Evidence Table 4KQ1: primary process outcomes for all technologies assisting drug administration

Article InformationHIT Studied
Integrated systems
SettingsOutcomes MeasuredResultsOutcome
Banet (2004)198
Design: Before- after
N = 55 nurses
Implementation: 05/2003
Study Start: 00/0000
Study End: 00/0000
CPOE/POE system, e-MAR, e-Medication administration system (e-MAR, e-TAR)
Integrated Imaging systems Laboratory system Pharmacy
Emergency department Academicdistribution of nurses’ time on activities, functions and contacts*Time-motion study demonstrated that after implementing the information system changes, nurses spent less time (mean percent of total time) on paper documentation (17% vs. 2%, RRR 90%, p <0.05) and searching for charts (0.4% vs. 0.1%, RRR 75%, p <0.05). They spent more time using computers (10% vs. 26%, RRR -157%, p <0.05), and charting in patients rooms (0.2% vs. 2.1%, RRR - 950%, p <0.05). They spent the same amount of time on documentation tasks overall (27% vs. 28%, RRR 3%, NS) and direct patient care (41% vs. 39%, RRR 4%, NS).+
Climent (2008)199
Design: Cross- sectional
N = 2,242 opportunities for error
Implementation: 00/0000
Study Start: 05/2005
Study End: 06/2006
3 different drug delivery systems, e-Medication administration system (e-MAR, e-TAR), e-RxAcute care/tertiary, 1,500 Beds Academicmedication error rate*, medication error rate- reaching patients*The integrated MMIT unit dose delivery system with e-Rx (DUPEA) had an error rate similar to the non-integrated unit dose system (DUTI), and the ward stock system (9.5% stock vs. 7.8% DUPEA vs. 4.7% DUTI). The error rate reaching patients with the DUPEA was lower than stock but higher than DUTI (8.1% stock vs. 5.5% DUPEA vs. 0.4% DUTI, p <0.05).
DeYoung (2009)200
Design: Before- after
N = 1,465 medication administrations in 92 patients
Implementation: 01/2007
Study Start: 12/2006
Study End: 05/2007
Integrated e-MAR
Critical care units (CCU, ICU, NICU)
38 in ICU, 744 in hospital Beds Academic
error rate-overall*, - excluding documentation errors*, - wrong administration time*The medication error rate was reduced by 56% after the implementation of BCMA (19.7% vs. 8.7%, p <0.001). This rate increased to 63% when documentation orders were excluded (p <0.001). The benefit was related to a reduction associated with errors of wrong administration time. Wrong administration time errors decreased from 18.8% during preimplementation to 7.5% postimplementation (p <0.001). There were no significant differences in other error types.+
Fontan (2003)46
Design: Cross- sectional
N = 4,532 prescriptions
Implementation: 00/1988
Study Start: 02/1999
Study End: 03/1999
Computerized UDDS
Integrated Hospital information system
Other specialty hospital (rehab, oncology) Pediatric stand alone hospital, 510 BedsPrescribing error rate, Administering error rateErrors were decreased with the use of the e-RX and computerized dispensing system compared with the hand-written prescriptions and ward distribution system. Prescribing errors were reduced from 87.9% to 10.6%, RRR 88%, p <0.00001 Administrative errors with time errors were reduced from 29.3% to 22.5%, RRR 23%, p <0.001.+
Franklin (2007)50
Donyai (2008)51
Barber (2007)52
Franklin (2008)53
Franklin (2007)54
Design: Before- after
N = 4,803 medication orders
Implementation: 06/2003
Study Start: 00/0000
Study End: 00/0000
Automated Dispensing Machine, e-Medication administration system (e-MAR, e-TAR) e-Rx
Integrated Pharmacy
Acute care/tertiary, 28 surgery bed ward of a teaching hospital Beds Inpatient hospital based, Academicerror rate for new prescriptions*, error rate for drug administrations*, %administered <1hr53, rate of pharmacist interventions51 Total pharmacy time taken on study wardThe prescription error rate for new orders dropped significantly after implementation of the system (3.8% vs. 2.0%, RRR 47%, p = 0.0004) Medication administration error rate also significantly declined (8.6% vs. 4.4%, RRR 49%, p = 0.0003).53 Postintervention medication timeliness was improved (%administered <1hr, 79% vs. 89%, p <0.001).51 The rate of pharmacist interventions declined significantly after implementation (3.0% vs. 1.9%, AR 1.1 (95% CI 0.2,2.0).54 Total pharmacy time taken on study ward increased after implementation (1h 8min vs. 1h 38min, p = 0.001). Pharmacists were required to endorse fewer orders (50% vs. 21%, RRR 58%, p <0.0001) and endorsed fewer orders (55% vs. 30%, RRR 45%, p <0.0001).+
Helmons (2009)201
Design: Before- after
N = 2,353 opportunities for error
Implementation: 05/2007
Study Start: 09/2007
Study End: 04/2008
Integrated Handheld
CPOE/POE system EHR/EMR system, e-MAR, Pharmacy
Critical care units (CCU, ICU, NICU) 386 Beds Academicerror rate-surgical medical unit*, error rate-ICU*The total medication administration error rates did not significantly decrease on the medical–surgical units (11% vs. 8%, RRR 23%, NS) the ICU (13% vs. 14% RRR - 7%, NS) or overall (13% vs. 14% RRR - 7%, NS) Accuracy measured by 6 indicators of accuracy reflecting error- prone process variations. Baseline medication administration accuracy higher in medical–surgical units compared with the ICUs. On the medical– surgical units, 3 accuracy indicators changed after the introduction of BCMA; improved compliance with checking patient identity after BCMA implementation was offset by more distractions and interruptions and less explanation of the medication to the patient. These 3 indicators did not change in the ICUs However, implementation of BCMA resulted in improved charting and labelling of medications administered in the ICUs.
Low (2002)202
Design: Before- after
N = not reported prescriptions
Implementation: 03/2000
Study Start: 03/1999
Study End: 03/2001
Integrated Hospital information system
Acute care/tertiary, Pharmacy Inpatient hospital basedrate of errors per 1,000 dosesThe rate of errors per 1,000 doses did not differ across the 24 month periods before and after BCMA (0.125 vs. 0.145, p = 0.6).
Mahoney (2007)99
Design: Before- after
N = 2,843,135 inpatient medication orders
Implementation: 02/2002
Study Start: 02/2002
Study End: 06/2006
CDSS/CDS/CCDS/reminders, CPOE/POE system, Pharmacy information system
Integrated EHR/EMR system, Hospital information system
General Hospital, Pediatric stand alone hospital, 966 in 2 hospitals Beds Pharmacy Inpatient hospital based, Academicrate of

