top of page
Search

Exploring the links between healthcare delivery and public health

  • Writer: Zach Wilson
    Zach Wilson
  • Mar 3, 2024
  • 2 min read

Healthcare delivery and public health influence one another in many ways. A way that deserves particular focus is in managing and responding to health crises like epidemics or pandemics. A particular example of this is syndromic surveillance which plays a crucial role in this interconnectedness, enabling the monitoring and detection of potential outbreaks by analyzing healthcare data in real-time. This involves the real-time collection, analysis, interpretation, and dissemination of health-related data (OpenAI, 2024). Two examples of this are the National Syndromic Surveillance Program (NSSP) ran by the Centers for Disease Control (CDC) and the Global Outbreak Alert and Response Network (GOARN) managed by the World Health Organization (WHO).




The NSSP collects and analyzes electronic health data from a network of participating healthcare facilities across the United States to monitor and respond to public health threats. To facilitate this the CDC provides software infrastructure, training, funding, collaboration, technical support, and data analysis assistance. This health data and data analysis yields trends which can be used to direct pubic health actions.


GOARN is a WHO network of over 250 technical institutions and network which address acute public health events with resources and staff. Their main focus is to rapidly deploy to an area while receiving WHO backside support to prevent and control infectious diseases outbreaks and public health emergencies. A recent example of these kinds of activations and management would be the COVID-19 pandemic. Below is a video giving a more in-depth overview of the program:



There are a few things on the horizon aligning for the future of advancing these lines of effort. One of these is the advancement of data analytics through artificial intelligence (AI). Advanced AI algorithms can analyze large volumes of healthcare data such as electronic health records, claims data, and social media feeds to identify patterns indicative of disease outbreaks and inform public health interventions. Before we worry too much about the rise of the machines though, the FDA has released guiding principles for machine learning to help keep things above board. This author is already incredibly impressed by the small glimpse into AI technology that he has experienced. It shows great promise to be a catalyst for positive change within the healthcare sphere.


References:



OpenAI. (2024). ChatGPT (4) [Large language model]. https://chat.openai.com 

 
 
 

Comments

bottom of page