Published on in Vol 16 (2024)

Preprints (earlier versions) of this paper are available at, first published .
Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review

Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review

Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review


1College of Medicine, University of Cincinnati, Cincinnati, OH, United States

2School of Medicine, Case Western Reserve University, Cleveland, OH, United States

3University of Cincinnati Libraries Research and Data Services, University of Cincinnati, Cincinnati, OH, United States

4Department of Human Development and Family Studies, The University of Illinois at Urbana-Champaign, Urbana, IL, United States

5The Family Resiliency Center, College of Agricultural, Consumer and Environmental Sciences, The University of Illinois at Urbana-Champaign, Urbana, IL, United States

6Department of Biomedical and Translational Sciences, The University of Illinois at Urbana-Champaign, Urbana, IL, United States

7School of Criminal Justice, University of Cincinnati, Cincinnati, OH, United States

Corresponding Author:

Danny T Y Wu, MS, PhD

College of Medicine

University of Cincinnati

3230 Eden Ave

Cincinnati, OH, 45267

United States

Phone: 1 5623916509


Background: Health literacy (HL) is the ability to make informed decisions using health information. As health data and information availability increase due to online clinic notes and patient portals, it is important to understand how HL relates to social determinants of health (SDoH) and the place of informatics in mitigating disparities.

Objective: This systematic literature review aims to examine the role of HL in interactions with SDoH and to identify feasible HL-based interventions that address low patient understanding of health information to improve clinic note-sharing efficacy.

Methods: The review examined 2 databases, Scopus and PubMed, for English-language articles relating to HL and SDoH. We conducted a quantitative analysis of study characteristics and qualitative synthesis to determine the roles of HL and interventions.

Results: The results (n=43) were analyzed quantitatively and qualitatively for study characteristics, the role of HL, and interventions. Most articles (n=23) noted that HL was a result of SDoH, but other articles noted that it could also be a mediator for SdoH (n=6) or a modifiable SdoH (n=14) itself.

Conclusions: The multivariable nature of HL indicates that it could form the basis for many interventions to combat low patient understandability, including 4 interventions using informatics-based solutions. HL is a crucial, multidimensional skill in supporting patient understanding of health materials. Designing interventions aimed at improving HL or addressing poor HL in patients can help increase comprehension of health information, including the information contained in clinic notes shared with patients.

Online J Public Health Inform 2024;16:e50898




In recent decades, medical providers, health systems, and legislators have prioritized increasing patient access to health information. For example, the 21st Century Cures Act mandates that patients must have access to their electronic health records, including clinic notes, in a rapid and convenient manner [1]. However, clinic notes and other health information can contain jargon that is difficult for patients to comprehend, reducing the utility of health information sharing. The Healthy People 2030 initiative, sponsored by the US Department of Health and Human Services, aims to address this issue by increasing patient comprehension of health information received from providers and web-based sources, such as their electronic health records [2].

A key part of health information comprehension is health literacy (HL), the ability to understand, contextualize, and make well-informed decisions based on health information [3]. Reducing HL gaps is crucial to meeting the goals set forth by Healthy People 2030 and maximizing the benefits of the 21st Century Cures Act.

Health Literacy

Having high HL correlates with greater shared decision-making between patients and physicians and promotes positive health outcomes because patients can better comprehend and act on the health information they receive [4]. Healthy People 2030 distinguishes between two dimensions of HL: personal, as previously described, and organizational [2]. Organizational HL holds health care systems and providers accountable for providing their patients with comprehensible health information to make informed decisions. This newer understanding of HL raises questions about how HL fits into the public health framework addressing disparities in health comprehension.

Social Determinants of Health and Health Literacy

Social determinants of health (SDoH) are nonmedical social and economic factors that fall into the following 5 domains: economic stability, education, health care and access quality, neighborhood and built environment, as well as social and community context [5,6]. SDoH affects health status and outcomes, and it can generate health disparities between population groups by influencing patient behavior and organizational responses. These determinants are also distinct from social factors or needs that exist at the individual level and instead exist as community- or population-level barriers [7-9].

HL has been categorized in different sources as an SDoH itself and as a midstream consequence of SDoH that can impede or improve patient interactions with health care institutions and health outcomes (ie, vaccination status and screening utilization) [10-12]. For example, a study by Schillinger et al [13] proposes that a higher education level improves HL, which was associated with better glycemic control among patients with diabetes. This is a unidirectional characterization of the relationship between SDoH, HL, and health outcomes, depicted in Figure 1.

Figure 1. Relationships characterized by the influence of social determinants of health on health literacy (personal and organizational), subsequently impacting health outcomes.

However, this may be an oversimplification. HL can evolve through continued exposure to health environments and interventions at the personal and organizational levels [14]. Moreover, even patients with high HL can struggle with comprehension in different contexts. Therefore, this relationship warrants further investigation, as there is a lack of systematic literature analyses that macroscopically evaluate how SDoH and HL are related across different SDoH domains [15]. Understanding the nature and role of HL in interactions with SDoH can also indicate the most effective approach to designing HL-targeting interventions for patients who struggle to understand health information.


Due to the literature gap in examining the complex relationship between HL and SDoH, we aimed to conduct a systematic literature review to (1) understand this relationship and (2) recommend informatics-based interventions to address low HL among patients.

Search Strategy

We systematically reviewed literature in PubMed and Scopus, two major biomedical and social science literature repositories. The initial database searches were conducted on June 22, 2020. The review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2009 guidelines to understand the relationship between HL and SDoH [16,17].

The search terms used were “health literacy” AND “social determinants of health.” After filtering for non-English articles and articles without abstracts, the remaining 281 articles were compiled in a Microsoft Excel sheet with their title, author, publication year, DOI or PMID, and abstract.

Screening Process

Two researchers (SB and CX) independently screened 281 papers by title and abstract and used the following exclusion criteria: (1) HL is a minor factor in the article; (2) the article is not an empirical study; (3) the article focuses on HL measurement tool development or evaluation; (4) the paper does not examine HL in relation to SDoH; (5) no abstract is available; and (6) the paper is not written in English.

