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GELLO can have the context of an IncomingObservation, for example a pathology result. Metadata within the GELLO can be set to specify LOINC codes for a host system to watch for and if present, run the GELLO.

This worked example will demonstrate using screen shots from the Medical Objects Explorer software. It will show some GELLO that looks for archetyped clinical patient data of height , weight, waist circumference and Blood pressure, such as might be obtained when a patient presents to a medical clinic. The code will take this data, and also look for recent blood tests results; and run its logic as to whether certain conditions are present such as being overweight or having abnormal metabolic profiles. We will use BMI and Triglyceride/HDL ratio. The result of the GELLO is another observation that presents in the UI as System Generated information, below the displayed Observation that has triggered the Comment GELLO.

The context is:

     Context HL7_v2_VMR_V1::IncomingObservation

Metadata is a tuple  we create as a local variable and is customisable. MO Explorer knows to look for this when the GELLO is uploaded to the server it uses and stores the Comment GELLO for clinical use.

Here's some metadata:

     Let metadata = Tuple {
                      title = 'calculate metabolic comments on admission vitals and recent pathology',
                       keywords = Sequence{'BMI','Weight', 'Metabolic'},
                      version = 1,
                       revision = 16,
                       date = '20180227',
                       author = 'Medical-Objects',
                      identifier = 'COMMENT_GELLO:CALCULATE_Metabolic_Comments',
                       purpose = 'For metabolic calculation after basic admission and registration vitals taken by nursing staff',
                      includesList = Sequence{'29463-7^^LN','8302-2^^LN','8280-0^^LN', '8480-6^^LN', '8462-4^^LN'},
                      excludesList = Sequence{}
                              }

Now we can set up come codes for the retrievable data:

     Let measured_weight_LN: CD = CD{code = '29463-7', codeSystem = '2.16.840.1.113883.6.1', codeSystemName ='LN'}
     Let measured_height_LN: CD = CD{code = '8302-2', codeSystem = '2.16.840.1.113883.6.1', codeSystemName ='LN'}
     Let measured_waist_LN: CD = CD{code = '8280-0', codeSystem = '2.16.840.1.113883.6.1', codeSystemName ='LN'}
     Let sys_BP_LN: CD = CD{code = '8480-6', codeSystem = '2.16.840.1.113883.6.1', codeSystemName ='LN'}
     Let diast_BP_LN: CD = CD{code = '8462-4', codeSystem = '2.16.840.1.113883.6.1', codeSystemName ='LN'}


These will be the codes that the archetyped data is mapped to.

Add the required lab data LOINC codes:

     Let trig_LN: CD = CD{code = '14927-8',codeSystem = '2.16.840.1.113883.6.1', codeSystemName ='LN'}
     Let hdl_LN: CD = CD{code = '14646-4',codeSystem = '2.16.840.1.113883.6.1', codeSystemName ='LN'}


Now we get the observation data and the vmr data:

     -- get the observations and vmr data
Let currentWeight:PQ = observations->select(o| o.observationCode.implies(measured_weight_LN).value)->sortedBy(dateTime)->last().value.oclAsType(PQ)
Let currentHeight:PQ = observations->select(o| o.observationCode.implies(measured_height_LN).value)->sortedBy(dateTime)->last().value.oclAsType(PQ)
Let currentWaist:PQ = observations->select(o| o.observationCode.implies(measured_waist_LN).value)->sortedBy(dateTime)->last().value.oclAsType(PQ)
Let last_trigObservation: Observation = If vmr.observations->select(o| o.observationCode.implies(trig_LN).value)->sortedBy(dateTime)->last().oclIsDefined() then
vmr.observations->select(o| o.observationCode.implies(trig_LN).value)->sortedBy(dateTime)->last() else null endif
Let last_hdlObservation: Observation = If vmr.observations->select(o| o.observationCode.implies(hdl_LN).value)->sortedBy(dateTime)->last().oclIsDefined() then
vmr.observations->select(o| o.observationCode.implies(hdl_LN).value)->sortedBy(dateTime)->last() else null endif
Let last_sysBPObservation: Observation = If observations->select(o| o.observationCode.implies(sys_BP_LN).value)->sortedBy(dateTime)->last().oclIsDefined() then
observations->select(o| o.observationCode.implies(sys_BP_LN).value)->sortedBy(dateTime)->last() else null endif
Let last_diastBPObservation: Observation = If observations->select(o| o.observationCode.implies(diast_BP_LN).value)->sortedBy(dateTime)->last().oclIsDefined() then
observations->select(o| o.observationCode.implies(diast_BP_LN).value)->sortedBy(dateTime)->last() else null endif

