Big Data Infrastructure and Analytics for Healthcare Applications Arjun Shankar, Ph.D.
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1 Big Data Infrastructure and Analytics for Healthcare Applications Arjun Shankar, Ph.D. Computational Sciences and Engineering Division
2 Outline Current big data context in healthcare Data processing and analysis for healthcare delivery and effectiveness Big data infrastructure to support scale, analytics, and visualization Innovative data representations and applications Population health and analysis of geographic variation Bridging clinical data and claims data Data formats for clinical data Linking clinical and claims data 2 Managed by UT-Battelle
3 Setting the Big Data Context 3 Managed by UT-Battelle
4 Individual Data Scales: Human Biology and Medicine Genotype to Phenotype Spectrum Petabytes, Terabytes, Gigabases/ second Gigabytes Megabytes 4 Managed by UT-Battelle
5 Population Data Scales: Healthcare Delivery and Outcomes Focus Individual to Population Megabytes Gigabytes Petabytes (300 Million Individuals 5B Medicare-related events/year) 5 Managed by UT-Battelle
6 Big Data Buzz? More sensors : connected devices, social media, literature, Better data management and processing: scale, interactive, networked, named, mobile, etc. Improved analytics: machine learning, semantics, visualization, Big data infrastructures and analytics: use of emerging tools and data-driven techniques to go beyond your traditional datacapacity, data-processing, and data-analysis capabilities. 6 Managed by UT-Battelle
7 7 Managed by UT-Battelle From Todd Park, SXSW 2011 Presentation
8 Transformation Challenges for National Healthcare Environment Person Centric Value Based Payment Outcome Driven Continuous Improvement 3 Outcomes Based 1 Value Based Payment Health Knowledge 2 Outcome Measures States Line of Sight to Benes Investments & Tools for Improvement Line of Sight to Providers 4 Payment Providers How is the Beneficiary Doing? (better care, better health, lower cost) 8 Managed by UT-Battelle Collect Performance Measures Outcomes Based Analysis Make Payment
9 Users of Healthcare Big Data Processing Providers and healthcare delivery systems Readmission prediction and reduction Care-coordination to reduce cost Measure outcomes Insurers/Payers Actuarial calculations by age, region, etc. Bundling payments Policy Makers Cost to quality ratios what works, what to cover, etc. Efficiency analysts Fraud Detection, Clinical Standards, System efficiencies Streamlining processes, clinical standards adherence, mitigating over payments, double charging, upcoding 9 Managed by UT-Battelle
10 Claims Streams Reflect Healthcare Pic credits: Bodenheimer, Grumbach, Understanding Health Policy, 6 th Edition Employer-paid Plans Example Taxpayer-paid Plans Example 10 Managed by UT-Battelle Payment Models Encoded in Claims and Codes
11 Data Vignettes Example of Medicare claim data-format elements Example of Medicare claim form 11 Managed by UT-Battelle
12 Setting up and Deploying a Big Data Infrastructure 12 Managed by UT-Battelle
13 ORNL Knowledge Discovery Infrastructure Knowledge Discovery for Infrastructure Big - Data Processing and Analysis Applications Analytics Data-Feeds, Extract- Transform-Load Infrastructure 13 Managed by UT-Battelle
14 A Use-Pattern for Big Data over Hadoop MapReduce is executed in Hadoop cluster Hive transform SQL to MapReduce User submits SQL to Hive DataNodes JobTracker MapReduce Job MapReduce Job Hive SQL NAS Data is loaded into Hadoop filesystem (HDFS) 14 Managed by UT-Battelle
15 Typical Recipe for Data Analysis Workflow Process using scalable infrastructure (Extract-Transform-Load) Data cleaning and organization Input into relational (Hive, MySQL, Column-databases, etc.) Run queries to extract relevant information SQL-based queries, SAS-based programs, etc. Learn correlations, patterns, causal behaviors, data-dependencies Visualize Re-run extractions of relevant data 15 Managed by UT-Battelle
16 Knowledge Discovery Infrastructure - Software Interconnections View Managed by UT-Battelle
17 Customized Population Health Views 17 Managed by UT-Battelle
18 18 Managed by UT-Battelle
19 Analyzer for Duals
20 20 Managed by UT-Battelle
21 21 Managed by UT-Battelle
22 22 Managed by UT-Battelle
23 Emerging Big Data Representation Approach Graphs and Linked Data Example 1: healthdata.gov, Hospital Compare 23 Managed by UT-Battelle Slide courtesy George Thomas, Clinical Quality Linked Data,
24 Example 2: Using Network Analysis to Infer Suspicious Activity 24 Managed by UT-Battelle 24
