Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care.

Importance: Health care algorithms are used for diagnosis, treatment, prognosis, risk stratification, and allocation of resources. Bias in the development and use of algorithms can lead to worse outcomes for racial and ethnic minoritized groups and other historically marginalized populations such as individuals with lower income. Objective: To provide a conceptual framework and guiding principles for mitigating and preventing bias in health care algorithms to promote health and health care equity. Evidence Review: The Agency for Healthcare Research and Quality and the National Institute for Minority Health and Health Disparities convened a diverse panel of experts to review evidence, hear from stakeholders, and receive community feedback. Findings: The panel developed a conceptual framework to apply guiding principles across an algorithm's life cycle, centering health and health care equity for patients and communities as the goal, within the wider context of structural racism and discrimination. Multiple stakeholders can mitigate and prevent bias at each phase of the algorithm life cycle, including problem formulation (phase 1); data selection, assessment, and management (phase 2); algorithm development, training, and validation (phase 3); deployment and integration of algorithms in intended settings (phase 4); and algorithm monitoring, maintenance, updating, or deimplementation (phase 5). Five principles should guide these efforts: (1) promote health and health care equity during all phases of the health care algorithm life cycle; (2) ensure health care algorithms and their use are transparent and explainable; (3) authentically engage patients and communities during all phases of the health care algorithm life cycle and earn trustworthiness; (4) explicitly identify health care algorithmic fairness issues and trade-offs; and (5) establish accountability for equity and fairness in outcomes from health care algorithms. Conclusions and Relevance: Multiple stakeholders must partner to create systems, processes, regulations, incentives, standards, and policies to mitigate and prevent algorithmic bias. Reforms should implement guiding principles that support promotion of health and health care equity in all phases of the algorithm life cycle as well as transparency and explainability, authentic community engagement and ethical partnerships, explicit identification of fairness issues and trade-offs, and accountability for equity and fairness.

Embryonic heart rate correlates with maternal temperature and developmental stage in viviparous snakes.

Interactions between the environment and maternal and embryonic physiology can have critical ramifications for early-life phenotypes and survival in a range of species. A major component of the environment-maternal-embryonic nexus is the regulation of embryonic heart rate, which can have important ramifications for developmental phenology, but remains relatively unexplored in viviparous reptiles. The goal of this study was to test for a relationship between embryonic heart rate and maternal body temperature in two species of viviparous garter snakes. The embryonic heart rates of Thamnophis elegans and T. sirtalis were assessed using a field-portable ultrasound. For both T. elegans and T. sirtalis, embryonic heart rate was strongly correlated to maternal temperature. Interestingly, there was also a strong correlation between embryonic and maternal heart rate that was most likely mediated by a common response to maternal body temperature, in spite of the effects of handling during ultrasound on maternal heart rate. Furthermore, embryos at earlier developmental stages had lower heart rates. To our knowledge, this study is the first to explore embryonic heart rate in viviparous reptiles, providing a foundation for future work using ultrasonography to test ecological and evolutionary hypotheses related to developmental dynamics in free-ranging viviparous species.