Utilization of Electronic Health Records for Chronic Disease Surveillance: A Systematic Literature Review

Guralnik, Elina

doi:10.7759/cureus.37975

Cureus

2023

DOI: 10.7759/cureus.37975

|View full text |Cite

Utilization of Electronic Health Records for Chronic Disease Surveillance: A Systematic Literature Review

Elina Guralnik¹

Abstract: This study reviews the current utilization of electronic health records (EHRs) for chronic disease surveillance, discusses approaches that are used in obtaining EHR-derived disease prevalence estimates, and identifies health indicators that have been studied using EHR-based surveillance methods.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

US public health surveillance, reimagined

Guralnik

2024

Learning Health Systems

View full text Add to dashboard Cite

IntroductionThis study presents two novel concepts for standardizing electronic health records (EHR)‐based public health surveillance through utilization of existing informatics methods and data platforms.MethodsDrawing from the collective experience in applied epidemiology, health services research and health informatics, the author presents a vision for an alternative path to public health surveillance by repurposing existing tools and resources, such as (1) computable phenotypes which have already been created and validated for a variety of chronic diseases of interest to public health and (2) large data platforms/collaboratives, such as All of Us Research Program and National COVID Cohort Collaborative. Opportunities and challenges are discussed regarding EHR‐based chronic disease surveillance, as well as the concept of phenotype definitions and large data platforms reuse for public health needs.Results/FrameworkReusing of computable phenotypes for EHR‐based public health surveillance would require secure data platforms and nationally representative data. Standardization metrics for reuse of previously developed and validated computable phenotypes are also necessary and are currently being developed by the author. This study presents a reimagined Learning Health System framework by incorporating Public Health and two novel concept sets of solutions into the healthcare ecosystem.Conclusion/Next StepsAlternative approaches to limited resources and current infrastructure of the US Public Health System, especially as applied to disease surveillance, are needed and may be possible when repurposing the resources and methodologies across the Learning Health System.

show abstract

US public health surveillance, reimagined

Guralnik

2024

Learning Health Systems

View full text Add to dashboard Cite

show abstract

CDR-Detector: a chronic disease risk prediction model combining pre-training with deep reinforcement learning

Lin,

Zhou,

Jiao

et al. 2024

Complex Intell. Syst.

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Utilization of Electronic Health Records for Chronic Disease Surveillance: A Systematic Literature Review

Cited by 2 publications

References 24 publications

US public health surveillance, reimagined

US public health surveillance, reimagined

CDR-Detector: a chronic disease risk prediction model combining pre-training with deep reinforcement learning

Contact Info

Product

Resources

About