Summarise clinical records • OmopSketch

Introduction

In this vignette, we will explore the OmopSketch functions designed to provide an overview of the clinical tables within a CDM object (for example, visit_occurrence, condition_occurrence, drug_exposure, procedure_occurrence, device_exposure, measurement, observation, and death). Specifically, there are two key functions that facilitate this:

summariseClinicalRecords(): creates summary statistics with key information about clinical tables, including the number of records, number of subjects, records per person, record duration, quality checks, concept summaries, and missing data.
tableClinicalRecords(): displays the results in a formatted table.

Create a mock cdm

Let’s see an example of these functionalities. To start with, we will load essential packages and create a mock CDM using the R package omock.

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(OmopSketch)
library(omock)

# Connect to mock database
cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")
#> ℹ Loading bundled GiBleed tables from package data.
#> ℹ Adding drug_strength table.
#> ℹ Creating local <cdm_reference> object.
#> ℹ Inserting <cdm_reference> into duckdb.

cdm
#> 
#> ── # OMOP CDM reference (duckdb) of GiBleed ────────────────────────────────────
#> • omop tables: care_site, cdm_source, concept, concept_ancestor, concept_class,
#> concept_relationship, concept_synonym, condition_era, condition_occurrence,
#> cost, death, device_exposure, domain, dose_era, drug_era, drug_exposure,
#> drug_strength, fact_relationship, location, measurement, metadata, note,
#> note_nlp, observation, observation_period, payer_plan_period, person,
#> procedure_occurrence, provider, relationship, source_to_concept_map, specimen,
#> visit_detail, visit_occurrence, vocabulary
#> • cohort tables: -
#> • achilles tables: -
#> • other tables: -

Summarise clinical tables

Let’s now use summariseClinicalRecords() from the OmopSketch package to create an overview of one clinical table in the CDM, condition_occurrence.

summarisedResult <- summariseClinicalRecords(
  cdm = cdm, 
  omopTableName = "condition_occurrence"
)
#> ℹ Adding variables of interest to condition_occurrence.
#> ℹ Summarising records per person in condition_occurrence.
#> ℹ The following estimates will be calculated:
#> • duration: mean, sd, median, q25, q75, min, max
#> ! Table is collected to memory as not all requested estimates are supported on
#>   the database side
#> → Start summary of data, at 2026-06-17 10:02:19.662668
#> 
#> ✔ Summary finished, at 2026-06-17 10:02:19.736468
#> ℹ Summarising subjects not in person table in condition_occurrence.
#> ℹ Summarising records in observation in condition_occurrence.
#> ℹ Summarising records with start before birth date in condition_occurrence.
#> ℹ Summarising records with end date before start date in condition_occurrence.
#> ℹ Summarising domains in condition_occurrence.
#> ℹ Summarising standard concepts in condition_occurrence.
#> ℹ Summarising source vocabularies in condition_occurrence.
#> ℹ Summarising concept types in condition_occurrence.
#> ℹ Summarising missing data in condition_occurrence.

summarisedResult
#> # A tibble: 91 × 13
#>    result_id cdm_name group_name group_level          strata_name strata_level
#>        <int> <chr>    <chr>      <chr>                <chr>       <chr>       
#>  1         1 GiBleed  omop_table condition_occurrence overall     overall     
#>  2         1 GiBleed  omop_table condition_occurrence overall     overall     
#>  3         1 GiBleed  omop_table condition_occurrence overall     overall     
#>  4         1 GiBleed  omop_table condition_occurrence overall     overall     
#>  5         1 GiBleed  omop_table condition_occurrence overall     overall     
#>  6         1 GiBleed  omop_table condition_occurrence overall     overall     
#>  7         1 GiBleed  omop_table condition_occurrence overall     overall     
#>  8         1 GiBleed  omop_table condition_occurrence overall     overall     
#>  9         1 GiBleed  omop_table condition_occurrence overall     overall     
#> 10         1 GiBleed  omop_table condition_occurrence overall     overall     
#> # ℹ 81 more rows
#> # ℹ 7 more variables: variable_name <chr>, variable_level <chr>,
#> #   estimate_name <chr>, estimate_type <chr>, estimate_value <chr>,
#> #   additional_name <chr>, additional_level <chr>

Notice that the output is in the summarised result format.

