• Healthcare often exists without feeling reachable. Even when systems offer care, design flaws, emotional friction, busy schedules, and past experiences keep patients from booking appointments or following through.
• Expanding access through mobile and digital care removes effort from the equation. Telehealth and mobile clinics lower travel and language barriers, meeting patients where they’re at—physically and cognitively.
• Reframing reminders turns passive scheduling into active follow-through. Using defaults, timely cues, and loss-framed messages can make attendance feel immediate and essential, not something that can wait.
• Trust begins long before the appointment. Behavioral design that prioritizes inclusive language, visible diversity, and cue consistency helps turn healthcare into a space people want to return to.

Across health systems, the hardest problem sometimes isn’t just getting patients in the door—it’s keeping them there. Despite unprecedented medical capacity, millions delay, reschedule, or skip visits entirely. These lapses may look administrative, but they carry real clinical and financial weight. In England, for instance, more than 15 million appointments go unattended each year, including 7.2 million with general practitioners—costing the National Health Service (NHS) an estimated £216 million annually.¹

Non-attendance is only one expression of a broader engagement gap. People avoid or postpone care for reasons that go far beyond cost or distance.² Sometimes the barriers are practical—like transport, scheduling, or childcare. Other times, they’re cognitive. Behavioral research shows that small friction costs—extra forms, long waits, or confusing portals—can make even motivated patients disengage.^3,4

Access itself is more than geography or timeliness; it’s the alignment between what patients need and what systems deliver. When that alignment breaks, care becomes harder to reach and easier to rationalize away. For disadvantaged populations, the consequences compound. Stigma, stereotype threat, and past experiences of discrimination can make healthcare feel unsafe or unresponsive, reinforcing avoidance patterns that can stretch across generations.^5,6

These gaps aren’t simply logistical—they’re behavioral and relational. Missed visits, late screenings, and silent disengagement all point to the same issue: a system that’s available, but not yet accessible in the ways people actually experience it.

Achieving equitable access to healthcare remains a central global objective, reinforced by the Universal Health Coverage agenda and the Sustainable Development Goals.⁷ However, the gap between services offered and services used is still wide—and nowhere is that clearer than in how people navigate space, systems, and language to reach care.

Spatial accessibility captures two essential components of access: “availability,” or the number of local service points a patient can choose from; and “accessibility,” defined as the travel impedance—distance or time—between where patients are and where care is delivered.⁸ It’s a term widely used in geography and increasingly adopted in health systems research. And it matters, because both dimensions shape behavior. A clinic might be nearby but fully booked. A hospital might offer excellent care, but 50 miles away on poor roads. Either way, patients don’t go.

Across geographies, these spatial frictions lead to predictable outcomes. In a systematic review covering 27 studies and over 716,000 cancer patients, researchers found that longer travel distances were linked to more advanced disease at diagnosis, lower adherence to recommended treatments, worse prognoses, and poorer reported quality of life.⁹ Patients traveling more than 50 miles were especially disadvantaged. These aren’t just access issues, they’re examples of effort discounting: when action feels too costly, people undervalue its benefits—even when the stakes are high.

Pediatric care shows a similar pattern. In Burkina Faso, where care for children under five has been free since 2016, researchers tracked over 12,000 visits across 226 communities.¹⁰ The median distance to a clinic was five kilometers—but even at that range, visit rates dropped as distance increased. Illnesses like pneumonia, diarrhea, and malaria went untreated more often in distant communities. The care existed. Still, families didn’t go because the barriers were simply too high.

Even in higher-income settings, transportation barriers remain a major reason patients skip care. In the United States, an estimated 5.8 million people delay medical visits each year because of issues like lack of a private vehicle, unreliable public transit, or the financial burden of travel.¹¹ In Australia, sparse settlement and uneven workforce distribution compound the challenge, particularly in rural and remote areas.¹²

Geography isn’t always the issue. Sometimes, the breakdown happens at the point of contact. The patient arrives, but the interaction stalls. With over 280 million people now living outside their country of birth, language mismatches between patients and providers are becoming an increasingly common occurrence.¹³ These mismatches create hesitation, misunderstandings, and longer visits that feel less productive. In one Australian study comparing Arabic- and English-speaking patients with diabetes, only the Arabic-speaking group reported intentionally delaying care—even when symptoms were clear.¹⁴ One of the primary reasons given was limited English proficiency paired with a lack of perceived language alliance. Patients didn’t just worry about being misunderstood: they doubted they’d be fully heard.

