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

Comprehensive Description of an Automated Drug Dispensing System Database

Medifilm is a company that blisters drug therapies recorded by pharmacists in the Medifilm software. The Medifilm dataset collates this information and provides details on drug substances, dosages, pharmacotherapy duration, the sequence of therapies, as well as demographic data on the patients. This article aims to provide an overview of the database, to describe the contents, and to demonstrate possibilities for researchers. The database and the recorded information were described. Furthermore, the data coverage was characterized in terms of the number of available pharmacies, patients, and their drug regimens. The database has been recording data since 2013 and has registered 470,801 blistered therapies for 45,594 patients ordered by 441 pharmacies so far. The longitudinal nature of the database allows researchers to study drug utilization, including medication changes, initiations, and discontinuations over time.

Introducing Unit Dose Dispensing in a University Hospital - Effects on Medication Safety and Dispensing Time

Purpose

Unit dose (UD) medications reduce manual steps in the medication management and use process and enable electronic documentation by barcode scanning. This study aimed to explore the effects of introduced unit doses on medication safety and time spent on medication dispensing.

Patients and Methods

Direct before-and-after observations were conducted in an inpatient internal medicine ward at Helsinki University Hospital. The prevalence of medication and procedural errors and time nurses spent dispensing medications at patient-specific doses were observed 10 weekdays before and after introducing unit doses of selected medications. To complement the observations, a separate survey was used to investigate nurses' perceptions of medication dispensing. Quantitative analysis was performed.

Results

During the observations, medications were dispensed for 208 patients (n=1359 medications) before and 221 patients (n = 1171) after introducing unit doses. After UD implementation, 45.3% (n=530/1171) of the medications were dispensed as UDs. Medication and procedural errors were reduced (from 3.2% to 1.7% and 37.4% to 13.9%, respectively; p<0.05). Barcode scanning-related problems decreased from 21.4% to 1.8% (p<0.05) after implementation. The unit doses did not change the median time used to dispense medications to the patient, although the time used to dispense a single medication increased. In the survey, nurses reported improvements in barcode scanning but also indicated problems with handling unit doses and were worried about increased plastic waste.

Conclusion

Piloted unit doses decreased medication and procedural errors. Barcode scanning improved, which supported electronic closed-loop medication management in the study hospital. Unit doses in a fully automated process should be further studied for their effects on the dispensing time. In addition, controlling the amount of plastic waste in the unit dose dispensing should be considered.

Hospital pharmacy implementation of a unit dose dispensing system: A qualitative interview study to determine experiences, views and attitudes of nursing staff

Background

Evolving automated technologies in the dispensing process promise effective solution to reduce medication error rates. The Unit-Dose-Dispensing-System (UDDS) is an essential element of the "Closed Loop Medication Management System" that enables the tracking of single medication items across the entire medication workflow thereby improving transparency, patient safety and healthcare efficiency".

Objective

The aim of this study was to determine hospital nurses' attitudes towards the unit dose dispensing system implemented by the hospital pharmacy department, examine their perceptions of opportunities and barriers in everyday practice and explore their experiences with its implementation.

Methods

A qualitative interview study with 23 nurses from the Barmherzige Brüder Hospital Linz, Austria was conducted. The validated and piloted semi-structured interview guide was based on best practice guidelines for qualitative interview studies and the Consolidated Framework for Implementation Research (CFIR). Interviews were transcribed verbatim and mapped against the Framework of Implementation of Services in Pharmacy (FISpH) by two researchers independently.

Results

Twenty-three nurses were interviewed. Despite the overwhelmingly positive experiences with ease of use, resultant time saving, improved collaboration, patient safety, patient autonomy and a better facilitated discharge process considered as some of the positive attributes, several barriers were identified. Nurses commented on a lack in standardised workflow, erosion of knowledge and sustainability.

Conclusion

Nurses highlighted key factors for implementation success and advantages of the unit dose dispensing system, suggesting a need to adapt to local conditions and full electronic integration into patient care. Suggestions to further improve the reliability and efficiency were made.

What does this paper contribute to the wider global clinical community?

•There is no research qualitatively exploring the implementation facilitators and barriers of a unit dose dispensing system at hospital ward level from the nurses' perspectives.•Insights are provided into the significance of good collaboration between all ward staff to ensure the necessary workflow adaptations, the necessity of a seamlessly integrated IT system and the adaptability of the system depending on the patient population.•The findings highlight key factors for a successful implementation of a unit dose dispensing system at ward level while underlining its advantages for workload, staff shortages and patient safety.

