Conditions and Visits

Last updated on 2026-04-28 | Edit this page

Overview

Questions

What are conditions in the OMOP CDM?
When do we need to consider visits in our analysis?

Objectives

Understand the structure and purpose of the conditions table in the OMOP CDM.
Know that visits are recorded in the visit_occurrence table.
Learn how and when to consider visits in data analysis.
Know that a visit is a period of time and patients can have multiple visits.
Understand that multiple measurements, conditions etc. can occur within a visit.
Understand that other tables link to visits.

Introduction

This episode covers the OMOP conditions and visits table.

Callout

For this episode we will be using a sample OMOP CDM database that is pre-loaded with data. This database is a simplified version of a real-world OMOP CDM database and is intended for educational purposes only.

(UCLH only) This will come in a similar form as you would get data if you asked for a data extract via the SAFEHR platform (i.e. a set of parquet files).

As part of the setup prior to this course you were asked to download and install the sample dataset. If you have not done this yet, please refer to the setup instructions. For now, we will assume that you have the sample OMOP CDM dataset available on your local machine at the following path: ./data/omop/ and the functions in a folder ./code/parquet_dataset.

You will then need to load the database as shown in the previous episode.

R

library(dplyr)
open_omop_dataset <- function(path) {
    # iterate over table level directories
    list.dirs(path, recursive = FALSE) |>
      # exclude folder name from path and use it as index for named list
      purrr::set_names(~ basename(.)) |>
      # "lazy-load" list of parquet files from specified folder
      purrr::map(arrow::open_dataset)
}

R

omop <- open_omop_dataset("./data/omop")

and the useful functions we created in the previous episode to look up concept names and ids.

R

get_concept_name <- function(omop_obj, id) {
  omop_obj$concept |>
    filter(concept_id == id) |>
    select(concept_name) |>
    collect()
}

R

get_concept_id <- function(omop_obj, name) {
  omop_obj$concept |>
    filter(concept_name == name) |>
    select(concept_id) |>
    collect()
}

Conditions

Conditions are a key part of the OMOP CDM. They represent diagnoses that have been made for patients. Conditions are stored in the condition_occurrence table. Each record in this table represents a single occurrence of a condition for a patient. The table contains records of diseases, medical conditions, diagnoses, signs, or symptoms observed by providers or reported by patients. Conditions are mapped from diagnostic codes and represented using standardized concepts in a hierarchical structure.

The `condition_occurrence` table contains the following columns (among others not listed here):

Column Names	Description of content
condition_occurrence_id	Unique identifier for each condition occurrence
person_id	Identifier for the patient
condition_concept_id	Concept identifier for the condition
condition_start_date	Date the condition started
condition_end_date	Date the condition ended
condition_type_concept_id	Concept identifier for the type of condition occurrence
condition_status_concept_id	Concept identifier for the status of the condition occurrence
visit_occurrence_id	Identifier for the visit during which the condition was recorded
condition_source_value	Original value from the source data
condition_source_concept_id	Concept identifier for the source value

Challenge

How many records are there in the condition_occurrence table?
List any of the conditions that occur more than once in the table along with their humanly readable names.
Choose one patient and list all the conditions they have and when they started?

Show me the solution

How many records are there in the condition_occurrence table?

R

omop$condition_occurrence |>
  tally() |>
  collect()

OUTPUT

# A tibble: 1 × 1
      n
  <int>
1    35

Answer: There are 35 records in the condition_occurrence table.

CODING NOTE: The tally() function is used to count the number of records in the table. It is more efficient to use tally() directly on the database without collecting the data into R, as this allows the database to perform the counting operation, which is optimized for large datasets. We then use collect() to run the calculation and produce the output. In place of tally() we could have used count(), which is a convenience function to group by a column and then tally(). We could have also used base R nrow(omop$condition_occurrence) in this example.

List any of the conditions that occur more than once in the table along with their humanly readable names.

R

omop$condition_occurrence |>
  group_by(condition_concept_id) |>
  summarise(occurrences = n()) |>
  filter(occurrences > 1) |>
  left_join(
    omop$concept,
    by = join_by(condition_concept_id == concept_id)
  ) |>
  select(concept_name, occurrences) |>
  collect()

OUTPUT

# A tibble: 4 × 2
  concept_name                             occurrences
  <chr>                                          <int>
1 Injury of head                                     2
2 Inflammatory disorder of digestive tract           2
3 Gastritis                                          2
4 Hemorrhoids                                        2

CODING NOTE: We first group the condition_occurrence table by condition_concept_id and count the number of occurrences for each condition. We then filter to keep only those conditions that occur more than once. Finally, we join with the concept table to get the humanly readable names of the conditions and select the relevant columns for display.

