Medication reconciliation is an im-portant method for accurately de-

termining discrepancies that occur at de-fined points of transition in the hospitalsystem.1-3 Discrepancies identified dur-ing reconciliation are brought to the at-tention of the prescriber and, if appropri-ate, changes are made to the orders. Poorcommunication has been shown to beconsistently responsible for more thanhalf of all medication errors that occur inhospitals during patient movementacross transition points of care as well asup to 20% of all adverse events.3,4

Past investigations of medication dis-crepancies have focused on admissionand discharge practices in general medi-cal and surgical settings.5-13 Little consid-eration has been given to the emergencydepartment (ED) context of care; howev-er, a recent survey of ED physicians hasshown that it is an environment that isprone to medication errors.14,15 Patientswho present to the ED with an un-planned medical event are most vulnera-ble to medication mismanagement be-cause they have just sustained a criticalillness and often cannot communicateadequately. The ED is also an environ-ment characterized by the need for deci-sions to be made rapidly under high lev-

The Annals of Pharmacotherapy n 2009 November, Volume 43 n 1755

Medication Use Across Transition Points from the Emergency

Department: Identifying Factors Associated with Medication

Discrepancies

Elizabeth Manias, Marie Frances Gerdtz, Tracey J Weiland, and Marnie Collins

theannals.com

Emergency Medicine

Author information provided at the end of thetext.

BACKGROUND: As patients move across transition points of care, medicationdiscrepancies are likely to occur. In the emergency department (ED), patients arevulnerable to medication discrepancies because they are in an environment inwhich rapid decisions need to be made under high levels of stress.

OBJECTIVE: To identify the patient-, environment-, and medication-related factorsinvolving unexplained medication discrepancies across transition points after EDpresentation.

METHODS: Using a retrospective chart review design, a stratified, randomsampling of data was undertaken over a 12-month period. Information wasobtained from an electronic administrative database and medical records aspatients moved from the ED to another transition point of care. Medicationdiscrepancies were classified into 2 outcome groups: (1) no discrepancies andsituations in which discrepancies were adequately explained and (2) discre-pancies that had no adequate explanation.

RESULTS: For the 12-month period, 210 randomly selected patients wereincluded; 73 (34.8%) had at least one unexplained medication discrepancy.Binary logistic regression modeling showed 4 factors that were statisticallysignificant in determining the incidence of at least one unexplained medicationdiscrepancy. Benefit card holders (individuals who receive benefits fromgovernment insurance programs comparable to the US-based Medicare andMedicaid initiatives, which include the elderly, the disabled, low income earners,and unemployed persons) had 3.73 greater odds of experiencing an unexplainedmedication discrepancy (95% CI 1.72 to 8.07; p = 0.001). Patients prescribed 5or more drugs at discharge from the ED had 12.22 greater odds of having at leastone unexplained medication discrepancy (95% CI 5.52 to 27.08; p < 0.001).Patients who were first seen by a physician within 1 hour of a change in workingshift had 3.70 greater odds of having an unexplained medication discrepancy(95% CI 1.67 to 8.18; p = 0.001). For each additional minute of wait time for aphysician, the odds of having an unexplained medication discrepancy increasedby a factor of 1.01 (95% CI 1.00 to 1.01; p = 0.042).

CONCLUSIONS: Patient-, environment-, and drug-related factors contribute to therisk of medication discrepancies across transition points from the ED.

KEY WORDS: care transition, communication, emergency department, medicationdiscrepancy, medication reconciliation.

Ann Pharmacother 2009;43:1755-64.

Published Online, 20 Oct 2009, theannals.com, DOI 10.1345/aph.1M206

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els of stress. In view of the great vulnerability of patients inthis context, it is important that medication managementbe examined comprehensively.16

The aim of this study was to measure the incidence of pa-tients having at least one unexplained medication discrepan-cy after presentation to the ED. The objectives were to identi-fy patient-, environment-, and medication-related factors as-sociated with these discrepancies during patients’ time in theED.

Methods

STUDY DESIGN

In this study, we used a retrospective chart review designfor the period from May 1, 2006, to April 30, 2007. The re-view was undertaken at a principal referral hospital in Mel-bourne, Australia. There were 37,860 ED visits during thestudy period. Approval for the research was obtained fromthe institutional ethics board of the hospital and the study wasconducted in accordance with ethical guidelines.17

DATA COLLECTION

A stratified random sampling technique was undertakenfor data collection. For each month of the review period, allpatients who had presented to the ED were assessed for eligi-bility. The variable used for stratification related to dischargedestination of patients. Attempts were made to ensure thatequal numbers of patients admitted to a surgical, medical, orshort-stay emergency unit were obtained. Remaining patientsrecruited over the study period were discharged home. Pa-tients were eligible for inclusion if they were aged 18 years orolder, if they presented to the ED for an unplanned admis-sion, and if they were seen in the ED by a physician. Patientswere excluded if they transferred from another hospital, weredead on arrival, were readmitted during the study period, orwere previously included in the study. A random numberstable was used to select which eligible patients’ records wereto be accessed for each month, and between 16 and 18 pa-tient records were accessed each month to derive the sample.

