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.
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
Objective: A key aspect of electronic health record (EHR) governance involves the approach to EHR modification. We report a descriptive study to characterize EHR governance at academic medical centers (AMCs) across the United States (US). Materials and Methods: We conducted interviews with the Chief Medical Information Officers (CMIOs) of 18 AMCs about the process of EHR modification for standard requests. Recordings of the interviews were analyzed to identify categories within pre-specified domains. Responses were then assigned to categories for each domain. Results: At our AMCs, EHR requests were governed variably, with a similar number of sites using quantitative scoring systems (7, 38.9%), qualitative systems (5, 27.8%), or no scoring system (6, 33.3%). 2 (11%) organizations formally review all requests for their impact on health equity. Though 14 (78%) organizations have trained Physician Builders/Architects, their primary role was not for EHR build. Most commonly reported governance challenges included request volume (11, 61%), integrating diverse clinician input (3, 17%), and stakeholder buy-in (3, 17%). The slowest step in the process was clarifying end-user requests (14, 78%). Few leaders had identified metrics for the success of EHR governance. Conclusions: Governance approaches for managing EHR modification at AMCs are highly variable, which suggests ongoing efforts to balance EHR standardization and maintenance burden, while dealing with a high volume of requests. Developing metrics to capture the performance of governance and quantify problems may be a key step in identifying best practices.
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