Big Data and CancerLinQ

Transcription

1 Big Data and CancerLinQ Peter Paul Yu, MD, FACP, FASCO Immediate-Past President American Society of Clinical Oncology TACOS Phoenix, Arizona November 13, 2015

2 Disruptive Change in Oncology driving change in oncology digital health value-based care precision medicine

3 To achieve the level of success in precision medicine for cancer care that U.S. President Barack Obama and others are anticipating, sequence data needs to be linked, in real time, to the patient sitting in front of his or her doctor. Integrated genomic and clinical data will also need to be available, in a searchable way, to a broad community of practitioners and researchers. Mark A. Rubin, Weill Cornell Medical College Nature; April 2015

4 Evolution in Complex Disease Management Multi-disciplinary cancer care Biology based, patient specific care Surgery Radiation Chemotherapy Genetically based Immune system-boosting treatments Role of metabolome in tumor growth

5 Current Disease Paradigm Presented By Manish Shah at 2015 ASCO Annual Meeting

6 Precision Medicine Personalized Medicine Transcriptomics Environment Genomics Behavior Epigenetic Data Patient Preference Metabolome Beliefs

7 What is Value? Value = Improved Patient Outcomes Financial & Non-financial Cost

8 Slide 2 Presented By Michael Porter at 2015 ASCO Annual Meeting

9 Outcomes Our goal is Health, not Healthcare Our benchmarks are outcomes not process Our goal is optimizing cancer patients health Palliative Survivorship

10 The Outcome Measures Hierarchy Presented By Michael Porter at 2015 ASCO Annual Meeting

11 Breast Cancer Outcomes (modified from Michael Porter N Engl J Med 2010;363: ) Tier 1 Primary Outcomes Tier 2 Short Term Toxicities Tier 3 Long Term Toxicities 5 year survival Breast preservation Treatment duration Febrile neutropenia Neuropathy Second malignancy Lymphedema

12 Full Cycle of Care Prevention Treatment Diagnosis Treatment Planning Survivorship Palliation

13 Creating a Value-Based Health Care Delivery System<br />The Strategic Agenda Presented By Michael Porter at 2015 ASCO Annual Meeting

14 BIG DATA OFFERS A NEW UNIVERSE OF POSSIBILITIES

15 A new era with big data analytics Volume Variety Velocity Veracity 15

16 From Data to Learning Real-world applicability Rapid learning Knowledge base Data Understanding

17 Van Allen EM, Wagle N, Levy MA J Clin Oncol 31:

18 Public BRCA1/2 Variants ClinVar: 6431 variants Breast Cancer Information Core (BIC) 3793 Sharing Clinical Reports Project (SCRP) 2148 InVitae 4220 Ambry Genetics 1318 GeneDx 286 Counsyl 112 OMIM 81 LOVD: 3262 variants UMD (France): 3913 variants

19 Clinical Practice Guidelines Policy Statements Provisional Clinical Opinions Systematic Reviews Published Research

20 Healthcare Systems KB Operational data: efficiency and cost Resource consumption Patient reported outcomes Variation in practice Pharmacy: compliance and adverse events Provides local context

21 Transforming Data Bases into Knowledge Precision Medicine DB Internet of Things DB Vocabularies Ontologies Knowledge Bases Clinical DB Cancer Registries DB Insurance DB

22 Learning Application of Knowledge Bases to patient care Clinical Decision Support Systems Validation by Outcomes Measurement

23 Clinical Decision Support Class Information Management Situational Awareness Patient Application Examples InfoButton, Up ToDate Alerts, Dashboards, Control Charts Chemotherapy Pathways

24 Healthcare Systems KB CPGs KB Precision Medicine KB Internet of Things Clinical Decision Support Shared Decision Making Outcomes

25 Promise of Health IT Change agent Accelerate learning Reduce healthcare cost Engage the ecosystem Patients and families Research: translational, clinical, population Payers, Regulators

26 Health Information Technology Reality Costly May 2015 Health system cost > $ 1 Billion Meaningful Use 4,816 hospitals & 535,000 providers $30.9 Billion Dissatisfaction high Disruption in workflow New root causes of error Lack of interoperability

27 Rapid Learning Hypothesis Generation Clinical Care Process & Outcomes Building Knowledge Bases Applying Clinical Decision Support Tool

28 Current Challenges 1. Learn from every patient 2. Harness data in powerful new ways

29 1.7 MM people diagnosed with cancer in the US Only3% 3% enroll in clinical trials.

