Personalized Care in the UAE : A Study on Precision Medicine Awareness and Accessibility among the general population

Study design and participants

A population-based cross-sectional study was conducted using an online questionnaire, distributed across multiple platforms, including university mailing lists, social media groups, and professional networks. Participants were invited to access the questionnaire link after receiving a brief description of the study. Both Arabic and English versions of the questionnaire were made available through Google Forms. The study commenced only after receiving IRB approval (Ref. no. IRB-COP-STD-3-JULY-2024). Participation was voluntary, and informed consent was obtained electronically prior to enrollment. UAE residents aged 18 years or older were eligible, and an eligibility check was implemented within the online tool. Participants were also given the option to share the questionnaire link on their social media platforms to increase reach.

Questionnaire development and validation

The questionnaire was adapted from previous studies^12,15,16 and modified in consultation with community and hospital pharmacists to ensure alignment with the study objectives and the local context. It was initially prepared in English and translated into Arabic using a forward–backward translation method to ensure linguistic and conceptual equivalence. A panel of five experts, including two university professors, two pharmacy students, and a community pharmacist familiar with the study objectives, reviewed the questionnaire for content and face validity. The instrument was further piloted with members of the general public to assess clarity, ease of understanding, and comprehensiveness, and feedback was incorporated accordingly. The final questionnaire consisted of 22 items across five sections: demographics (7 items), current health status (3 items), knowledge of precision medicine (2 items), acceptance of precision medicine (2 items), and utilization patterns of precision medicine services (8 items).

Key constructs and scoring

Familiarity/knowledge of precision medicine was defined as participants’ self-reported awareness and understanding of core concepts, including genetic testing, pharmacogenetics, targeted therapy, and precision medicine itself. This was assessed using four Likert-scale items ranging from 1 (“Not familiar”) to 5 (“Extremely familiar”), and responses were summed to generate a total score ranging from 4 to 20, with higher scores indicating greater knowledge. Acceptance of precision medicine captured participants’ willingness to adopt personalized medicine approaches, measured using Likert-scale items (1 = strongly disagree to 5 = strongly agree) addressing perceived value, willingness to use, and comfort with precision medicine; summed item scores reflected overall acceptance. Perceived benefits represented participants’ recognition of advantages such as improved treatment effectiveness, reduced adverse outcomes, cost-effectiveness, and early disease detection, assessed via multiple-choice and Likert-scale items, with higher composite scores indicating stronger perceived benefits. Preferences for pharmacogenetic testing measured interest in undergoing testing, using Likert-scale items from 1 (“Not at all interested”) to 5 (“Extremely interested”), with higher scores reflecting greater preference. Finally, utilization or practice of precision medicine captured actual engagement, including prior genetic testing, discussions with healthcare providers, or use of precision medicine services; responses were coded as 0 (“I don’t know”), 1 (“No”), and 2 (“Yes”), and summed across items to generate a total score (range 0–6), with higher scores indicating more active engagement.

To minimize duplicate responses, the Google Forms “one response per device” restriction was enabled, and entries with identical IP addresses or missing consent were excluded during data cleaning. Internal consistency was assessed to ensure reliability, with Cronbach’s alpha values of 0.852 for the knowledge section and 0.855 for the utilization section, indicating high reliability.

Scoring methods for knowledge, acceptance, and practice were developed based on previously published research on awareness and perceptions of precision medicine.^17,18 Each domain comprised Likert-scale items, with composite scores generated by summing individual responses within each category. Higher scores reflected greater knowledge, higher acceptance, and more favorable practice behaviors toward precision medicine.

Participants were given the option to complete the questionnaire in either Arabic or English to accommodate language preferences and improve response rates. This comprehensive approach ensured the questionnaire’s validity, reliability, and data integrity before its distribution.

Statistical analysis

Descriptive statistics for ordinal or non-normally distributed continuous variables are presented as medians with interquartile ranges (IQR), while categorical variables are reported as frequencies and percentages. The levels of Knowledge, Awareness, and Acceptance of Precision Medicine were assessed. Comparisons of continuous demographic and clinical variables with Knowledge, Awareness, and Acceptance scores were conducted using the Mann–Whitney U test, as the data were not normally distributed. A p-value < 0.05 was considered statistically significant. All statistical analyses were performed using IBM SPSS Statistics for Windows, Version 26.0 (IBM Corp., Armonk, NY, USA).

Conceptional definitions

Precision Medicine (PM): The use of diagnostic tools and treatments targeted to the needs of the individual patient on the basis of genetic, biomarker, or psychosocial characteristics. PM does not imply the creation of medicines or devices that are unique to a patient, but rather the ability to stratify individuals into groups that differ in their susceptibility to a specific disease or their response to a specific treatment.^1,11

Personalized Medicine: A synonym to precision medicine. However, “precision medicine” is the preferred term.^1,11

Pharmacogenomics: The use of genomic information to inform prescription or avoidance of pharmaceuticals.^1,11

Knowledge and familiarity with key terminologies in precision medicine

The majority were slightly or somewhat familiar with terms such as “genetic testing” (61.8%), “precision medicine” (55.3%), “pharmacogenetics” (59.3%), and “targeted therapy” (51.5%). However, a notable proportion of participants reported being not familiar with these terms, ranging from 20.3% to 33.8%. Only a small percentage (12.3%–18.0%) indicated moderate or extreme familiarity. Details Participants’ familiarity with key precision medicine terminologies given in Figure 1.

