Safety, time and cost evaluation of automated and semi-automated drug distribution systems in hospitals: a systematic review

Validation of a Questionnaire to Assess Patient Satisfaction with an Automated Drug Dispensing System

BACKGROUND AND OBJECTIVES

Automated drug dispensing systems (ADDs) have been introduced to improve the efficiency of dispensing and patient safety. The available questionnaires measure patient satisfaction with particular aspects of ADDs. Also, the level of patient satisfaction with ADDs is not widely established. This study aimed to develop and validate a novel questionnaire to assess patient satisfaction with ADDs.

METHODS

Content and construct validity procedures were used to validate the 20-item questionnaire with four domains, including pharmacy administration, dispensing practice, patient education, and the dispensing system. Two hundred consenting participants took part in this study, from those who visited the outpatient pharmacy in a government hospital.

RESULTS

The internal consistency of all four scale items shows acceptable reliability (>0.7). In the exploratory factor analysis, three items were removed due to poor factor loading and cross-loading. In the confirmatory factor analysis, the model has acceptable fit indices, including the comparative fit index (0.937), Tucker-Lewis's index (0.924), standardized root mean square residual (0.051), root mean square error of approximation (0.057), and χ2/df (1.67). The convergent and discriminant validity were established, since the average variance extracted (AVE) was ≥0.5 and the squared correlation (SC) values of one construct with other constructs were less than the AVE of the specific construct.

CONCLUSION

This study offered a reliable and valid 17-item questionnaire incorporating a multi-dimensional four-factor model to evaluate patient satisfaction with ADDs. The validated questionnaire can be utilized to explore patients' perspectives on ADDs.

Automated dispensing cabinets and their impact on the rate of omitted and delayed doses: A systematic review

Use of automated dispensing cabinets (ADCs) is increasing in hospital settings. ADCs bring various potential benefits, among which are improvements to patient safety and reduction of medication errors. A core function of ADCs is to prevent medication stock outs by triggering an order when stock is reaching low levels. A quantifiable patient safety measure is the occurrence of omitted or delayed doses, which can range in severity from being negligible, to potentially fatal. The purpose of this review is to identify and synthesise the existing evidence regarding the impact of ADCs situated in secondary and tertiary care inpatient settings, on the rate of omitted and delayed doses as a specific subsection of medication errors. In April 2024 searches were conducted in Embase, PubMed and CINAHL, with additional articles discovered through citation searching and from colleagues. A total of 375 articles were returned from the search. Nine articles met the inclusion criteria. The most common reason for exclusion was due to lack of relevance. The included papers were focused on centres which had implemented six or fewer ADCs. The studies mostly presented findings which suggest ADCs have a positive impact on the rate of omitted or delayed doses, although crucially only two papers correlated missed doses due to unavailability of medications The studies highlighted other factors which should be considered prior to the implementation of ADCs. Factors included staffing requirement, type of stock held in the cabinets, and interoperability with other systems. Studies only reported omitted or missed doses, none reported results on delayed doses. It is widely accepted that ADCs can prevent medication unavailability but there is a paucity of evidence linking the improved availability of medications through the utilisation of ADCs with the perceived impact on missed or delayed doses. Further multi-centre studies are needed to determine this causality.

Introduction of Unit-Dose Care in the 1,125 Bed Teaching Hospital: Practical Experience and Time Saving on Wards

Purpose

The shortage of nursing staff as well as the slow progress in the German health care system's digitalisation has gained much attention due to COVID-19. Patient-specific medication management using the unit-dose dispensing system (UDDS) has the potential for a lasting and positive influence on both digitalisation and the relief of nursing staff.

Methods

Nursing staff UDDS-acceptance was determined via a validated online survey. For the evaluation of stock keeping on the wards, the delivery quantities were determined for a comparative period before and after the introduction of the UDDS. The time required for on-ward medication-related processes on ward before and after the introduction of UDDS was recorded based on a survey form and the nursing relief in full-time equivalent (FTE) was calculated using the data obtained.

Results

We show that nurses appreciate the UDDS and confirm a significant reduction in drug stocks on the wards. The UDDS reduces the time needed to dispense medications from 4.52 ± 0.35 min to 1.67 ± 0.15 min/day/patient. In relation to the entire medication process, this corresponds to a reduction of 50% per day and per patient. Based on 40,000 patients/year and a supply of 1,125 beds with unit-dose blisters, 7.36 FTE nursing staff can be relieved per year. In contrast, 6.5 FTE in the hospital pharmacy are required for supplying the hospitals.

Conclusion

UDDS is well accepted by nurses, reduces stock levels on ward, and fulfils criteria as a nursing-relief measure.

