INTERACTIVE JOURNAL OF MEDICAL RESEARCH

Luke et al

Viewpoint

Narrowing the Digital Divide: Framework for Creating Telehealth
Equity Dashboards
Michael J Luke1,2,3,4, MD; Sansanee Craig1,5,6, MD; Suzinne Pak-Gorstein7, MD, MPH, PhD; Marlíse Arellano8, MBA;
Jessica Zhang8, MPH; S Margaret Wright9, MD, MPH; John Chuo1,6, MD, MS; Philip V Scribano1,6, DO, MSCE
1

Children's Hospital of Philadelphia, Philadelphia, PA, United States

2

Clinical Futures, Children's Hospital of Philadelphia, Philadelphia, PA, United States

3

Leonard Davis Institute of Health Economics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States4

PolicyLab, Children's Hospital of Philadelphia, Philadelphia, PA, United States

5

Department of Biomedical Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, United States

6

Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States

7

Department of Pediatrics, School of Medicine, University of Washington, Seattle, WA, United States

8

Boston Children's Hospital, Boston, MA, United States

9Children's Mercy Kansas City, University of Missouri-Kansas City School of Medicine, University of Kansas School of Medicine, Kansas City, MO,
United States

Corresponding Author:
Michael J Luke, MDChildren's Hospital of Philadelphia
3500 Civic Center Blvd
Philadelphia, PA, 19104
United States
Phone: 1 267 426 6634
Email: lukem1@chop.edu

Abstract
Telehealth presents both the potential to improve access to care and to widen the digital divide contributing to health care disparities
and obliging health care systems to standardize approaches to measure and display telehealth disparities. Based on a literature
review and the operational experience of clinicians, informaticists, and researchers in the Supporting Pediatric Research on
Outcomes and Utilization of Telehealth (SPROUT)–Clinical and Translational Science Awards (CTSA) Network, we outline astrategic framework for health systems to develop and optimally use a telehealth equity dashboard through a 3-phased approachof (1) defining data sources and key equity-related metrics of interest; (2) designing a dynamic and user-friendly dashboard; and(3) deploying the dashboard to maximize engagement among clinical staff, investigators, and administrators.
(Interact J Med Res 2024;13:e57435) doi: 10.2196/57435
KEYWORDS
telehealth; equity; dashboard; data; framework; televisit; healthcare; disparity; disparities; clinician; clinicians; informaticist;
informaticists; researcher; researchers; pediatric; pediatrics; health system; health systems; dashboards; access to care; data source
mapping

Telehealth Equity
The COVID-19 pandemic catalyzed a surge in telehealthadoption [1,2]. However, disparities in access to and adoptionof digital health care persist among Black, Hispanic,
public-insured, low-income, and rural populations [3,4]. This“digital divide” risks worsening health disparities in these
populations [5]. As such, Crawford and Serhal [6] created theDigital Health Equity Framework (DHEF) to guide the equitable
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design and implementation of future digital health interventions.
The DHEF takes into consideration, how individuals’sociocultural and economic contexts influence intermediatefactors, such as environmental stressors and health behaviors,
which then drive the digital determinants of health (eg,
acceptability of or access to digital health and digital healthliteracy) at the root of these disparities.
While health systems can use the DHEF to implement
equity-minded telehealth strategies, understanding and bolstering
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the quality of the digital infrastructure within the communities
they care for are critical steps to ensuring equitable access totelehealth [7]. Unfortunately, digital analytics are still lacking
in understanding patterns of use for those underserved bytechnology infrastructure. Dashboards that showcase key
performance indicators in real-time have become valuable tools
to track health care access, understand disparities, and applyinterventions. Yet, there are no consensus guidelines for thecreation of telehealth-specific equity dashboards, which canapply the nuanced considerations for telehealth equity outlined
through the DHEF to existing standards for data monitoring.
To standardize such dashboards, the Supporting PediatricResearch on Outcomes and Utilization of Telehealth

Luke et al(SPROUT)–CTSA Network formed the Telehealth Equity
Workgroup. Evidence on best practices for the collection anduse of equity-related data continues to evolve. Based on thereview of the existing literature and the operational experienceof clinicians, informaticists, and researchers in this workgroup,
we aim to describe a strategic framework for adult- andpediatrics-serving health systems to execute telehealth equity
dashboards through 3 phases: define, design, and deploy (Figure1). In addition, we offer a checklist for framework navigation(Figure 2) to motivate more critical monitoring and evaluationof health systems’ current telehealth practices and ultimately
identify service delivery gaps.

