Original Research ARTICLE
Text analysis of Electronic Medical Records to predict seclusion in psychiatric wards: proof of concept
- 1Parnassia Psychiatric Institute, Netherlands
- 2VU University Amsterdam, Netherlands
- 3Data Research Office, Antes, Parnassia Group, Netherlands
- 4InterSystems (United States), United States
- 5Leiden University, Netherlands
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 five years on an acute and non-acute psychiatric ward were analyzed using a text mining application. A period of fourteen 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 fourteen 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 day fourteen to seven 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.
Keywords: Data Mining, Electronic Medical Record (EMR), Psychiatric inpatient ward, seclusion, text mining
Received: 27 Jul 2018;
Accepted: 14 Mar 2019.
Edited by:Tilman Steinert, Center for Psychiatry Weissenau, Germany
Reviewed by:Christian Huber, University Psychiatric Clinic Basel, Switzerland
Eric Noorthoorn, Independent researcher
Copyright: © 2019 Hazewinkel, de Winter, van Est, van Hyfte, Wijnschenck, Miedema and Hoencamp. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ms. Mirjam C. Hazewinkel, Parnassia Psychiatric Institute, The Hague, 2553, Netherlands, email@example.com