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Health ICTs and transgender health equity: a research agenda
Katherine Wyers
Department of Informatics, University of Oslo, Oslo, Norway
ABSTRACT
While research in healthcare service provision for transgender and gender
diverse (TGD) people has seen rapid progress, health information
communication technologies (health ICTs) research on this key
population is falling behind. Blindspots in the literature risk perpetuating
systemic barriers to healthcare access. This critical, cross-disciplinary
literature review applies a health equity perspective alongside the lenses
of structural violence and intersectionality. It presents a research agenda
for building systematic knowledge in the Information Systems (IS) and
Information Communication Technologies for Development (ICT4D)
fields, identifying and addressing blindspots that risk amplifying existing
inequities for TGD people. It makes theoretical contributions to
discourse on health equity and ICTs by exploring how health ICTs shape
the inclusion and exclusion of marginalized communities. In facilitating a
scholarly and practice-based understanding of the effect of health ICTs
on TGD health provision, it paves the way for these fields to make
significant contributions to global health equity.
KEYWORDS
Health ICTs; health equity;
transgender; gender diverse;
intersectionality; structural
violence
1. Introduction
Research in healthcare provision for transgender and gender diverse people (TGD) has progressed
rapidly in multiple disciplines over the last decade and there are calls in the global health discipline
for cross-disciplinary engagement with the reduction of transgender health inequities (Pillay et al.,
2022). In contrast to this, the research around TGD people and health information communication
technologies (health ICTs) development and use has fallen behind, leaving blind spots that risk
the amplification of health inequities by health ICTs (Qureshi, 2016).
Health ICTs are digital technologies applied in the field of healthcare to improve the management,
delivery, and accessibility of healthcare services, and to improve communication and information
exchange between patients and healthcare providers. The term encompasses a wide range of
different tools for collecting, storing, and sharing health information. These include health monitoring
devices, electronic medical records (EMRs), and health information exchanges (Jen et al., 2022; OECD,
2013; Sheikh et al., 2021). These tools shape the distribution of resources within a health system,
impacting on who receives care and who is excluded (Kickbusch et al., 2021). While health ICTs can
improve healthcare provision, their influence on resource distribution risks amplifying existing
health inequities (Qureshi, 2016; Ram et al., 2022). This study focuses on the relationship between
health ICTs and TGD people, a key population who aredisproportionately affected by health inequities
© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed underthe terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this
article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
CONTACT Katherine Wyers katherwy@ifi.uio.no Department of Informatics, University of Oslo, Gaustadalléen 30, Oslo
0373, Norway
Silvia Masiero is the accepting Editor-in-Chief for this manuscript.
INFORMATION TECHNOLOGY FOR DEVELOPMENT
https://doi.org/10.1080/02681102.2023.2292740
(Wesp et al., 2019). Unless the needs of this population are considered in the development and use of
health ICTs, these digital technologies risk further amplifing inequities faced by TGD people.
Within health ICT literature, there is a subdomain that explores the relationship between health
ICTs and TGD people. This subdomain has several blind spots, and if they persist, groups within this
key population will continue to experience exclusion. To address these blindspots, the Information
Systems (IS) and Information Communication Technologies for Development (ICT4D) fields need sys-
tematic knowledge about how health ICTs shape TGD health equity. This knowledge can then be
used to reduce the risk of further amplification of health disparities, and the adverse implications
this can have on the health and wellbeing of the members of this key population.
Health equity is ‘the absence of health differences between more and less socially advantaged
groups’(Nolen et al., 2005, p. 597). For this study, Amartya Sen’sdefinition of development is
adopted, whereby development is viewed as a process of ‘expanding substantive freedoms’(Sen,
1999, p. 3). When development is viewed in this way, the reduction of health inequities becomes
a central concern in human development (Sen, 1999). The ‘D’in ICT4D is then taken in this study
to refer to the reduction of health inequities for all TGD people around the globe, rather than
placing a focus on a specific geographical region such as the Global South. This understanding of
development is in line with the global health community, which, in recent years, has turned its atten-
tion toward the reduction of global TGD health inequities, seeing them as an opportunity to reduce
global health inequities by exploring challenges faced by this key population (Lo & Horton, 2016).
The objective of this article is to promote health equity as a central concern in health ICT research,
drawing attention in particular to health equity for TGD people. The study takes a health equity per-
spective, focussing on health ICT research literature that explores TGD issues in relation to the devel-
opment and use of ICTs for health service planning and provision. It seeks to understand the blind
spots in this literature, in particular those that relate to health equity, and presents a research agenda
for IS and ICT4D researchers and practitioners to address these blind spots.
This study is guided by the following two broad research questions: (1) How are issues of health
equity for TGD people currently studied and discussed in health ICT literature? and (2) What insights can
be used to inform future research on health ICTs and TGD people? It adopts a narrative style literature
review methodology to address these questions by exploring the literature at the intersection of
health ICTs and TGD people. This methodology provides a broad overview of the manner that
themes are debated and discussed within the research domain (Ferrari, 2015).
A corpus of 24 peer-reviewed studies was selected from the fields of public health, transgender
health, medical informatics, laboratory medicine and the general medical fields, and analyzed using
a health equity perspective. Section 4 of this paper presents the findingsofthereview.Thefour
themes emerging from the corpus relating to health equity are (1) the need for gender identity data
in healthcare provision (2) standardization and system maintainability, (3) identifying TGD people in
health datasets, and (4) the representation of TGD voices in health ICT research. These findings are dis-
cussed in Section 5, where they are explored through the analytical lenses of structural violence and
intersectionality under three headings: (1) trans exclusion as a systemic problem, (2) visibility and invi-
sibilization, and (3) the need for diverse voices and contexts. Building on these three headings, Section 6
presents a future research agenda to address blind spots in the literature. The agenda promotes the
building of a body of systematic knowledge for IS and ICT4D fields to improve the development and
use of health ICTs that affect the lives of TGD people. It is presented for researchers and practitioners
to engage with the domain and contribute to the project of global health equity. It contains founda-
tional orientations, four research streams and three methodological approaches. So doing, it presents
not only the work that needs to happen, but also how the work should take place.
This study contributes to the discourse on health equity and ICTs by highlighting absences in health ICT
literature that can lead to the exclusion of key populations. It seeks to amplify the voices of marginalized
communitiesinhealthICTdiscourseandtoensurethat technologies are developed that consider the
needs and potential implications for key populations. This study uses the term transgender and gender
diverse (TGD) throughout this paper. This term was selected to be inclusive of people whose gender
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identity is not the identity assigned to them at birth, butwhodonotnecessarilyidentifywiththeconcept
of transgender. In the interest of making the paper coherent, all TGD-related terms in the direct quotations
were replaced with [TGD] unless the original term was necessary. The need to do this stems in part from the
lack of consensus around terminology, an issue that this study discusses further in the Results and Discus-
sion sections of this paper. Table 1 shows a glossary of the TGD-related terminology used in this article.
2. Background
This study explores the literature in an interdisciplinary domain at the intersection of health ICTs and
health equity for TGD people. From the global health field, there are calls for cross-disciplinary
engagement with transgender health equity, while informatics research has been exploring how
health ICTs impact on health equity and how ICTs can better meet the needs of TGD people.
2.1. Health equity and TGD people
Despite the vision of global health to ‘includ[e] the excluded’(Sharma, 2021, p. 902), TGD people, and
other members of the LGBTQ+ communities, are noticeably underrepresented in global health
research (Pillay et al., 2022). There is an increasing call for cross-disciplinary engagement to consider
the experiences of LGBTQ+ communities, and a consideration of the intersection between health
inequities and LGBTQ+ people which, to date, has remained generally neglected. If the global
health research field is to deliver on its vision of including the excluded, it must not be the bearer
of heteronormativity and cisnormativity (Pillay et al., 2022).
TGD people have the same health care needs as cisgender (non-transgender) people, such as
basic physical exams, preventative care, treatment control, disease prevention and treatment, inju-
ries or disabilities, and aftercare, along with concerns around privacy and safety. Many of the issues
faced by TGD people are also mentioned in the broader literature in relation to other groups.
However, within the context of the TGD community, these are exacerbated by the presence of struc-
tural inequities. For example, TGD people may have special needs, such as a transgender woman
needing a prostate exam or a transgender man needing a cervical cancer screening.
In recent years, the global health community has started to view the reduction of TGD health
inequities as an opportunity to reduce global health inequities by exploring challenges faced by
these key populations (Lo & Horton, 2016). Systemic barriers to healthcare exist for these commu-
nities, whether or not the care is specifically related to their gender identity. These barriers exist as
a result of social stigma, with TGD people being four times as likely to live in low-income households,
and twice as likely to be unemployed (Dunne et al., 2017). Minority groups within the TGD commu-
nities, such as indigenous people or people of color, can face further disadvantage through racism,
classism and other social processes of privilege and oppression. These interlocking structures of
oppression further shape the experiences of healthcare engagement among these TGD people.
