Impact of belt conveyor systems on patient waiting time and productivity in outpatient pharmacy settings: a comparative study

Impact of belt conveyor systems on patient waiting time and productivity in outpatient pharmacy settings: a comparative study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of belt conveyor systems on patient waiting time and productivity in outpatient pharmacy settings: a comparative study Siti Nur Su'aidah Nasarudin, Nurhairani Abdul Latif, Saidatul Sheeda Ahmad Shukri, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5954849/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The implementation of belt conveyor (BC) systems in outpatient pharmacy departments (OPDs) has been a national policy since 2009, aiming to streamline the dispensing process. However, limited evidence exists on their impact on OPD operations. This study aims to evaluate and compare the waiting time and productivity of OPDs with and without BC systems. Methods A cross-sectional study was conducted in which 17 facilities with BC systems were compared with 17 facilities without BC systems. As pre-implementation data were unavailable, facilities were matched via propensity score matching (PSM) with a 1:1 matching technique without replacement. Post-matching data were analyzed via an independent t -test for productivity and a Mann-Whitney test for waiting time. A significance level of p < 0.05 was set to ensure reliable results. Results A total of 34 facilities were included in the analysis. For waiting time, there was no statistically significant difference between the groups (U = 136.00 OR z = -0.327, p = 0.744). Similarly, productivity was not significantly different between facilities with BC systems (M = 3.60, SD = 1.192) and those without BC systems (M = 3.64, SD = 1.273); t( 32 )=-0.109, p = 0.914. These findings suggested that the use of the BC system did not significantly ncrease the waiting time or productivity in OPDs. Conclusion The study concluded that the implementation of a BC system alone did not improve productivity or reduce patient waiting time. The study also highlighted the importance of comprehensive planning and consideration of multiple factors when new technologies are introduced into OPD workflows. belt conveyor waiting time pharmacy productivity dispensing process outpatient pharmacy Figures Figure 1 Background The operational efficiency of outpatient pharmacy services is a critical component of healthcare delivery, directly influencing patient safety, patient satisfaction and overall healthcare outcomes. Patient waiting time and pharmacy productivity are widely recognized as key performance indicators in assessing the quality of pharmacy services ( 1 – 4 ). Many countries, including Malaysia, have established standards to ensure timely care and minimize harm caused by delays in medication dispensing ( 4 , 5 ). In Malaysia, waiting time is defined as the duration from when a prescription is received at the pharmacy counter to when the patient receives their medication ( 6 , 7 ). According to the Ministry of Health Malaysia’s Client Charter, 95% of prescriptions should be dispensed within 30 minutes ( 6 , 7 ). This benchmark underscores the need for efficient pharmacy workflows to meet patient expectations and ensure compliance with national standards. Equally important is the evaluation of pharmacy productivity, as it directly impacts the efficiency, quality and sustainability of pharmacy services. It informs decisions on resource distribution, allows the identification of bottlenecks and inefficiencies in the workflow and even provides a baseline for assessing the impact of innovations ( 1 , 2 ). As outpatient pharmacy operations often face challenges such as high prescription volumes and a limited workforce, innovations such as automation and workflow optimization have been increasingly adopted to address these issues ( 8 – 12 ). Automation technologies such as automated dispensing systems (ADSs), robotics and radio frequency identification (RFID)-enabled systems have been implemented in several countries to increase operational efficiency, reduce medication errors and improve patient safety ( 13 – 18 ). Belt conveyor (BC) systems are another innovation designed to streamline medication dispensing workflows. BC systems facilitate the efficient movement of prescriptions and medications within the pharmacy, reduce manual handling and improve workflow efficiency ( 19 ). These systems are characterized by their robust load-bearing capabilities, simple design, minimal maintenance requirements and reliable operation. In Malaysia, BC systems have been implemented in MOH outpatient pharmacies as part of the Ambulatory Pharmacy Care Service since 1998. A national policy on its implementation was introduced in 2009 ( 20 , 21 ). By automating the transfer of prescriptions between counters and filling areas, BC systems aim to increase productivity and reduce patient waiting time. Despite the growing adoption of automation technologies, research specifically focused on the impact of BC systems in outpatient pharmacy settings remains limited. While previous studies have highlighted the benefits of automation in improving pharmacy services ( 11 , 14 – 16 , 22 – 24 ), the effectiveness of BC systems in achieving these goals is not well-documented. This study aimed to bridge that gap by evaluating and comparing patient waiting times and productivity between outpatient pharmacies equipped with and without BC systems. The findings from this study provide valuable insight into the operational benefits and limitations of BC systems, guiding healthcare administrators in making evidence-based decisions regarding the adoption of technologies in pharmacy practice. Additionally, this study emphasizes the importance of tailoring technological solutions to the specific needs of healthcare facilities to maximize their impact on service delivery. Methods Study design & settings This cross-sectional study compared 17 outpatient department (OPD) facilities with BC systems to 17 facilities without BC. The inclusion criteria required facilities to have a pharmacy unit under the MOH and at least one pharmacist. The exclusion criteria included facilities with fewer than 50 prescriptions dispensed per day, newly approved MOH facilities after 2021, institutions, rural clinics, maternal and child health clinics and facilities without pharmacists. Propensity score matching (PSM) Since only post-implementation data were available, propensity score matching (PSM) was used to identify facilities without BC as the comparison group. The propensity scores were calculated using key facility characteristics: category of facility, total number of counters, type of OPD counter, range of prescriptions dispensed per day, total number of pharmacy staff and area of space. Given the limited availability of facilities eligible for matching on the basis of the scores, a 1:1 matching without replacement approach was applied. This method allowed pairing of each facility with BC to the most similar non-BC facility without exhausting the pool of potential matches. Additionally, 1:1 matching ensures a more balanced analysis and maintains the integrity of comparisons while accommodating the constraints of the available data ( 25 – 27 ). To assess the quality of matching, baseline covariates were compared between the BC and non-BC groups via t-tests and absolute standardized mean differences (SMDs). Since p-values are highly driven by sample size, the SMDs provide a more robust measure of balance, complementing the p-values ( 28 – 30 ). The SMDs were calculated for each baseline covariate, including both continuous and categorical variables. An SMD of < 0.1 indicates a negligible difference and excellent balance between groups for that covariate ( 27 , 31 ). Data collection and analysis Twelve months of data from January to December 2022 were retrieved from quarterly reports and the pharmacy infrastructure database for both the BC and non-BC groups. The data were categorized into two main sets. The first set included facility-related information, such as facility category and type, total number of counters, type of OPD counter, range of prescriptions dispensed per day, prescription volume dispensed, total number of pharmacy staff, average monthly dispensing or patient waiting time and area of the OPD space. The second set focused on BC data, including the brand, model, shape, length, speed and frequency of maintenance and breakdowns throughout the year. A Mann-Whitney test (p > 0.05) was conducted to assess the impact of BC systems on patient waiting time. Moreover, productivity was assessed by calculating the number of prescriptions dispensed per person per standard working hour. This metric was derived by dividing the average monthly number of pharmacy staff, which was based on 180 working hours per month. Finally, an