Abstract
Background
Robotic technology is being rapidly adopted worldwide. The purpose of this study was to quantify the prevalence of robotic technology use among UAE community pharmacists, evaluate their perceived benefits and concerns, and identify factors that predict heightened concern levels.
Research design and methods
The present study utilized a validated self-administered survey, which was distributed in person to community pharmacists in different regions of Abu Dhabi and other Emirates. The questionnaire comprised sociodemographic and job‑related items, an operational definition of robotics, a 5‑point Likert scale on perceived benefits, a 4‑point Likert scale on perceived concerns (recoded to a 0–14 score), and a checklist of potential robotic pharmacy services.
Results
Pharmacists holding only a bachelor’s degree and pharmacy owners reported higher median concern scores than those with postgraduate degrees and pharmacists in charge, respectively. Additionally, pharmacists without training on robotic systems and those with heavier workloads dispensing ≥30 prescriptions per day or serving ≥10 patients per day also showed significantly greater concerns than their counterparts.
Conclusion
It is necessary to implement training initiatives aimed at enhancing awareness and understanding of robotic technologies among pharmacists.
Keywords: Robotics, community pharmacies, pharmacists, perceived benefits, perceived concerns, services
PLAIN LANGUAGE SUMMARY
In this study, we surveyed 430 community pharmacists across the UAE between May and October 2024 about using robots in their everyday work. Only about one in five pharmacists said they regularly use robotic systems for tasks like stocking medicine and packing prescriptions. Many pharmacists told us they worry that robots might break down, make it harder to talk with patients, or cost too much to set up. Those most concerned were pharmacists with only a bachelor’s degree, pharmacy owners, those without any robot training, and those with heavy workloads. Our findings suggest that offering hands‑on training, clear guidelines, and ongoing support could help pharmacists feel more confident using robotic tools safely in their pharmacies.
ARTICLE HIGHLIGHTS
The study emphasizes the necessity of thorough training programs designed for pharmacy employees at all levels, especially for those with a bachelor’s degree in pharmacy and pharmacy owners who expressed more concerns to utilize robotics in community pharmacies.
It is essential to implement strong support and maintenance mechanisms for robotic systems due to widespread concerns about their reliability and technical issues.
Since robots may have an adverse effect on pharmacist-patient interactions, methods for using robotic technologies that complement human contact rather than replace it should be created. This can entail setting up automated processes to manage repetitive and time-consuming duties.
The study indicates that in order to regulate the use of robotic technology in pharmacy settings, precise legal and ethical criteria are required. Fostering a safe and secure use of these technologies requires the development of comprehensive rules that address possible legal difficulties, cybersecurity threats, and ethical considerations regarding patient privacy and data security.
To mitigate pharmacists’ concern, it is essential to prepare and enforce specialized solutions that can smoothly integrate into high-volume settings without interfering with operations. This involves making certain that systems are flexible and expandable to accommodate varying pharmacy sizes and service requirements.
1. Introduction
The field of pharmacy has experienced significant change, including drug discovery, pharmacy practice, and infectious diseases [1–4]. One of these changes is the artificial intelligence (AI)-driven robotic tools, which are now widely recognized and being used [5–7]. In 2023, pharmaceutical robot’s industry was valued at USD 199.6 million and is anticipated to increase at a compound annual growth rate (CAGR) of 8.8% until 2030 [8].
These robotic technologies include automated dispensing robots that accurately manage and distribute drugs, reducing human error and enhancing pharmacy operations [5]. AI-powered inventory systems utilize algorithms to predict medication demand, improve stock levels, and minimize waste, hence ensuring an efficient supply chain and drug availability [5,9]. These developments provide substantial advantages, including increased precision in medicine delivery, less burden for pharmacists, and greater patient safety. Nonetheless, the integration of robotic technology poses obstacles and concerns, such as substantial implementation costs, technical difficulties, and the possible effects on employment and the professional roles of pharmacists [10].
Over the past ten years, community pharmacies in the United States [11], Europe [12], and Asia [13] have embraced robotic technology more quickly due to its proven advantages. According to a German study, within the first 12 months, community pharmacies’ robotic dispensing equipment increased over-the-counter sales by 6.8% while reducing labor costs by an average of 4.6% [14]. Higher prescription productivity and less technical dispensing activities by pharmacists were linked to robotics in community pharmacies in the United States [15].
The United Arab Emirates (UAE) is a Middle Eastern country with a rapidly evolving healthcare sector [16]. UAE implemented its first telemedicine framework in 2013, outlining the legislative requirements for six crucial elements: telemonitoring, telediagnosis, teleconsultation, telemedical treatments, teleconsultation, and teleprescribing. Numerous studies have now been conducted on the models’ performance, safety, and acceptance [17,18]. Starting from 2017, the UAE has introduced many robotic technologies into pharmacy practice in both hospital and community settings [19].