Geographic and linguistic barriers don’t just delay care, but they also influence the decision to seek it in the first place. Behavioral science helps explain why. When a task feels time-consuming, uncertain, or effortful, people tend to overweigh those immediate costs and underweigh the long-term health benefits. That’s present bias at play: a tendency to favor short-term comfort, even when it carries long-term risk. It doesn’t mean patients aren’t motivated. It means the system makes showing up feel harder than staying home. Designing around that reality is where access reform begins.

Access problems reveal design possibilities. Each barrier—whether digital, geographic, or linguistic—highlights where healthcare can evolve to meet people where they are.

Let’s Talk Telehealth

Telehealth is designed to eliminate many of these burdens. For patients with mobility issues, tight work schedules, or limited transportation, it removes the biggest obstacle: the commute. It allows care to happen on the patient’s terms, not the clinic’s, which is especially important, especially for those managing chronic conditions who need frequent, lower-stakes check-ins.

However, telehealth isn’t universally accessible. Older adults in particular may struggle with interfaces, logins, or poor audio quality. In a large systematic review of over 200 trials, more than half of the studies cited a lack of familiarity with technology as a barrier to using telehealth.¹⁵

This isn’t just about digital literacy. It’s about cognitive load—the mental strain that builds when people are asked to manage multiple unfamiliar tasks in working memory at once. When the effort to log in outweighs the perceived benefit of being seen, patients often opt out.

Design-wise, this means healthcare platforms offering telehealth might need to focus more on usability, clarity, and fail-safe options: a single-button join link, visual cues instead of dense menus. These small design tweaks can help make the system feel navigable.

What Happens When the Clinic Moves

Not every patient can—or wants to—use a screen. That’s why mobile clinics, fully equipped vans or buses offering in-person services, remain a crucial, under-leveraged strategy. They reach into communities where infrastructure doesn’t, particularly rural towns and urban neighborhoods with limited transportation options.

The impact is measurable. In a national analysis of 811 mobile clinics that participated in the Mobile Health Map project between 2007 and 2017, researchers found these clinics served a higher percentage of Black (35%) and Latino (26.6%) patients than their representation in the general U.S. population (13.4% and 18.3%, respectively, based on the 2010 Census).¹⁶ Interestingly, the authors found that although mobile clinics operate across the country, many are concentrated in densely populated cities, leaving a coverage gap in rural areas, especially in parts of the Midwest.

What happens when mobile clinics are placed in these underserved regions? Mayo Clinic launched a mobile health clinic serving four rural towns in Minnesota: Blooming Prairie, Kenyon, Sherburn, and Butterfield—each with populations of roughly 3,000 or fewer.¹⁷ The clinic featured two exam rooms, onsite laboratory services, and telehealth equipment connecting patients to Mayo providers. Notably, more than 45% of appointments came from surrounding communities, saving patients considerable drive time. Patient feedback was overwhelmingly positive: “easy to find, easy to use, surprisingly spacious.” The barrier wasn’t need, it was distance. Once that dropped, uptake followed.

Speaking the Same Language—Or at Least Trying

Language is another form of distance. Not spatial, but psychological.

Patients who aren’t fluent in English often experience high levels of stress before they even walk through the door. Will I be understood? Will I understand enough to make the right decision?

Many clinics still rely on ad hoc solutions. A 2022 audit of orthopedic clinics in California found that 80% asked patients to use unqualified interpreters. 28% asked them to bring a friend or family member.¹⁸

But here’s the opportunity: when clinics proactively embed translation support into the care journey—and make that support visible up front—it may reduce both confusion and dropout. In decision science, this is a form of friction reduction as it significantly reduces the small effort barriers that disproportionately shape behavior. Each step that feels smoother, like seeing clear language options or hearing confirmation of understanding, removes psychological drag from the decision to engage in care. Additionally, leveraging novel technology, like artificial intelligence, can further ease these points of friction. In two recent simulations involving patient care scenarios, researchers tested Google Translate and ChatGPT-4 as real-time medical interpreters across multiple languages.

Google Translate showed strong performance, with accuracy rates ranging from 83.5% in Urdu to 95.4% in French, shaped largely by dialect sensitivity and pronoun structure.¹⁹ Most errors were literal or syntactic, not clinically significant.

ChatGPT-4, tested in a different scenario as a live interpreter between English-speaking clinicians and Spanish-speaking simulated patients, translated nearly 3,600 words over 10 mock visits. It reached a 99.4% raw accuracy rate, with only minor errors—small omissions, substitutions, and gender mismatches—none of which had meaningful clinical implications.²⁰

These tools aren’t flawless, and they shouldn’t replace trained interpreters in high-stakes settings. However, in under-resourced environments where staffing or funding may be limited, they (and other developing language tools) might help patients feel heard. When clinics make these tools visibly available to patients, it may shift the dynamic. A poster, a digital check-in screen, or a simple prompt that says “Language assistance available, just ask” might not just inform—it can lower anxiety before the interaction even begins. 

