What data is available for health systems with consolidated automated dispensing machine (i.e., Pyxis, Omnicell, etc) fulfillment services (e.g., centralized ADM fulfillment for multiple sites/hospitals)?

Comment by InpharmD Researcher

Implementation of consolidated automated dispensing cabinets (ADCs) has been consistently associated with reductions in medication dispensing error rates, although one simulation-based study (Table 1) suggests that a more ADC-dependent model resulted in an unfavorable shift in staff skill mix (i.e., reduced pharmacy technician workload at the expense of the nursing staff) and corresponding human resource costs compared to the hybrid distribution model, which was less dependent on ADC dispensing. Most available cost-analysis data are either outdated or limited for comparison. However, a mixed-methods, observational study of 11 hospitals within an integrated delivery network evaluated the impacts of an enterprise inventory optimization tool involving linked ADCs, centralized inventory software, and controlled substance vaults, reporting enterprise labor avoidance of 2,099 hours (~1 full-time equivalent), ADC cost reductions exceeding $680,000 in 82% of sites, and decreased central pharmacy stockout percentages across 100% of sites, suggesting improved labor efficiency, cost, and medication availability (Table 11). Additionally, a retrospective, single-center French study reported a return on investment of +$163,331 after 7 years of using ADCs, confirming ADCs to be a profitable investment (Table 12).

Background

Automated dispensing cabinets (ADCs) have been utilized in direct patient care areas since the 1990s, with studies since then evaluating its efficacy in medical error reduction and subsequent cost savings. One previous study using the McLaughlin Dispensing System observed lowered error rate compared to use of doses dispensed from a satellite pharmacy (10.6% vs 15.9%) over a two-week trial period, while another which utilized the Baxter ATC-212 automated dispensing system found daily cart filling time to be significantly reduced, but overall time savings was not significant (<0.5 full-time equivalent), and drug costs were increased due to the acquisition prices for bulk drugs used by the automated dispensing system. In a more recent study, ADCs were observed to reduce overall error rate, but errors of greater severity increased. Though a positive financial impact was observed based on both operation and investment costs, these findings were based on nurses’ time gained, and findings were presented in 2015 Euros. Overall, insufficient evidence is available to determine the extent of dispensing accuracy in patient care areas, necessitating further studies. [1], [2], [3]

A 2014 review examined the clinical and economic impacts of decentralized automated dispensing devices (ADDs), such as Pyxis and Omnicell, in hospital settings. The review explores how use of ADDs in intensive care units (ICUs), general medicine wards, surgical units, or a combination of these locations affect medication management processes, patient safety, and healthcare costs. Within the eight papers included for analysis, ADDs appear to have a benefit in medication storage errors reduction and controlled substance inventory management; however, there was a lack of evidence to support whether ADD use resulted in staff time savings, whether patient harm was reduced as a result of ADD use, or whether costs overall were reduced. Additionally, findings were specific to Canadian hospitals, and applicability to domestic hospitals is uncertain. [4]

A 2015 BD article describes a single institution's implementation of the BD™ Pyxis™ Enterprise solution (ES) in Saudi Arabia, which is a program to convert cart-fill pharmacy distribution of medications to the Pyxis ADC. The ADCs were installed on 31 different units, including operating rooms, labor and delivery units, recovery units, and catheter labs. The formulary was standardized and integrated into the ADC, which assessed the minimum quantity needed for each drug and allowed for timely refills. In the results, the authors found that medication turnaround time was reduced by ~57% while dispensing of the wrong medication was reduced by ~42%, and dispensing to the wrong location was reduced by ~85%. Improvements to clinical workflow were cited as a major benefit that lowered the need for medication preparation, medication checking, and medication delivery to nursing units. No formal cost-saving analysis was performed. Cost savings may be observed by the optimization of inventory use and storage in the APC. [5]

