An automated medication system reduces errors in the medication administration process: results from a Danish hospital study

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.

Contribution of an anticancer drug compounding robot in reducing the risks of manual preparation in a hospital pharmacy unit specialized in oncology

INTRODUCTION

In the last few years, pharmaceutical technology has evolved. In the field of oncology pharmacy, robots for the preparation of anti-cancer drugs have appeared to progressively replace manual preparation. The objective of this study is to evaluate the contribution of the robot in reducing the risk of manual preparation.

METHODS

The study was conducted at the pharmacy of the National Institute of Oncology in Rabat (May-August 2021). The method used to compare the two types of preparation is the method of analysis of failure modes, their effects and their criticality (FMECA). It will calculate the criticality index (CI = severity × frequency × detectability). The risks have been categorized into human, technical, and environmental risks.

RESULTS

The anticancer drugs reconstitution step was the most critical in manual preparation (CI = 126.7) and robotic preparation (CI = 40.7). The robot has made it possible to reduce several CIs of manual preparation including: musculoskeletal disorders of pharmacy operators -93 (89%), error in cancer drug and diluent selection -72 (60%), as well as lack of traceability -145 (97%).

CONCLUSION

The preparation robot has made it possible to reduce many of the risks of manual preparation, and constitutes an important advance in the field of oncology pharmacy.

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.

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.

Prevalence, types and severity of medication errors associated with the use of automated medication use systems in ambulatory and institutionalized care settings: A systematic review protocol

The use of automated systems within the medication use process has significantly reduce the occurrence of medication errors and the associated clinical and financial burden. However, automated systems lull into a false sense of security and increase the risk of medication errors that are often associated with socio-technical interactions, automation bias, workarounds and overrides. The objective of the systematic review is to determine the prevalence, types and severity of medication errors that are associated the use of automated systems in ambulatory and institutionalized care settings. The search strategy will be guided by PRISMA framework. Selected databases and relevant gray literature were searched and screening was done independently by two researchers between 01 April and 29 June 2021. These covered all relevant articles published from the inception of the use of automation in the medication use process (2000) until 2020. De-duplication and screening of all studies were done independently by two researchers with a clear inclusion / exclusion criteria. Data extraction and synthesis are currently on going (started on 06 July 2021) and being conducted independently but the validity and completeness of the processes will be confirmed by the third researcher. The Cochrane Risk of Bias tool and the Hoy et al's quality assessment checklist will be used for the assessment of methodological bias while the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system will be used for the quality of evidence assessment. Detailed qualitative synthesis of key findings will be done with thematic and descriptive analyses. If the number and types of included studies permit, fixed or random effect model meta-analysis will be conducted based on the degree of homogeneity in the sampling frame used in the included studies. Heterogeneity will be assessed with I2 statistics and I2 > 50% will be considered a high statistical heterogeneity. The systematic review may provide new perspective especially from developing settings about the prevalence, types and severity of medication errors associated with the use of automated systems at all the stages of medication use process, and in all categories of patients. This may add to global knowledge in the research area. Systematic review registration: The systematic review was registered and published by PROSPERO (CRD42020212900).

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.

Investigation of interventions to reduce nurses' medication errors in adult intensive care units: A systematic review

BACKGROUND

Medication errors in adult intensive care units (ICUs) are both frequent and harmful. For nurses, these errors may be multifactorial and multidisciplinary, extending from prescription stage to monitoring of patient response to medication. Therefore, diverse interventions have been developed to optimise the medication process to prevent such errors.

OBJECTIVES

The objective of this systematic review was to identify research investigating interventions that may be effective in reducing the rate of nurses' medication errors in adult ICUs.

METHODS

A systematic search was undertaken of three databases: Cumulative Index of Nursing and Allied Health Literature, Medical Literature Analysis and Retrieval System Online, and EMCARE using a combination of key terms related to "medication errors", "nurses", "interventions", and "intensive care units". The search was limited to studies published in English between 2009 and 2019. Independent screening, quality appraisal, and data extraction were undertaken by two reviewers.

RESULTS

A total of 464 records were identified from database searches. Eleven studies met inclusion criteria: ten were quasi-experimental designs and one was a randomised controlled trial. Studies examined six types of interventions: prefilled syringes, barcode-assisted medication administration, an automated dispensing system, nursing education programs, a protocolised program logic form, and a preventive interventions program with protocols and pharmacist-supported supervision and monitoring. Findings revealed that a prefilled syringe, nurses' education programs, and the protocolised program logic form were most effective in reducing medication errors. For the barcode-assisted medication administration, automated dispensing systems, and a preventive interventions program with protocols and pharmacist-supported supervision and monitoring, results showed wide variability in effectiveness.

CONCLUSION

This review found that the evidence for effective interventions to reduce nurses' medication errors in adult ICUs is limited, due largely to inconsistencies in research design and methods. Therefore, further studies such as randomised controlled trials focusing on a single intervention are required to provide robust evidence of the effectiveness of interventions.

