Smart big data's new role in optimizing clinical 4
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The document discusses how big data and analytics can optimize clinical trial efficiency. It notes that unstructured data makes up 80% of useful information and is growing faster than structured data. Traditional clinical trial protocols rely on limited data sources like medical histories and questionnaires, whereas expanded protocols could incorporate a wider range of data sources like other medical records. Graphical displays of contextual analyses and intuitive interfaces can provide insights at a glance. Actionable analytics derived from big data could drive clinical trial efficiency by defining measures, answering questions, and leading directly to meaningful actions.
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![Why Utilize Big Data Now?
12/3/2013
1. Unstructured big data is ~80% of all useful information
2. Unstructured big data is growing at >10x rate of structured data
Web 1.0
Google
Yahoo
MS
>$100B Market
Wave 3.0 …..
The Need to Extract Trends &
Meaning from Big Data Chaos
in ‘Real Time’
Web 2.0
Facebook
LinkedIn
Proprietary
Unstructured data growth
[Source: Merrill Lynch]
Twitter
2
2000s
UP 2013, Santa Clara
2010s
© Trendicy Corp
2020s](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Organizations are being bombarded with messages telling you that you must make an investment in Big Data, that without it your organization will be rendered obsolete, a mere bystander, on the road to increased growth and profitability.
But do you? How exactly will your organization benefit from Big Data? When do you invest – and does investing in Big Data mean leaving the rest of your data strategy stranded?
Oracle ACE Director Dan Morgan, an internationally recognized expert in database technology and former University of Washington lecturer, and Mark Swanholm, PTC’s Chief Strategy Officer and 22 year IT Veteran, will address the issue of Big Data from the standpoint of what it is, where the value can be found, what is actually required to turn this new technology into something of value.
This Performance Tuning Corporation online event will focus on strategy, management, planning, and budgeting, and will provide you and your management team the information they need to plan make the best possible decision with respect to an investment in Big Data technology.
Primary data and secondary data
This document discusses primary and secondary data sources. Primary data is originally collected by a researcher specifically for their study, through methods like interviews or surveys. Secondary data has already been collected by someone else for another purpose and includes sources like published books, government reports, media sources, and internal company records. When using secondary data, researchers must evaluate if the data is relevant to their research objectives, accurate, and collected through reliable methods. They should also consider if the data could have errors from the original study. Secondary data sources in Nepal include government organizations, research agencies, universities, and trade organizations.
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Service design teams can glean big data insights from social media, financial systems, emails, surveys, call centers, and digital and analog sensors. But companies that fixate on amassing new data sources put themselves at risk of neglecting small data insights gathered through qualitative research methods. How can firms achieve balance?
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This document discusses secondary and primary data collection methods in marketing research. It defines secondary data as data collected previously for other purposes, while primary data is collected specifically for the research project. Some key advantages of secondary data are that it is more economical and saves time compared to primary data collection. However, secondary data may not always fit the research needs or be up-to-date. The document outlines various sources of secondary data, both internal and external to an organization. It also discusses requirements for using secondary data and limitations of the observational primary data collection method.
DAMA Webinar - Big and Little Data Quality
While technological innovation brings constant change to the data landscape, many organizations still struggle with the basics: ensuring they have reliable, high quality data. In health care, the promise of insight to be gained through analytics is dependent on ensuring the interactions between providers and patients are recorded accurately and completely. While traditional health care data is dependent on person-to-person contact, new technologies are emerging that change how health care is delivered and how health care data is captured, stored, accessed and used. Using health care as a lens through which to understand the emergence of big data, this presentation will ask the audience to think about data in old and new ways in order to gain insight about how to improve the quality of data, regardless of size.
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The document discusses the challenges of health care data quality and realizing the promise of big data analytics in health care. While technological innovation enables new insights from health care data, many organizations still struggle with basic data quality issues. In health care specifically, person-to-person interactions that are recorded as data can vary in quality due to factors like technology limitations, lack of standards, and the "butterfly effect" where small changes in clinical decision-making conditions can impact outcomes. To fulfill the promise of health care analytics will require recognizing variation in data, reducing unnecessary variation, and ensuring data is collected and managed as a critical product rather than a byproduct of the health system.
How to Use Data Analytics and Response Models to Reach, Realize, and Retain C...
Caserta Concepts (http://casertaconcepts.com/) and PNT Marketing Services (http://www.pntmarketingservices.com/), a leading provider of Customer Intelligence-based database marketing and analytic services, discussed how to turn your customer data into increased sales with predictive analytics and response modeling in a recent webinar.
Communicating Data for Impact
Florence Nightingale used data visualization to communicate public health data and drive policy change in the mid-19th century. She partnered with a statistician to analyze mortality data from the Crimean War, which showed that most soldier deaths were from preventable diseases, not combat wounds. Nightingale created "coxcomb" diagrams to visually depict the data in a clear, compelling way. Her data storytelling had a significant impact, improving sanitary conditions in military hospitals. Today, effective communication of data requires identifying the right audience and tailoring the amount, format and delivery of data to meet their needs.
