multiple daily diabetic glucose measurements or more continuous control by insulin pumps , blood pressure readings, and EKGs.
Meanwhile, in many medical situations, constant real-time data trauma monitoring for blood pressure, operating room monitors for anesthesia, bedside heart monitors, etc. Future applications of real-time data, such as detecting infections as early as possible, identifying them swiftly and applying the right treatments not just broad-spectrum antibiotics could reduce patient morbidity and mortality and even prevent hospital outbreaks.
Already, real-time streaming data monitors neonates in the ICU, catching life-threatening infections sooner [ 6 ]. The ability to perform real-time analytics against such high-volume data in motion and across all specialties would revolutionize healthcare [ 4 ]. Therein lies variety.
As the nature of health data has evolved, so too have analytics techniques scaled up to the complex and sophisticated analytics necessary to accommodate volume , velocity and variety. Gone are the days of data collected exclusively in electronic health records and other structured formats. Increasingly, the data is in multimedia format and unstructured. The enormous variety of data—structured, unstructured and semi-structured—is a dimension that makes healthcare data both interesting and challenging.
Structured data is data that can be easily stored, queried, recalled, analyzed and manipulated by machine. Historically, in healthcare, structured and semi-structured data includes instrument readings and data generated by the ongoing conversion of paper records to electronic health and medical records. Historically, the point of care generated unstructured data: office medical records, handwritten nurse and doctor notes, hospital admission and discharge records, paper prescriptions, radiograph films, MRI, CT and other images.
Already, new data streams—structured and unstructured—are cascading into the healthcare realm from fitness devices, genetics and genomics, social media research and other sources.
But relatively little of this data can presently be captured, stored and organized so that it can be manipulated by computers and analyzed for useful information. Healthcare applications in particular need more efficient ways to combine and convert varieties of data including automating conversion from structured to unstructured data. The need to field-code data at the point of care for electronic handling is a major barrier to acceptance of EMRs by physicians and nurses, who lose the natural language ease of entry and understanding that handwritten notes provide.
On the other hand, most providers agree that an easy way to reduce prescription errors is to use digital entries rather than handwritten scripts. The potential of big data in healthcare lies in combining traditional data with new forms of data, both individually and on a population level.
We are already seeing data sets from a multitude of sources support faster and more reliable research and discovery. If, for example, pharmaceutical developers could integrate population clinical data sets with genomics data, this development could facilitate those developers gaining approvals on more and better drug therapies more quickly than in the past and , more importantly, expedite distribution to the right patients [ 4 ].
The prospects for all areas of healthcare are infinite. That is, the big data, analytics and outcomes are error-free and credible. Of course, veracity is the goal, not yet the reality. Data quality issues are of acute concern in healthcare for two reasons: life or death decisions depend on having the accurate information, and the quality of healthcare data, especially unstructured data, is highly variable and all too often incorrect. Veracity assumes the simultaneous scaling up in granularity and performance of the architectures and platforms, algorithms, methodologies and tools to match the demands of big data.
The analytics architectures and tools for structured and unstructured big data are very different from traditional business intelligence BI tools. They are necessarily of industrial strength. Likewise, models and techniques—such as data mining and statistical approaches, algorithms, visualization techniques—need to take into account the characteristics of big data analytics.
Traditional data management assumes that the warehoused data is certain, clean, and precise. Improving coordination of care, avoiding errors and reducing costs depend on high-quality data, as do advances in drug safety and efficacy, diagnostic accuracy and more precise targeting of disease processes by treatments. But there are other issues to consider, such as the number of architectures and platforms, and the dominance of the open source paradigm in the availability of tools.
Consider, too, the challenge of developing methodologies and the need for user-friendly interfaces. While the overall cost of hardware and software is declining, these issues have to be addressed to harness and maximize the potential of big data analytics in healthcare.
The conceptual framework for a big data analytics project in healthcare is similar to that of a traditional health informatics or analytics project. The key difference lies in how processing is executed. In a regular health analytics project, the analysis can be performed with a business intelligence tool installed on a stand-alone system, such as a desktop or laptop.
Because big data is by definition large, processing is broken down and executed across multiple nodes. The concept of distributed processing has existed for decades. What is relatively new is its use in analyzing very large data sets as healthcare providers start to tap into their large data repositories to gain insight for making better-informed health-related decisions. While the algorithms and models are similar, the user interfaces of traditional analytics tools and those used for big data are entirely different; traditional health analytics tools have become very user friendly and transparent.
Big data analytics tools, on the other hand, are extremely complex, programming intensive, and require the application of a variety of skills. They have emerged in an ad hoc fashion mostly as open-source development tools and platforms, and therefore they lack the support and user-friendliness that vendor-driven proprietary tools possess.
Big data in healthcare can come from internal e. Sources and data types include:. Web and social media data: Clickstream and interaction data from Facebook, Twitter, LinkedIn, blogs, and the like.
It can also include health plan websites, smartphone apps, etc. Machine to machine data: readings from remote sensors, meters, and other vital sign devices [ 6 ].
Big Data Analytics in Healthcare: Investigating the Diffusion of Innovation
To describe the promise and potential of big data analytics in healthcare. The paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the challenges, and offers conclusions. The paper provides a broad overview of big data analytics for healthcare researchers and practitioners. Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however there remain challenges to overcome. While most data is stored in hard copy form, the current trend is toward rapid digitization of these large amounts of data. Reports say data from the U.
The shortage of data scientists has restricted the implementation of big data analytics in healthcare facilities. This survey study explores big data tool and technology usage, examines the gap between the supply and the demand for data scientists through Diffusion of Innovations theory, proposes engaging academics to accelerate knowledge diffusion, and recommends adoption of curriculum-building models. For this study, data were collected through a national survey of healthcare managers. Results provide practical data on big data tool and technology skills utilized in the workplace. This information is valuable for healthcare organizations, academics, and industry leaders who collaborate to implement the necessary infrastructure for content delivery and for experiential learning.
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Raghupathi and V. Raghupathi , V. ObjectiveTo describe the promise and potential of big data analytics in healthcare. MethodsThe paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the challenges, and offers conclusions.
Challenges and Opportunities of Big Data in Health Care: A Systematic Review
Improving health outcomes while containing costs acts as a stumbling block. In this context, Big Data can help healthcare providers meet these goals in unprecedented ways. The potential of Big Data in healthcare relies on the ability to detect patterns and to turn high volumes of data into actionable knowledge for precision medicine and decision makers. In several contexts, the use of Big Data in healthcare is already offering solutions for the improvement of patient care and the generation of value in healthcare organizations.
Ashwin Belle, Raghuram Thiagarajan, S. The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems.
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