Light-M: An efficient lightweight medical image segmentation framework for resource-constrained IoMT

The Internet of Medical Things (IoMT) is being incorporated into current healthcare systems. This technology intends to connect patients, IoMT devices, and hospitals over mobile networks, allowing for more secure, quick, and convenient health monitoring and intelligent healthcare services. However, existing intelligent healthcare applications typically rely on large-scale AI models, and standard IoMT devices have significant resource constraints. To alleviate this paradox, in this paper, we propose a Knowledge Distillation (KD)-based IoMT end-edge-cloud orchestrated architecture for medical image segmentation tasks, called Light-M, aiming to deploy a lightweight medical model in resource-constrained IoMT devices. Specifically, Light-M trains a large teacher model in the cloud server and employs computation in local nodes through imitation of the performance of the teacher model using knowledge distillation. Light-M contains two KD strategies: (1) active exploration and passive transfer (AEPT) and (2) self-attention-based inter-class feature variation (AIFV) distillation for the medical image segmentation task. The AEPT encourages the student model to learn undiscovered knowledge/features of the teacher model without additional feature layers, aiming to explore new features and outperform the teacher. To improve the distinguishability of the student for different classes, the student learns the self-attention-based feature variation (AIFV) between classes. Since the proposed AEPT and AIFV only appear in the training process, our framework does not involve any additional computation burden for a student model during the segmentation task deployment. Extensive experiments on cardiac images and public real-scene datasets demonstrate that our approach improves student model learning representations and outperforms state-of-the-art methods by combining two knowledge distillation strategies. Moreover, when deployed on the IoT device, the distilled student model takes only 29.6 ms for one sample at the inference step.

Potential of Assistive Robots in Clinical Nursing: An Observational Study of Nurses' Transportation Tasks in Rural Clinics of Bavaria, Germany

Transportation tasks in nursing are common, often overlooked, and directly impact patient care time in the context of staff shortages and an aging society. Current studies lack a specific focus on transportation tasks, a gap our research aims to fill. By providing detailed data on transportation needs in nursing, our study establishes a crucial foundation for the development and integration of assistive robots in clinical settings. In July and September 2023, we conducted weekly observations of nurses to assess clinical transportation needs. We aim to understand the economic impact and the methods nurses use for transportation tasks. We conducted a participant observation using a standardized app-based form over a seven-day observation period in two rural clinics. N = 1830 transports were made by nurses and examined by descriptive analysis. Non-medical supplies account for 27.05% (n = 495) of all transports, followed by medical supplies at 17.32% (n = 317), pharmacotherapy at 14.10% (n = 258) and other other categories like meals or drinks contributing 12.68% (n = 232). Most transports had a factual transport time of under a minute, with patient transport and lab samples displaying more variability. In total, 77.15% of all transports were made by hand. Requirements to collect items or connect transports with patient care were included in 5% of all transports. Our economic evaluation highlighted meals as the most costly transport, with 9596.16 € per year in the observed clinics. Budget-friendly robots would amortize these costs over one year by transporting meals. We support understanding nurses' transportation needs via further research on assistive robots to validate our findings and determine the feasibility of transport robots.

Improving Inpatient Medication Dispensing with an Automated System

Introduction

Medication inventory management and error prevention are complex issues. Single interventions are insufficient to make improvement across the spectrum. A uniform system for dispensing and distributing medications can help reduce the risk of medication errors, improve efficiency, and minimize waste. This quality improvement project aims to: 1) decrease - the time from ordering medication to administration, including delay incidents, by > 70%; and 2) decrease the inpatient monthly total medication consumption by > 20% and ward medication stock items by > 70%, including decreasing returned items and loss from in-house expired medications by > 70%.

Methods

A Six-Sigma approach was applied to eliminate deficiencies throughout the medication management process. Failure mode effect analysis and staff surveys were used to evaluate implementation of automated dispensing cabinet (ADCs) and reengineered workflows for expensive, misused, and restricted medications.

Results

After the new processes were implemented, the turnaround time from ordering medication to administration was reduced by 83%, with zero delay incidents reported. Most nurses (64%) and pharmacists (67%) stated that implementation of ADCs increased their productivity by more than 40%. Monthly medication consumption was reduced by 24%, with an estimated annual saving of $4,100,000 USD. The number of returned items per month was reduced by 72%, and the estimated annual savings from loss of in-house expired medications was $750,000 USD.

Conclusions

This quality improvement project positively impacted stock control while reducing costs and turnaround time for inpatient medication dispensing. Medication delay incidents were reduced, and staff satisfaction levels were positive. Next steps are to reengineer narcotic, anesthesia, and refrigerated products' management.

Improving Pharmacist-Led Pediatric Patient Education on Oral Chemotherapy at Home

Oral chemotherapy (OC) has been increasingly used in pediatric patients diagnosed with cancer, which is primarily managed in the outpatient setting. Different from adults, pediatric patients face unique challenges in administering these hazardous medications at home. Because of the complexity of pediatric pharmaceutical care and the hazardous nature of chemotherapy agents, comprehensive patient education is imperative to mitigate the potential safety risks associated with OC administration at home. Pharmacists play a vital role in patient education and medication consultations. However, the lack of practice guidelines and limited resources supporting OC counseling are noted. Additional barriers include insufficient knowledge and training on OC, which can be improved by continuing education. In a regional children's hospital, a comprehensive OC education checklist was developed for pediatric patients and their caregivers to standardize consultations led by pharmacists. An infographic OC handout was also formulated to improve patient knowledge and awareness. Moreover, innovative approaches such as using telepharmacy, smartphone applications, and artificial intelligence have been increasingly integrated into patient care, which can help optimize OC consultations for children and adolescents. Further studies are warranted to enhance oral chemotherapy education specifically tailored for pediatric patients in outpatient settings.

