The Impact of Automation and Digitalization in Hospital Medication Management: Economic Analysis in the European Countries

Abstract

Background/Objectives: European healthcare systems are increasingly adopting automation technologies to improve efficiency. This study evaluates the economic viability of hospital automation and medication management digitalization. Methods: An economic evaluation was based on a standardized hospital model comprising 561 beds, representative of an average acute care hospital across EU27 + UK. For each technology, several cost items were estimated using country-specific parameters such as labor costs, medication error rates, healthcare expenditure, and money discount rate. The financial metrics (Return On Investment—ROI, Net Present Value—NPV, Payback Time—PBT) were first calculated at the hospital level. These results were then extrapolated to the national level by scaling the per-hospital estimates according to the total number of hospital beds reported in each country. Finally, national results were aggregated to derive the overall European impact. Results: The analysis estimated a total European investment of EUR 3.55 billion, with an average PBT of 4.46 years and annual savings of 1,96 billion. ROI averaged 167%, and the total NPV was 8.21 billion. A major saving driver was the reduction in Medication Administration Errors that has an impact of 37.2% on the total savings. Payback times ranged from 3 years in high-GDP countries, to 7 years in lower-GDP nations. Conclusions: These findings demonstrate how providing structured data on hospital automation benefits could support decision-making processes, highlighting the organizational and economic feasibility of the investment across different European national contexts.

1. Introduction

In recent years, healthcare systems across Europe have increasingly focused their attention on automation and digitalization in medication management to enhance the efficiency and safety of hospital organizations processes. Despite this relevance recognition, specifically at hospital level, the current adoption of such technologies across the continent remains low, primarily due to high initial costs, the complexity of integration with existing hospital information and management systems, and a lack of clear, demonstrable Return On Investment (ROI) and feasibility evidence that could justify such fundings. These challenges have prevented widespread adoption, particularly in smaller healthcare facilities or in regions with limited budgets [1].

As of 17 January 2025, Regulation (EU) 2021/2282 on Health Technology Assessment (HTA) is fully applicable in all member states. This study fits into this renewed regulatory framework, contributing to the economic evidence needed for the technologies under assessment to be sustainable throughout their life cycle, including adoption in local settings.

This study aims to evaluate the economic viability and organizational impact of implementing medication management automation technologies in acute care hospitals across the 27 EU countries and the United Kingdom over a 10-year period (2024–2034). By modeling financial outcomes on a standardized hospital of 561 beds and scaling results according to national hospital bed capacity, the analysis provides aggregated estimates of investment costs, Payback Time, ROI, NPV, and annual savings at both national and European levels. The goal is to offer hospital administrators and policymakers a data-driven framework to assess the feasibility of automation investments based on hospital size and national healthcare context [2]. The studied technologies, optimizing the resources allocation, minimizing medication errors, and improving patients’ outcomes, could ensure the delivery of sustainable, high-quality care. The suggested shift toward digitalization and automation acknowledges the essential role of technology in enhancing healthcare standards and meeting future demands, despite current adoption barriers [3,4].

2. Materials and Methods

Following Bonnabry and François’s (2020) methodology [2], this study’s economic analysis is structured into three main stages: initial investment, annual cost–benefit balance, and calculation of ROI, NPV, and PBT.

ROI is a percentage measure of the profitability of an investment, indicating the efficiency of the capital employed.

$$\mathrm{R}\mathrm{O}\mathrm{I}={\displaystyle \frac{(\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}\mathrm{C}\mathrm{o}\mathrm{s}\mathrm{t}\mathrm{s}-\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}\mathrm{S}\mathrm{a}\mathrm{v}\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{s})}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}\mathrm{c}\mathrm{o}\mathrm{s}\mathrm{t}\mathrm{s}}}$$

NPV evaluates the profitability of an investment by calculating the difference between the present values of cash inflows and outflows over the project’s duration (average European inflation rate 2.15%), with a positive NPV indicating that the project is expected to generate profit.