Despite extensive evidence of robotics improving workflow and safety in hospital settings in the Middle East, such as the recent 21‑month usability study in Saudi Arabia, and investigations of AI acceptance among pharmacy students and faculty across the region, there remains a conspicuous lack of research focused specifically on community pharmacy practice in the UAE [20,21]. Community pharmacists face unique operational demands, patient‐counseling roles, and regulatory considerations that differ markedly from hospital environments. Moreover, the acceptance rate and perception of robotic technologies among community pharmacists in the UAE remain unclear. Cultural, economic, and legal considerations specific to the UAE may affect the adoption and use of these technologies. Furthermore, apprehensions about job security, the dependability of robotic systems, and the possible repercussions on the pharmacist-patient interaction may influence pharmacists’ perceptions of robotics. Assessing the prevalence of robotic technology usage among community pharmacists in the UAE is essential for informing policy and directing future implementations. to our knowledge, this is the first nationwide study to quantify how community pharmacists in all seven Emirates utilize robotic technologies, what benefits they perceive, and what concerns they harbor. By employing a cross‑sectional survey with validated measures of technology acceptance and concern, we not only establish baseline uptake rates but also identify key predictors of pharmacists’ attitudes toward automation. These insights will directly inform tailored implementation strategies, regulatory guidance, and training programmes within the UAE’s rapidly evolving healthcare landscape.
The UAE embraces technology in pharmacy practice and healthcare. Recent studies highlighted the advantages of using these technologies in pharmacy settings. In specific, a study found that adopting telepharmacy service in the UAE could increase patient access, particularly COVID-19 probable and confirmed cases, to pharmacy care and could also reduce dispensing errors [22]. Another study found that remote pharmacy services in the UAE open the door for an evolved era of pharmacy services in terms of the types of interventions and recommendations provided by pharmacists [23]. A study from the UAE found that adopting technology into pharmacy settings improves pharmacist interventions on OTC misuse and allows patients with alarming symptoms, for example, a cough, to receive remote pharmaceutical care and reduces transmission of COVID-19. Furthermore, it enables older patients who are vulnerable to viruses to access pharmacies without the risk of contracting the infection [17]. The public in the UAE also encourages adopting these technologies into healthcare settings [18].
Therefore, this study aimed to 1) estimate the utilization of robotic technologies among community pharmacists in the UAE, 2) to assess their perceived benefits and concerns regarding these technologies, and 3) to identify predictors of concerns.
2. Materials and methods
2.1. Study design and participants
In this cross-sectional study, a validated self-administered survey was distributed to community pharmacists in various regions of the UAE between May and October 2024, using a convenience-sampling technique. Eligible participants were those who had graduated from universities accredited by the UAE Ministry of Higher Education and were registered as community pharmacists with the UAE Ministry of Health. Pharmacists with less than six months of experience, and those with a diploma in pharmacy, were excluded.
2.2. Sample size calculation
The sample size was calculated using the standard formula for estimating a population proportion with a specified confidence level and margin of error, as described by Pourhoseingholi et al. (2013) [24];
where: n is the required sample size, Z is the Z-score for the desired confidence level (1.96 for 95%), p is the estimated population proportion (assumed to be 0.5 to account for maximum variability), d is the margin of error (set at 0.05). The calculated minimum sample size was 385 participants. To compensate for possible non-responses and incomplete surveys, we increased this number by 10%, setting a target of 424 participants. This formula is commonly used in cross-sectional studies to determine the minimum sample size needed for estimating a proportion with desired level of precision, especially when no prior estimate is available. This aligns with the present study aim of assessing unknown proportions among a large and diverse group of pharmacists in the UAE. The formula is simple, conservative, and ensures adequate statistical power within a specific margin of error.