Challenges experienced during pharmacy automation and robotics implementation in JCI accredited hospital in the Arabian Gulf area: FMEA analysis-qualitative approach

Background

Pharmacy automation and robotics implementation are essential aspects of healthcare facilities. It streamlines the medication dispensing process and significantly reduces medication errors. However, implementing automation and robotics in pharmacies comes with its challenges. We aim to detect and rectify potential dangers in the pharmacy workflow by utilizing the Failure Mode and Effects Analysis (FMEA) methodology; this is expected to augment performance and increase profitability.

Materials and methods

In this study, we conducted an FMEA analysis using a qualitative approach to identify the challenges experienced during pharmacy automation and robotics implementation in a Joint Commission International (JCI) accredited hospital in the Arabian Gulf area. The pharmacy processes and procedures were mapped in a Flow chart to visualize the pharmacy workflow, including highlighting the risks that were found. Then these risks were arranged as Potential failure modes and added to the table as 9 main points for each RPNs were calculated, and then the 9 points were prioritized for the action plans.

Results

Via applying traditional Risk Priority Number (RPN) FMEA, the Pharmacy board identified the process stages marked risky failure modes through several FMEAs, calculating the total RPNs at the implementation phase. It revealed several challenges, including staff training, technical issues, and inadequate communication. Furthermore, the study resulted in corrective and intervention steps.

Conclusion

Pharmacy automation and robotics implementation is a complex process that requires proper planning, preparation, and execution. The FMEA approach effectively identifies potential problems and evaluates their impact on the pharmacy system. Nine major failure modes appeared to be risky stages with high RPN scores. Therefore, multiple interventions were done during the implementation to enhance the knowledge of challenges faced during the implementation of the automation process and solve it. Future studies should address the identified challenges and develop strategies to mitigate them.

A Novel Two-Stage Induced Deep Learning System for Classifying Similar Drugs with Diverse Packaging

Dispensing errors play a crucial role in various medical errors, unfortunately emerging as the third leading cause of death in the United States. This alarming statistic has spurred the World Health Organization (WHO) into action, leading to the initiation of the Medication Without Harm Campaign. The primary objective of this campaign is to prevent dispensing errors from occurring and ensure patient safety. Due to the rapid development of deep learning technology, there has been a significant increase in the development of automatic dispensing systems based on deep learning classification to avoid dispensing errors. However, most previous studies have focused on developing deep learning classification systems for unpackaged pills or drugs with the same type of packaging. However, in the actual dispensing process, thousands of similar drugs with diverse packaging within a healthcare facility greatly increase the risk of dispensing errors. In this study, we proposed a novel two-stage induced deep learning (TSIDL)-based system to classify similar drugs with diverse packaging efficiently. The results demonstrate that the proposed TSIDL method outperforms state-of-the-art CNN models in all classification metrics. It achieved a state-of-the-art classification accuracy of 99.39%. Moreover, this study also demonstrated that the TSIDL method achieved an inference time of only 3.12 ms per image. These results highlight the potential of real-time classification for similar drugs with diverse packaging and their applications in future dispensing systems, which can prevent dispensing errors from occurring and ensure patient safety efficiently.

Automated unit dose dispensing systems producing individually packaged and labelled drugs for inpatients: a systematic review

OBJECTIVES

Pharmacy automation is increasing in hospitals. The aim of this systematic review was to identify and evaluate the literature on automated unit dose dispensing systems (UDDS) producing individually packaged and labelled drugs for inpatients.

METHODS

The search was conducted on eight electronic databases, including Scopus, Medline Ovid, and Cinahl, and limited to peer reviewed articles with English abstracts published 2000-2020. Studies were included in the review if drug dispensing was performed by an automated UDDS where individually packaged and labelled unit doses were subsequently assembled patient specifically for inpatients. All outcomes related to UDDS functionality were included with specific interest in medication safety, cost-efficiency and stock management. Outcomes were categorised and results synthesised qualitatively.

RESULTS

664 publications were screened, one article identified manually, resulting in eight included articles. Outcomes of the studies were categorised as medication administration errors (MAEs), dispensing errors, costs and cost-effectiveness. Studies showed that automated UDDS reduced significantly MAEs of inpatients compared with traditional ward stock system (WSS), especially when UDs were dispensed patient specifically by unit dose dispensing robot. Patient specific drug dispensing with automated UDDS was very accurate. Of three different automated medication systems (AMSs), patient specific AMS (psAMS) was the most cost-effective and complex AMS (cAMS) the most expensive system across all error types due to the higher additional investments and operation costs of automated dispensing cabinets (ADCs). None of the studies investigated the impact on the medication management process such as efficiency, costs and stock management as primary outcome.

CONCLUSIONS

UDDS improved patient safety. However, automation is a costly investment and the implementation process is complex and time consuming. Further controlled studies are needed on the clinical and economical outcomes of automated UDDS to produce reliable knowledge for hospital decision makers on the cost-benefit of the investment and to support decision making.

