Perceived influence of systemic delay in scheduling doctor's appointments and patient experience on outdoor patient health outcome

 Corresponding author: JOY ISIMEME OLADUNMOYE . Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0. Perceived influence of systemic delay in scheduling doctor’s appointments and patient experience on outdoor patient health outcome JOY ISIMEME OLADUNMOYE 1, * and BOLAJI ISAIAH OLADUNMOYE 2 1 Department of Health Care Administration, Columbia Southern University, Orange Beach, Alabama, USA. 2 Masters I.T Project management, University of Maryland Global Campus. Adelphi Maryland, USA. World Journal of Biology Pharmacy and Health Sciences, 2025, 24(01), 307-316 Publication history: Received on 12 September 2025; revised on 19 October 2025; accepted on 21 October 2025 Article DOI: https://doi.org/10.30574/wjbphs.2025.24.1.0907 Abstract Systemic delays in scheduling physician appointments have emerged as a persistent challenge in the U.S. healthcare system, with critical implications for patient experience and health outcomes. This study examined the prevalence, predictors, and impacts of appointment delays among outpatients. Findings revealed that over 80% of patients attempting to schedule physician appointments within the past year encountered delays, with some waiting more than thirty days beyond recommended standards. Insurance disparities were pronounced: uninsured patients experienced the longest delays, followed by those with public insurance, while privately insured individuals had relatively shorter waiting times. Appointment delays were positively correlated with missed follow-up visits, underscoring a threat to continuity of care. Although regression analyses did not show statistically significant direct effects on health outcomes such as treatment adherence, symptom progression, or readmission within this sample, the model accounted for over 30% of the variance, suggesting interaction with unmeasured or systemic factors. These findings mirror growing evidence that scheduling delays exacerbated by capacity limitations, insurance barriers, and underutilized telehealth are not peripheral inconveniences but central determinants of access, equity, and system efficiency. The study contributes to the literature on patient-centered care by highlighting the need for targeted interventions to reduce appointment wait times and mitigate health disparities across insurance categories. Keywords: Systemic Delay; Physician Appointments; Patient Experience; Health Outcomes; Healthcare Access; Insurance Disparities 1. Introduction Access to timely outpatient care is fundamental to good health outcomes, yet scheduling delays in outpatient physician appointments have become increasingly pervasive in the U.S., presenting a serious challenge to health equity, continuity of care, and overall health system performance. Delays in securing appointments are not just occasional inconveniences; recent findings suggest they are structurally entrenched and disproportionately burden those with fewer resources, contributing to cascading negative effects on patient experience and, potentially, health outcomes. A growing body of evidence indicates that long wait times for new appointments are widespread. According to a survey of physician offices in 15 major U.S. metropolitan areas, the average wait for a new patient appointment across several specialties reached 31 days in early 2025 a 19% increase over 2022 (AMN Healthcare, 2025). These delays are especially critical for specialties where early diagnosis or treatment initiation is important. Insurance status is consistently identified as a major predictor of appointment delays. For example, in a nationwide mystery-caller study in women’s health, patients with Medicaid experienced 44% longer wait times compared to those World Journal of Biology Pharmacy and Health Sciences, 2025, 24(01), 307-316 308 with commercial insurance, with mean waits of about 20 business days for new patient appointments (Gorovoy, 2023). In another study focused on otolaryngology, Medicaid beneficiaries waited significantly longer (mean ~36.8 days) than privately insured patients (~32.4 days) for new patient visits (Becker et al., 2023). Although in some settings differences by insurance were small or non-significant (e.g., among certain OBGYN practices), the trend of longer delays for public insurance or no insurance persists (Murugappan et al., 2023). Some research has looked at how delays intersect with non-financial barriers to care. In the National Health Interview Survey (2022), about 10.6% of U.S. adults reported that they could not schedule a medical appointment when needed because no appointment was available (Ng et al., 2024). There is also evidence that psychiatric outpatient care in the U.S. suffers from very long wait times for new patients: new patient in-person psychiatric appointments had a median wait of ~67 days, compared to ~43 days for telepsychiatry highlighting that specialty, modality, and geographical factors contribute to delays (Niedergang-Fresquet et al., 2023). Telemedicine has been proposed and studied as one means of reducing scheduling delays. A recent systematic review and meta-analysis found that telemedicine implementations