What data is available for health systems with automated dispensing machine (i.e. Pyxis, Omnicell, etc) fulfillment services?

Comment by InpharmD Researcher

In general, implementation of automated dispensing cabinets (ADCs) reports a reduction in dispensing error rates, though one simulation 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. Available cost-analysis data are either outdated or limited for comparison.
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 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 automatic dispensing cabinet (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 2013 simulation study assessed different medication distribution models in an effort to find an alternative to the hospital’s existing hybrid distribution model (64% of doses dispensed via cart fill and 36% via ADCs; see Table 1). Relative to the base case (hybrid model), a simulation modeling of different distribution scenarios, one involving no use of cart fill, one involving no use of ADCs, and one heavily dependent on ADC dispensing (Decentralized; 89% via ADC and 11% via cart fill), showed that the calculated pharmacy technician labor requirements decreased in a decentralized medication distribution system which involved greater use of ADCs (-671.37 min no cart fill vs. -291.50 min no ADCs vs. 211.02 min Decentralized) but only at the expense of the nursing staff workload (859.21 min vs. -706.59 min vs. 977.16 min). Given the higher labor cost of the nurses than that of pharmacy technicians, the projected human resource opportunity cost of transitioning from the existing hybrid system to a suggested decentralized system was estimated to be $229,691 per annum. A more ADC-dependent model resulted in an unfavorable shift in staff skill mix and corresponding human resource costs at the medical center. [6]

Background References: [1] M Boyd A, W Chaffee B. Critical Evaluation of Pharmacy Automation and Robotic Systems: A Call to Action. Hosp Pharm. 2019;54(1):4-11. doi:10.1177/0018578718786942
[2] Barker KN, Pearson RE, Hepler CD, Smith WE, Pappas CA. Effect of an automated bedside dispensing machine on medication errors. Am J Hosp Pharm. 1984;41(7):1352-1358.
[3] Klein EG, Santora JA, Pascale PM, Kitrenos JG. Medication cart-filling time, accuracy, and cost with an automated dispensing system. Am J Hosp Pharm. 1994;51(9):1193-1196.
[4] Tsao NW, Lo C, Babich M, Shah K, Bansback NJ. Decentralized automated dispensing devices: systematic review of clinical and economic impacts in hospitals. Can J Hosp Pharm. 2014;67(2):138-148. doi:10.4212/cjhp.v67i2.1343
[5] BD. Medication Safety and Quality of Care. Published June 2015. Accessed July 16, 2026. https://web.archive.org/web/20200711030719/https://www.bd.com/documents/international/white-paper/medication-supply-management/DI_King-Faisal-Hospital_WP_EN.pdf
[6] 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
Literature Review

A search of the published medical literature revealed 9 studies investigating the researchable question:

What data is available for health systems with automated dispensing machine (i.e. Pyxis, Omnicell, etc) fulfillment services?

Level of evidence

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



Please see Tables 1-9 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