JMIR Publications

ABSTRACT

Background:

Implementing mass vaccination clinics for COVID-19 immunization has been a successful public health activity worldwide. However, this tightly coupled system has many logistical challenges, leading to increased workplace stress, as evidenced throughout the pandemic. Understanding the complexities of mass vaccination clinics that combine multi-disciplinary teams working within non-clinical environments has yet to be established through a human-systems perspective.

Objective:

The objective of this study is to holistically model mass COVID-19 vaccination clinics in the Region of Waterloo, Ontario, Canada, to understand the challenges centered around frontline workers and to inform clinic design and technological recommendations that can support systemic inefficiencies.

Methods:

An ethnographic approach was carried out guided by contextual inquiry to gather data on work-as-done in these ad-hoc immunization settings. Observation data were clarified by speaking with workers at the clinics, and the research team discussed the observation data regularly throughout the data collection period. Data were analyzed by combining aspects of the Contextual Design framework and Cognitive Work Analysis, building workplace models that can identify the stress points and interconnections within mass vaccination clinic flow, developed artifacts, culture, physical layouts, and decision-making.

Results:

Observations were conducted in six mass COVID-19 vaccination clinics over four weeks in 2021. The workflow model identified challenges with maintaining situational awareness about client intake and vaccine preparation among decision-makers. The artifacts model depicted how separately developed tools for the Vaccine Lead and Clinic Lead may support cognitive tasks through data synthesis, however, their effectiveness depends on sharing accurate and timely data. The cultural model indicated that the approach to mass immunization may impact stress depending on the aggressive or relaxed nature towards immunization and minimizing vaccine waste, which may adapt in response to changing policies, regulations, and vaccine scarcity. The physical model suggested that the co-location of workstations may influence decision-making coordination. Finally, the decision ladder described the decision-making activity for managing end-of-day doses, highlighting challenges with data uncertainty and areas for supporting expertise.

Conclusions:

Modelling mass COVID-19 vaccination clinics from a human-systems perspective identifies two high-level opportunities for improving the inefficiencies within this tightly coupled healthcare delivery system. First, by reducing uncertainties in the data required for decision-making by implementing strategies and artifacts that add redundancy while standardizing how data is synthesized through automation, clinics may become more resilient to unexpected data perturbations. Second, improving data sharing among stakeholders by co-locating their workstations and implementing collaborative artifacts that support a collective understanding of the state of the clinic may reduce system complexity by improving situational awareness. Future research should examine how the developed models apply to immunization settings beyond the Region of Waterloo and evaluate the impact of the recommendations on workflow coordination, stress, and decision-making.