Unit dose drug dispensing systems in hospitals: a systematic review of medication error reduction and cost-effectiveness

BACKGROUND

Medical errors pose significant risks to patient safety and public health. Automated unit dose drug dispensing systems (UDDSs) have emerged as valuable tools to reduce medication errors while optimising economic and logistical resources.

OBJECTIVES

This systematic review aims to evaluate studies specifically focused on the impact of automated UDDSs in reducing medication errors and streamlining processes.

METHODS

A literature search was performed on PubMed, Scopus, and Web of Science, focusing on peer-reviewed articles published between 2019 and 2024. The search, concluded on 24 September 2024, included studies conducted in inpatient hospital settings that assessed automated UDDS effects on medication errors, therapy management and inventory control. Outcomes examined included effects on patient safety, cost-effectiveness and inventory management. Results were synthesised qualitatively.

RESULTS

From 3346 references, four studies met the inclusion criteria: a cost-effectiveness analysis, an uncontrolled before-and-after study, and two observational studies. UDDS improved medication processes, reducing drug-related problems, medication handling and dispensing time by 50% per patient per day. Integrated with barcode scanning, UDDS lowered medication administration errors (MAEs) from 19.5% to 15.8% and harmful MAEs from 3.0% to 0.3%. Overall, medication errors dropped by 45-70%, enhancing safety and reducing manual handling risks. UDDS demonstrated cost-effectiveness by significantly reducing MAEs. The study estimated a reduction in MAEs, with a cost-effectiveness ratio of €17.69 per avoided MAE. For potentially harmful MAEs, the cost-effectiveness ratio was estimated at €30.23 per avoided error. These findings suggest substantial long-term savings potential, though the exact magnitude may vary depending on hospital size and implementation specifics CONCLUSIONS: Automated UDDSs improve patient safety by significantly reducing medication errors and delivering cost savings through better inventory management. Challenges such as high initial costs and workflow adjustments can be mitigated through gradual implementation and staff training. Further integration with other healthcare technologies, such as barcoding, real-time tracking, artificial intelligence (AI)-driven error prevention tools and fully automated restocking systems could enhance UDDS benefits and further support hospital processes.

Exploring the Benefits, Barriers and Improvement Opportunities in Implementing Automated Dispensing Cabinets: A Qualitative Study

Technology has increasingly influenced the provision of healthcare services by enhancing patient safety, optimising workflows, and improving efficiency. Large healthcare facilities have adopted automated dispensing cabinets (ADCs) as an advanced technological solution. A key gap exists in understanding the ADC implementation experience in different contexts. Therefore, this study seeks to fill this literature gap by exploring key stakeholders' perspectives on the benefits, barriers, and improvement opportunities related to ADCs, offering valuable insights to support their effective integration across various healthcare settings. This qualitative study was conducted in Saudi Arabia. The implementation of ADCs generally has positive outcomes for all staff. The system has brought about enhanced medication tracking, greater time efficiency, along with reduced workload and medication errors. However, there are barriers to their implementation, including changes in workflow and workload distribution, cabinet design, technical medication management challenges, and the need for staff training. To maximise the effectiveness of ADCs, healthcare organisations should focus on improving operational workflows, providing ongoing staff training, and maintaining robust system monitoring. Additionally, manufacturers should focus on advancing technology to further enhance the efficiency and functionality of ADCs.

Nursing home staff's experiences with the implementation of an automated blister packaging system for solid, orally ingestible drugs: a qualitative study in Northern Italy

OBJECTIVES

To explore nursing home (NH) staff's experiences implementing an automated blister packaging system for solid, orally ingestible drugs.

DESIGN

This was a descriptive qualitative study. Semistructured interviews were used for data collection, and qualitative content analysis was applied for data analysis.

SETTING

Four NHs in a region in Northern Italy participated in the piloting of an automated blister packaging system.

PARTICIPANTS

The purposive sample comprised 40 NH staff (seven registered nurses, 11 certified nursing assistants, three nursing aids, seven nursing managers, four directors and eight physicians) RESULTS: Six main categories emerged from the data analysis: (1) impacting safety, including improved medication process, perceived insecurities and remaining error sources; (2) creating free spaces, including time, individualised care and drug storage and logistics; (3) serving residents, (4) meeting expectations, including met and unmet expectations; (5) generating efforts and (6) producing waste. They all merged into one theme 'It pays off!'

