HIPAA, Privacy, and Reproductive Rights in a Post-Roe Era

Spectrum of Fetal Intraparenchymal Hemorrhage in COL4A1/A2-Related Disorders

BACKGROUND

COL4A1/A2 variants affecting the alpha 1 and 2 chains of type IV collagen are increasingly recognized as a cause of fetal and neonatal intracranial hemorrhage, porencephaly, and schizencephaly. Fetal magnetic resonance imaging (MRI) findings in COL4A1/A2-related disorders are not well characterized.

METHODS

This is a retrospective case series of fetal MRI findings in eight patients with intraparenchymal hemorrhage (IPH) and COL4A1/A2 variants, five of whom have postnatal imaging and clinical follow-up.

RESULTS

IPH was multifocal and bilateral in four of eight patients. IPH involved the frontal lobes in all cases and basal ganglia in six of eight. The median maximum diameter of IPH was 16 mm (range 6 to 65 mm). All patients had ventriculomegaly, and four of eight had intraventricular hemorrhage. Prenatal IPH size correlated clinically with motor outcomes, and none had clinically symptomatic recurrent hemorrhage.

CONCLUSION

COL4A1/A2 variants can present with a spectrum of IPH prenatally, including small and/or unifocal IPH, as well as multifocal and bilateral IPH, involving the frontal lobes and basal ganglia. Given the wide spectrum of IPH severity seen on fetal brain MRI, genetic testing for COL4A1/A2 variants should be considered in all cases of fetal IPH.

Availability of information needed to evaluate algorithmic fairness - A systematic review of publicly accessible critical care databases

BACKGROUND

Machine learning (ML) may improve clinical decision-making in critical care settings, but intrinsic biases in datasets can introduce bias into predictive models. This study aims to determine if publicly available critical care datasets provide relevant information to identify historically marginalized populations.

METHOD

We conducted a review to identify the manuscripts that report the training/validation of ML algorithms using publicly accessible critical care electronic medical record (EMR) datasets. The datasets were reviewed to determine if the following 12 variables were available: age, sex, gender identity, race and/or ethnicity, self-identification as an indigenous person, payor, primary language, religion, place of residence, education, occupation, and income.

RESULTS

7 publicly available databases were identified. Medical Information Mart for Intensive Care (MIMIC) reports information on 7 of the 12 variables of interest, Sistema de Informação de Vigilância Epidemiológica da Gripe (SIVEP-Gripe) on 7, COVID-19 Mexican Open Repository on 4, and eICU on 4. Other datasets report information on 2 or fewer variables. All 7 databases included information about sex and age. Four databases (57%) included information about whether a patient identified as native or indigenous. Only 3 (43%) included data about race and/or ethnicity. Two databases (29%) included information about residence, and one (14%) included information about payor, language, and religion. One database (14%) included information about education and patient occupation. No databases included information on gender identity and income.

CONCLUSION

This review demonstrates that critical care publicly available data used to train AI algorithms do not include enough information to properly look for intrinsic bias and fairness issues towards historically marginalized populations.

Understanding the impact of an AI-enabled conversational agent mobile app on users' mental health and wellbeing with a self-reported maternal event: a mixed method real-world data mHealth study

Background

Maternal mental health care is variable and with limited accessibility. Artificial intelligence (AI) conversational agents (CAs) could potentially play an important role in supporting maternal mental health and wellbeing. Our study examined data from real-world users who self-reported a maternal event while engaging with a digital mental health and wellbeing AI-enabled CA app (Wysa) for emotional support. The study evaluated app effectiveness by comparing changes in self-reported depressive symptoms between a higher engaged group of users and a lower engaged group of users and derived qualitative insights into the behaviors exhibited among higher engaged maternal event users based on their conversations with the AI CA.

Methods

Real-world anonymised data from users who reported going through a maternal event during their conversation with the app was analyzed. For the first objective, users who completed two PHQ-9 self-reported assessments (n = 51) were grouped as either higher engaged users (n = 28) or lower engaged users (n = 23) based on their number of active session-days with the CA between two screenings. A non-parametric Mann-Whitney test (M-W) and non-parametric Common Language effect size was used to evaluate group differences in self-reported depressive symptoms. For the second objective, a Braun and Clarke thematic analysis was used to identify engagement behavior with the CA for the top quartile of higher engaged users (n = 10 of 51). Feedback on the app and demographic information was also explored.

Results

Results revealed a significant reduction in self-reported depressive symptoms among the higher engaged user group compared to lower engaged user group (M-W p = .004) with a high effect size (CL = 0.736). Furthermore, the top themes that emerged from the qualitative analysis revealed users expressed concerns, hopes, need for support, reframing their thoughts and expressing their victories and gratitude.

Conclusion

These findings provide preliminary evidence of the effectiveness and engagement and comfort of using this AI-based emotionally intelligent mobile app to support mental health and wellbeing across a range of maternal events and experiences.

Protecting reproductive health information in the post-Roe era: interoperability strategies for healthcare institutions

On June 24, 2022, the US Supreme Court ended constitutional protections for abortion, resulting in wide variability in access from severe restrictions in many states and fewer restrictions in others. Healthcare institutions capture information about patients' pregnancy and abortion care and, due to interoperability, may share it in ways that expose their providers and patients to social stigma and potential legal jeopardy in states with severe restrictions. In this article, we describe sources of risk to patients and providers that arise from interoperability and specify actions that institutions can take to reduce that risk. Institutions have significant power to define their practices for how and where care is documented, how patients are identified, where data are sent or hosted, and how patients are counseled, and thus should protect patients' privacy and ability to receive medical care that is safe and legal where it is performed.

Prevalence of Third-Party Tracking on Abortion Clinic Web Pages

This cross-sectional study assesses how often third-party domains use tracking data from visitors to abortion clinic web pages.