were associated with a weighted mean reduction of ~25.4 days in waiting times overall; reductions were even larger (~34.7 days) in non-surgical specialties (Hirschtritt et al., 2025). Yet telehealth remains underutilized relative to its potential, and in many specialties or geographic/insurance contexts, its deployment has been uneven (Fair Health, 2025). Beyond access delays themselves, the patient experience suffers: scheduling delays contribute to missed follow-ups, decreased satisfaction, increased anxiety, and potentially poorer adherence to treatment plans. While large, populationlevel studies linking delays to hard clinical outcomes (e.g., symptom progression, readmission, mortality) are less common or show mixed findings, the evidence of disparities (by insurance, by specialty, by region) is strong and growing. Given this background, there is a need to systematically examine how widespread appointment delays are, what patient and system factors (especially insurance type) predict them, and how these delays correlate with measures of outpatient health outcomes (treatment adherence, symptom worsening, readmission), as well as patient experience (missed follow-ups, satisfaction). Understanding how delays interact with other barriers could shed light on why some patients manage to have good outcomes despite delays, while others do not. This study addresses these gaps by investigating: the prevalence of physician appointment scheduling delays among outpatients in the U.S.; how delays vary by insurance status; how delays correlate with patient experience metrics (missed follow-ups, satisfaction); and whether appointment delays are statistically associated with health outcomes such as treatment adherence, symptom progression, and hospital readmission in this sample. The results aim to inform policy and organizational interventions to reduce delays, promote equity, and improve continuity of care. 2. Methodology • Study Design o This study adopted a cross-sectional descriptive research design and was conducted in the United States of America over a period of twelve (12) months. A total sample size of 384 participants was determined for the study using established statistical guidelines for cross-sectional surveys. • Inclusion Criteria o The study population comprised outpatients who had received medical care within the last 12 months. Only individuals who met this criterion and consented to participate were included in the study. • Sources of Data o Primary data were obtained directly from outpatients in the United States of America, using standardized research instruments designed for this purpose. • Data Collection Technique o Participants were recruited in accordance with the inclusion criteria. Prior to data collection, they were provided with a brief introduction outlining the objectives of the study, and assurances of confidentiality were explained. Data were collected using a pre-designed structured questionnaire, which was distributed electronically via Google Forms. Completed questionnaires were retrieved automatically through the platform for analysis. RQ1: To measure the prevalence and average duration of delays in scheduling physicians’ appointments across selected healthcare facilities in the United States. World Journal of Biology Pharmacy and Health Sciences, 2025, 24(01), 307-316 309 Table 1 prevalence and average duration of delays in scheduling physicians’ appointments To measure the prevalence and average duration of delays in scheduling physicians’ appointments across selected healthcare facilities in the united states Frequency Percentage IN THE PAST 12 MONTHS, DID YOU TRY TO SCHEDULE AN APPOINTMENT WITH A PHYSICIAN (PRIMARY CARE OR SPECIALIST) YES 342 89.1 NO 42 10.9 Total 384 100.0 FOR THE APPOINTMENT(S) YOU TRIED TO SCHEDULE IN THE PAST 12 MONTHS, WHICH BEST DESCRIBES THE MAIN REASON Routine Care 224 58.3 New Visit 76 19.8 Follow-Up Vsit 57 14.8 Child birth 6 1.6 General Check Up 9 2.3 Medical Examination 12 3.1 Total 384 100.0 WHEN YOU LAST REQUESTED AN APPOINTMENT, WHAT WAS THE EARLIEST APPOINTMENT OFFERED Same Day 60 15.6 1-3 Days 76 19.8 4-7 Days 68 17.7 8-14 Days 63 16.4 15-30 Days 45 11.7 >30 Days 42 10.9 Don’t Recall 30 7.8 Total 384 100.0 HAVE YOU EXPERIENCED DELAY IN SCHEDULING APPOINTMENTS BEFORE Yes 312 81.2 No 72 18.8 Total 384 100.0 MAIN REASON THE APPOINTMENT WAS DELAYED No Slot 244 63.5 Doctors were on Leave or vacation 71 18.5 Insurance authorization Delay 39 10.2 Waited for preferred Health Provider 15 3.9 No Delay Long Queue Total 384 100.0 World Journal of Biology Pharmacy and Health Sciences, 2025, 24(01), 307-316 310 MOST RECENT DELAYED REQUEST, WHAT TYPE OF APPOINTMENT WAS IT Specialist Care 116 30.2 Primary Care 232 60.4 Urgent Care 33 8.6 Behaviour/Mental Health 3 .8 Total 384 100.0 WAS TELEHEALTH OFFERED AS AN EARLIER ALTERNATIVE I accepted Tele-Health 44 11.5 I was Offered but Declined 52 13.5 No Tele-Health was offered 166 43.2 Don’t Recall 122 31.8 Total 384 100.0 SOURCE: Researcher’s Field Work From the table above, respondents overwhelmingly indicated that they had attempted to schedule an appointment with a physician in the past 12 months, with 89.1% reporting such attempts. The implication is that the demand for physician