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

Mahmood, Tahir; Wittenberg, Philipp; Zwetsloot, Inez Maria; Wang, Hailiang; Tsui, Kwok Leung

doi:10.1016/j.ijmedinf.2019.03.011

Cited by 20 publications

(12 citation statements)

References 35 publications

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“…One limitation of this study is the lack of data quality monitoring during implementation. Based on the data collected, we developed a data quality monitoring method to signal issues with the accuracy of the collected data quickly [ 71 ], which could be beneficial for further studies. Moreover, our study was based on a 3-month design and cannot evaluate the influences of season/time of the year on data variation.…”

Section: Discussionmentioning

confidence: 99%

A Personalized Health Monitoring System for Community-Dwelling Elderly People in Hong Kong: Design, Implementation, and Evaluation Study

Wang¹,

Zhao²,

Yu³

et al. 2020

J Med Internet Res

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Background Telehealth is an effective means to assist existing health care systems, particularly for the current aging society. However, most extant telehealth systems employ individual data sources by offline data processing, which may not recognize health deterioration in a timely way. Objective Our study objective was two-fold: to design and implement an integrated, personalized telehealth system on a community-based level; and to evaluate the system from the perspective of user acceptance. Methods The system was designed to capture and record older adults’ health-related information (eg, daily activities, continuous vital signs, and gait behaviors) through multiple measuring tools. State-of-the-art data mining techniques can be integrated to detect statistically significant changes in daily records, based on which a decision support system could emit warnings to older adults, their family members, and their caregivers for appropriate interventions to prevent further health deterioration. A total of 45 older adults recruited from 3 elderly care centers in Hong Kong were instructed to use the system for 3 months. Exploratory data analysis was conducted to summarize the collected datasets. For system evaluation, we used a customized acceptance questionnaire to examine users’ attitudes, self-efficacy, perceived usefulness, perceived ease of use, and behavioral intention on the system. Results A total of 179 follow-up sessions were conducted in the 3 elderly care centers. The results of exploratory data analysis showed some significant differences in the participants’ daily records and vital signs (eg, steps, body temperature, and systolic blood pressure) among the 3 centers. The participants perceived that using the system is a good idea (ie, attitude: mean 5.67, SD 1.06), comfortable (ie, self-efficacy: mean 4.92, SD 1.11), useful to improve their health (ie, perceived usefulness: mean 4.99, SD 0.91), and easy to use (ie, perceived ease of use: mean 4.99, SD 1.00). In general, the participants showed a positive intention to use the first version of our personalized telehealth system in their future health management (ie, behavioral intention: mean 4.45, SD 1.78). Conclusions The proposed health monitoring system provides an example design for monitoring older adults’ health status based on multiple data sources, which can help develop reliable and accurate predictive analytics. The results can serve as a guideline for researchers and stakeholders (eg, policymakers, elderly care centers, and health care providers) who provide care for older adults through such a telehealth system.

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Section: Discussionmentioning

confidence: 99%

A Personalized Health Monitoring System for Community-Dwelling Elderly People in Hong Kong: Design, Implementation, and Evaluation Study

Wang¹,

Zhao²,

Yu³

et al. 2020

J Med Internet Res

Self Cite

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“…A plotting point refers to an in-control (IC) state if it lies between decision lines; otherwise, the plotting point is considered as out-of-control (OOC). Control charts have been used for numerous applications across various domains [65][66][67][68][69][70][71][72][73][74].…”

Section: Monitoring Methods Based On the Com-poisson Regressionmentioning

confidence: 99%

GLM-Based Flexible Monitoring Methods: An Application to Real-Time Highway Safety Surveillance

et al. 2021

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Statistical modeling of historical crash data can provide essential insights to safety managers for proactive highway safety management. While numerous studies have contributed to the advancement from the statistical methodological front, minimal research efforts have been dedicated to real-time monitoring of highway safety situations. This study advocates the use of statistical monitoring methods for real-time highway safety surveillance using three years of crash data for rural highways in Saudi Arabia. First, three well-known count data models (Poisson, negative binomial, and Conway–Maxwell–Poisson) are applied to identify the best fit model for the number of crashes. Conway–Maxwell–Poisson was identified as the best fit model, which was used to find the significant explanatory variables for the number of crashes. The results revealed that the road type and road surface conditions significantly contribute to the number of crashes. From the perspective of real-time highway safety monitoring, generalized linear model (GLM)-based exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts are proposed using the randomized quantile residuals and deviance residuals of Conway–Maxwell (COM)–Poisson regression. A detailed simulation-based study is designed for predictive performance evaluation of the proposed control charts with existing counterparts (i.e., Shewhart charts) in terms of the run-length properties. The study results showed that the EWMA type control charts have better detection ability compared with the CUSUM type and Shewhart control charts under small and/or moderate shift sizes. Finally, the proposed monitoring methods are successfully implemented on actual traffic crash data to highlight the efficacy of the proposed methods. The outcome of this study could provide the analysts with insights to plan sound policy recommendations for achieving desired safety goals.

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“…The first 30 multivariate observations (i.e., m = 30 for p = 5) are employed to estimate the process parameters. The Phase I estimates are computed based on Equation ( 9), we obtain the estimated process mean vector and sample covariance matrix ( Σ 0 ) respectively as µ 0 = 121.9 128.1 111.8 124.7 133.1 , Due to a change in the measurement system, for details see Zwetsloot and Ajadi [18] and Mahmood et al [23], there is a downward shift in the process variance from 12 February 2018 onwards. We try to detect this change with the different charts.…”

Section: Example To Monitor Elder Care Center Datasetmentioning

confidence: 99%

A New Robust Multivariate EWMA Dispersion Control Chart for Individual Observations

2021

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A multivariate control chart is proposed to detect changes in the process dispersion of multiple correlated quality characteristics. We focus on individual observations, where we monitor the data vector-by-vector rather than in (rational) subgroups. The proposed control chart is developed by applying the logarithm to the diagonal elements of the estimated covariance matrix. Then, this vector is incorporated in an exponentially weighted moving average (EWMA) statistic. This design makes the chart robust to non-normality in the underlying data. We compare the performance of the proposed control chart with popular alternatives. The simulation studies show that the proposed control chart outperforms the existing procedures when there is an overall decrease in the covariance matrix. In addition, the proposed chart is the most robust to changes in the data distribution, where we focus on small deviations which are difficult to detect. Finally, the compared control charts are applied to two case studies.

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

Cited by 20 publications

References 35 publications

A Personalized Health Monitoring System for Community-Dwelling Elderly People in Hong Kong: Design, Implementation, and Evaluation Study

A Personalized Health Monitoring System for Community-Dwelling Elderly People in Hong Kong: Design, Implementation, and Evaluation Study

GLM-Based Flexible Monitoring Methods: An Application to Real-Time Highway Safety Surveillance

A New Robust Multivariate EWMA Dispersion Control Chart for Individual Observations

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