Table 1. Terminology and definitions.
Term Definition
Cisgender person A person whose sex assigned at birth is the same as their gender identity
Transgender person A person whose sex assigned at birth differs from their gender identity
Gender diverse
person
An inclusive term to include people who do not necessarily identify with the term transgender, such as
communities where the term transgender is not relevant
Heteronormativity The societal assumption that all people are heterosexual
Cisnormativity The societal assumption that all people are cisgender
Gender-affirming
care
Any single or combination of a number of social, psychological, behavioral or medical (including
hormonal treatment or surgery) interventions designed to support and affirm an individual’s gender
identity (WHO, 2023)
TGD-related ICD
codes
Diagnostic codes that indicate engagement with trans-related gender affirming care
INFORMATION TECHNOLOGY FOR DEVELOPMENT 3
Existing research, although limited, has highlighted that TGD people experience multiple health dis-
parities due to stigma, discrimination, and unique barriers to accessing quality care (CDC, 2020; Restar
et al., 2021). TGD people are disproportionally affected by health inequities on account of the social
factors and social stressors, including challenges to accessing competent medical professionals,
family support structures and cultural norms related to gender and sexuality (Wesp et al., 2019).
TGD health equity should be seen as an effort that is part of the broad project to reduce global
health inequities by reducing inequities within marginalized communities. Therefore, the reduction
of TGD health inequities needs a concerted effort with cross-disciplinary engagement from the
medical, political, legal, scientific and informatics fields (Lo & Horton, 2016). These fields must
work together to understand the health needs of TGD people, to assume responsibility for their well-
being, and to ensure that there is a reduction in global health inequities (Landers & Bowleg, 2022).
Such a concerted effort must take an intersectional approach to research, by naming the power
relations at play and considering how structures of domination interlock to produce inequities.
Researchers should be self-reflexive by considering their own heteronormative, cisnormative
assumptions and how these assumptions can influence their work (Wesp et al., 2019).
2.2. Health ICTs and TGD equity
Society is becoming more reliant on the information that is created and communicated through
digital technologies. Assumptions are made that the information stored on these systems is accurate,
and that actions can be taken based on that information (Rogerson et al., 2019). Within the health
system, ICTs are increasingly being used to determine who gets access to which treatment
(Qureshi, 2016). They influence the diagnostic decision-making as well as the organizational prac-
tices within healthcare (Béranger, 2015). Through this mediation, they have the potential to
reduce health inequities by positively impacting on these processes. However, they can also
amplify existing inequalities and exacerbate the existing barriers to health (Qureshi, 2016). Infor-
mation systems mediate much of the information exchange, and information systems researchers
have an obligation to ensure their systems do not cause harm for TGD communities and other mar-
ginalized groups (Lo & Horton, 2016; Pillay et al., 2022). ‘[Digital] health interventions that are not
anchored to local contexts and understandings around health …might be ineffective or even
harmful’(Kickbusch et al., 2021, p. 1752). Considering the far-reaching potential of health ICTs,
their research can make a substantial contribution to TGD health equity. The field should seek to
understand the mechanisms behind the societal impact of health ICTs, considering the ethics of
digital health, privacy, self-determination, and autonomy, and to develop approaches to mitigate
the adverse effects they can have on a TGD individual’s health and wellbeing (Spiekermann et al.,
2022). Such research should be grounded in collaboration with TGD communities, centering the
voices and the embodied knowledge of members of the communities (Wesp et al., 2019).
Demographic data about sexual orientation and gender identity is essential to understanding the
needs of TGD populations. However, healthcare providers may be uncomfortable asking for the infor-
mation, or may not know how to elicit it, and patients themselves may also be hesitant to disclose (Insti-
tute of Medicine, 2013). In response to these concerns, and the growing need for this data in electronic
medical records (EMR), the World Professional Association for Transgender Health (WPATH) EMR Working
Group wasformed to explore the digital representation of TGD people in health ICTs. In 2011, it released
recommendations for how to design EMRs to be inclusive of TGD people, recommending that TGD
people must be adequately represented in EMR even though they are a minority group. EMRs should
store patient’s medical transition history and notify healthcare providers if the patient’s preferred
name differs from their legal name, to avoid mis-naming and misgendering (Deutsch & Buchholz, 2015).
In January 2018, the US government’s Department of Health mandated the collection of gender
identity data in EMRs as part of the Meaningful Use Stage 3 regulations that sought to improve
health data in the US health system. This narrative review explores how conversations are conducted
worldwide within the health informatics and health ICT domains since 2017.
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3. Methodology
This study used a health equity perspective to explore the relationship between health ICTs and the health
and wellbeing of TGD people. This research domain, which focuses on health ICTs and TGD people, is inter-
disciplinary, being at the intersection between informatics, social sciences and health. The study drew on a
selected corpus of 24 peer-reviewed papers from different disciplines that specifically discuss TGD people
in relation to health ICTs. This study explored the themes and debates from the corpus to identify blind
spots where health ICT research can contribute to the reduction of TGD health inequities. The findings
from the review were analyzed using the structural violence lens and the intersectionality lens.
3.1. The narrative review
The review methodology chosen for this study was a narrative style literature review, a non-systema-
tic approach that is ‘aimed at identifying and summarizing what has been previously published,
avoiding duplications, and seeking new study areas not yet addressed’(Ferrari, 2015, p. 4). The nar-
rative review differs in several ways from a systematic review. While the systematic review focuses on
one specific research question and has a very narrow focus, the narrative review is much broader. It
can address multiple questions in the same study and aims to build an understanding of the discus-
sions taking place within a research domain.
The narrative review approach was chosen for this study because it provided a broad overview of the
research domain at the intersection between health ICTs and TGD people. The study sought to understand
themannerthatissuesofhealthequityarebeingdiscussed within the literature, and how these have
changed over the six years from 2017 to 2022. The breadth of scope afforded by the narrative review
enabled tracking of the development of discourse over this period in a way that could be lost if the focus
is narrow, as is the case with the systematic literature review approach (Ferrari, 2015;Grant&Booth,2009).
3.2. Literature search strategy
PubMed and SCOPUS were selected as the databases to use for the literature search. PubMed
indexes literature from the major medical research outlets. SCOPUS indexes a broad range of
cross-disciplinary academic literature. The combination of a medical database and a general data-
base provided access to a broad range of sources from several domains. The corpus was selected
from the literature published since 2017.
This study proactively sought to mitigate the potential for selection bias in the corpus of literature.
A major source of potential bias here was in the keywords used in the search queries. Terminology
related to gender diversity is deeply rooted in context, with a wide variety of terms used among indi-
genous gender diverse people around the world to refer to their identities and their communities.
Examples of these indigenous gender diverse communities include the Hijra and Kinnar communities
in India (Singh et al., 2014), the Faa’fa’fine people of Samoa (Kanemasu & Liki, 2021), and the travesti
community in Brazil (Kulick, 1998). To be inclusive of such diversity within the search strategy, an
initial search of the literature was conducted based on queries that used a list of these indigenous
terms. It was noted from the search results that the vast majority of published studies used these indi-
genous terms together with the terms transgender,gender diverse,gender fluid, or other related terms,
indicating that scholars who were publishing research on indigenous gender diversity use these
terms to connect the studies to broader gender diverse discourse. This insight was then used to sim-
plify the search strategy. For this study, the terms transgender,gender diverse,gender fluid, and other
related terms were taken to be umbrella terms within the academic literature, albeit a set of terms that
are not necessarily used by the indigenous gender diverse people themselves.
To conduct the search, a list of related keywords was drafted for both transgender and gender
diverse communities and health ICT. Search queries were entered into PubMed and SCOPUS, using
AND/OR operands. Both transgender and gender diverse communities terms and health ICT terms
INFORMATION TECHNOLOGY FOR DEVELOPMENT 5
were used to search only the titles of literature. The lists of keywords are shown below, and the
stages of the literature search are shown in Figure 1.
3.2.1. Transgender and gender diverse communities
transgender, non-binary, nonbinary, transsexual, gender non-conforming,
gender divers*, genderqueer, gender fluid, transphobi*, gender*, trans*.
3.2.2. Health ICT
electronic medical record*, electronic health record*, EMR, EHR, health
data, health informatics, medical informatics, health technolog*, health-
care technolog*, health information system*, laboratory information
system*, digital health.
