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Observation Series for V3. A way to represent table-like, multi-dimensional Observations in HL7’s Version 3. Presented by Barry D. Brown Mortara Instrument, Inc. September 30, 2002. Background. FDA wishes to receive annotated ECG’s in support of New Drug Applications.
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Observation Series for V3 A way to represent table-like, multi-dimensional Observations in HL7’s Version 3. Presented by Barry D. Brown Mortara Instrument, Inc. September 30, 2002
Background • FDA wishes to receive annotated ECG’s in support of New Drug Applications. • Work is underway to create clinical trials information models in HL7 V3 (RCRIM TC, CDISC). • Need a way to cast ECG’s and similar types of information in HL7 V3 information model.
Single-valued Observations • The value attribute of Observation has the data type ANY. • ANY can hold a wide range of values. • It is clear how to communicate single-valued observations in V3, but not so clear when multiple values are observed.
Multi-valued Observations • An Observation instance has one cd attribute, so all values must represent the same type of observation. • Generic collections like BAG<>, LIST<> and SET<> can be used for the value attribute. • When LIST<> is used for a multi-valued observation, call it an Observation Sequence.
Example • Cardiac stress exam. • Technician asks patient perceived level of exertion at different points in the exam. • Technician records those observations in a list. • This is an Observation Sequence with a cd for perceived exertion.
Correlated Sequences • A single observation sequence communicates an ordinal relationship between the values, but we don’t know if it’s temporal or something else. • Correlating each value in the list with some other type of observation gives more information.
Example Continued • Technician observes the clock in the room and records the time in a parallel list. • There are 2 observation sequences, and the values in each row are correlated. • Call this Correlated Observation Sequences.
Table-like Observations • A set of observations that can be represented in a table can be Correlated Observation Sequences. • Each column is an Observation Sequence. • Each row is a correlation between the values of each sequence.
Example Continued • Observe HR and BP.
Example Continued • Observe ECG Leads.
Observation Series • Multiple tables (correlated sequences) observed in the same frame of reference can be grouped into an Observation Series. • Sequences of the same type (cd) appearing in different tables can be compared. • E.g. relative time, ECG electrode placement, patient position…
Example Continued Record relative time so observations can be compared with ECG.
Region • Secondary observations can be made from other observations. • Series observations contain many observation values. • Need a way to identify subsets of the Series from which the secondary observation is made. • Call it a Region.
Region Boundary • Each Region is defined by a set of boundaries, one for each observed Sequence type. • The Region Boundary cd is the same as the Observation Sequence cd. • The Region Boundary value defines the interval inside the Region.
Observations on Regions • Secondary observations can be made on the Regions within the Series. • No special Observation class is required. • Use the “supported by” relationship.