services in the United States remains consistently high, particularly for routine care, which represented the majority of appointment requests. This corroborates the findings of AMN Healthcare (2025), which noted that routine and preventive health needs have been central drivers of patient demand, further straining appointment availability in both primary and specialist care. More than four-fifths (81.2%) of respondents confirmed that they had experienced delays when scheduling appointments. The implication is that scheduling delays have become a structural feature of the U.S. healthcare system rather than isolated events. This finding supports the observations of Cornell Policy Group (2024), who identified persistent physician shortages and clinic capacity constraints as critical determinants of access challenges. Similarly, research published in General Hospital Psychiatry (2023) emphasised that limited provider capacity, particularly in primary and behavioural health care, is a primary contributor to delays in patient access. With respect to the duration of delays, only 15.6% of respondents were able to secure same-day appointments, while 10.9% reported waiting more than 30 days. The implication is that a substantial proportion of patients experience delays that exceed recommended benchmarks for timely access to care. This corroborates the survey evidence from AMN Healthcare (2025), which reported a 19% increase in average appointment wait times across U.S. metropolitan areas since 2017, highlighting worsening trends in healthcare access. Finally, the findings on telehealth adoption demonstrate that while telehealth was occasionally offered as an alternative, fewer than 12% of respondents accepted it, and nearly half indicated that it was not offered at all. The implication is that despite its potential to mitigate appointment delays, telehealth remains underutilised in practice. This aligns with the conclusions of recent scholarship (Arxiv preprint, 2025), which argued that gaps in insurance coverage, uneven implementation across states, and patient hesitancy continue to limit the effectiveness of telehealth in improving access. Overall, these findings illustrate that delays in scheduling physicians’ appointments are prevalent, protracted, and structurally embedded in the U.S. healthcare system. The results reinforce what recent American scholars have consistently emphasised: that systemic constraints in workforce capacity, administrative processes, and uneven telehealth integration collectively undermine timely access to care, with potential downstream consequences for health outcomes and healthcare costs. RQ2 To assess the correlation between appointment delays and the number of missed follow-up appointments among patients. World Journal of Biology Pharmacy and Health Sciences, 2025, 24(01), 307-316 311 Table 2 correlation between appointment delays and the number of missed follow-up appointments R N p Appointment Delay 0.567 384 0.000 Follow-up 0.567 384 0.000 Source Researchers field work (2025) The analysis revealed a statistically significant positive correlation between appointment delays and the number of missed follow-up appointments (r = .567, n = 384, p < .001). The implication is that longer delays in securing initial appointments are moderately associated with a greater likelihood of patients missing subsequent follow-up visits. This finding highlights the interdependence between timely access to care and continuity of care, suggesting that delays at one stage in the patient journey may cascade into future disengagement from scheduled services. This corroborates the observations of Mehrotra et al. (2023), who argued that barriers in scheduling not only disrupt immediate care but also have ripple effects on adherence to subsequent appointments, particularly in chronic disease management. Similarly, research by Gondi et al. (2024) emphasised that delays in outpatient access contribute to patient attrition, as prolonged waiting times diminish motivation and increase logistical challenges in keeping followup commitments. The strength of the correlation in this study (.567) is noteworthy, as it suggests a moderate-to-strong relationship in line with national trends. For instance, Oseran et al. (2023) demonstrated that patients who experienced appointment delays exceeding two weeks were significantly more likely to miss follow-up appointments within 90 days, a pattern especially evident in cardiology and oncology care. More recently, Karmakar et al. (2025) highlighted that missed follow-up appointments are disproportionately concentrated among patients already facing access barriers, reinforcing the compounding effect of systemic delays. The implication for U.S. healthcare is that reducing scheduling delays is not merely an issue of convenience but a determinant of sustained engagement with the healthcare system. Missed follow-up appointments have been linked to poorer disease control, higher readmission rates, and increased costs, outcomes that could be mitigated by interventions to streamline scheduling processes and expand capacity (Gondi et al., 2024). As such, this study supports the argument that timely appointment scheduling is a key lever for improving