People often avoid seeking medical care—even when they suspect it might be necessary. In one national U.S. survey, nearly one-third of adults reported avoiding the doctor, despite believing they may have needed medical attention.²¹ Across studies, the reasons go beyond cost or access. In a sample of 1,369 participants, most cited traditional barriers like high cost (24.1%), lack of insurance (8.3%), or time constraints (15.6%).²¹ But a meaningful subset, over 12%, reported low perceived need, often assuming their illness or symptoms would pass on their own (4.0%). This pattern extends beyond acute care and into preventive behaviors. Many people skip routine screenings, not because they lack access, but because they don’t see the point. It’s not always a conscious choice. Screening decisions are often shaped by cognitive inertia, the tendency to maintain the status quo unless symptoms force action, or fatalism, which is the belief that outcomes are predetermined and won’t be changed by early detection. Often, once those beliefs take hold, they’re hard to shake. These biases lead people to undervalue the uncertain—but potentially life-saving—benefits of preventive care. This type of avoidance isn’t always conscious—and it’s rarely irrational. Instead, it reflects two common behavioral tendencies: temporal discounting and normalcy bias. Temporal discounting means people undervalue long-term or uncertain health benefits when compared to short-term costs. The effort of booking, waiting, and missing work feels disproportionately heavy compared to the vague reward of “catching something early.” That benefit’s in the future—and right now, the hassle’s what stands out.

Layered onto that is normalcy bias. People tend to assume that because things have felt normal up to now, they’ll stay that way. It’s not just optimism; it’s how the brain protects routine. When nothing looks obviously wrong, the default is to stay the course. Warning signs get brushed off. Symptoms get reframed as temporary. 

However, behavioral drift doesn’t end with avoidance. Even when people take the step of booking care, many still fail to follow through. Medical non-attendance, or “no-shows,” happens when patients miss visits without canceling ahead of time. These missed appointments aren’t just logistical hiccups—they break continuity, reduce clinic efficiency, and predict greater reliance on emergency and inpatient services down the line.^22,23

Real-world data bears this out. At one community health center serving predominantly Latino, low-income immigrant adults, 927 of 5,604 analyzed appointments (16.5%) were no-shows.²⁴ When staff followed up, 35.5% of contacted patients said they’d simply forgotten. Another 31.5% cited miscommunication, they thought they'd canceled, got the date wrong, or couldn’t get through to the clinic. 

That pattern isn’t unique. In Buenos Aires, a longitudinal study of 473 patients (150 absences, 176 attendances, 147 cancellations) found the same trend: forgetting was the most common reason for missing care, cited by 44% of no-show patients.²⁵ Across very different health systems, the outcome was the same—patients falling off the path not from resistance, but from low saliency.

Even reminder systems haven’t fully solved this. A randomized trial examining straightforward text-message reminders among 173 patients with repeated no-show histories found no statistically significant reduction in nonattendance.²⁶ Of 415 appointments, 12% in the intervention group versus 17% in the control group were missed, though the difference was non-significant. Together, these findings suggest something more nuanced than outright avoidance. People don’t always skip care because they’re uninterested or resistant. They skip because they forget—or because it just doesn’t feel necessary anymore. No sharp symptoms? No immediate urgency? Then the appointment slips down the mental to-do list, or drops off entirely.

If people don’t see the appointment as essential, they won’t remember to go. Even with the best technology, preventive care depends on one simple act—showing up. Behavioral design can make that step harder to ignore. 

Cancer remains the leading cause of death worldwide, claiming nearly 9.7 million lives in 2022.²⁷ Screening could prevent up to 40% of these deaths, yet uptake is still far below potential.²⁸ The problem isn’t just awareness—it’s activation. People delay or skip screening because the task doesn’t feel urgent or rewarding enough in the moment. That’s where default design can help change these numbers. 

In a randomized trial on colorectal cancer screening, patients aged 50–74 were split into two groups: one had to opt in to receive a mailed fecal test, while the other received the test unless they opted out.²⁸ The results weren’t close. Only 9.6% completed screening in the opt-in group, compared to 29.1% under opt-out conditions. Among those contacted through electronic portals, completion rose from 9.5% to 37.5%. The findings clearly indicate that when participation is the default, inertia works for you, not against you. The same design could apply to cervical cancer or mammography: patients are automatically scheduled, and those who decline must do so intentionally.