A 2025 systematic review of 129 studies analyzed the different types of automation systems used in hospital pharmacies, assessed the impact of automation, and explored its benefits and challenges/limitations associated with its implementation. Centralized systems designed to prepare and dispense medications from a central pharmacy include distribution and storage robots, unidose preparation robots, intravenous preparation robots (Intravenous Workflow Management Systems, [IVWMS]), and pneumatic tube distribution systems. These systems use a combination of technologies such as barcodes, visual identification (via cameras), radiofrequency identification (RFID), and other advanced methods. On the contrary, ADCs alone are considered decentralized systems. A study in a Brazilian tertiary hospital is referenced, which showed that implementation of automated dispensing robots in the central pharmacy reduced the frequency of errors in both the prescription (from 26% in 2013 to 15% in 2017) and distribution phases (from 36% in 2013 to 33% in 2017). A further study determined that after 7 years of using ADCs at a single institution, the actual return on investment reached +$163,331, confirming ADCs as a profitable investment. Table 10 summarizes reported outcomes of automation on medication administration times and workflow. Automated technologies significantly contribute to reducing medication errors, strengthening traceability, optimizing inventory management, and alleviating the workload of healthcare professionals; however, remaining challenges include cost optimization, integration with existing processes, and need for specialized staff training. The authors note that artificial intelligence may provide new prospects for improving both the accuracy and operational efficiency of medication automation systems. [6]

Literature Review

A search of the published medical literature revealed 12 studies investigating the researchable question:

Level of evidence

C - Multiple studies with limitations or conflicting results Read more→

Please see Tables 1-12 for your response.

Comparison of a hybrid medication distribution system to simulated decentralized distribution models
Design	Two-part study with direct observation and simulation modeling
Objective	To estimate the human resource and cost implications of changing the medication distribution model at a large medical center
Methods	A two-part study was conducted involving direct observation of nurse, pharmacist, and pharmacy technician workloads within the hybrid system and a comparator hospital with a decentralized system. Time standards were calculated for each dispensing task. Simulation modeling was used to evaluate alternative distribution scenarios: one with no cart fill, one with no ADCs, and one heavily dependent on ADC dispensing (89% via ADC and 11% via cart fill).
Outcome Measures	Human resource and cost implications of changing the medication distribution model
Results		Pharmacy Technician Time, min	Pharmacist Time, min	Nurse Time, min	Total Time, min
	No ADCs	605	17.49	1,817.50	2,440.99
	Hybrid	605	28.11	2,524.09	3,157.20
	Decentralized	934.20	20.51	3,501.25	4,456.96
	No CF	951.69	20.53	3,383.30	4,355.52
Study Author Conclusions	Based on the simulation results, it was decided that a transition from the existing hybrid medication distribution system to a more ADC-dependent model would result in an unfavorable shift in staff skill mix and corresponding human resource costs at the medical center.
Critique	The study lacks specific participant data and does not account for potential differences in patient acuity and institutional practices, which may limit the generalizability of the findings. Additionally, the study does not address the potential impact on patient safety and medication turnaround time.

Table 1 References:
[7] Gray JP, Ludwig B, Temple J, Melby M, Rough S. Comparison of a hybrid medication distribution system to simulated decentralized distribution models. Am J Health Syst Pharm. 2013;70(15):1322-1335. doi:10.2146/ajhp120512

Work activities before and after implementation of an automated dispensing system
Design	Self-reported work-sampling study
Objective	To study the impact of an automated dispensing system (ADS) on medication-related work activities by nurses and pharmacists
Study Groups	Nurses (n= 7,797 observations) Health unit coordinators (n= 1,408 observations) Pharmacists (n= 4,286 observations)
Inclusion Criteria	Participants were nurses, health unit coordinators, and pharmacists working in the surgical intensive care unit (SICU) and a medicine unit (4NMU) of a 400-bed university hospital
Methods	A point-of-care ADS (Baxter Sure-Med) was installed in two nursing units. A self-reported work-sampling study was conducted over a seven-day period before and after ADS implementation. Observations of medication-related work activities were collected from nurses, health unit coordinators, and pharmacists.
Duration	Pre-ADS data collection: May 1-7, 1995 Post-ADS data collection: July 31 to August 6, 1995
Outcome Measures	Primary: Changes in medication-related work activities by nurses and pharmacists Secondary: Efficiency of pharmacists' time for patient care-related activities
Baseline Characteristics		Nurses (n= 7,797 observations)	Health unit coordinators (n= 1,408 observations)	Pharmacists (n= 4,286 observations)
	Medication-related activities before ADS	20.7% (4NMU)	17.5% (4NMU)	36.5% (Medicine Satellite)
	Medication-related activities after ADS	18.4% (4NMU)	25.3% (4NMU)	49.1% (Medicine Satellite)
Results		4NMU Nurses	SICU Nurses	4NMU Health Unit Coordinators	SICU Health Unit Coordinators	Medicine Satellite Pharmacists
	Medication-related activities before ADS	20.7%	10.8%	17.5%	16.6%	36.5%
	Medication-related activities after ADS	18.4%	11.0%	25.3%	10.7%	49.1%
	Non-medication-related activities before ADS	69.5%	73.3%	66.1%	75.3%	21.0%
	Non-medication-related activities after ADS	71.1%	73.9%	57.7%	82.1%	10.4%
Study Author Conclusions	Point-of-care ADSs did not affect the proportion of time spent by nurses on medication-related activities and seemed to give pharmacists more time for clinical work.
Critique	The study provides valuable insights into the impact of ADS on work activities, but it is limited by its self-reported nature and lack of specific participant numbers. The study's findings may not be generalizable due to the specific hospital setting and the pilot nature of the project.