Big data in healthcare
Big data is generating hype in healthcare, but true value will come as technical expertise and security improve. While most healthcare organizations currently have limited big data use beyond basic analytics, needs will grow as data sources expand through devices and the "internet of things". Predictive analytics using socioeconomic and other data could help predict patient outcomes and appointments. Prescriptive analytics may eventually provide personalized treatment paths for patients. Drug discovery may also be enhanced through big data. However, barriers like a lack of skills and integrated security currently limit big data to research applications.
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- 1. 12/3/2013 Smart Big Data’s New Role in Optimizing Clinical Trial Efficiency UP 2013 Santa Clara Proprietary Robert A. Downing II. 1
- 2. Why Utilize Big Data Now? 12/3/2013 1. Unstructured big data is ~80% of all useful information 2. Unstructured big data is growing at >10x rate of structured data Web 1.0 Google Yahoo MS >$100B Market Wave 3.0 ….. The Need to Extract Trends & Meaning from Big Data Chaos in ‘Real Time’ Web 2.0 Facebook LinkedIn Proprietary Unstructured data growth [Source: Merrill Lynch] Twitter 2 2000s UP 2013, Santa Clara 2010s © Trendicy Corp 2020s
- 3. The Provider-Centric View 12/03/13 Physician Nursing staff Proprietary Office staff PORs Patients 3 UP 2013, Santa Clara
- 4. The Patient-Centric View 12/03/13 Patients PORs Proprietary Office staff Nursing staff Physicians 4 UP 2013, Santa Clara
- 5. The SaaS View 12/03/13 Cloud Patients Nursing staff PORs Office staff Office staff PORs Nursing staff Patients Physicians Proprietary Physician 5 UP 2013, Santa Clara
- 6. SaaS Model Benefits 12/03/13 • Accessible • From any browser, anywhere • Reliable • Always up, with built-in fault tolerance • Robust • Scalable with demand • Multi-protocol support (including secure protocols), with address obfuscation Proprietary • Secure • Low Cost-of-Ownership • Installation, maintenance, support someone else’s problem UP 2013, Santa Clara 6
- 7. Big Data Obstacles • SaaS relatively new, & some vendors even newer 12/03/13 • Lack of history • Lack of selection criteria • Too many claims by too many participants • Too few recognized leaders • Security Proprietary • Distrust/Mistrust still exists, largely because of perceived shortcomings in standards for verification & vague external accountability • Legislation • PPACA • HIPAA UP 2013, Santa Clara 7
- 8. New Solutions 12/03/13 • History • Get references from any vendor, & ask for their competitors • Selection criteria • Nothing is too insignificant to ask, then demand if necessary • Security Proprietary • Demand the best, & ask for logical or physical isolation if needed • Vendor(s) should be able to provide HIPAA compliance • Follow your gut • But be realistic 8 UP 2013, Santa Clara
- 9. Making the Case [Study] Proprietary • Plan the protocol • Source(s) of data • Type(s) of data • Reliability / Veracity • Gather & ‘map’ the data • Evaluate additional sources, types of data • Look for better / more reliable sources • Plan the cost? 12/03/13 • Plan the Goals, & Path(s) to Get There • Dynamism Can Affect / Improve Confidence 9 UP 2013, Santa Clara
- 10. Traditional Protocol 12/03/13 • Medical Hx • Questionnaire • Visits • Additional questionnaires • Investigators’ notes • Verbalization transcription Proprietary 10 UP 2013, Santa Clara
- 11. Expanded Protocol • • • • • Other providers records Hospitalization records Licensed practitioner records Support communities Other… • Graphical • Contextual • Meaningful Proprietary • Information at A Glance 12/03/13 • Sponsor / PI EHR / EMR plus 11 UP 2013, Santa Clara
- 12. Contextual Look 12/03/13 Proprietary 12 UP 2013, Santa Clara
- 13. Contextual Analysis 13 Proprietary 13 UP 2013, Santa Clara
- 14. Intuitive User Interface 12/03/13 Proprietary 14 UP 2013, Santa Clara
- 15. Actionable Analytics 12/03/13 Proprietary 15 UP 2013, Santa Clara
- 16. Big Data’s New Role … Driving Clinical Trial Efficiency 12/03/13 • Graphical • Easy to understand graphics display insight at a glance • Points in time are data, not answers • Answers come from measuring change / time • Contextual Proprietary • Questions should be defining the shape of the answers • Environment should be defining the measures • Meaningful • Answers should lead directly to action, without any intervening step or steps • Analytics need to be actionable UP 2013, Santa Clara 16
- 17. Thank you…. Proprietary robert.downing@trendicy.com T: 510.516.1812 M: 510.468.0066 SkypeID: radowningii Twitter: TrendicyRob 12/03/13 Robert Downing II Founder & CEO www.trendicy.com 17 UP 2013, Santa Clara