Records per person

We can use the function arguments to specify which statistics to compute. For example, the recordsPerPerson argument controls the estimates returned for the number of records per person.

summarisedResult <- summariseClinicalRecords(
  cdm = cdm,
  omopTableName = "condition_occurrence",
  recordsPerPerson = c("mean", "sd", "q05", "q95")
)
#> ℹ Adding variables of interest to condition_occurrence.
#> ℹ Summarising records per person in condition_occurrence.
#> ℹ The following estimates will be calculated:
#> • duration: mean, sd, q05, q95
#> ! Table is collected to memory as not all requested estimates are supported on
#>   the database side
#> → Start summary of data, at 2026-06-17 10:02:23.892337
#> 
#> ✔ Summary finished, at 2026-06-17 10:02:23.958979
#> ℹ Summarising subjects not in person table in condition_occurrence.
#> ℹ Summarising records in observation in condition_occurrence.
#> ℹ Summarising records with start before birth date in condition_occurrence.
#> ℹ Summarising records with end date before start date in condition_occurrence.
#> ℹ Summarising domains in condition_occurrence.
#> ℹ Summarising standard concepts in condition_occurrence.
#> ℹ Summarising source vocabularies in condition_occurrence.
#> ℹ Summarising concept types in condition_occurrence.
#> ℹ Summarising missing data in condition_occurrence.

summarisedResult |>
  filter(variable_name == "Records per person") |>
  select(variable_name, estimate_name, estimate_value)
#> # A tibble: 4 × 3
#>   variable_name      estimate_name estimate_value
#>   <chr>              <chr>         <chr>         
#> 1 Records per person mean          24.2509       
#> 2 Records per person sd            7.4065        
#> 3 Records per person q05           14            
#> 4 Records per person q95           38

Quality

When the argument quality = TRUE is set, the results will include a quality assessment of the data.
This assessment provides information such as:

The proportion of records that fall outside the subjects’ observation periods.
Issues with date columns (e.g., start dates occurring after end dates, or dates preceding a subject’s birth date).
The presence of subjects in the clinical table that do not exist in the person table.

summarisedResult <- summariseClinicalRecords(
  cdm = cdm,
  omopTableName = "condition_occurrence",
  recordsPerPerson = NULL, 
  conceptSummary = FALSE,
  missingData = FALSE,
  quality = TRUE
)
#> ℹ Adding variables of interest to condition_occurrence.
#> ℹ Summarising records per person in condition_occurrence.
#> No analyses were selected.
#> ℹ Summarising subjects not in person table in condition_occurrence.
#> ℹ Summarising records in observation in condition_occurrence.
#> ℹ Summarising records with start before birth date in condition_occurrence.
#> ℹ Summarising records with end date before start date in condition_occurrence.

summarisedResult |>
  select(variable_name, estimate_name, estimate_value) 
#> # A tibble: 13 × 3
#>    variable_name                estimate_name estimate_value
#>    <chr>                        <chr>         <chr>         
#>  1 Number subjects              count         2694          
#>  2 Number records               count         65332         
#>  3 Number subjects              percentage    100           
#>  4 Subjects not in person table count         0             
#>  5 Subjects not in person table percentage    0.00          
#>  6 In observation               count         450           
#>  7 In observation               count         64882         
#>  8 Start date before birth date count         0             
#>  9 End date before start date   count         0             
#> 10 In observation               percentage    0.69          
#> 11 In observation               percentage    99.31         
#> 12 Start date before birth date percentage    0.00          
#> 13 End date before start date   percentage    0.00

Concept Summary

When the argument conceptSummary = TRUE is set, the results will include information about the concepts contained in the table, such as:

The domain to which each concept belongs.
Whether records use standard or non-standard concepts.
The concept class, concept type, standard vocabulary, and source vocabulary associated with the records.

summarisedResult <- summariseClinicalRecords(
  cdm = cdm,
  omopTableName = "drug_exposure",
  recordsPerPerson = NULL, 
  conceptSummary = TRUE,
  missingData = FALSE,
  quality = FALSE
)
#> ℹ Adding variables of interest to drug_exposure.
#> ℹ Summarising records per person in drug_exposure.
#> No analyses were selected.
#> ℹ Summarising domains in drug_exposure.
#> ℹ Summarising standard concepts in drug_exposure.
#> ℹ Summarising source vocabularies in drug_exposure.
#> ℹ Summarising concept types in drug_exposure.
#> ℹ Summarising concept class in drug_exposure.

