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1 qwertyuiopasdfghjklzxcvbnmqwert yuiopasdfghjklzxcvbnmqwertyuiop asdfghjklzxcvbnmqwertyuiopasdfg hjklzxcvbnmqwertyuiopasdfghjklzx Big Data Analytics in Healthcare cvbnmqwertyuiopasdfghjklzxcvbn Octocube Consulting mqwertyuiopasdfghjklzxcvbnmqwe 2/3/2014 Sumit Kumar Roy rtyuiopasdfghjklzxcvbnmqwertyuio pasdfghjklzxcvbnmqwertyuiopasdf ghjklzxcvbnmqwertyuiopasdfghjklz xcvbnmqwertyuiopasdfghjklzxcvbn mqwertyuiopasdfghjklzxcvbnmqwe rtyuiopasdfghjklzxcvbnmqwertyuio pasdfghjklzxcvbnmqwertyuiopasdf ghjklzxcvbnmqwertyuiopasdfghjklz xcvbnmqwertyuiopasdfghjklzxcvbn mrtyuiopasdfghjklzxcvbnmqwertyu iopasdfghjklzxcvbnmqwertyuiopas

2 Table of Contents Big Data...3 Big Data in Healthcare...3 Economic Value of Big Data Analytics...4 Example of Big Data analytics in Healthcare...4 Business Goal of Big Data Analytics...5 Open Source Software as Alternative to Costly Proprietary BI Tools...6 Cost Benefit Analysis of using Open Source Software...6 Some of the Projects that uses Open Source Software...7 Key Qualifications of Octocube Consulting...7 Big Data Analytics in HealthCare Page 2

3 Big Data Octocube Consulting ( Big data is a term used to describe a collection of data sets with the following three characteristics: Volume large amounts of data generated; Velocity frequency and speed of which data are generated, captured and shared; and Variety diversity of data types and formats from various sources. The size and complexity of big data makes it difficult to use traditional database management and data processing tools. Data is being created in much shorter cycles, from hours to milliseconds. There is also a trend underway to create larger databases by combining smaller data sets so that data correlations can be discovered. There are important distinctions and sufficient differentiating value between Big Data Analytics and DW/BI systems which make Big Data Analytics unique. Gartner defines a data warehouse as a storage architecture designed to hold data extracted from transaction systems, operational data stores and external sources. The warehouse then combines that data in an aggregate, summary form suitable for enterprise-wide data analysis and reporting for predefined business needs. Big Data Analytics solutions will not replace DW/BI, rather they will co-exist side-by-side to unlock hidden value in the massive amount of data that exists within and outside the enterprise. Big Data Analytics functions are unique because they: handle open ended "how and why" type questions whereas BI tools are designed to query specific "what and where"; and Process unstructured data to find patterns, whereas DW systems process structured and mostly aggregated data. Big Data in Healthcare There are many potential use cases for Big Data Analytics in health care. Big Data Analytics can be used to: help researchers find causes of, and treatments for diseases; actively monitor patients so clinicians are alerted to the potential for an adverse event before it occurs; and personalize care so precious resources associated with a treatment are not administered to a patient who cannot benefit from the intervention. Until the advent of Big Data Analytic solutions, scientists, government statisticians and market researchers routinely encountered problems with analyzing their large and complex data sets. Given these characteristics, big data requires the use of new frameworks, technologies and processes to manage it. Yet its arrival in the enterprise software space has created some confusion as business leaders try to understand the differences between it and traditional data warehousing (DW) and business intelligence (BI) tools. The types of data anticipated to be of use in Big Data Analytics include: Clinical data up to 80 per cent of health data is unstructured as documents, images, clinical or transcribed notes; Big Data Analytics in HealthCare Page 3

