From Master Data Management (MDM) to Big Data

Transcription

1 13 th November 2014 From Master Data Management (MDM) to Big Data A Cognizant Insight from Sowmya Srinivasan

2 Introduction Sowmya Srinivasan, R&D Solutions Lead, Cognizant Life Sciences Bringing innovative solution to Life Sciences organizations across Drug Discovery, Clinical Development, Pharmacovigilance and Regulatory Compliance. +15 years of experience in R&D. Responsible for building capabilities in the emerging transformation areas including Real World Evidence, Biomarkers/Translational Medicine & NGS among other use cases. As a part of his responsibility he also builds and manages an ecosystem of partners in the area of R&D informatics Prior to Cognizant, was part of the management team in a research informatics company, Strand Life Sciences focused on product development across bio and chem informatics. Actively involved in Pistoia Alliance and transmart Foundation Cognizant (NASDAQ:CTSH) is a leading provider of information technology, consulting, and business process outsourcing services. 187,400 employees globally $8.843bn Revenues in FY active Customers 26% of revenue in Life Sciences Cognizant works with: 9 of the top 10 Life Sciences Companies 12 Of Global Top 20 Med Tech Companies 28 Of the Global Top 30 Pharma Companies 75 Global Delivery Centers 1,000 Healthcare Clinical Experts 15,500 Global Life Sciences Associates 2

3 Agenda 1 Key Drivers of Clinical Transformation 2 Leveraging Big Data for Patient Centric Clinical Trials 3 R&D Big Data Use Cases 4 MDM as an Enabler for Big Data Use Cases Real World Evidence Getting Started Conclusions 3

4 Key Drivers For Clinical Transformation RnD Pressures Real World Pressures Cost Pressure Trials not completed on time and budget Personalized medicine leading to smaller patient population Outcome Focus Pressure to generate evidence at time of launch Scientific study outcomes differ from real world outcomes Patient Experience Complex medical literature for trials No consistency in patient experience SERVICE Differentiate product to payers and providers By Owning the Disease And Generating Evidence for Pill + Service model Cost Pressure Healthcare payers imposing new cost constraints on providers and scrutinizing the value of medicines more carefully Outcome Focus Focus on Real World Evidence for new treatments to make sure they offer more value than competing therapies Patient Experience Image of pharma as aggressive pushers of their products (not about patient wellness) Lack of brand differentiation in customer s mind 4

5 There is a new ecosystem of clinical information about patients (Big Data) Quantify me data Body Vitals Health surveys Risk Assessments Social interactions Health Reported Outcomes Information of things Device data App data Web data Sensor data Wearables data Information of me from others EMR / EHR Data Adherence Care planning & management 5

6 Leverage this new Big Data to enable Patient Centric Clinical Trials SPONSOR VALUE PROPOSITION Standard procedures and ICFs Better relationships with IRBs Predictive analytics Continuous improvements based on patient feedback Measure and evaluate site effectiveness PATIENT VALUE PROPOSITION Reminders and adherence tracking for appointments and dosage Pharma INVESTIGATOR / SITE VALUE PROPOSITION Easy scheduling of appointments Self-reported information from patients for better diagnosis Instant communication of Personalized instructions symptoms/adverse event to site Patient education ICF education Patient s Voice through feedback on site, procedures and ICF Site Patient Investigator Increase patient adherence and retention to produce better health outcomes Detect non-eligibility/drop-out rate, earlier in the trial Improve trial conduct Generate better health outcomes 6

7 R&D Big Data Use Cases DRUG DISCOVERY CLINICAL DEVELOPMENT DRUG SAFETY REGULATORY Genomic Technologies Disease & Mechanism of Action R&D Business Development New Market Identification Competitor- Compound Profiling Predictive Sciences Translational Medicine Biosensors and Imaging Drug Repositioning Site and Investigator Selection Patient Selection Safety Reporting from Social Media Regulatory Monitoring Post Launch Support Patient Centric Drug Design Real World Data/Evidence Patient Engagement Services 7

8 Leveraging Data to Deliver RWE Use Cases Across the Spectrum Internal Data Sources Final study reports Optimizing Study design (patient size and cohorts) Real World Data/Evidence Leverage existing non-clinical data and other external (EHR) data to increase the clinical trial efficiency Final study protocols Patient outcome insights To harness the power of structured and unstructured data to improve the patient outcomes and reduce costs CTMS, Observational Studies.. External Data Sources Project protocols Statistical analyses plans Submission dossiers Off-target (AE) identification and validation Enable Cross-Study Analysis Utilizing data and literature from the clinical and non-clinical data sources to conclude a hypothesis related to human risk assessment Gain insight from multiple clinical trials to improve other studies and therapies e.g. EHR/EMR, Patient registries. Commercial Data Sources Risk management plans Global and local Medical Affairs Plans Status information on studies in progress RWE Platform Investigator selection and profiling Support Biomarker Identification Integrates (Public) Genomic/ Genetic Study Data Identify and recruit right set of Investigators for a given therapeutic areas Potential to identify new biomarkers, track therapeutic area specific biomarkers in various phases of trials Disease / Patient stratification, translational medicine Approved abstracts e.g. Truven Value dossiers Contextualization of Real World drug use through social Listening Effectively use social media sources to conduct postmarketing surveillance will greatly enhance understanding of the safety & efficacy of their medicines in the Real World HC. data Virtual Clinical trails Leverage Large datasets of patient population to build simulation models. 8

