Opportunities and Challenges in Using [EHR] Systems to Study [long COVID]: Insights From the NIH RECOVER Initiative, 2025, Mandel et al

[forestglip]

Opportunities and Challenges in Using Electronic Health Record Systems to Study Postacute Sequelae of SARS-CoV-2 Infection: Insights From the NIH RECOVER Initiative

Hannah L Mandel, Shruti N Shah, L Charles Bailey, Thomas Carton, Yu Chen, Shari Esquenazi-Karonika, Melissa Haendel, Mady Hornig, Rainu Kaushal, Carlos R Oliveira, Alice A Perlowski, Emily Pfaff, Suchitra Rao, Hanieh Razzaghi, Elle Seibert, Gelise L Thomas, Mark G Weiner, Lorna E Thorpe, Jasmin Divers, RECOVER EHR Cohort

Abstract
The benefits and challenges of electronic health records (EHRs) as data sources for clinical and epidemiologic research have been well described. However, several factors are important to consider when using EHR data to study novel, emerging, and multifaceted conditions such as postacute sequelae of SARS-CoV-2 infection or long COVID.

In this article, we present opportunities and challenges of using EHR data to improve our understanding of long COVID, based on lessons learned from the National Institutes of Health (NIH)–funded RECOVER (REsearching COVID to Enhance Recovery) Initiative, and suggest steps to maximize the usefulness of EHR data when performing long COVID research.

Link | PDF (Journal of Internet Medical Research) [Open Access]

 

[rvallee]

Similar considerations may pertain to other chronic infection–associated diseases, that is, complex multisystemic conditions often occurring in the context of infection with other pathogens, including myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS).

Within the EHR, Long COVID may not be well-documented due to diagnostic complexities, as described earlier. The initial focus among clinicians on respiratory symptoms and widespread lack of knowledge around what were initially considered to be “atypical” presentations, such as postexertional malaise, ME/CFS, POTS, and dysautonomia, also contribute to missingness of relevant diagnoses within the EHR [43], which is compounded by the absence of or delay in introduction of ICD-10 codes for many of these conditions, including the U09.9 code for long COVID itself. Such manifestations can significantly impact daily function but may not be considered attributable to previous COVID-19 infection (or misdiagnosed as mental health issues), leading to their underrepresentation in research [43].

We caution that despite the dedication of the research community and high expectations for EHRs to quickly advance our understanding of long COVID, sustained, collaborative efforts are needed for this work.

 

[rvallee]

Sensible and smart overall, but it only emphasizes how disastrous the lack of coherent pathways to handle health issues that are not identifiable with current technology has been. The complete lack of a plan B, which is only made worse by excessive amounts of bias, cultural tropes and the widespread, and in some cases catastrophic, habit of dismissing what they don't understand. Most of which is what make their way into health records. Alongside some valid data, but all thoroughly mixed.

Because now that 5 years have fully passed, we don't have 5 years of good data to work with. Which is both scandalous and a tragedy. Instead we have a hodge-podge of bad data and unfit practices and methodologies working in systems that reject working any other way than the traditional methods. Methods that work in many cases, but fail miserably here.

But, really, this paper could have been an email that said: if we had done the work needed for this community, work that is different from the rigid ways we require, which this community constantly demanded of us, we wouldn't be in this mess, but we are in this mess, and it's all so messy.