Still, defaults don’t fix everything. Patients may agree to screening, yet still fail to attend. Missed appointments remain a global challenge, and most reminder systems barely move the needle when a simple “you have an appointment tomorrow” doesn’t carry enough weight.

However, behavioral science insights can enhance the impact of text reminders. Prospect theory tells us that people respond more strongly to losses than to equivalent gains.²⁹ When attending an appointment is framed as preventing waste, delays, or clinical harm—rather than as an opportunity for help—patients may feel greater urgency.³⁰ The same behavioral asymmetry applies here: the perceived loss of missing care feels more pressing than the abstract gain of receiving it. Also, when that message is delivered by a high-authority messenger (the clinician), it taps into authority bias, increasing follow-through.

One example: patients scheduled for chemotherapy could receive letters signed by their treating oncologist—not a generic system. The letter outlines what the visit is for, but also what’s wasted if they don’t show: drug doses, staff time, and a slot someone else could’ve used. It doesn’t shame; it clarifies. By pairing loss aversion with authority bias, the letter can help make attendance feel urgent and necessary. 

The impact of this kind of messaging has been tested at scale. In a large trial across 14 hospitals in Israel’s Clalit Health Services network, over 600,000 patients were randomized to receive different reminder messages before their outpatient visits.³¹ One group received emotionally framed nudges, emphasizing how missed appointments affect clinicians and other patients. Compared to the control group, those patients showed a 33% relative reduction in no-shows (14.2% vs. 21.1%) and a 50% increase in proactive cancellations (26.3% vs. 17.2%). When the appointment isn’t just a date in the system, attendance improves.

Encouraging more patient-doctor visits isn’t just about availability—it’s about perception. One of the most powerful deterrents isn’t distance, cost, or even time. It’s mistrust.

Medical mistrust refers to a deeply ingrained wariness toward healthcare providers, institutions, or treatment plans.³² It’s not some vague unease or simple reluctance. This is a defensive reflex, often developed by patients who’ve been dismissed, sidelined, or mistreated before. Once that trust is gone, it doesn’t just bruise feelings—it rewires expectations. Patients might downplay symptoms, brush off appointment reminders, ghost follow-ups, or walk away from care altogether. Not because they don’t care about their health, but because they’ve been burned before and don’t want to risk it again.

A telephone survey in Baltimore brought that into focus. Among 401 adults, those who scored high on the Medical Mistrust Index (MMI) were far more likely to delay or avoid care.³³ Furthermore, mistrust predicted skipping prescriptions and bailing on follow-up visits. In short, mistrust doesn’t just erode the patient-provider relationship—it keeps people from ever stepping through the door.

Part of what’s happening here is affective forecasting. Patients may anticipate not only the inconvenience of care, but also the added emotional discomfort that comes with it. They may expect to feel judged, dismissed, or overwhelmed. That anticipation alone can drive avoidance, even when care is technically accessible.

Then there’s stereotype threat—another invisible barrier.³⁴ Originally developed in psychology, it refers to the fear of being reduced to a negative stereotype about one’s group. In healthcare, it might mean a patient worries they’ll be seen as “noncompliant,” “drug-seeking,” “overdramatic,” or “uneducated.” And that concern isn’t unfounded. A large multi-site study in the U.S. found that Black patients were significantly more likely than White patients to report race-related stereotype threat in healthcare, even when controlling for age and gender.³⁵

This fear affects behavior. It may lead patients to withhold information, avoid follow-ups, or disengage altogether. In a qualitative study, Black and Hispanic patients reported feeling like “second-class citizens,” receiving less respect, time, or compassion than White patients.^34,36 Those perceptions aren’t just about feelings—they’re linked to real patterns of avoidance.

Even clinically beneficial interventions suffer. In a study of Black women at elevated risk for BRCA1/2 mutations, which help diagnose hereditary breast and ovarian cancer, higher mistrust predicted significantly lower engagement in genetic counseling and testing—despite its value being well-known.³⁷

Minority stress theory helps explain the pattern. It posits that individuals with marginalized identities, such as by race, gender, sexual orientation, or other factors, face chronic stress due to discrimination and stigma.³⁸ One study of 142 Black sexual minority women found that those with masculine-presenting gender were 28% less likely to report healthcare access while those who experienced more stigma were 44% less likely.³⁹

What does all this mean for healthcare leaders? That mistrust isn’t a side issue. It shapes screening rates, healthcare utilization rates, and long-term health outcomes. But here’s the good news: trust can be rebuilt. It may take time to earn, but when healthcare systems actively build safety, emotionally and interpersonally, patients begin to show up, speak up, and stay engaged.

