Observational studyBMC infectious diseases2026
A mixed-methods study comparing clinical and patient-reported perspectives on long COVID: insights from electronic health records and narrative journal entries.
Observational study in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundMany studies of Long COVID have used a variety of clinical and self-reported data to characterize this condition. Previous research has shown the many ways in which these two types of data sources can differ. Our study aims to expand on previous research by exploring descriptions of Long COVID using electronic health records (EHRs) and narrative journal entries collected from the same patients to understand how they align and differ.
methodsThis convergent mixed methods observational study design analyzed narrative patient-reported data (journal entries) and EHRs from adults with a clinical Long COVID diagnosis. From August 2020 to July 2023, adults across seven states who tested positive for COVID-19 were invited to participate in My COVID Diary (MCD) for up to a year; in 2023 they re-consented for EHR access. Of 18,941 MCD participants, 3,323 consented to EHR access (17.54% participation rate). Inclusion required at least one medical encounter and one journal entry between 4 and 52 weeks post-infection, plus an EHR-recorded Long COVID diagnosis. Ninety-two individuals met all criteria. EHR was used to descriptively analyze patient demographics, medical encounters, and diagnoses. MCD journal entries were coded and analyzed using a framework analysis to identify themes. Ten individuals with journal entries within 1 week of a clinical visit were randomly selected and the records were manually reviewed for semantic overlap to qualitatively illustrate alignment.
resultsThe most frequent types of medical encounters for participants in the study window were outpatient primary care or specialist visits. The most common diagnoses found in the EHR were respiratory conditions, joint pain, cardiovascular issues, malaise and fatigue, and ear, nose, and throat issues. Journal entries highlighted similar symptoms like tiredness/fatigue, pain, respiratory challenges, mental health concerns, and cardiovascular issues. Key differences were that journal entries had a greater emphasis on more subjective or functional symptoms and experiences such as fatigue, brain fog, and mental health and included the impact of long COVID on daily, work, and social activities. Comparison of co-occurring EHR and journal entries for individual encounters illustrated limited to no overlap in symptom description for the 10 selected cases.
conclusionsWhile our study is limited by a small sample size that impacts generalizability, our in-depth review of these data sources indicates that neither EHR nor patient-reported data alone conveys the complete experience of Long COVID. Integration of patient-reported and EHR data is likely needed to fully understand and treat patients struggling with Long COVID.
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