Reducing Medication Errors by Adopting Automatic Dispensing Cabinets in Critical Care Units

Medication errors can have severe consequences and threaten patient safety. The patient safety-related benefits of automated dispensing cabinets (ADCs) have been reported by several previous studies, including a reduction in medication errors in intensive care units (ICUs) and emergency departments. However, the benefits of ADCs need to be assessed, given the different healthcare practice models. This study aimed to compare the rates of medication errors, including prescription, dispensing, and administrative, before and after using ADCs in intensive care units. The prescription, dispensing, and administrative error data before and after the adoption of ADCs were retrospectively collected from the medication error report system. The severity of medication errors was classified according to the National Coordinating Council for Medication Error Reporting and Prevention guidelines. The study outcome was the rate of medication errors. After the adoption of ADCs in the intensive care units, the rates of prescription and dispensing errors reduced from 3.03 to 1.75 per 100,000 prescriptions and 3.87 to 0 per 100,000 dispensations, respectively. The administrative error rate decreased from 0.046 to 0.026%. The ADCs decreased National Coordinating Council for Medication Error Reporting and Prevention category B and D errors by 75% and category C errors by 43%. To improve medication safety, multidisciplinary collaboration and strategies, such as the use of automated dispensing cabinets, education, and training programs from a systems perspective, are warranted.

The Impact of Hospital Pharmacy Operation on the Quality of Patient Care

This paper presents the role, tasks, and place of a hospital pharmacy in the structure of the entire facility. The role of hospital drug management and pharmacy seems to be extremely important in providing patients with high-quality care. Particular emphasis was placed on the distribution systems of medicinal products and medical devices in the hospital. The advantages and disadvantages of the classical distribution system and modern systems such as unit-dose and multi-dose-and the most important differences between them-are presented. Difficulties related to implementing modern distribution systems in hospitals were also discussed. The information provided is presented in the context of the legal regulations in Poland.

Impact of automated drug dispensing system on patient safety

Objectives

Automated drug dispensing system (ADDs) is an emerging technology positively impacts drug dispensing efficiency by minimizing medication errors. However, the pharmacist perception of the impact of ADDs on patient safety is not well-established. This cross-sectional observational study aimed to evaluate the dispensing practice and pharmacist perception of ADDs towards patient safety through a validated questionnaire.

Methods

A self-designed questionnaire was validated and the pharmacist perception of dispensing practice was compared between two hospitals adopting ADDs and traditional drug dispensing system (TDDs).

Results

The developed questionnaire had an excellent internal consistency (both Cronbach's α and McDonald's ω coefficients were >0.9). Factor analysis retained three significant factors (subscales) that explained pharmacist perception of dispensing system, dispensing practice, and patient counseling (p<0.001 for each factor). The average number of prescriptions dispensed per day, drugs contained in each prescription, average time for labeling each prescription and inventory management were significantly varied between ADDs and TDDs (p=0.027, 0.013 0.044 and 0.004, respectively). The perception of pharmacists using ADDs on three domains were higher than the TDDs. The pharmacists in ADDs agreed that they had enough time to review the medications before dispensing than TDDs and this difference was found to be statistically significant (p=0.028).

Conclusions

ADDs was highly effective in improving dispensing practice and medication review; however, the pharmacists need to emphasize the importance of ADDs to translate the pharmacists' freed-time towards patient care.

Nurse managers' views regarding patients' long waiting time at community health centers in Gauteng Province, South Africa

Background

Patients' long waiting time still exceeds the set target of 120 minutes. As a result, the volume of complaints remains a concern that points to systems inefficiencies. Minimal attention has been given to the experiences of nurse managers regarding patients' long waiting time.

Objective

To explore and describe the experiences of nurse managers regarding patients' long waiting time at Community Health Centers (CHCs) in Gauteng Province, South Africa.

Methods

The research applied qualitative exploratory descriptive and contextual design. Non-probability purposive sampling techniques were used to select eight nurse managers. Individual semi-structured interviews were conducted and captured with an audio tape. Tesch's 8-steps of data analysis were followed to analyze the data.

Results

Three themes and seven categories emerged from the study: (1) The adverse effects of patients' long waiting time (early birds to evade long queues, increased patients' complaints and compromised quality care), (2) Factors that contribute to patients' long waiting time (records and patient administration system deficiencies, poor time management, patients' lack of adherence to booking system), (3) Measures to mitigate patients' long waiting time (embracing decongestion systems to mitigate patients' overflow at CHC).

Conclusion

The study recommends the optimal implementation of an appointment system to avert long waiting times. Collaboration between CHCs' management and clinic committees is encouraged to provide the best solutions to the reduction of patients' long waiting times. In addition, time management is one area that needs to be improved. A digital record management system can assist in better-sought problems related to filling. Studies are encouraged on a model to enhance collaboration in reduction of patients' long waiting time.

Interventions to reduce the incidence of medical error and its financial burden in health care systems: A systematic review of systematic reviews

Background and aim

Improving health care quality and ensuring patient safety is impossible without addressing medical errors that adversely affect patient outcomes. Therefore, it is essential to correctly estimate the incidence rates and implement the most appropriate solutions to control and reduce medical errors. We identified such interventions.

