Modernizing public health surveillance
As U.S. Congress considers proposals for increased funding for public health surveillance and contact tracing, governments should focus on the three capacities needed to modernize: data, analytics and technology.
Disasters are known to stress governmental systems and, as in the case of COVID-19, expose structural weaknesses, unmet needs and capacity gaps. As the pandemic subsides, attention will turn to recovery and lessons learned. Post-pandemic there appears to be a small window of attention and investment in public health preparedness, including a wave of funding focused on capacity building for existing infectious disease surveillance and epidemiologic systems.
Public health surveillance is the ongoing systematic collection, analysis, interpretation and dissemination of data regarding a health-related event to reduce morbidity and mortality, as well as improve health. Surveillance provides situational awareness that is critical to mounting a timely response.
However, there is no uniform national surveillance system in the United States. Rather, surveillance is a highly decentralized enterprise, siloed by categorical funding across programs, and balkanized into multiple separate and distinct “systems.” This period of disruption presents an opportunity to rationalize and improve the efficiency and effectiveness of our early warning system.
Vision for public health: Move from retrospective view to prospective preparedness
Leveraging modern tools, public health surveillance can move from a largely retrospective view of descriptive epidemiology based on counting cases, to a rapid, near real-time data collection system for situational awareness of emerging trends and prospective preparedness based on predictive modeling.
To do so, we must have the core infrastructure – in terms of people, policy, funding and technology (i.e., data collection, data management, storage, curation, governance, analysis, visualization and dissemination) – that can be used across the multitude of surveillance programs at the federal and state levels. Alignment must involve both horizontal and vertical approaches to build a scalable, interoperable platform that enables separate disease programs to migrate legacy systems and is configurable to meet unique categorical requirements.
Alignment must also support state and local health departments to collect, use and report state and local data back to the Centers for Disease Control and Prevention (CDC). Reducing the reporting and data collection burden of those in health systems is a key success factor, as well as identifying high-value, novel data to complement traditional public health sources.
Three capacities key to modernizing public health surveillance
I believe three cross-cutting capacities are key to modernizing public health surveillance: data, analytics and technology.
Traditional surveillance data are often kept in siloed categorical systems. Allowing these data to be aggregated across programs (e.g., all payer claims datasets, health information exchanges) with the appropriate safeguards, can deliver efficiency, timeliness and new insights. New technologies can also bring sources of information to the surveillance enterprise, such as data from electronic health records, smart phones, wearable devices and social media. With increasing progress toward interoperability, adding data from state and local social services programs and non-health data from the census and economic programs can help improve understanding of the social determinants of health.
Artificial intelligence can augment traditional epidemiology in helping public health sort through large amounts of data. Machine learning can enhance predictive modeling, improve cluster analysis and social network analyses to increase the sensitivity of surveillance systems.
Natural language processing can help mine large quantities of unstructured text data in notes to extract meaning from unstructured data contained in clinical notes, social media and news. Sophisticated data visualization techniques can provide additional insights on disease patterns and transmission.
With multi-faceted data sources, larger datasets and the use of more computationally intensive analytic techniques, cloud computing can be a cost-effective approach to meet the needs of modern complex surveillance systems. Blockchain has also emerged as a new way of record keeping that securely facilitates permission-based access to data, promoting trust and transparency. Additionally, modern application programming interfaces along with adoption of standards for exchanging healthcare information electronically (e.g., FHIR ) can reduce the burden of entering data and transferring from clinical sources into public health surveillance systems.
The current pandemic-driven focus on public health response presents an opportunity to harmonize and modernize existing surveillance systems to improve efficiency and effectiveness. Investment also offers a once in a generation chance to strategically build enhanced capacity within the national surveillance enterprise, by taking advantage of new developments in technology, big data and advanced analytics.
IBM Watson Health partners with social services and health and human services agencies to co-create user-centered solutions that enhance support programs, increase ease of use and access, and modernize service delivery. Contact us to learn more about transforming public health surveillance programs.