3.2.3. Inclusion/exclusion criteria
.Must be peer-reviewed
.Prefer empirical over review
.When a single author disseminates multiple papers on the same topic to different audiences,
select only one
.Only include papers published since 2017
.English language only
.If multiple studies form part of a larger project that is summarized, select only the summary paper
.The study must have published its methodology
3.3. Search results
Figure 1. Resulting counts of studies at each stage of the search process.
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3.4. Analytical lenses
Two lenses were used to analyze the findings with respect to systemic barriers and health inequities.
The first is the Structural Violence lens, which describes how institutional and societal structures limit
the capacity for an individual to achieve their potential. When this limitation of potential is avoidable,
an act of violence has taken place. The lens draws a distinction between direct violence and structural
violence, the former of which emerges at the interpersonal level and whose source can be traced to a
specific event, whilethe latter is systemic and it is difficult to trace since it emanates from the structures
(Farmer, 2004; Galtung, 1969). The second lens used is the Intersectionality lens, which sheds light on
bias by highlighting the overlapping group memberships, such as race, class, sexual orientation, and
gender identity, and examines how disadvantage manifests within society through the oppressions
that result from the interlocking of these group membership (Crenshaw, 1991).
4. Results
An overview is now presented of the findings from the review of the selected corpus of 24 papers.
First, the descriptive statistics are presented, followed by the themes that emerge that relate to TGD
health equity.
4.1. Descriptive statistics
4.1.1. Publications per year
Of the 24 papers, 13 were published in the final 18 months of the time range, from January 2021 to
June 2022. There is a steady growth of literature in the domain from multiple disciplines. While the
two papers from 2017 came from the medical field, since 2018 the medical informatics field has con-
sistently contributed to this domain, and it is now a domain with contributions from informatics,
health, and laboratory medicine (Table 2). This indicates that there is a growing awareness that the
domain needs a cross-disciplinary response. Notably absent from this corpus are studies emerging
from the IS and ICT4D fields. For further details about each paper in the corpus, refer to Appendix 2.
4.1.2. Methodologies used in the corpus of studies
Of the 24 studies in the corpus, seven collected new qualitative data, 10 reported on the findings
from searching existing health datasets, and the remaining seven studies were literature reviews
that assessed various themes and disseminated knowledge (Table 3).
Table 2. Publications by year and by discipline.
Domain 2017 2018 2019 2020 2021 2022* Total
Public Health 1 2 1 4
Trans-related Health 1 1 2 1 5
Medical Informatics 2 1 1 2 3 9
Interdisciplinary 1 2 3
Population Health 1 1
Laboratory Medicine 2 2
Total 2 2 3 4 8 5* 24
*Note: 2022 findings are from January to June.
Table 3. Publications by geographical focus.
Data Source Asia North America South America Europe Africa Total
New Qualitative Data from TGD people 3 3
New Qualitative Data from other sources 2 1 3
New Quantitative Data 1 1
Existing Health Datasets 10 10
Literature (Author institution and/or geographical focus) 6 1 7
Total 1 21 1 1 0 24
INFORMATION TECHNOLOGY FOR DEVELOPMENT 7
Of the seven studies collecting new primary data, five were conducted in North America, one in
Thailand and one in Argentina (Appendix 2). Three of the seven studies elicited qualitative data from
TGD people, three elicited qualitative data from training observations, administrator interviews and
health provider interviews, and one gathered quantitative data through survey and monitoring of
platform use. No study explicitly stated that there were TGD researchers involved in the research
team. However, it must be noted that TGD researchers may be cautious about disclosing their
status, and there may be studies in the corpus that have TGD researchers in their teams.
4.2. Themes
Within the corpus, there are four major themes being debated that relate to health equity. The
themes are (1) the need for gender identity data in healthcare provision, (2) standardization and
system maintainability, (3) identifying TGD people in health datasets, and (4) the representation
of TGD voices in health ICT research. The findings from these themes will now be presented.
4.3. The need for gender identity data in healthcare provision
Many of the studies call attention to the urgent need for gender identity data in the provision of
healthcare, and to address concerns around the collection of such data. There are three reasons
why such data is needed. Firstly, gender identity data is necessary for population health monitoring,
and for informing policy decisions. Secondly, it is needed for individual patient healthcare provision.
Thirdly, the data enables monitoring of hospitals and health clinics in the delivery of health
programs.
4.3.1. Population health monitoring
Accurate data related to gender identity ‘holds great utility with respect to tracking disparities over
time, and patient monitoring for prevention and treatment outreach’(Livingston et al., 2022, p. 98).
The data helps healthcare providers identify the services needed to support the TGD patients (Grasso
et al., 2020) and identify health disparities in the population by ‘enabl[ing] providers, researchers,
and policymakers to monitor progress toward the elimination of disparities in healthcare access,
risk screening and treatment’(Bosse et al., 2018, p. 269). The data has been used to show that
TGD communities have a higher prevalence of post-traumatic stress disorder (Livingston et al.,
2022) and HIV (Niforatos et al., 2020) compared to cisgender patients.
4.3.2. Individual patient healthcare provision
Healthcare providers must have access to this gender identity information to ensure they can ‘treat
[the patient] in a manner consistent with their gender identity’(Kirkland, 2021, p. 6). ‘Promoting the
highest standards of care for [TGD] populations requires collecting and documenting detailed infor-
mation about patient identity, including sex and gender information’(Patel et al., 2021, p. 210). It is
needed to ensure TGD people has access to ‘care that is compassionate and relevant to their unique
needs’(Bosse et al., 2018, p. 267) and ‘enables healthcare organisations to create a more patient-
centred care experience’(Goldhammer et al., 2022, p. 1303). This information is also needed for
the ‘effective provision of laboratory services for [TGD] patients’(Costelloe & Hepburn, 2021, p. 264).
4.3.3. Monitoring hospitals and health clinics
Gender identity data is needed to monitor the performance of health clinics and hospitals. These
facilities must reach certain targets for health programs. For example, cervical cancer screening
must to be conducted for all people who have a cervix. These checks are only required for patients
who have a cervix, which transgender women do not have. If this is not taken into consideration, the
system can flag a facility as being ‘out of compliance …even if the patient does not have a cervix’
(Grasso et al., 2021, p. 2533). Equally, transgender men may have a cervix, and a system that does not
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take into consideration that they may require a cervical cancer screening may subsequently fail to
flag that a facility is out of compliance.
4.3.4. Concerns around the collection of the data
The need for TGD patients to disclose their gender identity raises major concerns around privacy.
Many TGD patients have expressed concerns about exposing gender identity data to registration
staff, clinical staff, family members and third parties, as such disclosure can risk harm (Goldhammer
et al., 2022). Patients ‘might feel hesitant in communicating [the information] with providers’(Anand
et al., 2017, p. 21). The eliciting of gender identity data places the TGD patient in a concerning situ-
ation, where they ‘are motivated to forego personal privacy now, in order to gain access and equity
…[V]isibility brings immense vulnerabilities’(Thompson, 2021, p. 100) leading to a visibility paradox.
This process of foregoing personal privacy in exchange for access to healthcare services should be
seen as a concern for all involved in healthcare, including those in health ICTs. Anand et al. (2017)
suggest that ICTs can help reduce this concern using a computer portal at registration where
patients can add their gender identity data without discussing it aloud. However, such data much
be carefully stored until it is needed by the healthcare provider.
Within the corpus, several studies explore how gender identity can be appropriately collected. A
difficulty in gathering such data stems from ‘structural incompetency [that] translates into significant
pitfalls for [gender identity data] collection’(Kronk et al., 2022, p. 274). TGD healthcare is mired with a
‘lack of [healthcare] provider education, heterosexism, social stigma, and several structural barriers’
(Bosse et al., 2018, p. 268).
The collection of gender identity data as part of routine data collection is discussed in several
studies (Davison et al., 2021; Goldhammer et al., 2022; Niforatos et al., 2020). Collecting the data
through an EMR as routine data would help monitor health on a national level in a way that is
more effective than ‘primary reliance on survey studies that have significant limitations’(Niforatos
et al., 2020, p. 194). However, while tools for collecting such routine data are being introduced in
health ICT upgrades, ‘healthcare providers still rarely utilize these tools’(Ehrenfeld et al., 2019,
p. 442).
There are concerns that the new regulations mandating the collection of gender identity data can
only be practically implemented by large high-resource facilities who have the economies of scale to
absorb the costs. Small health clinics are concerned about the costs of new technological systems.
While some hospitals have the resources to make these changes themselves, ‘many rel[y] on the cor-
porate providers to update their software’(Kirkland, 2021, p. 6). Furthermore, they are concerned
that their low numbers of patients make it practically impossible to meet the demands of de-identi-
fying patient records, leading to potential ‘privacy-violating types of visibility for [TGD] patients’
(Thompson, 2021, p. 97). In response to this, one small health center has opted to ‘collect as little
information [about gender identity] as possible’(Thompson, 2021,p. 97).