The sample size was calculated using the confidence in-tervals method, based on an estimated prevalence of the out-come.18 It was hypothesized that the outcome, which per-tained to patients having had at least one medication discrep-ancy without appropriate clinical explanation, would have anestimated prevalence of 30%. Using this estimated preva-lence, a sample size of 191 would be required to produce aconfidence interval of ±6.5% precision or 0.13 for the desiredinterval width of the 95% confidence level. The sample wasobtained by collecting approximately 16–18 patient medicalrecords per month for the review.

Information was sought from the Patient AdmissionSystem, which is an electronic means of collecting demo-graphic and clinical data about patient care. Medical

records were also manually examined. Collectively, datainformation helped to identify patient-, environment-, andmedication-related factors that could influence the occur-rence of medication discrepancies as patients moved fromthe ED to another transition point, such as a surgical ormedical ward, or home (Appendix I).

Patient-related factors included the patient’s age, sex,chief type of complaint for admission to the ED, need for aninterpreter, visual deficit, hearing deficit, and benefit card sta-tus. Benefit card holders involved individuals receiving bene-fits from government insurance programs, which are compa-rable to the US-based Medicare and Medicaid initiatives, andincluded the elderly, disabled, low income earners, and un-employed. Medication-related factors included the number ofmedications taken by the patient at discharge from the EDand presence of a medication allergy. Environment-relatedfactors included the discharge destination, position of the at-tending physician, mode of arrival to the ED, arrival time atthe ED, discharge time from the ED, duration of stay in theED, time first seen by the ED physician, time first seen beingwithin 1 hour of change of working shift, wait time before anED physician examined the patient, and allocation of catego-ry on the Australasian Triage Scale. The Australasian TriageScale classifies the urgency of presentations to AustralianEDs and comprises 5 categories.19,20 A pharmacist was em-ployed in the ED halfway into the review period, and the ef-fect of this environmental factor was also examined.

Medications were audited and compared at 2 time points.At the first time point, a description of all medications at thepoint of entry to the ED was collected as determined by theED physician at first interview. This description included thedose and frequency of all medications taken, the method ofadministration and type of formulation, and the presence ofany known allergies. This was a routine component of theinitial patient assessment by the ED physician and was docu-mented in patients’ ED admission notes in their medicalrecords. At the second time point, a description of the drugsordered for patients at discharge from the ED was document-ed by the admitting physician of the inpatient department, inconsultation with the ED physician. If patients were not ad-mitted, the ED physician documented the medications to beprescribed for patients upon discharge from the hospital. Thedescription included the dose and frequency of all medica-tions taken, the method of administration and type of formu-lation, and the identification of any new medication allergiesas well as other known allergies. This information was docu-mented on the patient’s medication chart. Medication dis-crepancies between the 2 time points were examined.Sources of information for drugs at the point of entry includ-ed ED discussions with patients, ambulance officers, andfamily members or other people accompanying the patient;outpatient notes; ED nurses’ notes from communication theyhad with patients and accompanying people; staff pharma-cists’ notes; and community physicians’ notes.

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MEASURES

A medication discrepancy was defined as any differenceobserved between the drugs listed on the medication chartby the attending physician at the time of discharge fromthe ED and those contained in the medication history ob-tained at admission. The discrepancies were systematicallyidentified and categorized into 1 of 8 forms by a pharma-cist not involved in the patient’s care. These categoriza-tions have been validated in past research.9,21

DATA ANALYSIS

Statistical analysis was primarily carried out on patient-level data using SPSS version 16 (SPSS Inc., Chicago, IL).The outcome (dependent) variable of consideration was de-fined to be binary: (1) patients who had no medication dis-crepancies or whose medication discrepancies were ex-plained and (2) patients who had at least one unexplainedmedication discrepancy. For statistical analysis, categories ofdiscrepancy were collapsed into 2 outcome groups: no dis-crepancies, which also included situations in which discrep-ancies were adequately explained (categories 1–4), and dis-crepancies that had no adequate explanation (categories5–8). Descriptive statistics analysis was undertaken for allvariables. Univariate associations with the medication dis-crepancy outcome were first investigated for all patient-,medication-, and environment-related factors using cross-tabulations and χ2 tests for categorical variables and binarylogistic regression for continuous variables. Binary logisticregression modeling was then undertaken with all factors, us-ing a forward stepwise approach, which is an appropriatemethod if little previous work has been completed in the par-ticular context under investigation. In the forward stepwisemethod, the model begins with a constant and tests whetheradded variables have a substantial effect in terms of how wellthe model fits the observed data.22

