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Omicron, COVID Variants, and Data Chaos 

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One lesson that the pandemic has taught us is the difficulty of maintaining data systems with rapidly changing viral variants.

Clinical research and data analytics teams need tools that will not just fix data chaos but properly store and analyze data in the first place.  Nothing proves this so well as the cascade of COVID-19 variants.  

First, COVID-19 shut the world down for months. Then the Delta variant heralded a new wave of fear and uncertainty. Today, the Omicron variant is still running rampant as the dominant strain of the novel Coronavirus. Without worldwide herd immunity, nothing is stopping COVID from mutating further. And every new variant requires a study, research, and conclusions by professionals in medical science. 

The constant evolution of the virus has required increased capacity for public health to collect, process, and appropriately share data. Each new variant leads to its own collection of lab tests and results, the sheer mass of which for every variant can clutter up a system. This data chaos makes it difficult to attribute cases to individuals who tested positive and slowing or even confusing the research process. Though of course all variants are studied separately, the sheer mass of tests for new strains of COVID can lead to all sorts of possible mix-ups.  

Omicron is a perfect example of how new and more transmissible variants cause data chaos. The more contagious a strain, the more people it will infect, the more tests will be done. More tests mean more data crowding up an already overcrowded system. This causes the volume of data states receive from labs to grow considerably, which means more data to sift through, leading to slowdowns and disorganization. And each new strain creates another new round of testing.  

Data chaos can be a major hindrance to accurate and timely research. Unraveling an already tangled system can take even experts a long time. The longer researchers and analytics systems have to spend parsing through dense data for the right scrap of information, the later that information is useful for decision making.   

The solution is a cutting-edge, efficient data platform. Such a system would automate and simplify the storage and organization of data as it is entered and ensure compliance with national data exchange standards such as HL7 and FHIR. This standards-based approach makes sure all information is stored and recorded in a manner understandable to other systems using the same standards. 

Modern data platforms can also reconcile and translate files from different systems. This allows vital communication between multiple sources of data without letting either source muddle the other. Files are received and sent back without confusion about which data came from which source. Chaos is thus minimized so that systems and researchers alike can access the correct data faster and more confidently.  

Eliminating data chaos will have a powerful streamlining effect on our vital COVID analytics processes. Required information will be easier to query and plug into tests and studies. Removing bloat, breaking down silos, and refining information will reduce the overall strain on systems.   

With better organized data, we will save on storage and make research more cost-efficient. As new COVID variants arise (hopefully each less dangerous than the last), they will be kept from cluttering up old records. This will make each variant easier to study on its own and alongside other strains to find differences, similarities, and perhaps even a cure.  

Data chaos is an inevitable result of letting data pile up until it is too dense to sift through easily. By adopting the newest, most progressive data platforms, we can eliminate the chaos as soon as we input the data; minimizing slowdowns, confusion, and error. When platforms eliminate chaos, we are prepared for as many new variants as COVID throws our way.