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New Data Element: Long COVID Clinic
In service of our goal to provide the richest possible dataset to support COVID-19 and long-COVID research, we are seeking sites willing to add new data elements to their common data models (and therefore N3C payloads). It is unlikely that you are supporting any of the below data elements already (though let us know if you are!); for this reason, we are providing guidance for extracting and mapping data elements and templates that you can use to store these data in your local CDMs, which will then flow through to your N3C payloads.
So that we can process everyone’s payloads consistently, we ask that you use these design templates if you are going to provide these data elements.
Have questions? Need to discuss how to apply this guidance at your site? Please reach out via the PASC Slack in the #data-enhancements channel.
Why is this important? If your site has a long-COVID specialty clinic, knowing which patients visit that clinic and when will help improve our understanding of long-COVID care trajectories and patterns.
What sites should implement this? Sites with long-COVID specialty clinics. (Sites without such clinics should skip this data element.)
We have separate instructions for each data model below.
OMOP sites will use the OBSERVATION table to store this information. We have created a custom N3C concept_id to reflect a long-COVID clinic visit (concept_id = 2004207791). That concept_id has been added to the N3C OMOP vocabulary tables inside the enclave. The ID number chosen was arbitrary. If it collides with a custom concept_id that you use locally, please let us know.
To provide this data element, adjust your ETL to identify any encounters that occur at your site’s long-COVID specialty clinic, and use those data to fill in an OBSERVATION row as shown below. (Fields not shown in the example should be assumed to be null.) Note that these visits should also appear in your VISIT_OCCURRENCE table.
Template
| observation_id | observation_date | visit_occurrence_id | person_id | observation_concept_id | observation_source_concept_id | observation_type_concept_id |
| {generated by your ETL} | {date of clinic visit} | {visit id of the clinic visit} | {patient attached to the visit} | 2004207791 {N3C custom id} | 2004207791 {N3C custom id} | 32817 {EHR} |
Example
| observation_id | observation_date | visit_occurrence_id | person_id | observation_concept_id | observation_source_concept_id | observation_type_concept_id |
| 63728123 | 2021-05-13 | 463623455 | 18927867 | 2004207791 | 2004207791 | 32817 |
PCORnet sites will use the OBS_GEN table to store this information. We have created an OBSGEN_TYPE and OBSGEN_CODE to reflect a long-COVID clinic visit.
To provide this data element, adjust your ETL to identify any encounters that occur at your site’s long-COVID specialty clinic, and use those data to fill in an OBS_GEN row as shown below. (Fields not shown in the example should be assumed to be null.) Note that these visits should also appear in your ENCOUNTER table.
Template
| obsgenid | patid | encounterid | obsgen_start_date | obsgen_type | obsgen_code | obsgen_source |
| {generated by your ETL} | {patient attached to the visit} | {visit id of the clinic visit} | {date of clinic visit} | UD_CLINICTYPE {custom type} | PASC {custom code} | {whichever option is appropriate for your site} |
Example
| obsgenid | patid | encounterid | obsgen_start_date | obsgen_type | obsgen_code | obsgen_source |
| 63728123 | 18927867 | 463623455 | 2021-05-13 | UD_CLINICTYPE | PASC | OT |
ACT sites will use the OBSERVATION_FACT table to store this information. We have created a custom N3C concept_cd to reflect a long-COVID clinic visit (concept_cd =N3C:long-covid clinic). Please reserve the “N3C:” scheme for all N3C custom codes in the future.
To provide this data element, adjust your ETL to identify any encounters that occur at your site’s long-COVID specialty clinic, and use that data to fill in an OBSERVATION_FACT row as shown below. (Fields not shown in the example should be assumed to be null.) If the visits are already represented in the VISIT_DIMENSION table please link it to the new fact, otherwise generate a new visit with inout_cd=N3C:long-covid clinic.
Template
| encounter_num | start_date | patient_num | concept_cd |
| {visit number or generate if one does not exists} | {date of clinic visit} | {patient attached to the visit} | N3C:long-covid clinic {N3C custom id} |
Example
| encounter_num | start_date | patient_num | concept_cd |
| 12345678 | 12-NOV-21 | 3456789 | N3C:long-covid clinic |
Data will be flagged as long COVID in the encounter extract. This will most likely be based off specific locations or the encounter type. If you are populating your TriNetX data from one of the data models above, the best approach would be to use one of the above approaches in your upstream data, and then allow those data to flow through to TriNetX. If you are populating TriNetX directly from your EHR or a custom data warehouse, please reach out to n3c@trinetx.com.
It is also helpful if you let the N3C team know (via Slack, email, or GitHub issue) if and when you plan to add this enriched data, so that we know to look out for it.