Big data in Healthcare & Life Sciences
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Healthcare and Life Sciences organizations are leveraging Big Data technology to capture data in order to get a better insight into patient centric and research centric information. Combining these two requires extreme computing power. We will discuss use cases where Big Data technology was instrumental ; Merging Genomic and Clinical Data in order to advance personalized Medicine
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Big data in Healthcare & Life Sciences
- 2. Big Data Webinar Series
- 3. Big Data Webinar Series Customer Intimacy & Develop New BusinessMarch 11, 2014 Operational ExcellenceMarch 18, 2014 March 25, 2014 April 1, 2014 April 3, 2014 Big Data in Public Services Big Data in Healthcare & Life Sciences Big Data in Human Resources
- 4. Big Data Applications New Data Structured Semi Not structured Social Media Mobile Sensor data Analytics becomes 1-on-1 & Real-Time Enables a huge number of ‘new’ or improved Big Data Applications Open Data Analytics becomes 1-on-1 & Real-Time Business domains Operational Excellence Customer Intimacy New Business Risk Management Retail Banking/securities Healthcare Life Sci Media & entertainment Government Utilities Insurance Manufacturing Telecom Transport Services X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X … Source: Gartner
- 5. Big Data Applications Operational Excellence Customer Intimacy New Business Risk Management Generic Predictive asset maintenance Scenario testing Targeted Advertising Customer entity resolution Sentiment analysis Information-based products & services Crowdsourcing Cybersecurity Fraud detection Auditing for compliance Retail Dynamic pricing Dynamic forecasting Market basket analysis Shopping cart defection Real-time store management Fuzzy matching Recommendations Mall experience gamification Loyalty management Counter-dynamic pricing Sell retail data upstream Real-time offers Multichannel location analysis Customer-centric merchandising Healthcare Integrated patient data Assisted diagnosis Adaptive treatment planning Patient flow management Self evaluation Remote patient monitoring Hotspotting Fraud (ring) detection Life Sciences Clinical trials Translation medicine Virtual humans Global listening capabilities Personalized medication Adaptive treatment planning Self monitoring Drug & medical device safety Clinical trial fraud
- 9. Healthcare & Life Sciences Collaboration
- 10. Healthcare & Life Sciences Collaboration Source: Mount Sinai Icahn School of Medicine
- 11. US Health Care Provider • 1,2 Million Patients • 15 Hospitals • 185 Day Clinics • 70 Retail Pharmacies • 30.000 Employees • 6.000 Nurses • 1.500 Physicians Benchmark clinical performance against national standards • Treatment costs • Re-admission rate • Length of stay
- 12. Big Data Concept HealthCare Data Hub HealthCare Analytics Scheduling Radiology Home DevicesGenomicsTranscriptionsMedications … Lab Data PharmacySurveys PACS EMR Billing
- 13. Data Flow Concepts AnalyzeAcquire Organize Decide OLTP Data RDBMS ETL DWH BI
- 14. AnalyzeAcquire Organize Decide New Data OLTP Data Data Flow Concepts RDBMS ETL DWH BI
- 15. Use Case Drives the Data Flows AnalyzeAcquire Organize Decide OLTP Data RDBMS ETL DWH BI New Data
- 16. AnalyzeAcquire Organize Decide New Data OLTP Data RDBMS ETL Use Case Drives the Data Flows
- 17. Big Data Reference Architecture PACS DATA SOURCES Legacy EMR Financial RTLS Device Integratio n Clinical Trials Radiology Social Media Medication Laboratory Bio Repository Home Devices Genomics Quantifie d Self Pharmacy POS Transcript ions Security HadoopCluster Operations Linear Scale Compute & HDFS Storage Multitenant Processing: YARN Scrip t Pig SQL Hive Impala Online HBase Accumulo OthersReal- Time Storm In- Memory Spark Batch Map Reduce Governance Tag, Filter & Process Metadata Management Ingest Sqoop DATA REPOSITORIES EDW EDW EDW EDW EDW Surgical Data Mart Diagnosis Data Mart Quality Data Mart Clinical Info Data MartNeo4j APPLICATIONS • Cohort discovery • Predicting read mission • Detection of sepsis pathways • Analyzing test variances • Rapid bedside response • Tracking patient wait times • Home health monitoring • Chronic disease management • Patient scorecards
- 18. Big Data Journey Business Defines mandate and requirements IT Acquires and integrates data Data Scientists Build and refine analytic models IT Publishes new Insights Business Consumes insights and measures effectiveness
- 19. Cronos Big Data Services Offering Use Case Discovery Workshop Big Data Analytics Implementation Services Proof of Concepts
- 20. The role of the Data Scientist Business Strategy Analyst Hadoop System Administrator Hadoop Developer Data Architect Data Analyst/ Statistician Identify Business Pains & demonstrate through Analytical skills how the available data can be exploited on a Strategic Level Hadoop Cluster installation & administration. Data Loading Build the relevant dataset by cleansing, filtering, grouping and aggregating the data using parallel processing languages like MapReduce Hive, Pig, Impala, Spark,… ETL tooling, MDM, Data Cleaning & Matching Integration with Enterprise Architecture Data Governance & Security Through statistical Analysis, conceptual and predictive data modeling, machine learning,… Discover patterns, trends, insights. Translate these to Business Opportunities