drug allergy violations*,


excessive doses*,


incomplete or unclear orders*,


therapeutic duplication*

Medication errors decreased after implementation of the CIT with respect to drug allergy violations (OR 0.14, CI 0.11 to 0.17, p <0.001) excessive doses (OR 0.68, CI 0.62 to 0.74, p <0.001) and incomplete or unclear orders (0.35, CI 0.32 to 0.38, p <0.001) but no decease in therapeutic duplications. Turnaround time between drug ordering and administration decreased from 90 minuets to 11 minutes, no stats given. The override rate also decreased (7.1 to 2.9%, RRR 59%, p = 0.001).+
Mekhjian (2002)186
Design: Before- after
N = 28,898 patients
Implementation: 05/2000
Study Start: 02/2000
Study End: 01/2001
CPOE/POE system, e- Medication administration system (e-MAR, e-TAR)
Integrated Dietary system, EHR/EMR system, Imaging systems, Laboratory system
Acute care/tertiary, Other specialty hospital (rehab, oncology) Academicmedication turn-around time, proportion of verbal orders countersigned, rate of transcription errorsCombining the data showed that time from initiation of the prescription and administration was reduced after POE: mean 5:28 hours before vs. 1:51 hours after, 64% relative reduction, p <0.001. The proportion of signed verbal orders increased for both hospitals: OSUH 56.4% vs. 76%, RRR 76%, p <0.001 and James Cancer 72.8% vs. 99.0, RRR 36%, p <0.001. The volume of transcription errors was reduced after POE from 11.3% to 0%, RRR 100%, p <0.001.+
Mitchell (2004)187
Design: Cross- sectional
N = 4,297 prescriptions
Implementation: 0/1999
Study Start: 09/2002
Study End: 12/2002
e-Medication administration system (e-MAR, e-TAR), e-Rx
Integrated Formulary, Pharmacy
Acute care/tertiary, Academic15 aspects of data completeness for e- MAR were sought with implementation of the e-MAR.e-MAR was more accurate (more inclusion of important information) for nurses
9 of the 15 were statistically significantly improved including presence of dosing recommendations (30% v3 99%, RRR 230%, p <0.01), Errors detected by the pharmacist did not differ before and after implementation of the e-Rx system. Only minor errors were reduced with the system
Morriss (2009)203
Design: Cohort study
N = 958 patients; 92,398 doses administered
Implementation: 00/0000
Study Start: 00/0000
Study End: 00/0000
BCMA, e-Medication administration system (e-MAR, e-TAR)
Integrated Pharmacy
Critical care units (CCU, ICU, NICU) 36 beds NICU Beds AcademicMedication Error*, Potential ADEs*, preventable ADEs*When the BCMA system was not operative, the unadjusted medication error rates were 69.5/1,000 doses and mean 0.53 (SD 0.98)/subject/day. The unadjusted medication error rates increased in the study NICU when the BCMA system was operative to 79.7/1,000 doses and mean 0.60 (SD 0.99)/subject/day (p <0.001). The increase in medication error was associated with a 117% increase in detected wrong-time errors from 1412 before the BCMA system to 3075 when the system was operative. Significant decrease in potential ADEs [0.11 (0.47) vs. 0.033 (0.20), p <0.001], or unadjusted targeted, preventable ADEs [00.0065 (0.082) vs. 0.0032 (0.060) p <0.008] for subjects cared for in the BCMA system-equipped beds.+
Paoletti (2007)204
Design: Before- after
N = 1,868 Doses observed
Implementation: 08/2003
Study Start: 00/0000
Study End: 00/0000
BCMA, e-Medication administration system (e- MAR, e-TAR)
Integrated Hospital information system, Pharmacy
General Hospital, 521 Bedserror rate*The error rate compared between pre and postimplementation period in the three groups were: 19.6% vs. 20.6%, p = 0.762 (control); 25.3% vs. 19.2%, p = 0.065 (Intervention Group 1) and 15.6% vs. 10%, p = 0.035 (Intervention Group 2). Group 1 and 2 were noted to have different practices during baseline measurement. [unsure if this would be considered a positive trial].+
Persell (2008)205
Design: RCT
N = 242 patients
Implementation: 00/0000
Study Start: 10/2004
Study End: 03/2005
Integrated EHR/EMR system
Ambulatory care, Academicself-reported aspirin use* by all patients, self-reported aspirin use* by patients excluding long-term aspirin users and patients reporting medical contraindication
The control rate (reminders only) of self- reported aspirin use was not significantly different than the intervention (reminders plus clinician emails and patient phone calls) group (39% vs. 46%, p = 0.20). Excluding long-term aspirin users and patients reporting medical contraindication (30% vs. 43%, p = 0.013).
