Tahir Mahmood1, Philipp Wittenberg2, Inez Maria Zwetsloot3, Hailiang Wang4, Kwok Leung Tsui4. 1. Department of Systems Engineering and Engineering Management, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong. 2. Department of Mathematics and Statistics, Helmut Schmidt University, Hamburg, Germany. 3. Department of Systems Engineering and Engineering Management, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong; School of Data Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong. Electronic address: i.m.zwetsloot@cityu.edu.hk. 4. School of Data Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong.
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.
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.
Authors: Ridwan A Sanusi; Lin Yan; Amani F Hamad; Olawale F Ayilara; Viktoriya Vasylkiv; Mohammad Jafari Jozani; Shantanu Banerji; Joseph Delaney; Pingzhao Hu; Elizabeth Wall-Wieler; Lisa M Lix Journal: BMC Public Health Date: 2022-04-09 Impact factor: 3.295
Authors: Hailiang Wang; Yang Zhao; Lisha Yu; Jiaxing Liu; Inez Maria Zwetsloot; Javier Cabrera; Kwok-Leung Tsui Journal: J Med Internet Res Date: 2020-09-30 Impact factor: 5.428