2.3. Study instrument
The study questionnaire was developed after review of relevant previously published research studies [25–27]. The questionnaire consisted of close-ended questions and was structured into five sections. The first section collected sociodemographic and job-related information (e.g., age, gender, education, pharmacy type, years of experience, average number of prescriptions dispensed and patients served per day, time spent per patient, and main source of drug information) using multiple-choice formats. Next, we provided all participants with the operational definition of robotics to ensure they could provide appropriate feedback on the perceived benefits and concerns of robotics utilization as follows: “Robots are defined as mechanical (physically embodied) artificially intelligent agents with the ability to move in the physical environment to perform complex tasks [28]. This definition includes the implementation of automated robotic systems to perform tasks traditionally carried out by pharmacists, such as medication dispensing, packaging, labeling, sorting, and inventory management.” The second part assessed participants’ familiarity with and use of robotic systems through yes/no and multiple-choice questions. The third section assessed perceived benefits using 10 items on a 5-point Likert scale from strongly disagree (1 point) to strongly agree (5 points). The fourth section assessed the perceived concerns using 14 items on a 4-point Likert scale from not concerned (1 point) to highly concerned (4 points). For the purpose of the present analysis, responses were recoded so that “Not concerned at all” received a score of 0, signifying no worry, while “Slightly concerned,” “Moderately concerned,” and “Highly concerned” received a score of 1. The possible scores ranged from 0 to 14. This binary recoding approach was used to simplify the analysis and identify participants who expressed any level of concern. This adjustment enabled clearer interpretation of overall concern levels and facilitated the use of total concern scores in regression analysis. The final section included a multiple-choice question where participants selected pharmacy services they believed robotics could assist with, such as dispensing, counseling, and inventory management, No open-ended questions were included in the survey. In the questionnaire, perceived benefits were assessed through statements such as “Robots in community pharmacy can streamline workflow” and “Robots in community pharmacies can reduce medication dispensing errors.” Other examples included “Robots can reduce the time pharmacists spend on technical tasks” and “Robots in community pharmacies can improve patients’ satisfaction.” In terms of perceived concerns, participants were asked about issues like “Robots in pharmacies could reduce the demand for human pharmacists” and “Increased reliance on robots might reduce important pharmacist-patient interactions.” Additionally, concerns were raised about “High initial investment and ongoing costs” and “Robotic systems might not fully ensure the security of controlled substances. The questionnaire was reviewed by a panel of experts and piloted with ten pharmacists from various districts in the UAE to evaluate relevance and clarity. Participants in the pilot stage were not included in the final dataset. The Cronbach’s alpha was 0.76 for benefit scale and 0.85 for concern scale, demonstrating reliability of the survey.
2.4. Data collection
A team of five trained Bachelor of Pharmacy (B Pharm) students performed data collection. Prior to data collection, these students underwent comprehensive training that covered the study’s objectives, ethical considerations, participant engagement techniques, and standardized procedures for administering the survey. Each data collector was responsible for distributing the survey in person via Google Forms to community pharmacies within their respective residential areas. The targeted pharmacies included those located in malls, street-front locations, and medical complexes to capture a diverse range of pharmacy settings. Upon visiting each pharmacy, the data collectors outlined the objectives of the study and assured confidentiality, anonymity, and the voluntary participation. On average, participants spent ten minutes completing the questionnaire.
2.5. Statistical analysis
IBM SPSS, version 26.0, was used for the statistical analysis. Because of their non-normal distribution as shown by the Shapiro-Wilk test (p = 0.001), continuous data were reported as medians with interquartile ranges (IQR), whilst categorical variables were displayed as frequencies and percentages. A quantile regression analysis was used to determine the variables affecting the degree of concern about robots use. The composite score of concerns obtained from the Likert-scale questions in Table 3 served as the dependent variable. Age, gender, educational attainment, job title, pharmacy type, years of experience, average number of prescriptions filled daily, average number of patients served daily, average amount of time spent with each patient, familiarity with robotic systems, robotic system training, practical use of robotic systems, and sources of drug information were all considered independent variables. P-values below 0.05 were considered statistically significant.
Table 3.
Pharmacists’ perceived concerns about the utilization of robotics in community pharmacies.