- 21. Cronos Big Data References Use Case Discovery Workshop Proof Of Concept Implementation Services Big Data Analytics Call Center Healthcare Utility Editor Telco ISV Media Transport Manufacturing & Distribution Online Gaming
- 22. Clever usage of data to make better decisions Conclusions
- 23. Contact: matthias.vallaey@cronos.be` +32 496 57 66 27
Notas do Editor
- Preventive Healthcare
- What is Genomics?Genomics is the study of the complete genetic material (genome) of organisms. The field includes sequencing, mapping, and analyzing a wide range of RNA and DNA codes, from viruses and mitochondria to many species across the kingdoms of life. Most pertinent here are intensive efforts to determine the entire DNA sequence of many individual humans in order to map and analyze individual genes and alleles as well as their interactions. The primary goal that drives these efforts is to understand the genetic basis of heritable traits, and especially to understand how genes work in order to prevent or cure diseases.The amount of data being produced by sequencing, mapping, and analyzing genomes propels genomics into the realm of Big Data. Genomics produces huge volumes of data; each human genome has 20,000-25,000 genes comprised of 3 million base pairs. This amounts to 100 gigabytes of data, equivalent to 102,400 photos. Sequencing multiple human genomes would quickly add up to hundreds of petabytes of data, and the data created by analysis of gene interactions multiplies those further.Genomics Fuels Personalized MedicinePersonal genomics–understanding each individual’s genome–is a necessary foundation for predictive medicine, which draws on a patient’s genetic data to determine the most appropriate treatments. Medicine should accommodate people of different shapes and sizes. By combining sequenced genomic data with other medical data, physicians and researchers can get a better picture of disease in an individual. The vision is that treatments will reflect an individual’s illness, and not be a one treatment fits all, as is too often true today.Human Genome: Then and NowAs we’ve shown, research in the field of genomics has come a long way in the past 60 years. The pioneering effort in studying the human genome and its effect on disease is the Human Genome Project (1990-2003), which changed sequencing from a manual process to an automated one.Driven by advances in technology that have dramatically reduced costs, Genome Wide Association Studies (GWAS) are expanding on the Human Genome Project in discovering connections between genes and diseases. GWAS tests single nucleotide polymorphisms (SNPs) for association with diseases to find links. (A single nucleotide polymorphism is one in which there is a one nucleotide difference between two genes. For example, two sequenced DNA fragments, AAGCCTA versus AAGCTTA, have one differing nucleotide. In old-fashioned genetic terminology, these would be different alleles of the same gene.)More than 1600 genome publications have connected 2000 gene associations with more than 300 common human disease traits.So far, GWAS hasn’t proven directly useful for guiding individual health, but we may be on the brink of changing this. There are three near future clinical applications for GWAS:Predictive models to identify high risk patients, as in Type 1 Diabetes patients.Classifying disease subtypes of potential use for more precisely guided clinical trials, and targeted treatments (e.g. cancers).Provide better information for screening drug candidates for toxicity and efficacy before clinical trials.The Era of the $1000 Genome: the Archon X-prizeBy the time the Human Genome Project was completed, the cost of sequencing the human genome was $40 million, down from $95 million just two years before. Academics and companies have been working hard to make sequencing affordable and therefore available to the public. Today an individual human genome can be sequenced for around $5000 consistently and accurately.The current Holy Grail in genomics is the “$1000 Genome,” the attempt to make sequencing and mapping individual genomes cheap enough to be a part of every patient’s medical record. The Archon X-PRIZE “$1000 Genome” prize competition challenges researchers to decrease the price of sequencing to under $1000. Through advanced genomic sequencing and developing rapid, inexpensive, and accurate whole genome sequencing technologies, the ultimate goal is to usher in an era of personalized medicine. The competition will take place this fall, and will award a purse of $10 million to the first team to successfully sequence the whole human genome of 100 centenarians within 30 days at a maximum cost of $1000 per genome and an error rate no greater than 1 per million base pairs.