Each researcher independently gave the article a score of “1” for inclusion or “0” for exclusion. The scores were summed; articles scoring “2” were automatically included, and those scoring “0” were excluded from the full article eligibility review. Disagreements (any papers with a total score of “1”) were resolved by the authors after the initial screening. The process was repeated for the full-article eligibility review and subsequent reference screening from the included full articles. Reference screening was a precautionary step to ensure the inclusion of articles that may not have been included in the initial database search. Original exclusion criteria were consistently used.

Quality Assessment

Before the information extraction, all included articles were assessed by 2 researchers (SB and TG) for study quality. Using the Agency for Healthcare Research and Quality (AHRQ) guidelines, separate quality assessments were developed for each type of study included in the review—observational studies and randomized clinical trials (RCTs) [18]. Domains included in both study types were study questions, population, interventions, outcome measurement, statistical methods, results, discussion, and disclosure of funding or sponsorship. Domains evaluated in the RCT assessment also included blinding and randomization.

The reviewers created a 3-point scoring system for the quality assessment. Articles were rated by 2 team members (SB and TG) with scores of “good,” “fair,” and “poor” for each domain and assigned numerical values of 2, 1, and 0, respectively, as per the AHRQ guidelines [18]. Values were averaged and translated back to a rating of “good” (1.50 or higher), “fair” (1-1.49), and “poor” (0-0.99).

Information Extraction

Based on quality assessment results, 43 papers were included for information extraction. Four researchers (CB, CX, AN, and TV) extracted data for the following PRISMA-based criteria: title, author, article ID, year published, location, study design, sample demographics, results, and limitations [16]. To answer the research questions, information specific to SDoH focus, HL measurement, and health outcomes was collected. The information extraction sheet is attached as Multimedia Appendix 1.

Quantitative and Qualitative Data Analysis

Extracted data were analyzed both quantitatively and qualitatively. Location, year of publication, and study design were statistically summarized. AHRQ guidelines were used to categorize studies as RCT, cross-sectional, and qualitative designs, with the last two being types of observational studies [19].

Qualitative analysis was conducted in 2 steps. First, a narrative synthesis of the chosen articles summarized the relationships between SDoH and HL. Narrative synthesis involves analyzing the data from systematic reviews to create textual explanations of observed patterns or trends rather than relying solely on statistical data. This involves developing textual descriptions of the data by extracting key information pertinent to the research question (ie, methods used or results) and exploring commonalities and differences between and within studies (ie, through visually mapping relationships) [20]. These methods were also used in a systematic review previously published by the authors [21]. The included articles were classified by SDoH domains they addressed, per the 5 domains defined by Healthy People 2030: economic stability, education, health care access and quality, neighborhood and built environment, as well as social and community context. Then, information extraction data from article results and discussion sections were used to define roles for HL. Finally, a theme visualization was conducted that plotted HL roles against publication year to understand how HL perception has evolved.

In the second step, lessons learned were summarized regarding HL roles, again using the results and discussion sections. From these same sections, the authors then extrapolated possible interventions that use HL to improve patient comprehension of health information.

Literature Search Results

The PubMed and Scopus searches yielded 389 articles, resulting in 281 unique articles (Figure 2). After screening titles and abstracts, 43 articles remained for full-text eligibility assessment. Not discussing HL and SDoH together (n=95) was the largest cause for exclusion. Other papers were excluded because HL was not a substantial focus of the paper (n=62). A total of 19 articles were excluded from the full-text eligibility, once again for a minor focus on HL. References of the remaining 24 articles were screened for inclusion, yielding 20 additional articles. After the quality assessment, 1 low-quality article was excluded. Information extraction and narrative synthesis were conducted on a final sample of 43 articles.

Figure 2. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) diagram of articles from the PubMed and Scopus search. A total of 24 articles (underlined) were included from the first database search.

Quantitative Analysis

The final 43 articles were analyzed for study location, year of publication, role of HL, and study design (Table 1). A total of 14 (32.6%) studies took place in North America (12 in the United States and 18 in Europe). All articles from South America originated in Brazil (n=4). Publication year trends revealed an increased focus and discussion of the topic in recent years, with 90.7% (n=39) of the articles being published after 2010. Most articles (n=40, 93%) had a cross-sectional design and used surveys, while 4.7% (n=2) used a qualitative design with semistructured interviews and focus questions to assess SDoH and HL.

Table 1. Summary of articles included in the literature review (N=43).
CategoryValues, n (%)
Study location

Europe18 (41.9)

North America14 (32.6)

Asia6 (14)

South America4 (9.3)

Australia1 (2.3)
Year published

2006-2009a4 (9.3)

2010-20139 (20.9)

2014-201715 (34.9)

2018-202115 (34.9)
Study design

Cross-sectional40 (93)

Qualitative2 (4.7)

Randomized controlled trial1 (2.3)

aThe search was not limited to 2006 for publication year; this was the earliest date among the 43 articles.

Qualitative Analysis

Narrative Synthesis

The narrative synthesis generated 4 roles for HL in relation to SDoH (Table 2). Most of the articles discussed multiple SDoH domains, but all 43 articles discussed education access and quality [5].

The most common categorization of the HL role was as a “result of SDoH” (n=23), followed by “modifiable SDoH” (n=14), and finally, as a “mediator of SDoH” (n=6). HL can be a “result of SDoH” (n=23), which suggests that SDoH domains contribute to HL levels and that it is a downstream variable [22-44]. As mentioned, 14 studies identified HL as a “modifiable SDoH,” where they identified HL as an SDoH, often citing the World Health Organization’s categorization of it; these studies suggested that HL can be improved through interventions and is actionable at multiple levels [14,45-57]. Finally, the articles that categorized HL as a “mediator of SDoH” (n=6) discussed how HL is an intermediary between other SDoH domains, such as educational attainment or economic stability, and that high HL levels can compensate for lower domain levels that compromise positive health outcomes [58-63]. Occasionally, the same paper would suggest multiple roles for HL (eg, an article’s Results and Discussion sections would inform both modifiable and mediatory roles for HL), but the most prominent relationship that appeared was used to categorize each article.

