## 4 Discussion  
The present study aimed to explore the value added by information reported in narratives in characterizing patterns of falls. Present findings showed that adding data from narratives did not improve the understanding of such phenomenon, even though the performance of the cluster analysis was good.  
To our knowledge, no previous studies have assessed if the integration of information from narratives improves the understanding of falls in the hospital setting; however, similar studies have been conducted in other settings with promising results [12; 29]. Results of the present study did not demonstrate the hypothesis that the integration of the information improves the understanding of in-hospital falls since the cluster analysis did not improve by adding information derived from narratives. Such findings suggest that a validated, well-conceived, data collection tool including only pre-coded information could be enough to characterize this phenomenon and guaranteeing its surveillance. Thus, avoiding the collection of narratives, which is time-consuming for healthcare professionals, and the application of sophisticated techniques to derive information from free text, which requires specific expertise in the field of text mining, not always readily available in the hospital services. In addition to that, the results of the MCA analysis showed that the proportion of variability explained by the pre-coded variables of the data collection tool was very low (less than 20%). This means that, probably, pre-coded information collected was not especially useful to differentiate falls. Such findings highlight the need for validating data collection tools before their use, avoiding the collection of redundant information that is not useful for the differentiation of falls patterns.  
However, the present findings do not mean that free text information is not useful at all since we cannot rule out that such type of information would be useful in a different study setting using a different data collection form. We might hypothesize several reasons to understand the reason why in this specific study free-text information did not provide  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **V-Test** & **Cluster** \\ \hline Location: _Toilet_ & 10.92 & 1 \\ Action at time of fall: _Using toilet_ & 9.15 & 1 \\ Extrinsic cause (patient): _Slippers (no shoe style)_ & 4.66 & 1 \\ People witnessing the fall: _None (the patient was alone)_ & 3.64 & 1 \\ Mechanism: _Off balance_ & 3.51 & 1 \\ Location: _Hospital bedroom_ & 9.23 & 2 \\ Fall out off: _the bed_ & 5.03 & 2 \\ Action at time of fall: _Getting out from bed_ & 4.88 & 2 \\ Extrinsic cause (environment): _Lowered bed rails_ & 4 & 2 \\ Fall out off: _the bed (nised bed rails)_ & 3.74 & 2 \\ Location: _Outpatient clinic_ & 6.28 & 3 \\ Setting: _Outpatient_ & 5.95 & 3 \\ Injury treatment: _Suture_ & 3.39 & 3 \\ Fall prevention plan: _None_ & 3.19 & 3 \\ Fall out off: _the stretchers_ & 2.92 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the HCPC analysis with topic variable. V-test represents the influence of variables in the cluster composition. Cluster refers to the three distinct profiles of falls obtained from HCPC model.  
any additional value to the characterization of falls patterns. The main one is that the type of data collected are redundant and the data collection form needs to be improved through a validation process. For this reason, we might hypothesize to develop ad hoc strategies to overcome such issues, including the use of validated data collection forms and the improvement of narratives quality by involving health care professionals, e.g., by providing a training in the use of the data collection form.