Class: QME::MapReduce::Executor

Inherits:
Object
  • Object
show all
Includes:
DatabaseAccess
Defined in:
lib/qme/map/map_reduce_executor.rb

Overview

Computes the value of quality measures based on the current set of patient records in the database

Constant Summary collapse

SUPPLEMENTAL_DATA_ELEMENTS = 
{QME::QualityReport::RACE => "$value.race.code",
QME::QualityReport::ETHNICITY => "$value.ethnicity.code",
QME::QualityReport::SEX => "$value.gender",
QME::QualityReport::PAYER => "$value.payer.code"}

Instance Method Summary collapse

Methods included from DatabaseAccess

#get_db

Constructor Details

#initialize(measure_id, sub_id, parameter_values) ⇒ Executor

Create a new Executor for a specific measure, effective date and patient population.

Parameters:

  • measure_id (String) —

    the measure identifier

  • sub_id (String) —

    the measure sub-identifier or null if the measure is single numerator

  • parameter_values (Hash) —

    a hash that may contain the following keys: ‘effective_date’ the measurement period end date, ‘test_id’ an identifier for a specific set of patients



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# File 'lib/qme/map/map_reduce_executor.rb', line 18

def initialize(measure_id,sub_id, parameter_values)

  @measure_id = measure_id
  @sub_id =sub_id

  @parameter_values = parameter_values
  q_filter = {hqmf_id: @measure_id,sub_id: @sub_id}
  if @parameter_values.keys.index("bundle_id")
    q_filter["bundle_id"] == @parameter_values['bundle_id']
    @bundle_id = @parameter_values['bundle_id']
  end
  @measure_def = QualityMeasure.where(q_filter).first
end

Instance Method Details

#build_query ⇒ Object 



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# File 'lib/qme/map/map_reduce_executor.rb', line 32

def build_query
  pipeline = []

  filters = @parameter_values["filters"]


  match = {'value.measure_id' => @measure_id,
           'value.sub_id'           => @sub_id,
           'value.effective_date'   => @parameter_values['effective_date'],
           'value.test_id'          => @parameter_values['test_id'],
           'value.manual_exclusion' => {'$in' => [nil, false]}}

  if(filters)
    if (filters['races'] && filters['races'].size > 0)
      match['value.race.code'] = {'$in' => filters['races']}
    end
    if (filters['ethnicities'] && filters['ethnicities'].size > 0)
      match['value.ethnicity.code'] = {'$in' => filters['ethnicities']}
    end
    if (filters['genders'] && filters['genders'].size > 0)
      match['value.gender'] = {'$in' => filters['genders']}
    end
    if (filters['providers'] && filters['providers'].size > 0)
      providers = filters['providers'].map { |pv| {'providers' => BSON::ObjectId.from_string(pv) } }
      pipeline.concat [{'$project' => {'value' => 1, 'providers' => "$value.provider_performances.provider_id"}}, 
                       {'$unwind' => '$providers'}, 
                       {'$match' => {'$or' => providers}},
                       {'$group' => {"_id" => "$_id", "value" => {"$first" => "$value"}}}]
    end
    if (filters['languages'] && filters['languages'].size > 0)
      languages = filters['languages'].map { |l| {'languages' => l } }
      pipeline.concat  [{'$project' => {'value' => 1, 'languages' => "$value.languages"}},
                        {'$unwind' => "$languages"},
                        {'$project' => {'value' => 1, 'languages' => {'$substr' => ['$languages', 0, 2]}}},
                        {'$match' => {'$or' => languages}},
                        {'$group' => {"_id" => "$_id", "value" => {"$first" => "$value"}}}]
    end
  end

  pipeline.unshift({'$match' => match})

  pipeline
end

#calculate_cv_aggregation ⇒ Object

This method calculates the aggregated value for a CV measure. It extracts all the values for patients in the MSRPOPL and uses the aggregator to combine those values into an aggregated value. The currently supported aggregators are:

MEDIAN
MEAN

Raises:

  • (RuntimeError) 


