Model design
Ensure that the primary goal of the machine learning algorithm is achieving optimal equitable patient outcomes.
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Review the relevant literature and community-level data for your patient service area to learn about the existing health inequities related to the health condition(s) that will be addressed by the machine learning algorithm.
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Discuss how and why this new machine learning algorithm could create new disparities or could exacerbate existing disparities.
Ensure that the goals of the model developers and users are aligned together and consistent with overall goal of achieving health equity (as outlined in Fig 1).
Discuss how potential nonclinical goals such as efficiency, saving money, and increasing revenue relate to the clinical goals and how these nonclinical goals may impact the overall goal to achieve health equity.
Check that the model development and review team have sufficiently diverse expertise and perspectives.
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Required stakeholders include patients living with the health conditions that the machine learning algorithm will address and their family members.
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Identify a stakeholder leader or facilitator with the training and expertise to lead group discussions on potentially emotionally charged and difficult topics, such as racism and other forms of oppression and discrimination.
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Data collection
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Model training and validation
Intentionally consider model performance metrics, patient outcomes, and resource allocation during model development.
Data scientist to use machine learning techniques that reduce overfitting and have been shown to be accurate in predicting the same clinical outcome or similar clinical outcome in the literature.
Model developers should share the patient demographics of the training dataset transparently to ensure that this information is available to future data scientists who may want to use the model.
Evaluate performance metrics such as sensitivity, specificity, positive predictive value, false-positive rate, negative predictive value, area under the receiver operating characteristic curve, and area under the precision-recall curve for the outcome of interest in the test dataset across potential demographic variables. -
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This includes race, ethnicity, age, sex, sexual orientation, gender identity, socioeconomic status, payer status, religion, citizenship status, preferred language, and disability.
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Evaluation of model deploymentDiscuss with diverse stakeholders whether to deploy the model, explicitly considering the ethical concepts of health equity and transparency, and the analysis of model performance metrics, patient outcomes, and resource allocation.
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Based on the model’s validation data, key stakeholders can decide whether the model should be deployed.
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If differences are found in the model’s performance across patient groups, the stakeholders will have to decide how much difference is too much to deploy the model.
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How are stakeholders ensuring that interventions are tailored to meet the needs of different patient groups?
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In larger health systems, the modeling team needs to ensure that equity exists in the deployment locations of predictive models across their clinics and hospitals.
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Monitored deployment
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