independent t-test was used to compare the productivity between facilities with BC and those without BC. Significance was defined as p > 0.05. Results Identification of the comparison group: pre- and post-matching Table 1 lists the covariates used to generate propensity scores for each facility. However, the shape of BC was not included as a covariate in the propensity score calculation, as the goal of this calculation was to identify matching pairs between BC and non-BC facilities. Six types of BC designs were implemented: T, O, U, I, L and customized. Table 1 Descriptive subject characteristics and covariates used to measure propensity score. Type of Facility (BC / non-BC) Category of Facility Total Number of Counter Type of Counter (Open / Close) Range of Prescriptions Dispensed Per Day Total Number of Pharmacy Staffs Area of Space (m 2 ) Shape of Belt Conveyor (BC) Score Pair 1 BC *Major Specialist 8 Open > 500–800 35 1104.90 T 0.9569 Non-BC Major Specialist 7 Open > 500–800 16 893.63 - 0.8058 Pair 2 BC Major Specialist 8 Open > 1000 38 1038.63 O 0.9210 Non-BC Major Specialist 9 Open > 500–800 30 347.40 - 0.7722 Pair 3 BC Major Specialist 9 Open > 500–800 33 640.00 U 0.7698 Non-BC Major Specialist 9 Close > 1000 57 414.96 - 0.5919 Pair 4 BC Major Specialist 8 Open > 350–500 22 110.00 T 0.7183 Non-BC Major Specialist 8 Open > 500–800 27 394.02 - 0.4252 Pair 5 BC Major Specialist 14 Open > 1000 41 241.40 U 0.6097 Non-BC Major Specialist 7 Open > 500–800 30 81.00 - 0.3820 Pair 6 BC Major Specialist 9 Open > 1000 39 482.00 Customised 0.5559 Non-BC Major Specialist 7 Open > 500–800 27 67.00 - 0.3500 Pair 7 BC Major Specialist 6 Open > 500–800 13 675.00 O 0.5281 Non-BC Major Specialist 6 Open > 350–500 20 137.15 - 0.2890 Pair 8 BC Major Specialist 4 Open > 350–500 12 494.60 O 0.5020 Non-BC Major Specialist 9 Open > 1000 62 101.00 - 0.2855 Pair 9 BC Major Specialist 12 Open > 1000 33 296.60 I 0.4982 Non-BC Major Specialist 8 Open > 500–800 32 217.00 - 0.2741 Pair 10 BC Major Specialist 6 Open > 500–800 33 129.60 U 0.3921 Non-BC Major Specialist 8 Open > 1000 39 253.90 - 0.2585 Pair 11 BC Major Specialist 6 Open > 500–800 32 128.90 U 0.3854 Non-BC Major Specialist 5 Open > 350–500 21 93.50 - 0.2104 Pair 12 BC Major Specialist 6 Open > 500–800 36 364.50 I 0.3292 Non-BC Major Specialist 6 Open > 1000 25 380.00 - 0.2079 Pair 13 BC Major Specialist 6 Open > 500–800 32 130.00 U 0.1338 Non-BC Major Specialist 6 Open > 500–800 21 214.80 - 0.1468 Pair 14 BC *Type 2 6 Open > 500–800 11 63.00 I 0.6066 Non-BC Type 2 6 Open > 500–800 10 37.20 - 0.4129 Pair 15 BC Type 2 5 Open > 500–800 11 76.66 U 0.2439 Non-BC Type 2 4 Open > 500–800 14 26.00 - 0.2980 Pair 16 BC *Type 1 4 Open > 500–800 16 40.00 I 0.2011 Non-BC Type 2 7 Open > 500–800 16 25.88 - 0.2171 Pair 17 BC Type 2 5 Open > 500–800 20 32.20 L 0.1128 Non-BC Type 2 4 Open > 500–800 13 107.63 - 0.1237 *Major Specialist is classified as hospital that has up to 20 types of specialty or sub-specialty. *Type 1 is classified as clinics with an average number of daily patient arrivals over than 800 patients. *Type 2 is classified as clinics with an average number of daily patient arrivals around 500–800 patients. The PSM approach successfully facilitated the matching process, as evidenced by comparable characteristics across matched BC and non-BC facilities within each pair in Table 1 . In Pair 1, both BC and non-BC facilities had similar numbers of counters (8 versus 7), prescription volumes (> 500–800) and facility types (major specialists), with slight differences in the number of pharmacists (35 versus 16) and area of space (1104.90 m 2 versus 893.63 m 2 ). The calculated propensity scores for Pair 1 were also similar (0.9569 versus 0.8058). For several pairs, such as Pairs 2 and 3, the BC and non-BC facilities even presented the closest matching of covariates, supporting the effectiveness of the PSM method. However, in certain pairs, larger discrepancies in scores were observed. For example, Pair 5 displayed a sharp contrast between BC (0.6097) and non-BC (0.3820) facilities, despite the BC facility handling twice the prescriptions and having a larger number of pharmacists. Additionally, some pairs presented lower propensity scores for both the BC and non-BC facilities, which may be due to their lower operational capacity, smaller spaces or smaller number of pharmacists. Figure 1 shows the flowchart of the matching assessment. The proportions of the category of facility, type of counter and range of prescriptions dispensed were comparable and balanced between the two groups. For the variables of the total number of pharmacy staff, the total number of counters and area of space were not significantly different. The non-significant p-values might suggest improved balance, but it was not a definitive measure of balance ( 28 – 30 ). To complement the p-value analysis, the SMDs for all baseline covariates were calculated before and after matching (Table 2 ). Before matching, substantial imbalances were observed across several covariates. The largest imbalance was observed in the facility category (SMD = 1.227), followed by the total number of pharmacy staff (SMD = 0.941) and the area of space (SMD = 0.918). Table 2 SMDs of pre- and post-matched baseline covariates. Variables SMD Pre-Matching SMD Post-Matching Category of facility 1.227 0.041 Total number of counters 0.897 0.159 Type of counter 0.544 0.353 Range of prescriptions dispensed 0.607 0.116 Total number of pharmacy staffs 0.941 0.014 Area of space 0.918 0.464 After matching, the SMDs for most covariates were substantially reduced, indicating improved balance between the groups. The facility category achieved near-complete balance with an SMD < 0.1. The total number of pharmacy staff and range of prescriptions dispensed significantly improved, with the SMDs decreasing to 0.014 and 0.116, respectively. Despite these improvements, the covariates of the type of counter and area of space exhibited residual imbalances, although these imbalances were reduced compared with the initial SMD before matching. The reduction in SMD across all variables suggested that the matched groups were more comparable to enhance the validity of this study. Patient waiting time The results in Table 3 shows that the average patient waiting time at BC facilities (mean = 17.84 minutes, SD = 6.57) was slightly lower than at non-BC facilities (mean = 18.61 minutes, SD = 5.70). The variance in waiting time was also greater in BC facilities (43.16) than in non-BC facilities (32.53). Despite these differences, the mean waiting times for both facility types were relatively comparable, suggesting that the presence of BC may have a limited impact on reducing patient waiting time. Further analysis via the Mann-Whitney test was used to determine if these differences were significant. Table 3 Descriptive statistic on the average patient waiting time. Group of Facility N Mean Median SD Variance Average of patient waiting time Facilities with BC 17 17.84 18.00 6.57 43.16 Facilities without BC 17 18.61 16.91 5.70 32.53 Table 4 shows a comparison of the average waiting time between the two groups. The analysis revealed no statistically significant difference in the total patient waiting time for medication prescriptions between facilities equipped with BC and without BC (U = 135.00, z = -0.327, p = .744). In summary, the results indicated that the implementation of BC did not significantly reduce the total patient waiting time in the OPDs. Table 4 Mann-Whitney U test results in comparison of average waiting time between facilities with BC and facilities without BC. Group of Facility N Mean Rank Sum of Ranks U Z p-value Average of patient waiting time Facilities with BC 17 16.94 288.00 135.00 -0.327 0.744 Facilities without BC 17 18.06 307.00 Productivity Table 5 presents the independent t-test results comparing the productivity between facilities equipped with BC and those without BC. The mean productivity for facilities with BC was M = 3.60 (SD = 1.192), whereas for facilities without BC, the mean productivity was M = 3.64 (SD = 1.273). The t-test yielded no significant difference, t ( 32 ) = -0.109, p = .914. These findings indicated that the presence of BC had no measurable effect on productivity in OPDs, as the average productivity for both groups was similar, with 3.60 and 3.64 prescriptions processed per person per hour, respectively. Table 5 Independent t-test results for productivity in facilities with BC and without BC. Group Mean (M) Standard Deviation (SD) df p-value Facilities with BC 3.60 1.192 32 .914 Facilities without BC 3.64 1.273 Discussion The results revealed no significant differences in patient waiting time or productivity between outpatient pharmacy facilities with and without BC systems. These findings suggested that the implementation of a BC system alone did not automatically lead to measurable improvements