Nowadays, patients’ clinical data associated with sample and laboratory results is indispensable for accurate diagnosis, especially with COVID-19’s complex symptomatology. Unfortunately, the current pandemic situation exposed long known problems. This is mainly the miscommunication brought by unintegrated platforms that make patient information harder to obtain, so it consequently takes longer to get a straight forward diagnosis. Thankfully, Odoo allows the development of a graphic interface, and since it’s open source one can easily modify and personalize it. It can:
- Fit the user’s needs
- Build the connection between doctors and scientists
- Save time
- Enable collaborative work
Here we’ll focus on the CoVTec feature. If you want to learn more about other features, make sure you browse our articles.
The CoVTec platform stores all the information needed at first glance for an easy sample organization, whether in a doctor’s office or in a research laboratory. It provides information such as:
- Test results
- Who introduced the sample to the platform
It only takes a few minutes before sample collection for the doctor or individual (in case of a self collected sample) to fill in a few questions. The platform performs codification, to protect the patient’s privacy (according to ethics committee requirements). This code also makes for easy identification inside the lab.
One of its major benefits is the fact that both doctors (clinical data) and researchers (laboratory data) use the same platform. This allows for more accurate patient records and makes it more accessible for both. This is better compared to other scenarios where doctors often wait for sample results to make a more complete diagnosis and researchers need a more complete patient file to adapt and do more precise testing.
We added a function that enables an option to do patient follow-ups throughout the many sample testing timepoints, information regarding COVID-19 symptomatology/test results can be added, as well as laboratory data for future diagnosis. Having the option to update patient’s information, that can also be visible to both doctors and researchers without changing platforms.
Inside the platform there are two main pathways:
- Clinical Data: At first glance the Clinical Data helps keep everything organized according to the test results. For example, see if it passed through the testing stage or if it still has an untested or unknown result. Once you open a sample’s information, it gives you a general idea of a patient’s symptomatology upon sample recollection.
- Laboratory Analysis: This pathway divides into two parts:
- Laboratory data that splits the samples into “Collected” and “Stored”, so you get an indication if a sample has been processed or not. It also provides information regarding the test result.
- The Sample Info that lets you know who collected the sample or introduced the sample to the platform. At this stage the diagnosis is introduced by the lead researcher, allowing the doctor to see it on the Clinical Data tab.
This way of organization can be extremely helpful when dealing with a high number of samples. It also benefits you in terms of ethics, since not everyone gets access to all the patients’ information, knowing the sample only by the number associated with it.
In the near future, improvements will take place, such as:
- The ability to skip all pathology related questions when the patient doesn’t have any medical history.
- A way to answer all the questions at once instead of having to answer one by one, which can be time consuming.
- The option to select multiple samples and change their status, for example from “Collected” to “Stored”, at the same time; this is more practical since most tests are done with multiple samples, during information introduction.
Odoo opens the door to using clinical information and laboratory results, aiding in the doctor-researcher communication. It also allows easy access to medical diagnosis, essential to both parties. Data stored in the new platform allows graphical results interpretation and analysis based on AI strategies.