$$\sum _{\mathrm{t}=2024}^{2034}{\displaystyle \frac{\mathrm{N}\mathrm{e}\mathrm{t}\mathrm{C}\mathrm{a}\mathrm{s}\mathrm{h}\mathrm{F}\mathrm{l}\mathrm{o}\mathrm{w}}{({1+\mathrm{i}\mathrm{n}\mathrm{f}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e})}^{\mathrm{t}}}}$$

PBT is used to determine the period necessary for the investment to recoup its initial costs through the cash flows it generates (in years), thanks to cost savings, providing a straightforward metric for financial planning in healthcare facilities.

This study assessed five automation technologies—inventory robots, unit dose systems, automated dispensing cabinets (ADCs), a central system in ICU for smart pumps, and oncology medication traceability—across the 27 EU countries and the United Kingdom over a projected 10-year period from 2024 to 2034. The economic data, updated with the average European inflation rate, supports uniformity and comparability across this timeframe, allowing a comprehensive evaluation of both the immediate and long-term economic and operational impacts of these technologies in hospital settings. The 10-year horizon is chosen based on the typical lifecycle of these technologies in healthcare, covering installation, full deployment, and mature operation, to assess their initial effects and sustained benefits comprehensively. This period helps ensure that the economic assessments capture the full spectrum of benefits and costs, providing a practical framework for decision-makers to evaluate the long-term impacts of healthcare automation investments.

The analysis was conducted using a baseline model of a medium-sized hospital with 561 beds, reflecting the structural capacity of a medium-sized healthcare organization across the EU27 + UK (Eurostat, 2024). For each automation technology, the required investment and the associated economic benefits were estimated at the hospital level, using country-specific input data (e.g., staff salaries, drug prices, average cost per hospital day) to enable the results’ contextualization and adaptation based on local healthcare structures and resources availability. Subsequently, to estimate the national-level impact, the results from the 561-bed hospital model were proportionally scaled up to the total number of care hospital beds in each country, based on the data reported by Eurostat (2024). This scaling approach provides a structured and conservative estimation of the potential economic impact of automation technologies at the national level, allowing for contextual adaptation across different healthcare systems. The assumptions and limitations of this method are addressed in the Section 4.

Subsequently, national estimates were adjusted to account for the current penetration rate of each technology in the different countries, based on data from the European Collaborative Action on Medication Errors and Traceability (ECAMET). This adjustment acknowledges that some hospitals have already implemented specific automation systems, and that technology adoption levels vary significantly across countries. As such, both the additional investment required and the achievable benefits were proportionally scaled according to existing adoption rates, ensuring more realistic and context-sensitive projections of economic impact.

To further enhance the accuracy of the model, country-specific adjustments were incorporated, including data on hospital bed capacity, average healthcare salaries (from SalaryExpert), ICU and non-ICU stay costs, and average drug prices. This combined approach ensures a balance between standardization and national-level specificity, supporting a fair and comprehensive evaluation of automation investments across diverse healthcare environments.

The results will be presented from two complementary perspectives: first, at the hospital level, illustrating the investment and savings associated with each technology in a modeled 561-bed European hospital; and second, at the aggregated level, summarizing the projected economic impact across the EU27 + UK. Additionally, country-specific results for all 28 nations included in the analysis are reported in the Appendixe A and Appendixe B to provide greater transparency, facilitating per country interpretation of the data.

Investment Calculation. The investment costs of the five automation technologies were estimated through an in-depth review of publicly available procurement documents and tender databases at the European level (e.g., TED—Tenders Electronic Daily). This approach ensured the use of real-world pricing information while maintaining neutrality and avoiding conflicts of interest [5]. For consistency and comparability, net prices were assumed to be constant across all EU27 + UK countries, while country-specific VAT rates were applied to reflect local fiscal frameworks. To support medication management and logistics processes in the reference hospital (561 beds), five automation technologies were considered, each targeting a specific phase of the medication-use cycle. These technologies were selected based on their relevance to clinical safety, operational efficiency, and prevalence in the extant literature.