Unexplained discrepancies were assessed as having an un-likely, possible, or probable potential to cause patient discom-fort or clinical harm, using an instrument described in pastwork (Appendix II).5 This process of examining the clinicalimpact was undertaken independently by 2 researchers. A pvalue of less than 0.05 was considered statistically signifi-cant. Interrater reliability for assessing the potential of unex-plained discrepancies to cause patient discomfort or patientharm was analyzed using Cohen’s κ measure of agreement.22

Results

DEMOGRAPHICS

For the 12-month period, 210 randomly selected pa-tients were included in the review (Figure 1). The mean ±SD age of patients was 51.9 ± 21.7 years (range 18–97).Other demographic characteristics are shown in Table 1.

CHARACTERISTICS OF MEDICATION DISCREPANCIES

A total of 1087 medications were prescribed at the timeof discharge from the ED across the sample of 210 patients,according to the 8 categories of discrepancies (Table 2). For37.6% of the drugs, no discrepancy was detected. For all 210patients, 73 (34.8%) had at least one unexplained medicationdiscrepancy. Most notably, 13.2% (143/1087) of medicationswere associated with unexplained discrepancies for whichthere was a lack of clinical explanation for changes made. Ofthe unexplained discrepancies (n = 143), 84 (58.7%) wereunlikely to cause patient discomfort or clinical harm, while55 (38.5%) and 4 (2.8%) discrepancies had the potential tocause either possible or probable patient discomfort or clini-cal harm, respectively. The κ score obtained for agreementbetween the 2 researchers assessing clinical impact of medi-cation discrepancy was 0.97. Table 3 shows the formulationtype and medication type in relation to no discrepancy or anexplained discrepancy, and unexplained discrepancy deter-mined for all 1087 medications.

Factors in Medication Discrepancies Across Transition Points from the ED

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Figure 1. Flow diagram for sample selection over the audit period (May1, 2006–April 30, 2007). ED = emergency department.

UNIVARIATE ANALYSES

Univariate analyses for patient-related factors revealedthat increased patient’s age, the patient’s need to have aninterpreter present in the ED, benefit card status, and pres-ence of a visual deficit were statistically significantly relatedto the incidence of an unexplained medication discrepancy(Table 4). Two drug-related factors—having 5 or more medi-cations ordered at the time of discharge from the ED andhaving a known medication allergy—were statistically sig-nificant relative to the incidence of an unexplained medica-tion discrepancy in the univariate analysis (Table 4). Univari-ate analysis also revealed that 3 environmental factors werestatistically significantly associated with the incidence of anunexplained medication discrepancy: patients’ admission to ahospital inpatient setting, the type of discharge destination,and being seen by an ED physician within 1 hour of changeof the working shift (Table 4).

EXPLANATION OF PREDICTOR EFFECTS BY LOGISTIC

REGRESSION MODELING

Binary logistic regression modeling involving a forwardstepwise variable selection approach was used to identify thebest combination of patient-, medication-, and environment-related factors for predicting the occurrence of an unex-plained medication discrepancy. All factors were consideredfor inclusion in the model regardless of whether they showeda significant relationship in univariate analysis. One patient-related factor was independently associated with an unex-plained medication discrepancy. Patients who were benefitcard holders had 3.73 greater odds of experiencing an unex-plained medication discrepancy compared with those whowere not benefit card holders (95% CI 1.72 to 8.07; p =0.001) (Table 5). Regarding medication-related factors, pa-tients who were prescribed 5 or more drugs at ED dischargehad 12.22 greater odds of having at least one unexplainedmedication discrepancy compared with those who were pre-scribed fewer than 5 medications at ED discharge (95% CI5.52 to 27.08; p < 0.001). Two environment-related factorswere significant: the patient being seen by an ED physicianwithin 1 hour of change of the working shift and the physi-cian wait time were independently related to the incidence ofan unexplained medication discrepancy. Patients who werefirst seen by a physician within 1 hour of a change in work-ing shift in the ED had 3.70 greater odds of having an unex-plained medication discrepancy compared with those whowere first seen by a physician at other times (95% CI 1.67 to8.18; p = 0.001). As the physician wait time increased, pa-tients had 1.01 greater odds of having an unexplained medi-cation discrepancy (95% CI 1.00 to 1.01; p = 0.042).

