Create Health Connect to FHIR converter module

[vishnuravi]

Create Health Connect to FHIR converter module

The HealthConnectOnFHIR library provides a mapper that converts supported Android Health Connect Records to corresponding HL7® FHIR® R4 Observations with standardized codes (e.g. LOINC).

For more information, please refer to the API documentation.

Mapping Table

Health Connect Record	FHIR Observation Category	LOINC Code	Unit	Display
ActiveCaloriesBurnedRecord	Activity	41981-2	kcal	Calories burned
BloodGlucoseRecord		41653-7	mg/dL	Glucose Glucometer (BldC) [Mass/Vol]
BloodPressureRecord	Vital Signs	85354-9	mmHg	Blood pressure panel with all children optional
BodyFatRecord		41982-0	%	Percentage of body fat Measured
BodyTemperatureRecord	Vital Signs	8310-5	Cel	Body temperature
HeartRateRecord	Vital Signs	8867-4	/min	Heart rate
HeightRecord	Vital Signs	8302-2	m	Body height
OxygenSaturationRecord	Vital Signs	59408-5	%	Oxygen saturation in Arterial blood by Pulse oximetry
RespiratoryRateRecord	Vital Signs	9279-1	/min	Respiratory rate
StepsRecord	Activity	55423-8	steps	Number of steps
WeightRecord	Vital Signs	29463-7	kg	Body weight

Installation

HealthConnectOnFHIR can be installed into your Android Studio project via Jitpack.

Usage

// Initialize the mapper
val mapper = RecordToObservationMapperImpl()

// Query a `Record` from Health Connect
val record =  // ..
    
// Map the record to an HL7 FHIR Observation    
val observation = mapper.map(record)

Example

First, you will need to configure your application to use Android Health Connect. For more information, please see the official documentation.

// Initialize a `HealthConnectClient` (see Health Connect docs for full details)
val healthConnectClient = HealthConnectClient.getOrCreate(context)

// Define a time range for the query
val startTime = Instant.parse("2023-05-01T00:00:00Z")
val endTime = Instant.parse("2023-06-01T00:00:00Z")

// Query a list of `WeightRecord`s from Health Connect
val result = healthConnectClient.readRecords(
    ReadRecordsRequest(
        recordType = WeightRecord::class,
        timeRangeFilter = TimeRangeFilter.between(startTime, endTime)
    )
).records

// Initialize the mapper
val mapper = RecordToObservationMapperImpl()

// Convert each weight record to a FHIR Observation
result.forEach { weightRecord ->
    val observations = mapper.map(weightRecord)
    observations.forEach { observation ->
        // Do something with the observation
    }
}

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[PSchmiedmayer]

Thank you for adding this in the @StanfordSpezi ecosystem @vishnuravi!

I added my thoughts in the discussions below, thank you @eldcn & @Basler182 for the first reviews!

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 26.40%. Comparing base (b77e6ea) to head (4217b3b).

Additional details and impacted files

@@             Coverage Diff              @@
##               main      #15      +/-   ##
============================================
+ Coverage     19.38%   26.40%   +7.02%     
- Complexity      146      193      +47     
============================================
  Files            99      101       +2     
  Lines          2611     2860     +249     
  Branches        392      404      +12     
============================================
+ Hits            506      755     +249     
  Misses         2063     2063              
  Partials         42       42              

Flag	Coverage Δ
unittests	26.40% <100.00%> (+7.02%)	⬆️

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Files	Coverage Δ
...ava/edu/stanford/healthconnectonfhir/MappedUnit.kt	100.00% <100.00%> (ø)
...althconnectonfhir/RecordToObservationMapperImpl.kt	100.00% <100.00%> (ø)

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[PSchmiedmayer]

Thanks for the updates here @vishnuravi 🚀
Wondering if we can merge the PR in its current state for now?

[vishnuravi]

Thanks for the updates here @vishnuravi 🚀 Wondering if we can merge the PR in its current state for now?

We can merge it if everyone is OK with the updated method of implementing the mapper - can discuss at the meeting today.

[eldcn]

LGTM 🚀

[PSchmiedmayer]

Thank you @vishnuravi!