9 Epidemiology Analytics and Patient Cohort Analysis Client: Global Top Pharma MarketScan Business Need I3 Invision DataMart De-identified patient data is provided by third party data providers Datasets can range from 500 GB to 2-3 TB SAS analysis can take more than 10 hours due to the complexity of the processing. Preparation of the control and analytic datasets can take up to several days Solution Hadoop-based solution developed to leverage its parallel processing capabilities Pig used for converting the datasets from multiple providers into a common format Python used for applying the algorithms for the cohort analysis Analysis results stored in Hive for querying and analysis using SAS Use of HBase and Solr for fast search Understanding of prevalence of secondary conditions Better understanding of disease market Improved trial design Epidemiology Benefits Real time search of over million records in 2.5 seconds Reduced processing time of Epidemiology analytics to 20 minutes Technology Landscape 9

10 Type 2 Diabetes Research using Semantic Technology Patient Selection Mayo Clinic used Semantic Web technologies to develop a framework for high throughput phenotyping using EHRs to analyze multifactorial phenotypes 1 Mapped Clinical Database to Ontology Model Diseasome DBPedia ChemBL Find Genes or Biomarkers associated with T2D, as Published in the Literature 4 2 RxNorm DailyMed Clinical DB Diseasome RxNorm ChemBL DrugBank Clinical DB 5 Find All FDA-approved T2D Drugs; Find All Patients Administered these Drugs Selected Genes have Strong Correlation to T2D. Find All Patients Administered Drugs that Target those Genes. 3 RxNorm SIDER Clinical DB Diseasome RxNorm ChemBL DrugBank 6 Find Which of these Patients are having a Side Effect of Prandin Find All Patients that are on Sulfonylureas, Metformin, Metglitinides, and Thiazolinediones, or combinations of them Reprinted with permission from Jyotishman Pathak, Ph.D., Mayo Clinic 10

11 Enabling Biomarker Focused Approaches Using Genomics Data Use Case What Dataset? Key Observation Question? What is the survival probability between the two categories of patient population TCGA GBM UC 1 Analysis: Create a Kaplan Meier Plot to Level 1 Survival identify patient time to death between - Survival Time Analysis Primary and Secondary Stage Tumor -Tumor Stage progression Patients Inference: Patient Stratification based on survival time 4 Step Process. 1 Select the Cohorts. Cohort Explorer 2 Analyze R scripts (pre-configured 3 by Cognizant for Differential Expression and Survival analysis) Visualize.. Spotfire Genomic Data Spotfire integration: Seamless transition. No User Selection Question? What are the potential UC 2 Differential Expression TCGA GBM Level 2 - Normalized Gene Expression biological markers that are differentially expressed between the two Subsets? Analysis: Create a Volcano Plot to identify significant changing genes (up regulation or down regulation) Inference: A list of significant changing genes between the two patient population 4 Observe Gene Name Fold Change EGR GAP SERPINA

12 Building a KOL Network Client: Pilot Project for Top 10 Global Pharma Build a network of high performing investigators and partners to improve trial performance and establish thought leadership Be on the cutting edge of science and identify new focus areas Early to market Business Need Solution Semantic integration of data from external and internal sources Manual curation and delivered as actionable insights Monitors new trends and provides alerts and dashboards Assign a confidence level to each of the elements being tracked Data mart that will enable complex analytics and visualization Plan new market entry Identify partners for rare diseases in new/existing markets Quick start clinical trials with a master list of investigators Track and profile new/existing partners Cloud Site and Investigator Selection Benefits Technology Landscape 12

13 Building a KOL Network Geography Social Media Unmet Need Peer Reviews Expert? (based on confidence) Patent Warning Letters Publication Journal Collaboration Inspection Sentiment Performance Metrics Unmet Need Conferences Key Opinion Leader Investigators Clinical Trials Therapeutic Areas Research Focus Rare Diseases Geography Disease of Interest Emerging Countries Identify Patient Population Current Collaboration Site and Investigator Selection KOLs working on DPP IV inhibitors, based in emerging markets with positive performance metrics and publications in journals, conferences and social media Academia/Pharma/ Biotech? BRICS Working with competitors? Identify Patient Population Research Focus Clinical Trials China 13

