The format of the reporting data is in unstructured free form text. This makes it particularly difficult to extract identifying attributes so that the information can be made truly anonymous – a pre-requisite for open reporting. For example, to make an incident report anonymous, the statistician has to trawl through all the text (often several or many paragraphs) to find any occurrences of the name of the patient, staff member, ward and even in some cases the detail of the injury that may need to be made anonymous.
The agency was using a system which was manual, labor intensive and costly to run. If the agency needed to ‘white list’ or add a new word to be made anonymous and applied to the data, they had to buy professional services from the existing data quality vendor to add this into the code. This was very costly and to get the best value for money, any changes in white listing were stored up and then implemented in bulk to lower the cost of the professional services required.
This meant that the agency had a list that they were maintaining separately before implementing the bulk list through the vendor into the code (around every 6-9 months). They had to look for these separate list items specifically. For example they would know that they wouldn’t have ‘Martin Ward’ included in the code yet, so they would have to look for it manually in every case received.
The agency was looking for a solution that would reduce the costs and labor involved in the process of creating truly anonymous reporting.