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# File 'lib/qme/map/map_reduce_executor.rb', line 175

def calculate_cv_aggregation
  cv_pipeline = build_query
  cv_pipeline.first['$match']["value.#{QME::QualityReport::MSRPOPL}"] = {'$gt'=>0}
  cv_pipeline << {'$unwind' => '$value.values'}
  cv_pipeline << {'$group' => {'_id' => '$value.values', 'count' => {'$sum' => 1}}}

  aggregate = get_db.command(:aggregate => 'patient_cache', :pipeline => cv_pipeline)

  raise RuntimeError, "Aggregation Failed" if aggregate['ok'] != 1

  frequencies = {}
  aggregate['result'].each do |freq_count_pair|
    frequencies[freq_count_pair['_id']] = freq_count_pair['count']
  end
  QME::MapReduce::CVAggregator.send(@measure_def.aggregator.parameterize, frequencies)
end

#calculate_supplemental_data_elements ⇒ Object

Calculate all of the supoplemental data elements



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# File 'lib/qme/map/map_reduce_executor.rb', line 78

def calculate_supplemental_data_elements

  match = {'value.measure_id' => @measure_id,
           'value.sub_id'           => @sub_id,
           'value.effective_date'   => @parameter_values['effective_date'],
           'value.test_id'          => @parameter_values['test_id'],
           'value.manual_exclusion' => {'$in' => [nil, false]}}

  keys = @measure_def.population_ids.keys - [QME::QualityReport::OBSERVATION, "stratification"]
  supplemental_data = Hash[*keys.map{|k| [k,{QME::QualityReport::RACE => {},
                                             QME::QualityReport::ETHNICITY => {},
                                             QME::QualityReport::SEX => {},
                                             QME::QualityReport::PAYER => {}}]}.flatten]

  keys.each do |pop_id|
    _match = match.clone
    _match["value.#{pop_id}"] = {"$gt" => 0}
    SUPPLEMENTAL_DATA_ELEMENTS.each_pair do |supp_element,location|
      group1 = {"$group" => { "_id" => { "id" => "$_id", "val" => location}}}
      group2 = {"$group" => {"_id" => "$_id.val", "val" =>{"$sum" => 1} }}
      pipeline = [{"$match" =>_match},group1,group2]
      aggregate = get_db.command(:aggregate => 'patient_cache', :pipeline => pipeline)

      v = {}
      (aggregate["result"] || []).each  do |entry|
        code  = entry["_id"].nil? ? "UNK" : entry["_id"]
        v[code] = entry["val"]
      end
      supplemental_data[pop_id] ||= {}
      supplemental_data[pop_id][supp_element] = v
     end
  end
  supplemental_data
end

#count_records_in_measure_groups ⇒ Hash

Examines the patient_cache collection and generates a total of all groups for the measure. The totals are placed in a document in the query_cache collection.

Returns:

  • (Hash) —

    measure groups (like numerator) as keys, counts as values



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# File 'lib/qme/map/map_reduce_executor.rb', line 118

def count_records_in_measure_groups
  pipeline = build_query

  pipeline << {'$group' => {
    "_id" => "$value.measure_id", # we don't really need this, but Mongo requires that we group
    QME::QualityReport::POPULATION => {"$sum" => "$value.#{QME::QualityReport::POPULATION}"},
    QME::QualityReport::DENOMINATOR => {"$sum" => "$value.#{QME::QualityReport::DENOMINATOR}"},
    QME::QualityReport::NUMERATOR => {"$sum" => "$value.#{QME::QualityReport::NUMERATOR}"},
    QME::QualityReport::ANTINUMERATOR => {"$sum" => "$value.#{QME::QualityReport::ANTINUMERATOR}"},
    QME::QualityReport::EXCLUSIONS => {"$sum" => "$value.#{QME::QualityReport::EXCLUSIONS}"},
    QME::QualityReport::EXCEPTIONS => {"$sum" => "$value.#{QME::QualityReport::EXCEPTIONS}"},
    QME::QualityReport::MSRPOPL => {"$sum" => "$value.#{QME::QualityReport::MSRPOPL}"},
    QME::QualityReport::CONSIDERED => {"$sum" => 1}
  }}

