A case study on the impact of the COVID-19 vaccination reconciliation tool on primary care efficiency

COVID-19 vaccinations are provided across many clinics in Canada and patients are encouraged to be vaccinated. Being fully vaccinated provides many benefits, such as prevention against the variants of the COVID-19 virus, and prevention against severe illness, hospitalization and death resulting from COVID-19.¹ Within the current process, primary care practices manually update vaccination records based on patient and public health reported information, which is tedious and time-consuming. Updated vaccination records can help identify individuals who are experiencing vaccine hesitancy and can also keep track of any adverse reactions to vaccines, in addition to where and when the vaccine was administered. ​

To address the need for updating COVID-19 vaccination records in primary care, while reducing administrative burden for clinicians, the eHealth Centre of Excellence (eCE) has produced a solution. Using RPA capabilities, the eCE has created a COVID-19 Vaccination Reconciliation Bot, known as Bernie, that updates primary care patient records based on the COVaxON vaccination database (Figure 1.) RPA utilises a combination of workflow, rules and “presentation layer” integration within the EMR with its own user ID to action requests independently and can be designed to conduct tasks such as updating EMR, test ordering, repeating prescriptions, check-ins and bookings, telehealth and telemedicine, and much more.^2,3 This is a case study on the New Vision Family Health Team and their experience with the Bernie COVID-19 Bot that automates the process of updating patient vaccine status.

Impact at New Vision Family Health Team

An analysis of the data from New Vision Family Health Team (NVFHT), including 13 family physician records, from December 2021 to December 2022 illustrated that 7043 patient records were processed using the COVID-19 Vaccination Reconciliation Bot. This analysis demonstrated that 68% of all patient records were updated with current vaccination status in the EMR; reducing administrative workload for the family health team.

The Impact Across Clinician Sites

Since the launch of the COVID-19 Vaccination Reconciliation Tool in May 2021 to December 2022, 400 physicians have signed up, and 54 sites were enrolled with over 200 000 patient records processed. This bot was offered at no cost to the organizations with no program or software installation required, and there was no impact to the organization’s server. The Bot worked in the background, with no interference with day-to-day workflow, working efficiently and with high degree of accuracy. This bot improved clinic workflows by reducing the number of hours spent on administrative tasks by physicians and staff. Additionally, the bot contributed to proactive care by flagging patients without vaccines or who could benefit from an additional assessment or vaccines. This enables staff to place their focus where it needs to be – on delivering high quality patient care.

Bernie has also been used within diabetes care, where it can validate and generate a notification that a follow-up patient appointment is due based on the best practices for management of diabetic patients. Bernie can search within the past 12 months to identify patients that meet the criteria for a diabetes management billing incentive. It will also find patients that have had a diabetic visit, but a diabetic management assessment was not billed. Although Bernie started the COVID-19 vaccine and diabetes codes, it has the capacity to expand to other conditions.

Figure 3: Benefits of the COVID-19 Vaccination Reconciliation Automation Tool

1. Government of Canada. COVID-19: Effectiveness and benefits of vaccination [Internet]. Canada.ca/Gouvernement du Canada. 2021 [cited 2021Sep30]. Available from: https://www.canada.ca/en/public-health/services/diseases/coronavirus-disease-covid-19/vaccines/effectiveness-benefits-vaccination.html
2. Willis M, Duckworth P, Coulter A, Meyer E, Osborne M. The Future of Health Care: Protocol for Measuring the Potential of Task Automation Grounded in the National Health Service Primary Care System. JMIR Research Protocols. 2019;8(4):e11232.
3. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthcare Journal. 2019;6(2):94-98.