CONCLUSIONS

As experienced by NH staff, the automated blister packaging system improved medication safety and reduced the workload of registered nurses, but it is not an 'all-around carefree package'. Continuous quality improvement and risk management strategies are recommended to accompany and sustain implementation, as well as further clarification of roles and duties among the different care workers involved in medication management. Further research is needed to gain a better understanding of the impact of an automated blister packaging system on registered nurses' competencies in NHs regarding medication management and residents' therapies and safety.

Evaluating the impact of an automated drug retrieval cabinet and robotic dispensing system in a large hospital central pharmacy

PURPOSE

To determine the impact of implementing 2 technologies in succession, the Carousel system and XR2 robot, in a hospital central pharmacy. The study examined the technologies' impact on workload shifted from fully human-involved, labor-intensive filling from shelves to Carousel and/or XR2, prevention of filling errors, and efficiency.

METHODS

Implementation occurred in 3 phases from August 2021 through October 2022. In phase I, medications were manually filled from the shelves for immediate doses and automated dispensing cabinet stock. RobotRx was used for unit-dose (UD) carts. In phase II, the Carousel system was introduced, while RobotRx was used for UD carts. In phase III, the XR2 robot was added and RobotRx was decommissioned. Epic data and time studies were utilized and analyzed with ANOVA.

RESULTS

Over the 3 phases of implementation, workload shifted away from filling from the shelves, with 2,479, 1,044, and 864 orders filled from the shelves for phase I through phase III, respectively. The Carousel workload was 1,234 orders (phase II) and 348 orders (phase III). Nearly 71% of the workload was shifted to the XR2. The overall filling error (prevented) rate did not significantly change from phase I to phase II, remaining at 0.5%. In comparison, the error rate significantly decreased to 0.41% in phase III. Use of the Carousel system resulted in a significant reduction in filling errors compared to manual filling from the shelves. Use of the XR2 robot resulted in a filling error rate of 0%. The savings in time when using both Carousel and XR2 led to a decrease in full-time equivalents of 0.77 for pharmacists and 1.76 for pharmacy technicians.

CONCLUSION

Carousel and/or XR2 significantly shifted workload from manual filling to automated technologies, decreased filling errors, and improved efficiency, reducing pharmacist and technician workload. Time saved could allow staff to spend more time on patient-centric tasks.

Understanding medication recycling practices in Canadian hospitals

BACKGROUND

Medication recycling within hospitals has proven financial and possible environmental benefits according to local evaluations done in British Columbia. Despite this, the extent of medication recycling in Canadian hospitals remains unclear in the literature.

OBJECTIVE(S)

To determine if Canadian hospitals recycle medications, provide an estimate of how much medication is recycled by dosage form, and identify medication recycling barriers through the distribution of a cross-sectional survey.

METHODS

A nine-question survey was distributed to 171 hospital pharmacy departments across Canada that consented to complete the survey. The survey identified whether sites recycled unused medications, an estimate of how much is recycled based on dosage form, and barriers to recycling.

KEY FINDINGS

Of 62 respondents, the majority indicated they do have medication recycling procedures; however, the frequency of recycling is suboptimal (30-50% of medications are not recycled), and not all medication types are always recycled. Individually packaged oral tablets were most often recycled, and oral liquid medications were least often recycled. Many multi-dose medications were not tamper-proofed. Most respondents selected "sanitization/infection control" and "resource constraint" as reasons for not recycling all medications.

CONCLUSIONS

Among respondents, the proportion and type of unused medicines that are recycled varied. For sites that did not respond, this might suggest that medication recycling is not a priority. This could represent a missed opportunity to standardize practices and increase medication recycling in hospitals, both of which could represent a meaningful step towards responsible use of medications and reduction of negative impacts on human health and the environment.

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.

Leveraging code-free deep learning for pill recognition in clinical settings: A multicenter, real-world study of performance across multiple platforms

BACKGROUND

Preventable patient harm, particularly medication errors, represent significant challenges in healthcare settings. Dispensing the wrong medication is often associated with mix-up of lookalike and soundalike drugs in high workload environments. Replacing manual dispensing with automated unit dose and medication dispensing systems to reduce medication errors is not always feasible in clinical facilities experiencing high patient turn-around or frequent dose changes. Artificial intelligence (AI) based pill recognition tools and smartphone applications could potentially aid healthcare workers in identifying pills in situations where more advanced dispensing systems are not implemented.

