Insights (Essays)

Insights (Essays) November 2020

Optimizing Human Resource Management in the Time of COVID-19 at York Region

Rina Lamba and Julia Roitenberg

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When the pandemic started in February 2020, York Region was faced with constantly changing conditions and minimal data to help guide decision making. We were challenged to make informed decisions with limited information as we redeployed our staff of 500 to support the COVID-19 response. The situation was unprecedented.

As senior leaders, we were, and continue to be, in a situation that the US Army has called “VUCA” – a popular acronym used to describe and reflect on an environment of volatility, uncertainty, complexity and ambiguity. Scholars Bennett and Lemoine (2014) believe that understanding VUCA can enable organizational leadership to be more effective in preparing for, and responding to, uncontrollable events that are characterized by the four VUCA categories.

At York Region – which comprises nine municipalities and serves 1.2 million residents or about 8% of the provincial population (York Region 2020) – we found VUCA helpful in developing our strategy to deal with the ever-changing demands of this pandemic. Our biggest challenge was how to redeploy staff to the various COVID-19-response areas such as the telephone surge line, case management, contact-tracing and managing outbreaks.

In order to make this deployment as efficient as possible, we involved senior leaders, front-line staff and data analysts to launch an observational study on case investigators supplemented by focused interviews of subject matter experts in order to determine the workload required to conduct the various COVID-19 operations. The study included collecting data on

  • the current time required to complete operational tasks,
  • the operational requirements that must be met to successfully manage COVID-19, and
  • the complexity of cases

Based on this information, we created metrics that reflect the workload for each team as well as work flows to best match the actual number of staff needed to respond to changing COVID-19-related demands.

We were able to develop a staffing metrics calculator tool that supports decision making on staffing levels and is informed by COVID-19 activities and trends (for example, cases, call volumes or testing). This tool is currently being used by operations, scheduling and Human Resources (HR) logistics in our Incident Management Structure (IMS). Other benefits included an improvement of communication between operations and HR logistics as well as finding operational efficiencies such as combining two of our operational teams into one and refocusing our human resources. This has assisted us in our hiring decisions, such as how many new staff are required based on work volume. It is also helping us identify how to resume public health services that were put on hold, without compromising the requirements of the pandemic response.

Moving forward, we plan to layer on predictive mathematical modeling, which would forecast cases of COVID-19 in York Region, and utilize it to demonstrate the impact of changes in COVID-19 cases on staffing requirements. An example of such mathematical modeling for the transmission of severe acute respiratory syndrome virus 2 (SARS-COV-2) – the agent of the coronavirus disease 2019 (COVID-19) – was developed by the Public Health Agency of Canada (PHAC). The model uses knowledge obtained from studies around the world on the biology of transmission of the COVID-19 virus to mathematically represent how COVID-19 may spread in the Canadian population under the various non-pharmaceutical interventions such as physical distancing, case detection and isolation, and contact tracing and quarantine (Ogden et al. 2020).

Mathematical modelling of infectious diseases is primarily intended to study the spread and duration of an epidemic, understand the magnitude of the challenge posed by the disease and the potential impact of interventions on the disease (Ogden et al. 2020). However, from a health human resources management perspective, utilizing mathematical modeling of COVID-19 would allow us to plan ahead for the changing landscape of this pandemic and predict future staffing needs.

Prediction – the process of filling in missing information (Agrawal et al. 2018) – is at the heart of making decisions under uncertainty. Better prediction creates opportunities for new strategies and structures, and prediction tools increase productivity (Goldfarb 2019).

To conclude, although the majority of the attention with COVID-19 is outward-facing, including dealing with the number of new cases and outbreaks, there is an added dimension of internal management of resources that is equally as important to the situation and has long-term repercussions for healthcare.

With our staffing metrics calculator tool and additional analytical developments, we feel we are better positioned to anticipate and prepare for potential future COVID-19 waves and mass COVID-19 immunization.

About the Author(s)

Rina Lamba, RN, MHS, MBA, has close to 20 years of experience working in the public healthcare sector covering a broad spectrum of roles. She is currently an acting director in York Region Public Health and was involved with the redeployment of more than 400 staff as part of York Region’s COVID-19 response. LinkedIn: linkedin.com/in/rina-lamba-r-n-m-h-s-mba-97056a4a

Julia Roitenberg, RN, MN, MBA, has progressive senior management experience in a Municipal Government environment with focus on public health and the social determinants of health. She is currently a Director and Chief Nursing Officer in York Region Public Health and a Chief of Logistics HR as part of York Region’s COVID-19 response. LinkedIn: https://www.linkedin.com/in/juliaroitenberg/

References

Agrawal, A., J. Gans and A. Goldfarb. 2018, October 23. Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Review Press.

Bennett, N. and J. Lemoine. 2014. What VUCA Really Means for You. Harvard Business Review. Retrieved September12, 2020.
<https://hbr.org/2014/01/what-vuca-really-means-for-you>.

Goldfarb, A. 2019, April 29. How Prediction Builds Better Business Insights. Harvard Business Review Webinar. Retrieved September 13, 2020. <https://hbr.org/webinar/2019/05/how-prediction-builds-better-business-insights>.

Ogden, N. H., A. Fazil, J. Arino, P. Berthiaume, D. N., Fisman, A. L. Greer et al. 2020. Modelling Scenarios of the Epidemic of COVID-19 in Canada. Canada Communicable Disease Report 46(6): 198–204. doi:10.14745/ccdr.v46i06a08.

York Region. 2020, February. York Region’s Response to the Public Health Modernization: Discussion Paper. Retrieved November 7, 2020. <https://yorkpublishing.escribemeetings.com/filestream.ashx?DocumentId=10631>.

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