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Apervita Aims to Save Clinicians Time

Apervita, a provider of healthcare collaboration software, announced on Tuesday that Carta Healthcare’s AI-powered data abstraction technology had been incorporated into its platform’s interoperability layer. According to the firms, the functionality is intended to improve Apervita quality evaluation and clinical intelligence capabilities.

Unstructured data, which can account for up to 80% of the information in electronic health records, can make an interpretation, analysis, data management, and interoperability complex. According to the organizations, this can have an impact on quality score accuracy and reimbursement. With this in mind, Apervita new tool will evaluate patient information using a combination of technology, people, and procedures to provide quality reporting and clinical insights.

The system extracts data from medical records using AI-enabled natural language processing, which nurse data abstraction professionals subsequently vet to lower the time required for abstraction. The corporations say that human data abstraction, especially when it comes to quality measures, takes time away from professionals who could be caring for patients.

Rick Howard, Apervita chief product officer, said in an email to Healthcare IT News, “Provider organizations with great quality scores often have armies of chart abstractors. However, smaller and medium-sized organizations often only have the resources to do the bare minimum of chart abstraction.” According to the firms, the combination of AI and human power contributes to improved accuracy. Apervita cites sepsis as an example of an illness recognized in records using markers such as a yellow wound. In addition, studies have proven that unstructured data has a lot of predictive power when it comes to clinical research.

Of course, combing through all of that data may be difficult, which is why many software giants are turning to AI for help. Such tools can also assist health IT professionals in spotting “nightmares” lurking in unstructured data, such as potentially sensitive personal information that has been shared unintentionally, according to one expert.

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