: Streamlining Hospital Stays in Health Registry Data

A common problem in health registry research is to collapse overlapping hospital stays to a single stay while retaining all information registered for the patient. Let’s start with looking at some example data:

pat_id    <- c(1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2,
               3, 3, 3, 3, 4, 4, 4,4, 5, 5, 5, 5, 5, 5,
               5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6,
               7, 7, 7, 7)

hosp_stay_id <- 1:44

enter_date <- as.Date(c(19324, 19363, 19375, 19380, 19356,
                19359, 19362,19368, 19369, 19373, 19375,
                19376, 19382, 19423, 19423, 19425, 19429,
                19373, 19395, 19403, 19437, 19321, 19422,
                19437, 19438, 19443, 19444, 19445, 19454,
                19454, 19458, 19459, 19460, 19464, 19467,
                19468, 19510, 19510, 19511, 19511,
                19360, 19397, 19432, 19439), origin="1970-01-01")

exit_date <- as.Date(c(19380, 19363, 19375, 19380, 19359,
                19382, 19362, 19368, 19369, 19373, 19375,
                19376, 19382,  19423, 19429, 19425, 19507,
                47117, 19395, 19403,  19437, 19445, 19422,
                19437, 19438, 19443, 19444, 19445, 19454,
                 19468, 19458, 19459, NA, 19464,
                19467, 19468, 19510, 19511, 19511, 19513, 19450,
                19397, 19432, 19439), origin="1970-01-01")

example_data <- data.frame(pat_id,hosp_stay_id,
                     enter_date,exit_date)

In the example data, patient nr. 1 has 4 hospital episodes that we would like to identify as a single consecutive hospital stay. We still want to retain all the other information (in this case only the unique hosp_stay_id).

Since we want to keep all the other information, we can’t simply collapse the information for patient 1 to a single line if information with enter date 2022-11-28 and exit date 2023-01-23.

Let’s start by evoking data.table (my very favorite R package!) and change the structure of the data frame to the lovely data table structure:

library(data.table)
setDT(example_data)

# The code below will run but give strange results with missing data in exit date. Missing in exit date usually means patients are still hospitalized, and we could replace the missing date with the 31st December of the reporting year. Let's just exclude this entry for now:

example_data <- example_data[!is.na(exit_date)]


# Then order the datatable by patient id, enter date and exit date:

setorder(example_data,pat_id,enter_date,exit_date)

# We need a unique identifier per group of overlapping hospital stays.
# Let the magic begin!

example_data[, group_id:=cumsum(
  cummax(shift(as.integer(enter_date),
  fill=as.integer(exit_date)[1])) < as.integer(enter_date)) + 1,
             by=pat_id]

# The group id is now unique per patient and group of overlapping stays
# Let's turn it make it unique for each group of overlapping stays over the entire dataset:

example_data[,group_id := ifelse(seq(.N)==1,1,0),
             by=.(pat_id,group_id) ][,
              group_id := cumsum(group_id)]

# Let's make our example data a little prettier and easier to read by changing the column order:

setcolorder(example_data,
        c("pat_id", "hosp_stay_id","group_id"))

# Ready!

Now we can conduct our analyses.

In this simple example, we can only do simple things like counting the number of non-overlapping hospital stays or calculating the total length of stay per patient.

In more realistic examples, we will be able to solve more complex problems, like looking into medical information that might be stored in a separate table, with the hospital_stay_id as the link between the two tables.

R data table makes life so much easier for analysts of health registry data!

Acknowledgement: This solution was inspired by this Stack overflow post: https://stackoverflow.com/questions/28938147/how-to-flatten-merge-overlapping-time-periods

Continue reading: Linking overlapping hospital stays

Analysis: Collapsing Overlapping Hospital Stays in Health Registry Data

In medical research, there is a common challenge of representing a patient’s multiple, overlapping hospital stays as a single continuous stay while preserving all registered patient data. A solution to this problem is implemented using the R package ‘data.table’, which offers an efficient interface for handling and transforming large datasets.

A. Key Points of the Initial Implementation

1. Creating a dataset known as ‘example_data’, containing patient id’s, hospital stay ids, and respective entrance and exit dates for the hospital stays.
2. Changing the data structure from a data frame to a ‘data.table’ using the ‘data.table’ function.
3. Excluding entries with missing exit dates as it often suggests that the patient is still hospitalized. The unavailability of an exit date could result in misleading results during analysis.
4. Ordering the data by patient id, entrance date, and exit date to maintain a chronological sequence of events.
5. Generating a unique identifier, termed ‘group_id’, for each group of overlapping stays using the ‘cumsum’ and ‘shift’ functions. This ‘group_id’ is then made unique for every group across the dataset.

B. Long-Term Implications and Potential Future Developments

The methodology offered here has long-term implications and opportunity for future developments. Its ability to collapse multiple overlapping hospital stays into one unique ‘group_id’ provides way to more accurately represent each patient’s journey through their hospital visits. With this simplification, we can more effectively derive insights surrounding the duration and frequency of hospital stays and thus make evaluations regarding hospital efficiency and patients’ health status.

Going forward, there are opportunities to expand this methodology with more complex data and further refinements. For instance, adding additional medical information associated with each hospital stay could provide deeper insights into patient’s health progress. Furthermore, considering other variables like illness severity or treatment administered could also aid in creating a more comprehensive picture of a patient’s health journey.

C. Actionable Advice

Healthcare professionals involved in medical data analysis could use these insights to make informed decisions regarding patients’ healthcare and the management of health institutions. They should:

• Understand this methodology and leverage the R ‘data.table’ package to simplify their analyses of hospital stays.
• Continue refining this analysis by integrating more complex data to create comprehensive views of patients’ healthcare trajectories.
• Look for opportunities to apply this methodology in other healthcare analyses that require the linkage of overlapping events.
• Remember to handle missing data appropriately to avoid misleading results in their analysis or perhaps consider deploying a strategy to fill these missing values when essential, such as mean, median or mode.

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