14 No. of Publications Building a KOL Network KOL s in DPP IV Inhibitors Site and Investigator Selection Geographical Spread of KOL s KOL s with highest number of publications Geographical spread of KOLs and focus on state with maximum KOLs Key Opinion Leader Number of publications in journals, social media and conferences Positive Sentiment Based on FDA Inspections Identifying KOL s with positive FDA investigation report Publications in Media Charts highlighting publications in various media for KOL with an overall sentiment 14 Negative Positive

15 Digital Recruitment & Digital Site Selection Site and Investigator Selection SHARED INVESTIGATOR PORTAL Platform Single sign on (SSO) for seamless investigator experience. DIGITAL RECRUITMENT & DIGITAL SITE SELECTION INDUSTRY TRENDS Quality, streamline processes, regulatory compliance, capacity Costs related to: Training Document exchange Support & maintain Help desk Startup time Investigators Productivity (via reduced redundant tasks & streamlined processes), access to information Study startup time, redundant training Streamlined electronic audit process, insight into trial, harmonized information model Target Outcomes Launch of a common investigator portal, with early capabilities including: Investigator training, Site Feasibility Surveys, Document Exchange, Management of facility and investigator information Single technology platform for investigators to interact with multiple sponsors Enhanced user experience 15

16 Digital Recruitment & Digital Site Selection Leveraging MDM IMPLICATIONS FOR DIGITAL STRATEGY Site and Investigator Selection Master Data Management (MDM) Unique Site ID (and Investigator ID) Updates to Clinical Systems & Processes Greater Transparency on Investigator: Sponsor relationship DIGITAL RECRUITMENT & DIGITAL SITE SELECTION INDUSTRY TRENDS Single set of Standard Clinical Documents & Templates Updates to Clinical Systems & Processes Opportunity to standardise CRO outputs to drive reduced risk to the sponsor organisation Decommissioning of Existing Investigator Portals Reduced TCO through flexible pay-as-you go model Need to integrate with SaaS model Source: Forecasted for 2016 according to Frost & Sullivan 16

17 Master Data Management in Clinical Trials Summarized MDM Approve Protocol Select Investigator Enroll Subject Select Site Conduct Study Analyze & Report The clinical trial process entails a complex set of regulated processes involving multiple participants from heterogeneous domains. We have identified the following pain points in the process which can be optimized to drive more value to the entire drug lifecycle Process Pain Points Clinical MDM Entities X Absence of investigator knowledge resulting in dropouts and termination X Absence of site data repository resulting in penalties due to inaccurate audit reporting X Manual site selection process is inefficient and cause delays X Study and API relationship is not stored optimally for Statistical analysis X Subject enrolment takes longer due to lack of optimized process Study Drug Relation Site Selection API Study Investigator Selection X Multiple points of Study creation resulting in ambiguity and absence of an Unique Study ID Site Investigator 17

18 Biosensors and Imaging Using a BYOH strategy in Clinical Studies Combine, Correlate, and draw inference from Clinical & Device Data streams BT Inhaler Device Collect & Transmit Dosage Time/ Date logs Inhalation flow profile Evaluate 1 Pattern detection, by linking the behavior of biosignals to known phenomenon that occur within the body Data Aggregator Sensor data and adherence insight OUR POV ON BIOSENSORS Patient BT enabled Spirometer Real time pulmonary functions (FEV / PEF etc.) 3 Real World Evidence & Evidence based medicine 2 Clinical decision support for intelligent intervention Collect & Transmit Personalized Communication Engage Patient App 4 Pharma Dynamically reconfigure study based on patient characteristics Physician Intervene Health Coach 18

19 Using a BYOH strategy in Clinical Studies Biosensors and Imaging CONNECTING TECHNOLOGY WITH HUMAN TOUCH Patient Education Medication Reminders Appointment Reminders Virtual Coach Behavioral Change Tools Gamification Feedback & Surveys OUR POV ON BIOSENSORS Hi Tech Hi-Tech PATIENT CARE Hi-Touch Remo te Nurs e Healt h coac h Virtual Coach Remote Nurse Health Coach Virtual Coach Appt. Reminders Goal Setting Patient Follow-Up Drug Reminders Tips & Challenges Patient Education Real-time Messaging 19

20 Adopting Big Data requires a new model for experimental evaluation New Opportunity Data Sources New Technologies New Data Sources New Stakeholders New Processes Review scale up potential Generate idea Enumerate opportunity Technical assessment Refine opportunities as needed Review Design Concept Go/No Go Decision Pilot created Users informed Production project formed Performance optimization Additional requirements Business process redesign, if needed Training and roll out Review Design Concept Go/No Go Decision Pilot created Users informed 20

21 Conclusions Clinical is moving towards an health ecosystem leveraging new types of data. The implications of this shift would be LEVERAGING BIG DATA FOR CLINICAL TRANSFORMATION Need to integrate device data Collaboration with partners for site and investigator selection Patient selection and stratification leveraging genomics data Pharma can get started with an experimental approach and iteratively build the platform 21

22 Thank You 22