in waiting time and productivity, contrary to expectations. The BC system is outlined in Malaysia’s national policy guidelines; however, no standardized design is specified and the criteria for its implementation remain broad ( 20 , 21 ). Consequently, this has led to variability in BC designs, which may have resulted in differences in system utilization, potentially impacting waiting time and productivity outcomes. The waiting time data used in this study were obtained from secondary sources and not segregated by specific work processes, making it unclear which processes contributed the most to the overall waiting time. Besides that, other confounding factors, such as the total number of medications per prescription, prescriptions requiring pharmacist intervention and the experience level of the pharmacy staff handling prescriptions, also influence waiting time ( 5 , 32 ). In addition to dispensing medications, pharmacy staff at OPDs also perform various other responsibilities including compounding, extemporaneous preparations, patient medication counseling and providing Value Added Services (VAS) such as Medicine by Post, Drive-Through Pharmacy Service, Park & Take, Locker Medicine, and Drug Information Service ( 33 ). They also engage in health promotion and patient education, operate the Medication Therapy Adherence Clinic (MTAC), handle adverse drug reaction (ADR) reporting as well as smoking cessation pharmacotherapy ( 33 ). These additional tasks may increase the workload of pharmacy staff, thereby influencing overall waiting time and productivity in medication dispensing ( 8 , 34 ). Such workload increases may also mask the potential benefits of BC implementation. Currently, there is no gold standard for measuring the productivity of pharmacy staff in OPDs ( 1 , 2 ). Most studies have used different metrics, such as the number of full-time equivalents (FTEs) of pharmacists, changes in workload or the number of prescriptions dispensed per person per unit of time ( 35 – 38 ). These approaches highlight the variability in productivity metrics on the basis of environmental and workflow factors. Given that the context of this study was in Malaysia's public healthcare facilities, the mean workload of pharmacy staff, defined as the number of prescriptions dispensed per person per working hour, also provides a relevant and practical productivity measure. The absence of pre-implementation data and a pilot study to validate the ability of the BC system represent major limitations of this study, which restrict the ability to compare performance metrics before and after the implementation of the BC system. Without baseline data, assessing the true impact of the BC system is difficult. In addition, facilities with BC might have had different initial performance levels than those without BC. For example, BC may have been implemented in facilities that already experienced higher patient volumes or workflow challenges, which could influence post-implementation outcomes. Nonetheless, despite these limitations, the PSM approach allowed for the identification of comparable subjects in both BC and non-BC facilities on the basis of observable characteristics, enabling a fairer evaluation and comparison of waiting times and productivity outcomes. While the PSM process achieved a moderate balance in covariates distribution between the groups, discrepancies in facility characteristics, such as the number of pharmacists and space area, may have influenced the calculated scores, thus contributing to the observed variability in some matched groups. This also reflected the importance of including additional data or unobserved factors as mentioned previously, which might be significant in future analyses to achieve greater precision in matching. With the current advancements in robotics and artificial intelligence (AI), integrating fully automated systems powered by AI into pharmacy workflows could be a promising step forward. For example, the outpatient pharmacy at Singapore General Hospital has implemented an Automated RFID Prescription Drug Delivery System, which automates the prescription filling process from start to end. This system not only significantly improves the dispensing efficiency but also reduces the possibility of medication errors ( 13 ). Similarly, several hospitals in Thailand have implemented ADS in their outpatient pharmacies to streamline their outpatient pharmacy operations. Bangkok Hospital employs automated drug dispensing machines that utilize AI to dispense medications accurately ( 17 ) whereas Vejthani Hospital has introduced the EV220 Outpatient Pharmacy Robotic Dispensing system. In this system, after pharmacists verify a prescription, the robot selects the required drugs, fills a pill bottle and labels it with the patient’s information ( 18 ). These innovations reduce the overall workflow for pharmacists while improving patient safety and operational efficiency. Conclusions Future studies should incorporate pre-implementation data to enable a more comprehensive evaluation of the impact of the BC system. A longitudinal design, including pre- and post-BC implementation, would provide a clearer understanding of its effectiveness. Additionally, qualitative methods such as staff interviews or workflow analyses, could explore other contextual factors that influence the system’s performance, complementing the quantitative findings. Furthermore, there is a need to develop and adopt standardized productivity and waiting time metrics that are specific to pharmacy practices, enabling consistent and comparable evaluations across facilities. In conclusion, this study highlighted the importance of careful planning by identifying the conditions under which the BC system delivers the greatest benefits, ensuring that it is tailored to the operational needs of individual facilities. Similarly, introducing any innovation or automation in pharmacy practices should be accompanied by workflow optimization to ensure that it aligns with operational requirements and enhances overall effectiveness. Establishing standardized design and implementation guidelines for innovations or automations in national policies is equally critical to reducing variability in outcomes and supporting informed planning and decision-making by healthcare administrators. Abbreviations ADR: Adverse drug reaction ADS: Automated dispensing system AI: Artificial intelligence BC: Belt conveyor FTE: Full-time equivalent MOH: Ministry of Health MTAC: Medication Therapy Adherence Clinic OPD: Outpatient pharmacy departments PSM: Propensity score matching RFID: Radio frequency identification SMD: Standardized mean difference VAS: Value Added Services Declarations Ethical approval and consent to participate This study was registered in the National Medical Research Register (NMRR-22-01699-MOE) and approved by the Medical Research and Ethics Committee (MREC), Ministry of Health Malaysia. The need for informed consent was waived by the MREC because the study was a retrospective and used secondary data. Consent for publication Not applicable. Availability of data and materials The data sets that support for the findings in this study can be provided by the first author upon reasonable request and with the permission of the MOH Malaysia. Competing interests The authors declare that they have no competing interests. Funding The study did not receive any funding. Authors’ contributions Study design and conceptualization: AHMY, SNSN and SSAS. Data collection and interpretation: NHL, SSSMH, HAAK and MFL. Data cleaning and analyses: SNSN, SSAS and AHMY. Literature review: SNSN, NHL, SSSMH, HAAK, SSAS and MFL. Preparation of first draft: SNSN and SSAS. Review and editing: AHMY and NAL. All authors approved the final version of the manuscript. Acknowledgements We would like to thank the Director General of Health, Ministry of Health Malaysia, for his permission to publish this article. References Rough SS, McDaniel M, Rinehart JR. Effective use of workload and productivity monitoring tools in health-system pharmacy, part 1. American Journal of Health-System Pharmacy [Internet]. 2010 Feb 15;67(4):300–11. 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Available from: https://arxiv.org/pdf/1010.5586 Flury BK, Riedwyl H. Standard Distance in Univariate and Multivariate Analysis. Am Stat [Internet]. 1986 Aug;40(3):249–51. Available from: http://www.jstor.orgURL:http://www.jstor.org/stable/2684560http://www.jstor.org/stable/2684560?seq=1&cid=pdf-reference#references_tab_contents Fahrurazi FE, Ibrahim NH, Mafauzy NM, Wan Ismail WNA, Mohamed Rusli SS. Factors affecting waiting time in Outpatient Pharmacy at Hospital Raja Perempuan Zainab II (HRPZ II). Journal of Pharmacy [Internet]. 2022 Jan 31 [cited 2024 Dec 19];2(1):1–7. Available from: https://journals.iium.edu.my/ktn/index.php/jp/article/view/105/53 Program Perkhidmatan Farmasi KKM. Polisi Operasi Farmasi Ambulatori 2022 [Internet]. 