Evidence Table 2KQ1: primary process outcomes for all technologies assisting order communication

Article InformationHIT
Integrated systems
SettingsOutcomes MeasuredResultsOutcome
Astrand (2009)182
Design: Cross- sectional
N = 14,365 prescriptions
Implementation: 00/0000
Study Start: 02/2006
Study End: 03/2006
e-Rx, e-Transmission-of the prescription to/from doctor to pharmacyPharmacy, Other mail/email in pharmaciesproportion of new prescriptions needing clarification*Clarification contacts were made for 2.0% (147/7532) of new e-Prescriptions and 1.2% (79/6833) of new non-electronic prescriptions. RR 1.7 (95% CI 1.3 to 2.2) Increased RR was mainly due to ‘Dosage and directions for use’, RR 7.6 (95% CI 2.8 to 20.4) when compared to other clarification contacts. In all, 89.5% of the suggested pharmacist interventions were accepted by the prescriber, 77.7% (192/247) as suggested and an additional 11.7% (29/247) after a modification during contact with the prescriber.
Beer (2002)183
Design: Cross- sectional
N = 836 Medication orders
Implementation: 00/0000
Study Start: 06/2002
Study End: 06/2002
CDSS/CDS/CCDS/reminders
CPOE/POE system
Integrated Hospital information system
Pharmacy, Outpatient hospital based Academicmean time required to complete prescription review for OpTx order *The mean time required to complete the prescription review for an OpTx order was 11.11 min (95% CI 10.1 to 12.1; n = 140) compared to the mean time to review a paper order of 5.96 min (95% CI 5.6 to 6.4, p <0.001; n = 696). Therefore, the mean time required to review an order was increased by 5.15 min with the implementation of the direct electronic order entry system.
Ekedahl (2004)184
Design: Cross-sectional
N = 24,6991 prescriptions
Implementation: 00/0000
Study Start: 05/2000
Study End: 10/2001
e-Rx
Integrated Pharmacy
Pharmacy, Not specifiedrate of non compliance (unclaimed “all other” prescriptions vs. unclaimed e- Prescriptions)Rate of non compliance between unclaimed “all other” prescriptions 369/322754 (0.01%) vs. unclaimed e-Prescriptions 2,171/91,704 (2.37%).
Halkin (2001)65
Design: Time series
N = 775,186 prescriptions
Implementation: 11/1997 to 00/1998
Study Start: 01/1998
Study End: 06/1999
CDSS/CDS/CCDS/reminders
Integrated, Pharmacy
Pharmacy, HMO pharmacyrate of drug interaction prescriptions when 90% of pharmacies and 50% of physicians compared with baseline, rate of drug interaction prescriptions when 95% of pharmacies and 90% of physicians compared with baselineDispensing of drug interaction prescriptions reduced by 21.1% and by 67.5% in periods II and III compared with period I (OR, 0.79; 95% CI 0.75 to 0.83 and OR, 0.28; 95% confidence limit, 0.26 to 0.30, respectively).+
Humphries (2007)185
Design: Before-after
N = not given prescriptions
Implementation: 02/2002
Study Start: 07/2000
Study End: 05/2005
CDSS/CDS/CCDS/re minders
Integrated EHR/EMR system
Pharmacy
Ambulatory care, Outpatient hospital basedproportion of co-dispensings for interacting drugs per 10,000 prescriptionsThe proportion of prescriptions of any of the 8 drug pairs decreased after implementation of CDIX for all 8 drugs (21.3 to 14.7 per 10,000 prescriptions, RRR 31%, (CI 12.7 to 49.5, p = 0.01).+
Johnson (2010)75
Design: RCT
N = 3,285 patients
Implementation: 00/0000
Study Start: 04/2007
Study End: 08/2007
CDSS/CDS/CCDS/reminders
e-Rx
Integrated EHR/EMR system
Ambulatory care, Pharmacy, Not specified, Academicrate of callbacks generated*There was no significant difference in the callback rates between the “SYW off” and the “SYW on” periods (0.4% vs. 0.45%; p = 0.47).
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 Academic
rate 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, NR. 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, RRR 64%, 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 redueced with the system.+
Murray (1999)188
Design: Cohort
N = 11,102 observations of 28 pharmacists
Implementation: 03/1995
Study Start: 11/1995
Study End: 01/1996
Pharmacy information system
Integrated EHR/EMR system
Imaging systems
Laboratory system
Acute care/tertiary, Pharmacy, Inpatient hospital baseddistribution of pharmacist time on activities, functions and contacts*.The electronic guidelines and reminders were associated with the overall distribution of activities (more time discussing information and less time checking and preparing prescriptions) p <0.001; overall functions (more time advising or discussing information or problem solving and less time filling prescriptions) p <0.001 and distribution of contacts (more time with other pharmacy personnel, patients, and clinicians and less time working alone) p <0.001.+