Methods

We conducted a systematic review of systematic reviews by searching four databases (PubMed, Scopus, Ovid Medline, and Embase) until January 2021 to elicit interventions that have the potential to decrease medical errors. Two reviewers independently conducted data extraction and analyses.

Results

Seventysix systematic review papers were included in the study. We identified eight types of interventions based on medical error type classification: overall medical error, medication error, diagnostic error, patients fall, healthcare-associated infections, transfusion and testing errors, surgical error, and patient suicide. Most studies focused on medication error (66%) and were conducted in hospital settings (74%).

Conclusions

Despite a plethora of suggested interventions, patient safety has not significantly improved. Therefore, policymakers need to focus more on the implementation considerations of selected interventions.

Automation of a tertiary hospital pharmacy drug dispensing system in a lower-middle-income country: A case study and preliminary results

Background

Methods

Results

Conclusion

Reducing medication errors for adults in hospital settings

BACKGROUND

Medication errors are preventable events that may cause or lead to inappropriate medication use or patient harm while the medication is in the control of the healthcare professional or patient. Medication errors in hospitalised adults may cause harm, additional costs, and even death.

OBJECTIVES

To determine the effectiveness of interventions to reduce medication errors in adults in hospital settings.

SEARCH METHODS

We searched CENTRAL, MEDLINE, Embase, five other databases and two trials registers on 16 January 2020.  SELECTION CRITERIA: We included randomised controlled trials (RCTs) and interrupted time series (ITS) studies investigating interventions aimed at reducing medication errors in hospitalised adults, compared with usual care or other interventions. Outcome measures included adverse drug events (ADEs), potential ADEs, preventable ADEs, medication errors, mortality, morbidity, length of stay, quality of life and identified/solved discrepancies. We included any hospital setting, such as inpatient care units, outpatient care settings, and accident and emergency departments.

DATA COLLECTION AND ANALYSIS

We followed the standard methodological procedures expected by Cochrane and the Effective Practice and Organisation of Care (EPOC) Group. Where necessary, we extracted and reanalysed ITS study data using piecewise linear regression, corrected for autocorrelation and seasonality, where possible.  MAIN RESULTS: We included 65 studies: 51 RCTs and 14 ITS studies, involving 110,875 participants. About half of trials gave rise to 'some concerns' for risk of bias during the randomisation process and one-third lacked blinding of outcome assessment. Most ITS studies presented low risk of bias. Most studies came from high-income countries or high-resource settings. Medication reconciliation -the process of comparing a patient's medication orders to the medications that the patient has been taking- was the most common type of intervention studied. Electronic prescribing systems, barcoding for correct administering of medications, organisational changes, feedback on medication errors, education of professionals and improved medication dispensing systems were other interventions studied. Medication reconciliation Low-certainty evidence suggests that medication reconciliation (MR) versus no-MR may reduce medication errors (odds ratio [OR] 0.55, 95% confidence interval (CI) 0.17 to 1.74; 3 studies; n=379). Compared to no-MR, MR probably reduces ADEs (OR 0.38, 95%CI 0.18 to 0.80; 3 studies, n=1336 ; moderate-certainty evidence), but has little to no effect on length of stay (mean difference (MD) -0.30 days, 95%CI -1.93 to 1.33 days; 3 studies, n=527) and quality of life (MD -1.51, 95%CI -10.04 to 7.02; 1 study, n=131).  Low-certainty evidence suggests that, compared to MR by other professionals, MR by pharmacists may reduce medication errors (OR 0.21, 95%CI 0.09 to 0.48; 8 studies, n=2648) and may increase ADEs (OR 1.34, 95%CI 0.73 to 2.44; 3 studies, n=2873). Compared to MR by other professionals, MR by pharmacists may have little to no effect on length of stay (MD -0.25, 95%CI -1.05 to 0.56; 6 studies, 3983). Moderate-certainty evidence shows that this intervention probably has little to no effect on mortality during hospitalisation (risk ratio (RR) 0.99, 95%CI 0.57 to 1.7; 2 studies, n=1000), and on readmissions at one month (RR 0.93, 95%CI 0.76 to 1.14; 2 studies, n=997); and low-certainty evidence suggests that the intervention may have little to no effect on quality of life (MD 0.00, 95%CI -14.09 to 14.09; 1 study, n=724).  Low-certainty evidence suggests that database-assisted MR conducted by pharmacists, versus unassisted MR conducted by pharmacists, may reduce potential ADEs (OR 0.26, 95%CI 0.10 to 0.64; 2 studies, n=3326), and may have no effect on length of stay (MD 1.00, 95%CI -0.17 to 2.17; 1 study, n=311).  Low-certainty evidence suggests that MR performed by trained pharmacist technicians, versus pharmacists, may have little to no difference on length of stay (MD -0.30, 95%CI -2.12 to 1.52; 1 study, n=183). However, the CI is compatible with important beneficial and detrimental effects. Low-certainty evidence suggests that MR before admission may increase the identification of discrepancies compared with MR after admission (MD 1.27, 95%CI 0.46 to 2.08; 1 study, n=307). However, the CI is compatible with important beneficial and detrimental effects. Moderate-certainty evidence shows that multimodal interventions probably increase discrepancy resolutions compared to usual care (RR 2.14, 95%CI 1.81 to 2.53; 1 study, n=487). Computerised physician order entry (CPOE)/clinical decision support systems (CDSS) Moderate-certainty evidence shows that CPOE/CDSS probably reduce medication errors compared to paper-based systems (OR 0.74, 95%CI 0.31 to 1.79; 2 studies, n=88).  Moderate-certainty evidence shows that, compared with standard CPOE/CDSS, improved CPOE/CDSS probably reduce medication errors (OR 0.85, 95%CI 0.74 to 0.97; 2 studies, n=630). Low-certainty evidence suggests that prioritised alerts provided by CPOE/CDSS may prevent ADEs compared to non-prioritised (inconsequential) alerts (MD 1.98, 95%CI 1.65 to 2.31; 1 study; participant numbers unavailable). Barcode identification of participants/medications Low-certainty evidence suggests that barcoding may reduce medication errors (OR 0.69, 95%CI 0.59 to 0.79; 2 studies, n=50,545). Reduced working hours Low-certainty evidence suggests that reduced working hours may reduce serious medication errors (RR 0.83, 95%CI 0.63 to 1.09; 1 study, n=634). However, the CI is compatible with important beneficial and detrimental effects. Feedback on prescribing errors Low-certainty evidence suggests that feedback on prescribing errors may reduce medication errors (OR 0.47, 95%CI 0.33 to 0.67; 4 studies, n=384). Dispensing system Low-certainty evidence suggests that dispensing systems in surgical wards may reduce medication errors (OR 0.61, 95%CI 0.47 to 0.79; 2 studies, n=1775).