Figure 1. Participants' familiarity with key precision medicine terminologies.

Sources of information on precision medicine

Most participants (n = 154, 38.5%) identified family and friends as their primary source of information about precision medicine, followed by the internet and websites (n = 96, 24%). In contrast, a notable proportion of participants (n = 130, 32.5%) stated that they had never heard of precision medicine. Details are presented in Figure 2.

Figure 2. Sources of information on precision medicine.

Perceived benefits of precision medicine

As shown in Figure 3, 160 (40%) of participants considered precision medicine main benefit is to be effective treatment, while 154 (38.5%) believed it could play a crucial role in early disease identification and prevention. For 72 (18%) of respondents, the key purpose was cheaper treatment, and 97 (24.3%) reported that it was valuable for safe treatment. However, 119 (29.8%) of participants were unsure about the specific benefits.

Figure 3. Perceived benefits of precision medicine.

Perceptions of insurance coverage for precision medicine among participants

Figure 4 illustrates the limited awareness and uncertainty regarding insurance coverage for precision medicine. A majority of participants, 236 (59.0%), were unsure if their insurance coverage was sufficient, while 83 (20.8%) believed it was inadequate, and 81 (20.3%) reported that it was sufficient. Regarding challenges in obtaining insurance coverage for precision medicine treatments, 222 (55.5%) participants were uncertain, 132 (33.0%) had not faced any difficulties, and 46 (11.5%) had encountered issues, indicating potential barriers to access.

Figure 4. Perceptions of insurance coverage for precision medicine among participants.

Exploring participants’ engagement in genetic testing

The data indicate that most participants have limited experience or engagement with genetic testing and precision medicine. Regarding prior experience with genetic testing, 224 (56.0%) reported having no experience. In terms of discussions with doctors about genetic testing or related treatments, 222 (55.5%) had not had such conversations. Concerning the utilization of precision medicine services by themselves or their families, 192 (48.0%) had not used such services, 147 (36.8%) were uncertain, and 61 (15.3%) reported having utilized these services. Additionally, 179 (44.8%) participants were unsure about their engagement with information on precision medicine and genetic testing, 147 (36.8%) had not engaged with any information, and 74 (18.5%) had actively sought such information. These findings suggest that public awareness and engagement with precision medicine remain limited, highlighting the need for targeted education and outreach initiatives ( Table 2).

Demographic variables vs knowledge

Table 3, Gender differences in familiarity scores were not statistically significant (p = 0.056), with both females and males showing a median score of 8 (IQR: females 7–11, males 6–11). Age and education level did not show a significant association (p =0.419 and p = 0.327, respectively), occupation showed a strong association with familiarity (p < 0.001), with students having the highest median score (9, IQR: 8,12), indicating greater awareness, possibly due to academic exposure. Annual household income was significantly associated with familiarity (p = 0.004), with higher-income individuals (>60,000 AED) and those who preferred not to disclose their income showing the highest familiarity scores (10, IQR: 7,12). Those in the lowest income category (<5,000 AED) had a lower median familiarity (8, IQR: 5,10), indicating that economic factors may influence awareness levels.

Demographic variables vs acceptances of precision medicine

Table 4 evaluates the acceptance of precision medicine based on a combined score derived from perceptions of healthcare outcome improvements and preferences for pharmacogenetic testing. The total acceptance score ranged from 1 to 5 for each item, with higher scores indicating greater acceptance. Across all demographic groups, the median acceptance score was 5 (neutral) on the 1–5 scale, indicating a generally neutral to slightly positive stance toward precision medicine.

Overall, gender, age, education, occupation, and income did not show strong associations with acceptance, while chronic health conditions showed a statistically significant association with acceptance (p = 0.004). Individuals with chronic illnesses have a higher median score (6, IQR: 5,7) compared to those without (5, IQR: 4,6). This suggests that those managing chronic diseases may be more receptive to precision medicine, potentially due to a greater perceived need for personalized treatment options. This suggests that individuals managing chronic diseases may be more receptive to precision medicine, potentially due to a greater perceived need for personalized treatment options.

Demographic vs practice of precision medicine

Table 5 reports the variation in practice of precision medicine scores across different demographic and socioeconomic groups. Gender showed a statistically significant difference (p = 0.003), with females having a median score of 3 (IQR: 0,3) compared to males with a slightly higher range (3, IQR: 2,4), suggesting that males may have a broader practice distribution. Occupation, age and education was not significantly associated with practice scores. However, annual household income showed a highly significant association (p < 0.001), and detailed analysis indicates that individuals with an annual income between 30,000 and 60,000 AED (0, IQR: 0, 3) had lower levels of practice regarding precision medicine compared to those earning less than 5,000 AED (3, IQR: 2.5,3) and between 5,000 and 30,000 AED (3, IQR: 0,4). Lastly, chronic health conditions were significantly associated with awareness (p = 0.023), as individuals with such conditions had a higher median (3, IQR: 2,4) compared to those without (3, IQR: 0,3), implying that personal health experiences may contribute to greater awareness of precision medicine.