Poon (2006)206
Design: Before- after
N = 232 observation sessions
Implementation: 00/0000
Study Start: 02/2005
Study End: 10/2005
BCMAAcute care/tertiary, 735 Bedsproportion time on medication administration, proportion time nurses spent on direct careThe proportion of time nurses spent on the major activity groups remained stable. Before BCMA implementation, nurses spent 26.5% of their time on medication administration. After BCMA implementation, this proportion remained statistically unchanged at 24.5% (RRR 8%, p = 0.22). The proportion of time nurses spent on direct care activities unrelated to medication administration remained statistically unchanged (20.1% vs. 23.7%, RRR −18%, p = 0.15).
Poon (2010)207
Design: Before- after
N = 14041 medication administration
Implementation: 04/2005
Study Start: 02/2005
Study End: 10/2005
BCMA, e-Medication administration system (e-MAR, e-TAR)
Integrated EHR/EMR system, Pharmacy
Acute care/tertiary, 735 Beds AcademicNon-timing errors in medication administration*, Timing errors in medication administration*, transcription error (2ndary outcome)On units without the bar-code e-MAR, 776 (11.5%) non-timing medication- administration errors was observed compared to 495 (6.8%) on units with the bar-code e-MAR (p <0.001). The overall incidence of medication doses directly observed to be administered either early or late decreased from 16.7% without the bar-code e-MAR to 12.2% with its use (p = 0.001). The units without bar-code e- MAR observed 110 (6.1%) transcription errors while those with it observed no errors (p <0.001).+
Shirley (1999)208
Design: Before- after
N = 163 medication administrations
Implementation: 00/0000
Study Start: 05/1997
Study End: 08/1997
Automated drug dispensing system
Integrated, Pharmacy
Acute care/tertiary, 270 Bedsproportion of medications administered as scheduled*, mean time deviation between actual and scheduled administration times*,Before implementation of the automated dispensing system, 59% of 76 medication doses were administered as scheduled, after 77% of 87 doses were administered as scheduled (RRR −31%, p = 0.02). The mean time deviation between actual and scheduled administration times did not change significantly postimplementation (130 minutes vs. 101 minutes, p = 0.157).
Taylor (2008)209
Design: Before- after
N = 521 medication administrations
Implementation: 07/2005
Study Start: 09/2004
Study End: 04/2006
CPOE/POE system
Integrated e-MAR, Pharmacy
Critical care units (CCU, ICU, NICU)variance in medication administrationMedication variances were detected for 19.8% of administrations during the pre- CPOE period, compared with 11.6% with CPOE (RRR 41%, p <0.05). The reasons for medication administration variances during the pre- CPOE and CPOE were not statistically different. Overall, administration mistakes, pharmacy problems and prescribing problems accounted for 74% of all variances observed.+
Wax (2007)210
Design: Before- after
N = 14,465 patients
Implementation: 02/2005
Study Start: 06/2004
Study End: 12/2005
Anesthesia information management system (AIMS), CDSS/CDS/CCDS/reminders
Integrated EHR/EMR system
Acute care/tertiary, Academicoverall compliance with antibiotic administration before surgery, noncompliance due to late administration, noncompliance due to early administrationCompliance (antibiotics 60 min before surgery) for the entire data set increased from 82.4% to 89.1% (RRR −8%, p <0.01) following the event icon implementation. Noncompliance rates decreased following implementation for late administration (15.2% vs. 8.1%, RRR 47%, p <0.01), but remained unchanged for early administration (2.4% vs. 2.8%, RRR − 17%, p = 0.07).+
Wilson (1997)197
Design: Before- after
N = 00 not stated number of medications, etc
Implementation: 02/1994
Study Start: 07/1993
Study End: 06/1995
e-Medication administration system (e-MAR, e-TAR)
Integrated Formulary, Hospital information system
Acute care/tertiary, 362 Beds Inpatient hospital based, AcademicMedication occurrences per admission*, Medication occurrences per patient day*, Medication occurrences per order, Medication occurrences per doseSelf-reported medication occurrences (errors) per admission (11% vs. 7%, RRR 39%, p <0.001), per patient day (1.4% vs. 7%, RRR 34%, p <0.001), per order (0.4% vs. 0.3%, RRR 34%, p <0.001), and per dose (0.05% vs. 0.03%, RRR 40%, p <0.001) were all significantly reduced following implementation of a shared electronic medication record for pharmacists and nurses.+