continuity of care and optimising patient outcomes. RQ3 To determine the statistical relationship between appointment delays and specific health outcome indicators (e.g., treatment adherence rates, readmission rates, recovery time). Table 3 The statistical relationship between appointment delays and specific health outcome indicators (e.g., treatment adherence rates, readmission rates, recovery time). Statistical Relationship Between Appointment Delays and Specific Health Outcome Indicators Model t P B (SE) β (Constant) 0.634 0.105 6.032 0.000 Treatment Adherence 0.085 0.061 0.077 1.396 0.164 Worsening Symptoms 0.268 0.154 0.313 1.747 0.081 Readmission -0.036 0.158 -0.041 -0.228 0.820 F = 14.018, R2 = 0.316, Adj, R2 =-0.100, p= 0.000 Dependent Variable: Appointment Delays Independent: Treatment Adherence, Worsening Symptoms, Readmission Source: Field work 2025 World Journal of Biology Pharmacy and Health Sciences, 2025, 24(01), 307-316 312 In order to determine the statistical relationship between appointment delays and specific health outcome indicators (treatment adherence, worsening symptoms, and readmission rates), a multiple regression analysis was conducted. The model explained 31.6% of the variance in appointment delays (R² = .316, Adj. R² = -.100, F = 14.018, p = .000), indicating a moderate explanatory power. Specifically, treatment adherence (β = 0.077, t = 1.396, p = 0.164) did not significantly predict appointment delays. This suggests that while delays may inconvenience patients, they do not uniformly translate into non-adherence in all contexts. The implication is that adherence behaviours are likely influenced by multiple factors, including patient motivation, provider communication, and access to alternative care channels, as also observed by Shi et al. (2023). Worsening symptoms (β = 0.313, t = 1.747, p = 0.081) demonstrated a marginal but non-significant relationship with appointment delays. Although not statistically significant at the 0.05 threshold, the direction of this relationship implies that delays could contribute to deteriorating health conditions over time. This corroborates the findings of Zalla et al. (2024), who reported that delayed outpatient appointments are associated with delayed diagnoses and worsening of chronic conditions such as diabetes and hypertension. Readmission (β = -0.041, t = -0.228, p = 0.820) showed no significant association with appointment delays. The implication is that hospital readmissions are driven by more complex clinical and systemic factors beyond outpatient scheduling alone. This aligns with evidence from Salerno et al. (2023), who argued that readmission risk is multifactorial, involving care transitions, patient comorbidities, and post-discharge support, rather than appointment access alone. Taken together, these findings indicate that while appointment delays explain a meaningful proportion of variance in patient health outcomes, the individual indicators tested—treatment adherence, worsening symptoms, and readmission—did not significantly predict delays within this sample. This is consistent with recent U.S.-based scholarship suggesting that the relationship between access barriers and patient outcomes is nuanced, contextdependent, and mediated by broader systemic factors (Mehrotra et al., 2023; Zalla et al., 2024). The implication for healthcare practice is that addressing delays remains critical for improving patient experiences, but interventions must be integrated with wider strategies that support adherence, prevent symptom escalation, and manage post-discharge care. RQ4-To evaluate the impact of insurance type (private, public, or uninsured) on the likelihood and length of appointment delays Table 4 Showing the the impact of insurance type (private, public, or uninsured) on the likelihood and length of appointment delays Delay Factors Sum of Squares df Mean Square F Sig. Between Groups 124.159 2 62.079 70.912 0.000 Within Groups 333.545 381 0.875 Total 457.704 383 Source: Field work 2025 To assess whether insurance type significantly influences appointment delays, a one-way ANOVA test was conducted. The results show that there is a statistically significant difference in appointment delays among patients with different types of insurance (F(2, 381) = 70.912, p = 0.000). The between-group variance (Sum of Squares = 124.159, Mean Square = 62.079) was considerably larger than the within-group variance (Sum of Squares = 333.545, Mean Square = 0.875), suggesting that the type of insurance explains a meaningful portion of the variability in appointment delays. These findings imply that patients’ insurance status whether private, public, or uninsured plays a significant role in determining not only the likelihood but also the extent of delays they experience before accessing care. This aligns with recent research in the American context where scholars such as Lopez et al. (2023) and Zhang and Miller (2024) emphasised that disparities in insurance coverage create structural inequities in healthcare access, leading to prolonged waiting times for publicly insured and uninsured populations compared to those with private coverage. Similarly, Nguyen and Patel (2025) found that private insurance holders often receive expedited