Effect of Sociodemographic Factors on Uptake of a Patient-Facing Information Technology Family Health History Risk Assessment Platform

R. Ryanne Wu, Rachel A. Myers, Adam H. Buchanan, David Dimmock, Kimberly G. Fulda, Irina V. Haller, Susanne B. Haga, Melissa L. Harry, Catherine McCarty, Joan Neuner, Teji Rakhra-Burris, Nina Sperber, Corrine I. Voils, Geoffrey S. Ginsburg, Lori A. Orlando

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Objective Investigate sociodemographic differences in the use of a patient-facing family health history (FHH)-based risk assessment platform. Methods In this large multisite trial with a diverse patient population, we evaluated the relationship between sociodemographic factors and FHH health risk assessment uptake using an information technology (IT) platform. The entire study was administered online, including consent, baseline survey, and risk assessment completion. We used multivariate logistic regression to model effect of sociodemographic factors on study progression. Quality of FHH data entered as defined as relatives: (1) with age of onset reported on relevant conditions; (2) if deceased, with cause of death and (3) age of death reported; and (4) percentage of relatives with medical history marked as unknown was analyzed using grouped logistic fixed effect regression. Results A total of 2,514 participants consented with a mean age of 57 and 10.4% minority. Multivariate modeling showed that progression through study stages was more likely for younger (p -value = 0.005), more educated (p -value = 0.004), non-Asian (p -value = 0.009), and female (p -value = 0.005) participants. Those with lower health literacy or information-seeking confidence were also less likely to complete the study. Most significant drop-out occurred during the risk assessment completion phase. Overall, quality of FHH data entered was high with condition's age of onset reported 87.85%, relative's cause of death 85.55% and age of death 93.76%, and relative's medical history marked as unknown 19.75% of the time. Conclusion A demographically diverse population was able to complete an IT-based risk assessment but there were differences in attrition by sociodemographic factors. More attention should be given to ensure end-user functionality of health IT and leverage electronic medical records to lessen patient burden.

Original languageEnglish
Pages (from-to)180-188
Number of pages9
JournalApplied Clinical Informatics
Volume10
Issue number2
DOIs
StatePublished - 2019

Keywords

  • family health
  • health care disparities
  • health risk assessment
  • patient engagement

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