OBJECTIVE

Most of the published research on pill recognition focuses on theoretical aspects of model development using traditional coding and deep learning methods. The use of code-free deep learning (CFDL) as a practical alternative for accessible model development, and implementation of such models in tools intended to aid decision making in clinical settings, remains largely unexplored. In this study, we sought to address this gap in existing literature by investigating whether CFDL is a viable approach for developing pill recognition models using a custom dataset, followed by a thorough evaluation of the model across various deployment scenarios, and in multicenter clinical settings. Furthermore, we aimed to highlight challenges and propose solutions to achieve optimal performance and real-world applicability of pill recognition models, including when deployed on smartphone applications.

METHODS

A pill recognition model was developed utilizing Microsoft Azure Custom Vision platform and a large custom training dataset of 26,880 images captured from the top 30 most dispensed solid oral dosage forms (SODFs) at the three participating hospitals. A comprehensive internal and external testing strategy was devised, model's performance was investigated through the online API, and offline using exported TensorFlow Lite model running on a Windows PC and on Android, using a tailor-made testing smartphone application. Additionally, model's calibration, degree of reliance on color features and device dependency was thoroughly evaluated. Real-world performance was assessed using images captured by hospital pharmacists at three participating clinical centers.

RESULTS

The pill recognition model showed high performance in Microsoft Azure Custom Vision platform with 98.7 % precision, 95.1 % recall, and 98.2 % mean average precision (mAP), with thresholds set to 50 %. During internal testing utilizing the online API, the model reached 93.7 % precision, 88.96 % recall, 90.81 % F1-score and 87.35 % mAP. Testing the offline TensorFlow Lite model on Windows PC showed a slight performance reduction, with 91.16 % precision, 83.82 % recall, 86.18 % F1-score and 82.55 % mAP. Performance of the model running offline on the Android application was further reduced to 86.50 % precision, 75.00 % recall, 77.83 % F1-score and 69.24 % mAP. During external clinical testing through the online API an overall precision of 83.10 %, recall of 71.39 %, and F1-score of 75.76 % was achieved.

CONCLUSION

Our study demonstrates that using a CFDL approach is a feasible and cost-effective method for developing AI-based pill recognition systems. Despite the limitations encountered, our model performed well, particularly when accessed through the online API. The use of CFDL facilitates interdisciplinary collaboration, resulting in human-centered AI models with enhanced real-world applicability. We suggest that rather than striving to build a universally applicable pill recognition system, models should be tailored to the medications in a regional formulary or needs of a specific clinic, which can in turn lead to improved performance in real-world deployment in these locations. Parallel to focusing on model development, it is crucial to employ a human centered approach by training the end users on how to properly interact with the AI based system to maximize benefits. Future research is needed on refining pill recognition models for broader adaptability. This includes investigating image pre-processing and optimization techniques to enhance offline performance and operation on handheld devices. Moreover, future studies should explore methods to overcome limitations of CFDL development to enhance the robustness of models and reduce overfitting. Collaborative efforts between researchers in this domain and sharing of best practices are vital to improve pill recognition systems, ultimately enhancing patient safety and healthcare outcomes.

Evaluation of the automated dispensing cabinets users' level of satisfaction and the influencing factors in Al-Ahsa hospitals

Automated dispensing cabinets (ADCs) are decentralized, computer-controlled systems used to store, distribute, and track medications at the point of care in the wards.

Objective

The objective of the current study is to evaluate how healthcare practitioners are satisfied with ADCs and scrutinize some influencing factors that could affect this satisfaction.

Material

A cross-sectional survey study was designed and distributed online to healthcare providers in Al-hasa hospitals.

Results

A total of 166 participants. Regarding the frequency and pattern of ADC use, around 79.5% used ADC and 85.4% were informed about using ADC on a daily basis. As for the level of satisfaction with ADC, an exact 81.9% gave a high rate for overall satisfaction, 81.3% were highly satisfied with the system's accuracy, and 74.7% were highly satisfied with the time it takes to complete the task. Regarding usability of the system, 69.8% thought it was easy whereas 36.8% agreed that the time required for reloading medication is longer than before ADC. Furthermore, 79.5% agreed that ADC allowed them to accomplish their job safely, and 67.4% agreed that it improved their productivity. Regarding challenges, 74.7% agreed that all drawer types assure safe access and removal of medications, and 18.7% agreed that there is a significant potential for loss of data.

Conclusion

This study investigated healthcare staff's perceptions and satisfaction with ADCs in Al-hasa hospitals. The healthcare participants were mostly highly satisfied with the use of the ADCs which translated into better patient care and improved patient safety as well as higher productivity.

