The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Statistical process control as a tool for research and healthcare improvement
Open accessAbstract
Improvement of health care requires making changes in processes of care and service delivery. Although process performance is measured to determine if these changes are having the desired beneficial effects, this analysis is complicated by the existence of natural variation-that is, repeated measurements naturally yield different values and, even if nothing was done, a subsequent measurement might seem to indicate a better or worse performance. Traditional statistical analysis methods account for natural variation but require aggregation of measurements over time, which can delay decision making. Statistical process control (SPC) is a branch of statistics that combines rigorous time series analysis methods with graphical presentation of data, often yielding insights into the data more quickly and in a way more understandable to lay decision makers. SPC and its primary tool-the control chart-provide researchers and practitioners with a method of better understanding and communicating data from healthcare improvement efforts. This paper provides an overview of SPC and several practical examples of the healthcare applications of control charts.
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Cites
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Feng, J., Phillips, R. V., Malenica, I., Bishara, A., Hubbard, A. E., Celi, L. A., & Pirracchio, R. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00611-y · Cited by 423
Data drift in medical machine learning: implications and potential remedies
Sahiner, B., Chen, W., Samala, R. K., & Petrick, N. (2023) · The British Journal of Radiology · doi:10.1259/bjr.20220878 · Cited by 279
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Mohr, N. M., Wessman, B. T., Bassin, B., Elie-Turenne, M.-C., Ellender, T., Emlet, L. L., Ginsberg, Z., Gunnerson, K., Jones, K. M., Kram, B., et al. (2020) · Critical Care Medicine · doi:10.1097/ccm.0000000000004385 · Cited by 172
Detection of calibration drift in clinical prediction models to inform model updating
Davis, S. E., Greevy, R. A., Lasko, T. A., Walsh, C. G., & Matheny, M. E. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103611 · Cited by 160
Finding order in heterogeneity: types of quality-improvement intervention publications
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Self-citationRedesigning the ICU nursing discharge process: A quality improvement study.
Chaboyer, W., Lin, F., Foster, M., Retallick, L., Panuwatwanich, K., & Richards, B. (2012) · Worldviews on Evidence-Based Nursing · doi:10.1111/j.1741-6787.2011.00234.x · Cited by 33
Open questions and research gaps for monitoring and updating AI-enabled tools in clinical settings
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Practice Transformation Under the University of Colorado’s Primary Care Redesign Model
Smith, P. C., Lyon, C., English, A. F., & Conry, C. (2019) · The Annals of Family Medicine · doi:10.1370/afm.2424 · Cited by 28
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Syndromic surveillance for health information system failures: a feasibility study
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Metadata
- Authors
- Benneyan, J. C.
- Venue
- Quality and Safety in Health Care
- Publisher
- BMJ
- Published
- 2003-12-01
- Volume
- 12
- Issue
- 6
- Pages
- 458-464
- ISSN
- 1475-3898 (ISSN-L)
- 1475-3901
- OpenAlex
- W2161088630
- Cited by
- 1,045
- References
- 1
- FWCI
- 7.68
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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