Monitoring data quality for telehealth systems in the presence of missing data

TL;DR

This paper develops a real-time data quality monitoring method using control charts, specifically designed to handle missing data, to ensure accurate vital sign measurements in telehealth elder care systems.

Contribution

It introduces a Hotelling's T-squared control chart adapted for missing data, enabling effective real-time detection of data quality issues in healthcare monitoring.

Findings

Successfully detected measurement errors in blood pressure data

Validated method in retrospective case study

Supported real-time data quality control in telehealth systems

Abstract

Background: All-in-one station-based health monitoring devices are implemented in elder homes in Hong Kong to support the monitoring of vital signs of the elderly. During a pilot study, it was discovered that the systolic blood pressure was incorrectly measured during multiple weeks. A real-time solution was needed to identify future data quality issues as soon as possible. Methods: Control charts are an effective tool for real-time monitoring and signaling issues (changes) in data. In this study, as in other healthcare applications, many observations are missing. Few methods are available for monitoring data with missing observations. A data quality monitoring method is developed to signal issues with the accuracy of the collected data quickly. This method has the ability to deal with missing observations. A Hotelling's T-squared control chart is selected as the basis for our proposed method. Findings: The proposed method is retrospectively validated on a case study with a known measurement error in the systolic blood pressure measurements. The method is able to adequately detect this data quality problem. The proposed method was integrated into a personalized telehealth monitoring system and prospectively implemented in a second case study. It was found that the proposed scheme supports the control of data quality. Conclusions: Data quality is an important issue and control charts are useful for real-time monitoring of data quality. However, these charts must be adjusted to account for missing data that often occur in healthcare context.