Search

An editorial is presented which highlights the "Making Healthcare Safer IV" initiative, reviewing and updating patient safety practices after 25 years. It emphasizes the importance of adopting effective strategies for reducing medication errors, improving opioid stewardship, and minimizing adverse drug events.

Algorithms are used in health care to calculate organ function and predict patient risk or future outcomes, and, subsequently, for treatment decisions, further diagnostic procedures, or referral for further care. Inputs to algorithms and how they are calculated may result in racial or ethnic disparities in access to care, quality of care, and disparities in health outcomes. This paper systematically reviews studies that have evaluated the impact of algorithms on racial or ethnic disparities and what strategies have been used to mitigate such disparities. Background: There is increasing concern for the potential impact of health care algorithms on racial and ethnic disparities. Purpose: To examine the evidence on how health care algorithms and associated mitigation strategies affect racial and ethnic disparities. Data Sources: Several databases were searched for relevant studies published from 1 January 2011 to 30 September 2023. Study Selection: Using predefined criteria and dual review, studies were screened and selected to determine: 1) the effect of algorithms on racial and ethnic disparities in health and health care outcomes and 2) the effect of strategies or approaches to mitigate racial and ethnic bias in the development, validation, dissemination, and implementation of algorithms. Data Extraction: Outcomes of interest (that is, access to health care, quality of care, and health outcomes) were extracted with risk-of-bias assessment using the ROBINS-I (Risk Of Bias In Non-randomised Studies – of Interventions) tool and adapted CARE-CPM (Critical Appraisal for Racial and Ethnic Equity in Clinical Prediction Models) equity extension. Data Synthesis: Sixty-three studies (51 modeling, 4 retrospective, 2 prospective, 5 prepost studies, and 1 randomized controlled trial) were included. Heterogenous evidence on algorithms was found to: a) reduce disparities (for example, the revised kidney allocation system), b) perpetuate or exacerbate disparities (for example, severity-of-illness scores applied to critical care resource allocation), and/or c) have no statistically significant effect on select outcomes (for example, the HEART Pathway [history, electrocardiogram, age, risk factors, and troponin]). To mitigate disparities, 7 strategies were identified: removing an input variable, replacing a variable, adding race, adding a non–race-based variable, changing the racial and ethnic composition of the population used in model development, creating separate thresholds for subpopulations, and modifying algorithmic analytic techniques. Limitation: Results are mostly based on modeling studies and may be highly context-specific. Conclusion: Algorithms can mitigate, perpetuate, and exacerbate racial and ethnic disparities, regardless of the explicit use of race and ethnicity, but evidence is heterogeneous. Intentionality and implementation of the algorithm can impact the effect on disparities, and there may be tradeoffs in outcomes. Primary Funding Source: Agency for Healthcare Quality and Research. [ABSTRACT FROM AUTHOR]

Abstract Background Methods for describing one's confidence in the available evidence are useful for end-users of evidence reviews. Analysts inevitably make judgments about the quality, quantity consistency, robustness, and magnitude of effects observed in the studies identified. The subjectivity of these judgments in several areas underscores the need for transparency in judgments. Discussion This paper introduces a new system for rating medical evidence. The system requires explicit judgments and provides explicit rules for balancing these judgments. Unlike other systems for rating the strength of evidence, our system draws a distinction between two types of conclusions: quantitative and qualitative. A quantitative conclusion addresses the question, "How well does it work?", whereas a qualitative conclusion addresses the question, "Does it work?" In our system, quantitative conclusions are tied to stability ratings, and qualitative conclusions are tied to strength ratings. Our system emphasizes extensive a priori criteria for judgments to reduce the potential for bias. Further, the system makes explicit the impact of heterogeneity testing, meta-analysis, and sensitivity analyses on evidence ratings. This article provides details of our system, including graphical depictions of how the numerous judgments that an analyst makes can be combined. We also describe two worked examples of how the system can be applied to both interventional and diagnostic technologies. Summary Although explicit judgments and formal combination rules are two important steps on the path to a comprehensive system for rating medical evidence, many additional steps must also be taken. Foremost among these are the distinction between quantitative and qualitative conclusions, an extensive set of a priori criteria for making judgments, and the direct impact of analytic results on evidence ratings. These attributes form the basis for a logically consistent system that can improve the usefulness of evidence reviews.