• Inventory Management Robot

A robotic system installed in the central pharmacy to automate the storage, retrieval, and inventory control of medications. It uses barcode or RFID scanning to ensure accurate stock management, minimize picking errors, and optimize space utilization. It is designed to handle high volumes and improve traceability from the moment drugs enter the hospital supply chain.

• Unit Dose Distribution System (UDDS)

A centralized, automated packaging and labeling system that prepares individual unit doses for inpatients. It facilitates personalized and traceable drug dispensing at the patient level, reduces medication errors during preparation, and supports a closed-loop medication administration model.

• Automated Dispensing Cabinets (ADCs)

Eleven decentralized dispensing units located in clinical wards (e.g., medical, surgical, emergency departments) provide secure and on-demand access to medications. These systems are interfaced with the hospital information system and ensure controlled drug access, inventory tracking, and real-time documentation of drug dispensing activities.

• Centralized Dose Error Reduction System (DERS) for Smart Infusion Pumps in the ICU

A centralized digital library that standardizes infusion parameters (e.g., drug name, dose range, infusion rate) and programs smart pumps in critical care areas. It enables dose checking against predefined safety limits, reduces programming errors, and ensures consistent adherence to clinical protocols for high-risk intravenous therapies.

• Medication Traceability System for Oncologic Therapies

A dedicated platform that supports the safe prescription, preparation, and administration of chemotherapy drugs. It includes functionalities for computerized physician order entry (CPOE), gravimetric verification during compounding, automatic labeling, and digital documentation of each step. The system ensures full traceability of each dose, linking it to specific patients, operators, and preparation steps, and is specifically designed to address the complexity and risk profile of antineoplastic treatments.

Economic Benefits Classification. The economic benefits are systematically described and listed as follows.

• Reduction in Human Resource Costs: Automation technologies significantly reduce healthcare personnel time by eliminating or streamlining repetitive logistical tasks, such as drug picking, transportation, restocking, and manual documentation. To quantify the economic value of these time savings, we reviewed comparative studies from the literature reporting Full-Time Equivalent (FTE) workload differences between automated and non-automated hospital settings. Using these benchmarks, we estimated the annual workload (in hours per professional category) required to manage medication-related logistics in a 561-bed hospital without automation. These baseline values are reported in Table 1. For each automation technology, we applied corresponding efficiency improvement rates—also reported in Table 1—to estimate the reduction in workload. The time savings were monetized by multiplying the avoided hours by the average annual salary of the relevant personnel categories, using publicly available national wage data. This method was applied consistently across all technologies and countries, enabling a standardized estimation of human resource savings and a clearer understanding of how automation reallocates staff time from non-value-added tasks to clinical care [4].
• Reduction in Drug Wastage: Automated systems improve drug utilization by implementing stock rotation algorithms and real-time inventory visibility, which prioritize the dispensing and use of medications nearing expiration. This reduces the number of drug packages discarded due to expiry. Baseline wastage rates in non-automated settings and corresponding reduction percentages achievable with automation were obtained from published literature. For each drug class and automation technology, we estimated the annual volume of expired drugs, valued using the average purchase price per package. The difference in costs between the automated and non-automated scenarios was calculated as the economic benefit. All parameters used for this estimation—including baseline wastage rates, reduction percentages, and unit prices—are detailed in Table 1, along with the corresponding literature sources [6].
• Optimized Inventory Management: The optimization of inventory levels is increasingly seen as a critical goal for hospital logistics systems, especially in settings under economic pressure. Manual inventory management often leads to excess stock levels, especially of infrequently used pharmaceuticals, resulting in a high volume of capital tied up in unused inventory. Several studies have proposed predictive models to support hospital purchasing and supply planning activities, including approaches based on short time-series that have proven effective even in healthcare settings where long historical datasets are not always available [7]. Automation improves stock visibility, reorder accuracy, and rotation, thereby enabling hospitals to maintain optimal inventory levels. The economic benefit was estimated by comparing the average inventory value in automated versus non-automated settings, based on literature-derived benchmarks of stock reduction (expressed as a percentage of total drug value). Inventory holding costs were calculated by applying an annual holding cost rate—reflecting the opportunity cost of capital—sourced from the European Central Bank (ECB). All input parameters for this calculation, including baseline inventory levels, efficiency improvement rates, and holding cost percentage, are presented in Table 1, along with relevant references from the literature [8].
• Reduction in Medication Administration Errors (MAEs): Although the reduction in MAEs can be considered an indirect benefit, it was included in our analysis due to its significant clinical and economic implications, well documented in the literature (see references in Table 1). To estimate the effect of automation on MAEs, we relied on observational studies comparing automated and non-automated hospital workflows. For the first three technologies (inventory robots, Unit Dose Distribution Systems, and Automated Dispensing Cabinets), these studies provided error rates that we applied to a standardized 561-bed hospital model. For the remaining two technologies (Central DERS and Oncology Medication Traceability System), which primarily enhance error detection, we assumed an improvement in the recognition and correction of medication errors based on supportive evidence from the literature. We classified MAEs by severity—no harm, low harm, and mild/severe harm—and associated each category with an average increase in hospital length of stay, using values found in previous economic evaluations. To consolidate these values into a single figure, we calculated a weighted average increase in length of stay per error, based on the relative frequency of each severity category and its corresponding excess days of hospitalization. For each country, this average excess stay was multiplied by the mean inpatient cost per day, providing a country-specific estimate of the economic burden associated with a generic MAE. Automation-related savings were then derived by applying the expected reduction (or detection) rates to these cost estimates. Table 1 presents all input parameters used in this calculation, including baseline error rates, severity distribution, average LOS extension per error category, and unit costs across countries [9].