Table 2 References:
[8] Guerrero RM, Nickman NA, Jorgenson JA. Work activities before and after implementation of an automated dispensing system. Am J Health Syst Pharm. 1996;53(5):548-554. doi:10.1093/ajhp/53.5.548

The impact of automated medicine dispensing units on nursing workflow: A cross-sectional study
Design	Cross-sectional study N= 186
Objective	To evaluate the impact on the nursing workflow of a distributed automated medication dispensing system and to explore the acceptability and utility of this system in a variety of clinical settings
Study Groups	Registered nurses (n= 174) Pharmacy assistant staff (n= 12)
Inclusion Criteria	Registered nurses and pharmacy assistant staff from general ward and specialty areas using the automated medication dispensing cabinets
Exclusion Criteria	Not specified
Methods	Methods included a hospital-wide survey of users and an observation study of nursing workflow around the automated medication cabinets in specific clinical areas. The survey consisted of 33 items using a five-point Likert scale, and observation was conducted using a work sampling technique in four clinical areas
Duration	19 months after the opening of the hospital
Outcome Measures	Primary: Staff satisfaction with the automated dispensing system Secondary: Impact on nursing workflow, access delays, and time needed for medication administration
Results		Medical ward (n= 116)	Surgical ward (n= 82)	p-value
	Transaction time, min (IQR)	2 (2.25)	1 (1)	0.001
	Two staff checking dangerous or controlled drug	26 (22.4%)	22 (26.5%)	0.475
	Multiple meds withdrawn	54 (46.5%)	18 (29.1%)	0.025
	Interruption occurred	9 (7.8%)	6 (7.3%)	0.509
	Occasions when other staff were waiting	26 (22.4%)	12 (14.6%)	0.002
Study Author Conclusions	Automated medication dispensing cabinets were widely accepted by nurses in a large newly opened hospital in a variety of acute clinical areas despite disruptions to workflow. Adaptations for access were more acceptable to nurses in general wards than those in specialty areas prompting consideration of redesign to improve suitability.
Critique	The study provided valuable insights into the impact of automated dispensing cabinets on nursing workflow and staff satisfaction. However, the cross-sectional design and reliance on self-reported survey data may introduce bias. The study's setting in a newly opened hospital with unique design features may limit the generalizability of the findings to other settings. Additionally, the lack of integration with electronic medical records during the study period may have affected the perceived benefits of the system.

Table 3 References:
[9] Craswell A, Bennett K, Dalgliesh B, et al. The impact of automated medicine dispensing units on nursing workflow: A cross-sectional study. Int J Nurs Stud. 2020;111:103773. doi:10.1016/j.ijnurstu.2020.103773