Medical mistrust undermines care access. However, evidence-based design shifts that are easy to implement and rooted in behavioral science can reduce stigma, rebuild trust, and increase follow-through for patients with marginalized identities.

Use Language That Supports, Not Labels

Electronic health records aren’t just clinical tools—they’re behavioral cues. Patients today are increasingly accessing their notes, and how they’re described can influence whether they return.

Research shows that stigmatizing language in documentation—even when subtle—can shape both patient expectations and provider attitudes. Words like “non-compliant” or “drug abuser” signal blame, reinforcing the idea that care is conditional on “good” behavior. This shapes how future clinicians interpret that patient, creating bias before the visit even starts.⁴⁰

From a decision science perspective, this is a major framing issue. Language choices guide how others assess a situation—and how patients assess themselves. If records consistently frame people in terms of failure or risk, patients may feel judged, not supported. That decreases trust and discourages return visits.⁴⁰

Instead, switch to neutral, person-first phrasing. Use “person with substance use disorder” rather than “addict,” or “missed appointments” instead of “non-compliant.” Keep contextual notes factual, not interpretive: “patient reported stopping medication due to side effects,” not “refused meds again.”

Organizations can standardize inclusive phrasing in templates, build feedback prompts into Electronic Health Record (EHR) systems, and hold periodic language audits. When documentation affirms autonomy and avoids blame, it helps keep the door open for follow-up care.

 Signal Inclusivity Through Environmental and Digital Cues

Identity threat, triggered by cues that signal exclusion or bias, predicts lower trust and avoidance of care settings. Conversely, identity-safe cues—those that affirm belonging—can buffer stigma and increase engagement.⁴¹ In one set of experiments, participants exposed to environments with diversity-valuing statements and inclusive imagery reported higher comfort and perceived fairness, even when minority representation was low.⁴²

Practical steps translate easily to clinics:

• Display visible signals of inclusivity (e.g., multilingual signage, posters reflecting community demographics, or LGBTQ+ ally symbols).
• Highlight mission statements explicitly valuing cultural diversity.
• Ensure front-line staff reflect warmth and respect in greetings and body language, as these micro-behaviors shape perceptions of psychological safety.

Digital environments may carry just as much weight as in-person care. In one study, sexual minority patients rated providers as significantly less biased and more culturally competent when the clinic’s website featured both diverse patient reviews and an explicit diversity statement.⁴³ These cues—seemingly minor—might shape whether someone perceives a provider as safe before the first visit even happens.

This ties into cue consistency: the alignment between what an organization says it values and what patients actually see. When that alignment is missing, trust may wear away quickly. People notice when visual signals, written language, and frontline behavior don’t match, and draw corresponding conclusions.

Conversely, when digital and physical environments send a coherent message—one that reflects inclusion, not just claims it—patients are more likely to interpret the space as more credible, more accountable, and more worth returning to.

In practice, this means ensuring that every visible element—from website testimonials to clinic posters—echoes the same message of respect and belonging. When patients encounter consistent cues of safety across platforms and spaces, skepticism softens, and participation rises.

Behavioral interventions can’t outrun structural inequities. Telehealth platforms, artificial intelligence (AI) translators, and mobile clinics may extend reach, but only when the systems beneath them hold steady. Connectivity might fail, funding might lapse, and leadership might resist change. When those conditions unravel, even the most promising tools can start to reproduce the very inequities they were built to reduce.

Similarly, improving access doesn’t mean trust automatically returns. Many communities still carry the heavy weight of neglect and the burden of intergenerational trauma. A clinic can display welcoming cues, diverse staff, and inclusive messaging, yet those signals are unlikely to undo years of harm for everyone who walks in. Deep mistrust doesn’t dissolve on initial contact. It takes repeated, reliable experiences of respect to shift expectations. The more patients see that care is consistent, the more likely they are to come back—not because the space looks safe, but because it has proven to be.

Behavioral design makes healthcare easier to reach, while institutional commitment makes it worth returning to. When the two align, access stops being an aspiration and becomes a standard that can be sustained.

Across all three challenges, a consistent idea emerges: expanding care is necessary, but it rarely guarantees engagement. Access goes beyond proximity. It’s about whether people feel equipped and safe enough to use what’s offered. Many people still postpone visits—not because care doesn’t exist, but because it feels hard to reach, time-consuming, or emotionally uncertain. For communities long shaped by inequity, those perceptions are deeply rooted and slow to change.

Progress depends on pairing structural access with behavioral design. Mobile clinics and telehealth platforms can bridge distance, but only if the system beneath them is reliable. Simplified scheduling, timely reminders, and patient-centered defaults help turn good intentions into follow-through. Additionally, when care environments reflect cultural and linguistic diversity and demonstrate accountability and respect, they signal that engagement is both welcomed and worthwhile. Each small improvement builds familiarity and trust, but together, they sustain participation.