Figure 1. Telehealth equity dashboard framework.

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Figure 2. Telehealth Equity Dashboard Checklist (aSTEM: SPROUT Telehealth Evaluation and Measurement).

Engaging Interested Parties
Before beginning to create a telehealth equity dashboard, healthsystems must identify all interested parties to balance diverse
perspectives and priorities. This should include all potential
dashboard users such as clinical staff, investigators, andadministrators as well as dashboard experts and patientadvocates. Early engagement facilitates institutional buy-in toboth the development and use of a dashboard. In addition, as
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there is notable variation in data privacy regulations based on
patient age, type of medical problem, local health system policy,
and federal laws, early involvement of senior leadership canhelp ensure dashboards are implemented appropriately. Once
identified, interested parties must be continuously engaged
throughout all phases of the framework process to ensure thesedashboards are developed with the intended users in mind.

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Phase 1: DefineFirst, health systems should consider what data sources toleverage. Data source mapping is one useful technique toidentify usable sources for dashboard development. This
inventory process involves cataloging all available sources anddescribing potentially relevant data to allow teams to consider
the feasibility, reliability, and quality of these sources [9].
Poor data quality can have negative downstream impacts, as
inaccurate or incomplete data can mask disparities [10]. First,
patient and caregiver demographics can often be conflated inpediatric and elderly care settings. In addition, previous researchfound that non-White patients were less likely to have the correct
race in their health records and were often mislabeled as White,skewing disparities [11].
Several strategies can mitigate the limitations of missing or
inaccurate data [12]. Imputation or Bayesian modelingtechniques can help bolster existing data by addressing
missingness with inferred values. For example, imputing raceand ethnicity identified greater disparities in the COVID-19
pandemic compared with only excluding missing data [13].
Health systems can also enhance existing data by linking their
databases to external sources to conduct area-based monitoring[14]. To illustrate, health systems could integrate regional-level
population data from national datasets (eg, the National Surveyof Children’s Health or the American Community Survey for
United States health systems) with internal patient data by
census tract. Inequities can then be tracked between geographic
regions to further support patients from medically underserved
areas.
Unfortunately, these methods fail to address the root of datainaccuracy. Improvement of data collection processes is thebest long-term solution. Staff training, patient education, andoptions for self-reporting outside of clinical encounters are thekey to improved collection [10]. Greater transparency regarding
the purpose of data collection and improved framing of questions
to reduce discomfort in sharing sensitive data could also increase
self-reporting [11].
Once data sources are established, health systems can selectmetrics from the domains of the SPROUT Telehealth Evaluationand Measurement Framework [8], including health outcomes(ie, disease-specific measures), health delivery (ie, quality andcost), individual experience (ie, patient experience data), andkey performance indicators (ie, implementation measures), as
well as equity stratifiers (ie, environmental and patient
attributes). In addition, defining each metric’s performancetarget is critical. Targets can be based on peer organizations’performance, past institutional achievements, national-, state-,or county-wide standards, and public policy goals.