Cost-consequence analysis of self-administration of medication during hospitalization: a pragmatic randomized controlled trial in a Danish hospital setting

OBJECTIVES

The objective of this study was to evaluate the costs and consequences of introducing "self-administration of medication" (SAM) during hospitalization as compared with nurse-led dispensing and administration of medication.

METHODS

This pragmatic randomized controlled trial was performed in a Danish Cardiology Unit. Patients ⩾18 years old capable of self-administering medication were eligible. In the intervention group, patients self-administered their medication. In the control group, medication was dispensed and administered by nurses. The implementation of SAM was used to evaluate the cost-consequences. The micro-costing analysis used the hospital perspective and a short-term incremental costing approach. The costs for medication, materials, and nursing time were included. Consequences included the dispensing error proportion, patients' perceptions regarding medication, satisfaction, and deviations in the medication list at follow-up. In addition, the number of readmissions and general practitioner (GP) contacts within 30 days after discharge was included.

RESULTS

The total cost (TC) per patient in the intervention group was 49.9€ (95% CI: 46.6-53.2) compared with 52.6€ (95% CI: 46.6-58.6) in the control group. The difference between the groups was not statistically significant (p = 0.09). Sensitivity analysis consistently showed TCs favoring the intervention. The dispensing error proportion was 9.7% (95% CI: 7.9-11.6) in the intervention group compared with 12.8% (95% CI: 10.9-15.6) in the control group. The difference was statistically significant (p = 0.02). The analysis also found changes in the perceptions regarding medication (indicating higher medication adherence), increased satisfaction, and fewer patients with deviations in the medication list at follow-up. No statistically significant differences between the groups in relation to readmissions and GP contacts within 30 days were observed.

CONCLUSIONS

SAM seems to cost less although the cost difference was small and not statistically significant. As SAM had positive effects on patient outcomes, the results indicate that SAM may be cost-effective.

PLAIN LANGUAGE SUMMARY

Self-administration of medication: a research study of the costs and consequences Objectives To evaluate the costs and consequences of introducing "self-administration of medication" (SAM) during hospitalization compared to medication dispensed by nurses. Methods This research study included patients ≥18 years capable of self-administering medication and was performed in a Danish cardiology unit. Patients self-administered their own medication during hospitalization in the intervention group, whereas nurses dispensed and administered the medication in the control group. Patients were allocated between groups by randomization. The costs of SAM were analyzed from a hospital perspective and included costs for medication, materials, and nursing time. The consequences included the proportion of dispensing errors, patients' perceptions regarding medication, patient satisfaction, deviations in the medication list at follow-up, the number of readmissions and general practitioner (GP) contacts within 30 days after discharge. Results The total cost per patient was 49.9€ in the intervention group compared to 52.6€ in the control group (p = 0.09). The cost difference between groups was not significant. The proportion of dispensing errors was significantly lower in the intervention group compared to the control group. In addition the research study found changes in the perceptions regarding medication, increased satisfaction, and fewer patients with deviations in the medication list at follow-up. For readmissions and GP contacts within 30 days no significant differences between groups were found. Conclusion SAM cost less or equal to medication dispensing and administration by nurse. SAM had positive impacts on patient outcomes. Therefore, SAM may be cost-effective.

Self-administration of medication during hospitalization-a randomized pilot study

Background

Self-administration of medication (SAM) during hospitalization is a complex intervention where patients are involved in their course of treatment. The study aim was to pilot test the SAM intervention. The objectives were to assess the feasibility of conducting a randomized controlled trial on the safety and cost-consequences of SAM during hospitalization.

Methods

The study was performed in a Danish cardiology unit.

Patients ≥ 18 years capable of self-administering medication during hospitalization were eligible. Patients were excluded if they did not self-administer medication at home, were incapable of self-administering medication, were not prescribed medication suitable for self-administration, did not bring their medication, or were unable to speak Danish.

Feasibility was assessed as part of the pilot study. A future randomized controlled trial was considered feasible if it was possible to recruit 60 patients within 3 months, if outcome measurement method was capable of detecting dispensing errors in both groups, and if patients in the intervention group were more satisfied with the medication management during hospitalization compared to the control group.

Forty patients were recruited to gain experience about the intervention (self-administration). Additionally, 20 patients were randomized to the intervention or control group (nurse-led dispensing) to gain experience about the randomization procedure.

Dispensing error proportions were based on data collected through disguised observation of patients and nurses during dispensing. The error proportion in the control group was used for the sample size calculation. Patient acceptability was assessed through telephone calls.

Results

Of the 60 patients recruited, one withdrew and 11 were discharged before observation resulting in analysis of 39 patients in the intervention group and nine in the control group. A dispensing error proportion of 3.4% was found in the intervention group and 16.1% in the control group. A total of 91.7% of patients in the intervention group and 66.7% in the control group were highly satisfied with the medication management during hospitalization. The overall protocol worked as planned. Minor changes in exclusion criteria, intervention, and outcome measures were considered.

Conclusions

It may be feasible to perform a pragmatic randomized controlled trial of the safety and cost-consequences of self-administration of medication during hospitalization.

