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Healthcare is something that must be given utmost priority because it’s one of the most demanding and ever-growing industry in work. The healthcare sector generates approximately 30% of the world’s total data volume, making it one of the largest contributors to global data growth1. Unfortunately, providers of health care along with researchers have been finding it difficult to capitalize on the data made available. The main reason why this is happening is due to breakdown of communication among electronic health records, insurance claims, research databases leading to critical insights being slowed down. This results in subpar patient care and medical breakthroughs.
This is where OMOP CDM comes in. OMOP CDM was developed by medical researchers from around the globe and it provides the framework to bridge the gap between health data and segments. OMOP CDM aids in standardizing every bit of information. The ultimatum is transformed to actionable insights by allowing clinicians and researchers to collaborate compare and organize information.
At MCS, our team has been dedicated to fully utilizing OMOP CDM, resulting in remarkable advancements in healthcare. In this blog, we share detailed information about the model and how our team helps organizations effectively and comprehensively leverage the structure.
Consider this example, how would you assess patient outcomes in 10 hospitals where software is never standard and each has its own way of recording basic healthcare needs like medications and lab tests? Because it’s a time-consuming task, it becomes more difficult.
This problem can be solved by OMOP CDM. The OHDSI community saw this gap and set up Observational Health Data Sciences and Informatics², which transforms unstructured data into a standard form. This unstructured data can be from anywhere in the world – be it a researcher working in a remote clinic in Boston, or a public health official stationed in Berlin, or even a clinician based out of Bangalore. This converts unstructured data into a structured one that can be utilized and analyzed by a demographic with differing skill sets.
Our platform simplifies the transition to OMOP CDM. A New York-based Healthtech company specializing in cancer treatment partnered with Maxis Clinical Sciences to standardize their oncology data using OMOP CDM³. By unifying EHRs, claims, and genomic data, they uncovered patterns in treatment effectiveness that are now guiding clinical decisions³.
OMOP CDM stores data in these straightforward categories2:
These everyday tables all communicate, enabling researchers to tackle non-linear, multi-faceted queries such as:
Medical research primarily relies on Randomized Clinical Trials (RCTs) as its standard but such trials prove to be time-intensive and expensive and tend to omit diverse participants. This gap gets bridged by OMOP CDM through its work to normalize standardized real-world data across electronic health records (EHRs) as well as insurance claims together with patient registries². OMOP CDM enables researchers to study treatment outcomes in actual clinical practice where they discover vital information such as:
Through OMOP CDM researchers gain the capability to merge patient-reported outcomes such as pain levels and quality of life measurement into their clinical research databases². For example:
Healthcare organizations are training machine learning models on OMOP-standardized data to:
As health information becomes more global, OMOP CDM is being explored as a foundation to conduct international studies that reveal health disparities and inform health policies worldwide. This application is particularly important in the context of epidemics, where understanding the health status of different populations is crucial2.
OMOP CDM progressed substantially since the beginning of its development²:
Version 1.0 (2009): Introduced core tables for demographics, drug exposures, and conditions. The initiative concentrated efforts on establishing standardized methods to handle observational healthcare information.
Version 3.0 (2014): Added capabilities for lab assessment measurements together with diagnostic procedures. OMOP CDM allowed researchers to analyze complex relationships between drugs and laboratory results.
Version 5.0 (2022): Integrated support for AI/ML tools, genomics, and global terminologies (e.g., ICD-11). Current systems employ OMOP version 5.0 to execute predictive modeling and conduct cross-border investigations.
Maxis Clinical Sciences implements current OMOP versions to give its clients the opportunity to work with advanced standardized data tools.
The OMOP CDM exists as a technical framework which drives the establishment of a future healthcare system based on smart connectivity. The conversion of fragmented data through a universal language enables healthcare providers to provide precision care while promoting research progress and policymaking focused on inequality reduction.
Maxis Clinical Sciences dedicates itself to transforming our foreseen vision into practical achievement. The team at Maxis Clinical Sciences helps healthcare organizations of all sizes implement OMOP CDM through user-friendly tools while providing personalized analytics training since every healthcare provider needs access to their complete data resources. Through our efforts we establish standardized information systems which build up healthcare across every region of the globe.