4.4. Standardization and system maintainability
Several studies highlight the lack of standardized terminology around TGD people, and explore
the challenges this raises for building maintainable data systems and health ICTs.
Throughout the corpus of studies, there is an evident lack of coherence around TGD-related ter-
minology. This has been raised as a concern by several studies. ‘[S]tandardised data that allows a
correct identification of this population is essential to ensure the continuity of care’(Levi et al.,
2019, p. 1133) and the lack of standardized terminology risks the use of ad-hoc terminology in
healthcare delivery, or worse, the perpetuation of oppressive, pejorative terminology based on out-
dated assumptions (Kronk et al., 2022). Plural understandings of the term transgender are evident in
Thompson et al. (2021), where it is noted that 13 patients in their dataset had been marked as having
a sexual orientation of transgender, a term which does not refer to a sexual orientation, and that 28
patients were marked as having a sexual orientation of neither exclusively male or female, a term
INFORMATION TECHNOLOGY FOR DEVELOPMENT 9
typically used to indicate nonbinary gender identities. Both cases indicate a conflation of gender
identity and sexual orientation.
The plurality of meaning can also be seen in the methodologies used by the researchers. Anand
et al. (2017) combine the cohort of men who have sex with men (MSM) together with the cohort of
transgender women. They also use the term transgender women and transgender individuals inter-
changeably. Finally, they misclassify transgender as a sexual orientation. Sequeira et al. (2020)
shows how the lack of standardized terminology affects the research methodologies used. The
study collected data on a diverse range of different gender identities, including genderqueer,gen-
derfluid,demiboy,androgyne, and agender. However, this data was subsequently aggregated, redu-
cing the range of genders to three, namely transmasculine,transfeminine, and nonbinary. This
reduction of a wide range of genders into a ternary classification loses much of the nuance
offered by a diverse range of gender identities and leads to the invisibilization of a wide range of
these identities.
Within the current climate where terminology has not yet been standardized, there are concerns
about the exchange of meaningful information between digital health systems. A major issue is
posed by the ‘conflated use of sex and gender to represent a single binary concept’(Antonio
et al., 2022, p. 381). Mismatches between binary-based reference ranges and gender diverse identi-
ties can lead to warping of meaning (Patel et al., 2021), and this factors into the effectiveness of diag-
nostic decision-making (Costelloe & Hepburn, 2021). Furthermore, when TGD patients move
between care providers, warping of meaning during the exchange of the EMRs may lead to unin-
tended mistreatment due to incorrect name, pronouns, or medical categorization (Grasso et al.,
2020). The interoperability issues and lack of standardization are felt most acutely by small,
under-resourced health centers, where many providers need to exchange health data with entities
‘governed by a myriad of gender rules’(Thompson, 2021, p. 99). There is an urgent need to moder-
nise the information practices around gender, sex, and sexual orientation, without which ‘health
inequities of [sexual and gender minorities] are likely to persist or worsen’(Davison et al., 2021, p. 9).
There are several studies that discuss how to move toward standardized terminology. Thompson
et al. (2021, p. 99) raise a caveat that the ‘modes of categorization for analysis can reify types of indi-
viduals’, leading to the belief that a TGD person is a singular, concrete archetype, rather than a
person with one of a broad range of gender diverse identities, with each individual having
unique lived experiences and healthcare needs. When modifying structures and social represen-
tation ‘[i]t is important to take into consideration the cultural aspect since it constitutes a system
of shared meanings from which people interpret reality …Otherwise the mere IT tool that allows
the recording of data in question would be insufficient’(Levi et al., 2019, p. 1131). Without due con-
sideration for the diverse needs in the TGD communities, members of the community may find they
need to alter their behavior or presentation to fit within the category for accessing an intervention.
So doing, ‘the burden of change is …placed on the individual rather than in structural conditions’
(Thompson, 2021, p. 99). This process must therefore be conducted carefully, in collaboration
with the TGD communities and be culturally sensitive by engaging with the language of the com-
munity (Kronk et al., 2022). Despite the proactive, trans-inclusive methodology used by Kronk
et al. (2022) the study focuses primarily on the standardization of terminology used in the USA,
and does not consider terminology from a wider range of geographical contexts where gender diver-
sity exists.
Health ICTs ‘should be able to provide adequate tools for healthcare centres to provide inclusive
care and medical decision support for [TGD] individuals’(Patel et al., 2021, p. 210). One approach is to
change the data modeling and digital representation in EMRs to be inclusive of all body types. This
approach, known as the anatomical inventory, stores an inventory of body parts for each patient and
provides a means of storing patient health needs without encouraging healthcare providers to make
assumptions of body parts based on the patient’s sex at birth or the gender identity of the patient
(Antonio et al., 2022; Davison et al., 2021; Grasso et al., 2021). Such an approach seeks to improve the
quality of healthcare provision for the patient.
10 K. WYERS
4.5. Identifying TGD people in health datasets
We have seen that identifying TGD patients enables the monitoring of population health disparities.
However, many health datasets have been collected without gender identity data. An active stream
of research is underway to explore novel approaches to identify a ‘comprehensive and representa-
tive [TGD] cohort’(Thompson et al., 2021, p. 1) using available data-points.
4.5.1. Algorithms for identifying TGD people
Within the corpus, ten studies sought to develop algorithms to identify TGD patients in health data-
sets, all of which were based on datasets from the USA. Eight used International Classification of
Disease (ICD) diagnostic codes as part of their algorithm as an indicator that the individual has
engaged with TGD-related gender-affirming care. Several studies extended this by applying
various lists of TGD-related terminology to search the health datasets. Two studies exclusively
used gender identity data that was self-declared by the patient and stored in the EMR (Grasso
et al., 2020; Thompson et al., 2021). Alpert et al. (2021) added a further search dimension by including
cases where a sex-specific diagnosis did not pair with registered gender; for example a male with
ovarian cancer.
4.5.2. Variance in estimates
This diversity of approaches had led to wide variance in the estimates of the size of the TGD popu-
lation. Results varied from 0.003% (44; n= ±1,300,000) (Alpert et al., 2021), 0.019% (10,160; n=
53,449,400) (Niforatos et al., 2020), 0.0192% (10,270; n= 1,504,423) (Wanta et al., 2019), ±0.1%
(234; n= ±234,000) (Ehrenfeld et al., 2019), 0.4% (±499; n= 124,938) (Grasso et al., 2020), 0.4%
(100; n= 11,906) (Thompson et al., 2021) up to the highest estimate of 0.784% (552; n= 70,048)
(Dubin et al., 2020). Three further studies developed novel algorithms but did not publish their
sample-size (Blosnich et al., 2018; Blosnich & Boyer, 2022; Livingston et al., 2022).
4.5.3. Benchmarking
Notable across the studies is the lack of benchmarking of these estimates. Accurate estimations of
the size of the TGD population are hampered by a lack of accurate reliable data. However, a
review of 50 years of research on TGD people around the world has indicated that the ‘[TGD com-
munity] represent a sizable proportion of the general population …[with credible estimates
ranging] from 0.1% to 2.7%, depending on inclusion criteria, age of participants, and geographic
location’(Goodman et al., 2019, p. 318). To benchmark the corpus analyzed in this study, the USA
national estimate of 0.6% was used. This estimate was published by the Williams Institute at the
School of Law in the University of California, USA. It is a widely cited source and provides a credible
benchmark for this study (Flores et al., 2016; Herman et al., 2022).
Of the seven studies in the corpus that published their estimates, four were significantly below
the William Institute USA estimate. Only three studies (Alpert et al., 2021; Dubin et al., 2020;
Grasso et al., 2020) benchmarked their findings against findings from other studies. While Niforatos
et al. (2020) mentions the estimated number of TGD people in the USA, it does not compare its esti-
mate of 0.019% to the national estimate. The three studies most closely matching the national esti-
mate (Dubin et al., 2020; Grasso et al., 2020; Thompson et al., 2021) identify TGD people using
datasets where gender identity data was elicited directly from the patients, with one study correlat-
ing this data with TGD-related ICD codes.
4.5.4. Critical engagement with TGD-related ICD codes
‘Using ICD codes to define [TGD] status invites misclassification biases’and false negatives (Blosnich
& Boyer, 2022, p. 385). There are ‘logistical and cultural caveats to using ICD codes to define [TGD]
identity in [electronic health record] studies’(Blosnich et al., 2018, p. 907). Some TGD people do not
want or need a documented TGD-related diagnosis (Ehrenfeld et al., 2019), while others have a
INFORMATION TECHNOLOGY FOR DEVELOPMENT 11
diagnosis but do not necessarily identify as transgender or gender diverse (Blosnich & Boyer, 2022).
