Tuva Data Model Changes for person_id

docs/connectors/input-layer.md

@@ -16,10 +16,13 @@ The `Input Layer` is designed to accomodate both claims and clinical data source
 The eligibility table includes information about a patient's health insurance coverage and demographics (note: we use the word patient as a synonym for member). Every claims dataset should include some sort of eligibility data, otherwise it's impossible to calculate member months, which are needed to calculate measures like PMPM.  Each record in the table is intended to represent a unique eligibility (i.e. enrollment) span for a patient with a specific health plan.
 
 **Primary Key:**
-  * patient_id
-  * plan
-  * enrollment_start_date
-  * enrollment_end_date
+   * person_id
+   * member_id
+   * enrollment_start_date
+   * enrollment_end_date
+   * payer
+   * plan
+   * data_source
 
 <JsonDataTable jsonPath="nodes.model\.input_layer\.eligibility.columns" />
 
@@ -32,7 +35,7 @@ The medical_claim table contains information on healthcare services and supplies
   * claim_line_number
 
 **Foreign Keys:**
-  * patient_id
+  * person_id
   * member_id
 
 <JsonDataTable jsonPath="nodes.model\.input_layer\.medical_claim.columns" />
@@ -46,11 +49,15 @@ The pharmacy_claim table includes information about retail and specialty drug pr
   * claim_line_number
 
 **Foreign Keys:**
-  * patient_id
+  * person_id
   * member_id
 
 <JsonDataTable jsonPath="nodes.model\.input_layer\.pharmacy_claim.columns" />
 
+### person_id
+A new patient identifier field named `person_id` has been added to the Tuva data model for both claims and clinical sources. This is a required field and cannot be null. If you bought the Tuva MPI Engine or have your own patient matching solution, this field should be populated with the UUID (Universally Unique Identifier). If you do not have a UUID, we recommend mapping the source patient identifier to this field (`member_id` for claims, patient_id for `clincal`).
+
+
 ## Clinical Input
 
 ### condition
@@ -62,6 +69,7 @@ The condition table contains information related to medical conditions patients
 
 **Foreign Keys:**
   * patient_id
+  * person_id
   * encounter_id
 
 <JsonDataTable jsonPath="nodes.model\.input_layer\.condition.columns" />
@@ -75,6 +83,7 @@ The encounter table contains information about patients visits (i.e. encounters)
 
 **Foreign Keys:**
   * patient_id
+  * person_id
 
 <JsonDataTable jsonPath="nodes.model\.input_layer\.encounter.columns" />
 
@@ -87,6 +96,7 @@ The lab result table contains information about lab test results, including the
 
 **Foreign Keys:**
   * patient_id
+  * person_id
   * encounter_id
 
 <JsonDataTable jsonPath="nodes.model\.input_layer\.lab_result.columns" />
@@ -109,6 +119,7 @@ The medication table contains information on medications ordered and/or administ
 
 **Foreign Keys:**
   * patient_id
+  * person_id
   * encounter_id
 
 <JsonDataTable jsonPath="nodes.model\.input_layer\.medication.columns" />
@@ -122,6 +133,7 @@ The observation table contains information on measurements other than lab tests
 
 **Foreign Keys:**
   * patient_id
+  * person_id
   * encounter_id
 
 <JsonDataTable jsonPath="nodes.model\.input_layer\.observation.columns" />
@@ -132,6 +144,7 @@ The patient table contains demographic and geographic information on patients.
 
 **Primary Key:**
   * patient_id
+  * person_id
 
 <JsonDataTable jsonPath="nodes.model\.input_layer\.patient.columns" />
 
@@ -153,7 +166,11 @@ The procedure table contains information on procedures that were performed on pa
 
 **Foreign Keys:**
   * patient_id
+  * person_id
   * encounter_id
   * practitioner_id
 
 <JsonDataTable jsonPath="nodes.model\.input_layer\.procedure.columns" />
+
+### person_id
+A new patient identifier field named `person_id` has been added to the Tuva data model for both claims and clinical sources. This is a required field and cannot be null. If you bought the Tuva MPI Engine or have your own patient matching solution, this field should be populated with the UUID (Universally Unique Identifier). If you do not have a UUID, we recommend mapping the source patient identifier to this field (`member_id` for claims, patient_id for `clincal`).

docs/core-data-model/condition.md

@@ -19,6 +19,8 @@ have an encounter_id).
   * condition_id
 
 **Foreign Keys:**
+  * person_id
+  * member_id
   * patient_id
   * encounter_id
 

docs/core-data-model/eligibility.md

@@ -12,9 +12,12 @@ otherwise it's impossible to calculate member months, which are needed to
 calculate measures like PMPM.
 
