By Karla Richardson, Director, Healthcare Quality, Sellers Dorsey
LinkedIn: Karla Richardson
LinkedIn: Sellers Dorsey
The COVID-19 pandemic demonstrated the critical role that data analytics and visualization play in strengthening the public health response. Now, the worrying spread of the H5N1 bird flu is serving as a reminder of the power of predictive analysis and making data understandable and widely available in keeping people informed and safe.
Both government and non-government health organizations played a key role in monitoring and predicting the COVID-19 pandemic when it emerged in the United States five years ago. They showed how data visualization can aid public health decisions and help experts manage the response.
For example, the Centers for Disease Control and Prevention (CDC) compiled data from local governments and organizations on wastewater to track and predict hotspots and new COVID-19 outbreaks. By analyzing the presence of the virus in wastewater, it was possible to detect the presence of the virus even before patients began to show symptoms.
Even with the best data collection and analysis, you still need good communication to make data meaningful. Data visualization allows clear, effective, and immediate communication of information. For example, the CDC uses a visual display on the map to illustrate where COVID-19 is turning up in wastewater samples, so viewers can easily identify where potential outbreaks are geographically.
This information helped improve healthcare delivery by directing resources to prepare for localized outbreaks. It also alerted healthcare providers to expect more patients, helping them and local officials to better prepare their communities and mitigate the impact of disease.
In this way, data analytics and visualization are the foundation of planning, budgeting, and preparing for public health crises.
Today, the CDC is actively tracking bird flu cases across the United States, looking at exposure location and source. As of mid-March 2025, there were 70 confirmed human cases of H5N1 in the U.S. and one death, in Louisiana.
For now, the risk to the public from H5N1 is considered low, with no known human-to-human transmission of the virus. But when challenges like a pandemic arise, data analytics and visualization can help determine not just what happened, but potentially why. More importantly, it can suggest solutions.
Without it, we’ll see duplicative efforts, inefficiency, and wasted resources. Through data analytics and cross-collaboration within the healthcare landscape, we can create tailored solutions that enhance healthcare quality, access, and outcomes for the people who need it most.
Challenges and Opportunities
Each year, the healthcare industry creates enormous amounts of data from many sources, and in many different formats. Adding to this complexity is data from government sources, including Census data, demographics, socioeconomic information, data on crime, and more.
To be of value to the public, this data needs to be collected, collated, analyzed, communicated, and made understandable. Modern data analytics and visualization are critical for helping healthcare and public health organizations turn their data into actionable insights.
The two biggest hurdles to effective healthcare data utilization are the twin bottlenecks of data collection and data analysis.
Each source of healthcare data — electronic medical records, claims, research studies, and so on — generates different pieces of information that must be cleaned, combined, and analyzed to create a complete picture of the care delivered to patients. Furthermore, each of these data sources has advantages and limitations from an analysis perspective.
It also bears mentioning that most healthcare organizations generate data for purposes other than improving individual and public health. For example, claims data has the specific information payers need to decide whether to pay or deny a claim. On their own, claims data overlook important details that can help us understand a person’s overall health. They also don’t tell us much about a patient’s health-related social needs, including living conditions, safety, access to healthy food, and income.
Uncovering Impact Beyond Individual Patients
But using data science and analytics is just as useful for health systems as it is for public health agencies. It can improve efficiency, boost revenues, enhance care delivery, and generate better patient outcomes.
On the population level, unstructured “big data” and machine learning are enabling new insights. Big data involves the analysis of large datasets, potentially from thousands or tens of thousands of patients and individuals. When we look at big data, we can find groups and trends within various populations. This helps us understand the past and predict the future to improve outcomes.
Experts increasingly recognize the significant influence of environmental factors on health outcomes. These concerns go beyond pollution and bad air quality. They include simple things like access to fresh food, safe places to play, and living costs.
By examining these social determinants of health and environmental hazards, healthcare organizations can identify people who are at risk for various chronic health conditions earlier. Then, healthcare providers can deliver targeted care to prevent severe complications further down the line and improve overall outcomes.
Using the interdisciplinary approach of data science, we can uncover new insights that show how healthcare impacts whole communities, not just individual patients. By applying economic and actuarial methods, it is possible to identify deeper impacts.
For example, let’s say a county health department launched an initiative to increase the rate of diabetes screenings conducted in a population identified as being at high risk. As a result, the rate of screenings increases along with the number of cases detected early enough for treatment. This increase in early intervention then leads to a decrease in the number of amputations or other severe complications of diabetes within that population.
Effective data analysis can help practitioners and decision-makers use resources wisely, set priorities, and tackle complex challenges.
Excellent data analytics and visualization allow us to understand the challenges and opportunities facing healthcare and public health organizations at all levels. They are key tools in advancing improvements in healthcare quality, access, and outcomes and are essential to solving the public health challenges of today and the future.