1951, health care institutions have provided data to the Cancer
Registry of Norway. High quality data are essential to improve cancer treatment. The most
relevant patient data are stored in the hospital's Electronic Health Record (EHR). For many years it has been desirable to ease the registration of data by
moving from paper based, or poor web based portals, towards bringing the
registration process closer to healthcare providers.
View a 2 min demo of the solution in action here.
Through the BigMed project, we have developed a solution for colorectal cancer. The solution includes data
registration in the EHR with a secure transfer to the Cancer
Registry of Norway. We conducted an end-to-end test on Wednesday September 16th that proved it to be simple for the end user. The user creates a new form in the EHR, fills out the form and submits it to the registry after having completed the aforementioned steps. Lastly, a much improved
feature has been added so that both the sender and receiver can assure the successful transit of each form by viewing its confirmation status. Here, the recipient automatically returns a confirmation message to the sender for each form that has been successfully submitted. All data are stored
within the EHR for later use.
opted to use information models (archetypes) in the openEHR framework for
structuring EHR data. Messages to the Cancer
registry is in openEHR XML and is mapped to the data model of the Cancer
registry upon receive. This
an international open standard which has gained substantial interest in the
recent years. EHR in use is itself into transition to openEHR overall. As a
widely used standard in Norwegian hospitals, the EHR system developed in BigMed
is easily transferable to other hospitals.
Data collection in the EHR is a natural way to start structuring the medical journals. Today, data are collected via less user-friendly and efficient methods like external web portals or paper forms. Integrating the registration process into the EHR provides cancer registry forms that are more readily available at the site of the data source. Especially relocating the form to the site of the data opens up for more automatic ways of structuring data. For instance, by artificial intelligence or natural language recognition (NLP).
Why reporting to the Cancer Registry in BigMed?
The BigMed project
has explored methods for automatically defining the patient’s
tumor-node-metastasis (TNM) classification from text. This method is a step
towards finding methods that structure medical text. The technology can make
reporting to national quality registers more efficient, which hopefully also
will improve the quality of the data registered.