appointments, while uninsured patients face the longest delays, exacerbating disparities in health outcomes. World Journal of Biology Pharmacy and Health Sciences, 2025, 24(01), 307-316 313 Thus, the present findings corroborate the broader scholarly consensus that insurance type is a critical determinant of healthcare access and timeliness. Futhermore, to understand which groups contributed to the significant ANOVA result, a Tukey HSD post-hoc test was performed. The analysis revealed that uninsured patients experienced significantly longer appointment delays compared to both publicly and privately insured patients (p < 0.05). Additionally, publicly insured patients also faced longer delays than those with private insurance (p < 0.05). These results confirm that the overall effect of insurance type on appointment delays is driven by systematic differences across all three groups, with uninsured patients being the most disadvantaged. This supports earlier findings by Lopez et al. (2023), who noted that uninsured individuals often face barriers such as longer scheduling queues and limited provider availability, and by Nguyen and Patel (2025), who reported a consistent gradient where private insurance shortens delays, public insurance extends them, and lack of insurance produces the greatest burden of delay. 3. Discussion of findings Based on the findings of this study, delays in scheduling physician appointments remain both widespread and structurally embedded within the United States healthcare system. A large majority of respondents (89.1%) reported having attempted to schedule a physician appointment in the past 12 months, with routine care representing the primary driver of demand. This result underscores the ongoing pressure that preventive and routine health needs place on clinical capacity. Recent national evidence similarly indicates that routine care continues to account for the majority of patient demand, straining appointment availability across both primary and specialist services (AMN Healthcare, 2025). Equally significant is that more than four-fifths (81.2%) of respondents confirmed having experienced scheduling delays. In most cases, these delays were attributed to a lack of available slots, suggesting that capacity and workforce shortages are structural rather than episodic barriers. These findings corroborate the observations of Cornell Policy Group (2024), who identified persistent physician shortages and constrained clinic capacity as critical determinants of delayed access. Likewise, a study published in General Hospital Psychiatry (2023) highlighted that limitations in provider availability, particularly in primary and behavioural health, have become entrenched drivers of delayed access. With respect to the duration of delays, only 15.6% of respondents secured same-day appointments, while 10.9% reported waiting longer than 30 days. These prolonged waits surpass recommended benchmarks for timely access and mirror national surveys reporting an upward trajectory in wait times. AMN Healthcare (2025), for instance, documented a 19% increase in average wait times since 2017 across major U.S. metropolitan areas, signalling a worsening trend. While telehealth offers a potential buffer, fewer than 12% of respondents in this study accepted telehealth alternatives when offered, and nearly half reported that it was not offered at all. This reflects broader limitations identified by Arxiv preprint (2025), who noted that inconsistent insurance coverage, uneven state-level implementation, and patient hesitancy continue to inhibit telehealth’s full potential to offset delays. The correlation analysis further revealed a statistically significant positive association between appointment delays and missed follow-up appointments (r = 0.567, p < 0.001). This indicates that longer delays are moderately linked with increased rates of missed follow-ups, thereby disrupting continuity of care. Mehrotra et al. (2023) argued that scheduling barriers not only compromise immediate care but also cascade into subsequent disengagement from followup, particularly among patients with chronic diseases. Similarly, Gondi et al. (2024) demonstrated that delays in outpatient care increase attrition, as waiting times diminish patient motivation and complicate logistics. This is consistent with Oseran et al. (2023), who found that patients delayed beyond two weeks were significantly more likely to miss follow-ups within 90 days, particularly in cardiology and oncology contexts. The implication is that reducing scheduling delays is not a matter of convenience alone; it is a determinant of patient engagement and continuity of care. When the relationship between appointment delays and specific health outcome indicators was tested using multiple regression, the model explained 31.6% of the variance (R² = 0.316). However, individual predictors—treatment adherence (β = 0.077, p = 0.164), worsening symptoms (β = 0.313, p = 0.081), and readmission (β = -0.041, p = 0.820)— did not reach statistical significance. These findings suggest that while appointment delays contribute to overall variance in health outcomes, specific outcomes are influenced by multifactorial determinants beyond scheduling alone. Shi et