AUTHORS' CONCLUSIONS

Low- to moderate-certainty evidence suggests that, compared to usual care, medication reconciliation, CPOE/CDSS, barcoding, feedback and dispensing systems in surgical wards may reduce medication errors and ADEs. However, the results are imprecise for some outcomes related to medication reconciliation and CPOE/CDSS. The evidence for other interventions is very uncertain. Powered and methodologically sound studies are needed to address the identified evidence gaps. Innovative, synergistic strategies -including those that involve patients- should also be evaluated.

Participation of pharmacy technicians in clinical and patient-centered care practices: A state-wide survey in Nigeria

BACKGROUND

Identifying the involvement of pharmacy technicians (PTs) in nonclinical and clinical duties will provide insight for improved pharmaceutical services.

OBJECTIVES

This study assessed the involvement of PTs in nonclinical tasks, patient-centered services, and more specialized patient care services and the difference in practice between hospital and community PTs.

METHODS

A cross-sectional survey was conducted using a 5-point Likert scale, and an analysis of data was performed using IBM SPSS version 21.0 (IBM). Descriptive statistics was done, and P ≤ 0.05 was considered statistically significant. The study was conducted in community and hospital pharmacies in Ogun State, Nigeria, among 100 PTs. Outcome measures were the involvement of PTs in clinical roles and other pharmaceutical care practices.

RESULTS

A total of 73 (73.0%) participated in the study, 45 (61.6%) and 28 (38.4%) practiced in hospital and community pharmacies, respectively. From the 11 listed nonclinical activities, only stocking of medications 61 (83.6%) and processing clients' or patient's charges 48 (65.8%) were often or very often performed. More than half of the participants often performed each of all 7 listed patient-centered activities, particularly empathy and confidentiality 62 (84.9%), providing information and referrals to patients or clients 56 (76.7%), and identifying patients or clients for counseling 51 (69.9%), respectively. No statistically significant difference was observed between the practice areas (P > 0.05). Only 2 of the 11 listed specialized clinical activities were performed by more than half of the respondents: screening prescriptions for completeness and authenticity 43 (58.9%) and alerting the pharmacist of drug therapy problems 46 (63.0%). Differences in the practice of specialized tasks was statistically significant for consultations (P = 0.002) and resolution of clinical conflict (P = 0.040) between the practice areas.

CONCLUSION

Study participants were less frequently involved in nonclinical activities but often involved in clinical activities. They also participated in specialized clinical tasks at lesser frequencies. Differences were observed between the practice areas in performance of nonclinical and specialized activities. Strategies to fill in the observed gaps should be explored for improved practice.

Effect of automated unit dose dispensing with barcode scanning on medication administration errors: an uncontrolled before-and-after study

BACKGROUND

Medication administration errors (MAEs) occur frequently in hospitals and may compromise patient safety. Preventive strategies are needed to reduce the risk of MAEs.

OBJECTIVE

The primary aim of this study was to assess the effect of central automated unit dose dispensing with barcode-assisted medication administration on the prevalence of MAEs. Secondary aims were to assess the effect on the type and potential severity of MAEs. Furthermore, compliance with procedures regarding scanning of patient and medication barcodes and nursing staff satisfaction with the medication administration system were assessed.

METHODS

We performed a prospective uncontrolled before-and-after study in six clinical wards in a Dutch university hospital from 2018 to 2020. MAE data were collected by observation. The primary outcome was the proportion of medication administrations with one or more MAEs. Secondary outcomes were the type and potential severity of MAEs, rates of compliance with patient identification and signing of administered medication by scanning and nursing staff satisfaction with the medication administration system. Multivariable mixed-effects logistic regression analyses were used for the primary outcome to adjust for confounding and for clustering on nurse and patient level.