These 3 roles were plotted against the years of publication in Figure 3. In the few HL-focused articles published before 2010, HL was recognized as having a variety of roles, but only 1 article identified it as a modifiable SDoH. In the next 5-year period, being a result of SDoH was the most common role assigned to HL. In 2013, a total of 5 out of 7 articles identified HL as being a result of SDoH. As the number of published HL-focused articles increased in subsequent years, being a result of SDoH remained the most consistent and most prominent role assigned to HL to appear across all articles. Nevertheless, there has been increasing recognition of HL as a modifiable SDoH in the years 2015, 2018, and 2020, further cementing HL’s multidimensional nature.

Table 2. Summary of narrative synthesis themes.
CategoryValues, n (%)
SDoHa domainb
Education access and quality43 (100)
Economic stability38 (88)
Health care and access quality11 (26)
Social and community context11 (26)
Neighborhood and built environment9 (21)
HLc role
Result of SDoH23 (53)
Modifiable SDoH14 (33)
Mediator of SDoH6 (14)

aSDoH: social determinant of health.

bMost of the articles included more than 1 SDoH domain they studied.

cHL: health literacy.

Figure 3. Theme visualization of the evolution of health literacy roles over time. SDoH: social determinants of health.
Lessons Learned

In addition to analyzing articles for the HL role, each article was further examined to determine details about the nature of the relationship between HL and SDoH. These were titled “lessons learned.” Figure 4 [14,22-63] shows an idea map that organizes articles by the role of HL and lessons learned. Although most of the articles are cross-sectional and do not always draw a causal relationship between Hl and SDoH, the authors of the articles nevertheless offer hypotheses on factors influencing HL or how it interacts with SDoH and health outcomes.

Figure 4. Idea map of health literacy (HL) roles, lessons learned, and article breakdown. Each role (white oval) is broken down into lessons learned (blue oval) and then into the article title and author (white rectangle) and possible intervention focus (yellow oval). SDoH: social determinants of health [14,22-63].
HL as a Result of SDoH (n=23)

Being a result of SDoH was the most frequent role identified for HL. These articles characterized HL as being associated with, influenced by, or resulting from other SDoH. All articles addressed that a higher level of education, such as high school graduation, had implications on HL levels [22-44]. Bazaz et al [22], Berens et al [24], and Rocha et al [39] have suggested that HL is developed through interactions with health care due to age and disease condition, and more interaction with health care over time leads to an improvement in HL. Hou et al [31], Jovic-Vranes et al [32], Kamberi et al [34], and Todorovic et al [41] also note that lived environments have an important role in HL development. Kamberi et al [34] argue that rural versus urban environments influence SDoH, such as health care access and quality, thereby, impacting HL development [34]. Beauchamp et al [23], Berens et al [24], Cudjoe et al [26], and Sentell et al [40] observed that HL is also influenced by the patient’s primary language, especially if the patient’s primary language is different from the language of the health system. Bo et al [25] and Pop et al [37] elaborated on the relationship between education and HL; they found that lower levels of language proficiency and self-perceived health can indicate lower HL. Heizomi et al [30] and Dashti et al [27] notice gender disparities in HL among students in Iran, with the latter observing that cultural differences encouraged technology access for men at a younger age, leading to higher HL levels among men compared to women [27,30].

HL as a Modifiable SDoH (n=14)

The 14 articles that classified HL as an SDoH did so following the World Health Organization’s classification and previous research or by defining determinants as factors that impact or predict health outcomes. Aaby et al [45] classify HL as an SDoH because it is a combination of “personal competencies and situational resources” that affects individuals’ interaction with health care institutions. Some authors, despite describing HL as an SDoH, still note that it is related to other SDoH as well. Cheuhuen et al [47] identify that HL is associated with economic stability and education, and Lee et al [55] and Sentell et al [46] both associate it with the social context. Articles also identified various health outcomes that HL may impact. Cabellos-Garcia et al [48] and Zhou et al [57] identified that poor HL could lead to reduced understanding of disease conditions and engagement with providers. Nevertheless, all 14 articles emphasize that HL is a modifiable SDoH that can change over time through interventions [14,45-57].

HL as a Mediator of SDoH (n=6)

A total of 6 articles established that, as a mediatory variable, HL can both compensate for and contribute to disparities in SDoH. Some articles define HL as an SDoH itself but further classify it as a mediator for other determinants. All 6 articles included education and income as SDoH for which HL could serve as compensation [58-63]. Bennett et al [59] also suggest that having high HL can compensate for racial or ethnic disparities in health outcomes. Zanchetta et al [63] describe that HL mediates between disparities in health care access and quality as well as social cohesion and context. To address poor HL among patients, van der Heide et al [62] recommend simplifying medical jargon. When designing these interventions, Bennett et al [59] emphasize considering complex patient perspectives and unique demographic needs, such as those of the geriatric population, which differ from those of younger patients.

Informatics Interventions

The secondary objective of the study was to identify informatics-based interventions to improve HL. Articles rarely provided specific intervention recommendations but instead listed several potential problems, such as complicated medical jargon or low health awareness, that complicate patient understanding of health information. Therefore, 4 informatics-based solutions were proposed based on the research team’s knowledge and experience for the identified problems, as follows: (1) language or text simplification, (2) population-focused (or policy-based) interventions, (3) health education efforts, and (4) patient identification. Since the included articles were largely not interventional in nature, the following sections extrapolate on the recommendations with references to ongoing studies that have implemented these strategies.

Principal Findings

This systematic review included 43 papers and reported the results following the PRISMA guidelines. Most studies were conducted in Europe in the past 5 to 10 years. The studies examined HL in relation to the two themes of SDoH—health-focused and demographic—and generated 3 roles for HL, as follows: a mediator of SDoH, a result of SDoH, and modifiable SDoH. More than half of the studies had a cross-sectional design. However, HL is a complex, actionable variable that may be targeted by various strategies.