4 Publications clinical research and medical reference material; Clinical references text-based practice guidelines and health product (e.g.,drug information) data; Genomic data represents significant amounts of new gene sequencing data; Streamed data home monitoring, tele-health, handheld and sensor-based wireless or smart devices are new data sources and types; Web and social networking data consumer use of Internet data from search engines and social networking sites; and Business, organizational and external data administrative data such as billing and scheduling and other non-health data. Economic Value of Big Data Analytics McKinsey s research points out valuable insights such as patient behaviors, along with demands and efficiencies about the environment surrounding the patient, are buried in unstructured or highly varied data sources. The report cites successful pilot projects in the U.S. and internationally that have used Big Data Analytics to find efficiencies in clinical operations, analyze data from remotely monitored patients, assess clinical and cost efficiencies of new treatments, and use analytics in public health surveillance and disease response. Example of Big Data analytics in Healthcare Outcomes-based research to determine which treatments will work best for specific patients ( optimal treatment pathways ) by analyzing comprehensive patient and outcome data to compare the effectiveness of various interventions; Pre-diagnosis that automatically mines medical literature to create a medical expertise database capable of suggesting treatment options to clinicians based on patients health records; and Remote patient monitoring for chronically ill patients and analyzing the resulting data to monitor treatment adherence, reduce patient in-hospital bed days, cut emergency department visits, improve the targeting of nursing home care and outpatient physician appointments, and reduce long-term health complications. There is potential to layer Big Data Analytics -type applications, in a privacy-protective manner, on top of the foundational health IT infrastructure to derive value that might not otherwise be found. What follows are some innovative ideas and solutions. Clinical decision support Big Data Analytics technologies that sift through large amounts of data, understand, categorize and learn from it, and then predict outcomes or recommend alternative treatments to clinicians and patients at the point of care. Personalized care Predictive data mining or analytic solutions that can leverage personalized care (e.g., genomic DNA sequence for cancer care) in real time to highlight best practice treatments to patients. These solutions may offer early detection and diagnosis before a patient develops disease symptoms. Public and population health Big Data Analytics solutions that can mine web-based and social media data to predict flu outbreaks based on consumers search, social content and query activity. Big Data Analytics solutions can also support clinicians and epidemiologists Big Data Analytics in HealthCare Page 4

5 performing analyses across patient populations and care venues to help identify disease trends. Clinical operations Big Data Analytics can support initiatives such as wait-time management, where it can mine large amounts of historical and unstructured data, look for patterns and model various scenarios to predict events that may affect wait times before they actually happen. Policy, financial and administrative Big Data Analytics can support decision makers by integrating and analyzing data related to key performance indicators. Using analytics to gain better insights can help demonstrate value and achieve better outcomes, such as new treatments and technologies. Information leading to insight can help informed and educated consumers become more accountable for their own health. Analytics can improve effectiveness and efficiency. From managing small details to large processes, analytics can aid exploration and discovery; help design and plan policy and programs; improve service delivery and operations; enhance sustainability; mitigate risk; and provide a means for measuring and evaluating critical organizational data. Perhaps most important, it can expand access to healthcare, align pay with performance and help hold down growth in healthcare costs. Healthcare organizations are increasingly using analytics to consume, unlock and apply new insights from information. New methods of analytics can be used to drive clinical and operational improvements to meet business challenges. From a traditional baseline of transaction monitoring using basic reporting tools, spreadsheets and application reporting modules, analytics in healthcare is moving toward a model that will eventually incorporate predictive analytics and enable organizations to see the future, create more personalized healthcare, allow dynamic fraud detection and predict patient behavior. Business Goal of Big Data Analytics Improve clinical effectiveness and member/patient satisfaction Improve clinical quality of care. Improve patient safety and reduce medical errors. Improve wellness, prevention and disease management. Understand physician profiles and clinical performance. Improve customer satisfaction, acquisition and retention. Improve operational effectiveness Reduce costs and increase efficiency. Optimize catchment area and network management. Improve pay for performance and accountability. Increase operating speed and adaptability. Improve financial and administrative performance Increase revenue and ROI. Improve utilization. Optimize supply chain and human capital management. Improve risk management and regulatory compliance. Reduce fraud and abuse. Big Data Analytics in HealthCare Page 5

