# Increasing diagnoses per patient admission at a specialist children’s hospital: A retrospective study

## TL;DR

This study shows that children admitted to a specialist hospital in England have had a significant increase in the number of coexisting diagnoses over 24 years.

## Contribution

The study provides the first evidence of increasing diagnostic complexity in pediatric inpatient care over a long period.

## Key findings

- The mean number of diagnoses per admission increased from 2.72 to 10.43 over 24 years.
- The increase was consistent across all speciality areas and not due to coding changes.
- The trend suggests biological or referral factors rather than data artifacts.

## Abstract

In adult practice there is recognition that average patient complexity is increasing, with a greater proportion of patients having multiple diagnoses or comorbidities. This study aims to examine whether there has been a change in number of recorded coexisting diagnoses per patient over a 24-year period for children attending as in-patients to a specialist children’s hospital in England.

Following all in-patient admissions, patient episodes are allocated specific diagnosis codes (ICD-10) by a specialist clinical coding team according to standard NHS criteria and guidance. We examine the number of coexisting diagnoses allocated per patient admission over a 24-year period.

From a total of 278,579 overnight in-patient admissions during the study period (2000–2023) there were 1,023,276 ICD-10 patient diagnoses. The mean number of diagnoses per admission increased from 2.72 to 10.43 over the period (Kendall’s tau statistic of 0.93; p-value < 0.001), an increase of 284% (95% confidence interval 275% - 293%).

Over recent decades, the recorded complexity of patients attending a specialist children’s hospital appear to have increased significantly, with an almost 3-fold increase in the number of coexisting diagnoses present per admission. The cause of this finding cannot be determined from the data; however, it appears to be gradual and consistent, and across all speciality areas suggesting biological or referral factors rather than artefactual coding issues. Recognition of such a trend is important when interpreting retrospective data for AI, research, and planning purposes.