Proposed Interventions

As clinical note sharing becomes more popular, generating interventions that address low HL becomes even more crucial. In this vein, we generated 4 recommendations for focused HL interventions based on the key findings of this systematic review.

Interventions with an informatics focus could play a particularly vital role in improving patient comprehension of health information as the health care field becomes increasingly mobile and technology dependent. It is important to consider experimental methods to measure the efficacy of implementing these strategies. Including control groups and validated HL measuring tools can help monitor how different interventions influence patient HL levels. Validated measuring tools include the Rapid Estimate of Adult Literacy in Medicine (REALM), REALM-Short Form, Short Assessment of Health Literacy-Spanish and English (SAHL-S&E), Brief Health Literacy Screen (BHLS), and Test of Functional Health Literacy in Adults (TOFHLA) [64-66]. The REALM, REALM-SF, and SAHL-S&E have all been validated and recommended by the AHRQ. The REALM and SAHL-S&E are recommended for research purposes to assess participant HL, while the REALM-SF, BHLS, and TOFHLA have been validated for use in screenings in clinical settings [66,67]. The REALM-SF is particularly designed to identify limited literacy levels [67]. Therefore, the clinically usable metrics may be more relevant for interventions that take place in health care settings, such as patient identification.

Language and Text Simplification

Text simplification addresses the tendency of clinic notes and health information in general to include medical jargon that exceeds the comprehension levels of most patients [42,44,62]. Even patients with highly educated backgrounds have shown low scores on HL surveys. Therefore, text simplification can benefit patients across all HL competencies by reducing jargon and making health information more easily understandable and usable [62]. Text simplification does not replace the existing clinic note shared between providers; it provides a simplified version for patients in addition to the original note. Current research indicates that the most effective manner of text simplification relies on manual editing techniques using human oversight of a text simplification process, combined with information visualization [68]. Although simplification improves patient comprehension, manual editing could strain health care professionals’ workload. Therefore, developing informatics interventions that automate text simplification while retaining the grammatical and logical integrity of the clinical text is important. Current automated simplification methods scored poorly due to grammatical errors, repetition, and inconsistencies in the autogenerated documents [68]. Artificial intelligence–derived text simplification methods may overcome these barriers by matching a document’s reading level to the readers’ needs, as shown in a study where ChatGPT was able to modify answers to men’s health condition questions to accommodate lower reading levels [69,70]. However, popularly used AI tools, such as ChatGPT, need considerable evaluation to minimize inaccurate information delivery and improve comprehensibility. Current studies indicate that these tools lack citations for the information they provide and cannot differentiate between low-quality and high-quality information [70,71].

Population-Based Visualization and Cross-Cultural Communications

HL needs are different across populations and cultural contexts, and interventions should account for these differences. For example, non–English-speaking individuals are overlooked in many HL studies, and interventions targeting English speakers will not always suit those with a limited or nonnative grasp of English [23,72]. Realizing this limitation, the OPHELIA (OPtimising HEalth LIterAcy) [73] project is a multisite study that assesses HL strengths and weaknesses in their patient population at each study site and uses these responses to determine appropriate intervention methods. Equally important is including representatives from the community in intervention design. A systematic review looking at interventions that address HL among Aboriginal and Torres Strait Islander community members noted that many failed to include these patients in the design process and consequently had limited participant retention [74]. Another facet is implementing policy-level changes that increase access to HL support. This is particularly relevant for patients who face health inequity. However, implementing these changes has been slow. In the European Union, challenges such as funding constraints and obstacles to initiatives have prevented effective execution beyond a few countries [75]. The population-based and policy-level interventions should consider visual analytics to explore meaningful patterns in a large data set and use recent advances in natural language understanding and translation to promote cross-cultural communication [76].

Patient Identification

Although population-focused and policy-level interventions address low HL at the macro level, such methods may overlook the individual HL needs of a patient. Therefore, screening HL levels as a part of standard practices in health care settings can help identify patients who need additional support at the clinic visit and can expedite provider response [36]. For example, Vanderbilt University Medical Center and the University of Arkansas Medical Sciences incorporate HL screening as part of their educational health assessment and have done so since 2010 and 2016, respectively [77]. Screening may also involve various informatics tools. For example, patients can be actively screened using electronic data capture tools (eg, REDCap) [78]. These informatics tools should be integrated into clinical workflow to ensure the quality of data. On the other hand, patient cohorts can be identified by reports or dashboards of electronic health records or medical text search engines (eg, Electronic Medical Record Search Engine [EMERSE]) [79]. Once the patient group is targeted, inclusive HL interventions can be designed and executed. However, implementing screening practices should be done with caution to avoid perpetuating stigma or embarrassment. Integrating screening questions within the clinical workflow and training health professionals on screening administration can help address these concerns [77].

Health Education and Online Community Building

Given the relevance of socialization and environment on HL development, it is important to consider interventions that cultivate HL through health education. Health care providers, such as nurses and community health workers, have important roles in providing education and reinforcing patient understanding of their health conditions [63,80]. However, the burden on health education cannot be placed on providers alone. Health education programs implemented by health care organizations and community health centers can actively and effectively improve HL [81]. It is important to adapt these programs for cultural and demographic sensitivity and patient-provider communications. For example, a recent study targeting older adult needs emphasized the need to include the patient’s caregivers and to accommodate barriers in comprehension, especially cognitive ones [82]. Health education intervention should consider developing an online community, such as ImproveCareNow, to promote collaborative care and build repositories of patient education materials with well-designed education programs to help patients improve their HL [83]. Including the input of individuals who are well-integrated into and familiar with the needs of a patient population, such as community health workers, can also be helpful in this process [80].