The multivariate logistic regression model had a sensitivityof 70%, which is the proportion of the sample with at leastone unexplained medication discrepancy that was correctlyidentified by the model. The specificity was 86%, which re-

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Table 1. Demographics for Study Populationa

Pts., Portion of Total Demographic n (%) ED Pts.,b %

Sexmale 125 (59.5) 54.7female 85 (40.5) 45.3

Place of birthAustralasia 129 (61.4) 61.5Europe 43 (20.4) 21.8Asia 23 (10.9) 10.6US 10 (4.8) 4.2Africa 5 (2.4) 1.9

Primary language spoken at homeEnglish 191 (91.0) 89.2Italian 8 (3.8) 2.8Greek 2 (1.0) 1.6Vietnamese 2 (1.0) 1.9other 7 (3.3) 4.5

Australian Triage Code1 (seen immediately) 5 (2.4) 1.32 (seen within 10 min) 29 (13.8) 9.13 (seen within 30 min) 104 (49.5) 39.54 (seen within 60 min) 67 (31.9) 42.45 (seen within 120 min) 5 (2.4) 7.6

Transport to EDown transportation 130 (61.9) 66.7emergency vehicle 80 (38.1) 33.3

Chief complaintillness 169 (80.5) 81.5injury 41 (19.5) 18.5

Interpreter requiredyes 16 (7.6) 8.5no 194 (92.4) 91.5

Benefit card holderyes 70 (33.3) 32.5no 140 (66.7) 67.5

Discharge outcomehospital admission 57 (27.1) 24.8no hospital admission 153 (72.9) 75.2

Destinationhome 29 (13.8) NAshort-stay emergency unit 61 (29.0) NAmedical unit 60 (28.6) NAsurgical unit 60 (28.6) NA

Hearing deficityes 199 (94.8) NAno 11 (5.2)

Visual deficityes 38 (18.1) NAno 172 (81.9)

Mean ± SD Median (IQR) Range

Drugs prescribed 3.2 ± 4.0 1.0 (0.0 to 5.3) 0–18for pts. prior to ED presentationc

Drugs prescribed for 5.3 ± 4.5 4.0 (2.0 to 8.0) 1–30pts. by ED physician at discharge from ED

Length of stay in ED 335.6 ± 233.9 294.5 (198.0 1–1507(min) to 405.8)

Length of wait-time to 49.1 ± 91.2 19.0 (5.0 to 55.5) 0–765see ED physician (min)

ED = emergency department; IQR = interquartile range; NA = not avail-able.an = 210. bN = 37,860.cAs documented in medication history taken on admission.

lates to the percentage of the sample without at least one un-explained medication discrepancy that was correctly identi-fied by the model. The positive predictive value was 73%,while the negative predictive value was 84%. Using this mod-el, we were able to correctly classify 81% of cases overall.

Discussion

This review demonstrates the importance of consideringpatient-, medication-, and environment-related factors in a

systematic way to determine their effects on medicationdiscrepancies. The focus on patients presenting to the EDis pertinent because of the constantly changing dynamicsof patients in that setting and the demands being placed onhealth professionals managing their care. Very little workhas examined this transition point of care in a methodicaland comprehensive manner.

The unexplained medication discrepancy rate of 34.8%(73/210 patients) was lower than that reported by Lau etal.21 (67.0%) and Cornish et al.5 (53.6%), but was higher

Factors in Medication Discrepancies Across Transition Points from the ED

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Table 2. Characteristics of Medication Discrepanciesa

Discrepancy, Characteristic n (%) Examples

No discrepancy, drug 409 (37.6)reconciled

New drug prescribed based 526 (48.4) metoclopramide 10 mg po prn started for nausea and vomitingon changes in clinical status diazepam 2 mg po prn for acute anxiety

albuterol 5 mg nebulizer for shortness of breathcefazolin 2000 mg iv tid for treatment of cellulitisacetaminophen 500 mg/codeine 5 mg combination tablet qid for low back pain

Drug omitted or change in 6 (0.6) insulin detemir dose increased from 12 to 16 units; insulin aspart dose increased from 6 to 8 units to dose, route, or frequency stabilize glucose levels for type 1 diabetesbased on change in clinical candesartan dose changed from 12 to 16 mg once daily to treat worsening heart failurestatus digoxin dose decreased from 250 to 187.5 µg/day based on recent CrCl of 40 mL/min for 70-kg man

glyceryl trinitrate patch changed from 10 to 15 mg/24 h to better control anginal symptoms

