Literature DB >> 24308444

Comparing nursing handover and documentation: forming one set of patient information.

M Johnson1, P Sanchez, H Suominen, J Basilakis, L Dawson, B Kelly, L Hanlen.   

Abstract

AIM: The aim of this study was to explore the potential for one set of patient information for nursing handover and documentation.
BACKGROUND: Communication of patient information requires two processes in nursing: a verbal summary of the patients' care and another report within the nursing notes, creating duplication.
INTRODUCTION: Advances in speech recognition technology have provided an opportunity to consider the practicality of one set of information at the nursing end-of-shift.
METHODS: We used content analysis to compare transcripts from 162 digitally recorded handovers and written nursing notes for similar patients within general medical-surgical wards from two metropolitan hospitals in Sydney Australia.
FINDINGS: Using the Nursing Handover Minimum Dataset analysis framework similar content [n = 2109 (handover) n = 1902 (nursing notes)] was found within the handovers and notes at the end-of-shift (7:00 am and 2:00 pm). Analysis of the overarching categories demonstrated the emphasis within the differing data sources as: patient identification (31%), care planning or interventions (25%), clinical history (13%), and clinical status (13%) for handover, vs. care planning (47%), clinical status (24%), and outcomes or goals of care (12%) for nursing notes. DISCUSSION: This study has demonstrated that similar patient information is presented at handover and within documentation. Major categories are consistent with international nursing minimum datasets in use.
CONCLUSION: We can use one set of patient information (within some limitations) for two purposes with system design, practice change and education. Experiments are currently being conducted trialling speech recognition within laboratory and clinical settings. IMPLICATIONS FOR NURSING AND HEALTH POLICY: One set of patient information, verbally generated at handover delivering electronic documentation within one process, will transform international nursing policy for nursing handover and documentation.
© 2013 International Council of Nurses.

Entities:  

Keywords:  Acute Care < Nursing; Communication; Health Services Research < Research; Informatics; Information Technology < Information Technology; Nursing; Nursing Classification < Nursing; Patient Safety < Workforce Issues; Quantitative Methods < Research; Risk Management < Health Service Management

Mesh:

Year:  2013        PMID: 24308444     DOI: 10.1111/inr.12072

Source DB:  PubMed          Journal:  Int Nurs Rev        ISSN: 0020-8132            Impact factor:   2.871


  6 in total

1.  A usability framework for speech recognition technologies in clinical handover: a pre-implementation study.

Authors:  Linda Dawson; Maree Johnson; Hanna Suominen; Jim Basilakis; Paula Sanchez; Dominique Estival; Barbara Kelly; Leif Hanlen
Journal:  J Med Syst       Date:  2014-05-15       Impact factor: 4.460

2.  Capturing patient information at nursing shift changes: methodological evaluation of speech recognition and information extraction.

Authors:  Hanna Suominen; Maree Johnson; Liyuan Zhou; Paula Sanchez; Raul Sirel; Jim Basilakis; Leif Hanlen; Dominique Estival; Linda Dawson; Barbara Kelly
Journal:  J Am Med Inform Assoc       Date:  2014-10-21       Impact factor: 4.497

3.  Evaluation and perceptions of a nursing discharge plan among nurses from different healthcare settings in Spain.

Authors:  Gloria Reig-Garcia; Anna Bonmatí-Tomàs; Rosa Suñer-Soler; Mari Carmen Malagón-Aguilera; Sandra Gelabert-Vilella; Cristina Bosch-Farré; Susana Mantas-Jimenez; Dolors Juvinyà-Canal
Journal:  BMC Health Serv Res       Date:  2022-05-28       Impact factor: 2.908

4.  Benchmarking clinical speech recognition and information extraction: new data, methods, and evaluations.

Authors:  Hanna Suominen; Liyuan Zhou; Leif Hanlen; Gabriela Ferraro
Journal:  JMIR Med Inform       Date:  2015-04-27

Review 5.  A systematic review of speech recognition technology in health care.

Authors:  Maree Johnson; Samuel Lapkin; Vanessa Long; Paula Sanchez; Hanna Suominen; Jim Basilakis; Linda Dawson
Journal:  BMC Med Inform Decis Mak       Date:  2014-10-28       Impact factor: 2.796

6.  The expected and actual communication of health care workers during the management of intrapartum: An interpretive multiple case study.

Authors:  Doreen K M M'Rithaa; Sue Fawcus; Mikko Korpela; Retha De la Harpe
Journal:  Afr J Prim Health Care Fam Med       Date:  2015-12-03
  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.