Brief ReportUse of social media to assess the impact of equitable state policies on LGBTQ patient experiences: An exploratory study
Introduction
The Institute of Medicine recognizes that LGBTQ persons experience a disproportionate burden of disease and poorer health outcomes compared to the general population.1 Equitable policies at the state level that protect LGBTQ persons have been implemented to reduce discrimination towards this group. However, limited research has evaluated these equitable policies because of the difficulty of capturing LGBTQ patient experience. Previous studies have shown that LGBTQ persons report increased rates of discrimination across a wide variety of healthcare settings2, 3, 4 which may prevent them from disclosing their LGBTQ status.5,6 Traditional surveys such as the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey do not capture patient experience as it relates to sexual orientation and gender identity and few studies include LGBTQ-related questions.5 The goal of this research was to use a social media big dataset to evaluate the impact of equitable policies on patient experiences for LGBTQ persons.
Section snippets
Methods
A supervised machine learning classifier was built to identify tweets related to healthcare patient experience as documented in a previous study.7 Tweets related to patient experience were collected from February 2013 to February 2017. To assign LGBTQ status, we examined the Twitter user profile. A user who used any of the terms “lesbian,” “gay,” “bisexual,” “transgender”, “trans”, “queer”, “LGBT”, “LGBTQ”, “intersex”, “homosexual”, or “cis”, in their profile description was deemed an LGBTQ
Results
The total number of users in the patient experience dataset was 1,376,084 users. Out of these users, 13,689 (1.00%) self-identified as LGBTQ and 1,362,395 (99.00%) did not self-identify as LGBTQ. The number of LGBTQ users that had available geolocation data was 5545 and the number of non-LGBTQ users was 445,919; only tweets with geolocation data were used for analysis. A proportion test of the top 1000 most common words found no significant differences in proportion between LGBTQ users and
Discussion
Based on our findings, more supportive LGBTQ policies were associated with higher patient experience sentiment and this association between protective LGBTQ state policies was two times greater for LGBTQ versus non-LGBTQ users. Additionally, our results reveal that LGBTQ patients and non-LGBTQ patients use similar terminology to report on their healthcare experiences. This may indicate they have comparable experiences but different sentiment toward these experiences.
The MAP policy tally
Limitations
Findings in this study are associative and causal claims on the effect of LGBTQ policies on patient experience cannot be made from these results. Although our study evaluated tweet sentiment regarding healthcare experience, we did not study tweet sentiment regarding LGBTQ status. Our dataset included tweets describing any patient experience by LGBTQ users; we did not require that the tweets comment on how users perceived their LGBTQ status to have impacted the quality of healthcare received.
Conclusion
Measuring patient experience can be difficult and research has indicated that surveys that capture a breath of the healthcare experiences that include communications and interactions with providers and the care team are more strongly correlated with health outcomes.4,19 Therefore, measuring patient experiences through Twitter may enable us to capture a broader and more organic picture of patient experience compared to traditional surveys which can be limited by question specificity and social
Financial disclosure
No financial disclosures were reported by any of the authors of this paper.
Declaration of competing interest
This study was funded by the Robert Wood Johnson Foundation Grant 73495 (to YH, JBH). Additional support was received from the NIH/National Human Genome Research Institute Grant 5U54HG007963-04 (to JSB, JBH). YH reports receiving funding from the Canadian Institutes of Health Research. The funders played no role in the study design; collection, analysis, or interpretation of data; writing of the manuscript; or decision to submit the manuscript for publication.
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