Paging the Clinical Informatics Community: Respond STAT to Dobbs v. Jackson's Women's Health Organization

Arvisais-Anhalt, Simone; Ravi, Akshay; Weia, Benjamin; Aarts, Jos; Ahmad, Hasan B; Araj, Ellen; Bauml, Julie A; Benham‐Hutchins, Marge; Boyd, Andrew D.; Brecht-Doscher, Aimee; Butler-Henderson, Kerryn; Butte, Atul J.; Cardillo, Anthony; Chilukuri, Nymisha; Cho, Mildred K.; Cohen, Jenny K.; Craven, Catherine K.; Crusco, Salvatore Joseph; Dadabhoy, Farah; Dash, Dev; DeBolt, Claire; Elkin, Peter L.; Fayanju, Oluseyi A; Fochtmann, Laura J.; Graham, Justin V; Hanna, John; Hersh, William R.; Hoffard, Mackenzie R; Hron, Jonathan D.; Huang, Sean S; Jackson, Brian R.; Kaplan, Bonnie; Kelly, William R.; Ko, Kyungmin; Koppel, Ross; Kurapati, Nikhil; Labbad, Gabriel; Lee, Julie; Lehmann, Christoph U.; Leitner, Stefano; Liao, Zachary C; Medford, Richard J; Melnick, Edward R.; Muniyappa, Anoop; Murray, Sara G.; Neinstein, Aaron B.; Nichols-Johnson, Victoria; Novak, Laurie L.; Ogan, William Scott; Ozeran, Larry; Pageler, Natalie M.; Pandita, Deepti; Perumbeti, Ajay; Petersen, Carolyn; Pierce, Logan; Puttagunta, Raghuveer; Ramaswamy, Priya; Rogers, Kendall; Rosenbloom, S. Trent; Ryan, Angela M.; Saleh, Sameh N; Sarabu, Chethan; Schreiber, Richard; Shaw, Kate A.; Sim, Ida; Sirintrapun, S. Joseph; Solomonides, Anthony; Spector, Jacob D; Starren, Justin; Stoffel, Michelle; Subbian, Vignesh; Swanson, Karl; Tomes, Adrian; Trang, Karen; Unertl, Kim M.; Weon, Jenny L.; Whooley, Mary A.; Wiley, Kevin; Williamson, Drew F. K.; Winkelstein, Peter; Wong, Jenson; Xie, James; Yarahuan, Julia Kay Whitlow; Yung, Nathan; Zera, Chloe; Ratanawongsa, Neda; Sadasivaiah, Shobha

doi:10.1055/a-2000-7590

Cited by 3 publications

(2 citation statements)

References 20 publications

(45 reference statements)

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“…We also find a negative impact on the expressions of trust in the privacy of health information. Among clinicians, the medical literature has highlighted the risks associated with how health information, in the traditional care setting or otherwise, can be used to prosecute patients, clinicians, and others who in any form aid in the access to abortion care [13][14][15]17]. Among the general public, there has been increased attention to and awareness of how apps can collect and misuse data [18,19,47].…”

Section: Principal Findingsmentioning

confidence: 99%

“…Since the Dobbs ruling, there has been increased awareness surrounding how reproductive health data in both traditional health care settings and beyond can be used against patients. For example, reports have detailed the known limitations of major federal regulation, such as the Health Insurance Portability and Accountability Act (HIPAA), in protecting patients' reproductive health data in the context of patient care [12][13][14][15] and how the massive exchange of health care data across organizations and state lines could jeopardize patients who receive abortion care [13,16,17]. Additionally, outside traditional health care settings, reports have detailed how mobile apps collecting data about users' health, sexual behavior, and menstrual cycles have shared user data without the user's knowledge or consent [18] and how these data could be used to prosecute women who receive abortion care [19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Effect of Recent Abortion Legislation on Twitter User Engagement, Sentiment, and Expressions of Trust in Clinicians and Privacy of Health Information: Content Analysis

Swanson¹,

Ravi²,

Saleh³

et al. 2023

J Med Internet Res

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Background The Supreme Court ruling in Dobbs v Jackson Women’s Health Organization (Dobbs) overrules precedents established by Roe v Wade and Planned Parenthood v Casey and allows states to individually regulate access to abortion care services. While many states have passed laws to protect access to abortion services since the ruling, the ruling has also triggered the enforcement of existing laws and the creation of new ones that ban or restrict abortion. In addition to denying patients the full spectrum of reproductive health care, one major concern in the medical community is how the ruling will undermine trust in the patient-clinician relationship by influencing perceptions of the privacy of patient health information. Objective This study aimed to study the effect of recent abortion legislation on Twitter user engagement, sentiment, expressions of trust in clinicians, and privacy of health information. Methods We scraped tweets containing keywords of interest between January 1, 2020, and October 17, 2022, to capture tweets posted before and after the leak of the Supreme Court decision. We then trained a Latent Dirichlet Allocation model to select tweets pertinent to the topic of interest and performed a sentiment analysis using Robustly Optimized Bidirectional Encoder Representations from Transformers Pre-training Approach model and a causal impact time series analysis to examine engagement and sentiment. In addition, we used a Word2Vec model to study the terms of interest against a latent trust dimension to capture how expressions of trust for our terms of interest changed over time and used term frequency, inverse-document frequency to measure the volume of tweets before and after the decision with respect to the negative and positive sentiments that map to our terms of interest. Results Our study revealed (1) a transient increase in the number of daily users by 576.86% (95% CI 545.34%-607.92%; P<.001), tweeting about abortion, health care, and privacy of health information postdecision leak; (2) a sustained and statistically significant decrease in the average daily sentiment on these topics by 19.81% (95% CI −22.98% to −16.59%; P=.001) postdecision leak; (3) a decrease in the association of the latent dimension of trust across most clinician-related and health information–related terms of interest; (4) an increased frequency of tweets with these clinician-related and health information–related terms and concomitant negative sentiment in the postdecision leak period. Conclusions The study suggests that the Dobbs ruling has consequences for health systems and reproductive health care that extend beyond denying patients access to the full spectrum of reproductive health services. The finding of a decrease in the expression of trust in clinicians and health information–related terms provides evidence to support advocacy and initiatives that proactively address concerns of trust in health systems and services.