RESULTS

One or more MAEs occurred in 291 of 1490 administrations (19.5%) pre-intervention and in 258 of 1630 administrations (15.8%) post-intervention (adjusted odds ratio 0.70, 95% confidence interval 0.51-0.96). The rate of omission fell from 4.6% to 2.0% and of wrong dose from 3.8% to 2.1%, whereas rates of other MAE types were similar. The rate of potentially harmful MAEs fell from 3.0% (n = 44) to 0.3% (n = 5). The rates of compliance with scanning of patient and medication barcode post-intervention were 13.6% and 55.9%, respectively.The median overall satisfaction score of the nurses with the medication administration system on a 100-point scale was 70 (interquartile range 63-75, n = 193) pre-intervention and 70 (interquartile range 60-78, n = 145) post-intervention (P = 0.626, Mann-Whitney U test).

CONCLUSION

The implementation of central automated unit dose dispensing with barcode-assisted medication administration was associated with a lower probability of MAEs, including potentially harmful errors, but more compliance with scanning procedures is needed. Nurses were moderately satisfied with the medication administration system, both before and after implementation. In conclusion, despite low compliance with scanning procedures, this study shows that this intervention contributes to the improvement of medication safety in hospitals.

Implementation of an Automated Dispensing Cabinet System and Its Impact on Drug Administration: Longitudinal Study

Background

A technology that has been widely implemented in hospitals in the United States is the automated dispensing cabinet (ADC), which has been shown to reduce nurse drug administration errors and the time nurses spend administering drugs.

Objective

This study aimed to determine the impact of an ADC system on medication administration by nurses as well as safety before and after ADC implementation.

Methods

We conducted a 24-month-long longitudinal study at the National Taiwan University Hospital in Taipei, Taiwan. Clinical observations and questionnaires were used to evaluate the time differences in drug preparation, delivery, and returns in the inpatient ward by nurses before and after using the ADC. Drug errors recorded in the Medical Incident Events system were assessed the year before and after ADC implementation.

Results

The drug preparation time of the wards increased significantly (all P<.005). On average, 2 minutes of preparation time is needed for each patient. Only 1 unit showed an increase in the drug return time, but this was not significant. There were 9 (45%) adverse events during the drug administration phase, and 11 (55%) events occurred during the drug-dispensing phase. Although a decrease in the mean number of events reported was observed during the ADC implementation period, this difference was not significant. As for the questionnaire that were administered to the nurses, the overall mean score was 3.90; the highest score was for the item “I now spend less time waiting for medications that come from the pharmacy than before the ADC was implemented” (score=4.24). The item with the lowest score was “I have to wait in line to get my patient medications” (score=3.32).

Conclusions

The nurses were generally satisfied with ADC use over the 9 months following complete implementation and integration of the system. It was acknowledged that the ADC offers benefits in terms of pharmaceutical stock management; however, this comes at the cost of increased nursing time. In general, the nurses remained supportive of the benefits for their patients, despite consequences to their workflows. Their acceptance of the ADC system in this study demonstrates this.

Robotic Pharmacy Implementation and Outcomes in Saudi Arabia: A 21-Month Usability Study

Background

We describe the introduction, use, and evaluation of an automation and integration pharmacy development program in a private facility in Saudi Arabia. The project was specifically undertaken to increase throughput, reduce medication dispensing error rates, improve patient satisfaction, and free up pharmacists’ time to allow for increased face-to-face consultations with patients.

Objective

We forecasted growth of our outpatient service at 25% per annum over 5- and 10-year horizons and set out to prepare our outpatient pharmacy service to meet this demand. Initial project goals were set as a 50% reduction in the average patient wait time, a 15% increase in patient satisfaction regarding pharmacy wait time and pharmacy services, a 25% increase in pharmacist productivity, and zero dispensing errors. This was expected to be achieved within 10 months of go-live. Realignment of pharmacist activity toward counseling and medication review with patients was a secondary goal, along with the rapid development of a reputation in the served community for patient-centered care.

Methods

Preimplementation data for patient wait time for dispensing of prescribed medications as a specific measure of patient satisfaction was gathered as part of wider ongoing data collection in this field. Pharmacist activity and productivity in terms of patient interaction time were gathered. Reported and discovered dispensing errors per 1000 prescriptions were also aggregated. All preimplementation data was gathered over an 11-month period.

Results

From go-live, data were gathered on the above metrics in 1-month increments. At the 10-month point, there had been a 53% reduction in the average wait time, a 20% increase in patient satisfaction regarding pharmacy wait time, with a 22% increase in overall patient satisfaction regarding pharmacy services, and a 33% increase in pharmacist productivity. A zero dispensing error rate was reported.

Conclusions

The robotic pharmacy solution studied was highly effective, but a robust upstream supply chain is vital to ensure stock levels, particularly when automated filling is planned. The automation solution must also be seamlessly and completely integrated into the facility’s software systems for appointments, medication records, and prescription generation in order to garner its full benefits. Overall patient satisfaction with pharmacy services is strongly influenced by wait time and follow-up studies are required to identify how to use this positive effect and make optimal use of freed-up pharmacist time. The extra time spent by pharmacists with patients and the opportunity for complete overview of the patient’s medication history, which full integration provides, may allow us to address challenging issues such as medication nonadherence. Reduced wait times may also allow for smaller prescription fill volumes, and more frequent outpatient department visits, allowing patients to have increased contact time with pharmacists.