Clinicians may have ‘purposefully kept [TGD]-related terms out of the notes at the behest of their
patients or to protect their patients’privacy’(Blosnich et al., 2018, p. 907). There are also significant
financial, social, and medical barriers to accessing such a diagnosis, which leaves many TGD people
unable to access care and ‘[lacking the] demographic data that identifies them as [TGD]’(Dubin et al.,
2020, p. 6). Wanta et al. (2019, p. 314) note that the rates of mental health diagnoses among TGD
patients in their dataset ‘are likely inflated …by the prerequisite mental health assessment before
starting medical interventions for transitioning.’In the USA, a high proportion of TGD people are
uninsured, homeless and/or unemployed. Since much of the health data in the USA comes from
insurance data, their absence from the data leads to exclusion from these searches (Dunne et al.,
2017). Even if the study uses datasets where gender identity data was elicited, the number of
TGD people is still likely to be underrepresented because the disclosure of this data carries a risk
of stigma and discrimination. TGD people tend to remain private about their status unless it is absol-
utely necessary (Dunne et al., 2017). Wanta et al. (2019, p. 315) recommend that ‘the sensitivity of a
large database study such as this must be compared against smaller studies with a higher specificity’
to be attuned to potential biases and absences.
Eight studies use ICD codes in their algorithms. Five of these critically reflect on the validity of
their findings in light of the issues associated with TGD-related ICD codes (Alpert et al., 2021; Blosnich
et al., 2018; Blosnich & Boyer, 2022; Dubin et al., 2020; Ehrenfeld et al., 2019).
4.5.5. Monitoring population health
The reliance on evidence of engagement with gender-affirming care as the principal proxy to iden-
tity TGD people ‘devalues a range of experiences …[d]istilling gender diversity into a few medica-
lized, trans categories’(Thompson, 2021, p. 99) and erases nuance that is relevant for population
health monitoring. The experiences of TGD people are often discussed in relation to the medicaliza-
tion of their physical bodies, rather than in relation to their humanity. Furthermore, it ‘erases diversity
across the cisgender spectrum’(Thompson, 2021, p. 99). Gender-affirming surgeries and hormone
therapies have long been used by cisgender (non-transgender) people, and their use as data-
signals for identifying TGD people leads to misclassification of cisgender people as being transgen-
der or gender diverse.
4.6. The representation of TGD voices in health ICT research
Several studies call for the centering of TGD voices within research to highlight the lived experiences
of TGD people and to sensitize the research to the systemic barriers created by health ICTs. Under-
standing the heterogeneity of the TGD communities is crucial for the design of adequate systems
that meet their needs. Of the six studies based on qualitative data, three elicited new data directly
from members of the TGD communities (Antonio et al., 2022; Dunne et al., 2017; Sequeira et al.,
2020). Anand et al. (2017) are based on data collected from surveys and app-use by LGBTQ+
people. Several studies discussed heterogeneity, noting how each TGD person has unique
medical needs, may have chosen a distinct set of gender-affirming medical interventions (Grasso
et al., 2021) and can have privacy concerns that stem from their unique social circumstances (Thomp-
son, 2021). Centering the research on the voices of the TGD people serves to highlight this hetero-
geneity. By centering the research around the needs of the TGD young people, Goldhammer et al.
(2022) discovered that some of the patients have a wider diversity of different gender identities and
sexual orientations, and these should be accommodated within the health ICTs to facilitate patient-
centered care delivery. While studies in the corpus may have had TGD researchers in their team, none
of the studies stated this.
The language used within the health system should be culturally sensitive and reflect the termi-
nology used by the community. By foregrounding the voices of TGD people during the research
process, the research can be better sensitized to the nuance in terminology used within and
12 K. WYERS
about the community. The reality of language, and the power of language, is complex. Terminology
for transgender and gender diverse people has evolved considerably in past decades. Kronk et al.
(2022) present a history of the terminology with the goal of introducing the informatics field to
the diverse terms used for TGD people throughout the past 70 years. This is a vital aspect of under-
standing and identifying TGD people in data.
Given the heterogeneity of the TGD communities, there is a need for a diversity of voices to be
heard in order to understand health inequities. One study that advocates for an intersectional lens is
Davison et al. (2021). It draws explicit attention to its geographical bias, noting in its limitations
section that ‘[a]ll included articles were from Western countries and cultures, limiting generalizability
to other settings and creating a bias for Western ideals and strategies’(Davison et al., 2021, p. 10).
The study encourages researchers to involve gender minorities, elderly TGD people, Indigenous TGD
people, and TGD immigrants in the research process. The needs of these minority groups must be
understood so that ‘culturally-safe and [TGD] competent care practices’can be created that
reduce barriers to access (Davison et al., 2021, p. 7).
5. Discussion
This study seeks to understand how issues of health equity for TGD people are studied and dis-
cussed in health ICT literature, and how these insights can be used to inform future research on
health ICTs and TGD people. The study adopts Amartya Sen’sdefinition of development, under-
standing it as the expansion of substantive freedoms (Sen, 1999, p. 3). When development is
viewed in this way, the reduction of health inequities for all people becomes a central concern
in human development. Identifying and reducing blindspots related to transgender health inequi-
ties in this literature is a key effort to reduce the risk of health ICTs amplifying existing inequities
(Qureshi, 2016).
The findings presented in Section 4 highlight an awareness within health ICT literature of the
growing need for TGD people to have access to healthcare, and a growing trend toward research
that seeks to understand how the development and use of health ICTs should proceed in this
matter. However, there are biases and blindspots in the literature. Health ICTs form key parts of
the technical infrastructure within healthcare provision. Given the potential for the unintentional cre-
ation of systemic barriers, it is crucial that the research domain of health ICTs and TGD people is
understood and discussed, and that biases and blind spots are addressed. Three areas that need
further discussion are (1) trans exclusion as a systemic problem, (2) visibility and invisibilization,
and (3) the need for diverse voices and contexts, which will now be explored.
5.1. Trans exclusion as a systemic problem
The exclusion of TGD people is a systemic issue leading to structural violence. Within the literature,
the systemic issues are often presented alongside interpersonal forms of violence, such as physical
assault, discrimination, and stigmatization. While such forms of direct violence are highly relevant,
the violence experienced by the TGD communities with respect to health ICTs goes beyond interper-
sonal violence. Many of the problems are structural and systemic, and their sources are not clear,
distinct, and singular. The sources of such forms of violence cannot be easily traced to a specific
event, and therefore they should be studied and understood as distinct from other forms of violence
if they are to be resolved.
By applying the lens of Structural Violence (Farmer, 2004; Galtung, 1969), issues emerge from the
findings around the unintentional inscription of bias from cisnormativity and heteronormativity, the
burden that ICT-embedded classification and categorization places on TGD people, and the potential
for health ICTs to sensitize healthcare providers to deliver inclusive care for these key populations.
INFORMATION TECHNOLOGY FOR DEVELOPMENT 13
5.1.1. Unintentional inscription of bias
Biases can be unintentionally inscribed into health ICTs through their development and use, which
can amplify existing inequalities and systemic barriers for TGD people (Spiel, 2021). Two major
sources of this are cisnormativity and heteronormativity. These normativities around sex, sexuality,
and gender identity position heterosexuality and cisgender as the default, and lead to the systemic
othering of people with identities that do not fit within these boundaries. These normativities perme-
ate through society, and are deeply ingrained within the practices and structures of healthcare pro-
vision, whether through the binary set of reference ranges used in laboratory information systems
(Patel et al., 2021), in the pejorative terminology used in diagnostic codes (Kronk et al., 2022),
legal frameworks that do not protect TGD people (Levi et al., 2019), the lack of adequate training
for healthcare providers about gender diversity (Bosse et al., 2018) or reified classification systems
that mediate access according to an overly restrictive set of criteria (Thompson, 2021). On the
surface, there are welcome cultural changes taking place in some countries that are leading to a
more inclusive society for TGD people, which is indeed leading to a reduction in interpersonal,
direct violence. However, as Davison et al. (2021) note, these changes must be coupled with sys-
temic, structural change.
