The aim of this study was to implement statistical quality control to stroke care in Sweden by designing control charts for data from the Riksstroke registry to detect potential unnatural, or special cause variation in the years 2019-2020. Suitable control charts were designed for three quality indicators: the time elapsed from hospital admission to receiving reperfusion therapy (door-to-needle time), the proportion of patients directly admitted to stroke unit, and the fatality rate. The data was sourced from three anonymous hospitals from the Riksstroke registry. The data was split into two phases, one for calibration of the control charts (phase-I) and one for monitoring the process (phase-II). Phase-I consisted of data from 2015-2018 and phase-II of data from 2019-2020. The control charts X-bar and s charts were designed for the door-to-needle time, p charts for the proportion of patients directly admitted to stroke unit, while p charts in addition to EWMA charts were used for the fatality rate. The x-bar and s charts for the two larger hospitals signalled for special cause variation in some months in 2019-2020, whereas the process appeared to be in control during the same period at the smallest hospital. The p chart for the proportion of directly admitted at the largest hospital signalled for special cause variation which lasted throughout 2019-2020. As a consequence, this p chart was modified with recalibrated control limits and it could be seen that the proportion of patients directly admitted had increased in 2019-2020 from previous years. None of the p and EWMA charts for the fatality rate at each hospital signalled for any special cause variation. In conclusion, in this study it is shown how control charts could be useful tools for detecting and evaluating changes in values for quality indicators in stroke care. In order to design adequate control charts, the data should be collected at each time unit and the process should be in control during calibration. This way the control charts may retain good sensitivity of detecting special cause variation.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:umu-197011 |
| Date | January 2022 |
| Creators | Morin, Edvin, Novossad, Martiina |
| Publisher | Umeå universitet, Statistik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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