Table 1 details the parameters used to quantify both direct and indirect benefits for each automation technology examined. This format clearly delineates the specific impacts of deploying these technologies in hospital settings, emphasizing their contributions to operational efficiencies and cost reductions.

Table 1. Input parameters for economic evaluation of the technologies’ impact on an average European hospital.

Technology	Number of Medications Managed/Stocked in an Average European Hospital	Percentage of Expired or Wasted Drug/number. of Preparations	Percentage of Wasted Drugs’ Reduction Thanks to Automation	% Reduction in Product Stock Thanks to Automation	Number of Medications’ Errors Without Automation Technologies in a Year	Percentage of Medication Error Reduction Thanks to Automation	Professional	Number of Hours Dedicated to the Specific Activities Without Automation	Reduced Process Time Through Automation (% of hours)
Inventory Robots	1,563,133	0.46% [10]	−100.00% [11]	−26.40% [12]	128.96 [10]	−16% [10]	Technicians	7769.45 [10]	−31.40% [10]
Unit Dose System	545,143	0.55% [11]	−100.00% [11]	0.00% (Assumption)	77.79 [13]	−53% [13]	Nurses	20,640.59 [10]	−5.84% [14]
							Technicians	13,881.92 [10]	−10.00% [10]
Automated Dispensing Cabinets	2,850,352	0.55% [11]	−100.00% (Assumption)	−60.58% Elaboration from [12]	954.91 Elaboration from [15]	−53% [16]	Nurses	499.16 [10]	−80.00% [10]
							Pharmacists	1992.81 [10]	−50.00% [17]
							Technicians	1996.63 [10]	15.00% [18]
Smart pumps with DERS	26,690	-	-	-	12.82 Hypothesys: Baseline efficacy is equal to the one epressed in [19]	−100% (Assumption)	Nurses	394.37 [20]	−69.81% [20]
Med. Traceability System in Oncology	19,180	2.5153% (Elaboration from [21])	−100.00% (Assumption)	-	284.82 [22]	−100% (Assumption	Pharmacists (preparation)	2878.90 [23]	−44.38% [23]
		0.0960% [21]	−21.10% Elaboration from [21]		139.25 [24]	−75% [25,26,27]	Nurses (administration)	1917.99 [28]	−88.61% [28]

Table 1. Input parameters for economic evaluation of the technologies’ impact on an average European hospital.