Automated drug dispensing system reduces medication errors in an intensive care setting
Design	Preintervention and postintervention study involving a control and an intervention medical intensive care unit N= 115
Objective	To assess the impact of an automated dispensing system on the incidence of medication errors related to picking, preparation, and administration of drugs in a medical intensive care unit. To evaluate the clinical significance of such errors and user satisfaction
Study Groups	Control unit (n= 56) Study unit (n= 59)
Inclusion Criteria	Adult medical intensive care patients
Exclusion Criteria	Not specified
Methods	After a 2-month observation period, an automated dispensing system was implemented in one unit (study unit) chosen randomly, with the other unit being the control. Errors were collected by direct observation of picking, preparation, and administration of drugs by nurses. The severity of errors was classified according to National Coordinating Council for Medication Error Reporting and Prevention categories by an expert committee. User satisfaction was assessed through self-administered questionnaires completed by nurses.
Duration	4 months
Outcome Measures	Primary: Overall error rate during picking, preparation, and administration Secondary: Detailed opportunities for error in picking, preparation, and administration; user satisfaction
Baseline Characteristics		Control Unit	Study Unit
	Age, years	63 (54–73)	61 (53–74)
	Male	17 (63%)	21 (67.7%)
	Length of stay, days	11 (5–25)	6 (3–12)
	Simplified Acute Physiology Score II	41 (37–53)	44 (30–57)
	Deaths	8 (29.6%)	5 (16.1%)
Results		Control Unit	Study Unit	p-value
	%TOE before ADS	19.3%	20.4%	NS
	%TOE after ADS	18.6%	13.5%	<0.05
	%DOE - Picking	1.9%	1.5%	NS
	%DOE - Preparation	3.8%	3.4%	<0.05
	%DOE - Administration	3.1%	2.7%	NS
Adverse Events	Most errors caused no harm (National Coordinating Council for Medication Error Reporting and Prevention category C). The automated dispensing system did not reduce errors causing harm.
Study Author Conclusions	The implementation of an automated dispensing system reduced overall medication errors related to picking, preparation, and administration of drugs in the intensive care unit. Furthermore, most nurses favored the new drug dispensation organization.
Critique	While the study demonstrated a reduction in medication errors with the implementation of an automated dispensing system, the lack of significant reduction in errors causing harm and the potential for new error risks with technology implementation highlight the need for continuous quality monitoring. The study's design, involving shared staff between control and intervention units, may have underestimated the impact of the ADS due to improved practices in the control unit.

Table 4 References:
[10] Chapuis C, Roustit M, Bal G, et al. Automated drug dispensing system reduces medication errors in an intensive care setting. Crit Care Med. 2010;38(12):2275-2281. doi:10.1097/CCM.0b013e3181f8569b

Automated drug dispensing systems in the intensive care unit: a financial analysis
Design	Single-center, before-after study
Objective	To evaluate the economic impact of automated-drug dispensing systems (ADS) in surgical intensive care units (ICUs)
Study Groups	Three surgical ICUs: neurosurgical, cardiac, and trauma
Methods	Costs were estimated before and after ADS implementation based on floor stock inventories, expired drugs, and time spent by nurses and pharmacy technicians on medication-related activities. Financial analysis included operating cash flows, investment cash flows, global cash flow, and net present value. Observations were conducted over 40 day shifts to assess time spent on activities
Duration	Data collection in 2011
Outcome Measures	Primary: Economic impact of ADS implementation Secondary: Time spent by nurses and pharmacy technicians on medication-related activities, cost of drug storage, cost of expired drugs
Results		Before ADS	After ADS	Change
	Nurse time on medication activities , hours/day	Not specified	14.7 hours saved	Decrease
	Pharmacy technician time on floor-stock activities , hours/day	Not specified	3.5 additional hours	Increase
	Cost of drug storage , €	93,832	49,525	Decrease
	Cost of expired drugs , €	Not specified	14,772 saved	Decrease
	Global cash flow at 5 years , €	Not applicable	148,229	Positive
	Net present value , €	Not applicable	510,404	Positive
Study Author Conclusions	The financial modeling of ADS implementation in three ICUs showed a high return on investment for the hospital. Medication-related costs and nursing time dedicated to medications are reduced with ADS.
Critique	The study lacks detailed patient inclusion and exclusion criteria, and the generalizability may be limited due to the single-center design. Additionally, it does not address potential clinical outcomes or patient safety improvements directly associated with ADS. Findings were provided in Euros, further limiting applicability to U.S. health systems, and overhead cost in 2015 will differ from current overhead costs in 2026.

Table 5 References:
[11] Chapuis C, Bedouch P, Detavernier M, et al. Automated drug dispensing systems in the intensive care unit: a financial analysis. Crit Care. 2015;19(1):318. Published 2015 Sep 9. doi:10.1186/s13054-015-1041-3