| Item | Highly concerned | Moderately concerned | Slightly concerned | Not concerned at all |
|---|---|---|---|---|
| Robots in pharmacies could reduce the demand for human pharmacists taking over important aspects of the pharmacists’ professional responsibilities | 97 (22.6%) | 118 (27.4%) | 133 (30.9%) | 82 (19.1%) |
| Increased reliance on robots in pharmacy might reduce important pharmacist-patient interactions and necessary patient emotional support | 131 (30.5%) | 109 (25.3%) | 120 (27.9%) | 70 (16.3%) |
| Patients might have concerns or distrust about receiving medications from robots instead of human pharmacists | 143 (33.3%) | 106 (24.7%) | 120 (27.9%) | 61 (14.2%) |
| System malfunctions, technical glitches, or downtime that could disrupt pharmacy operations and affect patient services | 156 (36.3%) | 101 (23.5%) | 109 (25.3%) | 64 (14.9%) |
| Pharmacists with older age may not adopt to using robotics in pharmacy | 137 (31.9%) | 100 (23.3%) | 132 (30.7%) | 61 (14.2%) |
| High initial investment and ongoing costs associated with purchasing, installing, and maintaining robotic systems | 130 (30.2%) | 109 (25.3%) | 127 (29.5%) | 64 (14.9%) |
| Robotics in pharmacy may decrease patient access to health care due to high upfront costs. | 114 (26.5%) | 115 (26.7%) | 135 (31.4%) | 66 (15.3%) |
| Robots may have computer-based errors | 132 (30.7%) | 101 (23.5%) | 133 (30.9%) | 64 (14.9%) |
| Protecting sensitive patient information handled by robotic systems from data breaches | 109 (25.3%) | 115 (26.7%) | 130 (30.2%) | 76 (17.7%) |
| Robots may lack adequate power sources to operate continuously over extended periods\Robots may lack adequate power sources to operate continuously over extended periods | 115 (26.7%) | 108 (25.1%) | 134 (31.2%) | 73 (17.0%) |
| Robots might not fully relieve pharmacists of repetitive tasks like counting and labeling prescriptions | 107 (24.9%) | 109 (25.3%) | 141 (32.8%) | 73 (17.0%) |
| How quickly systems can be repaired or replaced in case of failure | 122 (28.4%) | 109 (25.3%) | 129 (30.0%) | 70 (16.3%) |
| Robotic systems might not fully ensure the security of controlled substances or prevent unauthorized access | 109 (25.3%) | 114 (26.5%) | 137 (31.9%) | 70 (16.3%) |
| Robots may not adhere to regulations like HIPAA (Health Insurance Portability and Accountability Act) | 116 (27.0%) | 98 (22.8%) | 130 (30.2%) | 86 (20.0%) |
3. Results
Out of 526 pharmacists approached, 430 completed the questionnaire, including 290 (67.4%) females, 373 (86.7%) pharmacists in-charge, and 116 (27.0%) with more than 10 years of work experience (Table 1). The median age was 32.0 years (IQR: 27.0-39.0) years, and 115 (26.7%) considered the British National Formulary (BNF) their most common source of drug information. Regarding the utilization of robotics in pharmacy practice in the UAE, 230 (53.5%) were familiar with the concept of robotic systems in community pharmacies, 91 (21.2%) had received training on them, and 86 (20.0%) regularly used them in their practice. Among the 86 pharmacists who reported using robotic systems, 34 (39.5%) used AI-driven inventory management and 31 (36.0%) used automated dispensing robots.
Table 1.
General characteristics of the study participants (n = 430).
| Item | Total, n (%) |
|---|---|
| Age (years), median (IQR) | 32.0 (27.0-39.0) |
| Sex, female | 290 (67.4%) |
| Highest educational degree obtained | |
| Bachelor’s in pharmacy | 330 (76.7%) |
| Doctor in pharmacy | 45 (10.5%) |
| Masters/PhD | 55 (12.8%) |
| Job role | |
| Pharmacy owner | 57 (13.3%) |
| Pharmacist in-charge | 373 (86.7%) |
| Type of pharmacy, chain community pharmacy | 278 (64.7%) |
| Work experience | |
| From 6 months to 1 year | 102 (23.7%) |
| From 1 year to 10 years | 212 (49.3%) |
| More than 10 years | 116 (27.0%) |
| Place of work, Abu Dhabi | 359 (83.5%) |
| Average prescriptions dispensed per day | |
| Less than 10 | 93 (21.6%) |
| 10-29 | 184 (42.8%) |
| 30 or more | 153 (35.6%) |
| Average number of patients served per day | |
| Less than 10 | 49 (11.4%) |
| 10-29 | 140 (32.6%) |
| 30 or more | 241 (56.0%) |
| Average time spent with each patient | |
| Less than 2 minutes | 40 (9.3%) |
| 2-5 minutes | 231 (53.7%) |
| More than 5 minutes | 159 (37.0%) |
| Sources of drug information | |
| Online sources | 101 (23.5%) |
| BNF | 115 (26.7%) |
| British pharmacopeia | 47 (10.9%) |
| US pharmacopeia | 91 (21.2%) |
| Lexicomp | 47 (10.9%) |
| Micromedex | 29 (6.7%) |
| Are you familiar with robotic systems used in pharmacy practice, familiar | 230 (53.5%) |
| Have you had training to use robotic systems in your practice, yes | 91 (21.2%) |
| Do you regularly use robotic systems in your pharmacy, yes | 86 (20.0%) |
| Which specific robotic systems tools are you currently using* | |
| AI driven inventory management | 34 (39.5%) |
| Automated dispensing robots | 31 (36.0%) |
| Robotic labeling machines | 6 (7.0%) |
| Robotic packaging systems | 15 (17.4%) |
Denominator is 86. IQR: interquartile range.