There are a few limitations in the methodology and generalizability of our research. First, we conducted a database search of only PubMed and Scopus, limiting the scope of the article search. However, PubMed and Scopus are two of the most popular and largest databases in biomedical and social science research. During the analysis, it was clear that the results were concise and supported one another. For example, several articles noted multiple roles for HL but tended to focus on one. Second, very few articles included noncorrelated results because of their cross-sectional designs. This prevented researchers from drawing a causative relationship between HL and SDoH, but they nevertheless had hypotheses for relationships that informed our classification. Third, the PRISMA guidelines were updated in 2020 with new standards and recommendations for systematic reviews. As we had already made considerable progress in this project before the revision was published in 2021, we completed the data analysis using the 2015 reporting standards that originally informed our methods. However, in cross-referencing our methods with the 2020 revisions, our research largely adheres to the new guidelines [84].


The articles included in this literature review indicate that HL can adopt various roles in conjunction with SDoH. This flexibility makes HL an appropriate topic for intervention to accommodate poor health outcomes and improve patient autonomy. However, the complex nature of HL means that it warrants further research to understand how HL-targeted interventions impact this process.


The authors would like to acknowledge and thank Ms Elyse Ku at the corresponding author’s lab for her contribution in collecting and reviewing data for the literature review and Ms Somya Pandey for her copyediting effort.

This research was funded by the following intramural programs and awards at the University of Cincinnati: Office of Research Art Humanities Social Science Research Achievement Award, the University Honors Program’s (UHP) Discover program, and the 2021 Summer Undergraduate Research Fellowship Medical Exportation Program (SURF-MedEx) supported by the Medical Sciences Baccalaureate Program (MSBP).

Conflicts of Interest

None declared.

Multimedia Appendix 1

Literature search and information extraction.

XLSX File (Microsoft Excel File), 61 KB

Multimedia Appendix 2

PRISMA Checklist.

