Processing errors in patient data

Errors or inconsistencies may be discovered during many of our #Quality control processes that might detect errors. This article describes how to process them.

When an error or inconsistency is found, #Correct error from available sources for the correct information where possible. If that does not provide the needed information, #Contact the data collector for additional info.

Process any false positive or uncorrectable records as Known data errors.

Correct error from available sources

If the required change is obvious, apply it. An example for this would be a switch of two digits in a field where several previous records are available for the same patient.

If there is no obvious fix, consult the EPR.

Contact the data collector for additional info

If the previous step does not fix the problem:

• contact the data collector using the "email collector about patient data" button or the "copy patient demographics for questioning" button as works better for the problem at hand.
  • This will set the RecordStatus field to "questioned" for "sent" and exempt it from being vetted.

_dev_CFE Automate the populating of notes so button just does it. raise an input box for a summary, if gets content put data and content into Notes, else put nothing.

added: no added date action: no action date

• Cargo

• Categories

when resolved

• When question is resolved Data processor
• removes the notes if it makes sense to do so
• resets RecordStatus from "questioned" back to "sent"

Editing Patient_ID, location or D_ID

Patient_ID and location are used to generate the D_ID. If one of these needs to be edited, follow Changing D IDs.

Incomplete patients

If errors are found in incomplete patients any fixes done to CFE will be overwritten the next time the data is sent. Because of this, make sure to ask the data collector to make the same changes to their record.

False Positives or known errors

If a query finds an error and review of the data shows that the data is correct then consider if this is a query that is can have false positives (e.g. visitAdmitDtTms that are really close together) or that should never have false positives (e.g. died and then readmitted).

• if can have false positives then treat as Known data errors
• if cannot have false positives then discuss problem with Tina or Herman to correct the query; don't treat as Known data errors

Quality control processes that might detect errors

• vetting, specifically
  • Pre-linking checks
  • Correcting suspect links
  • Quality Assurance queries in CFE