Electronic Health Record Applications in Public Health Surveillance

Electronic health records (EHRs) have transformed public health surveillance by providing rich, individual‐level data streams that can be analysed in near real time to detect, monitor and respond to disease threats. Originally developed for clinical care and billing, EHR systems now feed surveillance platforms that aggregate diagnostic codes, laboratory results, medication orders and demographic information across diverse healthcare settings. This integration enables syndromic surveillance of acute infections, automated detection of notifiable conditions and estimation of noncommunicable disease prevalence at population scale. Recent advances in data linkage, cloud‐based data warehousing and machine‐learning algorithms have improved the sensitivity and specificity of case detection, while standardised messaging formats facilitate interoperability between healthcare providers and public health agencies. Nevertheless, challenges remain, including variable data quality across institutions, incomplete capture of social determinants of health and the need for clear governance frameworks to protect patient privacy. Globally, EHR‐based surveillance has been deployed to track seasonal influenza, emerging zoonoses and chronic disease burdens, demonstrating its practical value for targeting interventions, allocating resources and evaluating public health responses.

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Electronic Health Record Applications in Public Health Surveillance publication trend

The graph below shows the total number of articles in electronic health record applications in public health surveillance across all publications each year (not limited to Nature Index journals).

Technical terms

Electronic health record (EHR): A digital repository of patient health information collected during routine clinical care, including diagnoses, laboratory results, medications and demographics.

Syndromic surveillance: The monitoring of symptoms and clinical features—rather than confirmed diagnoses—in order to detect outbreaks or changes in disease patterns at an early stage.

Health information exchange (HIE): A system for electronic transfer of health data across institutions, facilitating secondary uses of clinical data for public health and research.

Algorithmic case detection: A computational method that applies predefined rules or machine‐learning models to EHR data to identify probable cases of notifiable or reportable diseases.

Positive predictive value (PPV): The proportion of identified cases by an algorithm that are true positives, reflecting the accuracy of a surveillance metric.

Sensitivity: The ability of a surveillance system or algorithm to correctly identify all true cases within a population, minimising false negatives.

References

1. Early Release - Electronic Health Record Data for Lyme Disease Surveillance, Massachusetts, USA, 2017–2018 - Volume 30, Number 7—July 2024 - Emerging Infectious Diseases journal - CDC. Emerging Infectious Diseases (2024).
2. Database derived from an electronic medical record-based surveillance network of US emergency department patients with acute respiratory illness. BMC Medical Informatics and Decision Making (2023).
3. Concepts, objectives and analysis of public health surveillance systems. Computer Methods and Programs in Biomedicine Update (2024).