A Review on Statistical Process Control in Healthcare: Data-Driven Monitoring Schemes
In recent years, the adoption of statistical process monitoring (SPM) techniques in healthcare has been successful. For instance, biosurveillance and biosignal monitoring have demonstrated direct benefits. As the latest reviews of the literature show, parametric SPM techniques have been implemented...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10144935/ |
| _version_ | 1797805893046239232 |
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| author | Baruc E. Perez-Benitez Victor G. Tercero-Gomez Marzieh Khakifirooz |
| author_facet | Baruc E. Perez-Benitez Victor G. Tercero-Gomez Marzieh Khakifirooz |
| author_sort | Baruc E. Perez-Benitez |
| collection | DOAJ |
| description | In recent years, the adoption of statistical process monitoring (SPM) techniques in healthcare has been successful. For instance, biosurveillance and biosignal monitoring have demonstrated direct benefits. As the latest reviews of the literature show, parametric SPM techniques have been implemented to evaluate the quality-of-service hospitals provide, track medical equipment, monitor safety markers, or assess the improvements made by quality projects. However, as shown in this research, world-trending topics in data science that include data-driven approaches integrated with SPM have not been reviewed. To bridge this gap and shed light on new research, a systematic review of scientific databases and a taxonomic literature analysis were performed. For the scientometric analysis, a set of bibliometric indicators were obtained to portray the performance of each subtopic, such as examining growth kinetics, identifying top authors, journals, countries and affiliations, as well as creating network maps of co-authorship and keyword co-occurrence. Additionally, the taxonomic analysis involved grouping proposals by methodological approach. Each approach was explained and discussed to identify the advantages, limitations, and challenges that researchers and practitioners may encounter. SPM researchers and practitioners require more flexibility in data-driven approaches to account for frequency unbalance, complexity, dimensionality problems, and speed. Those working in data-driven and computer-oriented areas can expand their toolbox by incorporating sequential approaches to enhance the power of their classifiers, assess risk, reduce misspecification, and adopt model-oriented mindsets. |
| first_indexed | 2024-03-13T05:59:06Z |
| format | Article |
| id | doaj.art-75159b3931614fd3b2e19f19c61279e8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T05:59:06Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-75159b3931614fd3b2e19f19c61279e82023-06-12T23:02:01ZengIEEEIEEE Access2169-35362023-01-0111562485627210.1109/ACCESS.2023.328256910144935A Review on Statistical Process Control in Healthcare: Data-Driven Monitoring SchemesBaruc E. Perez-Benitez0https://orcid.org/0000-0002-0885-5738Victor G. Tercero-Gomez1https://orcid.org/0000-0002-5196-3451Marzieh Khakifirooz2https://orcid.org/0000-0002-1721-2646School of Engineering and Science, Tecnologico de Monterrey, Monterrey, Nuevo León, MexicoSchool of Engineering and Science, Tecnologico de Monterrey, Monterrey, Nuevo León, MexicoSchool of Engineering and Science, Tecnologico de Monterrey, Monterrey, Nuevo León, MexicoIn recent years, the adoption of statistical process monitoring (SPM) techniques in healthcare has been successful. For instance, biosurveillance and biosignal monitoring have demonstrated direct benefits. As the latest reviews of the literature show, parametric SPM techniques have been implemented to evaluate the quality-of-service hospitals provide, track medical equipment, monitor safety markers, or assess the improvements made by quality projects. However, as shown in this research, world-trending topics in data science that include data-driven approaches integrated with SPM have not been reviewed. To bridge this gap and shed light on new research, a systematic review of scientific databases and a taxonomic literature analysis were performed. For the scientometric analysis, a set of bibliometric indicators were obtained to portray the performance of each subtopic, such as examining growth kinetics, identifying top authors, journals, countries and affiliations, as well as creating network maps of co-authorship and keyword co-occurrence. Additionally, the taxonomic analysis involved grouping proposals by methodological approach. Each approach was explained and discussed to identify the advantages, limitations, and challenges that researchers and practitioners may encounter. SPM researchers and practitioners require more flexibility in data-driven approaches to account for frequency unbalance, complexity, dimensionality problems, and speed. Those working in data-driven and computer-oriented areas can expand their toolbox by incorporating sequential approaches to enhance the power of their classifiers, assess risk, reduce misspecification, and adopt model-oriented mindsets.https://ieeexplore.ieee.org/document/10144935/Data-drivenhealthcarescientometricstatistical process monitoring |
| spellingShingle | Baruc E. Perez-Benitez Victor G. Tercero-Gomez Marzieh Khakifirooz A Review on Statistical Process Control in Healthcare: Data-Driven Monitoring Schemes IEEE Access Data-driven healthcare scientometric statistical process monitoring |
| title | A Review on Statistical Process Control in Healthcare: Data-Driven Monitoring Schemes |
| title_full | A Review on Statistical Process Control in Healthcare: Data-Driven Monitoring Schemes |
| title_fullStr | A Review on Statistical Process Control in Healthcare: Data-Driven Monitoring Schemes |
| title_full_unstemmed | A Review on Statistical Process Control in Healthcare: Data-Driven Monitoring Schemes |
| title_short | A Review on Statistical Process Control in Healthcare: Data-Driven Monitoring Schemes |
| title_sort | review on statistical process control in healthcare data driven monitoring schemes |
| topic | Data-driven healthcare scientometric statistical process monitoring |
| url | https://ieeexplore.ieee.org/document/10144935/ |
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