Turning Data Into Value: from Hype to Wisdom

Transcription

1 Turning Data Into Value: from Hype to Wisdom Francisco Lupiáñez-Villanueva Profesor, Open University of Catalonia, Partner Open Evidence

2 Chair UOC-BSA: Health, Big data and Data analytics Universitat Oberta de Catalunya Scientific evidence for informed decision-making Integrated health and social care provider The UOC-BSA Chair was possible thanks to the legacy of Romà Núñez Lloreda, a resident of Badalona who generously granted an inheritance to BSA 2

3 Mission: towards Health Population Management To develop significant advances in the application of Data Analytics and Big Data within health and social care through an interdisciplinary approach and the participation of all actors within the health system. Intersection between Clinical and Social care practice and Management 3

4 Badalona Serveis Assistencials Based to the north of Barcelona Cities of Badalona, Montgat, Tiana, Teià, Masnou and Alella Property of the City Council 100 % public funding Complex financing system which varies according to the care level Providing from both health and social services 4

5 From Hype to Wisdom: Real World Evidence Data lab allows to translate raw data into meaningful insights and knowledge that support more efficient and effective healthcare decisions. Guidelines. Open software. Simplification of the learning curves. Heterogeneous data. Data Management / Metadata Repositories. Centralised and distributed storage and processing. 5

6 Platform oriented to problem solving Deploy the technology platform through concrete analytical projects avoiding the creation of technological silos. Strategy: Identify the health areas, define the analytical goals, manage the integration of these technologies into existing health care providers and facilitate the cultural change towards new health care paradigms. Primary care Secondary care Social care Pharmacy Medical laboratories Source Layer Storage Layer Data Layer Cognitive Layer Unstructured Data Structured Data ETL Anonymizer module Loockup table ETL Central Data Repository Document-oriented storage Text-oriented storage Distributed storage engine ETL Distributed Processing Business Intelligence Artificial Intelligence 6

7 Platform oriented to problem solving End users: Managers, physicians, researchers, staff Visual analytics OLAP/MD analysis Data mining Big data mining / Deep Learning Cognitive layer Classical Data production Distributed Data production Data layer Massive data distribution GeoSpatial data RDBMS Integration layer ACID data bases Semi structured data Flexible schemas Classical ETL Distributed ETL Source and storage layer Datasource layer Data Lake Powered by 7

8 From raw data to actionable knowledge Multidisciplinary team Identify analytical objectives Data Understanding Data gathering Collect and review data Get insights Actionable knowledge HEALTH DATA Data processing and transformation Select and clean data Data mining Develop & validate models Data storage & modelling Manioulate data & storage 8

9 Providing solutions to real problems: social care Snapshot of the metrics related to occupancy, resource consumption and adequacy over the defined dimensions at the current time or at a given time. Longitudinal analysis showing metrics in a given period of time to analyse the evolution of such indicators over time. Forecast of occupancy and use of resource based on historical data to provide a mechanism to better manage the activity of the different services. Prediction models of patient s hospitalization according to his or her case 9

10 Providing solutions to real problems: cancer Data lake identifying patients across the continuum of care Natural Language Processing: from unstructured text to structure data from Pathology Reports (TNM) Sistemas clínicos Requerimientos Analíticos Documentoriented storage Text-oriented storage Framework analítico Distributed storage engine Algoritmos de aprendizaje automático/profundo Cohorte de pac. Caso 1 Cohorte de pac. Caso 2 From care and cure to prevention: screening, health prevention, geo-localization Population Health Management across the continuum of care 10

11 From hype to wisdow High impact in the organization: towards data driven organisation Before: Lack of a single criterion in the data or indicators used in each department, resulting in silos of information. After: Consensus reached between the different stakeholders (general management, clinical departments, and the economic and financial management department) about the relevant information to be used. Better performance, better health Better management. More efficient use of resources. Quality of life and health status 11

12 From hype to wisdow: opening evidence Customized Predictive Boxes Real World Data Surveillance Agent Data lake and NLP corpus 12

13 Lessons learned Domain tailoring Avoid all-in-one suites Flexible tools Coexistence with existing solutions Training and skill Building ROI management (through different projects) Data governance And Metadata management 13

14 Thank you! Francisco Lupiáñez-Villanueva Profesor, Open University of Catalonia, Partner Open