2nd ed. Musa N, Shaik Rahmat S, editors. Program Perkhidmatan Farmasi, Kementerian Kesihatan Malaysia; 2022. Available from: www.pharmacy.gov.my Loh BC, Wah KF, Teo CA, Khairuddin NM, Fairuz FB, Liew JE. Impact of value added services on patient waiting time at the ambulatory pharmacy Queen Elizabeth Hospital. Pharm Pract (Granada) [Internet]. 2017 Jan 1 [cited 2024 Dec 19];15(1). Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC5386619/pdf/pharmpract-15-846.pdf Mobach MP. The merits of a robot: a Dutch experience. J Pharm Sci [Internet]. 2006;9(3):376–87. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17207420 Ong YSP, Chen LL, Wong JA, Gunawan Y, Goh WJ, Tan MC, et al. Evaluating the Impact of Drug Dispensing Systems on the Safety and Efficacy in a Singapore Outpatient Pharmacy. In: Value in Health [Internet]. 2014 [cited 2022 Sep 9]. p. A791–2. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1098301514023705 Shao SC, Chan YY, Lin SJ, Li CY, Yang YHK, Chen YH, et al. Workload of pharmacists and the performance of pharmacy services. PLoS One [Internet]. 2020 Apr 1 [cited 2024 Dec 17];15(4). Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC7173874/pdf/pone.0231482.pdf Coblio NA. The Impact of Pharmacy Work Design on Pharmacist Productivity [Internet]. USF Tampa Graduate Theses and Dissertations; 2011. Available from: https://digitalcommons.usf.edu/etd Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5954849","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":411483296,"identity":"ba331f6d-9927-4261-9613-5f8790bb4bec","order_by":0,"name":"Siti Nur Su'aidah Nasarudin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYBADHoYDPIwPGBgOkKaF2YAkLUDFPGwSRGkxl8h9wPizzU6G7/jZY1U3Ku4wyLv3GDDdbMOtxXJGugEzb1syj+SZvLTbOWeeMRieOWPAnItHi8GNNAZmxjZmHoMDOWa3c9sOMxjOSEsgqAXosHoeg/NvzIqJ1sLA23aYx+BGjhkzSIu8RPIB/FqArj/Mc+44j+SNN8bSOWeAenkOHziccw6PluNpjA9/lFXb853PMfycU3FYTr69sfFxThluLSBwAJkDDAegCCMbfi2oQL4BRP4hRcsoGAWjYBQMcwAARztS4jkSOkoAAAAASUVORK5CYII=","orcid":"","institution":"Ministry of Health Malaysia","correspondingAuthor":true,"prefix":"","firstName":"Siti","middleName":"Nur Su'aidah","lastName":"Nasarudin","suffix":""},{"id":411483297,"identity":"031a60d2-6389-41ac-b660-84ae72139221","order_by":1,"name":"Nurhairani Abdul Latif","email":"","orcid":"","institution":"Ministry of Health Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Nurhairani","middleName":"Abdul","lastName":"Latif","suffix":""},{"id":411483298,"identity":"f59b180f-0656-4e33-8289-875784a2bf4e","order_by":2,"name":"Saidatul Sheeda Ahmad Shukri","email":"","orcid":"","institution":"Ministry of Health Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Saidatul","middleName":"Sheeda Ahmad","lastName":"Shukri","suffix":""},{"id":411483299,"identity":"747fc7ca-ef8e-4ed0-9bb5-d897ec537463","order_by":3,"name":"Sharifah Shafawati Syed Mohd Hamdan","email":"","orcid":"","institution":"Ministry of Health Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Sharifah","middleName":"Shafawati Syed Mohd","lastName":"Hamdan","suffix":""},{"id":411483300,"identity":"23d8d309-89e6-4960-aecd-9bca28ce8bb0","order_by":4,"name":"Hajar Atiqah Abdul Kadir","email":"","orcid":"","institution":"Ministry of Health Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Hajar","middleName":"Atiqah Abdul","lastName":"Kadir","suffix":""},{"id":411483301,"identity":"a3759034-6293-41d1-838e-f3cb7850f7d3","order_by":5,"name":"Muhamad Fuad Ali","email":"","orcid":"","institution":"Ministry of Health Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Muhamad","middleName":"Fuad","lastName":"Ali","suffix":""},{"id":411483302,"identity":"43db7f82-025e-4b40-a180-500d728f78f9","order_by":6,"name":"Abdul Haniff Mohamad Yahaya","email":"","orcid":"","institution":"Ministry of Health Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Abdul","middleName":"Haniff Mohamad","lastName":"Yahaya","suffix":""}],"badges":[],"createdAt":"2025-02-04 04:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5954849/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5954849/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75707877,"identity":"f2e73413-6f53-460d-87d1-acb1874c97ea","added_by":"auto","created_at":"2025-02-07 10:38:43","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":110597,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the PSM assessment and distribution of the covariates between groups\u003c/p\u003e","description":"","filename":"BMCHSRFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5954849/v1/7d9aeb46672de8d846fb21da.jpg"},{"id":81823949,"identity":"4f2ed97f-c972-4217-99c0-495c1ea26717","added_by":"auto","created_at":"2025-05-02 12:01:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1277843,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5954849/v1/7356bdc3-d9dd-401a-a366-a149569d5d51.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of belt conveyor systems on patient waiting time and productivity in outpatient pharmacy settings: a comparative study","fulltext":[{"header":"Background","content":"\u003cp\u003eThe operational efficiency of outpatient pharmacy services is a critical component of healthcare delivery, directly influencing patient safety, patient satisfaction and overall healthcare outcomes. Patient waiting time and pharmacy productivity are widely recognized as key performance indicators in assessing the quality of pharmacy services (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMany countries, including Malaysia, have established standards to ensure timely care and minimize harm caused by delays in medication dispensing (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In Malaysia, waiting time is defined as the duration from when a prescription is received at the pharmacy counter to when the patient receives their medication (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). According to the Ministry of Health Malaysia\u0026rsquo;s Client Charter, 95% of prescriptions should be dispensed within 30 minutes (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). This benchmark underscores the need for efficient pharmacy workflows to meet patient expectations and ensure compliance with national standards.\u003c/p\u003e \u003cp\u003eEqually important is the evaluation of pharmacy productivity, as it directly impacts the efficiency, quality and sustainability of pharmacy services. It informs decisions on resource distribution, allows the identification of bottlenecks and inefficiencies in the workflow and even provides a baseline for assessing the impact of innovations (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs outpatient pharmacy operations often face challenges such as high prescription volumes and a limited workforce, innovations such as automation and workflow optimization have been increasingly adopted to address these issues (\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Automation technologies such as automated dispensing systems (ADSs), robotics and radio frequency identification (RFID)-enabled systems have been implemented in several countries to increase operational efficiency, reduce medication errors and improve patient safety (\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBelt conveyor (BC) systems are another innovation designed to streamline medication dispensing workflows. BC systems facilitate the efficient movement of prescriptions and medications within the pharmacy, reduce manual handling and improve workflow efficiency (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). These systems are characterized by their robust load-bearing capabilities, simple design, minimal maintenance requirements and reliable operation. In Malaysia, BC systems have been implemented in MOH outpatient pharmacies as part of the Ambulatory Pharmacy Care Service since 1998. A national policy on its implementation was introduced in 2009 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). By automating the transfer of prescriptions between counters and filling areas, BC systems aim to increase productivity and reduce patient waiting time.\u003c/p\u003e \u003cp\u003eDespite the growing adoption of automation technologies, research specifically focused on the impact of BC systems in outpatient pharmacy settings remains limited. While previous studies have highlighted the benefits of automation in improving pharmacy services (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), the effectiveness of BC systems in achieving these goals is not well-documented. This study aimed to bridge that gap by evaluating and comparing patient waiting times and productivity between outpatient pharmacies equipped with and without BC systems.