 **Primary Keys:**
-  * patient_id
+  * person_id
   * member_id
   * enrollment_start_date
   * enrollment_end_date
+  * payer
+  * plan
+  * data_source
 
 <JsonDataTable jsonPath="nodes.model\.the_tuva_project\.core__eligibility.columns" />

docs/core-data-model/encounter.md

@@ -20,6 +20,6 @@ this important information in a single table.
   * encounter_id
 
 **Foreign Keys:**
-  * patient_id
+  * person_id
 
 <JsonDataTable jsonPath="nodes.model\.the_tuva_project\.core__encounter.columns" />

docs/core-data-model/lab-result.md

@@ -13,6 +13,7 @@ result.
   * lab_result_id
 
 **Foreign Keys:**
+  * person_id
   * patient_id
   * encounter_id
 

docs/core-data-model/medical-claim.md

@@ -17,9 +17,10 @@ category and encounter that are useful for analytics.
 **Primary Keys:**
   * claim_id
   * claim_line_number
+  * data_source
 
 **Foreign Keys:**
-  * patient_id
+  * person_id
   * member_id
   * encounter_id
 

docs/core-data-model/medication.md

@@ -12,6 +12,7 @@ administered during a patient encounter.
   * medication_id
 
 **Foreign Keys:**
+  * person_id
   * patient_id
   * encounter_id
 

docs/core-data-model/observation.md

@@ -12,6 +12,7 @@ tests e.g. blood pressure, height, and weight.
   * observation_id
 
 **Foreign Keys:**
+  * person_id
   * patient_id
   * encounter_id
 

docs/core-data-model/patient.md

@@ -11,6 +11,6 @@ analytics use cases involve analyzing things that happen to patients, so it's
 critical to have a clean patient table that contains this information.
 
 **Primary Keys:**
-  * patient_id
+  * person_id
 
 <JsonDataTable jsonPath="nodes.model\.the_tuva_project\.core__patient.columns" />

docs/core-data-model/person_id_crosswalk.md

@@ -0,0 +1,19 @@
+---
+id: patient
+title: "Patient"
+---
+
+import { JsonDataTable } from '@site/src/components/JsonDataTable';
+
+The person_id_crosswalk table contains all source patient identifiers from 
+the input layer for claims and/or clinical.
+
+**Primary Keys:**
+  * person_id
+  * patient_id
+  * member_id
+  * payer
+  * plan
+  * data_source
+
+<JsonDataTable jsonPath="nodes.model\.the_tuva_project\.core__person_id_crosswalk.columns" />

docs/core-data-model/pharmacy-claim.md

@@ -12,9 +12,10 @@ paid by an insurer.  The pharmacy_claim table is at the claim-line grain.
 **Primary Keys:**
   * claim_id
   * claim_line_number
+  * data_source
 
 **Foreign Keys:**
-  * patient_id
+  * person_id
   * member_id
 
 <JsonDataTable jsonPath="nodes.model\.the_tuva_project\.core__pharmacy_claim.columns" />

docs/core-data-model/procedure.md

@@ -14,6 +14,8 @@ ICD-10-PCS and HCPCS.
   * procedure_id
 
 **Foreign Keys:**
+  * person_id
+  * member_id
   * patient_id
   * encounter_id
   * practitioner_id

docs/data-marts/ahrq-measures.md

@@ -84,7 +84,7 @@ This data mart computes the PQIs. The individual measures and definitions as of
 This model contains a row for each patient and data_source combination that is eligible for each pqi each year.
 
 **Primary Key:**
-  * patient_id
+  * person_id
   * data_source
   * pqi_number
   * year_number
@@ -109,7 +109,7 @@ This model contains a list of all the exclusions an encounter qualified for. An
 
 ### pqi_num_long
 
-This model contains a list of all encounters that qualified for a pqi. The patient_id and data_source are brought in for reference as well.
+This model contains a list of all encounters that qualified for a pqi. The person_id and data_source are brought in for reference as well.
 
 **Primary Key:**
   * data_source
@@ -241,7 +241,7 @@ with num as (
         data_source
       , year_number
       , pqi_number
-      , count(patient_id) as denom_count
+      , count(person_id) as denom_count
     from ahrq_measures.pqi_denom_long 
     group by
         data_source

docs/data-marts/ccsr.md

@@ -22,7 +22,7 @@ rows than the Tuva condition model. The model is equivalent to the CCSR's Output
 Option 1 - Vertical File Output.
 
