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

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.

Accuracy of the Preferred Language Field in the Electronic Health Records of Two Canadian Hospitals

“…A recent study found that a sociodemographically diverse patient population could provide health histories using a web-based platform. 20 A systematic review found that survey responses collected via mobile applications were equivalent to responses collected using other platforms (paper, laptop, and personal digital assistant) and may improve data completeness. 21 While the use of information technology tools at the point of registration could minimize data entry errors and improve accuracy, hospitals must continue to perform audits to assess the accuracy of collected data.…”

At the intersection of precision medicine and population health: an implementation-effectiveness study of family health history based systematic risk assessment in primary care

“…In this large multi-institutional study in diverse populations of a precision medicine tool for the systematic assessment of risk across 27 conditions, we found that a large percentage of the population (46%) is at hereditary or familial level of risk and meets criteria for more intensive risk management. This result might not seem extraordinary when considering common chronic diseases; however, it is significantly greater than what is widely perceived as the prevalence of hereditary (5%) [ 43 ] and familial risk (7–14%) [ 44 ] in primary care populations. Remarkably, despite the geographical and cultural differences in the participating healthcare systems (sites included rural, urban, and suburban environments; academic and private institutions, largely minority and largely Caucasian populations) (24), we found no differences in the percentage of participants at increased risk between the healthcare institutions, suggesting that these findings may translate across the broader U.S. population and potentially beyond as well given that risk assessment is not routinely collected in many healthcare systems across the globe [ 45 , 46 ].…”

“…A detailed analysis of the intervention’s implementation and the implementation outcomes, including participant uptake as compared to the general clinic population, is published and shows that there are some differences, such as participants were more frequently females, older, and Caucasian than the underlying clinic population [ 51 ]. For minorities who did enroll in the study, we found they were equally likely to complete the study (with the exception of Asians) and the quality of their data was equal to that of the overall study population [ 43 ]. This is supported by prior studies as well which indicate minorities have equal access to mobile devices and are equally likely to use mHealth applications [ 52 , 53 ].…”

Implementation-effectiveness trial of systematic family health history based risk assessment and impact on clinical disease prevention and surveillance activities

“…This study builds on our prior work showing that systematic risk assessment can be successfully implemented across diverse healthcare systems [ 8 , 9 ], has the potential to significantly impact population health as evidenced by increased risk identification [ 10 , 29 ], and can enhance guideline based risk mitigation efforts that are widely accepted to improve health outcomes. It is also evident that further HRA development is needed to continue to close the gap between risk identification and risk mitigation.…”

“…The HBM was used to guide the development, deployment, and evaluation of the HRA intervention. We have previously published this study’s implementation outcomes [ 8 , 9 ] and the potential impact of systematic HRA on population health [ 10 ]. This study was funded by the National Institutes of Health as part of the Implementing Genomics in Practice network [ 11 ].…”