Comparison on Human Resource Requirement between Manual and Automated Dispensing Systems
Design	Comparative study conducted at a 2100-bed university hospital (Siriraj Hospital, Bangkok, Thailand)
Objective	To compare human resource requirement among manual, automated, and modified automated dispensing systems
Study Groups	Manual system ADM system Modified ADM system
Inclusion Criteria	Data collected from the pharmacy department at Siriraj Hospital, Bangkok, Thailand
Exclusion Criteria	Not specified
Methods	Data on the duration of the medication distribution process were collected using self-reported forms for 1 month. The ADM system data were obtained from 1 piloted inpatient ward, while manual system data were averaged from other wards. The FTE of each model was estimated for comparison. The ADM system included additional pharmacist roles in screening and verification, and the modified ADM system canceled the return unused medication process.
Duration	October 2012 (data collection)
Outcome Measures	Primary: Human resource requirement in FTEs for pharmacists and pharmacy technicians Secondary: Efficiency of medication distribution process
Baseline Characteristics		Manual system	ADM system	Modified ADM system
	Pharmacist FTEs	46.84	117.61	69.78
	Pharmacy Technician FTEs	132.66	55.38	51.90
Results		Manual system	ADM system	Modified ADM system
	Total FTEs in all processes	46.84 pharmacists, 132.66 technicians	117.61 pharmacists, 55.38 technicians	69.78 pharmacists, 51.90 technicians
Adverse Events	Not applicable
Study Author Conclusions	The ADM system decreased the workload of pharmacy technicians, whereas it required more time from pharmacists. The increased workload of pharmacists was associated with more comprehensive patient care functions, resulting from the redesigned work process.
Critique	The study effectively highlights the differences in human resource requirements between manual and automated systems. However, it lacks specific participant numbers and does not account for variations in medication types across different wards. The reliance on self-reported data may introduce bias, and the study does not consider the impact of ADM on medication errors or patient outcomes.

Table 6 References:
[12] Noparatayaporn P, Sakulbumrungsil R, Thaweethamcharoen T, Sangseenil W. Comparison on Human Resource Requirement between Manual and Automated Dispensing Systems. Value Health Reg Issues. 2017;12:107-111. doi:10.1016/j.vhri.2017.03.007

Improving operational efficiency through automated dispensing cabinet analytics software
Design	Quality improvement project N= 25 ADCs
Objective	To evaluate the implementation of a pharmacy analytics software program on medication expense and pharmacy operational efficiency at an academic medical center
Study Groups	Emergency departments (n= 3) Adult inpatient care areas (n= 16) Adult intensive care units (n= 6)
Inclusion Criteria	Twenty-five automated dispensing cabinets (ADCs) located in emergency departments, adult inpatient care areas, and adult intensive care units at an academic medical center
Exclusion Criteria	Not specified
Methods	Omnicell Inventory Optimization Service was used for optimization of ADCs over a 3-month period. The process involved analyzing medication usage, adjusting periodic automatic replenishment levels, and relocating medications based on usage. Data points were extracted through ADC vendor software and wholesaler reports.
Duration	Preimplementation: January to March 2022 Postimplementation: July to September 2022
Outcome Measures	Primary: Cost of expired medications, total inventory cost per ADC, vend-to-fill ratio, medication stock-outs, pharmacy technician time logged into ADCs
Baseline Characteristics		No. of cabinets	Unique medications stocked	Opportunities acted on	Total optimization time, hours	Mean optimization time per ADC, hours
	ED	3	852	539	15.81	5.27
	Ward	16	4,669	3,911	114.72	7.17
	ICU	6	1,685	1,102	32.29	5.38
	Total	25	7,206	5,552	169.85	6.79
Results		Pre-implementation	Post-implementation	Change (%)
	Cost of expired medications	$14,330.33	$7,699.01	-46.27%
	Total inventory cost	$173,030.53	$150,850.93	-12.82%
	Vend-to-fill ratio	9.83	11.00	Increase
	Stock-out rates	0.86%	1.07%	Increase
	Items restocked per minute	2.53	2.68	+5.93%
	Average restock time per ADC	7.23 minutes	6.27 minutes	-14.34%
Adverse Events	Not applicable
Study Author Conclusions	The implementation of analytics software to assist with automated dispensing machine operational efficiency and quality can lead to a variety of improvements in pharmacy operational metrics.
Critique	The study effectively demonstrated improvements in operational efficiency and cost savings through the use of analytics software. However, the lack of established best practice benchmarks for KPIs and the exclusion of compounded products or items purchased outside of the wholesaler in cost savings calculations may limit the generalizability and accuracy of the reported savings.

