forecasting healthcare utilization has the potential to anticipate care needs, either accelerating needed care or redirecting patients toward care most appropriate to their needs. While prior research has utilized clinical information to forecast readmissions, analyzing digital footprints from social media can inform our understanding of individuals' behaviors, thoughts, and motivations preceding a healthcare visit. We evaluate how language patterns on social media change prior to emergency department (eD) visits and inpatient hospital admissions in this case-crossover study of adult patients visiting a large urban academic hospital system who consented to share access to their history of facebook statuses and electronic medical records. An ensemble machine learning model forecasted eD visits and inpatient admissions with out-of-sample cross-validated AUCs of 0.64 and 0.70 respectively. Prior to an ED visit, there was a significant increase in depressed language (Cohen's d = 0.238), and a decrease in informal language (d = 0.345). Facebook posts prior to an inpatient admission showed significant increase in expressions of somatic pain (d = 0.267) and decrease in extraverted/social language (d = 0.357). These results are a first step in developing methods to utilize user-generated content to characterize patient care-seeking context which could ultimately enable better allocation of resources and potentially early interventions to reduce unplanned visits.

Table 2. Statistical insights on differential language expression prior to an inpatient visit*. *Effect sizes of individual linguistic features (diff-of-diff b/w true and null events) for inpatient visits. Significance was measured using paired, two-tailed t-test with Benjamini-Hochberg p-correction. Feature Cohen's d p value (corrected) Mean diff-of-diff 95% CI Inpatient visits Change in Linguistic Style/ Mental Well-being Categories that increase in usage before inpatient visit Depressed 0