AI in Public Health: Machine Learning & AI in Public Health Surveillance
Artificial intelligence (AI) has changed the way public health officials track and predict health outcomes at the local, state, and federal levels. The latest public health surveillance tools use both health and non-health information to predict which people and communities face the most risk. The agency can also evaluate the impact of its initiatives on the local population to ensure they are having the intended effect.
Artificial intelligence is expanding public health surveillance capabilities by increasing the speed, accuracy, and deployment of various life-saving initiatives. Agencies can use this technology to replace or supplement traditional epidemiological surveillance techniques to unlock new insights into the spread of infectious diseases on the local and state levels while evaluating the effectiveness of various public health resources. Predictive models can highlight important trends that affect the spread of illness and disease within certain pockets of the population to ensure those most at risk receive the resources they need to protect themselves. AI can also automate routine data collection workflows to increase the scale and reach of the department’s surveillance efforts.
But the adoption of AI in public health surveillance has challenges. The algorithms that power these public health software programs are only effective if the department can collect and validate large quantities of data in a timely manner. Many organizations also lack the resources and knowledge to implement or participate in these programs, even though AI can ease the administrative burden of protecting the population from infection by optimizing existing resources. Different data collection, validation, and analysis methods across agencies or facilities further complicate matters and can restrict the AI’s capacity to make accurate predictions.
The COVID-19 pandemic illustrated the need for rapid decision-making in the face of a novel infectious disease. Many local, state, and federal public health agencies responded by collecting large volumes of data on the pandemic without a specific plan of action. The crisis also brought attention to the need for a more localized approach to public health. While effective in saving lives, state-wide stay-at-home orders resulted in a widespread backlash among certain sectors of the general public, leading to the harassment of public health workers.
So, what can AI do to help these agencies overcome these challenges?
Public health officials need to make decisions quickly when faced with an emerging trend, such as the spread of an infectious disease. That starts with collecting the right type of data. AI allows each agency to scale its collection efforts as the amount of public health information continues to grow. It allows agencies to automatically identify, validate, and classify data from a wide range of sources, including various file types and media files, according to the latest industry standards to improve accuracy and resource utilization.
Once the department has collected the information, it can increase its understanding of the situation by including supplemental information that may provide additional insights into what’s causing the trend, including demographic information such as age, religion, ethnicity, gender, and where they live or work. This information can be used to identify “hot spots” and the types of individuals most likely to be infected so the department can focus its resources on preventing additional outbreaks.
Predictive analysis, or machine learning (ML), takes this approach one step further by helping the department zero in on where the illness or virus is most likely to spread, such as on a county-by-county basis. The AI uses other types of public data to identify the community’s potential risk of infection, such as residents’ social media posts regarding the latest health measures, nearby hospitalization admission rates, county testing and contact tracing capacity, and personal protective equipment availability. Over time, ML algorithms will become increasingly intelligent as the algorithm learns more about the relationship between different factors and the spread of illness and disease.
AI uses this supplemental information to predict when and where the next outbreak is most likely to occur so the country can prepare for a potential spike in cases. This allows the department to tailor its approach when implementing new policies and procedures designed to prevent infection while deploying life-saving resources to go where they are needed most.
Once these initiatives are in place, the agency can use AI to evaluate the effectiveness of its efforts to determine whether changes are needed. In the case of a public health emergency like COVID-19, the algorithm can indicate whether the implementation of measures has resulted in a decrease in the number of infections or deaths. It can also help determine if the decline can be attributed to the current strategy employed by the department. This information can also be used in the research process when analyzing how various public health measures, such as vaccines, medication, and agency guidelines, affect different groups of people.
To use this technology, public health agencies need to train their workers on how to interact with these tools while sharing knowledge between departments to improve the flow of information. Users may have trouble implementing the findings if they don’t understand how they were created. The program should be easy to use for non-technical employees, with adjustable inputs that can be tailored to existing workflows.
How is AI being used in public health?
Artificial intelligence can track, monitor, and even predict health trends within a specific geographic location, such as at the local, state, and federal levels. This can include the spread of infectious diseases and viruses like the coronavirus, Zika, monkeypox, and Ebola as well the emergence of preventable health conditions and diseases like cancer, lead poisoning, and developmental disabilities among young children.
A program collects, validates, and analyzes various types of data from participating providers and facilities to identify the trends while helping the department make the best use of its resources by allowing it to focus on the areas and individuals who need them most.
What are the benefits of using AI in public health surveillance?
Using AI in public health surveillance helps these agencies act quickly in the face of an emerging crisis or trend. A program automatically ingests the data as it becomes available to create an accurate report in a matter of hours, if not minutes, instead of days. The algorithm can make informed predictions regarding the location and severity of the spread, so the agency can better target its prevention methods. AI gives the agency a clear plan of action once it has collected the necessary data, so it can implement effective policies as quickly as possible. The department can also easily scale its surveillance efforts when including additional information in its analysis without hiring additional workers. This approach ultimately saves lives by preventing and minimizing outbreaks before they occur.
What is the future outlook for AI in public health surveillance?
AI will eventually become the backbone of public health surveillance to allow officials to make informed decisions in real time when faced with a public health crisis. The COVID-19 pandemic is a key example of why these programs are essential when dealing with a novel virus. The rapid exchange of information in the digital world has changed the way people interact with public health agencies. Individuals may soon use AI public health tools to assess their own risk level in real time. This ultimately leads to better patient health outcomes and faster implementation of more informed public health policies.
While the future of AI in public health surveillance may be unknown, this technology continues to drive new innovations in the industry. Agencies can also expect to collect more data in the years to come as these programs continue to grow in scale and scope.
SSG is here to help public health departments adopt this technology. Learn more about how our public health surveillance systems incorporate artificial intelligence.