VCHRISTIANA CARE HEALTH SYSTEM VALUE INSTITUTE

Transcription

1 Working with VCHRISTIANA CARE HEALTH SYSTEM VALUE INSTITUTE Edward Ewen, MD 11/6/2015 Overview What is BIG DATA? Why is it important to us? How is it used? What are the challenges? What are some examples of Big Data? The Data Explosion The volume of global data is increasing exponentially. 90% of all data generated in the last 2 years. Only 20% is structured with unstructured data growing 15 x faster (e.g photos, audio, video, handwritten text) million terabytes billion terabytes billion terabytes 1

2 So What is a Terabyte? Byte = 8 bits Kilobyte = 1000 bytes Megabyte = 1000 KB Gigabyte = 1000 MB Terabyte = 1000 GB A single character 1/2 page typed A digital photograph A pickup truck filled with typed pages All the x ray films in a large hospital 10 TB = the printed collection of the Library of Congress What's a Petabyte 1 Petabyte = 1000 TB A petabyte of songs would play over 2,000 yrs. continuously Enough to store the DNA of the entire population of the US and then clone them, twice. A petabyte of 8.5 inch printed photos placed side by side would be 48,000 miles long and wrap around the earth nearly 2 times 2

3 What s a Zettabyte Exabyte = 1000 PB 50,000 years' worth of DVD quality video 5 exabytes = a transcript of every word ever spoken by every human being since the dawn of time Zettabyte = 1000 EB 42 ZB = the storage requirements for all human speech ever spoken if digitized for your ipod 35 ZB = a stack of DVDs reaching ½ way to Mars 3

4 How do we define BIG DATA? A term that describes large volumes of high velocity, complex, and variable data that require advanced techniques and technologies to enable capture, storage, distribution, management, and analysis of information. Definition Collection of data that pose challenges to traditional data processing approaches (e.g., relational databases) Characteristics volume (denoting size) variety (indicating heterogeneity) veracity (representing accuracy) velocity (designating processing speed) Combinations of these four characteristics can result in data that cannot be scaled using traditional databases and analysis systems 5 types of BIG DATA Streams Web and Social Media Machine to Machine Data Click steam and interaction data from social media Facebook, LinkedIn, Twitter, healthcare portals, smartphone apps Readings from sensors, meters, and other devices Big Transaction Data Biometric Data Healthcare claims and billing records Structured laboratory results Blood pressure, pulse oximetry, weight, activity ECGs, x rays and medical imaging, genetics, fingerprints, retinal scans Human Generated Data Unstructured data and semi structured data from EMRs written progress notes, s, other paper documents 4

5 4 Dimensions of BIG DATA Volume Variety Velocity Veracity = quantity, from terabytes to zettabytes = from structured to unstructured data = batch processing to real time streaming = quality, relevance, predictive value, meaningfulness Variety Healthcare Data Sources and Complexity Unstructured Data Historically most of the data collected at the point of care Structured and Semi structured Data Data that can be easily stored, read, manipulated by machine New Data Streams Fitness devices, genetics, social media Velocity In the past, most healthcare data has been relatively static Notes, labs, x ray reports Real time data streams becoming more common and accessible Medium velocity: glucometers, BP readings High Velocity: ICU and Anesthesia monitoring 5

6 Veracity Healthcare data vary in terms of quality, relevance, and meaning Quality Matters Decisions only as good as the supporting data Quality varies widely (esp. in unstructured data) High Volume and Velocity can impair our ability to clean data before use Why do we care? Healthcare costs are growing at an unsustainable rate. Demographics Technology Quality and Efficiency Structural issues Goal to enable more patient centered care. Tailored to specific individuals and populations 6

7 How can Big data help? Creating transparency Expose variability Monitor performance Enabling experimentation Identify opportunities to improve efficiency Comparative effectiveness Accelerate R&D How can BIG DATA help? Segment populations to customize actions Tailored health promotion Genomics Replacing/supporting human decisionmaking with automated algorithms Fraud detection Early disease detection Predictive modeling Public health 7

8 Time for a reality check an occasion on which one is reminded of the state of things in the real world 8

9 Time for a Reality Check Tools and Techniques Statistics/regression Split testing Association rule learning Classification Cluster analysis Ensemble learning Genetic algorithms Machine learning Time series analysis Natural language processing Neural networks Network analysis Signal processing Spatial analysis Sentiment analysis Data visualization Process simulation Analytic Spectrum 9