Multidose Drug Dispensing in Community Healthcare Settings for Patients With Multimorbidity and Polypharmacy

Multidose drug dispensing (MDD) is the dispensing of different drugs in dose bags containing one, some, or all units of medicine that a patient needs to take at specific times. The aim of this narrative review is to provide an overview of the literature describing the use of MDD systems in community healthcare settings in patients with multimorbidity and polypharmacy. A literature search identified 14 studies examining adherence, medication knowledge, quality of drug prescription (including inappropriate drug use, drug-drug interactions), medication incidents, and drug changes after MDD initiation, as well as healthcare professional (HCP) and patient perspectives. There are limited data on MDD in community healthcare settings, particularly on outcomes such as adherence. Studies are mostly from Northern Europe. Patients selected for MDD are more likely to be older, female, cognitively impaired, and have a higher number of disease diagnoses and drugs than those who do not receive drugs through MDD. MDD is generally initiated for patients who have decreased capacity for medication management. Several advantages of MDD have been reported by patients and HCPs, and studies indicate that MDD can be improved by medication review, defining clear roles and responsibilities of HCPs in the medication management chain, and comprehensive follow-up of patients. Future development, implementation, and assessment of MDD systems in community healthcare should be designed in collaboration with HCPs and patients, to identify ways to optimize the systems and improve patient outcomes.

Exploring nurses' multitasking in clinical settings using a multimethod study

Nurses often multitask in the process of managing patient care and communicating with healthcare providers simultaneously within a limited time, which can negatively affect patient care and safety. In this multimethod research, we conducted a time and motion study to record nursing activities using eye trackers for 23 participants (9 nurses and 14 patients). The frequency and duration of single and multitasking activities were analyzed. Additionally, we conducted focus group interviews (FGIs) with 12 nurses (2-5 nurses per group) to further investigate their multitasking experience. The total duration of the eye tracker recordings was 3,399 min. Daily nursing activities comprised 23.7%, 21.1%, and 12.5% of scheduled medication, documentation, and monitoring and measurement, respectively. Among these activities, nurses mostly carry out scheduled medication, monitoring, and measurement together. Three themes emerged in the FGIs: "Being involved in every little task regarding patient care," "Getting swamped by the complexity of symptoms and problems of the patients at a given time," and "Getting interrupted at work too often." Nurses performed multiple activities while cooperating with other healthcare providers and providing care to patients. It is important to create an environment where nurses can focus on essential nursing activities to improve patient safety.

Cost-effectiveness of central automated unit dose dispensing with barcode-assisted medication administration in a hospital setting

BACKGROUND

Central automated unit dose dispensing (cADD) with barcode-assisted medication administration (BCMA) has been shown to reduce medication administration errors (MAEs). Little is known about the cost-effectiveness of this intervention.

OBJECTIVE

To estimate the cost-effectiveness of cADD with BCMA compared to usual care.

METHODS

An economic evaluation was conducted alongside a prospective before-and-after effectiveness study in a Dutch university hospital. The primary effect measure was the difference between the rate of MAEs before and after implementation of cADD with BCMA, obtained by disguised observation in six clinical wards and subsequent extrapolation to the entire hospital. The cost-analysis was conducted from a hospital perspective with a 12-month incremental costing approach. The total costs covered the pharmaceutical service, nurse medication handling, wastage, and materials related to cADD. The primary outcome was the cost-effectiveness ratio expressed as costs per avoided MAE, obtained by dividing the annual incremental costs by the number of avoided MAEs. The secondary outcome was the cost-effectiveness ratio expressed as costs per avoided potentially harmful MAE (i.e. MAEs with the potential to cause harm).

RESULTS

The intervention was associated with an absolute MAE reduction of 4.5% and a reduction of 2.7% for potentially harmful MAEs. Based on 2,260,870 administered medications in the entire hospital annually, a total of 102,210 MAEs and 59,830 potentially harmful MAEs were estimated to be avoided. The intervention was associated with an increased incremental cost of €1,808,600 annually. The cost-effectiveness ratio was €17.69 per avoided MAE and €30.23 per avoided potentially harmful MAE.

CONCLUSIONS

The implementation of cADD with BCMA was associated with a reduced rate of medication errors, including harmful ones, at higher overall costs. The costs per avoided error are relatively low, and therefore, this intervention could be an important strategy to improve patient safety in hospitals.