Sixty participants (13.9%) believed that robots could assist with patient counseling, 77 (17.9%) believed they could assist with compounding medications, and 89 (20.7%) believed they could assist with advising on over-the-counter drugs (OTCs) (Figure 1).
Figure 1.
Pharmacy services that robots can assist with in the community pharmacies.
Across all domains, less than half of the participants perceived robotics in pharmacy as useful. For example, only 30.7% agreed or strongly agreed that robots can streamline workflow. Similarly, just 23.7% agreed or strongly agreed that robots can improve patient satisfaction. Additionally, only 27.9% and 29.8% agreed or strongly agreed that robots help reduce medication prescribing and dispensing errors, respectively (Table 2).
Table 2.
Pharmacists’ perceived benefits of using robotics in community pharmacies.
| Item | Strongly agree | Agree | Neutral | Disagree | Strongly disagree |
|---|---|---|---|---|---|
| Robots in community pharmacies can streamline workflow | 38 (8.8%) | 94 (21.9%) | 129 (30.0%) | 102 (23.7%) | 67 (15.6%) |
| Robots in community pharmacies can reduce medication prescribing errors | 28 (6.5%) | 92 (21.4%) | 130 (30.2%) | 115 (26.7%) | 65 (15.1%) |
| Robots in community pharmacies can reduce medication dispensing errors | 26 (6.0%) | 102 (23.7%) | 127 (29.5%) | 111 (25.8%) | 64 (14.9%) |
| Robots in community pharmacies can reduce the time pharmacists spend on technical tasks | 63 (14.7%) | 115 (26.7%) | 103 (24.0%) | 81 (18.8%) | 68 (15.8%) |
| Robots in community pharmacies can improve patients’ satisfaction | 31 (7.2%) | 71 (16.5%) | 133 (30.9%) | 112 (26.0%) | 83 (19.3%) |
| Robotic systems can handle the demands of high daily prescription volumes | 44 (10.2%) | 96 (22.3%) | 124 (28.8%) | 92 (21.4%) | 74 (17.2%) |
| Robots in community pharmacies can improve collaboration between pharmacists and physicians | 25 (5.8%) | 93 (21.6%) | 118 (27.4%) | 112 (26.0%) | 82 (19.1%) |
| Robots in community pharmacies can increase revenue | 31 (7.2%) | 81 (18.8%) | 139 (32.3%) | 111 (25.8%) | 68 (15.8%) |
| Because it is a closed system, robotic dispensing machines can reduce the risk of contamination caused by airborne pharmaceutical dust generated by some machines | 39 (9.1%) | 106 (24.7%) | 134 (31.2%) | 92 (21.4%) | 59 (13.7%) |
| Reports generated by robots in community pharmacy can help pharmacists preventing drug shortages | 61 (13.7%) | 104 (24.2%) | 126 (29.3%) | 80 (18.6%) | 59 (13.7%) |
The median score for concerns about robotics use in pharmacy was 9 (out of 14), indicating high levels of concerns. Additionally, most participants showed concerns in all measured items. For example, 257 participants (59.8%) were moderately or highly concerned about system malfunctions or technical glitches disrupting pharmacy operations (Table 3). Moreover, 249 (58.0%) participants were moderately or highly concerned that patients would feel uncomfortable obtaining pharmaceuticals from robots rather than human pharmacists. Furthermore, 240 (55.8%) participants expressed moderate or high apprehension that heightened dependence on robots could diminish crucial pharmacist-patient relationships and essential emotional support. Regarding adaptation, 237 (55.1%) participants indicated moderate or significant concerns that elderly pharmacists may struggle to utilize robotics. Additionally, 239 (55.5%) participants expressed considerable concerns regarding the substantial initial investment and recurring expenses linked to robotic technology.
The results of the regression analysis (Table 4) indicated that pharmacists holding a bachelor’s in pharmacy (B Pharm) had a significantly higher median concern score compared to those with postgraduate degrees (Master’s/PhD) (β = 3.159, CI: 0.888 – 5.430, p = 0.007). Additionally, pharmacy owners had a median total concern score that is 3.173 units higher than pharmacists in-charge (β = 3.173, CI: 0.716 – 5.629, p = 0.011). Furthermore, pharmacists who had not received training on robotic systems reported a higher median concern score than those who had received training (β = 3.755, CI: 1.251 – 6.259, p = 0.003). Workload indicators were significant predictors as well. Pharmacists dispensing 30 or more prescriptions per day had a higher median concern score compared to those dispensing less than 10 prescriptions (β = 2.710, CI: 0.462 – 4.959, p = 0.018). Similarly, serving 10–29 patients per day (β = 3.070, CI: 0.284 – 5.856, p = 0.031) and 30 or more patients per day (β = 2.872, CI: 0.066 – 5.678, p = 0.045) were associated with higher median concern scores compared to serving less than 10 patients per day. The variance inflation factor for every variable in the regression model were less than five, indicating that multicollinearity was not significant.