The HIT system studied is in bold, followed by the systems that it was integrated with. The outcome column indicates whether at least 50% of the relevant outcomes abstracted were positively impacted by the MMIT (+) or not (−).


indicates outcomes noted as being the primary outcome by the paper’s authors

Abbreviations: AR = Absolute Reduction; BCMA = Bar Code Medication Administration; CCDS = Computerized Clinical Decision Support; CCU = Critical Care Unit; CDS = Clinical/Computerized Decision Support; CDSS = Clinical Decision Support System; CI = CI; CPOE = Computerized Provider Order Entry; EHR = Electronic Health Record; e-MAR = Electronic Medication Administration Record; EMR = Electronic Medical Records; e-RX = Electronic Prescribing; e-TAR = Electronic Treatment Authorization Request; hr = Hour; ICU = Intensive Care Unit; MMIT = Medication Management Information Technology; N = Sample Size; NICU = Neonatal Intensive Care Unit; NS = Not specified; OR = OR; OSUH= Ohio State University Health System; p = Probability; POE = Provider Order Entry; RRR = Relative Risk Reduction; SD = Standard deviation; UDDS = Unit Dose Drug Dispensing System; vs. = Versus

From: Appendix C, Evidence Tables

Cover of Enabling Medication Management Through Health Information Technology
Enabling Medication Management Through Health Information Technology.
Evidence Reports/Technology Assessments, No. 201.
McKibbon KA, Lokker C, Handler SM, et al.

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