Patient Self-Report Superior to Electronic Medical Record Abstraction for Identifying Positive COVID-19 Symptoms at Illness Onset - Gretchen L. Mockler, MD; Samantha P. Novotny, BS; Wei Hou, PhD; Yuhang Liu, MS; Elinor R. Schoenfeld, PhD (AJPM Focus, 2022)
Introduction: Most initial COVID-19 research focused on hospitalized patients. Presenting symptomatology in the outpatient setting was poorly characterized, making it difficult for primary care physicians to predict which patients would require hospitalization. The purpose of this study was to characterize the presenting symptoms of COVID-19 infection and baseline patient characteristics and evaluate for correlation with disease severity, duration, and chronicity in the outpatient setting. Methods: A total of 107 adult, English-speaking patients with suspected and confirmed COVID-19 cases at the 3 primary care practices of Stony Brook University Hospital were studied between March and December 2020. Survey data were collected from patient telephone interviews and electronic medical record abstraction. The potential risk factors assessed included participant demographics, medical comorbidities, and the number and type of symptoms at illness onset. Outcome measures included symptom duration, hospitalizations, and persistence of symptoms at 12 weeks from study enrollment. Results: Patient self-report survey elicited nearly twice as many symptoms described at illness onset as those recorded in the electronic medical record (p<0.0001). A higher number of symptoms at illness onset was positively associated with symptom duration and chronicity. The presence of fever and hypoxia at the onset of illness were each positively associated with eventual hospitalization for COVID-19 disease. Conclusions: Early in the setting of newly emerging infectious diseases, particularly those such as COVID-19 that involve multiple organ systems, patient self-report of symptoms using a complete review of systems rather than electronic medical record abstraction alone may be key for accurate disease identification and characterization as well as prediction of eventual disease severity, duration, and chronicity.