Data Aggregation and Structuring
Understanding provider’s core profitability drivers, quality metrics, clinical outcomes and operational bottlenecks and efficiencies requires linking materials management, CPOE, practice management, EMR, Quality documentation hosts of operational workflow systems and inventory systems including Excel spreadsheets to a data model which will aid in strategic decision support and provide cutting edge healthcare analytics. Our ability to work with various data sources separates us from the rest.
TEXTUAL ETL – PROCESSING MEDICAL RECORDS
Since the beginning doctors have communicated their thoughts, prescriptions and diagnoses in the form of text, even in EMR. The problem with text – narrative - is that it cannot be easily analyzed and understood by the computer. In order for text to be easily analyzable by the computer, text must be read and transformed into a standard data base format.
Disambigution of Textual Data
Healthinfomap uses Textual ETL AI - driven technology to help us read medical records in a narrative format and transform those records into a standard data base format.
Textual ETL makes use of 67 (and counting) different internal algorithms to read text, interpret text, and identify the context of the text. The algorithms used by textual ETL are able to be dynamically controlled by the operator and the software itself.
The profound division between structured and unstructured data has prevented organizations from including unstructured, textual data from being used for analytical purposes. Using TextualETL™, you can bring together structured data and unstructured data to:
- Integrate textual data into a data base;
- Create an unstructured data base that is then integrated with structured data in a data warehouse;
- Read and process unstructured data so that textual analytics can be done;
- Leverage the investment you have already made in Business Intelligence;
- Optimize your analytical infrastructure so it can be used – as is – and include unstructured data as part of the decision making process
Once the unstructured data resides in any of the standard technologies such as Oracle, DB2, Teradata, and NT SQL Server, standard analytical tools such as Business Objects, Cognos, MicroStrategy, SAS, Tableau and other analytical and BI visualization technologies can be used to access, analyze, and present your unstructured data.
These new applications can look at and retrieve textual data and can address and highlight fundamental challenges of the healthcare providers that have previously been unrealized.
Textual ETL Features:
Identifies the context of text
Restructures the medical document text into a relational data base
Operates on any form of text and languages (formal, slang, doctor’s notes, etc.)
Operates on output from OCR and voice transcription
Produces output in any standard dbms – Oracle, Teradata, SQL Server, DB2, Netezza, Hadoop, and others
Operates in a parallel manner, so that the output is not constrained by the capacity of a machine
Makes use of externally created taxonomies
Has its own set of taxonomies and has a suite of tools for building and maintaining custom built taxonomies
The world of medicine is full of text. To be meaningful, the text needs to be gathered and integrated. Without integration of medical text, an organization merely has an interesting collection of words and in this industry PEOPLE ARE DYING without this information. Once our product collects the integrated medical text and analyzes it, all sorts of analytical possibilities arise.