 **Primary Keys:**
-  * patient_id
+  * person_id
   * normalized_code
   * ccsr_category
 
@@ -40,7 +40,7 @@ mapping of an ICD-10 code to a CCSR category. The model is equivalent to the
 CCSR's Output Option 1 - Vertical File Output.
 
 **Primary Keys:**
-  * patient_id
+  * person_id
   * normalized_code
   * ccsr_category
 
@@ -55,7 +55,7 @@ This model contains only the CCSR's default category assignment for the
 ICD-10 code, and only for the first-listed ICD-10 code (`diagnosis_code = 1`).
 
 **Primary Keys:**
-  * patient_id
+  * person_id
   * ccsr_category
 
 **Foreign Keys:**

docs/data-marts/chronic-conditions.md

@@ -27,7 +27,7 @@ This table is created by running the CMS chronic conditions data mart on data
 that's been mapped to the core data model.
 
 **Primary Keys:**
-  * patient_id
+  * person_id
   * condition
 
 **Foreign Keys:**
@@ -43,7 +43,7 @@ particular chronic condition they will have a 1 in that particular column and
 0 otherwise.
 
 **Primary Keys:**
-  * patient_id
+  * person_id
 
 <JsonDataTable  jsonPath="nodes.model\.the_tuva_project\.chronic_conditions__cms_chronic_conditions_wide.columns"  />
 
@@ -56,7 +56,7 @@ recent diagnosis, and the total count of diagnosis codes that were recorded
 that are relevant for the condition.
 
 **Primary Keys:**
-  * patient_id
+  * person_id
   * condition
 
 <JsonDataTable  jsonPath="nodes.model\.the_tuva_project\.chronic_conditions__tuva_chronic_conditions_long.columns" />
@@ -69,7 +69,7 @@ particular chronic condition they will have a 1 in that particular column and
 0 otherwise.
 
 **Primary Keys:**
-  * patient_id
+  * person_id
 
 <JsonDataTable  jsonPath="nodes.model\.the_tuva_project\.chronic_conditions__tuva_chronic_conditions_wide.columns"  />
 
@@ -83,8 +83,8 @@ In this query we show how often each chronic condition occurs in the patient pop
 ```sql
 select
   condition
-, count(distinct patient_id) as total_patients
-, cast(count(distinct patient_id) * 100.0 / (select count(distinct patient_id) from core.patient) as numeric(38,2)) as percent_of_patients
+, count(distinct person_id) as total_patients
+, cast(count(distinct person_id) * 100.0 / (select count(distinct person_id) from core.patient) as numeric(38,2)) as percent_of_patients
 from chronic_conditions.tuva_chronic_conditions_long
 group by 1
 order by 3 desc
@@ -101,8 +101,8 @@ In this query we show how often each chronic condition occurs in the patient pop
 select
   condition_category
 , condition
-, count(distinct patient_id) as total_patients
-, cast(count(distinct patient_id) * 100.0 / (select count(distinct patient_id) from core.patient) as numeric(38,2)) as percent_of_patients
+, count(distinct person_id) as total_patients
+, cast(count(distinct person_id) * 100.0 / (select count(distinct person_id) from core.patient) as numeric(38,2)) as percent_of_patients
 from chronic_conditions.cms_chronic_conditions_long
 group by 1,2
 order by 4 desc
@@ -117,22 +117,22 @@ In this query we show how many patients have 0 chronic conditions, how many pati
 
 ```sql
 with patients as (
-select patient_id
+select person_id
 from core.patient
 )
 
 , conditions as (
 select distinct
-  a.patient_id
+  a.person_id
 , b.condition
 from patients a
 left join chronic_conditions.tuva_chronic_conditions_long b
- on a.patient_id = b.patient_id
+ on a.person_id = b.person_id
 )
 
 , condition_count as (
 select
-  patient_id
+  person_id
 , count(distinct condition) as condition_count
 from conditions
 group by 1
@@ -141,7 +141,7 @@ group by 1
 select 
   condition_count
 , count(1)
-, cast(100 * count(distinct patient_id)/sum(count(distinct patient_id)) over() as numeric(38,1)) as percent
+, cast(100 * count(distinct person_id)/sum(count(distinct person_id)) over() as numeric(38,1)) as percent
 from condition_count
 group by 1
 order by 1