Literature DB >> 30295820

Unstructured brainstorming is not enough: structured brainstorming based on four verification and validation questions yields better hazard identification in healthcare.

Ayala Kobo-Greenhut1, Haim Reuveni2, Izhar Ben Shlomo3,4, Racheli Megnezi1.   

Abstract

OBJECTIVES: (1) To introduce the Methodical Hazard Identification Checklist (MHIC) for structured brainstorming and the four V&V categories on which it is based, and (2) to compare its efficacy with that of brainstorming (BS) in identifying hazards in healthcare.
DESIGN: Comparative analysis of MHIC and team BS results.
SETTING: Baruch Padeh Medical Center, Poriya, Israel. STUDY PARTICIPANTS: Quality engineering students, facilitators, validation teams and hospital staff who were familiar with the specific processes. INTERVENTION(S): The number of hazards identified by team BS were compared with those deduced by applying the four V&V hazard categories to each step (the MHIC) of 10 medical and 12 administrative processes. MAIN OUTCOME MEASURE(S): The total number of hazards (1) identified by BS, (2) identified by MHIC, (3) validated by the validation team and (4) hazards identified by both methods that the validation team deemed unreasonable.
RESULTS: MHIC was significantly more successful than BS in identifying all hazards for the 22 processes (P < 0.0001). The estimated probabilities of success for BS for administrative and medical processes were 0.4444, 95%CI = [0.3506, 0.5424] and 0.3080, 95%CI = [0.2199, 0.4127], respectively. The estimated probabilities of success for MHIC for administrative and medical processes were 0.9885, 95%CI = [0.9638, 0.9964] and 0.9911, 95%CI = [0.9635, 0.9979], respectively.
CONCLUSIONS: Compared to traditional BS, MHIC performs much better in identifying prospective hazards in the healthcare system. We applied MHIC methodology to administrative and medical processes and believe it can also be used in other industries that require hazard identification.
© The Author(s) 2018. Published by Oxford University Press in association with the International Society for Quality in Health Care. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  human resources; quality improvement; quality management; teamwork

Mesh:

Year:  2019        PMID: 30295820     DOI: 10.1093/intqhc/mzy208

Source DB:  PubMed          Journal:  Int J Qual Health Care        ISSN: 1353-4505            Impact factor:   2.038


  1 in total

1.  Algorithmic prediction of failure modes in healthcare.

Authors:  Ayala Kobo-Greenhut; Ortal Sharlin; Yael Adler; Nitza Peer; Vered H Eisenberg; Merav Barbi; Talia Levy; Izhar Ben Shlomo; Zimlichman Eyal
Journal:  Int J Qual Health Care       Date:  2021-02-20       Impact factor: 2.038

  1 in total

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