summarisedResult |>
  select(variable_name, variable_level, estimate_name, estimate_value) 
#> # A tibble: 41 × 4
#>    variable_name       variable_level      estimate_name estimate_value
#>    <chr>               <chr>               <chr>         <chr>         
#>  1 Number subjects     NA                  count         2694          
#>  2 Number records      NA                  count         67707         
#>  3 Number subjects     NA                  percentage    100           
#>  4 Domain              Drug                count         67707         
#>  5 Standard concept    Standard            count         67707         
#>  6 Standard vocabulary CVX                 count         25710         
#>  7 Standard vocabulary RxNorm              count         41997         
#>  8 Source vocabulary   No matching concept count         35            
#>  9 Source vocabulary   CVX                 count         25710         
#> 10 Source vocabulary   NDC                 count         2694          
#> # ℹ 31 more rows

Missingness

When the argument missingData = TRUE is set, the results will include a summary of missing data in the table. This output is analogous to the results produced by the OmopSketch function summariseMissingData().

summarisedResult <- summariseClinicalRecords(
  cdm = cdm,
  omopTableName = "condition_occurrence",
  recordsPerPerson = NULL, 
  conceptSummary = FALSE,
  missingData = TRUE,
  quality = FALSE
)
#> ℹ Adding variables of interest to condition_occurrence.
#> ℹ Summarising records per person in condition_occurrence.
#> No analyses were selected.
#> ℹ Summarising missing data in condition_occurrence.

summarisedResult |>
  select(variable_name, variable_level, estimate_name, estimate_value) 
#> # A tibble: 53 × 4
#>    variable_name   variable_level          estimate_name   estimate_value
#>    <chr>           <chr>                   <chr>           <chr>         
#>  1 Number subjects NA                      count           2694          
#>  2 Number records  NA                      count           65332         
#>  3 Number subjects NA                      percentage      100           
#>  4 Column name     condition_occurrence_id na_count        0             
#>  5 Column name     condition_occurrence_id na_percentage   0.00          
#>  6 Column name     condition_occurrence_id zero_count      0             
#>  7 Column name     condition_occurrence_id zero_percentage 0.00          
#>  8 Column name     person_id               na_count        0             
#>  9 Column name     person_id               na_percentage   0.00          
#> 10 Column name     person_id               zero_count      0             
#> # ℹ 43 more rows

Strata

It is also possible to stratify the results by sex and age groups. When sex = TRUE, summaries are stratified by sex. When ageGroup is provided, records are assigned to the specified age groups using the clinical record start date.

summarisedResult <- summariseClinicalRecords(
  cdm = cdm,
  omopTableName = "condition_occurrence",
  recordsPerPerson = c("mean", "sd", "q05", "q95"),
  quality = TRUE,
  conceptSummary = TRUE,
  sex = TRUE,
  ageGroup = list("<35" = c(0, 34), ">=35" = c(35, Inf))
)
#> ℹ Adding variables of interest to condition_occurrence.
#> ℹ Summarising records per person in condition_occurrence.
#> ℹ The following estimates will be calculated:
#> • duration: mean, sd, q05, q95
#> ! Table is collected to memory as not all requested estimates are supported on
#>   the database side
#> → Start summary of data, at 2026-06-17 10:02:38.357908
#> 
#> ✔ Summary finished, at 2026-06-17 10:02:38.668715
#> ℹ Summarising subjects not in person table in condition_occurrence.
#> ℹ Summarising records in observation in condition_occurrence.
#> ℹ Summarising records with start before birth date in condition_occurrence.
#> ℹ Summarising records with end date before start date in condition_occurrence.
#> ℹ Summarising domains in condition_occurrence.
#> ℹ Summarising standard concepts in condition_occurrence.
#> ℹ Summarising source vocabularies in condition_occurrence.
#> ℹ Summarising concept types in condition_occurrence.
#> ℹ Summarising missing data in condition_occurrence.

