Discrimination, trust, and withholding information from providers: Implications for missing data and inequity

Clinical Decision Support for Precision Dosing: Opportunities for Enhanced Equity and Inclusion in Health Care

“…48 The lack of information sharing can negatively impact the quality of data in a patient's medical record, which has consequences for future medical decision making. 49 Finally, the level of evidence required to leverage RWD to inform drug development and update drug labels is unclear. The FDA recently created a framework for evaluating the potential use of realworld evidence to help support the approval of a new indication for a drug or to help support or satisfy drug postapproval study requirements.…”

“…Flexibility also includes allowing missing data in any or all of these fields, as patients from marginalized groups may feel unwilling to disclose personal information to providers. 49 However, if these data are required for model predictions, either the patient would be excluded from precision dosing, or the software would need to use a default value for race, gender, or other demographic variables, which in its own way constitutes a normative judgment. This stresses again why it is important for models to be reported both with and without demographic covariates: this way, CDSS can avoid assigning a default race or sex to patients when their demographic information is unknown.…”

“…Another challenge is that some patients, typically those most historically underrepresented in medical research, are reluctant to share information, potentially due to past experiences of discrimination or the poor historical track record regarding equity in clinical care and research 48 . The lack of information sharing can negatively impact the quality of data in a patient's medical record, which has consequences for future medical decision making 49 . Finally, the level of evidence required to leverage RWD to inform drug development and update drug labels is unclear.…”

“…Flexibility also includes allowing missing data in any or all of these fields, as patients from marginalized groups may feel unwilling to disclose personal information to providers 49 . However, if these data are required for model predictions, either the patient would be excluded from precision dosing, or the software would need to use a default value for race, gender, or other demographic variables, which in its own way constitutes a normative judgment.…”

An Ecosystem Approach to Earning and Sustaining Trust in Health Care—Too Big to Care

“…People in the US do not feel like they have access to high-quality care, frequently report experiences of discrimination, 1 and withhold information from clinicians because they do not trust them with their information. 2 Even when people operating within systems, like clinicians and other health professionals, attempt to gain patients' trust, they may still be perceived as part of a system that is too big to care. For example, a patient may have a great experience with their clinician, only to find the visit is not covered by insurance and the billing office of the hospital uses predatory practices to pursue payment.…”