Choose one patient and list all the conditions they have and when they started

R

patient_id <- 1111  # Replace with the desired person_id 

omop$condition_occurrence |>
  filter(person_id == patient_id) |>
  left_join(
    omop$concept,
    by = join_by(condition_concept_id == concept_id)
  ) |>
  select(condition_concept_id, concept_name, condition_start_date) |>
  collect()

OUTPUT

# A tibble: 1 × 3
  condition_concept_id concept_name                         condition_start_date
                 <int> <chr>                                <date>
1              4230399 Closed fracture of lateral malleolus 2025-07-22

CODING NOTE: We filter the condition_occurrence table for the specified person_id to get all conditions for that patient. We then join with the concept table to get the humanly readable names of the conditions and select the relevant columns for display.

Question three can be repeated for different patients by changing the patient_id variable. You might be surprised to see that if you choose patient 31 you will see that the entry for their condition and start date is repeated. This isn’t an error!

Challenge

Investigate why patient 31 has repeated entries for their condition and start date in the condition_occurrence table. Look at the condition_type_concept_id, conditions_status_concept_id, and condition_source_value columns to understand the differences between these entries.

Show me the solution

R

patient_id <- 31  

omop$condition_occurrence |>
  filter(person_id == patient_id) |>
  left_join(
    omop$concept,
    by = join_by(condition_concept_id == concept_id)
  ) |>
  rename(condition_concept_name = concept_name) |>
  relocate(condition_concept_name, .after = condition_concept_id) |>
  left_join(
    omop$concept,
    by = join_by(condition_type_concept_id == concept_id)
  ) |>
  rename(condition_type_concept_name = concept_name) |>
  relocate(condition_type_concept_name, .after = condition_type_concept_id) |>
  left_join(
    omop$concept,
    by = join_by(condition_status_concept_id == concept_id)
  ) |>
  rename(condition_status_concept_name = concept_name) |>
  relocate(condition_status_concept_name,
           .after = condition_status_concept_id) |>
  select(
    condition_concept_id, 
    condition_concept_name,
    condition_start_date, 
    condition_type_concept_id,
    condition_type_concept_name,
    condition_status_concept_id,
    condition_status_concept_name,
    condition_source_value
    ) |>
  collect()

OUTPUT

# A tibble: 2 × 8
  condition_concept_id condition_concept_name condition_start_date
                 <int> <chr>                  <date>
1               375415 Injury of head         2019-05-10
2               375415 Injury of head         2019-05-10
# ℹ 5 more variables: condition_type_concept_id <int>,
#   condition_type_concept_name <chr>, condition_status_concept_id <int>,
#   condition_status_concept_name <chr>, condition_source_value <chr>

Answer: The output shows that for patient 31, the condition_concept_id and condition_start_date are identical, but the condition_type_concept_id and condition_status_concept_id differ. This indicates that the same condition was recorded in different contexts or with different statuses, which explains the repeated entries in the condition_occurrence table. This is commonly found in hospital records!

CODING NOTE: We filter the condition_occurrence table for person_id 31 and join with the concept table multiple times to get the humanly readable names for the condition_concept_id, condition_type_concept_id, and condition_status_concept_id. We then select and arrange the relevant columns for display to understand the differences between the entries for this patient.

Visits

The visit_occurrence table contains events where each person engages with the healthcare system for a duration of time.

The `visit_occurrence` table contains the following columns (among others not listed here):

Column Names	Description of content
visit_occurrence_id	Unique identifier for each visit occurrence
person_id	Identifier for the patient
visit_concept_id	Concept identifier for the type of visit
visit_start_date	Date the visit started
visit_start_datetime	Date and time the visit started
visit_end_date	Date the visit ended
visit_end_datetime	Date and time the visit ended
visit_type_concept_id	Concept identifier for the type of visit
discharged_to_concept_id	Concept identifier for where the patient was discharged to
preceding_visit_occurrence_id	Identifier for the previous visit occurrence

The main clinical tables condition_occurrence, measurement, observation and drug_exposure contain a visit_occurrence_id that links to this table.

visit_concept_id specifies the kind of visit that took place using standardised OMOP concepts. These include Inpatient visit, Emergency Room Visit and Outpatient Visit. Inpatient visits can last for longer than one day.