\u003c/p\u003e \u003cp\u003eThe findings from this study provide valuable insight into the operational benefits and limitations of BC systems, guiding healthcare administrators in making evidence-based decisions regarding the adoption of technologies in pharmacy practice. Additionally, this study emphasizes the importance of tailoring technological solutions to the specific needs of healthcare facilities to maximize their impact on service delivery.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design \u0026amp; settings\u003c/h2\u003e \u003cp\u003eThis cross-sectional study compared 17 outpatient department (OPD) facilities with BC systems to 17 facilities without BC. The inclusion criteria required facilities to have a pharmacy unit under the MOH and at least one pharmacist. The exclusion criteria included facilities with fewer than 50 prescriptions dispensed per day, newly approved MOH facilities after 2021, institutions, rural clinics, maternal and child health clinics and facilities without pharmacists.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePropensity score matching (PSM)\u003c/h3\u003e\n\u003cp\u003eSince only post-implementation data were available, propensity score matching (PSM) was used to identify facilities without BC as the comparison group. The propensity scores were calculated using key facility characteristics: category of facility, total number of counters, type of OPD counter, range of prescriptions dispensed per day, total number of pharmacy staff and area of space.\u003c/p\u003e \u003cp\u003eGiven the limited availability of facilities eligible for matching on the basis of the scores, a 1:1 matching without replacement approach was applied. This method allowed pairing of each facility with BC to the most similar non-BC facility without exhausting the pool of potential matches. Additionally, 1:1 matching ensures a more balanced analysis and maintains the integrity of comparisons while accommodating the constraints of the available data (\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo assess the quality of matching, baseline covariates were compared between the BC and non-BC groups via t-tests and absolute standardized mean differences (SMDs). Since p-values are highly driven by sample size, the SMDs provide a more robust measure of balance, complementing the p-values (\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The SMDs were calculated for each baseline covariate, including both continuous and categorical variables. An SMD of \u0026lt;\u0026thinsp;0.1 indicates a negligible difference and excellent balance between groups for that covariate (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eData collection and analysis\u003c/h3\u003e\n\u003cp\u003eTwelve months of data from January to December 2022 were retrieved from quarterly reports and the pharmacy infrastructure database for both the BC and non-BC groups. The data were categorized into two main sets. The first set included facility-related information, such as facility category and type, total number of counters, type of OPD counter, range of prescriptions dispensed per day, prescription volume dispensed, total number of pharmacy staff, average monthly dispensing or patient waiting time and area of the OPD space. The second set focused on BC data, including the brand, model, shape, length, speed and frequency of maintenance and breakdowns throughout the year.\u003c/p\u003e \u003cp\u003eA Mann-Whitney test (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) was conducted to assess the impact of BC systems on patient waiting time. Moreover, productivity was assessed by calculating the number of prescriptions dispensed per person per standard working hour. This metric was derived by dividing the average monthly number of pharmacy staff, which was based on 180 working hours per month. Finally, an independent t-test was used to compare the productivity between facilities with BC and those without BC. Significance was defined as p\u0026thinsp;\u0026gt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of the comparison group: pre- and post-matching\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e lists the covariates used to generate propensity scores for each facility. However, the shape of BC was not included as a covariate in the propensity score calculation, as the goal of this calculation was to identify matching pairs between BC and non-BC facilities. Six types of BC designs were implemented: T, O, U, I, L and customized.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive subject characteristics and covariates used to measure propensity score.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType of Facility (BC / non-BC)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategory of Facility\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Number of Counter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eType of Counter (Open / Close)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRange of Prescriptions Dispensed Per Day\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal Number of Pharmacy Staffs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eArea of Space (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eShape of Belt Conveyor (BC)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePair 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*Major Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;500\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1104.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.9569\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;500\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e893.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.8058\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePair 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1038.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.9210\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;500\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e347.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.7722\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePair 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;500\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e640.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.7698\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e414.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.5919\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePair 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;350\u0026ndash;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e110.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.7183\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;500\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e394.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.4252\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePair 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e241.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.6097\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;500\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e81.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.3820\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePair 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e482.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCustomised\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.5559\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;500\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e67.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.3500\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePair 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;500\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e675.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.5281\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;350\u0026ndash;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e137.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.2890\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePair 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;350\u0026ndash;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e494.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.5020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e101.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.2855\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePair 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e296.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.4982\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;500\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e217.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.2741\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePair 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;500\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e129.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.3921\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e253.