Table 4.
Factors influencing concerns about the use of robotics in community pharmacies.
| Independent variable | Coefficient (β) | p-value | 95% CI |
VIF | |
|---|---|---|---|---|---|
| Lower | Upper | ||||
| Gender (Ref: Male) | |||||
| Female | 0.594 | 0.460 | −0.986 | 2.175 | 1.624 |
| Educational level (Ref: Postgraduate) | |||||
| Bachelor’s in Pharmacy (B Pharm) | 3.159 | 0.007 | 0.888 | 5.430 | 1.258 |
| Doctor in pharmacy | 1.533 | 0.330 | −1.559 | 4.625 | 1.114 |
| Job title (Ref: Pharmacist in-charge) | |||||
| Owner Pharmacist | 3.173 | 0.011 | 0.716 | 5.629 | 2.611 |
| Pharmacy type (Ref: Independent pharmacy) | |||||
| Chain pharmacy | 1.417 | 0.084 | −0.194 | 3.027 | 1.741 |
| Years of experience (Ref: more than 10 years) | |||||
| From 6 months to 1 year | −2.097 | 0.132 | −4.825 | 0.631 | 1.258 |
| From 1 year to 10 years | −1.108 | 0.275 | −3.103 | 0.887 | 1.310 |
| Average Prescriptions Dispensed Per Day (Ref: <10) | |||||
| 10-2910-29 | 0.023 | 0.982 | −1.949 | 1.995 | 2.447 |
| ≥30 | 2.710 | 0.018 | 0.462 | 4.959 | 1.897 |
| Average Patients Served Per Day (Ref: <10) | |||||
| 10-29 | 3.070 | 0.031 | 0.284 | 5.856 | 2.111 |
| ≥30 | 2.872 | 0.045 | 0.066 | 5.678 | 2.317 |
| Average time spent with each patient (Ref: more than 5 minutes) | |||||
| 2-5 minutes | 1.254 | 0.117 | −0.314 | 2.822 | 1.482 |
| Less than 2 minutes | −1.091 | 0.452 | −3.936 | 1.754 | 1.336 |
| Familiarity with robotic systems (Ref: not familiar) | |||||
| Familiar | −0.593 | 0.477 | −2.230 | 1.045 | 1.744 |
| Training in Robotic Systems (Ref: Yes) | |||||
| No | 3.755 | 0.003 | 1.251 | 6.259 | 2.092 |
| Utilize robotic systems in practice (Ref: Yes) | |||||
| No | −0.509 | 0.679 | −2.926 | 1.907 | 2.331 |
| Sources of information about drugs (Ref: US pharmacopeia) | |||||
| BNF | −1.749 | 0.118 | −3.945 | 0.448 | 1.749 |
| British pharmacopeia | −0.738 | 0.595 | −3.462 | 1.986 | 1.698 |
| Lexicomp | −1.248 | 0.68 | −3.986 | 1.472 | 1.921 |
| Micromedex | 0.403 | 0.806 | −2.862 | 3.630 | 1.089 |
| Online sources | 0.386 | 0.749 | −1.896 | 2.632 | 2.449 |
3.1. Summary of the results
Overall, over half of respondents were familiar with pharmacy robotics (53.5%), yet only one in five had received training (21.2%) or regularly used these systems (20.0%), primarily for inventory management and automated dispensing. Fewer than one‑third of participants agreed that robotics could streamline workflow, enhance patient satisfaction, or reduce prescribing and dispensing errors, and the median concern score was high (9 out of 14), reflecting widespread worries about technical failures, impacts on patient interaction, adaptation by older pharmacists, and costs. In quantile regression, higher concern scores were significantly associated with holding only a BPharm degree, owning rather than managing a pharmacy, lack of robotics training, and heavier workloads (dispensing ≥30 prescriptions or serving ≥10 patients per day).
