Detailed Agenda for 2015 OHDSI Symposium now available!

Posted on August 24, 2015 by

Time	Description
7:30 – 8:30am	Registration
8:00 – 8:30am	Introductions
8:30 – 10:00am	Welcome to the journey: Overview of OHDSI : past, present, future Speaker: Patrick Ryan,PhD, Sr. Director and Head, Epidemiology Analytics, Janssen Research & Development Description: Observational Health Data Sciences and Informatics (OHDSI, pronounced “Odyssey”, http://ohdsi.org) is an international collaborative whose goal is to create and apply open-source data analytic solutions to a large network of health databases to improve human health and wellbeing. OHDSI’s mission is to transform medical decision making by creating reliable scientific evidence about disease natural history, healthcare delivery, and the effects of medical interventions through large-scale analysis of observational health databases. We will provide an overview of OHDSI’s focus to research, develop, and apply shared solutions for 3 key analytical use cases: clinical characterization, population-level estimation, and patient-level prediction. We will highlight the progress to date and provide a vision for how an open-science approach to evidence generation can accelerate observational research around the world.
10:00 – 10:15am	Break
10:15 – 11:15am	OHDSI in action: Real-world evidence for clinical characterization Speaker: George Hripcsak MD, MS, Chair of the Department of Biomedical Informatics at Columbia University Medical Center Description: The Observational Health Data Sciences and Informatics (OHDSI) collaboration created an international data network with hundreds of millions of patient records from countries on four continents. To characterize the diversity of populations and the variance in care, OHDSI studied treatment pathways for three common diseases. The time from envisioning the study to analyzing the results from the first seven sites was just three weeks. Heterogeneity among treatment pathways was studied, looking at country, type of practice, and type of record (health record versus claims data) as sources of variance. Trends in monotherapy were studied, as well as uniqueness of the pathways. Large-scale international observational research appears to be feasible.
11:15 – 12:15pm	OHDSI in action: Open-source analytics for patient-centered evidence Speaker: Jon Duke, MD, Director of Drug Safety Informatics, Regenstrief Center for Biomedical Informatics Description: OHDSI’s mission is to transform medical decision-making by creating reliable scientific evidence from observational health data. Implicit in this mission is the translation of the knowledge generated by the OHDSI community into tools that benefit and inform patients and providers directly. In this session, we will introduce one such tool recently developed by the community that connects traditional health information resources with data generated by the OHDSI network. Specifically, we will delve into the drug product labeling for several commonly prescribed medications and show how OHDSI can complement and illuminate the safety information found in these important documents.
12:15 – 2:45pm	OHDSI collaborator showcase Poster session of OHDSI research Software demonstrations of OHDSI open-source tools: HERMES for vocabulary exploration HERMES (Health Entity Relationship and Metadata Exploration System) is a web based tool for searching and navigating the vocabulary within the OMOP Common Data Model (CDM). In addition to the search and navigation capabilities, HERMES also provides features to curate and export custom sets concept identifiers for use in cohort definitions. CALYPSO for study population exploration CALYPSO (Criteria Assessment Logic for Your Population Study in Observational data) is a web user interface to define, instantiate and evaluate a study population and the implications of inclusion criteria CIRCE for cohort definition CIRCE (Cohort Inclusion and Restriction Criteria Expression) is a cohort definition and syntax compiler tool for the latest version of the OMOP common data model HERACLES for quality of care HERACLES (Health Enterprise Resource And Care Learning Exploration System) is an application that allows you to explore healthcare quality, cost, and practice patterns using the OMOP Common Data Model. HERACLES provides high-level visualization tools and deep-dive capabilities to look at standardized quality metrics (e.g., NQF) as well as utilization across a variety of patient cohorts. ACHILLES for data characterization ACHILLES (Automated Characterization of Health Information at Large-scale Longitudinal Evidence Systems) is a platform which enables the characterization, quality assessment and visualization of observational health databases. ACHILLES provides users with an interactive, exploratory framework to assess patient demographics, the prevalence of conditions, drugs and procedures, and to evaluate the distribution of values for clinical observations. Methods Library We are developing a library of open-source tools for large-scale analytics, including population-level estimation and patient-level prediction. Our population-level estimation work is focused on developing open-source software for safety surveillance and comparative effectiveness. Already available is a tool for new-user cohort studies using propensity and outcome models generated through large-scale regularized regressions. Still under development but soon available are tools for patient-level prediction and other study designs such as self-controlled case series and self-controlled cohorts, as well as tools for evaluating and calibrating population-level estimation methods Vocabulary Resources The Standard Vocabulary is a foundational tool initially developed by some of us at OMOP that enables transparent and consistent content across disparate observational databases, and serves to support the OHDSI research community in conducting efficient and reproducible observational research. During this time, lunch will be provided
2:45 – 3:45pm	Panel Discussion – Experiences from the OHDSI international data networkDescription: A common data model (CDM) allows for the systematic analysis of disparate observational databases. The concept behind this approach is to transform data contained within disparate databases into a common format (data model), and then perform systematic analyses using a library of standard analytic routines that have been written based on the common format. The OHDSI data network has adopted the Observational Medical Outcomes Partnership (OMOP) CDM which currently covers over 600 million patients within 11 countries around the world. During this panel session we will hear from OHDSI data holders from America, Europe, Asia and Africa. Each panelist will share their perspectives on: (1) Why they chose to participate in the network (2) The benefits and challenges of the data network and (3) Shared strategies to make our collaboration stronger.Panelists: Christian Reich, MD, PhD, Vice President of Real World Evidence Systems, IMS Health Rae Woong Park, MD, PhD, Professor, Ajou University School of Medicine, South Korea Peter Rijnbeek, PhD Assistant Professor, Erasmus Medical Center Parsa Mirhaji, MD, PhD, Director of Clinical Research Informatics at Montefiore Healthcare System, Albert Einstein College of Medicine Paul Biondich, MD, Founder and President, OpenMRS
3:45 – 4:00pm	Break
4:00 – 5:30pm	Panel Discussion – The Value and Challenges of Evidence from Observational Data: A Multi-Stakeholder PerspectiveDescription: To close out the day, we want to hear back from the broader healthcare community. The aim of this panel is to give each stakeholder group an opportunity to share their perspectives about the OHDSI program and how OHDSI tools could benefit their work. This discussion will focus on: (1) Multi-stakeholder perspectives on the current state of observational data use for generating evidence to support decision making (2) The most immediate / largest needs that require evidence from observational data (3) Reflections about the objectives of the OHDSI community and progress that has made to date (4) Key drivers within each stakeholder group which will enable reliable evidence generation from observational data Moderator: David Madigan, PhD, Executive Vice President and Dean of the Faculty of Arts and Sciences at Columbia University Panelists to include stakeholder representatives from academia, government, industry and health systems
5:30pm	Closing remarks