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Creating Data Systems to Promote Health Equity.

Yuen Lie Tjoeng1,2, Mjaye Mazwi1,2, Andrew Goodwin3

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Summary

Systematic disparities can arise when collecting patient data for health services research. This review examines how biases impact data, modeling, and healthcare delivery, proposing solutions to improve health equity.

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Area of Science:

  • Health Services Research
  • Health Equity
  • Biomedical Data Science

Background:

  • Health equity is a growing concern in health services research and healthcare delivery.
  • Existing research highlights the potential for systematic disparities to be compounded during data collection and analysis.
  • Structural discrimination and human biases can influence data equity, impacting research findings and care processes.

Purpose of the Study:

  • To review mechanisms that introduce disparities in patient data collection and health modeling.
  • To identify how these disparities affect the translation of findings into healthcare delivery.
  • To propose initial steps for addressing these systemic inequities.

Main Methods:

  • Literature review of studies on health equity, data collection, and algorithmic bias.
  • Analysis of mechanisms introducing disparities in data and modeling.
  • Synthesis of potential strategies for mitigating bias in health research and implementation.

Main Results:

  • Data collection and modeling processes are susceptible to structural discrimination and human biases.
  • These biases can lead to the perpetuation and amplification of health disparities.
  • Implementation of derived algorithms and care processes may also be inequitable.

Conclusions:

  • Addressing health equity requires scrutinizing data collection and modeling practices for inherent biases.
  • Proactive strategies are needed to mitigate disparities at each stage of the research and implementation pipeline.
  • Further research and intervention are necessary to ensure equitable health outcomes.