Phase 2: Design
Next, health systems should carefully consider the design of
their dashboards, as literature demonstrates how data
aggregation and visualization influence the ability to detectdisparities. Common broad racial or ethnic categories such as
Black or Hispanic obscure within-group differences that can
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Luke et al
have significant clinical implications [15]. For example, when
Asian is grouped with Native Hawaiian and Other PacificIslanders, such aggregated statistics conceal meaningful
differences between subpopulations [16]. Thus, it is importantto present data as disaggregated by equity stratifiers as possible,
acknowledging that some level of aggregation is necessary given
data quality limitations. A recent proposal for revised federal
government standards for race or ethnicity classification may
guide new best practices [17].
We recommend, at a minimum, comparing data from medicallyunderserved populations tailored to each health system with anaggregated “catch-all” category. Health systems may consider
including a reference, which is often the total population, or thegroup with the largest population, the most favorable healthoutcomes, or the greatest socioeconomic advantage [18].
However, there are risks of identifying a “reference” group.
Selecting White, for example, as the “reference” population
may inherently imply “nonreference” populations require
assimilation or acculturation or are generally “abnormal.”
In addition, designing dashboards with filter functionality acrossmultiple metrics can provide more robust analytics and displays.
Irrespective of the population that a health system serves,intersectionality, or the connection between personal identities,
is another key attribute to dashboard design, allowing for a morein-depth look at identified disparities. Race as a stratifier on its
own could be a proxy for other variables underlying why thesedisparities exist. However, through filter functionality, users
might consider assessing telehealth equity across races withanother key attribute such as social determinants of health orinternet access [18].
Designers should follow best practices for data visualization[19], including maximizing data-ink ratios and selecting theappropriate software for desired displays. Commercial
visualization tools can be found in Figure 2. When choosing
visualizations, it is essential to consider ease of interpretationand potential risks of misrepresentation. Tables explicitly lay
out comprehensive information but can be difficult to digest.
Interpretation can be supported through bolding or color-coding.
Graphs can simplify data presentation and draw attention to
specific insights, but this simplicity can be misleading [18]. It
is essential to include missing data percentages to illustrate
uncertainty and incorporate features to understand the context
of the data for accurate interpretation. For instance, wheninterpreting a narrowed disparity, the availability of hover
functionality to display numerators, denominators, and countbreakdowns for each data point can help users understand thesource of this change. In addition to reporting current statistics,
the ability to view metrics over time permits the detection oftrends and postintervention changes in disparities, which is an
essential dashboard function.
Once a preliminary design has been determined, teams candevelop a draft dashboard. From this point forward, design anddevelopment should proceed concurrently. The draft dashboard
should undergo pretesting with sample end users, which cansubsequently inform alterations to the design. Keep in mind,
multiple designs are likely needed to accommodate different
audiences, from frontline staff implementing care andInteract J Med Res 2024 | vol. 13 | e57435 | p. 4
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monitoring day-to-day activity to administrators interested inquarterly or annual trends.

Phase 3: Deploy
Finally, intentional deployment of a telehealth equity dashboard
is critical to increase use, inform and monitor operational andclinical interventions, preserve institutional buy-in, and create
a data-driven culture to improve health equity.
Socialization, the process of organizations adjusting to, learning
about, and buying into a new initiative, is a key aspect of
successful dashboard deployment. Socializing with leadershipand clinical providers allows teams to create relationships forlong-term reporting and inspires clinicians to use the dashboard
in day-to-day operations. Normalizing the use of equity
dashboards at all levels can stimulate maintained awareness andaction to improve telehealth equity hence laying the foundation
for a culture of accountability and quality data collection toaddress disparities in telehealth and beyond.
In this phase, it is also essential to identify a cadence ofdashboard review and updates, given the likely differing
preferences among users. For example, leadership may expect
a quarterly update on high-level telehealth equity experience,
while interpreter services may desire monthly check-ins tomonitor progress on their practice changes. Socialization withregular review allows for opportunities for feedback, which
studies have shown improve data quality [20]. By recognizing
the appropriate set of interested parties, health systems cancontinue to enhance their dashboards with the right feedback
from a broader and inclusive user group.
Once the dashboard has been deployed, data can be used andupdated to advocate for new programs or workflows supporting
medically underserved populations. The implementation of adashboard is an ongoing, iterative process through each phase.
For example, the telehealth equity dashboard may highlight a
disparity that motivates the creation of a new intervention. The
implementation of a new intervention may then require new
metrics to be added to the existing dashboard or identify otherLuke et always to track performance. The dashboard development team
may thus return to phase 1 to re-evaluate their sources andmetrics. In addition, periodic usability testing by end users canallow for the identification of these key areas of improvement
for subsequent iterations. This process, akin to theplan-do-study-act cycle in improvement science, can ensure theadaptability and continual advancement of a dashboard to meetthe demands of a dynamic health system [21].

