Healthcare data often contains numerous intracacies that may go overlooked and can cost invaluable time and money. At Baltimore Health Analytics we specialize in solving present challenges in healthcare data management and have developed tools to streamline processes for our clinets.
Our tools are desgned with the end-user in mind and the flexibility to adapt to unique client needs.
The time it takes to create aesthetically pleasing reports creates a level of diffculty to consistantly track stars progress.
Prebuilt reports that help you track rates, gaps, cutpoints, year over year progress, and the improvement measures. These are designed to be easy to interpret, to use in executive presentations and to manage your Stars improvement through HEDIS®.
Creating realistic test data is a challenge that is further hindered by PHI and HIPPA concerns when using production data.
BHA's Extraction and Scrambling data tool uses production data as a template and creates a deidentified dataset that maintains the same referential integrity that can't be traced back to the original patient. Our ExScrambler script that unwinds the table dependencies, scrambles the patient names, birth dates and service dates and builds a copy of each table for a random sample of members.
Exscrambler does not just use random data. Instead, it maintains the utility of the patient record for development. For example, Ruiz Gonzalez of 123 Oak St who had cancer when he was 56 becomes the template for William Ahmed, of 4245 Main St who also had cancer at age 56 but on a different date.
Your vendors, your medical management departments, your network providers -- all of your partners -- have medical charts readily available for your chart abstraction efforts. Unfortunately, you can't use them because they all have different systems for storing and naming the files. One system has SMITH_JOHN_09091945.pdf another has John Smith Sr 9-9-1945.pdf and another has SMITH_J_356245.pdf, yet they are all the same person. You could go back and ask each partner to create a mapping file, however it is not the most efficent method.
We created MemberFilenameMatcher to solve this situation and make charts usable for HEDIS® abstraction. It figures out all the ways you might write a member id, date of birth and name, then applies search rules to search the filesystem for a match -- including zip files. It even gathers the files together and creates a manifest list for upload into BHA's Ocular OCR assisted abstraction system.
You have dozens of files with mostly the same columns, but not quite. You can't just concatenate the files because one of them has a lab value or or other extra content. It'd be much easier to manage if you could put it all into the same table.
member_id, birth_date, member_name, address, a1c_value, a1c_dosOur file merger will pick through all the files in all of your directories, read the column headers and then create a single file that has all the common fields, plus all the unique fields.
You have different files files in different formats and have different databases. You can't control the upstream source -- if they add a column or remove a column then you have to revise your script even if it's a column you don't need. That's easy enough to do if it's one source to one database, but what if you have .csv, text, and xlsx and need to go to Teradata and SQL Server?
DataMover takes input files and copies them to a database table. It analyzes the source file to determine the datatypes and collect instrumentation, No additional tools are needed, it just works. If the input file format changes, the target table matches. Once the files are staged, we have libraries to run additional stored procedures and queries to complete the processing.
The BHA data tools are written as Perl modules. They are designed so that a single Perl script can handle data load through reporting on Linux or Windows platforms.