Implementation of distributed automated medication dispensing units in a new hospital: Nursing and pharmacy experience

AIMS AND OBJECTIVES

To explore the structures, processes and outcomes involved in an Automated Medication Dispensing system implementation and its impact on patient safety.

BACKGROUND

Increasing digitalisation of medication prescribing, dispensing, administration and stock management has occurred over the past two decades. While automated medication dispensing units aim to provide safe, high-quality, patient-centred care, the implementation may result in unintended consequences leading to suboptimal outcomes.

DESIGN

This study uses a qualitative approach guided by Donabedian's structure, process and outcome framework.

METHODS

Twenty-six registered nurses and pharmacy assistant staff, from clinical areas equipped with automated medication dispensing cabinets, participated in semi-structured interviews. In-depth, thematic analysis explored the structures and processes. Together with interview data, content analysis of text data generated by internal risk management and critical incident reporting systems was undertaken to evaluate outcomes. Findings were considered in light of the Interactive Sociotechnical Analysis approach to health information technology. The COREQ checklist was used in preparation of this article.

RESULTS

Pharmacy assistants reported better satisfaction with the system at implementation than nurses. Training provided for nurses and their involvement in system implementation was reported as insufficient; however, nurses' use of and satisfaction with the system improved over time. A recursive relationship between the changes imposed by the system and nurses' creative problem solving (workarounds) used to manage these changes, impacted work productivity for nurses and safety for patients.

CONCLUSIONS

The individualised nature of "workarounds" employed offered both risks and opportunities which require further identification, investigation and management.

RELEVANCE TO CLINICAL PRACTICE

Nurses are the majority of the health workforce. Digitalisation of traditionally paper-based activities in health care, impacting nursing work, requires similar strategies to any practice change.

Automation of in-hospital pharmacy dispensing: a systematic review

OBJECTIVES

The current systematic review (SR) was undertaken to identify and summarise the published literature reporting on the clinical and economic value of automated in-hospital pharmacy services with a primary focus on systems supporting the dispensing of medicines.

METHODS

Literature searches were conducted in MEDLINE, Embase and the Cochrane Library on 17 December 2017 to identify English-language publications investigating any automated dispensing systems (ADSs) in the inpatient setting to include central pharmacy and ward-based systems.

RESULTS

4320 publications were screened by title and abstract and 45 of 175 full publications screened were included. Grey literature searching identified an additional three publications. Therefore, 48 publications relating to ADSs were eligible for inclusion. Although a relatively large evidence base was identified as part of the current SR, the eligible studies were inconsistent in terms of their design and the format of reporting of outcomes. The studies demonstrate that both pharmacy and ward-based ADSs offer benefits over traditional manual dispensing methods in terms of clinical and economic outcomes. The primary benefits following implementation of an ADS include reductions in medication errors, medication administration time and costs. Studies examining optimisation/inventory management strategies/refill programmes for these systems suggest that optimal implementation of the ADS is required to ensure that clinical success and economic benefits are maximised.

CONCLUSIONS

The published evidence suggests positive impacts of ADS and should encourage hospitals to invest in automation, with a global strategy to improve the reliability and the efficiency of the medication process. However, one of the key findings of the current SR is the need for further data from adequately powered studies reporting clinically relevant outcomes which would allow for robust, evidence-based recommendations on the return on investment of the technologies. These studies would probably contribute to a larger adoption of these technologies by European hospitals.

Identification and safe storage of look-alike, sound-alike medicines in automated dispensing cabinets

OBJECTIVES

Automated dispensing cabinets (ADCs) are used in hospitals to improve medication safety and decrease costs. However, ADCs do not completely eliminate the risk of mistakes between look-alike, sound-alike (LASA) medicines. The aim of this study was to identify the characteristics of LASA medicines and determine the factors related to their safe storage in ADCs.

METHODS

The medication selection of one hospital pharmacy's ADC located in an intensive care unit was observed. The study consisted of five parts: a determination of criteria to identify LASA medications, an analysis of an ADCs' inventory reports, assessment of the storage of identified LASA medicines, a visual observation of the medicine packages stored in the same storage compartment and qualitative analysis of the medication-use process from prescribing a medicine to removing it from an ADC.

RESULTS

Approximately 70% (n=355/488) of the ADCs selection had a LASA risk with at least one product. Moreover, 20% (n=84/355) of the LASA medicines identified were high-alert medications. Approximately 16% (n=58/355) of the identified LASA medicines were stored unsafely close to at least one other LASA medicine. Less than 4% (n=13/355) of the LASA medicines were unsafely stored high-alert medications.

CONCLUSIONS

ADCs reduce the risks of LASA medication errors when used correctly, but automation can also increase them, for example, when placing multiple LASA medicines in the same storage compartment. Attention should be paid to the identification and safe storage of LASA medicines to promote safe use of ADCs in hospitals.