Similar alternative drug 3 (0.3) pt. on controlled-release metoprolol 47.5 mg at home but had difficulty swallowing; preparation prescribed based on change changed to conventional tablets (50 mg)in clinical status or availability drug changed from quinapril 10 mg daily to perindopril 10 mg dailyin hospital formulary pt. on indomethacin 25 mg tid at home for lower back pain; changed to ibuprofen 200 mg 3–4 times

daily in hospital

Drug omitted without clinical 50 (4.6) stopped on pt. discharge from ED:explanation quetiapine 25 mg bid for schizophrenia

omeprazole 20 mg bidranitidine 150 mg bid for peptic ulcer disease initially prescribed 3 wk prior to ED admissionisosorbide mononitrate 60 mg once daily for prevention of angina acetaminophen 1330 mg bid for osteoarthritiscitalopram 20 mg daily for depression

New drug ordered without 42 (3.9) 2 formulations of metformin noted on medication chart after addition of 1 formulation in the ED; only clinical explanation 1 formulation was administered, according to nurse’s signature

fentanyl patch 25 µg once daily for symptomatic relief of dyspnea; fentanyl not usually helpful in dyspnea; patch should not be used in opiate-naïve pt.

tiotropium 18 µg ordered for symptomatic relief of exacerbation of chronic obstructive pulmonary disease; no short-acting β-agonists, oral corticosteroids, or antibacterials ordered

potassium chloride 2400 mg tid ordered for potassium supplementation; pt. already receiving amiloride 10 mg bid (potassium-sparing diuretic) for hypertension

Different dose, route, or 45 (4.1) glibenclamide dose changed from 5 mg bid to 10 mg bid without change in clinical status or blood frequency of medication glucose levels without clinical explanation pt. on nifedipine 60 mg controlled-release formulation before ED presentation; 60 mg/day (includes illegible or missing conventional tablets ordered on discharge from EDinformation about dose) pt. previously on controlled-release metformin 500 mg/day; formulation changed to conventional

tablet 500 mg/day; no reason providedwrong dose for glyceryl trinitrate patch noted on medication chart (should be 5 mg/patch over 14 h/day); dose of 600 µg was noted, which is for sublingual tablets; not identified by clinicians but signed that patch had been given

salmeterol 25 µg/dose metered-dose inhaler ordered; pt. was on dry powder Accuhaler at home (50 µg/dose)

Different drug from same 6 (0.6) pt. on omeprazole before ED presentation; changed to pantoprazole with no clinical explanationtherapeutic class ordered pt. taking perindopril arginine 2.5 mg once daily before presentation; changed to perindopril erbuminewithout clinical explanation 2 mg once daily on discharge from ED

not clear why alprazolam 2 mg tid and oxazepam 30 mg once daily were both ordered for acute anxiety

CrCl = creatinine clearance; ED = emergency department.aTotal medications = 1087.

than that observed by Miller et al.23 (4.3%). Variations indiscrepancy rates are likely due to the different environ-ments and time periods in which studies were undertaken.In the Lau et al.21 study, patient records were accessedfrom general internal medicine wards of 2 acute care hos-pitals over a 2-year period from 1993 to 1995. Cornish etal.5 accessed records during 3 months in 2003 from pa-tients admitted to the general internal medicine clinicalteaching units of a tertiary care teaching hospital. In Milleret al.’s23 study involving patients admitted to a trauma cen-ter during 2005, the urgency of treatment decisions thatneeded to be made was likely to have been similar to thatof the ED in our study. The large difference in rates in the2 settings may have been due to the formalized medicationreconciliation process instituted by Miller et al.

Previous studies have also shown that omission of med-ication was the most common form of unexplained dis-crepancy, which corresponds with our findings.13,24 In ourstudy, the omission of medication pertained to the transi-

tion period from the ED to another setting, which occurredduring the course of 1 day. The clinical impact of this find-ing is not clear because of the short time involved. Furtherclarification is needed as to what happens to these omis-sions at the time of discharge from inpatient hospital envi-ronments.

The logistic regression modeling process identified acombination of 4 factors that significantly statistically pre-dicted the incidence of at least one unexplained medicationdiscrepancy. The patient factor that was shown to be sig-nificant was the patient’s status as a benefit card holder. Themedication factor shown to be significant was patients whohad 5 or more medications ordered at the time of dischargefrom the ED. Patients presenting to the ED with these 2characteristics can be targeted by ED pharmacists to pre-empt possible medication problems at the time of dischargefrom the ED. Two significant factors were of an environ-mental nature: the ED physician seeing a patient within 1hour of the change of shift and the physician wait time.