5.1.2. Burden of change from classification and categorization
Within the research domain, there is a drive to standardize, to classify, and to categorize TGD iden-
tities so they can be efficiently represented in health ICTs and other information systems. Such stan-
dardization is needed to ensure continuity of care (Levi et al., 2019). However, it creates a tension
between the need for order and the need to consider the rich diversity of contexts and lived experi-
ences (Livingston et al., 2022; Thompson et al., 2021). Access to healthcare interventions is mediated
according to an individual meeting the specific requirements of a category. As Costelloe and
Hepburn (2021) point out, these categories lead to systemic barriers for those who need care, but
who fall outside the criteria. This process of classification leads to the burden of change being
placed firmly on the shoulders of the TGD individual (Thompson, 2021). A reified definition of a
TGD identity forces TGD people to change themselves and how they relate to and describe their
identities to fit within a restrictive mold. It is then the TGD individual who must change, rather
than the system that should accommodate them. For example, if a health system provides
gender-affirming healthcare, but only recognizes trans men and trans women without recognizing
non-binary and other gender diverse people, this classification of identities implicitly excludes non-
binary people who need gender-affirming healthcare. Wæhre and Schorkopf (2019) outline such a
case.
Such categorization must be considered carefully and must not aggregate a broad diverse range
of identities and healthcare needs into a singular, concrete, reified category. It places huge demands
on the TGD individual to advocate for themselves (Bosse et al., 2018), forego personal privacy and
subsume their personal identity to access health and equity because the structures used to organize
society do not account for their needs and identities.
5.1.3. Sensitizing healthcare providers through health ICTs
TGD people experience systemic barriers through the lack of awareness and sensitization among
healthcare providers in relation to the provision of care for them (Goldhammer et al., 2022), a situ-
ation that Kronk et al. (2022) refers to as structural incompetence. Health ICTs shape the care that is
given to patients by influencing the professional norms and the conceptions of justice and deserv-
ingness (Kirkland, 2021) and potentially reinforcing and perpetuating outdated, pejorative terminol-
ogy. Through their ongoing use by healthcare providers, health ICTs have the potential to provide
guidelines to assist in the provision of inclusive care, becoming a vehicle for change within the health
system (Grasso et al., 2020).
14 K. WYERS
To address these forms of trans exclusion, systemic barriers must first be foregrounded within the
literature. Trans exclusion must be studied beyond the interpersonal level and understood as a sys-
temic problem that can manifest unintentionally within health ICTs. This seeks to understand how
systemic barriers in health ICTs inhibit health and well-being for TGD people. The Structural Violence
lens offers a means to understanding such issues and how they affect TGD health equity. Researchers
and practitioners should be sensitized to the potential bias from cisnormativity and heteronormativ-
ity within health ICTs, reducing the inscription of bias within these systems. Health ICTs can help to
guide healthcare providers to deliver better, more appropriate practices when treating TGD patients,
becoming a vehicle for positive change. It is important that researchers and practitioners are prag-
matic when addressing such deeply ingrained issues. Understandings around the needs of TGD
people are evolving at a fast pace, and health ICTs and the health system cannot keep up with
this pace of change. Despite this, TGD people need to be able to access healthcare. Researchers
and practitioners should therefore seek to identify changes that can be made quickly to reduce
the burden for TGD people in the short term, while also making recommendations for systemic
change.
5.2. Visibility and invisibilization
There is a tension between the need for TGD identities to become visible, and the need for their
TGD status to remain hidden, what Thompson (2021) refers to as a visibility paradox. Visibility is
necessary for monitoring population health disparities and policy decisions, and health ICTs play
a role here. Furthermore, underestimation of the population size can impact on the public percep-
tion of the community, leading to erasure from public health decisions, and barriers in access
to public services, education, welfare, pensions, parental rights and other citizenship rights
(Sandvik, 2018). TGD issues are undergoing an ongoing debate and the publication of underesti-
mates can lead to politicization of the data to support arguments that deny human rights to
TGD people. Simultaneously, the increased collection of gender identity data brings with it
immense vulnerabilities for TGD people. Members of TGD communities should not have to
forego personal privacy for access and equity. If certain groups are rendered invisible within
research and practice, systemic barriers manifest that exclude their needs from being met within
the health system.
5.2.1. Bias in the research samples
There appear to be several studies with significant overrepresentations of certain demographics,
indicating potential biases and invisibilization. Anand et al. (2017) have a sample from Thailand
where 80% (n= 186) had received bachelor degrees or higher. Thailand has considerable barriers
to higher education for low-income families, and more so for TGD people. Niforatos et al. (2020)
identified a sample of 10,160 TGD people in a dataset of 53.5 million people in the USA. Of the
sample, 65% were Caucasian, aged 35–39, and 54% were privately insured. With an awareness of
the higher risk of invisibilization of the uninsured within the TGD population, and how this can
lead to exclusion from healthcare records (Dunne et al., 2017), a sample of TGD people who are
54% privately insured and also 65% Caucasian signals a potential bias toward the inclusion of the
privileged, white community. 86% (n= 192) of the TGD people interviewed in Sequeira et al.
(2020) self-described their race/ethnicity as white. Given that the empirical site was Pittsburgh, a
racially diverse city, such a high representation of self-described white participants indicates that
there may be barriers for racially marginalized groups from participating in the research, thus ampli-
fying existing inequalities and creating further systemic barriers.
5.2.2. Underestimation of the TGD population size
Evidence of engagement with gender-affirming healthcare is used in many studies as a proxy to indi-
cate a TGD identity. However, these proxies appear inaccurate when benchmarked against the
INFORMATION TECHNOLOGY FOR DEVELOPMENT 15
estimated size of the TGD population, leading to the invisibilization of large groups of the TGD popu-
lation. This concurs with Goodman et al. (2019, p. 318), who state that ‘clinic-based studies [for esti-
mating the number of TGD people] seem to capture only a subset of the [TGD] population.’
Estimations of 0.019% (Niforatos et al., 2020) and 0.003% (Alpert et al., 2021) are significantly
lower than the Williams Institute USA national estimate of 0.6% (Flores et al., 2016; Herman et al.,
2022). The three studies that identified a population size close to the USA national estimate used
proxies that went beyond the presence or absence of TGD-related ICD codes, using gender identity
data that was self-declared by the patients. The results from these three studies indicate that, by col-
lecting self-declared gender identity data, it would be possible to more-accurately monitor health
within the TGD population. However, as noted earlier, this must be conducted with individual
privacy as a core concern.
5.2.3. Critique of proxies for TGD identities
Studies exploring the algorithmic identification of TGD people tend to implicitly assume that the
presence of a TGD-related ICD code implies that the person has a TGD identity, and that the
absence of such a code indicates that the person is cisgender. This disregards the contextual
factors that influence whether a person has such a code in their health record (Blosnich & Boyer,
2022; Wanta et al., 2019), lacking the nuanced appreciation of the challenges of algorithmic identifi-
cation of TGD people. To gain access to gender-affirming care and subsequently have a health record
with a TGD-related ICD codes, the patient must have received a diagnosis that is only accessible to
the privileged few who have the means and opportunity to engage with the diagnostic process.
There are many reasons why a TGD individual does not have a TGD-related ICD code in their
health record. Any patient who is unable to access gender-affirming care, or chooses not to, may
still be subject to the social determinants of health that affect the TGD population, and their TGD
status must therefore also be counted to monitor the health disparities within the population.
Social determinants of health play a key role in access to gender-affirming healthcare. In the USA,
for example, many TGD people cannot access such care because they do not have health insurance
(Dunne et al., 2017). Others choose not to disclose their TGD status to each healthcare provider, or
the healthcare provider decides not to include the ICD code in the health record, either at the
request or behest of the patient (Blosnich et al., 2018). While some studies propose alternatives to
the reliance on TGD-related ICD codes in health records by expanding the digital representation
of health records to include an anatomical inventory (Antonio et al., 2022; Davison et al., 2021;
Grasso et al., 2021), such a change has limited value as an indicator to TGD status. Much like the limit-
ations of TGD-related ICD codes, an anatomical inventory can only help identify TGD patients who
were able to engage with gender-affirming care, and excludes those who could not. It would also
include cisgender people who engage with certain gender-affirming medical interventions like
breast-augmentation for cisgender women, or testosterone replacement therapy for cisgender men.
5.2.4. Holistic healthcare provision
There is also a broader issue in connection to the provision of holistic care for the population. Even if
a TGD patient has a TGD-related ICD code because of accessing specific gender-affirming care, such a
code tells little about the lived experience of the individual, and how social determinants of health
may be impacting them. While much of the public perception of TGD people tends to focus on
gender-affirming medical interventions like surgeries and hormone replacement therapies, much
of the interpersonal violence and structural violence experienced by the community results from
issues of gender identity and gender performance, rather than medical gender-affirming treatment.
To provide holistic care for patients, it is important to understand the social determinants and the
systemic barriers that shape the lived experiences of the individual, and reliance on TGD-related
ICD codes in algorithms does not provide this level of detail. TGD-related ICD codes tend to aggre-
gate the rich diversity of gender expression, removing much of the nuance that would benefit the
researcher (Sequeira et al., 2020). This makes it difficult, or even impossible, to disaggregate the
16 K. WYERS
patients even into such broad groups as trans feminine, trans masculine and non-binary persons,
details that could be highly significant within the research and within the monitoring of health dis-
parities (Sequeira et al., 2020).