PDF File (Adobe PDF File), 109 KB

  1. Blease C, Walker J, DesRoches C, Delbanco T. New U.S. law mandates access to clinical notes: implications for patients and clinicians. Ann Intern Med. Jan 2021;174(1):101-102. [FREE Full text] [CrossRef]
  2. Health literacy in healthy people 2030. US Department of Health and Human Services. URL: https:/​/health.​gov/​our-work/​national-health-initiatives/​healthy-people/​healthy-people-2030/​health-literacy-healthy-people-2030 [accessed 2024-02-07]
  3. Sørensen K, Van den Broucke S, Fullam J, Doyle G, Pelikan J, Slonska Z, et al. Health literacy and public health: a systematic review and integration of definitions and models. BMC Public Health. Jan 25, 2012;12(1):80. [FREE Full text] [CrossRef] [Medline]
  4. Chang H, Li F, Lin C. Factors influencing implementation of shared medical decision making in patients with cancer. PPA. Nov 2019;13:1995-2005. [CrossRef]
  5. Social determinants of health. US Department of Health and Human Services. URL: [accessed 2024-02-07]
  6. Social determinants of health. World Health Organization. URL: [accessed 2024-02-07]
  7. Castrucci B, Auerbach J. Meeting individual social needs falls short of addressing social determinants of health. Health Affairs Blog. Jan 17, 2019. URL: [accessed 2024-01-16]
  8. Hacker K, Houry D. Social needs and social determinants: the role of the centers for disease control and prevention and public health. Public Health Rep. Sep 09, 2022;137(6):1049-1052. [FREE Full text] [CrossRef] [Medline]
  9. When talking about social determinants, precision matters. HealthAffairs. 2019. URL: [accessed 2024-02-07]
  10. Meyers AG, Salanitro A, Wallston KA, Cawthon C, Vasilevskis EE, Goggins KM, et al. Determinants of health after hospital discharge: rationale and design of the Vanderbilt Inpatient Cohort Study (VICS). BMC Health Serv Res. Jan 08, 2014;14(1):10. [FREE Full text] [CrossRef] [Medline]
  11. Social determinants of health literature summaries. US Department of Health and Human Services. URL: [accessed 2024-02-07]
  12. Schillinger D. The intersections between social determinants of health, health literacy, and health disparities. Stud Health Technol Inform. Jun 25, 2020;269:22-41. [FREE Full text] [CrossRef] [Medline]
  13. Schillinger D, Barton LR, Karter AJ, Wang F, Adler N. Does literacy mediate the relationship between education and health outcomes? A study of a low-income population with diabetes. Public Health Rep. Aug 02, 2006;121(3):245-254. [FREE Full text] [CrossRef] [Medline]
  14. Rowlands G, Shaw A, Jaswal S, Smith S, Harpham T. Health literacy and the social determinants of health: a qualitative model from adult learners. Health Promot Int. Feb 01, 2017;32(1):130-138. [CrossRef] [Medline]
  15. Stormacq C, Van den Broucke S, Wosinski J. Does health literacy mediate the relationship between socioeconomic status and health disparities? Integrative review. Health Promot Int. Oct 01, 2019;34(5):e1-e17. [CrossRef] [Medline]
  16. Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. Jan 01, 2015;4(1):1. [FREE Full text] [CrossRef] [Medline]
  17. Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med. Aug 18, 2009;151(4):264-9, W64. [FREE Full text] [CrossRef] [Medline]
  18. West S, King V, Carey T, Lohr K, McKoy N, Sutton S, et al. Systems to rate the strength of scientific evidence. Evid Rep Technol Assess (Summ). Mar 2002.(47):1-11. [Medline]
  19. Carey T, Sanders G, Viswanathan M, Trikalinos T, Kato E, Chang S. Framework for considering study designs for future research needs. In: AHRQ Methods for Effective Health Care. Rockville, FL. Agency for Healthcare Research and Quality (US); 2012.
  20. Popay J, Roberts H, Sowden A, Petticrew M, Arai L, Rodgers M. Guidance on the conduct of narrative synthesis in systematic reviews: a product from the ESRC Methods Programme. In: ESRC Methods Programme. Lancaster, UK. Lancaster University; 2006.
  21. Wu DTY, Xu C, Kim A, Bindhu S, Mah KE, Eckman MH. A scoping review of health information technology in clinician burnout. Appl Clin Inform. May 07, 2021;12(3):597-620. [FREE Full text] [CrossRef] [Medline]
  22. Bazaz M, Shahry P, Latifi SM, Araban M. Cervical cancer literacy in women of reproductive age and its related factors. J Cancer Educ. Feb 10, 2019;34(1):82-89. [CrossRef] [Medline]
  23. Beauchamp A, Buchbinder R, Dodson S, Batterham RW, Elsworth GR, McPhee C, et al. Distribution of health literacy strengths and weaknesses across socio-demographic groups: a cross-sectional survey using the Health Literacy Questionnaire (HLQ). BMC Public Health. Jul 21, 2015;15(1):678. [FREE Full text] [CrossRef] [Medline]
  24. Berens E, Vogt D, Messer M, Hurrelmann K, Schaeffer D. Health literacy among different age groups in Germany: results of a cross-sectional survey. BMC Public Health. Nov 09, 2016;16(1):1151. [FREE Full text] [CrossRef] [Medline]
  25. Bo A, Friis K, Osborne RH, Maindal HT. National indicators of health literacy: ability to understand health information and to engage actively with healthcare providers - a population-based survey among Danish adults. BMC Public Health. Oct 22, 2014;14(1):1095. [FREE Full text] [CrossRef] [Medline]
  26. Cudjoe J, Budhathoki C, Roter D, Gallo JJ, Sharps P, Han H. Exploring health literacy and the correlates of pap testing among African immigrant women: findings from the AfroPap study. J Cancer Educ. Jun 14, 2021;36(3):441-451. [FREE Full text] [CrossRef] [Medline]
  27. Dashti S, Peyman N, Tajfard M, Esmaeeli H. E-Health literacy of medical and health sciences university students in Mashhad, Iran in 2016: a pilot study. Electron Physician. Mar 25, 2017;9(3):3966-3973. [FREE Full text] [CrossRef] [Medline]
  28. Friis K, Lasgaard M, Osborne RH, Maindal HT. Gaps in understanding health and engagement with healthcare providers across common long-term conditions: a population survey of health literacy in 29 473 Danish citizens. BMJ Open. Jan 14, 2016;6(1):e009627. [FREE Full text] [CrossRef] [Medline]
  29. Haerian A, Moghaddam MHB, Ehrampoush MH, Bazm S, Bahsoun MH. Health literacy among adults in Yazd, Iran. J Educ Health Promot. 2015;4:91. [FREE Full text] [CrossRef] [Medline]
  30. Heizomi H, Iraji Z, Vaezi R, Bhalla D, Morisky DE, Nadrian H. Gender differences in the associations between health literacy and medication adherence in hypertension: a population-based survey in Heris County, Iran. VHRM. Apr 2020;Volume 16:157-166. [CrossRef]
  31. Hou W, Huang Y, Lee Y, Chen C, Lin G, Hsieh C. Validation of the integrated model of health literacy in patients with breast cancer. Cancer Nurs. 2018;41(6):498-505. [CrossRef]
