Disparity Dashboards: An Evaluation of the Literature and Framework for Health Equity Improvement
Jack Gallifant1Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA, Emmett Alexander Kistler2Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA, Luis Filipe Nakayama1,3Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Ophthalmology, São Paulo Federal University, São Paulo, Brazil, Chloe Zera2Department of Obstetrics, Gynecology and Reproductive Biology, Beth Israel Deaconess Medical Center, Boston, MA, USA, Sunil Kripalani4Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA, Adelline Ntatin5Department of Health Equity, Beth Israel Lahey Health, Boston, MA, USA, Leonor Fernandez2Department of Medicine Beth Israel Deaconess Medical Center, Boston, MA, USA, David Bates2,6Department of Medicine, Harvard Medical School, Boston, MA, USA; Division of General Internal Medicine and Primary Care, Brigham and Womens Hospital, Boston, MA, USA;, Irene Dankwa-Mullan7,8Merative & Center for AI, Research, and Evaluation, IBM Watson Health, Cambridge, MA, USA; Department of Health Policy and Management, Milken Institute School of Public Health, George Washington University, Washington, DC, USA, Leo Anthony Celi1,2,9Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA; Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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.
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 Questions | Explanation |
|---|---|
| Clear audience and use case | Clarifying 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 outcomes | Dashboards 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 exploration | Functionality 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 design | Important to present absolute and relative values with uncertainty measures, using contextual language. Visual cues can simplify information and emphasize key results. |
| Maximum transparency | Transparency in data sources and methods builds trust. Data should be accessible to researchers and patients, with consideration of legal and privacy issues. |
| Continuous sampling | Continuous 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 disaggregation | Moving 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 use | Diverse backgrounds of users and designers are crucial to prevent biased assessments. Consultation with patient partners and stakeholders is important in the design process. |
| Process evaluation | Data 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 funding | Benchmarks 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:
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}
}