Technology	Number of Medications Managed/Stocked in an Average European Hospital	Percentage of Expired or Wasted Drug/number. of Preparations	Percentage of Wasted Drugs’ Reduction Thanks to Automation	% Reduction in Product Stock Thanks to Automation	Number of Medications’ Errors Without Automation Technologies in a Year	Percentage of Medication Error Reduction Thanks to Automation	Professional	Number of Hours Dedicated to the Specific Activities Without Automation	Reduced Process Time Through Automation (% of hours)
Inventory Robots	1,563,133	0.46% [10]	−100.00% [11]	−26.40% [12]	128.96 [10]	−16% [10]	Technicians	7769.45 [10]	−31.40% [10]
Unit Dose System	545,143	0.55% [11]	−100.00% [11]	0.00% (Assumption)	77.79 [13]	−53% [13]	Nurses	20,640.59 [10]	−5.84% [14]
							Technicians	13,881.92 [10]	−10.00% [10]
Automated Dispensing Cabinets	2,850,352	0.55% [11]	−100.00% (Assumption)	−60.58% Elaboration from [12]	954.91 Elaboration from [15]	−53% [16]	Nurses	499.16 [10]	−80.00% [10]
							Pharmacists	1992.81 [10]	−50.00% [17]
							Technicians	1996.63 [10]	15.00% [18]
Smart pumps with DERS	26,690	-	-	-	12.82 Hypothesys: Baseline efficacy is equal to the one epressed in [19]	−100% (Assumption)	Nurses	394.37 [20]	−69.81% [20]
Med. Traceability System in Oncology	19,180	2.5153% (Elaboration from [21])	−100.00% (Assumption)	-	284.82 [22]	−100% (Assumption	Pharmacists (preparation)	2878.90 [23]	−44.38% [23]
		0.0960% [21]	−21.10% Elaboration from [21]		139.25 [24]	−75% [25,26,27]	Nurses (administration)	1917.99 [28]	−88.61% [28]

Summary Economic Metrics for Investment Evaluation: ROI, NPV over a 10-year horizon (country-specific discount rates, as published by the European Central Bank (ECB) in May 2024, were applied to ensure methodological consistency with national economic contexts), and PBT—were calculated for each country to provide a comprehensive economic assessment. Each metric provides unique insights into the financial viability of the investment, offering a holistic view of the automation economic impact in various contexts [29].

The model inputs, contents and economic results, were validated by five hospitals pharmacists, from different European Countries (two from Italy, and one each from France, Germany, and the UK). All participants had over ten years of experience in the hospital setting, with specific expertise in logistics and management. To ensure a structured validation process, the Nominal Group Technique (NGT) was implemented. This methodological approach facilitated the free expression of individual perspectives on critical and relevant elements, while subsequently fostering consensus around the findings to enhance their robustness. The CHEERS checklist was implemented to guarantee robustness of the study design.

Sensitivity Analysis: To assess the robustness of the economic model and incorporate contextual variability, a sensitivity analysis was conducted, focusing on two key parameters: hospital size and country-specific discount rates.

