Data Integration: Exploring Accuracy Issues

This is where I get to use the popular phrase, “Garbage in, garbage out.”  Transcriptionists are very meticulous about document accuracy and having access to patient and healthcare provider data during the document creation process is critical to this.  But what happens when you find errors and holes in the data you are provided?  Your documents can only be as accurate as the system where the data originates.  So, if someone in patient registration misspells a patient’s name or mistypes their date of birth, most likely that error will flow through to everything associated with the patient, and fixing it can get rather complicated.

In order to address errors in the data being moved between systems, it is important to establish which system is the “system of record,” and have a process in place so that all corrections and additions to the patient data are made there.  These days, many organizations have interfaces in place that will push out any corrections from the system of record to the receiving systems as soon as they are available, but this is not always the case.  It’s helpful to receive those database updates automatically, but how and when are those updates applied to the documents in your downstream systems?  For example, if a patient’s date of discharge changes after the discharge summary has been transcribed, will the date automatically be updated on the document and redistributed, or will the change occur only if someone manually edits the document?  Either way, everyone involved needs to understand the issues and how they are addressed.

When I think of accuracy concerns, I also think of errors that dictators make when entering patient information via the phone, and these errors carry over to the transcriptionist.  Should the transcriptionist just make corrections to the demographic fields when she creates the document, or is it important to also go back to the dictation system and make corrections there so the document matches the dictation job record?  Something to think about.  In a perfect documentation utopia, all systems would match all the time.  It may not be practical or realistic to expect this, but you need to determine where those lines are drawn for your organization.

 

Jill Devrick is a Product Solutions Advisor with 3M Health Information Systems.

One response to “Data Integration: Exploring Accuracy Issues

  1. Pingback: Data Integration: Consistency Concerns & Archival Policies | 3M Health Information Systems

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s