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The data required to measure quality across numerous clinical variables exists deep within the Electronic Health Record (EHR) systems in the form of unstructured clinical notes. The unstructured nature of this valuable data makes it difficult for hospitals and health systems to automate analysis and reporting.
Registry reporting is costly due to the significant manual effort of abstracting clinical data including key patient information from medical records.
Patient registries are important to health and life sciences organizations because they provide details about a patient's disease, progression, and outcomes. MetiStream’s Ember Platform automates the clinical abstraction and curation of clinically relevant data from unstructured healthcare data to automate and streamline the process for registry reporting.
Identify, extract, and format patient data from the EHR and clinical documents.
Connect and upload data to designated registry datastore.
Identify population of patients for reporting based on inclusion and exclusion criteria.
Healthcare organizations manually abstract and analyze clinical data across more than 150 variables to support surgical quality reporting and care programs.
MetiStream’s Ember Platform decreases the cost of NSQIP reporting while analyzing and processing a larger population of surgical cases to increase the overall quality of care. Today, this process can take a surgical clinical reviewer or quality nurse anywhere from 30 to 120 minutes or more to review one surgical case — impacting cost and time. Using AI & NLP technology, along with proprietary clinical rules and extraction algorithms, MetiStream automates the end-to-end NSQIP reporting process to impact the overall quality of care.
Analyze all patient cases to extract those that meet NSQIP requirements for reporting.
Easily integrate to the NSQIP Registry for data upload and reporting.
Take action on the extracted data to conduct a deeper quality, cost, and outcomes analysis.
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