Regarding hospital size, the baseline scenario assumes a 561-bed facility, reflecting the average size of acute care hospitals across EU27 + UK (Eurostat, 2022). To simulate the influence of scale, the analysis includes two additional configurations, representing a smaller hospital with 449 beds and a larger one with 673 beds (±20%). Presenting results for hospitals of varying bed capacities equips European decision-makers with actionable insights tailored to their own facility size—whether larger or smaller than the 561-bed baseline—thereby enhancing the study’s generalizability and practical relevance for real-world investment decisions. The benefits associated with automation technologies were adjusted according to hospital size using a nonlinear coefficient β = 0.18. This coefficient was derived from comparative efficiency estimates reported in the OECD (2017) study “Tackwling Wasteful Spending on Health”, where observed savings in facilities of different sizes were used to estimate the elasticity of benefit with respect to volume. Specifically, by applying a log-transformation to the reported savings and hospital capacities, the resulting β-value captures how efficiency gains scale with hospital size. This allows the model to reflect economies of scale in a way that is consistent with real-world observations, ensuring that investment returns and operational savings vary realistically with institutional volume. The sensitivity analysis also includes national variability in discount rates, which directly affect Net Present Value (NPV) estimates.

Country-specific rates were collected from the European Central Bank (ECB, May 2024), and the model tested scenarios with ±20% deviations from each baseline rate to simulate potential macroeconomic shifts. This approach ensures that the financial outcomes remain robust across varying fiscal environments.

3. Results

Table 2. Economic impact of automated technologies on an average European hospital.

Hospital-Based results
Technology	Investment	Annual Savings	NPV
Inventory Robot	−190,163 EUR	140,488 EUR	780,692 EUR
UDDS	−151,171 EUR	55,301 EUR	186,550 EUR
ACDs	−495,031 EUR	151,310 EUR	96,363 EUR
DERS	−99,017 EUR	65,300 EUR	304,488 EUR
Med, Traceability System	−78,549 EUR	111,783 EUR	696,935 EUR
Total	−1,013,931 EUR	524,183 EUR	2,065,029 EUR

summarizes the average economic results for each technology at the hospital level, based on a 561-bed model. Findings account for country-specific variation in technology penetration, labor costs, and pharmaceutical pricing. Among the five technologies analyzed, the oncology medication traceability system demonstrates the most favorable economic profile, with the lowest average investment (EUR −78,549), high annual savings (EUR 106,777), and a Net Present Value (NPV) of EUR 656,242 over ten years—second only to the inventory robot. The inventory robot, while requiring a higher investment (EUR −190,163), yields the highest NPV overall (EUR 712,751), driven by substantial reductions in wastage and stock inefficiencies. Automated Dispensing Cabinets (ADCs), despite generating the highest annual savings (EUR 142,255), show a very low NPV (EUR 23,216) due to their significantly higher upfront costs (EUR −495,031). The unit-dose distribution system (UDDS) and DERS for ICU smart pumps occupy an intermediate position, combining moderate investment with solid savings and ROI profiles. These results highlight the importance of balancing investment requirements with achievable savings and suggest that technologies with strong safety and traceability components (such as oncology-focused systems) can offer substantial long-term value even with modest capital requirements.

To complement the hospital-level analysis,

Table 3. Economic impact of automated technologies on all European Healthcare Services.

Technology	Investment	HR Efficiency Savings	Wastage Reduction Savings	Inventory Reduction Savings	MAE Reduction Savings (Indirect Benefit)	Total Annual Savings	ROI	NPV	Payback Time
Inventory Robot	−600,130,638 EUR	105,401,590 EUR	298,558,225 EUR	61,611,921 EUR	8,807,528 EUR	474,379,264 EUR	253%	2,871,105,529 EUR	2.75
UDDS	−507,585,002 EUR	100,732,484 EUR	98,798,224 EUR	0 EUR	14,016,421 EUR	213,547,129 EUR	88%	843,421,857 EUR	5.5
ACDs	−1,760,264,736 EUR	98,356,058 EUR	182,768,227 EUR	72,386,386 EUR	238,727,714 EUR	592,238,384 EUR	22%	887,897,647 EUR	7.33
DERS	−385,739,250 EUR	19,952,327 EUR	0 EUR	0 EUR	208,915,013 EUR	228,867,340 EUR	114%	1,031,345,807 EUR	3.75
Med, Traceability System	−304,110,137 EUR	162,230,625 EUR	36,029,794 EUR	0 EUR	262,662,674 EUR	460,923,093 EUR	360%	3,046,279,644 EUR	2.25
Total	−3,557,829,764 EUR	486,673,083 EUR	616,154,470 EUR	133,998,307 EUR	733,129,349 EUR	1,969,955,209 EUR	167%	8,213,739,492 EUR	4.46