Call to Action
Dashboards offer an avenue to improve data transparency. Datasharing, especially as it relates to equity, may be limited due tolack of incentives, fear of public scrutiny, or perceived
opportunity costs if data are used for research by external parties[22]. However, this creates silos between and even within healthsystems. Data sharing has the potential to establish sharedstandards and cross-institutional efforts to improve health on
the population level. Therefore, as technology use in health careadvances, we must pay close attention to what the data aretelling us, be transparent with our progress and shortcomings,
and push for change in our care models to ensure equitable
quality of and access to care for all patients.

Conclusions
The COVID-19 pandemic laid bare the implications of thedigital divide on health disparities. Nevertheless, telehealthcontinues to serve as a potential cost-effective care model andpromising access point for patients with barriers to in-person
services. As such, our strategic framework for developing atelehealth equity dashboard offers a valuable means to trackpatterns of use and outcomes to provide the evidence needed
to support continued investment in an equitable telehealthoffering. Telehealth equity dashboards present a promisingmeans to build a culture of data transparency, equity-centeredimplementation, and continuous improvement to narrow thedigital divide and improve access to care for all patients in thisexpanding world of digital health care.

Acknowledgments
MJL’s contribution to this manuscript was in part funded by the National Institute of Child Health and Human Development
Grant (#T32HD060550). JC, Christina Coleman, and the Supporting Pediatric Research on Outcomes and Utilization of Telehealth(SPROUT)–Clinical and Translational Science Awards (CTSA) Network Health Equity Topic Working Group Dashboard’s task
force’s effort contributing to this manuscript was in part funded by National Institutes of Health (NIH)/National Center for
Advancing Translational Sciences (NCATS) SPROUT-CTSA Collaborative Telehealth Network Grant (#U01TR002626). The
content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Authors' Contributions
MJL contributed to conceptualization, writing of the original draft, visualization, and reviewing and editing. SC managed
conceptualization and reviewing and editing. SPG handled conceptualization and reviewing and editing. SMW contributed toconceptualization and reviewing and editing. MA managed conceptualization, writing of the original draft, investigation, andreviewing and editing. JZ contributed to conceptualization and reviewing and editing. JC handled reviewing and editing. PVS
managed conceptualization, supervision, project administration, and reviewing and editing.

Conflicts of Interest
None declared.
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Abbreviations
CTSA: Clinical and Translational Science Awards
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Luke et al

DHEF: Digital Health Equity Framework
NCATS: National Center for Advancing Translational Sciences
NIH: National Institutes of Health
SPROUT: Supporting Pediatric Research on Outcomes and Utilization of Telehealth
STEM: SPROUT Telehealth Evaluation and Measurement

Edited by T de Azevedo Cardoso; submitted 27.02.24; peer-reviewed by R Bidkar, D Chong, J Jansen; comments to author 23.04.24;
revised version received 14.05.24; accepted 17.07.24; published 04.09.24
Please cite as:
Luke MJ, Craig S, Pak-Gorstein S, Arellano M, Zhang J, Wright SM, Chuo J, Scribano PV
Narrowing the Digital Divide: Framework for Creating Telehealth Equity Dashboards
Interact J Med Res 2024;13:e57435
URL: https://www.i-jmr.org/2024/1/e57435
doi: 10.2196/57435
PMID:©Michael J Luke, Sansanee Craig, Suzinne Pak-Gorstein, Marlíse Arellano, Jessica Zhang, S Margaret Wright, John Chuo, Philip
V Scribano. Originally published in the Interactive Journal of Medical Research (https://www.i-jmr.org/), 04.09.2024. This is an
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