To rectify these problems, researchers and practitioners should adopt an intersectional perspec-
tive. They should assess whether bias exists in the sample and engage with the potential contextual
factors that limit the usefulness of proxies. More accurate proxies are needed to determine whether a
patient has a TGD identity. To assess this, population estimates should be benchmarked against the
best-estimates to detect inclusion/exclusion bias. They should stay sensitive to the potential for poli-
ticization of research findings.
5.3. The need for diverse voices and contexts
The literature lacks much of the diversity of voices and contexts from the TGD communities along
socio-economic, geographical, ethnic, cultural, and racial lines (Table 3, Appendix 2). Contexts
outside of the USA are underrepresented, as are low-resource contexts. This raises concerns that
there may be blind spots within the research that have practical implications for TGD people.
Within the TGD population, there is a great deal of diversity with regards to racial group member-
ships, class, social status, sexual orientation, geographical locations, family and social support struc-
tures, and a range of other personal and institutional dimensions. There are those who have greater
privilege than others, and a diversity of contexts and lived experiences that must be considered
within the literature. The focus on a narrow set of lived experiences of TGD people, and a narrow
set of contexts, is leading to the reification of restrictive terminology and the exclusion of voices
and contexts outside of high-resource North America.
5.3.1. Underrepresentation of low-resource contexts
There is an underrepresentation of low-resource healthcare clinics. The legislative requirements
being introduced around the collection of gender identity data appear to be placing a greater
burden on small, under-resourced health centers who lack the economies of scale to absorb the
costs of a new digital EMR to accommodate gender identity data (Kirkland, 2021). In one case, a
low-resource clinic chose to protect the privacy of their patients by limiting the amount of data col-
lected with regards to gender identity (Thompson, 2021) a practice that goes contrary to the spirit of
the legislation and may indicate a blind spot in the research used to develop the legislation.
5.3.2. Underrepresentation of contexts outside North America
As can be seen in Table 3, there is a high degree of bias in the geographical focus of the literature
surveyed for this study, with most of the studies either conducted in or conducted about contexts
in the United States (Table 3). This bias exists despite that the study methodology proactively
sought to include studies from outside North American contexts. This underrepresentation of
research from and about contexts outside of North America excludes the great variety of
gender diverse identities that exist all around the world. This bias has also been noted by
Davison et al. (2021). While this bias points to a limitation in what can be seen and understood
through the specific studies surveyed for this article, it is the lack of diversity in the literature
that is most noteworthy and most concerning. Without a broad range of contexts that accurately
represent the huge range of diversity in TGD people’s lives around the world, the publication of
scientific, peer-reviewed health ICT research risks legitimizing the voices and experiences from
certain contexts over others. This leads to forms of testimonial injustice by reenforcing the pre-
existing assumption that the needs of TGD people are universal and homogenous, and that the
TGD voices from North America accurately represent the voices of all members of the global
TGD communities (Fricker, 2007).
INFORMATION TECHNOLOGY FOR DEVELOPMENT 17
5.3.3. Terminology is heavily biased toward that used in North America
The research bias toward the high-resource North American contexts is evident when reviewing the
terminology used in the literature on health ICTs. There is a lack of nuance in the literature around
the terminology being used and how its meaning and relevance changes across both geographical
contexts and with the progression of time. There is a heavy bias toward the terminology currently in
use in the United States, and an absence of the many terms and concepts related to gender identity
that exist elsewhere. This is leading to a reification of narrow, restrictive definitions of terminology
that excludes groups, and subsequently leads to systemic barriers for those whose voices and con-
texts are not considered. The concept of a transgender person does not necessarily map comfortably
to concepts of gender diversity in other parts of the world, and these differences are largely absent
from health ICT literature, leading to certain voices being deemed more authoritative than others, a
process referred to by Miranda Fricker (2007)astestimonial injustice. For example, in North India
there is a historical concept of gender diversity that is in many ways distinct from the western
concept of transgender (Chatterjee, 2018; Stryker, 2006).
Standard terminology is needed for the effective continuity of care (Levi et al., 2019), and to mini-
mize the use of ad-hoc terms that may be offensive or inappropriate (Kronk et al., 2022). However,
such standard terminology must be understood as being contextually contingent because its
meaning and relevance changes between contexts. While Kronk et al. (2022) go to great lengths
to draw attention to the concerns of bias in terminology, their study is also heavily focused on ter-
minology used in the United States and does not include terminology used in many of the other
gender diverse communities around the world. In the process of standardizing the terminology, it
is important to consider that terminology used about TGD people in the United States differs
from terminology used in, for example, India, or Thailand, or Brazil. As the drive for standardization
continues, it needs to consider that terminology differs around the world, and there should be
sufficient flexibility to ensure that the standardization practices do not lead to the reification of a
westernized concept of gender diversity that is not relevant for all people around the world.
5.3.4. Terminology changes relevance with the passage of time
The TGD-related terminology changes in meaning and relevance with the passage of time. Several
studies in the corpus use lists of International Classification of Diseases (ICD) codes and keywords
from previous studies without critically considering the implications of this. Many of the terms
used to represent TGD people and gender variance in ICD version 9 (ICD-9), released in 1977, and
ICD version 10 (ICD-10), released in 1992, are now considered pejorative and derogatory. Dubin
et al. (2020, p. 3) sought to identify TGD identities by converting ‘[a]ll ICD-9 codes, including
[TGD]-specific codes …to equivalent ICD-10 codes in 2015.’It is problematic to perceive such
codes as comparable because the terms used in 1977 do not easily map to terms used in 1992, or
those used in 2023. Ehrenfeld et al. (2019, p. 448) draw attention to this by publishing a caveat
with their algorithm, stating ‘it will be important to keep in mind that as terminology and diagnoses
change, the suitability of the algorithm may also vary.’Many old terms are now considered pejora-
tive, and their use must be handled with care. Their use raises an ethical dilemma with regards to the
perpetuation of outdated, pejorative terminology, as critiqued by Kronk et al. (2022). Similarly,
several studies use keyword lists from previous studies when searching for TGD identities. Such prac-
tices should be conducted with care and due consideration to the context and the potential for per-
petuating outdated terms.
Fundamental to addressing these issues is for researchers and practitioners to orient themselves
to the contextual contingency of the research domain. They should commit to understanding the
context and remain sensitive to the fact that the context they study is unlikely to be universal for
all TGD people. They should proactively seek to involve the voices of TGD people and domain
experts within the research, as participants and as members of the researcher team. They should
18 K. WYERS
also seek to study contexts that are absent from the literature, particularly geographical regions
outside the USA and low-resource contexts.
6. Research agenda
The overall aim of this research agenda is to promote the building of a body of systematic knowl-
edge that improves the development and use of health ICTs that affect the lives of TGD people. It
contains four research streams, together with three methodological approaches, and two foun-
dations to orient the research (Figure 2). So doing, it presents not only the work that needs to
happen, but also how the work should take place.
6.1. Orientations
Two foundations orient the research within this area. (1) All research in this domain is critical
research, and (2) the research domain is contextually contingent (Figure 2).
First, all research in this domain is critical research. The nature of the domain is one of improve-
ments, of seeking positive change connected to social justice for all members of society, not just for
TGD people. There is no neutral stance and research within this domain should be conducted from a
critical perspective with the implicit or explicit goal of changing the status quo. Researchers must
stay aware of their own positionality, their own agendas, interests, and identities, and how these
can create biases that amplify certain voices and silence others.
Second, the research domain is contingent on context. Context refers to ‘the meaning of human
environments to the people who live and work in them’(Davidoff,2019, p. 1). Researchers should
avoid treating any one context as universal, as the experiences within one context may not be
held by all members of the TGD communities. They should commit to understanding the social
determinants that shape TGD health, and identify the nuance in their findings. To achieve this,
they should get to know members of the community and make a commitment to understanding
the community while they conduct research that affects its members.
6.2. Research streams
Four streams of research are presented. Together, they address major blind spots in the literature in
relation to health ICTs and TGD health equity by building systematic knowledge and exploring
Figure 2. Future research agenda –orientations, research streams, and methodologies.
INFORMATION TECHNOLOGY FOR DEVELOPMENT 19
solutions. The streams of research are (1) structural, systemic issues, (2) burden of change, (3) proxies
for TGD identities and (4) privacy and invisibilization (Figure 2).
