Health Data Interoperability: OMOP

 The Observational Medical Outcomes Partnership (OMOP) was a public-private partnership established to inform the appropriate use of observational healthcare databases for studying the effects of medical products. Over the course of the 5-year project and through its community of researchers from industry, government, and academia, OMOP successfully achieved its aims to:

1) conduct methodological research to empirically evaluate the performance of various analytical methods on their ability to identify true associations and avoid false findings,
2) develop tools and capabilities for transforming, characterizing, and analyzing disparate data sources across the health care delivery spectrum, and
3) establish a shared resource so that the broader research community can collaboratively advance the science.
The results of OMOP's research has been widely published and presented at scientific conferences, including the annual OMOP Symposium.
The OMOP Legacy continues...
The community is actively using the OMOP common data model and vocabulary for their various research purposes. Those tools will continue to be maintained and supported, and information about this work is available at: http://omop.org/CDM.
The OMOP Research Lab, a central computing resource developed to facilitate methodological research, has been transitioned to the Reagan-Udall Foundation for the FDA under the Innovation in Medical Evidence Development and Surveillance (IMEDS) Program, and has been re-branded as the IMEDS Lab. Learn more at: http://imeds.reaganudall.org/
Observational Health Data Sciences and Informatics (OHDSI) has been established as a multi-stakeholder, interdisciplinary collaborative to create open-source solutions that bring out the value of observational health data through large-scale analytics. The OHDSI collaborative includes all of the original OMOP research investigators, and will develop its tools using the OMOP common data model and vocabulary. Learn more at: http://ohdsi.org‏/


Design Principles
The CDM is designed to store observational data to allow for research, under the following principles:
1. Data protection. The CDM is aims at providing data storage optimal for analysis, instead of
reflecting transactions in the course of patient care. In addition, all data that might jeopardize the
identity and protection of patients, such as names, precise birthdays etc. are limited. Exceptions
are possible where the research expressly requires more detailed information, such as precise
birth dates for the study of infants.
2. Reuse of existing models. In designing the CDM, industry-leading data modeling efforts are
leveraged, such as HL7 RIM, the HIMSS EHR Definitional Model, the i2b2 Hive framework, the
HMORN Virtual Data Warehouse, etc.
3. Design of domains. The domains are modeled in a person-centric relational data model, where
for each record the identity of the person and a date is captured as a minimum.
4. Standard vocabulary. To standardize the content of those records, the CDM relies on a
Standard Vocabulary containing all necessary and appropriate corresponding standard
healthcare concepts.
5. Reuse of existing vocabularies. If possible, these concepts are leveraged from national or
industry standardization or vocabulary definition organizations or initiatives, such as the National
Library of Medicine, the Department of Veterans' Affairs, the Center of Disease Control and
Prevention, etc.
6. Technology neutrality. The CDM does not require a specific technology. It can be realized in
any relational database, such as Oracle, MySQL etc., or as SAS analytical datasets. The tools
the OMOP team or collaborators publish will be instantiated in a specific technology (OMOP uses
both Oracle and SAS to store and analyze data) and may require some small adaptation if other
technologies are utilized.
7. Scalability. The CDM is optimized for data processing and computational analysis to
accommodate data sources that vary in size, up to and including databases with tens of millions
of persons and billions of clinical observations.

Comments

Post a Comment

Popular posts from this blog

Building a Powerful Chatbot with OpenAI and Elasticsearch Integration

 In the realm of modern technological advancements, harnessing the capabilities of artificial intelligence (AI) has become indispensable. One such manifestation of AI prowess lies in the creation of intelligent chatbots that not only understand user queries but also interact with data repositories to fetch relevant information. In this article, we'll delve into the process of developing a sophisticated chatbot using OpenAI's cutting-edge technology and seamlessly integrating it with Elasticsearch for efficient data retrieval. Understanding the Components: Before diving into the intricacies of building our chatbot, let's dissect the key components involved: OpenAI: OpenAI provides state-of-the-art natural language processing (NLP) capabilities, empowering developers to build conversational AI agents. By leveraging OpenAI's API, we can imbue our chatbot with the ability to comprehend and respond to user queries effectively. Elasticsearch: Elasticsearch serves as a powerfu...
Read more

Legal Text Analysis Project: Leveraging NLP for Enhanced Understanding and Insight

Image
 A key point in the development of NLP solutions is the consideration of language proficiency. The terms and jargon used in specialized sectors, such as the medical sector, differ greatly from those used in the financial sector, which in turn are not comparable to those in the legal sector. The legal texts contain a series of specifications such as: The breadth of the domain in terms of textual typology. The variety of target groups. The linguistic features of the domain. Furthermore, in the latter case, not only is the legal terminology of the domain covered, but it also tends to co-occur with terminologies from all areas. The limited number of NLP resources and tools tailored to the overall domain. The predominance of English, since most of the resources and tools available are developed for processing texts in English. A slowed adoption of smart technologies in the legal and administrative sector compared to other sectors such as the biomedical or financial sector. The heterogen...
Read more

Streamlining Log Analysis with Logstash, Filebeat, and Elasticsearch

Image
 In the realm of IT infrastructure management, effectively monitoring and analyzing application logs is crucial for maintaining system reliability and performance. To address this imperative, I've devised a robust system harnessing the capabilities of Logstash, Filebeat, and Elasticsearch. At its core, this system is designed to seamlessly ingest, process, and index application logs into Elasticsearch for comprehensive analysis and visualization. Let's delve into the key components and functionality of this integrated solution: Log Ingestion with Filebeat: Filebeat serves as the lightweight shipper responsible for tailing application log files and forwarding them to Logstash for processing. Its efficient design ensures minimal resource overhead while guaranteeing real-time log collection from diverse sources. Data Processing with Logstash: Logstash acts as the central processing engine, facilitating data transformation, enrichment, and filtering before indexing it into Elastics...
Read more