6 Open Source Software as Alternative to Costly Proprietary BI Tools For over a decade, Business Intelligence (BI) has been sold as an investment--typically a six-to-seven figure investment that promised to make organizations smarter and more competitive. Admittedly, the approach is valid: many enterprises have achieved handsome returns on investments in data warehousing and BI. But the high investment/high return scenario priced BI out of the reach of most organizations. A 2006 survey of IT and line of business professionals conducted by Ventana Research concludes that interest in and adoption of open source BI is widespread and growing, driven primarily by cost considerations. The report is careful to point out, however, an interesting disparity. While 48% of respondents expect open source BI to cost half as much as equivalent proprietary BI software, one-fourth of the respondents expect it to cost the same. Evaluating open source BI is not cost-free, either, but the software is freely available for download, and the labor costs should be dramatically lower. This is because the evaluation of open source BI is more likely to be bottom up rather than top down. This means that the technical evaluation and even a pilot implementation may occur at minimal cost and effort before executives are involved. When and if a project becomes large enough to require executive-level approval and budgeting, the business case is based on actual results already achieved, not academic projections or third-party endorsements. One of the biggest problems with proprietary solutions is that all the costs are born upfront by the customer before there is any reward. This dynamic is one of the key reasons for the growth and popularity of the open source movement. While open source is evangelized on many fronts, including security, flexibility, and competitive advantage, organizations adopt open source primarily because of the price/performance ratio. With open source BI, organizations can adjust spending as they go, depending on the perceived returns. Almost all of the investment is spent configuring or customizing the solution to meet the organization s needs, not on a generic system that needs to be customized just to work. Cost Benefit Analysis of using Open Source Software Open-source software is free to use, distribute, and modify. It has lower costs, and in most cases this is only a fraction of the cost of their proprietary counterparts. Open-source software is more secured as the code is accessible to everyone. Anyone can fix bugs as they are found, and users do not have to wait for the next release. The fact that is continuously analyzed by a large community produces secure and stable code. Open source is not dependent on the company or author that originally created it. Even if the company fails, the code continues to exist and be developed by its users. Also, it uses open standards accessible to everyone; thus, it does not have the problem of incompatible formats that exist in proprietary software. Lastly, the companies using open-source software do not have to think about complex licensing models and do not need anti-piracy measures like product activation or serial number. Big Data Analytics in HealthCare Page 6

7 Cost Cumulative Implementation often 3x-7x license $1.5MM to $3.5MM Initial License Maintenance 20%/year + FTEs = $300K/year All Cost borne upfront by Starts to customer before get return reward Can Balance Benefits against cost as Go Proprietary Start to Get Return Open Source BI Time Some of the Projects that uses Open Source Software AADHAAR UIDAI GOI: This project uses Open Source Software from Top to Bottom and that too on Community Edition. Some of the OSS software used in the project is: MULE ESB, Pentaho Suit, Spring Framework, Spring Integration. Rabbit MQ etc. Germany Federal government has earmark using Open Source Software for all of their projects. UK Government has made it mandatory to use Open Source Software to reduce cost. Key Qualifications of Octocube Consulting Strength of the company desired for this project execution with more than 55+ man years industrial expertise We are qualified to meet your DWBI & Big Data Analytics needs with our Fortune 1000 Organizational experience in delivering Data warehousing, BI & Big Data Analytics. We try to provide all our solutions using Open Source Tools/Software. We can meet your required project deliverables well within schedule as we got a vast pool of developers, testers and administrators to manage your project. We currently got development centers in Chennai. Centre in Kolkata is being setup. We got required expertise in building DWBI & Analytics using industry leading Open source technologies. We design & build simple to use BI & Analytics solutions for the business and Power users We provide comprehensive training on Business Analytics and the solutions we provide to both business and Power users Big Data Analytics in HealthCare Page 7