BIG DATA ANALYTICS FOR HEALTHCARE

Transcription

1 International Journal of Research in Engineering, Technology and Science, Volume VI, Special Issue, July ISSN BIG DATA ANALYTICS FOR HEALTHCARE Vibha Ganjir 1, Dr. B.K. Sarkar 2, Ravi Ranjan Kumar 3 1, 2, 3 Department of Computer Engineering Birla Institute of Technology, Ranchi, India ABSTRACT: Health care is one of the greatest concerns of the world. Big Data in healthcare refers to electronic health data sets that are related to patient healthcare and well-being. Data in the healthcare sector is growing beyond dealing capacity of the health care organizations and is expected to increase significantly in the coming years. This paper defines big data analytics and its characteristics, advantages and challenges in health care. Keywords: Analytics, Big Data, Impact, Healthcare, 5v. [1] INTRODUCTION Big Data generally refers to data that exceeds the typical storage, processing, and computing capacity of conventional databases and data analysis techniques. The healthcare industry historically has generated large amounts of data, driven by record keeping, compliance & regulatory requirements, and patient care. By definition, big data in healthcare refers to electronic health data sets so large and complex that they are difficult (or impossible) to manage with traditional software and/or hardware; nor can they be easily managed with traditional or common data management tools and methods. Big data in health-care includes patient care data such as physician notes, Lab reports, X-Ray reports, case history, social media post, sensor data, diet regime, list of doctors and nurses in a particular hospital, national health register data, medicine and surgical instruments expiry date identification based on RFID data. Big data analytics can be defined as the process of examining large amounts of data, from a variety of data sources and in different formats, to deliver insights that can enable decisions in real or near real time. Various analytical concepts such as data mining, natural language processing, artificial intelligence and predictive analytics can be applied to analyze, contextualize and visualize the data [1]. [2] HEALTHCARE AND BIG DATA S 5VS There are five main dimensions to Big Data, commonly referred to as the Five V s: Volume, Velocity, Variety, Veracity and Value. By understanding the 5Vs of Big Data, we can employ their power in research and for real problems solving. So, let us discuss how these dimensions can be applied to health care [2]. Vibha Ganjir and Ravi Ranjan Kumar 1

2 BIG DATA ANALYTICS FOR HEALTHCARE FIGURE: 1. 5VS OF BIG DATA Volume: Health care data are in terabytes and petabytes. These systems include information such as: personal information, radiology images, personal medical records, 3D imaging, genomics, and biometric sensor readings. Healthcare systems can now have the potential to manage and analyze this complex data. According to KPMG report [3], the volume of healthcare data reached 150 Exabyte in 2013, and it is increasing at a prominent rate of Exabyte a year. Variety: Variety of data are structured, unstructured and semi-structured. Structured information, such as clinical data, are easy to manipulate, store and analyze by machine. Most of health care data, such as office medical records, doctor notes, paper prescriptions, images, and radiograph films, are unstructured or semi-structured. Velocity: 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.) can mean the difference between life and death. Veracity: Some practitioners and researchers have introduced a fourth characteristic, veracity, or data assurance. 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. (Inaccurate translations of poor handwriting on prescriptions are perhaps the most infamous example). 2

3 International Journal of Research in Engineering, Technology and Science, Volume VI, Special Issue, July ISSN Value: Variety Unlike the above-mentioned 4Vs, this V is too special because it represents the desired outcomes of big data processing. We are always interested in collecting and extracting maximum true value from big data we work with. In fact, we must look for data storage investment because value of data depends on quality of governance strategy and mechanism. For example, storing clinical data for new diseases on unreliable storage can save money today, but may affect data tomorrow. Another important point that is often ignored is that true value lies on the eyes of the customer of business data. Another serious point to consider is that some data in time of collection have different value to risk, but this risk can be developed in time. [3] BIG DATA ANALYTICS Analytics can be classified in to three types they are: Predictive Analytics, Descriptive Analytics and Prescriptive analytics [4]. Descriptive analytics: The simplest class of analytics," one that allows you to condense big data into smaller, more useful nuggets of information. Predictive analytics: It is the next step up in data reduction. It utilizes a variety of statistical, modeling, data mining, and machine learning techniques to study recent and historical data, thereby allowing analysts to make predictions about the future. Prescriptive analytics: It is a type of predictive analytics. It's basically when we need to prescribe an action, so the business decision-maker can take this information and act. [4] IMPACT OF BIG DATA ANALYTICS ON THE HEALTHCARE Right living: Patients can build value by taking an active role in their own treatment, including disease prevention. The right-living pathway focuses on encouraging patients to make lifestyle choices that help them remain healthy, such as proper diet and exercise and take an active role in their own care if they become sick. Right care: This pathway involves ensuring that patients get the most timely, appropriate treatment available. In addition to relying heavily on protocols, right care requires a coordinated approach across settings and providers, all caregivers should have the same information and work toward the same goal to avoid duplication of effort and suboptimal strategies. Right provider: This pathway proposes that patients should always be treated by highperforming professionals that are best matched to the task and will achieve the best outcome. Right provider therefore has two meanings: the right match of provider skill set to the complexity of the assignment for instance, nurses or physicians assistants performing tasks that do not require a doctor but also the specific selection of the provider with the best proven outcomes. Right value: To fulfill the goals of this pathway, providers and payers will continuously enhance healthcare value while preserving or improving its quality. This pathway could Vibha Ganjir and Ravi Ranjan Kumar 3