Table 7 References:
[13] Braham MJ, Carter J, O'Neil DP, Phillips M, Miller K, Chaffee K. Improving operational efficiency through automated dispensing cabinet analytics software. Am J Health Syst Pharm. 2026;83(14):771-774. doi:10.1093/ajhp/zxag016

Automated Dispensing Cabinet Stocking Schedule and Inventory Management Optimization
Design	Pre-test/post-test observational quality improvement study N= 51 ADCs
Objective	To determine the effect of ADC restocking changes on medication availability
Study Groups	Inpatient, non-procedural ADCs (n= 51)
Inclusion Criteria	ADCs located in inpatient, non-procedural areas, including progressive and intensive care units
Exclusion Criteria	ADCs in procedural areas, labor and delivery unit, and emergency department
Methods	Monthly reports of ADC refilling and dispensing transactions were compiled. Stock-out transactions were tracked, and overlap ratios were calculated by dividing refill by dispense activities during peak times. The new process involved every-other-day ADC replenishment with staggered technician shifts and a dedicated technician for ADC inventory optimization.
Duration	March 2022 to September 2023
Outcome Measures	Frequency of stock-out transactions, overlap ratios during peak medication administration times
Baseline Characteristics		All ADCs (n= 51)
	Location - Inpatient, non-procedural areas	100%
	Type - Progressive and intensive care units	100%
Results		Pre-Implementation	Post-Implementation
	Average monthly stock-outs	471	293
	Overlap ratio at 7 am	290%	23%
	Overlap ratio at 8 am	91%	9%
	Overlap ratio at 8 pm	4%	37%
Adverse Events	No adverse events reported
Study Author Conclusions	Converting to an every-other-day ADC restocking schedule with staggered shifts and a dedicated technician role for ADC inventory optimization may increase medication availability and reduce technician-nurse overlap during peak medication administration times.
Critique	The study effectively demonstrated a reduction in stock-outs and improved workflow efficiency. However, limitations include the lack of granular stock-out data and potential unaccounted factors affecting stock-out frequency. The study did not assess the impact on overall drug inventory changes or nurse workflow disruption due to technician presence in medication rooms.

Table 8 References:
[14] Elkes D, Timmons V. Automated Dispensing Cabinet Stocking Schedule and Inventory Management Optimization. HCA Healthc J Med. 2025;6(3):225-232. Published 2025 Jun 1. doi:10.36518/2689-0216.1822

Effects of technological interventions on the safety of a medication-use system
Design	Prospective study with preimplementation and postimplementation phases
Objective	To assess the effects and outcomes of implementing new technology into the medication-use process
Study Groups	Preimplementation phase Postimplementation phase
Inclusion Criteria	All patients admitted to the general medical and medical intensive care units receiving medications, except emergency medications
Exclusion Criteria	Emergency medications were excluded due to different distribution and administration systems
Methods	Implementation of a pharmacy computer system, automated dispensing cabinets, and point-of-care products. Data collection involved interviews, medication error reports, accuracy checks of medication administration records, and evaluation of preparation and dispensing processes. Observations were made before and after technology implementation, with a six-month washout period post-implementation.
Duration	November 2002 to July 2005
Outcome Measures	Primary: Decrease in system errors in each phase of the medication-use process Secondary: Increase in workload measures such as staffing and inventory levels
Baseline Characteristics		Preimplementation	Postimplementation
	Wristband not present	4.5%	0%
	Wristband not checked	37.3%	0.04%
	Wristband present and checked but other identification not checked	29.5%	64.2%
	Allergies not documented on MAR	7.8%	0.1%
	Allergies not documented on wristband	8.0%	25.6%
	Incorrect patient	0.7%	0%
	Correct patient receiving medication with no wristband present	4.3%	0.4%
	Medication administered without an active order	0.3%	1.3%
	Incorrect administration time	3.9%	0.2%
	Incorrect medication	0.2%	1.6%
	Incorrect dose	2.9%	1.1%
	Incorrect dosage form	1.0%	2.7%
	Incorrect route	5.5%	0.2%
	Administration time not documented	2.7%	0.0001%
Results		Preimplementation	Postimplementation	p-Value
	Wristband not present	4.5%	0%	0.0001
	Wristband not checked	37.3%	0.04%	0.0001
	Wristband present and checked but other identification not checked	29.5%	64.2%	0.0001
	Allergies not documented on MAR	7.8%	0.1%	0.0001
	Allergies not documented on wristband	8.0%	25.6%	0.0001
	Incorrect patient	0.7%	0%	0.003
	Correct patient receiving medication with no wristband present	4.3%	0.4%	0.0001
	Medication administered without an active order	0.3%	1.3%	0.445
	Incorrect administration time	3.9%	0.2%	0.0001
	Incorrect medication	0.2%	1.6%	0.677
	Incorrect dose	2.9%	1.1%	0.002
	Incorrect dosage form	1.0%	2.7%	0.674
	Incorrect route	5.5%	0.2%	0.0001
	Administration time not documented	2.7%	0.0001%	0.0001
Adverse Events	Not specified
Study Author Conclusions	Implementation of new technology into the medication management system standardized the medication administration processes, decreased turnaround time for processing medication orders, and increased accuracy of medication administration to patients.
Critique	The study demonstrated significant improvements in medication administration accuracy and process efficiency following technology implementation. However, it was limited by its single-center design and lack of control group, which may affect the generalizability of the findings. Additionally, the study did not evaluate patient outcomes, which could provide a more comprehensive assessment of the technology's impact on patient safety.