presents the aggregated economic impact of implementing each technology across all acute care hospitals in the EU27 + UK. The estimates are based on standardized 561-bed hospital models, scaled by country-specific hospital counts and contextual variables (e.g., labor costs, drug prices, and penetration rates). Table 3 summarizes the key economic results per technology, highlighting their distinct profiles. The oncology medication traceability system emerged as the most economically favorable, with a 339% ROI, the shortest Payback Time (2 years), and substantial savings in both direct and indirect cost categories. Inventory robots also performed well, achieving a 232% ROI and over EUR 445 million in annual savings, primarily due to reductions in drug wastage and improved stock efficiency. Conversely, although Automated Dispensing Cabinets (ADCs) generated the highest total annual savings (EUR 556 million), their initial investment of EUR 1.76 billion resulted in a modest ROI of 14% and the longest Payback Time (8 years). These findings support differentiated investment strategies, depending on available capital, hospital size, and national healthcare cost structures, and offer policymakers a comparative overview of return potential across technologies at the system level.

The sensitivity analysis explored the effects of varying two structural assumptions: the size of the hospital infrastructure (±20% around the baseline of 561 beds) and the discount rate (±20%), which directly affects the Net Present Value (NPV) of the investments. Varying hospital size influenced the total economic returns at the European level, with the overall NPV ranging from approximately EUR 7.66 billion in the low-volume scenario to EUR 8.78 billion in the high-volume case. This confirmed the presence of economies of scale: larger infrastructures can dilute fixed investments more efficiently and generate proportionally higher returns. Nonetheless, ROI and Payback Time remained stable across scenarios, suggesting that the investment rationale holds even in smaller hospital settings. Varying the discount rate had a more selective impact. ROI and Payback Time remained unchanged, as they are not affected by discounting. However, NPV proved to be highly sensitive to this parameter, as expected. At the European level, reducing the discount rate by 20% increased the total NPV to approximately EUR 8.59 billion, while increasing it by 20% reduced NPV to EUR 7.86 billion, a difference of nearly EUR 730 million. Similar shifts were observed at the hospital level, although on a smaller scale. Across both sensitivity dimensions, the Medication Traceability System consistently delivered the strongest performance, maintaining high ROI and low Payback Time in all scenarios. Technologies with higher initial investments and longer return horizons—such as ACDs—were more exposed to variations in discount rate, with NPV differences of nearly EUR 160 million between scenarios.

Table 4. Main sensitivity analysis results.

Scenario	ROI	Total NPV (EUR)	Payback Time (Years)
Baseline	167%	EUR 8,213,739,492	4.46
Hospital size −20%	133%	EUR 7,661,066,552	4.71
Hospital size +20%	164%	EUR 8,778,311,040	4.93
Discount rate −20%	167%	EUR 8,585,215,964	4.39
Discount rate +20%	167%	EUR 7,859,352,036	4.46

resumes the main results for sensitivity analysis.

4. Discussion

The results of this study reinforce the economic viability of adopting automation and digital technologies in hospital medication management across Europe. With an estimated investment of EUR 3.55 billion and an average Payback Time (PBT) of 4.46 years, the financial sustainability of these technologies emerges clearly, especially in countries with high labor and drug costs. These findings are consistent with Bonnabry et al. (2022) [2], who estimated a PBT of 3.8 years for similar technologies in a Swiss context, thereby validating our approach and estimates across different settings.