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.2585\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePair 11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;500\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e128.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.3854\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;350\u0026ndash;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e93.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.2104\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePair 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;500\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e364.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.3292\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e380.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.2079\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePair 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;500\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e130.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.1338\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;500\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e214.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.1468\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePair 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*Type 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;500\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e63.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.6066\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;500\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e37.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.4129\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePair 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;500\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e76.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.2439\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;500\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e26.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.2980\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePair 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*Type 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;500\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e40.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.2011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;500\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e25.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.2171\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePair 17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;500\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e32.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.1128\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;500\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e107.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.1237\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e*Major Specialist is classified as hospital that has up to 20 types of specialty or sub-specialty.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e*Type 1 is classified as clinics with an average number of daily patient arrivals over than 800 patients.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e*Type 2 is classified as clinics with an average number of daily patient arrivals around 500\u0026ndash;800 patients.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe PSM approach successfully facilitated the matching process, as evidenced by comparable characteristics across matched BC and non-BC facilities within each pair in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In Pair 1, both BC and non-BC facilities had similar numbers of counters (8 versus 7), prescription volumes (\u0026gt;\u0026thinsp;500\u0026ndash;800) and facility types (major specialists), with slight differences in the number of pharmacists (35 versus 16) and area of space (1104.90 m\u003csup\u003e2\u003c/sup\u003e versus 893.63 m\u003csup\u003e2\u003c/sup\u003e). The calculated propensity scores for Pair 1 were also similar (0.9569 versus 0.8058).\u003c/p\u003e \u003cp\u003eFor several pairs, such as Pairs 2 and 3, the BC and non-BC facilities even presented the closest matching of covariates, supporting the effectiveness of the PSM method. However, in certain pairs, larger discrepancies in scores were observed. For example, Pair 5 displayed a sharp contrast between BC (0.6097) and non-BC (0.3820) facilities, despite the BC facility handling twice the prescriptions and having a larger number of pharmacists. Additionally, some pairs presented lower propensity scores for both the BC and non-BC facilities, which may be due to their lower operational capacity, smaller spaces or smaller number of pharmacists.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the flowchart of the matching assessment. The proportions of the category of facility, type of counter and range of prescriptions dispensed were comparable and balanced between the two groups. For the variables of the total number of pharmacy staff, the total number of counters and area of space were not significantly different. The non-significant p-values might suggest improved balance, but it was not a definitive measure of balance (\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo complement the p-value analysis, the SMDs for all baseline covariates were calculated before and after matching (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Before matching, substantial imbalances were observed across several covariates. The largest imbalance was observed in the facility category (SMD\u0026thinsp;=\u0026thinsp;1.227), followed by the total number of pharmacy staff (SMD\u0026thinsp;=\u0026thinsp;0.941) and the area of space (SMD\u0026thinsp;=\u0026thinsp;0.918).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSMDs of pre- and post-matched baseline covariates.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSMD \u003c/p\u003e \u003cp\u003ePre-Matching\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSMD\u003c/p\u003e \u003cp\u003ePost-Matching\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCategory of facility\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal number of counters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of counter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRange of prescriptions dispensed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal number of pharmacy staffs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArea of space\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAfter matching, the SMDs for most covariates were substantially reduced, indicating improved balance between the groups. The facility category achieved near-complete balance with an SMD\u0026thinsp;\u0026lt;\u0026thinsp;0.1. The total number of pharmacy staff and range of prescriptions dispensed significantly improved, with the SMDs decreasing to 0.014 and 0.116, respectively. Despite these improvements, the covariates of the type of counter and area of space exhibited residual imbalances, although these imbalances were reduced compared with the initial SMD before matching. The reduction in SMD across all variables suggested that the matched groups were more comparable to enhance the validity of this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePatient waiting time\u003c/h2\u003e \u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that the average patient waiting time at BC facilities (mean\u0026thinsp;=\u0026thinsp;17.84 minutes, SD\u0026thinsp;=\u0026thinsp;6.57) was slightly lower than at non-BC facilities (mean\u0026thinsp;=\u0026thinsp;18.61 minutes, SD\u0026thinsp;=\u0026thinsp;5.70). The variance in waiting time was also greater in BC facilities (43.16) than in non-BC facilities (32.53). Despite these differences, the mean waiting times for both facility types were relatively comparable, suggesting that the presence of BC may have a limited impact on reducing patient waiting time. Further analysis via the Mann-Whitney test was used to determine if these differences were significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistic on the average patient waiting time.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup of Facility\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAverage of patient waiting time\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFacilities with BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e18.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e43.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFacilities without BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e16.