LORENZ drugTrack can help you prepare for IDMP by identifying and understanding compliance gaps and allowing you to already implement IDMP data in the system.
How drugTrack can help you with the transition to IDMP compliance
Database is ready for the first iteration of IDMP
drugTrack's database already has all fields in place which will be necessary for the first iteration of IDMP.
Identify your missing data
Prepare your data for the ISO IDMP standards: let drugTrack show you which fields are relevant for XEVMPD and which are relevant for SPOR/IDMP with colored tagging and popovers outlining the exact EMA requirements for these fields. Furthermore, you can filter by these tags as well as other fields such as ‘Mandatory’ or ‘Recommended’.
Update your controlled vocabulary
You can update your existing CV entries and link them to the new EMA RMS lists and OMS list. The SMS and PMS lists will be integrated as soon as the EMA publishes them.
Import EVWEB data
Our EVWEB tool allows you to easily import data which you have previously sent via EMA’s EVWEB. To become IDMP compliant you then only need to add the missing data.
IDMP Training
LORENZ offers IDMP Preparedness Training to help you prepare for IDMP structured data submissions. We will provide you an overview of EMA's ISO IDMP implementation guide and the EU Telematics Roadmap. The training will show you how to use drugTrack to help you become IDMP compliant.
drugTrack offers in addition Data Management capabilities which go perfectly hand-in-hand with IDMP
Freely configurable data model (which in contrast to customization simplifies validation) allows additional data where needed
Manage your data in the most efficient way by editing multiple related records
With drugTrack you can re-use your data for different applications and capabilities, as well as for messages to other health authorities
You can validate your data within drugTrack to make sure your data is technically compliant. Any error can directly be corrected in drugTrack before submission
Minimize entries of duplicate data or free-text data to improve your data quality by using controlled vocabularies or industry standard dictionaries