Elsen, Sandra J.

The COVID-19 pandemic challenged healthcare organisations in safely providing healthcare to patients while protecting employees and reducing the spread of the virus. This paper shows how the Mayo Clinic responded by implementing innovations, including screening, testing and tracing patients for COVID-19; expanding telehealth services; adjusting surgical and procedural operations and reallocating staffing and supplies. These changes were operationalised rapidly through the coordinated response from its leadership, partnership of experts across the organisation, prompt decision-making driven by data, redistribution of resources and expedited training. These innovations and methods of rapid implementation enabled Mayo Clinic to safely continue its mission of providing healing and hope to its patients worldwide, and quickly return to financial sustainability.

The COVID-19 pandemic has created unique logistical challenges for vaccine transportation, inventory management, allocation and distribution at multiple levels — the federal government, states and healthcare institutions. Unpredictable weekly vaccine allocation from state health departments, changing population priorities, stringent vaccine requirements for ultracold storage, transportation, reconstitution and 2-dose administration intervals have presented challenges never seen before in the history of mass vaccination programmes, including those at Mayo Clinic. To meet the challenges, an efficient system of allocation and administration for COVID-19 vaccines was developed through collaboration with process engineering. To understand the challenges, ten health systems engineers from the Department of Management Engineering & Consulting at Mayo Clinic facilitated the institution-wide COVID-19 vaccine project, collaborating closely with diverse multidisciplinary teams that included physicians, nurses, pharmacists, administrative services, information technology, human resources, scheduling operations and public affairs. The internal consultants designed tools and solutions based on systems and process engineering methodologies to solve a myriad of complex problems, including identifying priority populations, using resources efficiently and minimising vaccine waste. Tools designed included a vaccine resource playbook; dynamic staffing models based on vaccine allocation, storage, inventory and distribution processes using a hub-andspoke model; workflows and staffing models for face-to-face and drive-through vaccine administration sites; and end-of-day workflows to reduce vaccine waste. Through the collaboration, modelling and engineering, multiple sites across Mayo Clinic have implemented successful COVID-19 vaccination programmes that are efficient in resource utilisation and have minimal waste. In this paper, we share what we have learned to help other healthcare organisations prepare for future mass vaccination scenarios.

The demand for telehealth services in the United States saw an unprecedented increase following the 2020 COVID-19 pandemic. Prior to the pandemic, intake and virtual rooming functions for scheduled, synchronous video appointments at Mayo Clinic were supported by a small centralised team of virtual agents within its Center for Connected Care (Connected Care). In response to the pandemic-generated demand, Connected Care leveraged its existing model and the support of temporary staff to rapidly scale support for the increased video visit demand across the enterprise. Once demand stabilised in the summer of 2020, Mayo Clinic’s destination medical centres in Minnesota, Florida and Arizona, and Mayo Clinic Health System’s sites in the Midwest, launched pilots to evaluate alternative telehealth delivery models. The goal of these pilots was to test the flexibility of alternative intake models for supporting long-term scalability and growth. Through these pilots, each site developed unique workflows and staffing plans with the overall intake function remaining standardised across all locations. The workflows delineated responsibilities between clinical departments and virtual agents, leveraged current infrastructure and maximised best practices from the original model. This paper outlines the models implemented, and presents lessons learned and best practices garnered from each implementation.