Implementation of the Synergy Tool: A Potential Intervention to Relieve Health Care Worker Burnout.
Farinaz HavaeiMaura MacpheeAndy MaVivien W WongCecilia LiIrene CheungLina SciglianoAmera TaylorPublished in: International journal of environmental research and public health (2022)
(1) Background: Healthcare workers experienced rising burnout rates during and after the COVID-19 pandemic. A practice-academic collaboration between health services researchers and the surgical services program of a Canadian tertiary-care urban hospital was used to develop, implement and evaluate a potential burnout intervention, the Synergy tool. (2) Methods: Using participatory action research methods, this project involved four key phases: (I) an environmental scan and a baseline survey assessment, (II), a workshop, (III) Synergy tool implementation and (IV) a staffing plan workshop. A follow-up survey to evaluate the impact of Synergy tool use on healthcare worker burnout will be completed in 2023. (3) Results: A baseline survey assessment indicated high to severe levels of personal and work-related burnout prior to project initiation. During the project phases, there was high staff engagement with Synergy tool use to create patient care needs profiles and staffing recommendations. (4) Conclusions: As in previous research with the Synergy tool, this patient needs assessment approach is an efficient and effective way to engage direct care providers in identifying and scoring acuity and dependency needs for their specific patient populations. The Synergy tool approach to assessing patient needs holds promise as a means to engage direct care providers and to give them greater control over their practice-potentially serving as a buffer against burnout.
Keyphrases
- healthcare
- quality improvement
- primary care
- randomized controlled trial
- case report
- tertiary care
- emergency department
- social media
- cross sectional
- computed tomography
- mental health
- human health
- clinical practice
- risk assessment
- affordable care act
- drug induced
- artificial intelligence
- pain management
- magnetic resonance