summarisedResult |>
  select(variable_name, strata_level, estimate_name, estimate_value) 
#> # A tibble: 717 × 4
#>    variable_name      strata_level estimate_name estimate_value
#>    <chr>              <chr>        <chr>         <chr>         
#>  1 Number subjects    overall      count         2694          
#>  2 Records per person overall      mean          24.2509       
#>  3 Records per person overall      sd            7.4065        
#>  4 Records per person overall      q05           14            
#>  5 Records per person overall      q95           38            
#>  6 Number records     overall      count         65332         
#>  7 Number subjects    <35          count         2694          
#>  8 Number subjects    >=35         count         2656          
#>  9 Records per person <35          mean          14.3901       
#> 10 Records per person >=35         mean          10.0019       
#> # ℹ 707 more rows

Notice that, by default, the “overall” group will also be included, as well as crossed strata (for example, sex == "Female" and ageGroup == ">=35").

The analysis can also be conducted for multiple OMOP tables at the same time:

summarisedResult <- summariseClinicalRecords(
  cdm = cdm,
  omopTableName = c("visit_occurrence", "drug_exposure"),
  recordsPerPerson = c("mean", "sd"),
  quality = FALSE,
  conceptSummary = FALSE,
  missingData = FALSE
)
#> ℹ Adding variables of interest to visit_occurrence.
#> ℹ Summarising records per person in visit_occurrence.
#> ℹ The following estimates will be calculated:
#> • duration: mean, sd
#> → Start summary of data, at 2026-06-17 10:02:46.554472
#> 
#> ✔ Summary finished, at 2026-06-17 10:02:46.71803
#> ℹ Adding variables of interest to drug_exposure.
#> ℹ Summarising records per person in drug_exposure.
#> ℹ The following estimates will be calculated:
#> • duration: mean, sd
#> → Start summary of data, at 2026-06-17 10:02:48.383765
#> 
#> ✔ Summary finished, at 2026-06-17 10:02:48.541129

summarisedResult |>
  select(group_level, variable_name, estimate_name, estimate_value)
#> # A tibble: 14 × 4
#>    group_level      variable_name       estimate_name estimate_value   
#>    <chr>            <chr>               <chr>         <chr>            
#>  1 visit_occurrence Number subjects     count         890              
#>  2 visit_occurrence Records per person  mean          1.1652           
#>  3 visit_occurrence Records per person  sd            0.4145           
#>  4 visit_occurrence Number records      count         1037             
#>  5 visit_occurrence Number subjects     percentage    33.0363771343727 
#>  6 visit_occurrence Duration of records mean          1.25843780135005 
#>  7 visit_occurrence Duration of records sd            0.719782966295868
#>  8 drug_exposure    Number subjects     count         2694             
#>  9 drug_exposure    Records per person  mean          25.1325          
#> 10 drug_exposure    Records per person  sd            5.2457           
#> 11 drug_exposure    Number records      count         67707            
#> 12 drug_exposure    Number subjects     percentage    100              
#> 13 drug_exposure    Duration of records mean          33.2970298492032 
#> 14 drug_exposure    Duration of records sd            321.513935968042

Date Range

We can also filter the clinical table to a specific time window by setting the dateRange argument.


summarisedResult <- summariseClinicalRecords(
  cdm = cdm, 
  omopTableName = "drug_exposure",
  dateRange = as.Date(c("1990-01-01", "2010-01-01"))
) 
#> ℹ Adding variables of interest to drug_exposure.
#> ℹ Summarising records per person in drug_exposure.
#> ℹ The following estimates will be calculated:
#> • duration: mean, sd, median, q25, q75, min, max
#> ! Table is collected to memory as not all requested estimates are supported on
#>   the database side
#> → Start summary of data, at 2026-06-17 10:02:51.224114
#> 
#> ✔ Summary finished, at 2026-06-17 10:02:51.28771
#> ℹ Summarising subjects not in person table in drug_exposure.
#> ℹ Summarising records in observation in drug_exposure.
#> ℹ Summarising records with start before birth date in drug_exposure.
#> ℹ Summarising records with end date before start date in drug_exposure.
#> ℹ Summarising domains in drug_exposure.
#> ℹ Summarising standard concepts in drug_exposure.
#> ℹ Summarising source vocabularies in drug_exposure.
#> ℹ Summarising concept types in drug_exposure.
#> ℹ Summarising concept class in drug_exposure.
#> ℹ Summarising missing data in drug_exposure.

