Using Barriers Analysis to Refine a Novel Model of Neurocritical Care

• DOI: 10.1007/s12028-013-9905-3
• Publication Date: 2013-10-07T19:23:09Z
• Authors: Marianne J. Botting; Nicolas Phan; Gordon D. Rubenfeld; Anna K. Speke; Martin G. Chapman
• Keywords: Adult; Patient Care Team; Intensive Care Units; 03 medical and health sciences; 0302 clinical medicine; Critical Care; Brain Injuries; Models, Organizational; Process Assessment, Health Care; Humans; Quality Improvement; 3. Good health

In order to deliver specialized neurocritical care (NCC) without a dedicated neurological intensive care unit (ICU), we established a virtual NCC unit within an existing mixed level III ICU. This initiative required changes to patient allocation, physician staffing, and care protocols. In advance of its implementation, we gaged readiness, assessed barriers, and solicited feedback from staff.Clinicians at our academic hospital and trauma centre in Toronto, Ontario were the subjects of this concurrent mixed methods study. Eighteen stakeholders were individually interviewed. 116 of 217 eligible ICU staff participated in the survey and 36 staff attended the focus group sessions.From the survey, the most significant barriers to this reorganization were staff anxiety about coping (28 %) and a concern that patients would not receive better care (24 %). Noteworthy obstacles about the use of protocols were their lack of flexibility (19 %) and that implementation was seen as impractical (16 %). Seventeen barriers were proposed through an open-ended survey question. Content analysis revealed general resistance, educational challenges, workflow adjustment to a diagnosis-based rounding pattern and coordination conflicts to be the central barriers. These findings were confirmed in focus group discussions, with a lack of resources as an additional important challenge.A new workable model for NCC has been developed, facilitated by this analysis. Steps to overcome barriers demonstrated in this study include additional educational measures, changes to the rounding protocols, and patient allocation algorithms.