As we have seen we don’t need to consider visits to answer all research questions. For example if we can count the number of patients with a particular condition without considering visits. However, in some cases visits are important. For example, if we want to know how many emergency room visits resulted in a hospital admission we need to consider visits.

Challenge

Find out how many different types of visits are recorded in the visit_occurrence table and link these to get their name.
Find patients who had more than one visit.
How many patients had both an emergency room visit and an inpatient visit?

Show me the solution

Find out how many different types of visits are recorded in the visit_occurrence table and link these to get their name.

R

omop$visit_occurrence |>
  count(visit_concept_id) |>
  left_join(
    omop$concept,
    by = join_by(visit_concept_id == concept_id)
  ) |>
  select(visit_concept_id, concept_name, n) |>
  arrange(desc(n)) |>
  collect()

OUTPUT

# A tibble: 4 × 3
  visit_concept_id concept_name                           n
             <int> <chr>                              <int>
1             9203 Emergency Room Visit                   8
2             9201 Inpatient Visit                        7
3             9202 Outpatient Visit                       4
4              262 Emergency Room and Inpatient Visit     1

Answer: The different types of visits recorded in the visit_occurrence table along with their names are: - Emergency Room Visit - Inpatient visit - Outpatient Visit - Emergency Room and Inpatient Visit

CODING NOTE: We use the count() function to count the number of occurrences of each visit_concept_id in the visit_occurrence table. We then join with the concept table to get the humanly readable names of the visit types and select the relevant columns for display. Finally, we arrange the results in descending order of count using the desc() function in conjunction with the arrange() function.

Find patients who had more than one visit.

R

omop$visit_occurrence |>
  group_by(person_id) |>
  tally() |>
  filter(n > 1) |>
  collect()

OUTPUT

# A tibble: 4 × 2
  person_id     n
      <int> <int>
1      1112     2
2        31     2
3         2     2
4        58    10

CODING NOTE: We group the visit_occurrence table by person_id and use tally() to count the number of visits for each patient. We then filter to keep only those patients with an n greater than 1 and collect the results into R for display. We could have also used the summarise() function instead of tally().

How many patients had both an emergency room visit and an inpatient visit?

R

patients_with_both_in_one_visit <- omop$visit_occurrence |>
  filter(visit_concept_id == 262) |>
  count(person_id) |>
  collect()

patients_with_both_visits_seperately <- omop$visit_occurrence |>
  filter(visit_concept_id %in% c(9203, 9201)) |>
  group_by(person_id) |>
  summarise(visit_types = n_distinct(visit_concept_id)) |>
  filter(visit_types  == 2) |>
  collect()

patients_with_both_in_one_visit |>
  bind_rows(patients_with_both_visits_seperately) |>
  distinct(person_id) |>
  tally()

OUTPUT

# A tibble: 1 × 1
      n
  <int>
1     2

Answer: The number of patients who had both an emergency room visit and an inpatient visit is 2.

CODING NOTE: We filter the visit_occurrence table for the visit_concept_id values corresponding to emergency room visits and inpatient visit coded in a single concept. We then use count(person_id) to find the total number of visits per patient. For the next section we look at patients who have had separate inpatient and emergency room visits by filtering the visit_occurrence table for the visit_concept_id for both concepts. We then group by person_id and use summarise() to count the number of distinct visit types for each patient. Finally, we count the number of patients who had more than one distinct visit type to get the number of patients who had both types of visits. Then we join together both tables using bind_rows() and get the distinct patients before counting the number of rows with tally().

Key Points

Conditions are stored in the condition_occurrence table in the OMOP CDM.
Visits are stored in the visit_occurrence table and linked to other clinical tables via visit_occurrence_id.
A visit represents a period of time during which a patient interacts with the healthcare system and there can be multiple types of visits.
Visits may be important to consider in analyses depending on the research question.

Conditions and Visits

Overview

Questions

Objectives

Introduction

R

R

R

R

Conditions

The condition_occurrence table contains the following columns (among others not listed here):

Challenge

Show me the solution

R

OUTPUT

R

OUTPUT

R

OUTPUT

Challenge

Show me the solution

R

OUTPUT

Visits

The visit_occurrence table contains the following columns (among others not listed here):

Challenge

Show me the solution

R

OUTPUT

R

OUTPUT

R

OUTPUT

The `condition_occurrence` table contains the following columns (among others not listed here):

The `visit_occurrence` table contains the following columns (among others not listed here):