Disparity Dashboards: An Evaluation of the Literature and Framework for Health Equity Improvement

Jack Gallifant1, Emmett Alexander Kistler2, Luis Filipe Nakayama1,3, Chloe Zera2, Sunil Kripalani4, Adelline Ntatin5, Leonor Fernandez2, David Bates2,6, Irene Dankwa-Mullan7,8, Leo Anthony Celi1,2,9

Evaluating Systematic Equality Improvement

This paper addresses the growing need for systematic, continuous, and transparent reporting of patient outcomes across diverse populations. It evaluates studies that have successfully developed disparity dashboards, highlighting their role in visualizing data to identify clinical outcome disparities. This aids in guiding quality and equality improvement efforts that aim to enhance health equity.

Monitoring Health Outcomes

The COVID-19 pandemic starkly exposed health inequities, especially among racial and ethnic subgroups, with these groups experiencing higher rates of infection, hospitalization, and mortality. This scenario is not unique to COVID-19 but extends to other health disparities influenced by interconnected social determinants of health. Additionally, artificial intelligence (AI) in healthcare, while offering personalized care and improved quality, poses risks of exacerbating existing biases. This highlights the need for infrastructure to evaluate, validate, and update AI models and monitor their impact on patient subgroups.

The need for continuous monitoring and evaluation of health disparities is critical to address these issues effectively. This necessitates systematic reporting of patient outcomes in specific subgroups and the development of infrastructure to capture differences over time.

Disparity Dashboard Diagram

Electronic patient data flows in NHS England Data flows go upwards and are coloured by destination. For data source and extractors, node size is proportional to population catchment (eg, NHS Digital=55 million). For data consumers, node size is proportional to the number of projects (eg, University of Oxford=178). NHS=National Health Service.

Current State of Disparity Dashboards

We identified 22 studies that published disparity dashboards, covering areas like COVID-19, maternal mortality, pediatric healthcare, emergency departments, HIV cases, rural healthcare, and Medicare Health Equity Summary Score outcomes. Key findings from these studies are summarized in the table below.

Important Questions for Developing Disparity Dashboards

Key QuestionsExplanation
Clear audience and use caseClarifying the intended use and user is essential, with different interfaces for various groups such as management, governments, physicians, and patients. Multilanguage functionality is crucial for engaging diverse cohorts.
Focused outcomesDashboards must collect data addressing the root causes of outcomes and disparities. Outcomes should be tailored to individual groups, with inclusion of process measures for tracking intermediate steps.
Interaction and explorationFunctionality should allow analysis of various population sizes and permit interactive exploration with different levels of detail. Providing multiple views and exploring data for biases is essential.
Context-appropriate designImportant to present absolute and relative values with uncertainty measures, using contextual language. Visual cues can simplify information and emphasize key results.
Maximum transparencyTransparency in data sources and methods builds trust. Data should be accessible to researchers and patients, with consideration of legal and privacy issues.
Continuous samplingContinuous monitoring is necessary to track disparities over time and in relation to policies. Dashboards should have flexibility for challenging assumptions and integrating new data.
Appropriate disaggregationMoving beyond demographic criteria to underlying social risk factors is crucial. Data should be collected on key areas like REGAL, and a variety of composites should be created to represent patients accurately.
Diversity in design and in useDiverse backgrounds of users and designers are crucial to prevent biased assessments. Consultation with patient partners and stakeholders is important in the design process.
Process evaluationData integrity checks, forecasting, and exploratory analysis are key for calibration and evaluation. Findings should be distributed transparently for honest discourse and solution development.
Oversight and fundingBenchmarks and aligned incentives are necessary for organizations to strive towards goals. Local accountability measures should ensure active identification and deployment of interventions.

Advancing Health Equity with Disparity Dashboards

Disparity dashboards extend beyond traditional clinical dashboards by not only identifying and monitoring disparities but also aiding in understanding their underlying causes. These dashboards emphasize the importance of considering a broad range of factors including social or structural determinants of health and the need for actionable information. However, challenges exist in achieving interoperability between sites, regions, and countries, and in standardizing health equity data for comparative assessment.

Despite these challenges, disparity dashboards hold immense potential in improving health equity. As institutions increasingly align their strategies to promote equitable outcomes, the use of disparity dashboards becomes even more crucial. These tools, developed by diverse, interdisciplinary teams, are vital for safeguarding patient outcomes, improving health policies, and reducing health inequities. They empower health systems and providers to track, measure, and understand their capabilities in delivering equitable care, ensuring accountability and supporting the overarching goal of improving healthcare equity and quality.

Related Work

Our work builds upon work using digital tools to evaluate health inequities:

First Image Description

Joe Zhang, Jack Gallifant, Robin L Pierce, Aoife Fordham, James Teo, Leo Celi, Hutan Ashrafian. Quantifying digital health inequality across a national healthcare system. 2023.

Notes: This study quantified factors associated with differential utilisation of digital tools in the National Health Service (NHS). Results are concerning for technologically driven widening of healthcare inequalities. Targeted incentive to digital is necessary to prevent digital disparity from becoming health outcomes disparity.

How To Cite

This study can be cited as follows.

Bibliography

Zhang J, Gallifant J, Pierce RL, et al. "Quantifying digital health inequality across a national healthcare system." BMJ Health & Care Informatics 2023;30:e100809. doi: 10.1136/bmjhci-2023-100809.

BibTeX

@article{zhang2023quantifying,
                title={Quantifying digital health inequality across a national healthcare system},
                author={Zhang, Joe and Gallifant, Jack and Pierce, Robin L and Fordham, Aoife and Teo, James and Celi, Leo and Ashrafian, Hutan},
                journal={BMJ Health & Care Informatics},
                volume={30},
                pages={e100809},
                year={2023},
                publisher={BMJ Publishing Group},
                doi={10.1136/bmjhci-2023-100809}
              }