Rapid review: Identification of digital health interventions in atherosclerotic-related cardiovascular disease populations to address racial, ethnic, and socioeconomic health disparities

Abstract

Disparities in cardiovascular disease (CVD) and associated health and healthcare delivery outcomes have been partially attributed to differential risk factors, and to prevention and treatment inequities within racial and ethnic (including language) minority groups and low socioeconomic status (SES) populations in urban and rural settings. Digital health interventions (DHIs) show promise in promoting equitable access to high-quality care, optimal utilization, and improved outcomes; however, their potential role and impact has not been fully explored. The role of DHIs to mitigate drivers of the health disparities listed above in populations disproportionately affected by atherosclerotic-related CVD was systematically reviewed using published literature (January 2008-July 2020) from multiple databases. Study design, type and description of the technology, health disparities information, type of CVD, outcomes, and notable barriers and innovations associated with the technology utilized were abstracted. Study quality was assessed using the Oxford Levels of Evidence. Included studies described digital health technologies in a disparity population with CVD and reported outcomes. DHIs significantly improved health (eg, clinical, intermediate, and patient-reported) and healthcare delivery (eg, access, quality, and utilization of care) outcomes in populations disproportionately affected by CVD in 24 of 38 included studies identified from 2104 citations. Hypertension control was the most frequently improved clinical outcome. Telemedicine, mobile health, and clinical decision support systems were the most common types of DHIs identified. DHIs improved CVD-related health and healthcare delivery outcomes in racial/ethnic groups and low SES populations in both rural and urban geographies globally.

Keywords: Cardiovascular disease; Clinical decision support; EHR; Health information technology; Patient portals; Telemedicine; mHealth.

Graphical abstract

Figure 1

Disposition of articles and literature screening flow diagram. This flow diagram depicts the process and flow of information (including number of records of identified, included and excluded study numbers, and reasons for exclusion) through the phases of the rapid review.

Figure 2

Visual summary of the study characteristics and findings. Stepwise summarization of study development and execution including research question, identification of literature, and characterization of results stratified by population, intervention, and outcomes. CVD = cardiovascular disease.

Figure 3

Quality of the evidence and stratification of outcomes by technology. The x-axis indicates the result of the intervention (technology favors nondisparate group, no difference between groups, technology improves outcome in disparity group, technology improves outcome in all populations) on study outcome(s) organized by (A) access to care, (B) quality of care, (C) utilization of care, and (D) health outcomes including clinical, intermediate, and patient-reported measures. The y-axis lists the study quality from low (IV) to high (Ia) according to Oxford Levels of Evidence assessment. The type of technology is denoted by colored circles and the number in each circle corresponds to the study reference ID (enumerated below). Some studies improving outcomes in disparate populations did not have a nondisparity group comparator. See Appendix B for more details regarding study follow-up and/or adherence to technology in context to reported outcomes and our summary of study conclusions. Numbers in circles represent the following studies: (1) Akar et al (2015), (2) Asche et al (2016), (3) Bennett et al (2012), (4) Bove et al (2013), (5) Dang et al (2017), (6) Finkelstein et al (2010), (7) Gross-Schulman et al (2017), (8) Hebert et al (2012), (9) Jackson et al (2012), (10) Kerby et al (2012), (11) Kim et al (2011), (12) Kingue et al (2013), (13) McCant et al (2009), (14) Migneault et al (2012), (15) Pekmezaris et al (2019), (16) Rosen et al (2017), (17) Bobrow et al (2016), (18) Chandler et al (2019), (19) Dang et al (2017), (20) Davidson et al (2015), (21) Hacking et al (2016), (22) Kamal et al (2015), (23) Lewinski et al (2019), (24) Nundy et al (2013), (25) Patel et al (2013), (26) Piette et al (2012), (27) Piette et al (2013), (28) Piette et al (2014), (29) Saleh et al (2018), (30) Skolarus et al (2018), (31) Anchala et al (2013), (32) Evans et al (2017), (33) Shelley et al (2011), (34) Bettano et al (2019), (35) Schoenthaler et al (2020), (36) Manard et al (2016), (37) Price-Haywood et al (2018), (38) Green et al (2011).