# Skin tone and clinical dataset from a prospective trial on acute care patients

## TL;DR

This study creates a dataset combining skin tone measurements and health records to better understand pulse oximetry disparities.

## Contribution

The paper introduces a novel dataset integrating skin tone data from multiple sources with EHR data to study health disparities.

## Key findings

- Skin tone data from 16 body locations were collected using various devices and linked to EHR data for 128 patients.
- A dataset with 167 features per skin location and 2,438 mobile phone images was published for AI development.
- The dataset is de-identified and formatted for use in PhysioNet to evaluate health disparities.

## Abstract

Although hypothesized to be the root cause of the pulse oximetry disparities, skin tone and its use for improving medical therapies have yet to be extensively studied. Studies that previously used self-reported race as a proxy variable for skin tone cannot account for skin tone variabilities within race groups. This study aimed to create a unique baseline dataset that included skin tone and electronic health record (EHR) data to better evaluate health disparities associated with pulse oximetry. We collected skin tone data at 16 different body locations using multiple devices, including administered visual scales, colorimetric, spectrophotometric, and photography via mobile phone cameras. All patients’ data were converted into a common data model and de-identified before publication in PhysioNet. We assessed 167 features per skin location on 128 patients linked with their EHR data, such as laboratory data, vital sign recordings, and demographic information. We also include 2,438 images from mobile phones to assist in developing artificial intelligence tools to combat health disparities.