30 and the patients we see every day tend to be older less healthy and more diverse 25 % of clinical trial patients are vs 61 % of real-world patients are % of kidney cancer patients were not healthy enough to qualify for the trials that supported the approval of their treatments 2 90 % 23 % vs white 3 of patients in NCI trials are of the US POPULATION is non-white 3 than clinical trial patients. 1. Lewis JH, et al. Participation of patients 65 years of age or older in cancer clinical trials. J Clin Oncol. 2003;21: Mitchell AP, et al. Clinical trial subjects compared to "real world" patients: generalizability of renal cell carcinoma trials. J Clin Oncol. 2014;32(suppl): Taking action to diversify clinical cancer research. National Cancer Institute Web site. Accessed July 23, 2014.

31 Real-world patients Data aggregation Population health outcomes Improved healthcare operations Improved quality of care Clinical decision support

32 When deployed CancerLinQ will 1 2 Analyze medical records to uncover patterns that can improve patient care Provide guidance by identifying the best evidence-based plan of care 3 Provide insights for data exploration and hypothesis generation 32

33 Improving Quality for Patients, Providers, Researchers CancerLinQ improving QUALITY of care and enhancing outcomes; additional benefits: Patients Improved outcomes Clinical trial matching Safety monitoring Real-time side effect management Patient-reported outcomes Providers Real-time second opinions Observational and guideline-driven clinical decision support Real-time access to resources at the point of care Quality reporting and benchmarking Research/Public Health Mining big data for correlations Comparative effectiveness research Hypothesis-generating exploration of data Identifying early signals for adverse events and effectiveness in off label use 33

34 CLQ platform: Solution architecture Data Extraction Data Transformation Data Load Data Ingestion Apply Ontologies Data Integration Data Processing Database Application Server Services Content Data Model Portal (User Interface) Quality Reports Data Exploration Patients Like Mine Applications Tier 1 Tier 2 Tier 3 34

35 HANA Healthcare Platform SAP HANA Health Platform Analytics Extended App Services (Web Server) Rules Engine Procedural App Logic DB-oriented Logic Applications R Integration Text Mining Predictive Decision Tables Unstructured SQL Scripts Clinical EHR Financial Fin Omics Biometric

36 SAP HANA: Enabling CancerLinQ Supports Any Device Any Apps Any App Server SAP Healthcare and Life Science Applications SQL MDX R JSON Open Connectivity SAP HANA Platform SQL, SQLScript, JavaScript Spatial Search Text Mining Application & UI Services Stored Procedure & Data Models Business Function Library Predictive Analysis Library Database Services Planning Engine Rules Engine Omics Engine Integration Services Healthcare Integration Services Transaction Unstructured Machine HADOOP Real Time Locations Other Apps SAP HANA platform for healthcare industry-specific extensions of SAP HANA. Providing breakthrough capabilities for healthcare and life sciences applications from SAP and its partners while reducing time-to-value and TCO.

37 Interoperability: A Key Challenge An interoperable health IT ecosystem makes the right data available to the right people at the right time across products and organizations in a way that can be meaningfully used by recipients. -Office of the National Coordinator for Health Information Technology (ONC)

38 Vanguard Practices 15 practices Geographically dispersed Roughly 350 physicians Records from ~ 500,000 patients 38

39 CancerLinQ Clinical User Portal My Favorites 39

40 CancerLinQ Quality Performance Indicators 40

41 Real-Time Quality Measurement and Improvement My Favorites 41

42 Patient Care Timeline My Favorites 42

43 Patient Care Timeline My Favorites 43

44 Drawing Insights from Combined Data 44

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49 How Will CancerLinQ Transform Clinical Research? Hypothesis generation from observational data, e.g., off label use, risk stratification Patterns of care and trend analysis Cohort identification, frequency of target pop. Cohort assembly, location of target pop. Eligibility assessment, trial matching Registry-driven RCTs Comparative effectiveness assessments Collection of PROs 49