The Racial Disparities in Maternal Mortality and Impact of Structural Racism and Implicit Racial Bias on Pregnant Black Women: A Review of the Literature

Automatic Enrollment in Patient Portal Systems Mitigates the Digital Divide in Healthcare: An Interrupted Time Series Analysis of an Autoenrollment Workflow Intervention

PURPOSE

Racial and ethnic healthcare disparities require innovative solutions. Patient portals enable online access to health records and clinician communication and are associated with improved health outcomes. Nevertheless, a digital divide in access to such portals persist, especially among people of minoritized race and non-English-speakers. This study assesses the impact of automatic enrollment (autoenrollment) on patient portal activation rates among adult patients at the University of California, San Francisco (UCSF), with a focus on disparities by race, ethnicity, and primary language.

MATERIALS AND METHODS

Starting March 2020, autoenrollment offers for patient portals were sent to UCSF adult patients aged 18 or older via text message. Analysis considered patient portal activation before and after the intervention, examining variations by race, ethnicity, and primary language. Descriptive statistics and an interrupted time series analysis were used to assess the intervention's impact.

RESULTS

Autoenrollment increased patient portal activation rates among all adult patients and patients of minoritized races saw greater increases in activation rates than White patients. While initially not statistically significant, by the end of the surveillance period, we observed statistically significant increases in activation rates in Latinx (3.5-fold, p = < 0.001), Black (3.2-fold, p = 0.003), and Asian (3.1-fold, p = 0.002) patient populations when compared with White patients. Increased activation rates over time in patients with a preferred language other than English (13-fold) were also statistically significant (p = < 0.001) when compared with the increase in English preferred language patients.

CONCLUSION

An organization-based workflow intervention that provided autoenrollment in patient portals via text message was associated with statistically significant mitigation of racial, ethnic, and language-based disparities in patient portal activation rates. Although promising, the autoenrollment intervention did not eliminate disparities in portal enrollment. More work must be done to close the digital divide in access to healthcare technology.

Racial Differences in Stigmatizing and Positive Language in Emergency Medicine Notes

OBJECTIVE

Language used by providers in medical documentation may reveal evidence of race-related implicit bias. We aimed to use natural language processing (NLP) to examine if prevalence of stigmatizing language in emergency medicine (EM) encounter notes differs across patient race/ethnicity.

METHODS

In a retrospective cohort of EM encounters, NLP techniques identified stigmatizing and positive themes. Logistic regression models analyzed the association of race/ethnicity and themes within notes. Outcomes were the presence (or absence) of 7 different themes: 5 stigmatizing (difficult, non-compliant, skepticism, substance abuse/seeking, and financial difficulty) and 2 positive (compliment and compliant).

RESULTS

The sample included notes from 26,363 unique patients. NH Black patient notes were less likely to contain difficult (odds ratio (OR) 0.80, 95% confidence interval (CI), 0.73-0.88), skepticism (OR 0.87, 95% CI, 0.79-0.96), and substance abuse/seeking (OR 0.62, 95% CI, 0.56-0.70) compared to NH White patient notes but more likely to contain non-compliant (OR 1.26, 95% CI, 1.17-1.36) and financial difficulty (OR 1.14, 95% CI, 1.04-1.25). Hispanic patient notes were less likely to contain difficult (OR 0.68, 95% CI, 0.58-0.80) and substance abuse/seeking (OR 0.78, 95% CI, 0.66-0.93). NH NA/AI patient notes had twice the odds as NH White patient notes to contain a stigmatizing theme (OR 2.02, 95% CI, 1.64-2.49).

CONCLUSIONS

Using an NLP model to analyze themes in EM notes across racial groups, we identified several inequities in the usage of positive and stigmatizing language. Interventions to minimize race-related implicit bias should be undertaken.

Effectiveness and safety of telehealth medication abortion in the USA

Telehealth abortion has become critical to addressing surges in demand in states where abortion remains legal but evidence on its effectiveness and safety is limited. California Home Abortion by Telehealth (CHAT) is a prospective study that follows pregnant people who obtained medication abortion via telehealth from three virtual clinics operating in 20 states and Washington, DC between April 2021 and January 2022. Individuals were screened using a standardized no-test protocol, primarily relying on their medical history to assess medical eligibility. We assessed effectiveness, defined as complete abortion after 200 mg mifepristone and 1,600 μg misoprostol (or lower) without additional intervention; safety was measured by the absence of serious adverse events. We estimated rates using multivariable logistic regression and multiple imputation to account for missing data. Among 6,034 abortions, 97.7% (95% confidence interval (CI) = 97.2-98.1%) were complete without subsequent known intervention or ongoing pregnancy after the initial treatment. Overall, 99.8% (99.6-99.9%) of abortions were not followed by serious adverse events. In total, 0.25% of patients experienced a serious abortion-related adverse event, 0.16% were treated for an ectopic pregnancy and 1.3% abortions were followed by emergency department visits. There were no differences in effectiveness or safety between synchronous and asynchronous models of care. Telehealth medication abortion is effective, safe and comparable to published rates of in-person medication abortion care.