6.2.1. Stream 1: structural, systemic issues
This stream of research explores how systemic issues exist within health ICTs, describing them and
studying how they influence health inequities for TGD people. These systemic issues become
inscribed within health ICTs, exerting an influence on TGD people in a way that is difficult to
detect, causing problems whose sources are difficult to trace to specific events. The issues facing
TGD people within this domain should be explored using a lens to study the structural, systemic
issues. This should be distinct from the interpersonal forms of violence, including acts of discrimi-
nation and stigma. The Structural Violence lens can be used to foreground systemic issues and to
understand how the experiences of TGD people are shaped by systems that do not accommodate
them.
6.2.2. Stream 2: burden of change
This stream explores who carries the burden of change when there is a mismatch between the TGD
identity and the systems they interact with. Does the burden of change rest on the shoulders of the
TGD individual, the developers of the health ICT, or on the users of the health ICT? Systems of cat-
egorization may require a TGD person to meet the criteria of a category that has been created based
on a reified definition of the TGD identity. This places the burden of change on the TGD individual to
meet the criteria of a category, rather than the creation of a category that can accommodate the
TGD individual. Within this topic, it is important to be pragmatic. The understandings about TGD
identities and health needs are progressing faster that health ICTs can accommodate, creating a
time lag, and the systems that are currently in place will take many years to improve and align
with the needs of the TGD population. Therefore, researchers and practitioners should make rec-
ommendations for all stakeholder on ways to navigate the systems as it is to reduce the burden
of change being placed on the shoulders of TGD people. This is conducted concurrently with a
drive for systemic change.
6.2.3. Stream 3: proxies for TGD identities
This stream focuses on the nuanced development of proxies for the algorithmic identification of TGD
people using current and historical health records. It explores the use of terminology, how medical
indicators can be used, and the benchmarking of results. Terminology changes meaning with the
passage of time, and across different contexts. A term that is outdated or pejorative may be valuable
for identifying TGD people in historical data sets. Equally, a term that is seen as offensive in one
context may be appropriate in another. Use of TGD-related terminology must therefore be con-
ducted with sensitivity and in consultation with experts with knowledge of the terms and potential
pitfalls. Evidence of engagement with gender-affirming healthcare is proving to be an unreliable sole
indicator for TGD status because of the many contextual factors that influence the presence or
absence of such evidence on a patient’s health record. This research stream explores how such evi-
dence, together with other indicators, can be used to accurately identify TGD individuals. The
reliability of a proxy should be determined by benchmarking it based on the established estimates
of the TGD population. This enables greater transparency and comparability when novel algorithms
and their published results veer significantly from established estimates. This reduces the concerns
around over- or underestimation of population size, which is both an epistemological issue and one
that has practical implications.
6.2.4. Stream 4: privacy and invisibilization
There is a tension in the domain between the need to identify TGD people and the privacy concerns
for the exposure of such identities. Despite the need for population health monitoring, the privacy of
the patient must remain a central concern. While such issues are also being explore with other key
20 K. WYERS
populations, the context of the TGD population demands specific focus. Concerns about disclosure
of TGD status vary from one individual to the next, depending on their context. The context changes
with the passage of time. It changes across socio-cultural groups, and differs from one geographical
region to the next. This research stream explores how to navigate this variance and understand
privacy and invisibilization across different contexts. It explores how systemic barriers and social
determinants are expanded or constrained by health ICTs, and how they affect the tension
between privacy and invisibilization.
6.3. Methodological approaches
Through the Discussion in Section 5, three methodological approaches emerged that are valuable
within this research domain. (1) Adopt an intersectional perspective, (2) stay sensitive to the politi-
cization of the research domain, and (3) expand the circle of participation (Figure 2).
6.3.1. Adopt an intersectional perspective
The intersectional perspective sheds light on the research bias toward certain groups. Researchers
and practitioners should adopt an intersectional perspective to identify bias in sample distributions,
research findings, proxies for TGD identities, and when declaring the limitations of the study. This
perspective orients one to seek out transgressive voices that have been excluded and can help in
identifying biases that may signal potential issues of equity, whereby members of those demo-
graphics have been invisibilized by the intersection of their identities. This helps to become sensi-
tized to the potential for interlocking forms of oppression that result from an individual’s
membership to racial groups, class, social status, sexual orientation and a range of other factors. It
can indicate a significant overrepresentation of certain groups in the literature. As researchers and
practitioners carry out their work, they should seek to identify the potential absences in their
sample, and question whether there may be an underrepresentation which could lead to the
research reinforcing existing inequities through the invisibilization of the issues faced by a particular
group.
6.3.2. Stay sensitive to the politicization of the research domain
In recent years, debates around the TGD communities have become highly politicized. Debates
around bodily autonomy, legal identity, and healthcare provision have practical implications
for the wellbeing of members of the TGD communities. When sample bias exists and TGD sub-
groups are excluded from findings, the results can be used in such debates to push for policy
decisions and legislation that denies TGD people their human rights. Researchers conducting
studies within this domain must stay sensitive to this politicization, as their research can affect
these debates.
6.3.3. Expand the circle of participation
To reduce the blind spots in the literature, researchers and practitioners should expand the range of
voices and contexts that are involved in the research. Simultaneously, they must stay sensitive to
their own positionality, and the potential biases can be introduced by amplifying some voices
and not others. Further research is needed to understand how research methodologies can
include a diversity of TGD voices while minimizing bias. TGD people should be involved as research-
ers and participants, particularly those who have lived experiences from contexts that are underre-
presented in the literature, such as low-resource contexts or geographical contexts in the Global
South. Where TGD people are involved in the research, their voices should be central to the work.
Members of this community have the greatest awareness of their own lived experiences, and
these experiences should inform the research and the recommendations drawn from them. By
seeking to foreground transgressive voices in this way, the blind spots can be reduced within the
INFORMATION TECHNOLOGY FOR DEVELOPMENT 21
literature, drawing attention to the systemic barriers that affect subgroups within the TGD
community.
7. Conclusion
This critical study explored literature addressing both health ICTs and topics of TGD people, seeking
to understand how the research domain studies and discusses issues of transgender health equity. It
then further sought to identify how blindspots in the literature can be reduced in future health ICT
research. A cross-disciplinary narrative literature review of 24 published studies assessed the major
themes being discussed. It found a steady growth of awareness of the need for TGD people to have
access to healthcare, and a growing trend from multiple disciplines seeking to understand how the
development and use of health ICTs should proceed to this goal, focussing on four themes related to
health equity. Using a health equity perspective, and applying an intersectional lens and a structural
violence lens for the analysis, the study then discussed these findings under the headings of (1) trans
exclusion as a systemic problem, (2) visibility and invisibilization, and (3) the need for diverse voices
and contexts. It highlighted key blind spots in the literature that lead to the amplification of health
inequities by health ICTs, and the results of this analysis formed the scientific basis for the research
promoted in the future research agenda.
The blind spots that were identified have practical implications for the health and wellbeing of all
marginalized people and addressing them would have a positive impact on the cross-disciplinary
effort to promote health equity for all. Making this contribution to global health equity will
require the participation of a wide range of scholars and practitioners within the IS and ICT4D
fields. The research agenda presented here calls on these scholars and practitioners to join in this
project. It is a guiding document that outlines several research streams, methodologies, and founda-
tional orientations to be used when designing and conducting research in this area. It serves as an
entry point into an important field where solutions can have a major positive impact on equity for all
marginalized people. The research agenda emerges from a critical analysis of the themes and meth-
odologies currently used in the research domain.
This study makes both practical and theoretical contributions. First and foremost, it is a call to
action for IS and ICT4D researchers and practitioners, highlighting a research domain where contri-
butions can have a positive impact on all marginalized populations, thereby making a significant
contribution to the global effort to reduce health inequities for all. Secondly, the study makes a
theoretical contribution to the discourse on health equity and health ICTs by exploring how
health ICTs shape inclusion and exclusion of marginalized people. By making these contributions,
the study and its research agenda promote a path that pushes forward the field of health ICT
research by focussing on the reduction of health inequities for a key population. By building sys-
tematic knowledge and practical solutions in this domain, scholars and practitioners will contribute
to the cross-disciplinary effort to reduce health inequities around the globe.
Acknowledgements
I would like to extend my gratitude to Dr. Arunima Sehgal Mukherjee and Dr. Johan Ivar Sæbø of the Department of
Informatics, University of Oslo, and Dr. Tony Joakim Ananiassen Sandset at the Centre for Sustainable Healthcare Edu-
cation, University of Oslo for their support, guidance, and encouragements throughout this study. I am grateful to Dr.
Sune Dueholm Müller of the Department of Informatics, University of Oslo, who provided thoughtful feedback on a
previous draft of this paper. I gratefully acknowledge that this paper has been much improved with insightful sugges-
tions and critique from anonymous reviewers at Information Technology for Development.
Disclosure statement
No potential conflict of interest was reported by the author.
22 K. WYERS
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