An observation is a measure of the physical, physiological, or psychological state of an individual. It can be subjective (i.e. patient reported) or objective (reported by a provider or a device). They are reported without bias (as much as possible) and without interpretation by the observer. A serum potassium level of 5.5 mg/dL is an example of an observation. A pain score of 1, using a scale 0-3, is an example of a subjective observation.
Observations are used as inputs to assessments. The assessment represents an assessor's interpretation or analysis of the observations. The result of an assessment is generally a medical condition and its properties (e.g. severity, change from last assessment). I would like to say "...is always a medical condition..." but I try never to say never or always, because invariably an exception emerges. Because an assessment includes an assessor's interpretation, bias can be a problem. The same set of observations can, and not infrequently, be interpreted differently by different assessors. Formal adjudication processes are sometimes put in place in clinical trials to minimize this type of bias. In health care, second opinions are quite commonly solicited to seek a better assessment; one that is closer to the truth.
As a simple example, let's take the serum potassium of 5.5 mg/dL. To do a proper assessment, more information is needed. What is considered the normal range for the laboratory that conducted the test? (e.g. 3.0-4.5 mg/dL). Are there other clinical observations suggesting clinical hyperkalemia (e.g. EKG findings)? Is the patient on medications or does the patient have a known medical condition that can cause hyperkalemia (does the finding make sense)? Could this be due to hemolysis of the sample (this is a common cause of falsely elevated potassium measurement; it may require calling the lab and getting missing information about the biospecimen)? Could it be laboratory error (is a repeat measurement necessary)? Depending on the assessment, the assessor may determine that a new medical condition: hyperkalemia is indeed present, and may need to measure additional observations to determine its cause, and may need to order an intervention to bring the level down. In this example, the patient was recently placed on an angiotensin converting enzyme (ACE) inhibitor for the treatment of hypertension. ACE inhibitors are associated with hyperkalemia. The hyperkalemia was an adverse event related to ACE inhibitor use.
An important clinical distinction between observation results and assessment results is that only assessment results get treated and tracked on a patient's problem list. As a medical student, it was ingrained into me "never treat the lab test or the x-ray; always treat the patient."
Another important conclusion is that an Adverse Event is a Medical Condition; a special type of medical condition: one that is temporally associated with some medical intervention. In this example, the intervention was the administration of an ACE inhibitor.
So when I look at BRIDG 4.0, I don't see the distinction between observations and assessments. In fact, the results of assessments are modeled as other observations. Specifically, a PerformedObservationResult is a generalization of AdverseEvent in the model. I believe this is incorrect. Furthermore, the BRIDG definition of an Adverse Event is:
Any unfavorable and unintended sign, symptom, disease, or other medical occurrence with a temporal association with the use of a medical product, procedure or other therapy, or in conjunction with a research study, regardless of causal relationship.
I disagree with this definition. A sign or symptom is an observation and, for the reasons I state here, is not an adverse event. I would modify the definition to read:
Any unfavorable and unintended disease or other medical condition with a temporal association with the use of a medical product, procedure or other therapy, or in conjunction with a research study, regardless of causal relationship.
There are other BRIDG classes that have this same issue (e.g. PerformedMedicalConditionResult). I don't attempt to provide a comprehensive list here.
In discussions with the BRIDG modeling team, my understanding is that observation results and assessment results are handled the same way from a data management perspective, so the current modeling paradigm works from that respect. They propose developing a higher, conceptual presentation layer that draws the distinction between observations and assessments without necessarily changing the underlying model. I am not a modeler so I don't know if this is the right approach. I'm certainly willing to explore what a more subject-matter-expert-friendly presentation layer for BRIDG might look like and how that would address my concern. But I do have an underlying unease that these two very different concepts in clinical medicine: observations and assessments, can be collapsed in this way in an information model without some adverse consequences downstream from a computational perspective.
I welcome other thoughts on this issue.