  aggregate = get_db.command(:aggregate => 'patient_cache', :pipeline => pipeline)
  if aggregate['ok'] != 1
    raise RuntimeError, "Aggregation Failed"
  elsif aggregate['result'].size !=1
     aggregate['result'] =[{"defaults" => true,
                           QME::QualityReport::POPULATION => 0,
                           QME::QualityReport::DENOMINATOR => 0,
                           QME::QualityReport::NUMERATOR =>0,
                           QME::QualityReport::ANTINUMERATOR => 0,
                           QME::QualityReport::EXCLUSIONS => 0,
                           QME::QualityReport::EXCEPTIONS => 0,
                           QME::QualityReport::MSRPOPL => 0,
                           QME::QualityReport::CONSIDERED => 0}]
  end

  nqf_id = @measure_def.nqf_id || @measure_def['id']
  result = QME::QualityReportResult.new
  result.population_ids=@measure_def.population_ids


  if @measure_def.continuous_variable
    aggregated_value = calculate_cv_aggregation
    result[QME::QualityReport::OBSERVATION] = aggregated_value
  end

  agg_result = aggregate['result'].first
  agg_result.reject! {|k, v| k == '_id'} # get rid of the group id the Mongo forced us to use
  # result['exclusions'] += get_db['patient_cache'].find(base_query.merge({'value.manual_exclusion'=>true})).count
  agg_result.merge!(execution_time: (Time.now.to_i - @parameter_values['start_time'].to_i)) if @parameter_values['start_time']
  agg_result.each_pair do |k,v|
    result[k]=v
  end
  result.supplemental_data = self.calculate_supplemental_data_elements
  result

end

#get_patient_result(patient_id) ⇒ Object

This method runs the MapReduce job for the measure and a specific patient. This will not create a document in the patient_cache collection, instead the result is returned directly.



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# File 'lib/qme/map/map_reduce_executor.rb', line 225

def get_patient_result(patient_id)
  measure = Builder.new(get_db(), @measure_def, @parameter_values)
  result = get_db().command(:mapreduce => 'records',
                            :map => measure.map_function,
                            :reduce => "function(key, values){return values;}",
                            :out => {:inline => true},
                            :raw => true,
                            :query => {:medical_record_number => patient_id, :test_id => @parameter_values["test_id"]})

  raise result['err'] if result['ok']!=1
  result['results'][0]['value']
end

#map_record_into_measure_groups(patient_id) ⇒ Object

This method runs the MapReduce job for the measure and a specific patient. This will create a document in the patient_cache collection. This document will state the measure groups that the record belongs to, such as numerator, etc.



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# File 'lib/qme/map/map_reduce_executor.rb', line 210

def map_record_into_measure_groups(patient_id)
  measure = Builder.new(get_db(), @measure_def, @parameter_values)
  get_db().command(:mapreduce => 'records',
                   :map => measure.map_function,
                   :reduce => "function(key, values){return values;}",
                   :out => {:reduce => 'patient_cache', :sharded => true},
                   :finalize => measure.finalize_function,
                   :query => {:medical_record_number => patient_id, :test_id => @parameter_values["test_id"]})
  QME::ManualExclusion.apply_manual_exclusions(@measure_id,@sub_id)

end

#map_records_into_measure_groups(prefilter = {}) ⇒ Object

This method runs the MapReduce job for the measure which will create documents in the patient_cache collection. These documents will state the measure groups that the record belongs to, such as numerator, etc.



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# File 'lib/qme/map/map_reduce_executor.rb', line 196

def map_records_into_measure_groups(prefilter={})
  measure = Builder.new(get_db(), @measure_def, @parameter_values)
  get_db().command(:mapreduce => 'records',
                   :map => measure.map_function,
                   :reduce => "function(key, values){return values;}",
                   :out => {:reduce => 'patient_cache', :sharded => true},
                   :finalize => measure.finalize_function,
                   :query => prefilter)
  QME::ManualExclusion.apply_manual_exclusions(@measure_id,@sub_id)
end