Transgender People's Experiences Sharing Information With Clinicians: A Focus Group-Based Qualitative Study.

PURPOSE: Investigating transgender people's experiences sharing health information in clinical encounters may yield insights for family medicine clinicians. METHODS: This was a qualitative study using a community-based participatory research approach and interpretive description methodology. Seven qualitative focus groups were conducted with 30 transgender adults living in North America. We used purposive sampling to ensure diversity. The focus groups were transcribed verbatim, and 2 investigators independently reviewed and coded each transcript, then they mutually reviewed the transcripts, reconciled their coding, and summarized the codes into themes. Themes were reviewed with community members, participants, and uninvolved clinically oriented investigators for member checking and peer debriefing. RESULTS: Four themes were noted: (1) transgender people often perceive clinicians' questions as voyeuristic, stigmatizing, or self-protective; (2) patients describe being pathologized, denied or given substandard care, or harmed when clinicians learned they are transgender; (3) transgender people frequently choose between risking stigma when sharing information and risking ineffective clinical problem solving if clinicians do not have all the information about their medical histories; (4) improving the safety of transgender people is difficult in the context of contemporary medical systems. CONCLUSIONS: Transgender people often must choose between stigma and potentially suboptimal care. Improvements in medical culture, policies, procedures, and data collection tools are necessary to improve the quality and safety of clinical care for transgender people. Institutional and systems changes may be required to safely and effectively implement sexual orientation and gender identity (SOGI) data collection in clinical settings.

Experiences of Transgender People Reviewing Their Electronic Health Records, a Qualitative Study.

BACKGROUND: The 21st Century Cures Act and the OpenNotes movement have brought patients immediate access to their electronic health records (EHRs). The experiences of marginalized people, including transgender people, accessing and reviewing their EHRs could inform documentation guidelines to improve patient-clinician rapport and reduce harm. OBJECTIVE: To investigate the experiences of transgender people reviewing EHRs. DESIGN: Qualitative study using community-engaged research and an interpretive description methodology. Participants were recruited via social media, snowball sampling was employed, and purposive sampling was used to ensure diversity in terms of age, race/ethnicity, and other factors. In focus groups, participants were asked to discuss their experiences reviewing their EHRs and, for those participants who were clinicians, their experiences reviewing other clinicians' documentation. PARTICIPANTS: Thirty transgender adults aged 20 to 67 years, including 10 clinicians. APPROACH: Digital audio-recordings of focus groups were transcribed verbatim. Content was analyzed to identify emerging essential elements and analysis was continued until no new themes emerged (i.e., saturation). KEY RESULTS: Four themes were noted. (1) Using the wrong name, pronoun, or gender marker for patients is common in the EHR, erodes trust, and causes trauma. (2) Various aspects of clinicians' notes contradict, blame, or stigmatize patients, across multiple axes of oppression. (3) Limitations of EHR capabilities create barriers to quality care. (4) Certain medical customs set the stage for marginalizing, objectifying, and pathologizing transgender people. CONCLUSIONS: Transgender people experience harm via various aspects of EHR documentation, suggesting that changes must be made to improve patient-clinician relationships and reduce ill-effects for patients.

Kankanet: An artificial neural network-based object detection smartphone application and mobile microscope as a point-of-care diagnostic aid for soil-transmitted helminthiases.

BACKGROUND: Endemic areas for soil-transmitted helminthiases often lack the tools and trained personnel necessary for point-of-care diagnosis. This study pilots the use of smartphone microscopy and an artificial neural network-based (ANN) object detection application named Kankanet to address those two needs. METHODOLOGY/PRINCIPAL FINDINGS: A smartphone was equipped with a USB Video Class (UVC) microscope attachment and Kankanet, which was trained to recognize eggs of Ascaris lumbricoides, Trichuris trichiura, and hookworm using a dataset of 2,078 images. It was evaluated for interpretive accuracy based on 185 new images. Fecal samples were processed using Kato-Katz (KK), spontaneous sedimentation technique in tube (SSTT), and Merthiolate-Iodine-Formaldehyde (MIF) techniques. UVC imaging and ANN interpretation of these slides was compared to parasitologist interpretation of standard microscopy.Relative to a gold standard defined as any positive result from parasitologist reading of KK, SSTT, and MIF preparations through standard microscopy, parasitologists reading UVC imaging of SSTT achieved a comparable sensitivity (82.9%) and specificity (97.1%) in A. lumbricoides to standard KK interpretation (97.0% sensitivity, 96.0% specificity). The UVC could not accurately image T. trichiura or hookworm. Though Kankanet interpretation was not quite as sensitive as parasitologist interpretation, it still achieved high sensitivity for A. lumbricoides and hookworm (69.6% and 71.4%, respectively). Kankanet showed high sensitivity for T. trichiura in microscope images (100.0%), but low in UVC images (50.0%). CONCLUSIONS/SIGNIFICANCE: The UVC achieved comparable sensitivity to standard microscopy with only A. lumbricoides. With further improvement of image resolution and magnification, UVC shows promise as a point-of-care imaging tool. In addition to smartphone microscopy, ANN-based object detection can be developed as a diagnostic aid. Though trained with a limited dataset, Kankanet accurately interprets both standard microscope and low-quality UVC images. Kankanet may achieve sensitivity comparable to parasitologists with continued expansion of the image database and improvement of machine learning technology.