  32. Jovic-Vranes A, Bjegovic-Mikanovic V, Marinkovic J. Functional health literacy among primary health-care patients: data from the Belgrade pilot study. J Public Health (Oxf). Dec 19, 2009;31(4):490-495. [CrossRef] [Medline]
  33. Jovic-Vranes A, Bjegovic-Mikanovic V, Marinkovic J, Kocev N. Health literacy in a population of primary health-care patients in Belgrade, Serbia. Int J Public Health. Apr 2011;56(2):201-207. [CrossRef] [Medline]
  34. Kamberi H, Hysa B, Toçi E, Jerliu N, Qirjako G, Burazeri G. Functional health literacy among primary health care users in transitional Kosovo. Med Arch. 2013;67(3):209-211. [CrossRef] [Medline]
  35. Marques S, Escarce A, Lemos S. Letramento em saúde e autopercepção de saúde em adultos usuários da atenção primária. CoDAS. 2018. URL: [accessed 2024-02-07]
  36. Megwalu UC, Lee JY. Health literacy assessment in an otolaryngology clinic population. Otolaryngol Head Neck Surg. Dec 03, 2016;155(6):969-973. [CrossRef] [Medline]
  37. Pop OM, Brînzaniuc A, Sirlincan EO, Baba CO, Chereches RM. Assessing health literacy in rural settings: a pilot study in rural areas of Cluj County, Romania. Glob Health Promot. Dec 04, 2013;20(4):35-43. [CrossRef] [Medline]
  38. Rikard RV, Thompson MS, McKinney J, Beauchamp A. Examining health literacy disparities in the United States: a third look at the National Assessment of Adult Literacy (NAAL). BMC Public Health. Sep 13, 2016;16(1):975. [FREE Full text] [CrossRef] [Medline]
  39. Rocha P, Rocha D, Lemos S. Letramento funcional em saúde na adolescência: associação com determinantes sociais e percepção de contextos de violência. CoDAS. 2017. URL:​S2317-17822017000400307&lng=pt&tlng=pt [accessed 2024-02-07]
  40. Sentell T, Braun KL. Low health literacy, limited English proficiency, and health status in Asians, Latinos, and other racial/ethnic groups in California. J Health Commun. Oct 2012;17 Suppl 3(Suppl 3):82-99. [FREE Full text] [CrossRef] [Medline]
  41. Todorovic N, Jovic-Vranes A, Djikanovic B, Pilipovic-Broceta N, Vasiljevic N, Lucic-Samardzija V, et al. Assessment of health literacy in the adult population registered to family medicine physicians in the Republic of Srpska, Bosnia and Herzegovina. Eur J Gen Pract. Jan 22, 2019;25(1):32-38. [FREE Full text] [CrossRef] [Medline]
  42. van der Heide I, Rademakers J, Schipper M, Droomers M, Sørensen K, Uiters E. Health literacy of Dutch adults: a cross sectional survey. BMC Public Health. Feb 27, 2013;13(1):179. [FREE Full text] [CrossRef] [Medline]
  43. Vilella KD, Alves SGA, de Souza JF, Fraiz FC, Assunção L. The association of oral health literacy and oral health knowledge with social determinants in pregnant Brazilian women. J Community Health. Oct 24, 2016;41(5):1027-1032. [CrossRef] [Medline]
  44. Rowlands G, Protheroe J, Winkley J, Richardson M, Seed PT, Rudd R. A mismatch between population health literacy and the complexity of health information: an observational study. Br J Gen Pract. May 25, 2015;65(635):e379-e386. [CrossRef]
  45. Aaby A, Beauchamp A, O'Hara J, Maindal H. Large diversity in Danish health literacy profiles: perspectives for care of long-term illness and multimorbidity. Eur J Public Health. Feb 01, 2019;30(1):75-80. [CrossRef] [Medline]
  46. Sentell T, Baker KK, Onaka A, Braun K. Low health literacy and poor health status in Asian Americans and Pacific Islanders in Hawai'i. J Health Commun. Sep 30, 2011;16 Suppl 3(sup3):279-294. [CrossRef] [Medline]
  47. Chehuen Neto JA, Costa LA, Estevanin GM, Bignoto TC, Vieira CIR, Pinto FAR, et al. Functional Health Literacy in chronic cardiovascular patients. Cien Saude Colet. Mar 2019;24(3):1121-1132. [FREE Full text] [CrossRef] [Medline]
  48. Cabellos-García AC, Castro-Sánchez E, Martínez-Sabater A, Díaz-Herrera MÁ, Ocaña-Ortiz A, Juárez-Vela R, et al. Relationship between determinants of health, equity, and dimensions of health literacy in patients with cardiovascular disease. Int J Environ Res Public Health. Mar 20, 2020;17(6):2082. [FREE Full text] [CrossRef] [Medline]
  49. Sudore RL, Mehta KM, Simonsick EM, Harris TB, Newman AB, Satterfield S, et al. Limited literacy in older people and disparities in health and healthcare access. J Am Geriatr Soc. May 02, 2006;54(5):770-776. [CrossRef] [Medline]
  50. Davis SN, Wischhusen JW, Sutton SK, Christy SM, Chavarria EA, Sutter ME, et al. Demographic and psychosocial factors associated with limited health literacy in a community-based sample of older Black Americans. Patient Educ Couns. Feb 2020;103(2):385-391. [CrossRef]
  51. Divaris K, Lee JY, Baker AD, Vann WF. The relationship of oral health literacy with oral health-related quality of life in a multi-racial sample of low-income female caregivers. Health Qual Life Outcomes. Dec 1, 2011;9(1):1-9. [CrossRef]
  52. Fleary S, Ettienne R. Social disparities in health literacy in the United States. Health Lit Res Pract. Jan 2019;3(1):e47-e52. [FREE Full text] [CrossRef] [Medline]
  53. Fretian A, Bollweg TM, Okan O, Pinheiro P, Bauer U. Exploring associated factors of subjective health literacy in school-aged children. Int J Environ Res Public Health. Mar 06, 2020;17(5):1720. [FREE Full text] [CrossRef] [Medline]
  54. Kobayashi LC, Wardle J, Wolf MS, von Wagner C. Cognitive function and health literacy decline in a cohort of aging English adults. J Gen Intern Med. Jul 14, 2015;30(7):958-964. [FREE Full text] [CrossRef] [Medline]
  55. Lee HY, Rhee TG, Kim NK, Ahluwalia JS. Health literacy as a social determinant of health in Asian American immigrants: findings from a population-based survey in California. J Gen Intern Med. Aug 26, 2015;30(8):1118-1124. [FREE Full text] [CrossRef] [Medline]
  56. Pelikan JM, Ganahl K, Roethlin F. Health literacy as a determinant, mediator and/or moderator of health: empirical models using the European Health Literacy Survey dataset. Glob Health Promot. Nov 14, 2018;25(4):1757975918788300-1757975918788366. [CrossRef] [Medline]
  57. Zhou AQ, Lee HY, Lee RM. Who has low health literacy and does it matter for depression? Findings from aggregated and disaggregated racial/ethnic groups. Cultur Divers Ethnic Minor Psychol. Jan 2019;25(1):73-81. [CrossRef] [Medline]
  58. von Wagner C, Knight K, Steptoe A, Wardle J. Functional health literacy and health-promoting behaviour in a national sample of British adults. J Epidemiol Community Health. Dec 01, 2007;61(12):1086-1090. [FREE Full text] [CrossRef] [Medline]
  59. Bennett IM, Chen J, Soroui JS, White S. The contribution of health literacy to disparities in self-rated health status and preventive health behaviors in older adults. Ann Fam Med. May 11, 2009;7(3):204-211. [FREE Full text] [CrossRef] [Medline]
