Evaluating Social Determinants of Health Variables in Advanced Analytic and Artificial Intelligence Models for Cardiovascular Disease Risk and Outcomes: A Targeted Review.

Introduction/Purpose: Predictive models incorporating relevant clinical and social features can provide meaningful insights into complex interrelated mechanisms of cardiovascular disease (CVD) risk and progression and the influence of environmental exposures on adverse outcomes. The purpose of this targeted review (2018-2019) was to examine the extent to which present-day advanced analytics, artificial intelligence, and machine learning models include relevant variables to address potential biases that inform care, treatment, resource allocation, and management of patients with CVD. Methods: PubMed literature was searched using the prespecified inclusion and exclusion criteria to identify and critically evaluate primary studies published in English that reported on predictive models for CVD, associated risks, progression, and outcomes in the general adult population in North America. Studies were then assessed for inclusion of relevant social variables in the model construction. Two independent reviewers screened articles for eligibility. Primary and secondary independent reviewers extracted information from each full-text article for analysis. Disagreements were resolved with a third reviewer and iterative screening rounds to establish consensus. Cohen's kappa was used to determine interrater reliability. Results: The review yielded 533 unique records where 35 met the inclusion criteria. Studies used advanced statistical and machine learning methods to predict CVD risk (10, 29%), mortality (19, 54%), survival (7, 20%), complication (10, 29%), disease progression (6, 17%), functional outcomes (4, 11%), and disposition (2, 6%). Most studies incorporated age (34, 97%), sex (34, 97%), comorbid conditions (32, 91%), and behavioral risk factor (28, 80%) variables. Race or ethnicity (23, 66%) and social variables, such as education (3, 9%) were less frequently observed. Conclusions: Predictive models should adjust for race and social predictor variables, where relevant, to improve model accuracy and to inform more equitable interventions and decision making.

Examining the Asian American leadership gap and inclusion issues with federal employee data: Recommendations for inclusive workforce analytic practices.

In April 2021, a coalition of employee resource groups called the Federation of Asian American, Native Hawaiian, and Pacific Islander Network, or FAN, was established at the National Institutes of Health (NIH). The coalition aims to be a unifying voice that represents and serves these diverse communities. Discussion within the group centered around the persistent inequities and the lack of inclusion that the Asian American communities have long endured. Two common themes emerged from these discussions: (1) a leadership gap for Asian Americans in senior leadership and managerial positions, and (2) the everyday experience of exclusion. Asian Americans represent nearly 20% of the NIH permanent workforce yet make up only 6% of the senior leadership positions. These two issues reflect the sentiment that Asian Americans often feel invisible or forgotten in the discourse of structural racism and organizational inequities, especially in organizations in which they are numerically overrepresented. The purpose of this manuscript is to raise awareness of Asian American concerns in the federal workforce and how current employment and workforce analytic practices in this domain might contribute to the invisibility. To accomplish this goal, we will (1) describe relevant historical and contemporary contexts of Asian American experience undergirding their inclusion and visibility concerns; (2) present data analyses from available data sources to provide a deeper understanding of the Asian American leadership gap and lack of inclusion concerns; (3) highlight data availability and analytic challenges that hinder the ability to address the inequity and invisibility issues; and (4) recommend practices in data collection, measurement, and analysis to increase the visibility of this community in the federal workforce.

Role of Health Information Technology in Addressing Health Disparities: Patient, Clinician, and System Perspectives.

Over the last decade, health information technology (IT) has dramatically transformed medical practice in the United States. On May 11-12, 2017, the National Institute on Minority Health and Health Disparities, in partnership with the National Science Foundation and the National Health IT Collaborative for the Underserved, convened a scientific workshop, "Addressing Health Disparities with Health Information Technology," with the goal of ensuring that future research guides potential health IT initiatives to address the needs of health disparities populations. The workshop examined patient, clinician, and system perspectives on the potential role of health IT in addressing health disparities. Attendees were asked to identify and discuss various health IT challenges that confront underserved communities and propose innovative strategies to address them, and to involve these communities in this process. Community engagement, cultural competency, and patient-centered care were highlighted as key to improving health equity, as well as to promoting scalable, sustainable, and effective health IT interventions. Participants noted the need for more research on how health IT can be used to evaluate and address the social determinants of health. Expanding public-private partnerships was emphasized, as was the importance of clinicians and IT developers partnering and using novel methods to learn how to improve health care decision-making. Finally, to advance health IT and promote health equity, it will be necessary to record and capture health disparity data using standardized terminology, and to continuously identify system-level deficiencies and biases.

Behavioral Interventions Using Consumer Information Technology as Tools to Advance Health Equity.

The digital divide related to consumer information technologies (CITs) has diminished, thus increasing the potential to use CITs to overcome barriers of access to health interventions as well as to deliver interventions situated in the context of daily lives. However, the evidence base regarding the use and impact of CIT-enabled interventions in health disparity populations lags behind that for the general population. Literature and case examples are summarized to demonstrate the use of mHealth, telehealth, and social media as behavioral intervention platforms in health disparity populations, identify challenges to achieving their use, describe strategies for overcoming the challenges, and recommend future directions. The evidence base is emerging. However, challenges in design, implementation, and evaluation must be addressed for the promise to be fulfilled. Future directions include (1) improved design methods, (2) enhanced research reporting, (3) advancement of multilevel interventions, (4) rigorous evaluation, (5) efforts to address privacy concerns, and (6) inclusive design and implementation decisions.