At The Decision Lab, we work with health systems and policymakers around the world to apply behavioral science in ways that make engagement more likely and more lasting. Some projects start small: a clinic redesign, a pilot to streamline scheduling. Others scale to national programs that reshape how access, communication, and continuity connect. Each one aims to turn opportunity into participation.

If you’re rethinking how patients connect with care, we’d love to be part of that process. Let’s design systems that meet people where they are—and build the kind of access that ensures.

Health Belief Model

Health behavior goes far beyond simply knowing what’s good for us. The Health Belief Model (HBM) offers a framework for understanding why people act or hold back when it comes to their health. With those insights, practitioners can design interventions that address hesitation and help turn intention into action.

Overlooked: Implicit Bias in Health Care

Patient mistrust doesn’t appear out of nowhere. It often follows patterns of exclusion or mistreatment. In healthcare, implicit bias in clinical decision-making remains a stubborn barrier, shaping how providers interpret symptoms and allocate care. These biases can emerge based on gender, race, age, weight, marital status, income, and other sociodemographic variables, often overlapping in subtle but significant ways. So how do we begin to undo their impact? This article breaks it down.

1. Parsons, J., Abel, G., Mounce, L. T., & Atherton, H. (2023). The changing face of missed appointments. The British Journal of General Practice, 73(728), 134–135. https://doi.org/10.3399/bjgp23X732249 
2. Dawkins, B., Renwick, C., Ensor, T., Shinkins, B., Jayne, D., & Meads, D. (2021). What factors affect patients’ ability to access healthcare? An overview of systematic reviews. Tropical Medicine & International Health, 26(10), 1177–1188. https://doi.org/10.1111/tmi.13651 
3. Anthony, D. L., Campos-Castillo, C., & Lim, P. S. (2018). Who isn’t using patient portals and why? Evidence and implications from a national sample of US adults. Health Affairs, 37(12), 1948–1954. https://doi.org/10.1377/hlthaff.2018.05117 
4. Smith, K. T., Monti, D., Mir, N., Peters, E., Tipirneni, R., & Politi, M. C. (2018). Access is necessary but not sufficient: Factors influencing delay and avoidance of health care services. MDM Policy & Practice, 3(1), 2381468318760298. https://doi.org/10.1177/2381468318760298 
5. LeBlanc, M. E., Trinh, M. H., Zubizarreta, D., & Reisner, S. L. (2024). Healthcare stereotype threat, healthcare access, and health outcomes in a probability sample of US transgender and gender diverse adults. Preventive Medicine Reports, 42, 102734. https://doi.org/10.1016/j.pmedr.2024.102734 
6. Turpin, R. E. (2025). Health care stereotype threat: The danger and damage of stigma and discrimination in health care. American Journal of Public Health, 115(3), 265–267. https://doi.org/10.2105/AJPH.2024.307989 
7. United Nations. (2015). Transforming our world: The 2030 agenda for sustainable development. New York, NY: UN.
8. Guagliardo, M. F. (2004). Spatial accessibility of primary care: Concepts, methods and challenges. International Journal of Health Geographics, 3(1), 3. https://doi.org/10.1186/1476-072X-3-3 
9. Ambroggi, M., Biasini, C., Del Giovane, C., Fornari, F., & Cavanna, L. (2015). Distance as a barrier to cancer diagnosis and treatment: Review of the literature. The Oncologist, 20(12), 1378–1385. https://doi.org/10.1634/theoncologist.2015-0110 
10. Oldenburg, C. E., Sié, A., Ouattara, M., Bountogo, M., Boudo, V., Kouanda, I., ... & Étude CHAT Study Group. (2021). Distance to primary care facilities and healthcare utilization for preschool children in rural northwestern Burkina Faso: Results from a surveillance cohort. BMC Health Services Research, 21, 212. https://doi.org/10.1186/s12913-021-06226-5 
11. Labban, M., Chen, C. R., Frego, N., Nguyen, D. D., Lipsitz, S. R., Reich, A. J., ... & Trinh, Q. D. (2023). Disparities in travel-related barriers to accessing health care from the 2017 National Household Travel Survey. JAMA Network Open, 6(7), e2325291. https://doi.org/10.1001/jamanetworkopen.2023.25291 