summarisedResult |>
  settings() |>
  glimpse()
#> Rows: 1
#> Columns: 10
#> $ result_id          <int> 1
#> $ result_type        <chr> "summarise_clinical_records"
#> $ package_name       <chr> "OmopSketch"
#> $ package_version    <chr> "1.1.0"
#> $ group              <chr> "omop_table"
#> $ strata             <chr> ""
#> $ additional         <chr> "type_concept_id &&& is_required"
#> $ min_cell_count     <chr> "0"
#> $ study_period_end   <chr> "2010-01-01"
#> $ study_period_start <chr> "1990-01-01"

Tidy the summarised object

tableClinicalRecords() will help you to tidy the previous results and create a formatted table. The table type can be set with the type argument; supported formats are provided by visOmopResults::tableType(). If type = NULL, global table options are used when available; otherwise, a gt table is created by default.

summarisedResult <- summariseClinicalRecords(cdm,
  omopTableName = "condition_occurrence",
  recordsPerPerson = c("mean", "sd", "q05", "q95"),
  quality = TRUE, 
  conceptSummary = TRUE,
  sex = TRUE
)
#> ℹ Adding variables of interest to condition_occurrence.
#> ℹ Summarising records per person in condition_occurrence.
#> ℹ The following estimates will be calculated:
#> • duration: mean, sd, q05, q95
#> ! Table is collected to memory as not all requested estimates are supported on
#>   the database side
#> → Start summary of data, at 2026-06-17 10:02:56.733915
#> 
#> ✔ Summary finished, at 2026-06-17 10:02:56.873296
#> ℹ Summarising subjects not in person table in condition_occurrence.
#> ℹ Summarising records in observation in condition_occurrence.
#> ℹ Summarising records with start before birth date in condition_occurrence.
#> ℹ Summarising records with end date before start date in condition_occurrence.
#> ℹ Summarising domains in condition_occurrence.
#> ℹ Summarising standard concepts in condition_occurrence.
#> ℹ Summarising source vocabularies in condition_occurrence.
#> ℹ Summarising concept types in condition_occurrence.
#> ℹ Summarising missing data in condition_occurrence.

tableClinicalRecords(result = summarisedResult, type = "gt")

