A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
Semantic clustering to augment qualitative content analysis in exploring reasons for emergency department transfer delays
Tekijät: Laura-Maria Peltonen, Sanna Salanterä, Hans Moen
Toimittaja: Alpo Värri, Jaime Delgado, Parisis Gallos, Maria Hägglund, Kristiina Häyrinen, Ulla-Mari Kinnunen, Louise B. Pape-Haugaard, Laura-Maria Peltonen, Kaija Saranto, Philip Scott
Painos: 275
Konferenssin vakiintunut nimi: European Federation for Medical Informatics
Kustantaja: IOS Press BV
Julkaisuvuosi: 2020
Journal: Studies in Health Technology and Informatics
Kokoomateoksen nimi: Integrated Citizen Centered Digital Health and Social Care: Citizens as Data Producers and Service co-Creators: Proceedings of the EFMI 2020 Special Topic Conference
Tietokannassa oleva lehden nimi: Studies in Health Technology and Informatics
Sarjan nimi: Studies in Health Technology and Informatics
Vuosikerta: 275
Aloitussivu: 162
Lopetussivu: 166
ISBN: 978-1-64368-144-3
eISBN: 978-1-64368-145-0
ISSN: 0926-9630
DOI: https://doi.org/10.3233/SHTI200715
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/51366518
The aim of the study was to explore emergency department transfer
delays and to assess the potential of using a semantic clustering approach to augment
the content analysis of transfer delay data. Data were collected over a period of 5
months from two hospitals. A set of (unique) phrases describing reasons for transfer
delays (n=333) were clustered using the k-means with 1) cluster centroids initiated
in an unsupervised fashion and 2) a semi-supervised version where the cluster
centroids were initiated with keywords. The unsupervised algorithm clustered 77 %
and the semi-supervised 86 % of the phrases to suitable clusters. We chose the better
performing approach to augment our content analysis. Three main categories for
transfer delays were found as a result. These included 1) insufficient staffing
resources, 2) transportation and bed issues, and 3) patient and care related reasons.
The findings inform the audit of organisational processes, accuracy of staffing and
workflow to reduce transfer delays. Future research should explore implications of
semantic clustering approaches to other narrative data sets in health service research.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
