Using LOINC to link results from labs
2014-10-27 at 02:33 #16361
We are a fairly new multifacility health system with several labs across the system. Currently there are 3 different LIS’s being used but we are in the process of implementing a new LIS to be used in all labs. We have not standardized instrumentation across the labs but we would like to be able to build single a result for the common chemistry and hematology results utilizing the LIS’s ability to link different instruments with different reference ranges, reportable ranges, rules, etc. The results would all post in the EMR so that they can be trended and the reference ranges are easily viewed by hovering on the result. We are using LOINC to determine which results can be build as a single results. If the LOINC’s don’t match then individual results will be built for the analyte. Our pathologists are resisting this plan and feel that there should be unique results built for each analyte and don’t believe that LOINC is acceptable for determining when a singe result can be built. Patients are now being seen at multiple hospitals in the system and the providers, especially oncology and transplant providers, are very vocal about wanting to be able to trend results easily. I have shared information about LOINC with the pathologists but they still aren’t convinced. Can anyone point me to any information that might help us come to compromise that will make everyone happy?2014-10-27 at 14:31 #16720
After all these years of applying LOINC to existing LIS systems, it’s exciting for me to hear it’s being thought of at the forefront of an implementation. I’m curious if the chosen LIS has the ability to store different LOINCs within different workstation definitions, just as it may store different units of measure? I’m also curious if any of the pathologists have been involved in an LIS build before?
The scenario you are describing; LIS test descriptions being shared across different order panels or different workstations, has been in use for decades across LIS platforms. It saves database administration time, being able to make changes one time for an assay, instead of remembering all locations in the LIS where a change needs to be implemented for one analyte. As a former national reference lab clinical systems coordinator, we would rely on “mirror images” of an analyte if a reflex algorithm needed to be implemented, or if it was to have special charting information (as two examples). But maintaining mirror images over time is cumbersome and error prone. I personally don’t know of publications that describe database builds, but certainly the vendor has an implementation guide that describes best case.
I like to remind people that LOINC is like a name tag, added to the test catalog to identify what assay is being reported. I imagine you’ve made some sort of a grid based on analyte, specimen, units, reference range, and lab/workstation for the LIS build. This grid is independent of LOINC at the phase of finding your common shared assays. Based on their six attributes, you’re able to add the LOINC [ie name tag]. It might help the pathologists if the grid is shown why these assays can share a common LOINC? Please forgive is this is too simplistic.
Please review the LOINC User’s Guide for specific explanations. The LOINC User’s Guide explains why many of the chemistries are methodless. As for hematology, the large difference comes between automated and manual differentials. Some sites use the same field for each differential cell type, in which case methodless LOINCs are used.
Hope this helps in any regard!2014-10-27 at 18:32 #16721
Thank you – this is helpful.
Our LIS will have the ability to store different LOINCs within each method definition. Each result can have multiple methods each with their own reference ranges, reportable ranges, LOINC, even reflex testing and billing rules.
We do have a grid of sorts for building all of the results so it’s nice to know we’re going down the right path. I think it’s just a matter of making the pathologists comfortable with using LOINC to group results that are measured using the same methodology.
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