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The healthcare sector is one of the industries that generate the most data. Tons and tons of sensitive information aggregate just for a single patient. According to the study published by Statista, the amount of global healthcare data is expected to increase dramatically by the year 2020 with a projected 2,314 exabytes of new data from the early estimation of 153 exabytes in 2013. It is hard to keep up with electronic healthcare records (EHR) this big, let alone handle it. Due to this, clinical professionals have adapted and learned a way to navigate through volumes of data, including clinical claims, authorizations, and eligibility just to name a few.
In modern clinical environments, electronic healthcare records (EHRs) are stored in centralized databases that contain thousands — even millions — of entries. To retrieve only the relevant data from this sea of information, healthcare organizations rely on Structured Query Language (SQL).
SQL is a programming language that is used to communicate and manipulate databases. SQL programs are implemented in healthcare businesses and organizations as a means to access and manage the information stored in their database.
SQL is an efficient configuration that works to deliberately extract data by writing codes and speaking various SQL dialects. It is how the system filters data down to the last set of criteria and produces the exact report that you’re searching for. The SQL database serves as the foundation for the artificial intelligence (AI) model, allowing the system to encapsulate and store integrated data.
Every healthcare claim or authorization is tied to a complex web of patient information — from demographics to medical history to financial data.
Medical record number (MRN) or patient identifier is the primary way to link all your records together. By knowing the MRN and other patient information, you can quickly assemble your criteria to create certain SQL queries. The dataset generated from your query can be used as the basis for making adjudication rules in claims and authorizations.
With QuickCap’s SQL Query Builder platform, you can easily create database queries and filters from the system. The AI-based generated report can be used to decide the right adjudication rules of certain claims and authorizations. This is an efficient way to piece the data together to set up accurate auto adjudication parameters. The system can perform even complex data-gathering operations without you having to memorize codes. The platform has its own list of tag holders that corresponds to a code to retrieve the needed data that you might require for any reports and most especially for the auto adjudication rules.
QuickCap’s tools and functionalities are very extensive. It even has the option to export the results of the queries to other specified formats and view its charts and tables. The SQL query that you build will be linked to an analyzed report where you can make a graph out of to refer and use.
QuickCap provides the support that you might need in handling big healthcare data for authorization and claims adjudication rules.
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