10 Challenges with Big Data Privacy Security Integration Quality Management Analytics Challenges with Big Data Data and Information Privacy Provider patient confidentiality Disclosure to 3 rd parties Conflicting goals between stakeholders Government regulation Pooling of data sets Data Security Cloud computing Integration of social media/wearable biometrics 10

11 4 Data Pools R&D Clinical Patient Payor The Challenge Integration Standard Content Terminology Timing Identification Bringing Order to Chaos 11

12 The Result? An Example: Linking Data for Kidney Care VCHRISTIANA CARE HEALTH SYSTEM VALUE INSTITUTE Work supported by an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institute of Health under grant number U54 GM (PI: Binder Macleod) Team Claudine Jurkovitz, PI; Edward Ewen, co PI Tom Laughery, Data Analyst Bayo Gbadebo, Data Analyst Richard Caplan, Biostatistician Sarahfaye Heckler, Research Associate Hagit Shatkay, Investigator Kaidi Ma, Post Doc Xi Li, Post Doc Tim Bunnell, Investigator Joshua Zaritsky, Investigator (Nephrologist) John Swanson, Investigator (Transplant) Rubeen Israni, Investigator (Nephrologist) CCHS CCHS CCHS CCHS CCHS Univ.Del Univ. Del Univ. Del Nemours Nemours CCHS NAPA 12

13 Problem Chronic Kidney Disease is highly prevalent in the US and in Delaware One in 10 American adults (>20 million) have some level of CKD Delaware 2011: 2071 chronic renal replacement therapy patients, incidence 338 patients/year Multiple comorbid conditions Medicare Patients Characteristics age 65+, 2012 Data Source: Medicare 5 percent sample. Period prevalent patients, 2012, without ESRD, age 65 or older (Medicare). Abbreviations: CHF, congestive heart failure; DM, diabetes mellitus; HTN, hypertension. United States Renal Data System, 2014 Annual Data Report Cardiovascular Disease in Medicare Patients with or without CKD, 2012 Data Source: Medicare 5 percent sample. Abbreviations: AFIB, atrial fibrillation; AMI, acute myocardial infarction; ASHD, atherosclerotic heart disease; CHF, congestive heart failure; CKD, chronic kidney disease; CVA/TIA, cerebrovascular accident/transient ischemic attack; CVD, cardiovascular disease; PAD, peripheral arterial disease; SCA/VA, sudden cardiac arrest and ventricular arrhythmias. United States Renal Data System, 2014 Annual Data Report 13

14 Unadjusted and Adjusted all cause Mortality Rates (per 1,000 patient years at risk) for Medicare Patients, (A) Unadjusted (B) Adjusted Data source: Medicare 5 percent sample. January 1 point prevalent Medicare patients age 66 and older. Adj: age/sex/race/prior year hospitalization/comorbidities. United States Renal Data System, 2014 Annual Data Report Big data pilot grant from ACCEL CTR Overall hypothesis: by creating patients longitudinal records, we will have a better understanding of the care that is delivered and will be able to improve the coordination of care Specific Aims Aim 1: Acquire and integrate information from multiple data sources to create a unified longitudinal description of care, for patients with chronic kidney disease Aim 2: Develop models and methods for predicting hospital admission after an outpatient visit, in patients with chronic kidney disease Aim 3: Assess transitions of care of children with CKD to adult care 14

15 Data sources Common Data Model Desired Functionality Open model Well specified vocabularies Ability to convert data from relational data model to model that supports time series analysis Compatible with scalable analysis engine Patient centric Longitudinal patient record that includes demographics, events, observations, measurements, medications, orders and problems Potential Solution: OMOP Lewis Frey, PhD Medical University of South Carolina Observational Medical Outcomes Partnership (OMOP) Lewis Frey, PhD Medical University of South Carolina 15

16 Data Flow Common Data Model (OMOP) Another Example A very early effort using predictive analytics nearly 15 years ago at CCHS Attempted predict pneumonia hospitalizations based on rates of ED utilization for respiratory illness. Some important lessons were learned. Predicting Admissions 16

17 Count 11/4/2015 Predicting Admissions Predicting Admissions 1500 ED Visits for respiratory Illness Pneumonia admissions YearMonth Predicting Admissions ED Visits for respiratory Illness Pneumonia admissions 17

18 Predicting Admissions Conclusions ED utilization for respiratory illness can be used to anticipate hospital pneumonia admissions The seasonal nature of these admissions renders the predictive model of marginal value Questions? 18