Unlike previous studies focused on single countries or individual technologies, this research offers a comprehensive, cross-national economic evaluation analyzing EU27 + UK. By using a standardized hospital model and integrating country-specific parameters (e.g., wage levels, medication error rates, technology penetration), we provide robust estimates of Return on Investment (ROI), Net Present Value (NPV), and PBT both at the hospital and system level. This contributes new evidence to support strategic planning and investment decisions, especially in healthcare systems that are hesitant to invest in automation due to budget constraints or lack of detailed data on organizational benefits.

Notably, ROI and NPV remain positive even in lower-GDP countries, suggesting that automation is financially viable even in resource-constrained environments. The variation in ROI across nations highlights how contextual factors—such as workforce costs, existing infrastructure, and medication error prevalence—significantly influence the value generated from automation. These findings align with the literature emphasizing the importance of tailoring health technology investments to local economic and organizational contexts [30,31].

To improve the robustness of our estimates, we approached the sensitivity analysis by varying key parameters such as the number of hospital beds and the discount rate. Even under conservative assumptions (e.g., lower bed capacity or higher discount rates), the investment maintains a positive ROI and an acceptable PBT, confirming the model’s reliability across a range of economic scenarios.

In addition to economic benefits, automation technologies offer qualitative improvements—enhancing patient safety, reducing human error, and fostering more efficient workflows. These advantages are widely acknowledged in the literature [32] and are particularly relevant for reducing medication administration errors (MAEs), which represented the most significant driver of savings in our model.

However, several limitations must be acknowledged. First, our model assumes a standardized medication-use process across all countries, especially in oncology settings. Treatment protocols and workflows vary not only between countries, but often within regions. These variations can influence the actual impact of automation. Nonetheless, by focusing on core phases of medication management—prescription, preparation, dispensing, and administration—we ensure generalizability of results across care settings.

Second, the economic estimates rely on average values for variables such as labor costs, drug prices, and automation penetration, which introduces an inevitable approximation. Future research could explore hospital-level simulations using locally collected data to enhance precision.

We also recognize that the assumption of a 561-bed hospital may not fully reflect the heterogeneity of hospital sizes in Europe. In smaller hospitals, savings might be overestimated, while in larger facilities, economies of scale could yield even greater benefits. Nonetheless, this approach enables consistent and comparative analysis across countries and supports scalable policy recommendations. Furthermore, our two-step extrapolation—from hospital to national level, and then to EU27 + UK—ensures both micro- and macro-level insights.

Finally, it is worth noting that several countries have already implemented some of the evaluated technologies, creating asymmetries in current maturity and readiness. Countries such as Germany, France, and Sweden benefit from a stronger tradition of automation and thus serve as reference models. Conversely, countries newer to these technologies may face steeper learning curves and require phased implementation strategies, as previously discussed in the literature [33]. These differences, however, do not appear to significantly compromise the economic rationale for adoption.

Our analysis demonstrates the substantial economic and organizational value of investing in hospital automation. By combining empirical modeling with a sensitivity analysis and contextual interpretation, the study provides decision-makers with actionable evidence to guide sustainable innovation in healthcare.

5. Conclusions

In an era of constrained healthcare budgets, rigorous economic evaluations are crucial to justify investments in automation and digitalization. This study offers a structured and scalable methodology for calculating ROI, NPV, and Payback Time, equipping hospital pharmacists and decision-makers with essential tools to guide strategic investments in medication-use technologies.

Our findings confirm the economic sustainability of automation, with a total estimated investment of EUR 3.55 billion and an average Payback Time of 4.46 years across EU27 + UK. Even under varying economic assumptions—such as changes in hospital size or inflation rates—the model remains robust, highlighting the resilience of these investments over time and across contexts.

Beyond financial metrics, the study supports a broader vision of sustainable healthcare transformation. The ability to quantify both direct and indirect benefits enhances the case for adoption, particularly in systems where automation uptake has been limited by lack of economic evidence.

This methodology can be adapted to local contexts and evolving policy frameworks, offering a decision-support tool for national health authorities and hospital managers alike. Future research could refine the model using real-world data from diverse hospital settings, contributing to even more precise and contextualized investment planning.