| Autor: |
Hazewinkel MC; Clinical Centre for Acute Psychiatry, Parnassia, Parnassia Group, The Hague, Netherlands., de Winter RFP; Clinical Centre for Acute Psychiatry, Parnassia, Parnassia Group, The Hague, Netherlands.; Department of Clinical Psychology, VU University, Amsterdam, Netherlands., van Est RW; Data Research Office, Antes, Parnassia Group, Rotterdam, Netherlands., van Hyfte D; InterSystems BV Benelux, Vilvoorde, Belgium., Wijnschenk D; InterSystems BV Benelux, Vilvoorde, Belgium., Miedema N; Clinical Centre for Acute Psychiatry, Parnassia, Parnassia Group, The Hague, Netherlands., Hoencamp E; Clinical Centre for Acute Psychiatry, Parnassia, Parnassia Group, The Hague, Netherlands.; Department of Clinical Psychology, Institute of Psychology, Leiden University, Leiden, Netherlands. |
| Abstrakt: |
Aim: With the introduction of "Electronic Medical Record" (EMR) a wealth of digital data has become available. This provides a unique opportunity for exploring precedents for seclusion. This study explored the feasibility of text mining analysis in the EMR to eventually help reduce the use of seclusion in psychiatry. Methods: The texts in notes and reports of the EMR during 5 years on an acute and non-acute psychiatric ward were analyzed using a text mining application. A period of 14 days was selected before seclusion or for non-secluded patients, before discharge. The resulting concepts were analyzed using chi-square tests to assess which concepts had a significant higher or lower frequency than expected in the "seclusion" and "non-seclusion" categories. Results: Text mining led to an overview of 1,500 meaningful concepts. In the 14 day period prior to the event, 115 of these concepts had a significantly higher frequency in the seclusion category and 49 in the non-seclusion category. Analysis of the concepts from days 14 to 7 resulted in 54 concepts with a significantly higher frequency in the seclusion-category and 14 in the non-seclusion category. Conclusions: The resulting significant concepts are comparable to reasons for seclusion in literature. These results are "proof of concept". Analyzing text of reports in the EMR seems therefore promising as contribution to tools available for the prediction of seclusion. The next step is to build, train and test a model, before text mining can be part of an evidence-based clinical decision making tool. |