4 BIG DATA ANALYTICS FOR HEALTHCARE involve multiple measures for ensuring cost-effectiveness of care, such as tying provider reimbursement to patient outcomes, or eliminating fraud, waste, or abuse in the system. Right innovation. This pathway involves the identification of new therapies and approaches to delivering care, across all aspects of the system, and improving the innovation engines themselves for instance, by advancing medicine and boosting R&D productivity. To capture value in this pathway, stakeholders must make better use of prior trial data such as by looking for high-potential targets and molecules in pharma. They could also use the data to find opportunities to improve clinical trials and traditional treatment protocols, including those for births and inpatient surgeries. [5] ADVANTAGES TO HEALTH CARE Big Data could be useful in improving health in two significant ways-population health and personalized health care. Big Data is also helping consumers acquire more reliable and timely information about the cost and quality care. Data is an important tool in developing new types of personalized healthcare. The main benefits can be detecting diseases at earlier stages, detecting healthcare abuse and fraud faster, and reducing costs. [5.1] Benefits to Patients: Big data can help patients make the right decision in a timely manner. From patient data, analytics can be applied to identify individuals that need proactive care or need change in their lifestyle to avoid health condition degradation. For example, patients in early stages of some diseases (e.g., heart failure often caused by some risk factors such as hypertension or diabetes) should be able to benefit from preventive care thanks to big data. A concrete example of this is the Virginia health system Carillion Clinic project, which uses predictive models for early interventions [5]. [5.2] Benefits to Researchers and Developers (R & D): Collecting different data from different sources can help improving research about new diseases and therapies. R & D contribute to new algorithms and tools, such as the algorithms by Google, Facebook, and Twitter that define what we find about our health system. Google, for instance, has applied algorithms of data mining and machine learning to detect influenza epidemics through search queries [6] [7]. R & D can also enhance predictive models to produce more devices and treatment for the market. [5.3] Benefits to healthcare Providers: Providers may recognize high risk population and act appropriately (i.e. propose preventive acts).therefore, they can enhance patient experience. Moreover, approximately 54% of US hospitals are members in local or regional Health-Information Exchanges (HIEs) or try to be in the future. These developments give the power to access a large array of information. For example, the HIE in Indiana connects currently 80 hospitals and possesses information of more than ten million patients. 4

5 International Journal of Research in Engineering, Technology and Science, Volume VI, Special Issue, July ISSN [7] LIMITATIONS AND CHALLENGES OF DATA ANALYTICS IN HEALTHCARE Big data analytics platform in healthcare must support the key functions necessary for processing the data. The criteria for platform evaluation may include availability, continuity, ease of use, scalability, ability to manipulate at different levels of granularity, privacy and security enablement, and quality assurance [8]. 1. The source of data from organizations (hospital, pharmacies, companies, medical centers) is in different formats. These organizations have data in different systems and settings. To use this huge amount of data, those organizations must have a common data warehouse in order to get homogeneous information and be able to manage it. However, having such systems requires huge costs. 2. Quality of data is a serious limitation. Data collected are, in some cases, unstructured, improper, and no standardized.so, the industry has to apply additional effort to transform information into usable and meaningful data. 3. A big investment is required for companies to acquire staff (data scientists), resources and also to buy data analytics technologies. In addition, companies must convince medical organizations about using big data analytics. 4. Using data mining and big data analytics requires a high level of expertise and knowledge. It is a costly affair for companies to hire such persons. 5. Due to serious constraints regarding the quality of collected data, variations and errors in the results are not excluded. [6] CONCLUSION Big data analytics has the potential to transform the way healthcare providers use sophisticated technologies to gain insight from their clinical and other data repositories and make informed decisions. In the future we ll see the rapid, widespread implementation and use of big data analytics across the healthcare organization and the healthcare industry. Big data, including predictive analytics tools, have the potential to change healthcare system from reporting to predicting results at earlier stages. In this paper, we have presented a global vision of big data for collecting, analyzing and managing healthcare data in different forms and from multiple systems. Big data analytics and applications in healthcare are at a nascent stage of development, but rapid advances in platforms and tools can accelerate their maturing process. Vibha Ganjir and Ravi Ranjan Kumar 5

6 BIG DATA ANALYTICS FOR HEALTHCARE REFERENCES [1] Hiba Asri, Hajar Mousannif, Big Data in healthcare: Challenges and Opportunities. [2] M. A. U. D. Khan, M. F. Uddin, and N. Gupta, Seven V s of Big Data understanding Big Data to extract value, Proc Zo. 1 Conf. Am. Soc. Eng. Educ. - Engineering Educ. Ind. Involv. Interdiscip. Trends, ASEE Zo , [3] V. Galloro, Prime numbers, Mod. Healthc, vol. 38, pp , [4] L. Aniket Bochare Heterogeneous Data Integration for Clinical Decision Support System [5] IBM News room IBM Predictive Analytics to Detect Patients at Risk for Heart Failure - United States. 19-Feb [6] K. R. Ghani, K. Zheng, J. T. Wei, and C. P. Friedman, Harnessing big data for health care and research: are urologists ready? Eur. Urol., vol. 66, no. 6, pp , Dec [7] D. Lazer, R. Kennedy, G. King, and A. Vespignani, The Parable of Google Flu: Traps in Big Data Analysis, Science (80-.), vol. 343, pp , [8] Ohlhorst F: Big Data Analytics: Turning Big Data into Big Money. USA: John Wiley & Sons;