Big Data, Better Data, Better Health. Dr Michaela Black

Transcription

1 Big Data, Better Data, Better Health Dr Michaela Black

2 What is Big Data? Not a single technology, technique or initiative Refers to technologies and initiatives that involve data that is too diverse, fast-changing or massive for conventional technologies, skills and infra- structure to address efficiently Volume, velocity or variety of data is too great "Big Data" describes data sets so large and complex they are impractical to manage with traditional software tools

3 Knowledge Value

4 Benefits & Issues Data continually being generated/stored Real Time Multiple Sources: multiple databases, files, internal, external, structured, unstructured, web, sensors Formats: dates, textual, numeric, images, s, personal information, HTML Data over time Opportunities for LEARNING Enhanced services & customers care Efficiency

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8 Manage Data Cleaning Data Noisy Data Unstructured Data Joining Data Analytics: Knowledge & Learning Analytics Visualisation Descriptive models what is currently happening? Clustering, patterns Decision models Rules to ensure consistency of care, rehabilitation, institutional learning Predictive Fraud detection, risk or opportunity calculations, prediction of disease progression Change models Knowledge about disease over time or across demographics

9 Who is already benefiting? Big Data Search Engine Helps Cancer Patients A tool called CordMatch, by Berlin-based Cytolon, uses big data techniques and a unique matching algorithm to quickly find cord blood matches for cancer patients in need of a stem cell transplant. CordMatch, an Internet-based donor matching system, can search and compare over 2 million HLA Codes. Enables blood banks, transplant centres and registries to communicate

10 Who is already benefiting? The Harvard Medical School Division at Brigham and Women s Hospital in Boston, Massachusetts, is a globally recognized leader in drug safety and effectiveness research Research tool that could learn over time, bringing the very latest drug effectiveness and interaction data right to patients bedsides The solution is enabling researchers to design, test and apply brand-new algorithms to help identify drug risk warning signals far more quickly With access to the markedly larger amounts of patient data, the more traditional reactive approaches to adverse drug events can no longer suffice. Examining data after the fact to confirm a suspected drug safety problem will always be a crucial element of safety research Powerful computation simultaneously opens up avenues for identifying issues after a drug has entered the market, but before it is in widespread use

11 Who is already benefiting? For Keith Figlioli, senior vice president of healthcare informatics at the Premier healthcare alliance, finding a way to deal with fragmented healthcare information isn t just a job it s personal. Sharing and analysing healthcare information to help physicians identify the best treatments for their patients Serving more than 2,600 U.S. hospitals and 84,000-plus other healthcare sites, the Premier healthcare alliance is undertaking a ground-breaking initiative that will help hospitals, doctors and other health providers to identify which treatments benefit patients the most so that no matter where patients live, or are treated, they can be assured the best care. The potential benefits for saving lives, helping people enjoy healthier lives, and reducing healthcare costs are staggering. In one Premier project, 157 participating hospitals saved an estimated 24,800 lives while reducing healthcare spending by USD 2.85 billion

12 Data Management, Data Modelling and Big Data Analytics Predictive Patient Modelling NETCOM Hub for Next Generation Internet and Computational Modelling Mathematical / Simulation Modelling for Patient Pathways Large-scale processing of images and video Big Data Analytics for healthcare, including database and cloud: image, video and text. Integrated Hospital and Social Services data for predictive data modelling Research in computer vision for diagnosis based on clinical images.

13 Telecommunications, Cloud, Mobile Technology and Performance Management IU-ATC UK Co-ordinator: Planned Competence Centre in Cloud Computing Technologies, focusing on big data, security and resilience; content delivery networks; semantic models and network instrumentation. SUAAVE: Sensing with Unmanned Autonomous Aerial Vehicles Digital Innovation Infrastructure for Next Generation Internet Cloud-aware communication protocols and adaptation; data traffic profiling and autonomic network management in response to large scale application requirements Data capture, analysis and management for Wireless Sensor Networks and the Internet of Things

14 Patient Stratification for Prediction of Outcomes. In MATCH+ we developed and deployed methods for prediction of technology uptake Technology adoption and prediction tools (TAUT) We are developing methods for appropriate risk stratification of Alzheimer s patients with regard to their suitability for and adoption of technology. By using the University of Utah Huntsman Cancer Institute s UPDB (Utah Population Database) we can create matched samples and compare health outcomes with our target group. This should translate into better patient stratification and therapeutic intervention strategies.

15 Behavioural analysis and assistive technologies Assistive Technologies Research Environment Processing and Visualisation of Vital Sign Information

16 The Problem Project put forward by Dr Alexander Davey of the RVH. Researching issues with the current care monitoring system for acutely ill patients in the 24 hours before being admitted into the Intensive Care Unit. Problems identified Unsatisfactory quality care given to acutely or critically ill patients Lengthy processes for patient note reviewing, auditing and monitoring The amount of different paper-based documents per patient assessment.

17 The Solution CTT: A web based portal system Patient statistics collection application Patient timeline of events Management monitor portal Review practices Identify trends in quality of care Organisational learning