BIG DATA, BIOBANKS AND PREDICTIVE ANALYTICS FOR A BETTER CLINICAL OUTCOME

Transcription

1 BIG DATA, BIOBANKS AND PREDICTIVE ANALYTICS FOR A BETTER CLINICAL OUTCOME Π. Ε. Βάρδας MD, PhD(London)

2 DISCLOSURES My great love to innovative ideas

3 BIG DATA It is a broad term for data sets, so large or complex that traditional data processing applications are inadequate.

4 BIG DATA To qualify as big in the sense that information scientists use the term, a dataset much reach a level of size and complexity, that it becomes a challenge to store, process and analyze by standard computational methods. It is estimated that per capita computing capacity has been doubling every 40 months since the 1980 s

5 BIG DATA Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization and information privacy. Data sets grow in size in part, because they are increasingly being gathered by cheap and numerous information sensing mobile devices, remote sensing, cameras, microphones, radiofrequency identification readers & wireless sensors.

6 LARGE DATA SIZES BYTE 1 byte: A single character 10 bytes: A single word 100 bytes: A telegram KILOBYTE (1.000 bytes) 1 kilobyte : A very short story 10 kilobytes: An encyclopedic page 50 kilobytes: A compressed document image page

7 LARGE DATA SIZES MEGABYTE ( bytes) 1 Megabyte: A small novel 10 Megabytes: A minute of high fidelity sound 100 Megabytes: One meter of shelved books 500 Megabytes: A CD-ROM GIGABYTE ( bytes) 1 Gigabyte : A pickup truck filled with paper, or a movie at TV quality 100 Gigabytes: A floor of academic journals

8 LARGE DATA SIZES TERABYTE ( bytes) 1 Terabyte: All the X-Ray films in a large hospital 10 Terabytes: The printed collection of the US Library of Congress 50 Terabytes: The contents of a large Mass Storage System

9 LARGE DATA SETS PETABYTE ( bytes) 2 Petabytes: All US academic research libraries 20 Petabytes: All production of hard-disk drivers in Petabytes: All printed material

10 LARGE DATA SETS EXABYTE ( bytes) 5 Exabytes: All words ever spoken by human beings ZETABYTE ( bytes) YOTTABYTE XENOTTABYTE SHILENTNOBYTE DOMEGEMEGROTTEBYTE

11 LARGE DATA SETS According to International Data Corporation, the total amount of global data was expected to grow to 2.7 zettabytes during This is 48% up from 2011 In 2020 it is estimated there will be 44 times more data than in 2009 That means 35 zettabytes compared to Petabytes PLEASE IMAGING..

12 VOLUME BIG DATA BASICS In 2020, it is estimated there will be 44 times more data than Thirty-nine Zetabytes compared to Petabytes VELOCITY Represents the increasing frequency with which data is delivered VARIETY It signifies the many forms in which data exists

13 THE NEED FOR ELECTRICAL MEDICAL RECORDS (EMR) Development of EMR will permit integration of biological data, clinical information, patient information and clinical outcomes Large population or specific groups of patient with selected characteristics could be easily identified with the availability of electronic medical records In genomic research EMR, facilitate analysis genetic and molecular information from large subject populations allowing studies to be more powerful than small cohort studies.

14 PREDICTIVE ANALYTICS

15 PREDICTIVE ANALYTICS It Is the practice of extracting information from existing data sets, in order to determine patterns and predict future outcomes and trends. Predictive analytics does not tell you what will happen in future.

16 PREDICTIVE ANALYTICS Predictive Analytics (PA) uses technology and Statistical methods to search through massive amounts of information, analyzing it to predict outcomes for individual patients. IN HEALTHCARE That information can include data from past treatment outcomes as well as the latest medical research published in peer-reviewed journals and databases.

17 BIG DATA AND PREDICTIVE ANALYTICS They will study whether we can predict an individual's disease course as early as possible, by inferring their subtype. In many diseases, is not one, but many different subtypes. Analyzing patterns allow us to ask why different individuals show different disease trajectories

18 BIG DATA AND CLINICAL OUTCOMES

19 HOW BIG DATA HELPS HEALTHCARE Big Data has tremendous potential to add value in all healthcare settings. Big Data solutions can help organizations personalize care, engage patients, reduce variability and cost and improve quality. Personalization whether based on genomic data, standard test data, or a combination of the two, requires the integration and analysis of much larger volumes of data.

20 BIG DATA IN THE DIGITAL HEALTH Usually addresses the following six categories of information 1. Web & Social media data(smart phone apps, health plan websites) 2. Machine-to-machine data(sensors, meters, different devices) 3. Transactions data(health care claims, billing records, in both semi-structured and unstructured formats) 4. Biometric data 5. Human general data 6. Pharmaceutical & Medtech, R&D data

21 FACTORS DRIVING THE BIG DATA MARKET IN THE HEALTHCARE SECTOR The need for improved clinical outcomes The need for increased efficiency in managing healthcare data The presence of Federal healthcare mandates in some segments The double digit growth in the HER The increased focus on value-based medicine The need for personalized medicine that s based on analytics The need for improved decision support The need to reduce pharmaceutical cost The need to reduce clinical testing costs

22 FACTORS INHIBITING THE GROWTH OF BIG DATA A resistance to a systems-approach by the medical community The operational gap between payer & provider front office The acute IT staff shortage in healthcare A lack of comparable & transparent data in healthcare Financial constraints Concerns regarding ensuring patient confidentiality The low costs of traditional analytics techniques The lack of interoperability between healthcare systems

23 BIOBANKS

24 BIOBANKS A collection of biological material (e.g. animal, plant, human-skin, blood, organs, hair, saliva etc) with corresponding documentation that can be used for research purposes.

25 CAN BIOBANKS BE USED FOR PERSONALISED MEDICINE? Personalized medicine is a new model of healthcare treatment. It delivers targeted diagnostics, treatment and advice on nutrition, which are tailored to an individual. Personalized or precision medicine will be effective for the majority of people once genetic and molecular information derived from their samples, has been systematically understood.

26 CAN BIOBANKS BE USED FOR PERSONALISED MEDICINE? To achieve the targets we need to study genetic and molecular information. We need to predict the risk of disease, identify new targets for treatments and also identify markers predicting positive or negative reaction to treatment options. Therefore biobanks are needed as they contain a large pool of resources in the form of coded genetic materials

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28 WHAT WE RE TALKING ABOUT HERE IS THE TRANSFORMATION OF MEDICINE Scott Zeger, Vice Provost for Research Johns Hopkins University, USA The biomedical sciences have been the pillar of the health care system for a long time. The new system will have two equal pillars: The biomedical sciences and the data sciences

29 To physicians of the time, the appropriate treatment for apparent death was warmth and stimulation. For this purpose, artificial respiration and the blowing of smoke into the lungs or the rectum were thought to be interchangeably useful. The smoke enema was considered the most potent method, however, due to the warming and stimulating properties associated with tobacco in the pharmacopoeia of the period. At the turn of the 19th century, tobacco smoke enemas had become an established practice in Western medicine, considered by Humane Societies to be as important as artificial respiration. In the 1780s, the Royal Humane Society installed resuscitation kits, including smoke enemas, at various points along the