Benefit card holders, who included the elderly, disabled,unemployed, and low income earners, had significantly in-creased odds of an unexplained medication discrepancy. Itis possible that being a benefit card holder is a confound-ing factor in our study because, typically, these individualsare prescribed many medications and are therefore at highrisk of experiencing an unexplained medication discrepan-cy. Nevertheless, benefit card holders have been shown tohave multiple chronic diseases and are regular users ofEDs.25 In our study cohort, benefit card holders were pre-scribed significantly more medications compared withthose without benefit card status. We are not aware of priorstudies that evaluated the influence of insurance status onmedication discrepancy. Social and economic disadvan-tage plays an enormous role in healthcare access and equi-ty, as individuals without insurance are often least able toadvocate for their needs. It is therefore possible that, due tothe complexity of their situation, benefit card holders maynot receive adequate health care. Such a disadvantage hasan impact on how medications are managed in EDs; clear-ly, this is an area for consideration in future work.

Patients prescribed 5 or more drugs at the time of dis-charge from the ED had greatly increased odds of experi-encing an unexplained medication discrepancy. The meannumber of medications prescribed for patients before EDpresentation was 3.2, while the mean number prescribed atdischarge from the ED was 5.3. Hence, medication regi-mens were significantly altered during a patient’s stay in theED. An acute crisis can cause physicians to withhold certainmedications, cease medications, or change the dosage regi-men.25 These alterations become even more pronounced asthe number of drugs taken by patients increases.8 Further-more, at many hospitals, the presence of closed formulariesand the stocking of certain preparations lead to automaticsubstitution of one drug for another in the same class.26

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Table 3. Formulation and Medication Type According to No or Explained Discrepancy and Unexplained Discrepancya

DiscrepancyNone or Explained Unexplained

Type n (%) (n = 944) n (%) (n = 143)

Formulation (n)oral (eg, conventional or 717 (76.0) 112 (78.3)enteric-coated tablets) (829)parenteral (eg, intravenous 164 (17.3) 14 (9.8)injections) (178)

locally applied, systemically 46 (4.9) 16 (11.2)acting (eg, skin patches, inhalers, nebulizers) (62)

locally applied, topically acting 17 (1.8) 1 (0.7)(eg, creams, ointments, eye drops) (18)

Medication analgesics 195 (20.7) 15 (10.5)anesthetics 1 (0.1) 0 (0)anti-allergy 4 (0.4) 2 (1.4)antidotes 7 (0.7) 0 (0)antiinfectives 67 (7.1) 18 (12.6)cardiovascular agents 147 (15.6) 33 (23.1)coagulation and blood 99 (10.5) 8 (5.6)formation

dermatologicals 3 (0.3) 0 (0)ear, nose, and throat 1 (0.1) 0 (0)endocrine 81 (8.6) 13 (9.1)eye 6 (0.6) 1 (0.7)gastrointestinal 165 (17.5) 10 (7.0)genitourinary 1 (0.1) 1 (0.7)immunomodulatory and 26 (2.8) 3 (2.1)antineoplastics

musculoskeletal 30 (3.2) 7 (4.9)neurological 19 (2.0) 7 (4.9)gynecological 1 (0.1) 0 (0)psychotropics 50 (5.3) 13 (9.1)respiratory 41 (4.3) 12 (8.4)

aNumber of medications = 1087.

A notable factor that had significant impact on unex-plained medication discrepancy was patients being seen byan ED physician within 1 hour of change of a workingshift. Interestingly, discharge time of the patient was not asignificant factor for the incidence of an unexplained med-ication discrepancy. A possible reason for lack of effect ofthe timing of discharge is that all treatments in the ED are

undertaken at different times depending on the patient’s ur-gency of need and severity of illness. Once a patient’s con-dition has stabilized, there may be considerable delay inorganizing appropriate transfer arrangements from the ED.On the other hand, as ED physicians near the end of theirworking shifts and are still required to complete a numberof tasks, there may be opportunities for errors to occur.

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Table 4. Univariate Analysis of Factors Associated with Unexplained Medication Discrepanciesa

FactorReference Level Comparator Level Odds Ratio 95% CI χχ2 Degrees of Freedom p Value

PatientAge (y) 1.05 1.03 to 1.06 36.54 1 <0.001Chief complaint 2.53 1 0.124illness injury 0.54 0.25 to 1.18

Need for interpreter 5.54 1 0.021no yes 3.47 1.21 to 9.96

Benefit card holder 32.47 1 <0.001no yes 5.84 3.12 to 10.94

Sex 3.61 1 0.058female male 1.75 0.98 to 3.11

Hearing deficit 1.90 1 0.168no yes 2.36 0.70 to 8.03

Visual deficit 4.58 1 0.032no yes 2.19 1.07 to 4.46

MedicationDrugs ordered at discharge from ED 68.94 1 <0.001<5 ≥5 14.41 7.11 to 29.22