12. Russell, D. J., Humphreys, J. S., Ward, B., Chisholm, M., Buykx, P., McGrail, M., & Wakerman, J. (2013). Helping policy-makers address rural health access problems. Australian Journal of Rural Health, 21(2), 61–71. https://doi.org/10.1111/ajr.12023 
13. Whitaker, K. L., Krystallidou, D., Williams, E. D., Black, G., Vindrola-Padros, C., Braun, S., & Gill, P. (2021). Addressing language as a barrier to healthcare access and quality. The British Journal of General Practice, 72(714), 4. https://doi.org/10.3399/bjgp22X718013 
14. Alzubaidi, H., Mc Namara, K., Browning, C., & Marriott, J. (2015). Barriers and enablers to healthcare access and use among Arabic-speaking and Caucasian English-speaking patients with type 2 diabetes mellitus: A qualitative comparative study. BMJ Open, 5(11), e008687. https://doi.org/10.1136/bmjopen-2015-008687 
15. Foster, M. V., & Sethares, K. A. (2014). Facilitators and barriers to the adoption of telehealth in older adults: An integrative review. CIN: Computers, Informatics, Nursing, 32(11), 523–533. https://doi.org/10.1097/CIN.0000000000000105 
16. Malone, N. C., Williams, M. M., Smith Fawzi, M. C., Bennet, J., Hill, C., Katz, J. N., & Oriol, N. E. (2020). Mobile health clinics in the United States. International Journal for Equity in Health, 19, 40. https://doi.org/10.1186/s12939-020-1135-7 
17. Iqbal, A., Anil, G., Bhandari, P., Crockett, E. D., Hanson, V. M., Pendse, B. S., Eckdahl, J. S., & Horn, J. L. (2022). A digitally capable mobile health clinic to improve rural health care in America: A pilot quality improvement study. Mayo Clinic Proceedings: Innovations, Quality & Outcomes, 6(5), 475–483. https://doi.org/10.1016/j.mayocpiqo.2022.08.002 
18. Greene, N. E., Fuentes-Juárez, B. N., & Sabatini, C. S. (2019). Access to orthopaedic care for Spanish-speaking patients in California. Journal of Bone and Joint Surgery, 101(18), e95. https://doi.org/10.2106/JBJS.18.01080
19. Brandenberger, J., Stedman, I., Stancati, N., Sappleton, K., Kanathasan, S., Fayyaz, J., & Singh, D. (2025). Using artificial intelligence based language interpretation in non-urgent paediatric emergency consultations: A clinical performance test and legal evaluation. BMC Health Services Research, 25(1), 138. https://doi.org/10.1186/s12913-025-12263-1 
20. Hampers, N., Thieme, R., & Hampers, L. (2024). Artificial intelligence as a language barrier application in a simulated health care setting: A proof-of-concept study. Pediatric Emergency Care, 10–1097. https://doi.org/10.1097/PEC.0000000000003369 
21. Taber, J. M., Leyva, B., & Persoskie, A. (2015). Why do people avoid medical care? A qualitative study using national data. Journal of General Internal Medicine, 30(3), 290–297. https://doi.org/10.1007/s11606-014-3089-1 
22. Nguyen, D. L., & Dejesus, R. S. (2010). Increased frequency of no-shows in residents’ primary care clinic is associated with more visits to the emergency department. Journal of Primary Care & Community Health, 1(1), 8–11. https://doi.org/10.1177/2150131909359930 
23. Kheirkhah, P., Feng, Q., Travis, L. M., Tavakoli-Tabasi, S., & Sharafkhaneh, A. (2016). Prevalence, predictors and economic consequences of no-shows. BMC Health Services Research, 16, 13. https://doi.org/10.1186/s12913-015-1243-z 
24. Kaplan-Lewis, E., & Percac-Lima, S. (2013). No-show to primary care appointments: Why patients do not come. Journal of Primary Care & Community Health, 4(4), 251–255. https://doi.org/10.1177/2150131913498513 
25. Briatore, A., Tarsetti, E. V., Latorre, A., Gonzalez Bernaldo de Quirós, F., Luna, D., Fuentes, N. A., ... & Giunta, D. H. (2020). Causes of appointment attendance, nonattendance, and cancellation in outpatient consultations at a university hospital. The International Journal of Health Planning and Management, 35(1), 207–220. https://doi.org/10.1002/hpm.2890 
26. Fairhurst, K., & Sheikh, A. (2008). Texting appointment reminders to repeated non-attenders in primary care: Randomised controlled study. BMJ Quality & Safety, 17(5), 373–376. https://doi.org/10.1136/qshc.2006.020768 
27. Ghufran, M. S., Soni, P., & Duddukuri, G. R. (2023). The global concern for cancer emergence and its prevention: A systematic unveiling of the present scenario. In Bioprospecting of Tropical Medicinal Plants (pp. 1429–1455). Cham: Springer Nature Switzerland.