Notice that for the data obtained from the IncomingObservation ie the measured height, weight, waist circumference, systolic blood pressure and diastolic blood pressure; we don't need to say 'vmr.observations', but when we are looking outside the context of the incoming observation, we do- e.g. for recent pathology. This code keeps the convention of searching for Observations, then ordering them by dateTime, then getting the last Observation. This is probably unnecessary for the archetyped clinical data, but it is possible to repeat a blood pressure reading if high ; so it is retained.

Do a BMI calculation: ( we know the units  will be cm for height and kg for weight.

     -- do BMI calculation
Let currentHeightInMeters: PQ = currentHeight/100
Let bmi: Real = currentWeight.value/currentHeightInMeters.value.power(2)
Let bmi_Rounded: Real = bmi.round()
Let bmi_AsString: String = If bmi_Rounded.oclIsDefined() then bmi_Rounded.toChar() else '' endif

Do a triglyceride / HDL ratio:

     --TG HDL ratio
Let trig_HDL_Ratio : Real = If last_trigObservation.oclIsDefined() and last_hdlObservation.oclIsDefined() then
last_trigObservation.value.oclAsType(PQ).value / last_hdlObservation.value.oclAsType(PQ).value
else null endif
Let trig_HDL_Ratio_raised: Boolean = If last_trigObservation.oclIsDefined() and last_hdlObservation.oclIsDefined() then
If last_trigObservation.value.oclAsType(PQ).value / last_hdlObservation.value.oclAsType(PQ).value > 2
then True
else False endif
else False endif

     Prepare comments:

     -- prepare comments
     Let commentCode:CD =CD{code = '8251-1', codeSystem = '2.16.840.1.113883.6.1', codeSystemName ='LN', displayName = ST{value='Comment'}}
     Let bmiString: String = 'BMI is '
     Let additionalBMIComment: String = If bmi.oclIsDefined() then
        If bmi <18.6 then '. Patient is considered to be Underweight.' else
        If bmi >25 and bmi < 30 then '. Patient is considered to be Overweight.' else
        If bmi >29.9 then '. Patient is considered to be Obese.' else
         ''
       endif endif endif else '' endif
     Let trig_HDL_ratio_asChar: String = If trig_HDL_Ratio.oclIsDefined() then trig_HDL_Ratio.toChar() else '' endif
     Let trig_HDL_ratio_comment1: String = If trig_HDL_Ratio_raised then
        '\nTriglyceride-HDL ratio is ' else '' endif
     Let trig_HDL_ratio_comment2: String = If trig_HDL_Ratio_raised then trig_HDL_ratio_asChar.concat('. Levels above 2 are significant.')
        else '' endif
     Let trig_HDL_ratio_comment: String = trig_HDL_ratio_comment1.concat(trig_HDL_ratio_comment2)
     Let comment: String = bmiString.concat(bmi_AsString).concat(additionalBMIComment).concat(trig_HDL_ratio_comment)


Produce the result:

     -- result 
Let result_comment:ST = ST{value=comment}
Let result:Sequence(Observation)
= Sequence{Observation{
value = result_comment,
observationCode = commentCode
}
}
result


OK! So here's the input screen in MO Explorer, for the archetype that becomes the Incoming Observation that the GELLO is watching for the LOINC codes for (Comment GELLO just as easily runs against HL7 lab data Observations):


Here's what happens when we run the gello explicitly against this patient's data:


Here's the UI of the Observation showing the comment as a string under 'System Generated information':


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