Known drug allergy 6.17 1 0.013no yes 2.31 1.20 to 4.45

EnvironmentalLength of time in ED (min) 1.00 0.99 to 1.00 0.01 1 0.943 Physician wait time in ED (min) 1.00 1.00 to 1.01 3.44 1 0.087Triage (urgency) code 0.90 2 0.6421 & 2 3 0.93 0.42 to 2.07 0.8591 & 2 4 & 5 0.71 0.30 to 1.67 0.433

Mode of arrival 2.38 1 0.123own transport ambulance 1.58 0.88 to 2.82

Inpatient admission 20.01 1 <0.001no yes 4.13 2.19 to 7.76

Discharge destination 39.54 3 <0.001home emergency short- 0.49 0.20 to 1.20 0.118

stay unithome medical unit 1.50 0.62 to 3.66 0.372home surgical unit 0.10 0.03 to 0.31 <0.001

Physician level 0.42 1 0.516junior senior 1.30 0.59 to 2.87

Absence of pharmacist in ED 1.45 1 0.230no yes 0.71 0.40 to 1.25

Seen by physician within 1 h of shift change 9.57 1 0.002no yes 2.54 1.40 to 4.58

Arrival time at ED 4.48 2 0.1160700–1530 1531–2130 1.42 0.73 to 2.74 0.3000700–1530 2131–0659 0.60 0.28 to 1.28 0.186

Discharge time from ED 1.14 2 0.5680700–1530 1531–2130 1.63 0.62 to 2.45 0.5540700–1530 2131–0659 1.29 0.72 to 3.06 0.288

Time first seen by physician 2.84 2 0.2430700–1530 1531–2130 1.49 0.75 to 2.96 0.2580700–1530 2131–0659 0.77 0.38 to 1.55 0.458

ED = emergency department.aNumber of patients = 210.

Another environment-related factor, physician wait timein the ED, demonstrated that as wait time increased, the oddsof experiencing an unexplained medication discrepancy alsoincreased. Many reasons could account for this. ED physi-cians can delay making a definitive diagnosis as they wait forpathologic and radiologic tests to be completed.14,27 Ambigu-ous responsibilities in the designation of a definitive diagno-sis may also be associated with a prolonged wait time to seean ED physician, thereby contributing to communicationproblems and medication discrepancies.14,28 A lengthy waittime may also be related to high workloads, which occurwhen an ED is crowded or when admitting physicians re-ceive several new patients simultaneously. High workloadcan have an enormous impact on the quality of communica-tion. When clinicians are busy, handoffs around the point oftransfer from the ED can be rushed and not interactive.14

While the physician wait time was shown to be a statisticallysignificant factor affecting medication discrepancies, furtherresearch is needed on larger samples of patients to determinethe clinical significance of this effect.

Problems related to transitions in the ED are likely tohave complex causes that are resistant to single interventionsin promoting medication reconciliation. The ED has beenidentified as an environment associated with a number ofvulnerable areas, including lack of effective communication,high workload, ineffective physical design, insufficient ac-cess to changes occurring in patient status and treatment or-ders, ineffective patient flow, and lack of follow-up on pend-ing information.14 Targeted strategies and systems-based in-terventions that focus on the actual context of the EDenvironment could help to ameliorate medication discrepan-cy problems and improve patient safety.29,30

Limitations of this study include its retrospective nature;thus, reasons for changing medication regimens may nothave been properly documented. In addition, the single-center design may limit the applicability to institutionswith a different focus. Medication reconciliation was not aformalized process in the hospital during the time whendata were collected. In addition, no attempt was made to

examine how unexplained medication discrepancies thatoccurred in the ED were resolved or identified by clini-cians as patients progressed through the hospital system.

The novel approach used in this study identifies the com-plexity of patient-, medication-, and environment-relatedfactors that have an impact on medication discrepanciesacross transition points of care from the ED. It is importantto examine the influence of these factors in different ED en-vironments. Confirmation is required about the influence ofvarious factors by direct observation of clinical practicewhere patients are mapped across various environments asthey move through the hospital from admission to discharge.