25 Example 3: Semantic Inference from Literature From: Semantic MEDLINE: A Web Application for Managing the Results of PubMed Searches, Kilicoglu, Fiszman, Rodriguez, Shin, Ripple, Rindflesch; Proceedings of the Third International Symposium for Semantic Mining in Biomedicine: Managed by UT-Battelle
26 Population Health Analysis Case Study 26 Managed by UT-Battelle
27 Knowledge Discovery Process in Big Data Many methods and tools available statistical methods and packages visual analytics tools and frameworks textbooks and courses on data mining Most analysts specialize in a small number of techniques Few sources offer comprehensive and systematic approach to healthcare knowledge discovery in large scale What data and what recipe can an analyst use? 27 Managed by UT-Battelle
28 Online Public Health Data Sources 28 Managed by UT-Battelle
29 29 Managed by UT-Battelle
30 Categories of CMS Indicators Demographics Disease Prevalence Home Health Hospital Compare_ Congestive Heart Failure Measures Hospital Compare_ Disease Specific Readmissions and Mortality Rate Hospital Compare_ Heart Attack Measures Hospital Compare_ Pneumonia Care Measures Hospital Compare_ Surgical Care Measures Hospital Inpatient Imaging Inpatient Rehabilitation Facility Laboratory Tests Long-Term Care Hospital Other Tests (Non-laboratory) Outpatient Other Tests (Non-laboratory) 30 Managed by UT-Battelle Patient Safety Indicators Physician Evaluation & Management Physician Procedures Prevention Quality Indicators Readmissions & ED Visits Skilled Nursing Facilities Utilization PPS ASC CAH DME Hospice Other hospital IP Part B Post acute care CMS Indicators are expressed at State and Hospital Referral Region (HRR) Levels
31 31 Managed by UT-Battelle 1 Breastcancerdeathsper100000females 2 Colorectalcancerdeathsper100000pop. 3 Deathsallcausesper100000pop. 4 Diabetes% 5 Fewfruitsvegetables% 6 Fluvaccinationadults% 7 Heartdiseasedeathsper100000pop. 8 Highbloodpressure% 9 UnintentionalInjurydeathsNoMVAper100000pop. 10 Lungcancerdeathsper100000pop. 11 Mammogram% 12 Noexercise% 13 Papsmear% 14 Physicallyormentallyunhealthydays% 15 Pneumococcalvaccination% 16 Proctoscopy% 17 Populationdensitypersquaremile
32 A Collaborative HIW Analysis Platform 1. Correlation Views Zoomed In Zoomed Out 32 Managed by UT-Battelle
33 33 Managed by UT-Battelle Howard Wactlar
34 34 Managed by UT-Battelle
35 Analyzing HIW via Partial Correlation By adjusting for the effect of High Blood Pressure %, the correlation between No Exercise % Pop. and CHF Admission Rate is reduced from to What is the true causal model? No X HBP CHF 35 Managed by UT-Battelle
36 Analyst s Workflow 36 Managed by UT-Battelle
37 Integrating Claims and Clinical Data 37 Managed by UT-Battelle
38 Benefit to integrating clinical and claims data Can measure quality and cost relationship accurately Currently quality is derived from diagnosis codes, recurrence, preventability, etc. Longitudinal analysis What interventions are working in what delivery system? Fraud detection Are the clinical procedures necessary and being followed accurately? 38 Managed by UT-Battelle
39 Clinical data integration obstacles Data formats concerns Unified Medical Language System (UMLS) Systematized NOmenclature of Human and Veterinary MEDicine Clinical Terms (SNOMED-CT) Logical Observation Identifiers Names and Codes (LOINC) Digital Imaging and Communications in Medicine (DICOM) Health Level 7 (HL7) Data coding progression ICD-9, ICD-10 etc. for CPT, HCPCS codes. Privacy concerns 39 Managed by UT-Battelle
40 Linking Strategies Example Steps for Linking Inpatient Registry Data to Medicare Inpatient Claims Data Subset registry data Limit to records for patients aged 65 years or older. Subset Medicare data Limit to records for patients aged 65 years or older. Limit using broadly defined inclusion criteria to match registry entry criteria. Link hospital identifiers Link records between data sources on exact values of admission date, discharge date, patient date of birth or age, and patient sex. Use resulting number of matches to inform links. Determine specifications for hospitalization-level linking Choose rules to apply. Decide whether Medicare records should be allowed to link to multiple registry records. Link hospitalization records From Linking Inpatient Clinical Registry Data to Medicare Claims Data Using Indirect Identifiers, Bradley G. Hammill, MS, a Adrian F. Hernandez, MD, MHS, a,b Eric D. Peterson, MD, MPH, a,b Gregg C. Fonarow, MD, c Kevin A. Schulman, 40 Managed by UT-Battelle MD, a,b and Lesley H. Curtis, PhD a,b ;
41 Thank you! Questions? 41 Managed by UT-Battelle
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