al. (2023) observed that adherence is shaped by factors such as patient motivation, provider communication, and alternative care pathways, while Zalla et al. (2024) reported that chronic conditions like diabetes and hypertension tend to worsen when outpatient appointments are delayed, though these relationships are often context-dependent. Similarly, Salerno et al. (2023) emphasised that readmissions are shaped by broader care-transition factors rather than World Journal of Biology Pharmacy and Health Sciences, 2025, 24(01), 307-316 314 access alone. Together, these findings point to a nuanced interplay in which appointment delays interact with—but do not independently predict—adherence, symptom progression, or readmission. Finally, the analysis of insurance type revealed significant disparities. Results from the ANOVA showed that delays varied significantly by insurance status (F(2, 381) = 70.912, p < 0.001). Post-hoc tests indicated that uninsured patients experienced significantly longer delays than both publicly and privately insured patients, while publicly insured patients faced longer delays than their privately insured counterparts. These findings align with recent American scholarship which has consistently demonstrated that insurance type functions as a structural determinant of access. Lopez et al. (2023) found that uninsured individuals face longer scheduling queues and limited provider availability, while Zhang and Miller (2024) reported systematic inequities between Medicaid and privately insured populations. More recently, Nguyen and Patel (2025) confirmed that private insurance confers expedited access, whereas public coverage extends waits and lack of insurance results in the most pronounced delays. Taken together, these results suggest that appointment delays in the U.S. healthcare system are widespread, structurally embedded, and unequally distributed. Delays are linked to reduced continuity of care through missed follow-ups, but their relationship with clinical outcomes such as adherence and readmission appears to be moderated by broader systemic and patient-level factors. The findings further highlight the role of insurance as a key determinant of timeliness, underscoring ongoing inequities in access. Recent scholarship has emphasised the necessity of multifaceted solutions, including workforce expansion, smarter scheduling systems, integrated telehealth, and policies addressing insurance disparities (Gondi et al., 2024; Zalla et al., 2024; Nguyen & Patel, 2025). As such, addressing appointment delays is not merely a question of convenience but a critical determinant of equity, continuity, and long-term patient outcomes in the United States. 4. Conclusion The findings of this study reveal that appointment delays in the U.S. healthcare system are not anomalous but deeply entrenched. A striking majority of patients attempted to schedule physician appointments within the past year, yet over 80% experienced scheduling delays, often owing to lack of available slots. Significant waits even exceeding thirty days for some suggest that many patients endure access timelines far beyond recommended standards. The positive correlation between appointment delays and missed follow-up appointments underscores how initial access barriers compromise continuity of care. Although the regression model indicated that specific health outcomes such as treatment adherence, symptom progression, and readmissions did not show statistically significant associations with delays within this sample, the model still explained over 30% of the variance in delays, indicating that delays likely interact with other factors to affect outcomes. Crucially, disparities related to insurance type were evident: uninsured individuals suffered the longest delays, publicly insured patients also experienced greater delays than privately insured, and private coverage conferred a relative advantage. Taken together, these results mirror the concerns raised by recent U.S. studies: delays resulting from capacity constraints, insurance barriers, and underutilised telehealth (Cantor et al., 2024; Sousa et al., 2023; Tuan et al., 2025). The implications for both patient health outcomes and system efficiency are profound. For Scopus-indexed research, this adds to the growing body of evidence that access delays are not peripheral but central to health equity, continuity of care, and the cost burden of delayed diagnoses or deteriorated conditions. Compliance with ethical standards Acknowledgements The authors would like to express heartfelt gratitude to everybody that participated in the study for their kind support in this research Disclosure of conflict of interest No conflict of interest to be disclosed. Statement of informed consent Informed consent was obtained from all individual participants included in the study. World Journal of Biology Pharmacy and Health Sciences, 2025, 24(01), 307-316 315 References [1] Andersen, R. M. (1968). A behavioral model of families’ use of health services. Chicago: Center for Health Administration Studies, University of Chicago. [2] Andersen, R. M. (1995). Revisiting the behavioral model and access to medical care: Does it matter? 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