Over the first three years of the COVID-19 pandemic, the US suffered over twice the deaths per capita as Canada — over 600,000 potentially avoidable deaths. Experts agree that this was a hard-won victory of Canada’s public health response. While political and demographic realities were a major factor, an unsung hero is the data modernization efforts that helped Canada track and contain the sudden rise in infections and meet the demand for public health services.
When it comes to public health emergencies, Canadian authorities have long been committed to learning from crises, applying technology and transforming systems to position both state and federal agencies for success when the next challenge arrives. Even within imperfect political realities, public health organizations around the world can learn and emulate Canada’s response through data modernization, potentially saving millions of lives in the next public health emergency.
A proactive approach to the threat of a global health crisis
After the SARS outbreak in 2003, federal and provincial governments in Canada recognized that their existing public health systems and IT were inadequate. Canadian authorities proactively worked with IBM to develop a solution known as IBM Panorama, an end-to-end public health disease surveillance and immunization system. This solution would later be key to Canadian public health agencies’ response to COVID-19.
Meanwhile, across the US, public health officials continued to use paper-based systems that would later fail to keep up with the explosive spread of COVID-19.
Lesson 1: Use a data model built for public health.
Canadian provinces used a disease surveillance solution featuring a person-centered public health data model, which meant it captured the requisite information needed for public health professionals to forecast and identify emerging trends and outbreaks (as well as analyze interventions and report to stakeholders). US public health agencies would benefit from choosing disease surveillance solutions that come with a proven, public health data model that offers relevant terminology, relationships and models.
Lesson 2: Make key decisions with high-quality data.
High-quality data is the bedrock of any public health response. US public health agencies should seek solutions that ingest data in multiple formats and have built-in processes for data cleansing to maintain the integrity of data. Data silos often occur when data is not shared or accessible, leading to workarounds and incomplete information. It is crucial to establish data sharing agreements in advance of an emergency.
Lesson 3: Handle data volumes with system integration.
Both Canadian and US public health agencies were overwhelmed by huge volumes of data during the COVID-19 pandemic. US agencies invested emergency COVID-19 funding into new case management and contact training solutions. However, these were often stand-alone solutions that didn’t address the underlying issues of siloed, incomplete or duplicative data.
Canada used integrated public health information systems, like Panorama, for seamless data ingestion, cleansing and import processing. Through systems integration via open APIs and other means of integrating data into patient health records, this served as the single source of truth for all exposure, case investigation, contact tracing, outbreak management and case management information for each resident.
Lesson 4: Adopt a cloud-native architecture to ensure elastic scalability.
Many of the investments in new technology solutions from the COVID-19 pandemic are now being sunset. For sustainable investments, public health agencies need the ability to scale data and information systems to the data volumes experienced during steady-state operations as well as during emergency responses. Investing in a cloud-native solution offers a flexible architecture and a future-proof solution that allows the public health agency to have elastic scalability.
Lesson 5: Prioritize agile configurability to adapt to developing disease scenarios.
Canadian public health agencies benefited from IBM Panorama’s ability to serve all 100+ reportable diseases and conditions. When the novel coronavirus started to emerge from lab tests, signs and symptoms led epidemiologists to code this data in a parking lot within Panorama, until LOINC and SNOWMED officially coded COVID-19. This kind of configurability is necessary to adapt to emerging diseases and conditions, instead of systems that only serve single diseases and conditions.
Lesson 6: Empower end users to respond rapidly with an easy, customizable system.
The public health field has experienced a large turnover as people retire or experience burnout. Data modernization initiatives afford public health agencies the opportunity to attract new talent by offering solutions using human-centered design. These solutions use augmented intelligence, guided workflows, machine learning and even consider generative AI to embed domain expertise within workflows and advance the efficiency and training of new users.
Lesson 7: Use storytelling to engage stakeholders and the public.
While epidemiologists are equipped to report and analyze data and communicate it to other scientists, accessible storytelling is crucial to fostering greater trust and impact when translating science into public communications and public policy. In Canada, there are “data storytelling” trainings for public health professionals that empower public health information officers and state health officers to frame data insights to maximize public understanding and action.
Create a proactive data modernization plan to prepare for the unexpected
COVID-19 revealed opportunities to change the way public health is managed, as well as the need to invest in technology. With the CDC Data Modernization Initiative and Public Health Infrastructure Grants, now is the time for US public health agencies to learn from fellow public health agencies, like those in Canada, as they modernize.
By taking a proactive data modernization approach, US public health agencies can manage steady-state operations and be better prepared to respond to the unknowns of the next public health emergency.
Conclusion
In conclusion, Canada’s public health agencies’ data modernization efforts played a crucial role in their response to the COVID-19 pandemic. To prepare for the next public health emergency, US public health agencies can learn from Canada’s experiences and implement similar data modernization solutions.
FAQs
Q: What was Canada’s data modernization plan for the COVID-19 pandemic?
A: Canada used IBM Panorama, an end-to-end public health disease surveillance and immunization system, to track and contain the sudden rise in infections and meet the demand for public health services.
Q: What can US public health agencies learn from Canada’s response?
A: US public health agencies can learn from Canada’s proactive approach to data modernization, including the use of a data model built for public health, high-quality data, and agile configurability.
Q: What is the significance of data storytelling in public health?
A: Accessible storytelling is crucial to fostering greater trust and impact when translating science into public communications and public policy, empowering public health information officers and state health officers to frame data insights to maximize public understanding and action.
Q: How can data modernization initiatives attract new talent in public health?
A: Data modernization initiatives can attract new talent by offering solutions using human-centered design, augmented intelligence, guided workflows, machine learning, and generative AI to embed domain expertise within workflows and advance the efficiency and training of new users.
