Developing a social vulnerability index from real-world electronic health records using natural language processing

  1. Anna Tirkkonen
  2. Esha Khan
  3. Juho Kaijansinkko
  4. Markus J. Haapanen
  5. Kaisa Koivunen
  6. Anna A. Peltola
  7. Marko Niemelä
  8. Emiel O. Hoogendijk
  9. Jake Lin
  10. Juulia Jylhävä
  11. Mikaela B. von Bonsdorff
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Abstract

Background

Social vulnerability is associated with adverse health outcomes, but existing social vulnerability indices often rely on cohort-based and self-reported data, potentially limiting their population-level applicability. Therefore, this study aimed to develop an SVI from routinely collected electronic health records (EHRs) using natural language processing (NLP) and examine its association with mortality and healthcare utilization.

Method

EHR data from 193,629 individuals aged 35–102 from the Wellbeing Services County of Central Finland between years 2010 and 2023 were used in this study. A 6-item SVI (total score 0-6) was developed from free-text clinical notes using deep-learning-based NLP. SVI scores were calculated at baseline and annually for individuals. Associations between baseline SVI and all-cause mortality and healthcare utilization were assessed over 12-year follow-up using Cox and count models.

Results

T he mean baseline SVI among all participants was 0.35 (range 0-6). Women had higher SVI (0.39) than men (0.32, p<0.001). SVI increased with age. One unit increase in SVI was associated with a higher risk of mortality (Hazard Ratio (HR) 1.23; 95% Confidence Interval (CI) (1.21-1.24), a higher odds of using healthcare services (Odds Ratio (OR) = 1.20; 95% CI): 1.19–1.21) and a greater healthcare service use (Incidence Rate Ratio (IRR) = 1.07; 95% CI: 1.06–1.07).

Discussion

An index capturing multiple domains of social vulnerability can be derived from EHRs using NLP. Higher SVI was associated with increased mortality and healthcare utilization. These findings support the potential of EHR data for assessing social vulnerability and related health risks.

Highlights

  • SVI can be derived from free-text EHR data using NLP in an unselected population.

  • Higher EHR-derived social vulnerability was associated with mortality and healthcare use.

  • EHR data may help identify individuals needing targeted assessment or support

Article activity feed

  1. Version published to 10.64898/2026.10.07.26364944 on medRxiv