Summary of condition_occurrence table
Variable name	Variable level	Estimate name	Database name
Variable name	Variable level	Estimate name	GiBleed
condition_occurrence; overall
Number records	–	N	65,332.00
Number subjects	–	N (%)	2,694 (100.00%)
Subjects not in person table	–	N (%)	0 (0.00%)
Records per person	–	Mean (SD)	24.25 (7.41)
		q05	14.00
		q95	38.00
Duration of records	–	Mean (SD)	53.17 (423.43)
		q05	0.00
		q95	90.00
In observation	No	N (%)	450 (0.69%)
	Yes	N (%)	64,882 (99.31%)
Domain	Condition	N (%)	65,332 (100.00%)
Standard vocabulary	Snomed	N (%)	65,332 (100.00%)
Source vocabulary	Icd10cm	N (%)	479 (0.73%)
	No matching concept	N (%)	27 (0.04%)
	Snomed	N (%)	64,826 (99.23%)
Standard concept	Standard	N (%)	65,332 (100.00%)
Type concept	Ehr encounter diagnosis (32020)	N (%)	65,332 (100.00%)
Start date before birth date	–	N (%)	0 (0.00%)
End date before start date	–	N (%)	0 (0.00%)
Column name	Condition concept id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Condition end date	N missing data (%)	8,652 (13.24%)
	Condition end datetime	N missing data (%)	8,652 (13.24%)
	Condition occurrence id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Condition source concept id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Condition source value	N missing data (%)	0 (0.00%)
	Condition start date	N missing data (%)	0 (0.00%)
	Condition start datetime	N missing data (%)	0 (0.00%)
	Condition status concept id	N missing data (%)	0 (0.00%)
		N zeros (%)	65,332 (100.00%)
	Condition status source value	N missing data (%)	65,332 (100.00%)
	Condition type concept id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Person id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Provider id	N missing data (%)	65,332 (100.00%)
		N zeros (%)	0 (0.00%)
	Stop reason	N missing data (%)	65,332 (100.00%)
	Visit detail id	N missing data (%)	0 (0.00%)
		N zeros (%)	65,332 (100.00%)
	Visit occurrence id	N missing data (%)	64 (0.10%)
		N zeros (%)	0 (0.00%)
condition_occurrence; Female
Number records	–	N	33,744.00
Number subjects	–	N (%)	1,373 (50.97%)
Records per person	–	Mean (SD)	24.58 (7.59)
		q05	14.00
		q95	38.00
Duration of records	–	Mean (SD)	52.04 (421.43)
		q05	0.00
		q95	90.00
In observation	No	N (%)	227 (0.67%)
	Yes	N (%)	33,517 (99.33%)
Domain	Condition	N (%)	33,744 (100.00%)
Standard vocabulary	Snomed	N (%)	33,744 (100.00%)
Source vocabulary	Icd10cm	N (%)	242 (0.72%)
	No matching concept	N (%)	15 (0.04%)
	Snomed	N (%)	33,487 (99.24%)
Standard concept	Standard	N (%)	33,744 (100.00%)
Type concept	Ehr encounter diagnosis (32020)	N (%)	33,744 (100.00%)
Column name	Condition concept id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Condition end date	N missing data (%)	4,397 (13.03%)
	Condition end datetime	N missing data (%)	4,397 (13.03%)
	Condition occurrence id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Condition source concept id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Condition source value	N missing data (%)	0 (0.00%)
	Condition start date	N missing data (%)	0 (0.00%)
	Condition start datetime	N missing data (%)	0 (0.00%)
	Condition status concept id	N missing data (%)	0 (0.00%)
		N zeros (%)	33,744 (100.00%)
	Condition status source value	N missing data (%)	33,744 (100.00%)
	Condition type concept id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Person id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Provider id	N missing data (%)	33,744 (100.00%)
		N zeros (%)	0 (0.00%)
	Stop reason	N missing data (%)	33,744 (100.00%)
	Visit detail id	N missing data (%)	0 (0.00%)
		N zeros (%)	33,744 (100.00%)
	Visit occurrence id	N missing data (%)	24 (0.07%)
		N zeros (%)	0 (0.00%)
condition_occurrence; Male
Number records	–	N	31,588.00
Number subjects	–	N (%)	1,321 (49.03%)
Records per person	–	Mean (SD)	23.91 (7.20)
		q05	13.00
		q95	37.00
Duration of records	–	Mean (SD)	54.37 (425.56)
		q05	0.00
		q95	93.00
In observation	No	N (%)	223 (0.71%)
	Yes	N (%)	31,365 (99.29%)
Domain	Condition	N (%)	31,588 (100.00%)
Standard vocabulary	Snomed	N (%)	31,588 (100.00%)
Source vocabulary	Icd10cm	N (%)	237 (0.75%)
	No matching concept	N (%)	12 (0.04%)
	Snomed	N (%)	31,339 (99.21%)
Standard concept	Standard	N (%)	31,588 (100.00%)
Type concept	Ehr encounter diagnosis (32020)	N (%)	31,588 (100.00%)
Column name	Condition concept id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Condition end date	N missing data (%)	4,255 (13.47%)
	Condition end datetime	N missing data (%)	4,255 (13.47%)
	Condition occurrence id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Condition source concept id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Condition source value	N missing data (%)	0 (0.00%)
	Condition start date	N missing data (%)	0 (0.00%)
	Condition start datetime	N missing data (%)	0 (0.00%)
	Condition status concept id	N missing data (%)	0 (0.00%)
		N zeros (%)	31,588 (100.00%)
	Condition status source value	N missing data (%)	31,588 (100.00%)
	Condition type concept id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Person id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Provider id	N missing data (%)	31,588 (100.00%)
		N zeros (%)	0 (0.00%)
	Stop reason	N missing data (%)	31,588 (100.00%)
	Visit detail id	N missing data (%)	0 (0.00%)
		N zeros (%)	31,588 (100.00%)
	Visit occurrence id	N missing data (%)	40 (0.13%)
		N zeros (%)	0 (0.00%)

Disconnect from CDM

Finally, disconnect from the mock CDM.

cdmDisconnect(cdm = cdm)