Martindale-Mathern, Sonia C.

Abstract Purpose Excessive purchasing of influenza vaccine can lead to costly overages and waste of resources. Insufficient quantities, however, can jeopardize population health. Our project aimed to use predictive analytics to determine the influenza vaccine quantities that would be needed for the next influenza season while minimizing vaccine waste and meeting patient care demands. Methods Several data sources were evaluated to develop a predictive analytics model to better estimate future influenza vaccine orders during upcoming influenza seasons. A retrospective analysis of influenza vaccine administrations over the last 4 influenza seasons allowed the team to develop an algorithm to predict influenza vaccine needs. Two regions within Mayo Clinic were selected to determine the validity of our ordering process. These 2 regions, identified as regions 3 and 5, ordered influenza vaccines based on the algorithm, while the other 3 regions acted as control groups, ordering though traditional methods based on purchasing data. Results Predictive analysis for the 2 intervention regions resulted in a savings of over $1 million when compared to traditional ordering methods. The model predicted that the quantity of vaccine ordered should be 17,574.16 and 9,164.29 quadrivalent influenza vaccines for regions 3 and 5, respectively. On the basis of actual administration data, 15,902 vaccines for region 3 and 9,016 vaccines for region 5 will be administered by the end of the season, both of which are less than the predicted amount needed, demonstrating the accuracy of the analytics. Conclusion Compared to the traditional ordering method, ordering using predictive analytics allowed the team to more accurately determine future order volumes and spend, yielding significant cost savings.

Implementation of Epic as an integrated electronic health record and revenue cycle management system was the largest software implementation in the history of Mayo Clinic. It converged thousands of workflows, eliminated hundreds of legacy systems and affected thousands of end users across Mayo Clinic campuses. Strong physician leadership, project management and deliberate change management were important components in this US$1.5bn project. Mayo’s leadership used a distinctive approach to prepare staff to make these important changes. At the beginning of the project, leaders developed a deliberate and robust change management strategy and formed a team focused on change management and communications. The change management team comprised Mayo Clinic health systems engineers, who know healthcare and Mayo’s culture and values and have expertise in systems engineering and quality improvement principles. The team developed an approach using various tools to support operational readiness and change readiness and to communicate important messages throughout the project cycle. In this paper, crucial components of the project for successfully managing the people side of change are described: development of an approach to change management that fits the organisation, creation of a dynamic team with the right skills mix and execution of appropriate timing of tactics.

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.