# Cohort profile: The Dutch wound monitor cohort and the Swedish Region Halland Integrated Platform (RHIP) wound cohort

## TL;DR

This paper describes two wound care datasets from the Netherlands and Sweden, analyzing their potential for improving wound care through AI and data-driven approaches.

## Contribution

The paper introduces and characterizes two large wound care cohorts, highlighting their potential for predictive modeling and machine learning applications.

## Key findings

- The Dutch Wound Monitor cohort includes over 17,000 patients with wound data collected from 2005 to 2022.
- The RHIP Wound Cohort in Sweden includes nearly 39,000 patients with wound-related diagnoses from 2008 to 2021.
- The datasets offer detailed demographic and wound type information suitable for AI-driven healthcare improvements.

## Abstract

Hard-to-heal wounds are a growing human and financial concern, constituting approximately 1–3% of the healthcare budget. Wound care is not a medical specialty and is often not prioritized within healthcare. A large portion of the cost and suffering caused by wounds has the potential to be mediated through improved knowledge and effectivised workflows. One potential way to achieve this is through the implementation of AI-tools to support clinicians in planning and executing wound care. Information-driven care is a framework for implementing AI-technology in healthcare. Wound Monitor is a Dutch database containing data collected from home-care visits conducted by wound specialists during 2005 to 2022, mostly in Limburg. It contains data of more than 17000 patients. Region Halland, Sweden, created a platform of integrated clinical, financial and operational data called “The Regional Healthcare Information Platform” (RHIP). The platform contains data on over 500 000 patients during 2008–2021. Within this data, a subset of almost 39000 patients have been diagnosed with wounds or wound related conditions. This subset of patients are defined as the RHIP Wound Cohort. This article characterizes the two wound cohorts in terms of demographics and wound types. Further, it examines the quality, quantity and granularity of the respective databases. The discussion section evaluates the strengths and weaknesses of the datasets in terms of the perspective they provide on the patient and wound journey. Lastly, the discussion section also explores how the cohorts may be utilized for predictive modeling and other machine learning-based applications in order to enable information-driven wound care.