Nam (2007)189
Design: Before-after
N = 39 Patients
Implementation: 00/0000
Study Start: 06/2003
Study End: 05/2005
CPOE/POE system
Integrated EHR/EMR system
Laboratory system
Emergency departmentTime to arrival to tPA treatment in minutes*Time from arrival to tPA treatment was reduced by 23 minutes (from 79 to 56 minutes; p <0.01).+
Nilsson (2007)190
Design: Cohort study
N = 2,563 prescriptions (acute)
Implementation: 00/0000
Study Start: 02/2005
Study End: 03/2005
e-Rx
Integrated Pharmacy
Pharmacy, Otherrate of prescription pick up by patients within 5 days*e-RX accounted for 84% of the prescriptions. Among the patients with e- prescriptions 91% picked up their prescriptions in 5 days compared to 85% in the paper group, (RRR -7%, p <0.01).+
Pearce (2010)191
Design: Before-after
N = 332 medication refill orders
Implementation: 05/2006
Study Start: 02/2006
Study End: 03/2007
e-Rx
Integrated EHR/EMR system
Pharmacy
Ambulatory care, Pharmacy, Pharmacy chaintime to a response for refill request*The average time to a response to a pharmacy refill request decreased from 1.57 days to 1.04 days (p <0.004).+
Senholzi (2003)192
Design: Before-after
N = 349 pharmacist interventions
Implementation: 11/2001
Study Start: 00/0000
Study End: 00/0000
CDSS/CDS/CCDS/reminders
CPOE/POE system
Integrated e-MAR: Nursing Medication Administration Record
Acute care/tertiary, 633 Beds Inpatient hospital based, AcademicNumber of pharmacist interventionsThe number of pharmacist interventions remained the same before and after CPOE implementation in the control unit (80 before and 84 after) In the CPOE unit the number of pharmacist interventions increased from 76 to 109, p <0.01.+
Varkey (2007)172
Design: Cross- sectional
N = 4,527 prescriptions
Implementation: 00/0000
Study Start: 00/1996
Study End: 00/2002
CPOE/POE system
Integrated CDSS/CDS/CCDS/re minders
Ambulatory care, Other institution basedfrequency of intercepted prescription errors*Statistically significant decrease in frequency of intercepted prescription errors among handwritten and computerized prescriptions was observed (7.4% vs. 4.9%, p = 0.0048).+
Wess (2007)193
Design: Before-after
N = 3,791 medication orders
Implementation: 06/2005
Study Start: 00/0000
Study End: 00/0000
CPOE/POE system
Integrated EHR/EMR system
Hospital information system
General Hospital, Inpatient hospital based, Academicmean time from provider order entry to pharmacist verification, -community hospital, - university hospital, proportion of clarification calls placed, - community hospital, - university hospitalThe mean time from provider order entry to pharmacist verification decrease for both community (152 vs. 32 minutes, p <0.0001) and university hospitals (108 vs. 50 minutes, p <0.0001) The call back percentage also decreased for both community (2.8 vs. 0.4%, RRR 86%, p <0.0001) and university hospitals (2.8% vs. 0.5%, RRR 82%, p <0.0001).+
Wietholter (2009)194
Design: Before-after
N = 2,988 medication orders
Implementation: 00/0000
Study Start: 00/0000
Study End: 00/0000
CPOE/POE system
Integrated Pharmacy
Acute care/tertiary, 761 Beds Inpatient hospital basedmean order-processing time (min)*The mean order-processing time before CPOE implementation was 115 minutes from prescriber composition to pharmacist verification. After CPOE implementation, the mean order-processing time was reduced to 3 minutes (p <0.0001).+

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: CCDS = Computerized Clinical Decision Support; CDIX = Critical Drug Interaction Alert Program; CDS = Clinical/Computerized Decision Support; CDSS = Clinical Decision Support System; CI = Confidence interval; 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; GP = General Practitioner; HIT = Health Information Technology; HMO = Health Maintenance Organization; N = Sample Size; NS = Not specified; OSUH = Ohio State University Health System; p = Probability; POE = Provider Order Entry; RCT = Randomized Controlled Trial; RR = Relative Risk; RRR = Relative Risk Reduction; vs. = Versus

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

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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