Knowledge Bank

Statement of the Problem

Patient handovers have been described as the process of transferring primary authority and responsibility for providing clinical care to a patient from one departing caregiver to one oncoming caregiver. Patient handovers with incomplete and inaccurate information have repeatedly been identified as a patient safety risk. Recently a study using the mnemonic IPASS found that post-intervention the use of these strategies decreased medical errors by 23% and the rate of preventable adverse effects by 30%. The coding analysis used in the IPASS study and many others can be time consuming and cost-ineffective. As a result there is a desire to automate this analysis.

Methodology 20 existing transcripts from a previously IRB-approved data collection of audio-recorded Intensive Care Unit Registered Nurse handovers containing 27 patient discussions collected from a single, academic tertiary care institution were analyzed for this study. First all 20 transcripts were manually coded using a codebook (IPASS) adapted from Starmer et. al’s paper Changes in Medical Errors after Implementation of a Handoff Program. Next, a novel codebook was manually generated from the transcripts in an effort to more accurately model nurse handovers. The categories which emerged were grouped into INURSE (Identification, Narrative, Unusual Symptoms, Response, Status, Expected Challenges). Finally, Linguistic Inquiry and Word Count (LIWC) software was used to identify family terms that fell under the INURSE codes Narrative Family and Expected Family Challenges. The manually coded transcripts were the gold standard against which the LIWC coded transcripts were compared.

Findings The I and Sa codes from the IPASS code set were not observed. Each INURSE code was compared to the corresponding IPASS codes to observe differences between them. Overall the P code was described by 13 INURSE sub codes. The Noise category of IPASS was also described by 5 INURSE codes with few continued instances of Noise. The LIWC software had no misses and 9 false positives.

Discussion Overall these findings point towards major differences between nurses and physician’s handover needs. Additionally it points to mixed usefulness of LIWC software for automated analysis. Next steps could include looking into more advanced software for automated analysis, or altering INURSE to train nurses by taking the best of IPASS as well.