28. Mehta, S. J., Khan, T., Guerra, C., Reitz, C., McAuliffe, T., Volpp, K. G., Asch, D. A., & Doubeni, C. A. (2018). A randomized controlled trial of opt-in versus opt-out colorectal cancer screening outreach. The American Journal of Gastroenterology, 113(12), 1848–1854. https://doi.org/10.1038/s41395-018-0151-3 
29. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 363–391. https://doi.org/10.2307/1914185 
30. Aggarwal, A., Davies, J., & Sullivan, R. (2016). “Nudge” and the epidemic of missed appointments: Can behavioural policies provide a solution for missed appointments in the health service? Journal of Health Organization and Management, 30(4), 558–564. https://doi.org/10.1108/JHOM-04-2015-0061 
31. Berliner Senderey, A., Kornitzer, T., Lawrence, G., Zysman, H., Hallak, Y., Ariely, D., & Balicer, R. (2020). It’s how you say it: Systematic A/B testing of digital messaging cut hospital no-show rates. PLOS ONE, 15(6), e0234817. https://doi.org/10.1371/journal.pone.0234817 
32. Shukla, M., Schilt-Solberg, M., & Gibson-Scipio, W. (2025). Medical mistrust: A concept analysis. Nursing Reports, 15(3), 103. https://doi.org/10.3390/nursrep15030103 
33. LaVeist, T. A., Isaac, L. A., & Williams, K. P. (2009). Mistrust of health care organizations is associated with underutilization of health services. Health Services Research, 44(6), 2093–2105. https://doi.org/10.1111/j.1475-6773.2009.01017.x 
34. Burgess, D. J., Warren, J., Phelan, S., Dovidio, J., & Van Ryn, M. (2010). Stereotype threat and health disparities: What medical educators and future physicians need to know. Journal of General Internal Medicine, 25(Suppl 2), 169–177. https://doi.org/10.1007/s11606-009-1221-4 
35. Abdou, C. M., Fingerhut, A. W., Jackson, J. S., & Wheaton, F. (2016). Healthcare stereotype threat in older adults in the Health and Retirement Study. American Journal of Preventive Medicine, 50(2), 191–198. https://doi.org/10.1016/j.amepre.2015.07.034 
36. Gaston-Johansson, F., Hill-Briggs, F., Oguntomilade, L., Bradley, V., & Mason, P. (2007). Patient perspectives on disparities in healthcare from African-American, Asian, Hispanic, and Native American samples including a secondary analysis of the Institute of Medicine focus group data. Journal of National Black Nurses' Association, 18(2), 43–52.
37. Molina, Y., Kim, S., Berrios, N., & Calhoun, E. A. (2015). Medical mistrust and patient satisfaction with mammography: The mediating effects of perceived self-efficacy among navigated African American women. Health Expectations, 18(6), 2941–2950. https://doi.org/10.1111/hex.12278 
38. Frost, D. M., & Meyer, I. H. (2023). Minority stress theory: Application, critique, and continued relevance. Current Opinion in Psychology, 51, 101579. https://doi.org/10.1016/j.copsyc.2023.101579 
39. Thorpe, S., Tanner, A. E., & Hargons, C. N. (2023). Sexual identity factors and minority stressors associated with healthcare stereotype threat and access to care among Black sexual minority women. Journal of Gay & Lesbian Social Services, 35(2), 183–203. https://doi.org/10.1080/10538720.2022.2056398 
40. Healy, M., Richard, A., & Kidia, K. (2022). How to reduce stigma and bias in clinical communication: A narrative review. Journal of General Internal Medicine, 37(10), 2533–2540. https://doi.org/10.1007/s11606-022-07609-y 
41. Cipollina, R., & Sanchez, D. T. (2019). Reducing health care disparities through improving trust: An identity safety cues intervention for stigmatized groups. Translational Issues in Psychological Science, 5(4), 315. https://doi.org/10.1037/tps0000207 
42. Purdie-Vaughns, V., Steele, C. M., Davies, P. G., Ditlmann, R., & Crosby, J. R. (2008). Social identity contingencies: How diversity cues signal threat or safety for African Americans in mainstream institutions. Journal of Personality and Social Psychology, 94(4), 615–630. https://doi.org/10.1037/0022-3514.94.4.615 
43. Cipollina, R., & Sanchez, D. T. (2022). Identity cues influence sexual minorities’ anticipated treatment and disclosure intentions in healthcare settings: Exploring a multiple pathway model. Journal of Health Psychology, 27(7), 1569–1582. https://doi.org/10.1177/1359105321995984