4. Discussion
This study is the first one that provided insights into the utilization of robotic technologies in community pharmacies in the UAE as many participants reported using them for inventory management and automated dispensing. The study revealed that 20% of community pharmacists consistently utilize robotic systems and equipment in their operations, underscoring the increasing acceptability and incorporation of such technologies in the UAE’s pharmaceutical sector. However, in this study, only a limited number of participants perceived the benefits of robotics in community pharmacies. This could stem from the fact that only 53.5% of the participants were familiar with robotics in pharmacy and only 21.2% received training in this technology. In Saudi Arabia, several studies have discussed the implementation and effectiveness of robotic systems in improving inventory management and the dispensing process in outpatient pharmacies [21,29]. Although several studies indicated that robotic systems could alleviate pharmacists’ routine tasks, thereby enhancing patient interaction and counseling [29–31], it is essential to adopt a balanced approach in which robotics augment rather than supplant the vital human components of pharmacy practice. As stated by Rosenberg et al [32], robots may serve as a supplementary resource to human pharmacists for specific consumer demographics, particularly in the context of workforce shortages; nonetheless, they cannot entirely replace human pharmacists.
There are many studies that demonstrate the effectiveness of robots in streamlining workflow and enhancing medication safety in pharmacies [33,34]. For example, the Health Technology Assessment Office in the UAE revealed that an automated pharmacy surpassed a conventional one, accommodating 9.4% more patients and dispensing 11.8% more prescriptions over a 12-month period. The automated system decreased prescription fulfillment time by 95.7% and enhanced the accuracy of dispensed prescriptions by 28.8% [35]. However, in this study, only a limited number of participants perceived the benefits of robotics in community pharmacies. This could stem from the fact that only 53.5% of the participants were familiar with robotics in pharmacy and only 21.2% received training in this technology. Therefore, implementing educational programs that familiarize community pharmacists with this technology and pilot projects that demonstrate the benefits firsthand is necessary. A longitudinal study found that pharmacy leaders, better education, simplicity of use, and relevance to individual jobs can improve pharmacists’ perceptions of robotics in pharmacy [36].
Participants from this study demonstrated high levels of concern regarding implementation of robotics in community pharmacies. They were mainly concerned about technical failures disrupting pharmacy operations, the impact on patient-pharmacist relationships, adaptation by older pharmacists, and the cost of the technology. Concerns about the implementation of robotics in pharmacies have been documented in other countries as well. In New Zealand, pharmacists were mostly concerned about the cost, consequences of medication error, fear that technology may replace people, and an over-reliance on technology [37]. Similarly, some pharmacists in Saudi Arabia were concerned about being replaced by artificial intelligence tools [38]. While these concerns are valid, the implementation of stringent quality assurance and regular maintenance methods can substantially reduce these hazards.
The heightened concerns among pharmacists with a Bachelor in pharmacy (B Pharm) compared to those with advanced degrees in the present study suggest that the current curriculum may not adequately prepare B Pharm graduates for technological innovations like robotic systems. This aligns with a study that reported an urgent need for digital health education for next-generation health professionals [39].
Our findings that pharmacist without robotics training and those holding only a BPharm experienced the greatest concerns underscore the need to integrate formal robotics education across the pharmacy learning continuum. Pharmacy schools should embed competency‑based modules on automation principles into the PharmD curriculum, combining didactic instruction with hands‑on simulation (e.g., mock dispensing with automated systems) and interprofessional exercises alongside IT personnel and pharmacy technicians. Continuing professional development (CPD) programs must similarly offer modular robotics workshops and online micro‑credentials, ensuring that recent graduates and practicing pharmacists alike acquire practical skills in robot operation, troubleshooting, and workflow optimization. These initiatives should align with international competency frameworks, such as the FIP Digital Health policy, which calls for pharmacy graduates to “ensure that pharmacy and pharmaceutical sciences students graduate with adequate knowledge and skills in digital health” and for educators to “develop a core curriculum and assessment process for pharmacy students, graduates and qualified pharmacists.”
To translate training into safe, effective practice, institutions must adopt clear policies governing the selection, implementation, and maintenance of robotic systems. Pharmacies should require formal service-level agreements with vendors that guarantee preventive maintenance schedules, rapid-response technical support, and regular software updates. Standard operating procedures must mandate initial system validation and periodic Failure Mode and Effects Analyses (FMEA) to identify and mitigate workflow risks—approaches proven to reduce downtime in healthcare automation [10].
Pharmacy owners exhibited greater apprehension about the integration of robotic technology in pharmacy practice. Pharmacy owners may exhibit greater apprehension about the integration of robotic technology due to increased responsibility and accountability associated with these roles. A recent study found that pharmacists are worried about the lack of legal regulations for technology in pharmacy and possible disruption of their business due to cyberthreats [40]. This underscores the necessity for tailored communication methods that address the financial and operational issues faced by pharmacy owners.
Pharmacists without experience in robotic systems in the present study exhibited a higher median concern score compared to their trained counterparts, which aligns with studies highlighting the importance of training in promoting technology acceptance within healthcare environments [41,42].