Table 9 References:
[15] Skibinski KA, White BA, Lin LI, Dong Y, Wu W. Effects of technological interventions on the safety of a medication-use system. Am J Health Syst Pharm. 2007;64(1):90-96. doi:10.2146/ajhp060060

Summary of Studies on the Impact of Automation on Medication Administration Times
Type of automated system studied	Year, location/type of study	Criteria studied	Results	Author/reference
ADC decentralized	2014, 377-bed teaching hospital Retrospective analysis	Total time from prescription to administration	1.7-h reduction in mean from prescription to administration time	Lo et al.
ADC decentralized	2012 Observational, retrospective	Average antibiotic prescription time administration	Time from prescription to administration reduced from 55 to 26 min. Average time from arrival to antibiotic intake was reduced by 70 min	Hitti et al.
ADC decentralized	2017, university hospital Retrospective/ prospective study	Impact on administration time	Reduction of: 40% in administration time	Douglas et al.
ADC decentralized	2014, East London NHS Foundation Trust Prospective study	Impact of automation on reducing administration time	Reduction in administration time from 2.94 to 2.37 min per dose	Cottny et al.
ADC decentralized	1997, four departments within a major academic hospital Prospective study	Prescription processing time	88% decrease compared to pharmacy distribution	Martin et al.
ADC decentralized	Mercy hospital in Scranton, Pennsylvania, a 270-bed tertiary care Observational before/after study	Effects of an automated dispensing system on medication administration	Mean time difference between actual and scheduled drug administration decreased from 129.84 to 101 min	Shirely et al.

Table 10 References:
[16] Meknassi Salime G, Bhirich N, Cherif Chefchaouni A, El Hamdaoui O, El Baraka S, Elalaoui Y. Assessment of Automation Models in Hospital Pharmacy: Systematic Review of Technologies, Practices, and Clinical Impacts. Hosp Pharm. 2025 Feb 27;60(4):338-352. doi:10.1177/00185787251315622

Impact of connected dispensing technology with advanced analytics in a multicenter health system
Design	Mixed-methods, postmarket, observational study N= 11 acute care facilities
Objective	To evaluate the impact of enterprise inventory optimization (EIO) technology and analytics on pharmacy labor, costs, and medication availability within a large integrated delivery network (IDN)
Inclusion Criteria	Facilities within a 15-hospital integrated delivery network (IDN) in North Carolina using automated dispensing cabinets (ADCs), centralized pharmacy inventory software, and controlled substance vaults
Exclusion Criteria	Not specified
Methods	The study involved implementing an EIO that linked 600 decentralized ADCs (BD Pyxis MedStation ES), centralized pharmacy inventory software (BD Pyxis Logistics) with and without medication carousels, and controlled substance vaults (BD Pyxis CII Safe) via an inventory optimization analytics tool. Data was collected from transactional records and user perception surveys pre- and post-intervention. Descriptive and comparative statistical testing was used to assess outcomes.
Duration	January 2022 to October 2022
Outcome Measures	Primary: Impact on pharmacy labor, costs, and medication availability Secondary: User perceptions of inventory management complexity
Baseline Characteristics (reflects post-intervention data)		Bed count	ADC count	Average adjusted patient days/month	Unique inventory items	Average total dispenses monthly
	1	59	28	4,793	1,627	51,848
	2	36	21	1,974	1,245	32,656
	3	908	142	26,639	2,428	437,670
	4	127	52	7,984	1,788	85,504
	5	50	26	3,403	1,572	44,855
	6	123	41	8,743	1,795	94,448
	7	22	22	1,325	996	18,609
	8	28	20	3,335	1,498	31,938
	9	598	164	22,417	2,482	324,807
	10	189	57	10,246	2,062	126,214
	11	78	27	4,912	1,799	51,792
	Totals	2,218	600	95,771 (change from pre: -1,906)	19,292 (change from pre: 196)	1,300,341 (change from pre:
Results	Enterprise-wide, periodic automated replenishment (PAR) level changes increased 2.8-fold, yielding an estimated labor avoidance of 2,099 hours (roughly 1 full-time equivalent [FTE]) annually; 9 of 11 sites (82%) saw ADC inventory cost reductions exceeding $680,000 despite an enterprise policy shift from 3- to 5-day ADC supply targets; 7 sites (64%) had reduced ADC stockout rates, and all 11 sites showed decreased central pharmacy stockout percentages. Despite these advantages with the EIO model, overall enterprise inventory value rose $195,239, which likely reflected a shift in stock to the central pharmacy. User perception survey results complimented the objective metrics. Seventy-three percent of all users (n= 11/15) indicated they could manage PARs once a quarter, with 64% saying they could manage 5 times more than before the intervention. Forty-seven percent of respondents indicated a reduction in unscheduled refill trips, and 60% felt there was a reduction in pockets not used for more than 90 days. When asked about pharmacy waste, 50% of users felt there was a decrease in expired medi cations after solution implementation.
Adverse Events	Not applicable
Study Author Conclusions	Integration and optimization of connected inventory management technology improved labor productivity, reduced ADC inventory carrying costs, and increased medication availability.
Critique	This study suggests connected inventory automation can concurrently improve labor efficiency, cost, and medication availability, but industry funding (from Becton, Dickinson and Company), a single-enterprise design, uncontrolled confounders like drug price inflation and seasonality, and a short observation window limit generalizability and causal certainty, warranting longer-term, multi-vendor validation. Additionally, the study's findings may not be generalizable to all healthcare settings due to the specific technologies and processes used.