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Section: Principal Findingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effect of Recent Abortion Legislation on Twitter User Engagement, Sentiment, and Expressions of Trust in Clinicians and Privacy of Health Information: Content Analysis

Swanson¹,

Ravi²,

Saleh³

et al. 2023

J Med Internet Res

Self Cite

View full text Add to dashboard Cite

show abstract

Effect of Recent Abortion Legislation on Twitter User Engagement, Sentiment, and Expressions of Trust in Clinicians and Privacy of Health Information: Content Analysis (Preprint)

Arvisais-Anhalt¹,

Saleh²,

Ravi³

et al. 2023

Preprint

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BACKGROUND The supreme court ruling in Dobbs v Jackson Women’s Health Organization overrules precedents established by Roe v Wade and Planned Parenthood v Casey and allows states to individually regulate access to abortion care services. While many states have passed laws to protect access to abortion services since the ruling, the ruling has also triggered the enforcement of existing laws and creation of new ones that ban or restrict abortion. In addition to denying patients to the full spectrum of reproductive health care, one major concern in the medical community is how the ruling will undermine trust in the patient-clinician relationship by influencing perceptions of the privacy of patient health information. OBJECTIVE To study the effect of recent abortion legislation on twitter user engagement, sentiment, and expressions of trust in clinicians and privacy of health information. METHODS We scraped tweets containing keywords of interest between Jan 1st 2020 – Oct 17th, 2022, to capture tweets posted before and after the supreme court decision. We the trained a Latent Dirichlet Allocation model to select tweets pertinent to the topic of interest and performed a sentiment analysis using a RoBERTa model and a causal impact time series analysis to examine engagement and sentiment. In addition, we used a Word2Vec model to study the terms of interest against a latent trust dimension to capture how expressions of trust for our terms of interest changed over time and used TFIDF to measure to volume of tweets before and after the decision with respect to negative and positive sentiment that map to our terms of interest. RESULTS Our study revealed 1) a transient increase in the number of daily users by 576.86% (95% CI: 545.34% - 607.92%), p < 0.001, tweeting about abortion, healthcare, and privacy of health information post decision; 2) a sustained and statistically significant decrease in the average daily sentiment on these topics by 19.81% (95% CI: 5-22.98%, -16.59%, p , 0.001 post decision ; 3) a decrease in the association of the latent dimension of trust across most clinician related and health information related terms of interest; 4) an increased frequency of tweets with these clinician related and health information related terms and concomitant negative sentiment in the post decision period. CONCLUSIONS The study suggests that the Dobbs ruling has consequences for health systems and reproductive healthcare that extend beyond denying patients access to the full spectrum of reproductive health services. The finding of a decrease in expression of trust in clinicians and health information-related terms provides evidence to support advocacy and initiatives that proactively address concerns of trust in health systems and services. CLINICALTRIAL N/A

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Bad Boundaries: Geofences and the Intimacies of Location Data

Noone,

Jacob

2024

TOPIA: Canadian Journal of Cultural Studies

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Locational data are a key part of platform function. They organize people and environments according to position and proximity. One technique through which platforms collect and circulate locational data is through geofences—virtual perimeters established around target locations that mark who and what crosses their thresholds. Applying the lens of data intimacies, the authors look at two applications of geofences in the United States: (a) to target abortion seekers through geofencing brokers like CellHawk that extrapolate and sell Google Maps locational data and (b) to accelerate the tenant eviction process through platforms such as CIVVL, a so-called property preservation platform. Through the examples of CellHawk and CIVVL, this article argues that geofences enable platforms to organize space and make claims on the body via location data. Geofencing is a practice that exploits the intimacy of locational data not simply by accessing private data but also by using intimate data to index the body in relation to risk and safety and property and trespass. Geofencing is a mechanism through which platforms police and patrol space, reifying unequal terms of autonomy and access.

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Paging the Clinical Informatics Community: Respond STAT to Dobbs v. Jackson's Women's Health Organization

Abstract: n/a

Cited by 3 publications

References 20 publications

Effect of Recent Abortion Legislation on Twitter User Engagement, Sentiment, and Expressions of Trust in Clinicians and Privacy of Health Information: Content Analysis

Effect of Recent Abortion Legislation on Twitter User Engagement, Sentiment, and Expressions of Trust in Clinicians and Privacy of Health Information: Content Analysis

Effect of Recent Abortion Legislation on Twitter User Engagement, Sentiment, and Expressions of Trust in Clinicians and Privacy of Health Information: Content Analysis (Preprint)

Bad Boundaries: Geofences and the Intimacies of Location Data

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