Pharmacists who regularly handle more prescriptions and patients reported more concerns about using robotics in community pharmacies, which might be due to potential workflow disruptions. A systematic review [43] found that the use of digital devices and automated systems may result in increased workload because these systems need to be managed or monitored, or because they can provide notifications and alarms that demand human intervention. Effective workflow mechanisms are crucial for pharmacists in high-volume environments, and any slowdown can have a major impact on operations. The growing reliance on automated systems also raises concerns about the potential for medical errors, which could have detrimental effects in pharmacy settings, particularly for those handling a high volume of medications.
In this regard, we suggest creating competency-based education modules that are tiered and incorporate online coursework, hands-on simulation sessions, and continuing professional development credits in order to meet the increased concerns of chemists who have not had any prior robotics training. Workshops on failure mode and effects analysis (FMEA) should be a part of training programs so that chemists may proactively detect and prevent possible system failures in their local environments. Community pharmacies should enter into formal service agreements with robotics providers that ensure planned preventive maintenance, prompt technical support, and frequent software upgrades, given the pervasive concerns about system failures interfering with productivity. Such organized vendor collaborations significantly lower downtime and operational disturbances, according to evidence from hospital installations.
Prior to full deployment, we recommend that pharmacy managers take a phased implementation approach, starting with small-scale pilot locations where staff feedback loops and workflow simulations may be leveraged to improve procedures. Iterative process mapping and staged rollouts improve user acceptability, reveal hidden bottlenecks, and maximize integration into current processes, according to hospital automation literature. Health authorities should revise national and organizational policies to incorporate clear guidelines on the use of robotic technology in community pharmacies, addressing cybersecurity, patient consent, data security, and professional responsibility in order to promote confidence and standardize practice. Integrating robots with a clear legal and moral framework will safeguard patient confidentiality, guarantee responsibility, and encourage ethical innovation.
Although baseline adoption and concerns were established by our cross-sectional approach, multicenter, longitudinal research is required to measure the long-term effects of robots on clinical outcomes (such as mistake rates and counseling time) and patient satisfaction. To elucidate the causal impact on workflow efficiency and patient health indicators, randomized or stepped-wedge studies comparing community pharmacies with and without robots are needed. Future research should include qualitative techniques, including focus groups, in-depth interviews, and Failure Mode and Effects Analysis (FMEA), to examine the lived experiences of patients and chemists utilizing robotic systems in order to supplement quantitative data. Such research can reveal latent obstacles (such as usability and trust) and enablers (like perceived autonomy support) that influence the adoption of technology in practical contexts [1,32,44].
Researchers should carry out thorough cost-effectiveness and cost-benefit studies of robots in community pharmacy, building on frameworks for evaluating health technologies. In order to help owners and governments allocate resources, these studies would assess both direct financial indicators (such as labor savings and return on investment) and more general value-based outcomes (such as fewer hospitalizations brought on by dispensing mistakes). Future studies must examine how AI-based clinical decision support, electronic health records, and Internet of Things sensors may be seamlessly connected with automated dispensing and inventory systems as pharmacy robots develop. Research using frameworks like the Autonomous Pharmaceutical Model may map out the phases of development, pinpoint interoperability issues, and suggest strategies for delivering completely independent, data-driven pharmaceutical services.
5. Limitations
The cross-sectional design does not allow for the establishment of a cause-and-effect relationship. Furthermore, convenience sampling may not fully reflect the whole pharmacy population in the UAE. Additionally, the self-reported data is susceptible to social desirability bias by the participants. Lastly, future studies could consider integrating the Technology Acceptance Model or other validated frameworks to gain a more comprehensive understanding of technology adoption within the field of pharmacy practice.
Conclusion
In this nationwide survey, one in five community pharmacists in the UAE reported using robotics—mostly for inventory management and dispensing—yet many remained unconvinced of their benefits and expressed concerns about system failures, patient interactions, older pharmacist adaptation, and costs. Education level, role, prior training, and workload significantly influenced these concerns. These findings highlight the need for targeted robotics training and clear institutional policies to support safe, effective integration. Future longitudinal evaluations should assess how such initiatives affect pharmacists’ proficiency and attitudes toward automation.
Acknowledgements
The authors would like to thank the pharmacists who completed the study questionnaire
Funding Statement
This paper was not funded.
Ethics approval and consent to participate
The current research received ethical approval from the research ethics committee at Al Ain University- Abu-Dhabi Campus (Ref #: COP/AREC/AD/02). Pharmacists who agreed to participate were required to consent.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The data that support the findings of this study are available from the corresponding author AA upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author AA upon reasonable request.