- What is Genomics?Genomics is the study of the complete genetic material (genome) of organisms. The field includes sequencing, mapping, and analyzing a wide range of RNA and DNA codes, from viruses and mitochondria to many species across the kingdoms of life. Most pertinent here are intensive efforts to determine the entire DNA sequence of many individual humans in order to map and analyze individual genes and alleles as well as their interactions. The primary goal that drives these efforts is to understand the genetic basis of heritable traits, and especially to understand how genes work in order to prevent or cure diseases.The amount of data being produced by sequencing, mapping, and analyzing genomes propels genomics into the realm of Big Data. Genomics produces huge volumes of data; each human genome has 20,000-25,000 genes comprised of 3 million base pairs. This amounts to 100 gigabytes of data, equivalent to 102,400 photos. Sequencing multiple human genomes would quickly add up to hundreds of petabytes of data, and the data created by analysis of gene interactions multiplies those further.Genomics Fuels Personalized MedicinePersonal genomics–understanding each individual’s genome–is a necessary foundation for predictive medicine, which draws on a patient’s genetic data to determine the most appropriate treatments. Medicine should accommodate people of different shapes and sizes. By combining sequenced genomic data with other medical data, physicians and researchers can get a better picture of disease in an individual. The vision is that treatments will reflect an individual’s illness, and not be a one treatment fits all, as is too often true today.Human Genome: Then and NowAs we’ve shown, research in the field of genomics has come a long way in the past 60 years. The pioneering effort in studying the human genome and its effect on disease is the Human Genome Project (1990-2003), which changed sequencing from a manual process to an automated one.Driven by advances in technology that have dramatically reduced costs, Genome Wide Association Studies (GWAS) are expanding on the Human Genome Project in discovering connections between genes and diseases. GWAS tests single nucleotide polymorphisms (SNPs) for association with diseases to find links. (A single nucleotide polymorphism is one in which there is a one nucleotide difference between two genes. For example, two sequenced DNA fragments, AAGCCTA versus AAGCTTA, have one differing nucleotide. In old-fashioned genetic terminology, these would be different alleles of the same gene.)More than 1600 genome publications have connected 2000 gene associations with more than 300 common human disease traits.So far, GWAS hasn’t proven directly useful for guiding individual health, but we may be on the brink of changing this. There are three near future clinical applications for GWAS:Predictive models to identify high risk patients, as in Type 1 Diabetes patients.Classifying disease subtypes of potential use for more precisely guided clinical trials, and targeted treatments (e.g. cancers).Provide better information for screening drug candidates for toxicity and efficacy before clinical trials.The Era of the $1000 Genome: the Archon X-prizeBy the time the Human Genome Project was completed, the cost of sequencing the human genome was $40 million, down from $95 million just two years before. Academics and companies have been working hard to make sequencing affordable and therefore available to the public. Today an individual human genome can be sequenced for around $5000 consistently and accurately.The current Holy Grail in genomics is the “$1000 Genome,” the attempt to make sequencing and mapping individual genomes cheap enough to be a part of every patient’s medical record. The Archon X-PRIZE “$1000 Genome” prize competition challenges researchers to decrease the price of sequencing to under $1000. Through advanced genomic sequencing and developing rapid, inexpensive, and accurate whole genome sequencing technologies, the ultimate goal is to usher in an era of personalized medicine. The competition will take place this fall, and will award a purse of $10 million to the first team to successfully sequence the whole human genome of 100 centenarians within 30 days at a maximum cost of $1000 per genome and an error rate no greater than 1 per million base pairs.
- Mount Sinai Icahn School of Medicine brengt Clinical Trial data samen met anoniemgemaaktpatientendatauithunElectronischPatienten dossier.Erwordtgeanalyseerdwat de impact is van bepaaldemedicijnen op eenhelegrotepopulatie van patiënten en in reëleomstandigheden.Dezetestresultatenwordenvergeleken met de initiële analyses van de Clinische Trials. Eventueleafwijkingenwordenverderonderzocht. Door de data aandiepeanalyseteonderwerpenkwamenerbepaaldepatronennaar de oppervlakte. Patientenwerdengeclusterdnaargelijkaardigekenmerken en het In mindere of meerdere mate positiefreageren op eenspecifiekmedicijn. Aan de hand van dezenieuweinzichtenDezeoefeningbrengtnieuweinzichtenvoor de onderzoekers. Per gevondendoelgroepwerd de dosis of samenstelling van de medicatieaangepast met eenbeterresultaat tot gevolg.Dit is de eerstestapnaar 1op1 gepersonaliseerdemedicatie
- These actors create a huge amount of data. They want to use this data for better decision making
- 18 databronnenLength of stayComplicationsRe-admissionsPatient satisfaction ratesMortality ratesZiekenhuisbacterieSharen van de resultaten per afdeling en benchmarkentegen het nationaalgemiddelde.De kwaliteit van de zorgverstrekkingwerd heel visibel, met alsgevolgdat de zorgverstrekkershungedraggingenaanpassen en meer op kwaliteitgingenwerkenMeer Best Practices werdeningevoerd.Ditwerd pas mogelijk door alle data aanwezig in het ziekenhuis (recente en historische) Alle data bijhouden (Volume)Alle data combineren (Variety) vooranalysedoeleinden.Kanenkel maar door introductie van Big Data architectuur. Alle data silo’s samenbrengen in een Enterprise Data Hub, en complexeanalyseuitvoeren over de data silo’s heen.Slechtsindien je alle data combineert is het mogelijkombepaaldepatronenteontdekken of nieuweinzichtentekrijgenResultaat: KPI’s sterkverbeterdNext step: Remote patient care voorpatiënten met chronischeaandoeningen. Data wordtgecapteerd op regelmatigetijdstippen, Verpleegstershouden de resultaten in het oog.
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