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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 key patient information from medical records.
Patient registries are important to health and life sciences organizations because they provide details into patient’s disease progression and outcomes. Our platform identifies and extracts important insights 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.
The Ember Platform decreases the cost of NSQIP reporting while also analyzing and processing a larger population of surgical cases to increase the overall quality of care. Today, it can take a Surgical Clinical Reviewer or quality nurse between 30-120 minutes or more to review one surgical case, impacting cost and time. Using AI and NLP along with our proprietary clinical rules and extraction algorithms, we automate the end to end NSQIP reporting process and 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 deeper quality, cost, and outcomes analysis
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