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows a comparison of the average waiting time between the two groups. The analysis revealed no statistically significant difference in the total patient waiting time for medication prescriptions between facilities equipped with BC and without BC (U\u0026thinsp;=\u0026thinsp;135.00, z = -0.327, p\u0026thinsp;=\u0026thinsp;.744). In summary, the results indicated that the implementation of BC did not significantly reduce the total patient waiting time in the OPDs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMann-Whitney U test results in comparison of average waiting time between facilities with BC and facilities without BC.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup of Facility\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Rank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSum of Ranks\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAverage of patient waiting time\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFacilities with BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e288.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e135.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFacilities without BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e307.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProductivity\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the independent t-test results comparing the productivity between facilities equipped with BC and those without BC. The mean productivity for facilities with BC was M\u0026thinsp;=\u0026thinsp;3.60 (SD\u0026thinsp;=\u0026thinsp;1.192), whereas for facilities without BC, the mean productivity was M\u0026thinsp;=\u0026thinsp;3.64 (SD\u0026thinsp;=\u0026thinsp;1.273). The t-test yielded no significant difference, t (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) = -0.109, p\u0026thinsp;=\u0026thinsp;.914. These findings indicated that the presence of BC had no measurable effect on productivity in OPDs, as the average productivity for both groups was similar, with 3.60 and 3.64 prescriptions processed per person per hour, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndependent t-test results for productivity in facilities with BC and without BC.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Deviation (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFacilities with BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e.914\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFacilities without BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results revealed no significant differences in patient waiting time or productivity between outpatient pharmacy facilities with and without BC systems. These findings suggested that the implementation of a BC system alone did not automatically lead to measurable improvements in waiting time and productivity, contrary to expectations.\u003c/p\u003e \u003cp\u003eThe BC system is outlined in Malaysia\u0026rsquo;s national policy guidelines; however, no standardized design is specified and the criteria for its implementation remain broad (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Consequently, this has led to variability in BC designs, which may have resulted in differences in system utilization, potentially impacting waiting time and productivity outcomes.\u003c/p\u003e \u003cp\u003eThe waiting time data used in this study were obtained from secondary sources and not segregated by specific work processes, making it unclear which processes contributed the most to the overall waiting time. Besides that, other confounding factors, such as the total number of medications per prescription, prescriptions requiring pharmacist intervention and the experience level of the pharmacy staff handling prescriptions, also influence waiting time (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to dispensing medications, pharmacy staff at OPDs also perform various other responsibilities including compounding, extemporaneous preparations, patient medication counseling and providing Value Added Services (VAS) such as Medicine by Post, Drive-Through Pharmacy Service, Park \u0026amp; Take, Locker Medicine, and Drug Information Service (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). They also engage in health promotion and patient education, operate the Medication Therapy Adherence Clinic (MTAC), handle adverse drug reaction (ADR) reporting as well as smoking cessation pharmacotherapy (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). These additional tasks may increase the workload of pharmacy staff, thereby influencing overall waiting time and productivity in medication dispensing (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Such workload increases may also mask the potential benefits of BC implementation.\u003c/p\u003e \u003cp\u003eCurrently, there is no gold standard for measuring the productivity of pharmacy staff in OPDs (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Most studies have used different metrics, such as the number of full-time equivalents (FTEs) of pharmacists, changes in workload or the number of prescriptions dispensed per person per unit of time (\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). These approaches highlight the variability in productivity metrics on the basis of environmental and workflow factors. Given that the context of this study was in Malaysia's public healthcare facilities, the mean workload of pharmacy staff, defined as the number of prescriptions dispensed per person per working hour, also provides a relevant and practical productivity measure.\u003c/p\u003e \u003cp\u003eThe absence of pre-implementation data and a pilot study to validate the ability of the BC system represent major limitations of this study, which restrict the ability to compare performance metrics before and after the implementation of the BC system. Without baseline data, assessing the true impact of the BC system is difficult. In addition, facilities with BC might have had different initial performance levels than those without BC. For example, BC may have been implemented in facilities that already experienced higher patient volumes or workflow challenges, which could influence post-implementation outcomes.\u003c/p\u003e \u003cp\u003eNonetheless, despite these limitations, the PSM approach allowed for the identification of comparable subjects in both BC and non-BC facilities on the basis of observable characteristics, enabling a fairer evaluation and comparison of waiting times and productivity outcomes. While the PSM process achieved a moderate balance in covariates distribution between the groups, discrepancies in facility characteristics, such as the number of pharmacists and space area, may have influenced the calculated scores, thus contributing to the observed variability in some matched groups. This also reflected the importance of including additional data or unobserved factors as mentioned previously, which might be significant in future analyses to achieve greater precision in matching.\u003c/p\u003e \u003cp\u003eWith the current advancements in robotics and artificial intelligence (AI), integrating fully automated systems powered by AI into pharmacy workflows could be a promising step forward. For example, the outpatient pharmacy at Singapore General Hospital has implemented an Automated RFID Prescription Drug Delivery System, which automates the prescription filling process from start to end. This system not only significantly improves the dispensing efficiency but also reduces the possibility of medication errors (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Similarly, several hospitals in Thailand have implemented ADS in their outpatient pharmacies to streamline their outpatient pharmacy operations. Bangkok Hospital employs automated drug dispensing machines that utilize AI to dispense medications accurately (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) whereas Vejthani Hospital has introduced the EV220 Outpatient Pharmacy Robotic Dispensing system. In this system, after pharmacists verify a prescription, the robot selects the required drugs, fills a pill bottle and labels it with the patient\u0026rsquo;s information (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). These innovations reduce the overall workflow for pharmacists while improving patient safety and operational efficiency.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eFuture studies should incorporate pre-implementation data to enable a more comprehensive evaluation of the impact of the BC system. A longitudinal design, including pre- and post-BC implementation, would provide a clearer understanding of its effectiveness. Additionally, qualitative methods such as staff interviews or workflow analyses, could explore other contextual factors that influence the system\u0026rsquo;s performance, complementing the quantitative findings. Furthermore, there is a need to develop and adopt standardized productivity and waiting time metrics that are specific to pharmacy practices, enabling consistent and comparable evaluations across facilities.