Nepal HMIS FHIR Implementation Guide – 🚨 DRAFT VERSION - Local Development build (v0.0.1-ballot) built by the FHIR (HL7® FHIR® Standard) Build Tools. See the Directory of published versions

Indicators

Indicators are a machine-readable expressions that define the indicator and its input variables, population, and stratifiers. Indicators are expressed using FHIR Measure resource.

Inputs

• L2 indicator definition
• Data dictionary
• CQL dependencies

Outputs

• Measure FHIR artifact
• Example MeasureReport corresponding to the test / example data included

Activities

Measures are FHIR resources and can refer to CQL libraries.

Summary: For each indicator in the L2, the L3 author creates a Measure resource. This includes adding populations and stratifiers (consulting the CQF-Measures guidance). The create the CQL definitions needed for the calculations, which will be encoded into the Library resources.

1. For each indicator in the L2, create a Measure
   • a. The Measure SHALL conform to the appropriate scoring profile based on the scoring type:
     • i. Proportion - CQFMProportionMeasure
     • ii. Ratio - CQFMRatioMeasure
     • iii. Cohort - CQFMCohortMeasure
     • iv. ContinuousVariable - CQFMContinuousVariableMeasure
   • b. NOTE: Proportion measures with an estimated denominator are modeled as continuous variable measures to allow the metric to be collected and analyzed downstream as a proportion measure when the estimated denominator is known
   • c. The Measure ID should be derived from the indicator code, e.g. IMMZ.IND.08 -> IMMZIND08
   • d. Url: The URL SHALL be: [base canonical]/Measure/[id]
   • e. Version: Do not set the version element, it will be set by the publication process
   • f. Name: The Name SHALL be the same as the id
   • g. Title: The L2 Indicator ID e.g. IMMZ.IND.08 Immunization coverage for Measles containing vaccine (Estimated Denominator)
   • h. Description: The long description of the indicator (i.e. the indicator description)
2. Create an "indicator" logic library specific to the measure, e.g. IMMZIND08Logic
   • a. The logic library SHALL contain expressions for each population criteria appropriate to the scoring type of the measure
   • b. The logic library SHALL make use of an IndicatorElements library to reference data elements from the guideline
   • c. The logic library MAY make use of an IndicatorLogic library to share common logic between multiple indicators in the guideline
3. Create a group appropriate to the scoring type (only one group is supported)
   • a. group.id SHALL be the same as the name of the measure
   • b. create populations appropriate to the scoring type (https://build.fhir.org/ig/HL7/cqf-measures/measure-conformance.html#criteria-names)
   • c. each population references an expression in the indicator library

• Create or reuse a CQL library that contains the definitions and functions that are needed for the Measure
  • ​see Library authoring and CQL authoring

• Add the canonical URL of the Library to the Measure

• Depending on the type/purpose of the indicator, define the value for the measure scoring.

• Add the type and improvementNotation

• From the scoring, see what populations are permitted - according to the CQF Guidance

• For each population, define the code and id, the description, and the cql expression that evaluates the population. For example, ```
• group
  • id = "IMMZIND08"
  • population[0]
    • id = "measure-population"
    • code = $measure-population#measure-population
    • description = "Number of administrations of vaccinations containing a Measles component during reporting period"
    • criteria
      • language = #text/cql
      • expression = "measure-population" ```
• For each stratifier, define the id and the cql expression that evaluates the population. For example,
  • L3 Authors must create a set of stratifiers which is the permutations that are considered important. The stratifiers produce aggregate values and it may be impossible to disaggregate inside a stratifier. ```
  • stratifier[+]
    • id = "age-group-stratifier"
    • criteria
      • language = #text/cql
      • expression = "Age Group Stratifier" ```

NOTE: Determining effective data requirements is a detailed process and should be done through the use of tooling such as the CQF Tooling to process Measure and Library resources

• Add a contained Library to the resource and refer to it using the expression EffectiveDataRequirements.

• If known, add the data requirements:

  • add codes that are used directly in the measure Add the libraries that contain the functions

Output Criteria / Definition of Done

• All the input variables shall be in the measure's terms.

• All terms shall part of the Data Dictionary

• At least one example MeasureReport should be provided
  • The example MeasureReport should indicate in which conditions
• There should be a Bundle (Transaction) with all that is needed to evaluate the Measure and the $evaluate-measure operation, with example data included or available.
• Measures SHALL conform to the profile appropriate to their scoring type (as described above)