Leveraging data and digital health technologies to assess and impact social determinants of health (SDoH)

Abstract

Objective: Identify how novel datasets and digital health technology, including analytics- and artificial intelligence (AI)-based tools, can be used to assess non-clinical, social determinants of health (SDoH) for population health improvement.
 Methods: A targeted review with systematic methods was performed on three databases and the grey literature to identify recently published articles (2013-2018) for evidence-based qualitative synthesis. Following single review of titles and abstracts, two independent reviewers assessed eligibility of full-texts using predefined criteria and extracted data into predefined templates.
 Results: The search yielded 2,714 unique database records of which 65 met inclusion criteria. Most studies were conducted retrospectively in a United States community setting. Identity, behavioral, and economic factors were frequently identified social determinants, due to reliance on administrative data. Three main themes were identified: 1) improve access to data and technology with policy – advance the standardization and interoperability of data, and expand consumer access to digital health technologies; 2) leverage data aggregation – enrich SDoH insights using multiple data sources, and use analytics- and AI-based methods to aggregate data; and 3) use analytics and AI-based methods to assess and address SDoH – retrieve SDoH in unstructured and structured data, and provide contextual care management sights and community-level interventions.
 Conclusions: If multiple datasets and advanced analytical technologies can be effectively integrated, and consumers have access to and literacy of technology, more SDoH insights can be identified and targeted to improve public health.

Health Equity in Clinical Research Informatics

“…Patra et al [46] and Pham et al [47] are both citing Hazlehurst et al [48]. Patra et al [46] also share an article with Craig et al [49], both citing Navathe et al [50]. However, as the included sources have different focus and have contributed different findings, we did not consider the reviews as overlapping.…”

“…However, as the included sources have different focus and have contributed different findings, we did not consider the reviews as overlapping. No overlapping articles were identified between Pham et al [47] and Craig et al [49]. With the exception of one Canadian study [47], all studies were conducted in the United States.…”

“…The remaining five papers examined ways in which CRI may enable and promote health equity. Craig et al [49] and Patra et al [46] did so in an indirect manner, through literature reviews examining and promoting the utility of AI to actively include and use SDoH data from electronic records. However, it appears to be beyond the scope of both reviews [46,49], to discuss value in subjective data, as well as potential bias introduced by the source of data.…”

“…Pierson et al [54] and Hammarlund [52] both have in common the use of AI in exposing health inequity in clinical practices and providing alternative solutions, while Lu et al [53] uses AI to promote health equity in a setting known to be characterized by disparities in health and access to health. All the included sources of this scoping review address health equity in CRI with patient implications, either by exposing health inequality in AI-based solutions or by examining possibilities for AI to extract data of importance to address health equity [46,47,49,51,55]. However, Pierson et al [53], Lu et al [53], and Hammarlund [52] all stand out in their application of AI to drive health equity.…”

“…Craig et al [49] and Patra et al [46] did so in an indirect manner, through literature reviews examining and promoting the utility of AI to actively include and use SDoH data from electronic records. However, it appears to be beyond the scope of both reviews [46,49], to discuss value in subjective data, as well as potential bias introduced by the source of data. As clinical text include subjective data in EHR [36], this illustrates the issue of possibly overlooked subjective bias in algorithmic performance [30,36,37].…”