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



18
19
20
21
22
23
24
25
26
27
28
29
30
 
# 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 



32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
 
# 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'] = {'$in' => filters['genders']}
    end
    if (filters['patients'] && filters['patients'].size > 0)
      match['value.patient_id'] = {'$in' => filters['patients']}
    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) 


185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
 
# File 'lib/qme/map/map_reduce_executor.rb', line 185

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)
  aggregate_document = aggregate.documents[0]

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

  frequencies = {}
  aggregate_document['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



81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
 
# File 'lib/qme/map/map_reduce_executor.rb', line 81

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|
    pline = build_query

    _match = pline[0]["$match"]
    _match["value.#{pop_id}"] = {"$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 = pline.clone
      pipeline << group1
      pipeline << group2

      aggregate = get_db.command(:aggregate => 'patient_cache', :pipeline => pipeline)
      aggregate_document = aggregate.documents[0]
      v = {}
      (aggregate_document["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



125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
 
# File 'lib/qme/map/map_reduce_executor.rb', line 125

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::MSRPOPLEX => {"$sum" => "$value.#{QME::QualityReport::MSRPOPLEX}"},
    QME::QualityReport::CONSIDERED => {"$sum" => 1}
  }}

  aggregate = get_db.command(:aggregate => 'patient_cache', :pipeline => pipeline)
  aggregate_document = aggregate.documents[0]
  if !aggregate.successful?
    raise RuntimeError, "Aggregation Failed"
  elsif aggregate_document['result'].size !=1
     aggregate_document['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::MSRPOPLEX => 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_document['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.



236
237
238
239
240
241
242
243
244
245
246
247
248
249
 
# File 'lib/qme/map/map_reduce_executor.rb', line 236

def get_patient_result(patient_id)
  measure = Builder.new(get_db(), @measure_def, @parameter_values)
  operation = 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 operation.documents[0]['err'] if !operation.successful?
  return nil if operation.documents[0]['results'].empty?
  operation.documents[0]['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.



221
222
223
224
225
226
227
228
229
230
231
 
# File 'lib/qme/map/map_reduce_executor.rb', line 221

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.



207
208
209
210
211
212
213
214
215
216
 
# File 'lib/qme/map/map_reduce_executor.rb', line 207

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