Trial registration

ClinicalTrials.gov, NCT03541421, retrospectively registered on 30 May 2018.

Self-administration of medication: a pragmatic randomized controlled trial of the impact on dispensing errors, perceptions, and satisfaction

BACKGROUND

Our aim was to investigate whether self-administration of medication (SAM) during hospitalization affects the number of dispensing errors, perceptions regarding medication, and participant satisfaction when compared with nurse-led medication dispensing.

METHODS

A pragmatic randomized controlled trial was performed in a Danish cardiology unit. Patients aged ⩾ 18 years capable of SAM were eligible for inclusion. Patients were excluded if they did not self-administer medication at home, were not prescribed medication suitable for self-administration, or did not speak Danish.Intervention group participants self-administered their medication. In the control group, medication was dispensed and administered by nurses.The primary outcome was the proportion of dispensing errors collected through modified disguised observation of participants and nurses. Dispensing errors were divided into clinical and procedural errors.Secondary outcomes were explored through telephone calls to determine participant perceptions regarding medication and satisfaction, and finally, deviations in their medication list two weeks after discharge.

RESULTS

Significantly fewer dispensing errors were observed in the intervention group, with 100 errors/1033 opportunities for error (9.7%), compared with 132 errors/1028 opportunities for error (12.8%) in the control group. The number of clinical errors was significantly reduced, whereas no difference in procedural errors was observed. At follow up, those who were self-administering medication had fewer concerns regarding their medication, found medication to be less harmful, were more satisfied, preferred this opportunity in the future, and had fewer deviations in their medication list after discharge compared with the control group.

CONCLUSION

In conclusion, the reduced number of dispensing errors in the intervention group, indicate that SAM is safe. In addition, SAM had a positive impact on (a) perceptions regarding medication, thus suggesting increased medication adherence, (b) deviations in medication list after discharge, and (c) participant satisfaction related to medication management at the hospital.

Comparative Cost-Effectiveness Analysis of Three Different Automated Medication Systems Implemented in a Danish Hospital Setting

INTRODUCTION

Automated medication systems have been found to reduce errors in the medication process, but little is known about the cost-effectiveness of such systems. The objective of this study was to perform a model-based indirect cost-effectiveness comparison of three different, real-world automated medication systems compared with current standard practice.

METHODS

The considered automated medication systems were a patient-specific automated medication system (psAMS), a non-patient-specific automated medication system (npsAMS), and a complex automated medication system (cAMS). The economic evaluation used original effect and cost data from prospective, controlled, before-and-after studies of medication systems implemented at a Danish hematological ward and an acute medical unit. Effectiveness was described as the proportion of clinical and procedural error opportunities that were associated with one or more errors. An error was defined as a deviation from the electronic prescription, from standard hospital policy, or from written procedures. The cost assessment was based on 6-month standardization of observed cost data. The model-based comparative cost-effectiveness analyses were conducted with system-specific assumptions of the effect size and costs in scenarios with consumptions of 15,000, 30,000, and 45,000 doses per 6-month period.

RESULTS

With 30,000 doses the cost-effectiveness model showed that the cost-effectiveness ratio expressed as the cost per avoided clinical error was €24 for the psAMS, €26 for the npsAMS, and €386 for the cAMS. Comparison of the cost-effectiveness of the three systems in relation to different valuations of an avoided error showed that the psAMS was the most cost-effective system regardless of error type or valuation.

CONCLUSION

The model-based indirect comparison against the conventional practice showed that psAMS and npsAMS were more cost-effective than the cAMS alternative, and that psAMS was more cost-effective than npsAMS.

Cost-Effectiveness Analysis of an Automated Medication System Implemented in a Danish Hospital Setting

OBJECTIVES

To evaluate the cost-effectiveness of an automated medication system (AMS) implemented in a Danish hospital setting.

METHODS

An economic evaluation was performed alongside a controlled before-and-after effectiveness study with one control ward and one intervention ward. The primary outcome measure was the number of errors in the medication administration process observed prospectively before and after implementation. To determine the difference in proportion of errors after implementation of the AMS, logistic regression was applied with the presence of error(s) as the dependent variable. Time, group, and interaction between time and group were the independent variables. The cost analysis used the hospital perspective with a short-term incremental costing approach. The total 6-month costs with and without the AMS were calculated as well as the incremental costs. The number of avoided administration errors was related to the incremental costs to obtain the cost-effectiveness ratio expressed as the cost per avoided administration error.

RESULTS

The AMS resulted in a statistically significant reduction in the proportion of errors in the intervention ward compared with the control ward. The cost analysis showed that the AMS increased the ward's 6-month cost by €16,843. The cost-effectiveness ratio was estimated at €2.01 per avoided administration error, €2.91 per avoided procedural error, and €19.38 per avoided clinical error.

CONCLUSIONS

The AMS was effective in reducing errors in the medication administration process at a higher overall cost. The cost-effectiveness analysis showed that the AMS was associated with affordable cost-effectiveness rates.