\u003c/p\u003e \u003cp\u003eIn conclusion, this study highlighted the importance of careful planning by identifying the conditions under which the BC system delivers the greatest benefits, ensuring that it is tailored to the operational needs of individual facilities. Similarly, introducing any innovation or automation in pharmacy practices should be accompanied by workflow optimization to ensure that it aligns with operational requirements and enhances overall effectiveness. Establishing standardized design and implementation guidelines for innovations or automations in national policies is equally critical to reducing variability in outcomes and supporting informed planning and decision-making by healthcare administrators.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eADR: Adverse drug reaction\u003c/p\u003e\n\u003cp\u003eADS: Automated dispensing system\u003c/p\u003e\n\u003cp\u003eAI: Artificial intelligence\u003c/p\u003e\n\u003cp\u003eBC: Belt conveyor\u003c/p\u003e\n\u003cp\u003eFTE: Full-time equivalent\u003c/p\u003e\n\u003cp\u003eMOH: Ministry of Health\u003c/p\u003e\n\u003cp\u003eMTAC: Medication Therapy Adherence Clinic\u003c/p\u003e\n\u003cp\u003eOPD: Outpatient pharmacy departments\u003c/p\u003e\n\u003cp\u003ePSM: Propensity score matching\u003c/p\u003e\n\u003cp\u003eRFID: Radio frequency identification \u003c/p\u003e\n\u003cp\u003eSMD: Standardized mean difference\u003c/p\u003e\n\u003cp\u003eVAS: Value Added Services\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthical approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was registered in the National Medical Research Register (NMRR-22-01699-MOE) and approved by the Medical Research and Ethics Committee (MREC), Ministry of Health Malaysia. The need for informed consent was waived by the MREC because the study was a retrospective and used secondary data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe data sets that support for the findings in this study can be provided by the first author upon reasonable request and with the permission of the MOH Malaysia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe study did not receive any funding.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eStudy design and conceptualization: AHMY, SNSN and SSAS. Data collection and interpretation: NHL, SSSMH, HAAK and MFL. Data cleaning and analyses: SNSN, SSAS and AHMY. Literature review: SNSN, NHL, SSSMH, HAAK, SSAS and MFL. Preparation of first draft: SNSN and SSAS. Review and editing: AHMY and NAL. All authors approved the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe would like to thank the Director General of Health, Ministry of Health Malaysia, for his permission to publish this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRough SS, McDaniel M, Rinehart JR. Effective use of workload and productivity monitoring tools in health-system pharmacy, part 1. American Journal of Health-System Pharmacy [Internet]. 2010 Feb 15;67(4):300\u0026ndash;11. Available from: https://academic.oup.com/ajhp/article/67/4/300/5130009\u003c/li\u003e\n\u003cli\u003eBrummond P, Forsyth L, Johnson M, Moore K, Norman C, O\u0026rsquo;reilly K, et al. A primer on pharmacy benchmarking and productivity monitoring [Internet]. 2018 Aug [cited 2024 Dec 17]. 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Available from: https://www.pharmacy.gov.my/v2/sites/default/files/document-upload/requirement-development-pharmacy-fsacilities-1.pdf\u003c/li\u003e\n\u003cli\u003ePharmaceutical Services Division M of HM. Hospital Pharmacy Procurement and Supply (Hospital Pharmacy Store). In: Requirement for the Development of Pharmacy Facilities in Hospitals, Health Clinics and Other Health Facilities [Internet]. 3rd ed. Pharmaceutical Services Division, Ministry of Health Malaysia; 2009 [cited 2024 Dec 17]. p. 77\u0026ndash;130. Available from: https://pharmacy.moh.gov.my/sites/default/files/document-upload/requirement-development-pharmacy-fsacilities-2.pdf\u003c/li\u003e\n\u003cli\u003eFitzpatrick R, Cooke P, Southall C, Kaldhaur K, Waters P. Evaluation of an automated dispensing system in a hospital pharmacy dispensary. Pharm J [Internet]. 2009 Jun 16 [cited 2022 Jun 24]; Available from: https://pharmaceutical-journal.com/article/research/evaluation-of-an-automated-dispensing-system-in-a-hospital-pharmacy-dispensary\u003c/li\u003e\n\u003cli\u003eJames KL, Barlow D, Bithell A, Hiom S, Lord S, Pollard M, et al. The impact of automation on workload and dispensing errors in a hospital pharmacy. International Journal of Pharmacy Practice. 2013 Apr;21(2):92\u0026ndash;104. \u003c/li\u003e\n\u003cli\u003eAlanazi MF, Shahein MI, Alsharif HM, Alotaibi SM, Alanazi AO, Alanazi AO, et al. Impact of automated drug dispensing system on patient safety. Pharm Pract (Granada). 2022 Oct 1;20(4). \u003c/li\u003e\n\u003cli\u003eRosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika [Internet]. 1983;70(1):41\u0026ndash;55. Available from: https://academic.oup.com/biomet/article-lookup/doi/10.1093/biomet/70.1.41\u003c/li\u003e\n\u003cli\u003eAustin PC. 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Evaluating the Impact of Drug Dispensing Systems on the Safety and Efficacy in a Singapore Outpatient Pharmacy. In: Value in Health [Internet]. 2014 [cited 2022 Sep 9]. p. A791\u0026ndash;2. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1098301514023705\u003c/li\u003e\n\u003cli\u003eShao SC, Chan YY, Lin SJ, Li CY, Yang YHK, Chen YH, et al. Workload of pharmacists and the performance of pharmacy services. PLoS One [Internet]. 2020 Apr 1 [cited 2024 Dec 17];15(4). Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC7173874/pdf/pone.0231482.pdf\u003c/li\u003e\n\u003cli\u003eCoblio NA. The Impact of Pharmacy Work Design on Pharmacist Productivity [Internet]. USF Tampa Graduate Theses and Dissertations; 2011. Available from: https://digitalcommons.usf.edu/etd\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"belt conveyor, waiting time, pharmacy productivity, dispensing process, outpatient pharmacy","lastPublishedDoi":"10.21203/rs.3.rs-5954849/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5954849/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe implementation of belt conveyor (BC) systems in outpatient pharmacy departments (OPDs) has been a national policy since 2009, aiming to streamline the dispensing process. However, limited evidence exists on their impact on OPD operations. This study aims to evaluate and compare the waiting time and productivity of OPDs with and without BC systems.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted in which 17 facilities with BC systems were compared with 17 facilities without BC systems. As pre-implementation data were unavailable, facilities were matched via propensity score matching (PSM) with a 1:1 matching technique without replacement. Post-matching data were analyzed via an independent \u003cem\u003et\u003c/em\u003e-test for productivity and a Mann-Whitney test for waiting time. A significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was set to ensure reliable results.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 34 facilities were included in the analysis. For waiting time, there was no statistically significant difference between the groups (U\u0026thinsp;=\u0026thinsp;136.00 OR z = -0.327, p\u0026thinsp;=\u0026thinsp;0.744). Similarly, productivity was not significantly different between facilities with BC systems (M\u0026thinsp;=\u0026thinsp;3.60, SD\u0026thinsp;=\u0026thinsp;1.192) and those without BC systems (M\u0026thinsp;=\u0026thinsp;3.64, SD\u0026thinsp;=\u0026thinsp;1.273); t(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)=-0.109, p\u0026thinsp;=\u0026thinsp;0.914. These findings suggested that the use of the BC system did not significantly ncrease the waiting time or productivity in OPDs.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe study concluded that the implementation of a BC system alone did not improve productivity or reduce patient waiting time. The study also highlighted the importance of comprehensive planning and consideration of multiple factors when new technologies are introduced into OPD workflows.\u003c/p\u003e","manuscriptTitle":"Impact of belt conveyor systems on patient waiting time and productivity in outpatient pharmacy settings: a comparative study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-07 10:38:18","doi":"10.21203/rs.3.rs-5954849/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"11fe6197-659f-4932-ada4-4eb717f2d81b","owner":[],"postedDate":"February 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-02T11:53:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-07 10:38:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5954849","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5954849","identity":"rs-5954849","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}