Background: before using a speech recognition system for the neonatal documentation, the underlying neonatal information has to be specified and structured. Up to now, the pre-structuring the first comprehensive examination of newborn (U2) and the respective data set entries has not been described in literature, yet. The common booklet for the documentation of the German U2 does not contain all examinations required nor does it show the choice of all respective finding statements. Objectives: to set up a documentation standard for the U2 distinguishing the most important diseases/disorders at a limited level of detailing. Methods: the finding scheme of the U2 has been specified based on the German national recommendation for the U2. Here, the U2 is the first exhaustive examination of the newborn. Due to a lack of detailed descriptions, the U2 has been formalized and arranged in cooperation with experienced medical experts, which carry out the U2 in daily routine. Results: if all possible finding statements are presented in a hierarchical structure, – even with a small font size – it would cover more than 20 pages. Hence, a more condensed structure has been set up for presentation. If the general practitioner (GP) is to see (a) the finding statements necessary but (b) no more, additional rules can be set up for the masking of finding statements excluded by the results of the prior investigation. Conclusions: the proposed structure for neonatal documentation serves as a basis for statistical analysis. On its basis, investigation can be carried out about (a) problems during the individual examination, (b) problem with the documentation and (c) the benefits of automated speech recognition systems.
|Faculties / Institutes:||Medizinische Fakultät Heidelberg > Institut für Medizinische Biometrie und Informatik
Medizinische Fakultät Heidelberg > Universitätskinderklinik
|Subjects:||610 Medical sciences Medicine|
|Uncontrolled Keywords:||Entscheidungsunterstützung , Spracherkennung , DokumentationNeonatology Clinical Problem Solving Decision Support Systems , Clinical Decision , Trees , Automated Speech Recognition , Medical Documentation|