Table 11 References:
[17] Freeman-Muhammad S, Chipman-Ashley R, Martin RE 3rd, et al. Impact of connected dispensing technology with advanced analytics in a multicenter health system. Am J Health Syst Pharm. 2024 Nov 22;81(23):e760-e769. doi:10.1093/ajhp/zxae198

Return on Investment after Implementation of a Centralized Automated Storage System in a Hospital Pharmacy
Design	Retrospective, single-center analysis in France
Objective	To compare the projected and the real return on investment (ROI) after seven years of using and an important upgrade of the centralized automated dispensing system (ADS) in a hospital pharmacy
Methods	In 2008, a centralized pharmacy-based ADS, Rowa® system (ARX®), was implemented in the pharmacy (Rowa Extent). The pharmacy staff for medication global dispensing process was then composed of 3 pharmacy technicians and 2 pharmacy technician aides. This ADS was a tandem robot with a duplicate stock. In 2013, due to the widespread of datamatrix codes on drug boxes and recurrent failures and technical problems, the ADS was upgraded (Rowa Vmax). The new robot can read datamatrix but does not have a refrigerated unit; refrigerated drugs are stored outside the ADS. The hospital is equipped with a patient information system integrating an electronic patient record and a computerized physician order entry system (DxCare®, Medasys). Forty-three secure and automated medication cabinets (Omnicell® Inc.) are available in the 43 care units. ROI was calculated annually by deducting the cost of investment (ADS buying and implementation, maintenance, repairs, and upgrade) from the cost savings (drugs stock reduction and decrease of pharmacy staff dedicated to global dispensing).
Duration	2008 to 2015
Outcome Measures	Primary: Real ROI after seven years Secondary: Projected ROI, payback period
Results		Real ROI ($)	Projected ROI ($)
	2015	+163,331	+409,786
	As the robot was upgraded, the real payback period increased by approximately two years (six years of use, instead of two years as expected), explaining the difference between projected and real ROI observed.
Study Author Conclusions	Despite the initial cost and an unplanned upgrade, the real ROI was positive, leading to a ROI within a few years. Economic considerations should be balanced with benefits such as optimization of drug stock management and redeployment of pharmacy staff.
Critique	The study effectively demonstrates the financial impact of ADS implementation, but it lacks a detailed analysis of non-financial benefits such as staff satisfaction and error reduction. The retrospective nature and focus on financial metrics may overlook other significant impacts of ADS implementation.

Table 12 References:
[18] Berdot S, Savoldelli V, Zaugg V, et al. Return on investment after implementation of a centralized automated storage system in a hospital pharmacy. J Pharm Pharmacol. 2016;4(10):526-532. doi:10.17265/2328-2150/2016.10.002