  60. Furuya Y, Kondo N, Yamagata Z, Hashimoto H. Health literacy, socioeconomic status and self-rated health in Japan. Health Promot Int. Sep 16, 2015;30(3):505-513. [CrossRef] [Medline]
  61. Oliffe JL, McCreary DR, Black N, Flannigan R, Goldenberg SL. Canadian men's health literacy: a nationally representative study. Health Promot Pract. Nov 19, 2020;21(6):993-1003. [CrossRef] [Medline]
  62. van der Heide I, Wang J, Droomers M, Spreeuwenberg P, Rademakers J, Uiters E. The relationship between health, education, and health literacy: results from the Dutch Adult Literacy and Life Skills Survey. J Health Commun. Dec 04, 2013;18 Suppl 1(Suppl 1):172-184. [FREE Full text] [CrossRef] [Medline]
  63. Zanchetta M, Taher Y, Fredericks S, Waddell J, Fine C, Sales R. Undergraduate nursing students integrating health literacy in clinical settings. Nurse Educ Today. Sep 2013;33(9):1026-1033. [CrossRef] [Medline]
  64. Lee SD, Stucky BD, Lee JY, Rozier RG, Bender DE. Short assessment of health literacy-Spanish and English: a comparable test of health literacy for Spanish and English speakers. Health Serv Res. Aug 08, 2010;45(4):1105-1120. [FREE Full text] [CrossRef] [Medline]
  65. Arozullah A, Yarnold P, Bennett C, Soltysik R, Wolf M, Ferreira R, et al. Development and validation of a short-form, rapid estimate of adult literacy in medicine. Med Care. Nov 2007;45(11):1026-1033. [CrossRef] [Medline]
  66. Sand-Jecklin K, Coyle S. Efficiently assessing patient health literacy: the BHLS instrument. Clin Nurs Res. Dec 30, 2014;23(6):581-600. [CrossRef] [Medline]
  67. Personal health literacy measurement tools. AHRQ. 2022. URL: [accessed 2024-02-08]
  68. He X, Zhang R, Alpert J, Zhou S, Adam T, Raisa A, et al. When text simplification is not enough: could a graph-based visualization facilitate consumers' comprehension of dietary supplement information? JAMIA Open. Jan 2021;4(1):ooab026. [FREE Full text] [CrossRef] [Medline]
  69. Liu T, Xiao X. A framework of AI-based approaches to improving eHealth literacy and combating infodemic. Front Public Health. Nov 30, 2021;9:755808. [FREE Full text] [CrossRef] [Medline]
  70. Shah YB, Ghosh A, Hochberg AR, Rapoport E, Lallas CD, Shah MS, et al. Comparison of ChatGPT and traditional patient education materials for men’s health. Urol Prac. Jan 2024;11(1):87-94. [CrossRef]
  71. Golan R, Ripps S, Reddy R, Loloi J, Bernstein A, Connelly Z, et al. ChatGPT's ability to assess quality and readability of online medical information: evidence from a cross-sectional study. Cureus. Jul 2023;15(7):e42214. [FREE Full text] [CrossRef] [Medline]
  72. Karthik N, Barekatain K, Vu H, Wu DT, Ehrlich JR. A readability comparison of online Spanish and English patient education materials about vision health. Ophthalmic Epidemiol. Apr 08, 2022;29(2):182-188. [CrossRef] [Medline]
  73. Batterham RW, Buchbinder R, Beauchamp A, Dodson S, Elsworth GR, Osborne RH. The OPtimising HEalth LIterAcy (Ophelia) process: study protocol for using health literacy profiling and community engagement to create and implement health reform. BMC Public Health. Jul 07, 2014;14(1):694. [FREE Full text] [CrossRef] [Medline]
  74. Nash S, Arora A. Interventions to improve health literacy among Aboriginal and Torres Strait Islander Peoples: a systematic review. BMC Public Health. Jan 30, 2021;21(1):248. [FREE Full text] [CrossRef] [Medline]
  75. Rademakers J, Hofstede J, van DHI, Devillé W, Heijmans M. A snapshot across Europe: policies, interventions and actions on health literacy improvement in Europe uncovered in the HEALIT4EU study. Europ J Public Health. Oct 2015.:44. [FREE Full text] [CrossRef]
  76. Chishtie JA, Marchand J, Turcotte LA, Bielska IA, Babineau J, Cepoiu-Martin M, et al. Visual analytic tools and techniques in population health and health services research: scoping review. J Med Internet Res. Dec 03, 2020;22(12):e17892. [FREE Full text] [CrossRef] [Medline]
  77. Hadden KB, Kripalani S. Health literacy 2.0: integrating patient health literacy screening with universal precautions. Health Lit Res Pract. Oct 2019;3(4):e280-e285. [FREE Full text] [CrossRef] [Medline]
  78. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. Apr 2009;42(2):377-381. [FREE Full text] [CrossRef] [Medline]
  79. Hanauer DA, Barnholtz-Sloan JS, Beno MF, Del Fiol G, Durbin EB, Gologorskaya O, et al. Electronic Medical Record Search Engine (EMERSE): an information retrieval tool for supporting cancer research. JCO Clinical Cancer Informatics. Nov 2020.(4):454-463. [CrossRef]
  80. Freibott CE, Sprague Martinez LS, Rajabiun S, Drainoni M. Health literacy, health outcomes and community health worker utilization: a cohort study in HIV primary care. BMC Health Serv Res. Oct 17, 2022;22(1):1254. [FREE Full text] [CrossRef] [Medline]
  81. Wang M, Lo Y. Improving patient health literacy in hospitals – a challenge for hospital health education programs. RMHP. Oct 2021;Volume 14:4415-4424. [CrossRef]
  82. Kim MY, Oh S. Nurses' perspectives on health education and health literacy of older patients. Int J Environ Res Public Health. Sep 04, 2020;17(18):6455. [FREE Full text] [CrossRef] [Medline]
  83. Hartley DM, Keck C, Havens M, Margolis PA, Seid M. Measuring engagement in a collaborative learning health system: the case of ImproveCareNow. Learn Health Syst. Apr 06, 2021;5(2):e10225. [FREE Full text] [CrossRef] [Medline]
  84. Page M, Moher D, Bossuyt P, Boutron I, Hoffmann T, Mulrow C, et al. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ. Mar 29, 2021;372:n160. [FREE Full text] [CrossRef] [Medline]

AHRQ: Agency for Healthcare Research and Quality
BHLS: Brief Health Literacy Screen
EMERSE: Electronic Medical Record Search Engine
HL: health literacy
OPHELIA: OPtimising HEalth LIterAcy
PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
RCT: randomized clinical trial
REALM: Rapid Estimate of Adult Literacy in Medicine
SAHL-S&E: Short Assessment of Health Literacy-Spanish and English
SDoH: social determinants of health
TOFHLA: Test of Functional Health Literacy in Adults

Edited by E Mensah; submitted 17.07.23; peer-reviewed by A Sheon, S Kaur; comments to author 23.11.23; revised version received 28.01.24; accepted 31.01.24; published 20.03.24.


©Shwetha Bindhu, Anunita Nattam, Catherine Xu, Tripura Vithala, Tiffany Grant, Jacinda K Dariotis, Hexuan Liu, Danny T Y Wu. Originally published in the Online Journal of Public Health Informatics (, 20.03.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Online Journal of Public Health Informatics, is properly cited. The complete bibliographic information, a link to the original publication on, as well as this copyright and license information must be included.