Elizabeth Manias MPharm RN PhD, Professor, Melbourne Schoolof Health Sciences, Faculty of Medicine Dentistry and Health Sci-ences, The University of Melbourne, Carlton, Victoria, AustraliaMarie Frances Gerdtz PhD, Senior Research Fellow, MelbourneSchool of Health Sciences, Faculty of Medicine Dentistry and HealthSciences, The University of MelbourneTracey J Weiland PhD, Honorary Senior Fellow, Department ofMedicine, Faculty of Medicine Dentistry and Health Sciences, TheUniversity of MelbourneMarnie Collins BComm/BSci (Hons Applied Statistics), StatisticalConsultant, Statistical Consulting Centre, Department of Mathe-matics and Statistics, The University of MelbourneReprints: Dr. Manias, Melbourne School of Health Sciences, Fac-ulty of Medicine Dentistry and Health Sciences, The University ofMelbourne, Level 5, 234 Queensberry St., Carlton, Victoria 3053,Australia, fax 61 3 9317 4375, emanias@unimelb.edu.au.

Financial disclosure: None reported

References

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2. Institute for Safe Medication Practices. Building a case for medicationreconciliation. www.ismp.org/Newsletters/acutecare/articles/20050421.asp (accessed 2008 Dec 19).

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Table 5. Factors Associated with Unexplained Medication Discrepanciesa

Odds Factor Ratio 95% CI p Value

Benefit card holder (no, yes) 3.73 1.72 to 8.07 0.001

Drugs ordered at discharge from ED 12.22 5.52 to 27.08 <0.001(<5, ≥5)

Seen by physician within 1 h of shift 3.70 1.67 to 8.18 0.001change (no, yes)

Physician wait time (min) 1.01 1.00 to 1.01 0.042

ED = emergency department.aBinary logistic regression (forward selection) used; n = 210 pts.

10. Kwan Y, Fernandes OA, Nagge JJ, et al. Pharmacist medication assessmentin a surgical preadmission clinic. Arch Intern Med 2007;167:1034-40. DOI 10.1001/archinte.167.10.1034

11. Manley HJ, Drayer DK, McClaran M, Bender W, Muther RS. Drugrecord discrepancies in an outpatient medical record: frequency, type,and potential impact on patient care at a hemodialysis center. Pharma-cotherapy 2003;23:231-9.

12. Slain D, Kincaid SE, Dunsworth TS. Discrepancies between home medi-cations listed at hospital admission and reported medical conditions. AmJ Geriatr Pharmacother 2008;6:161-6. DOI 10.1016/j.amjopharm.2008.07.001

13. Vira T, Colquhoun M, Etchells E. Reconcilable differences: correctingmedication errors at hospital admission and discharge. Qual Saf HealthCare 2006;15:122-6. DOI 10.1136/qshc.2005.015347

14. Horwitz LI, Meredith T, Schuur JD, Shah NR, Kulkarni RG, Jenq GY.Dropping the baton: a qualitative analysis of failures during the transitionfrom emergency department to inpatient care. Ann Emerg Med 2009;53:701-10. Epub 16 Jun 2008. DOI 10.1016/j.annemergmed.2008.05.007

15. Croskerry P, Sinclair D. Emergency medicine: a practice prone to error.Can J Emerg Med 2001;3:271-6.

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17. National Health and Medical Research Council. National statement onethical conduct in human research. www.nhmrc.gov.au/publications/synopses/_files/e72.pdf (accessed 2008 Dec 19).

18. Peat J. Health science research: a handbook of quantitative methods.Sydney, Australia: Allen & Unwin, 2001.

19. Gerdtz MF, Collins M, Chu M, et al. Optimizing triage consistency inAustralian emergency departments: the Emergency Triage EducationKit. Emerg Med Australas 2008;20:250-9. Epub 6 May 2008. DOI 10.1111/j.1742-6723.2008.01089.x

20. Australasian College for Emergency Medicine (ACEM). Australasian Col-lege for Emergency Medicine Policy Document—The Australasian TriageScale. Emerg Med 2002;14:335-6. DOI 10.1046/j.1442-2026.2002.00371x

Factors in Medication Discrepancies Across Transition Points from the ED

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Appendix I. Description of Patient-, Medication-, and Environment-Related Factors

Factor Description

Patient-related age yearssex male, female chief type of complaint for illness, injuryadmission to ED

need for an interpreter no, yesvisual deficit no, yeshearing deficit no, yesbenefit card holder no, yes

Medication-related number of drugs taken by 0–4 or ≥5patient at discharge from ED

drug allergy no, yesdrugs taken by patient at descriptive informationpresentation to ED; dose, frequency, and formulation

Environment-relateddischarge destination home, emergency short-stay unit,

medical unit, surgical unitposition of attending junior, senior physician

mode of arrival to ED own transportation, ambulancearrival time to ED 0700–1530, 1531–2130, or 2131–

0659; times classified according to the 3 working shifts of the setting

discharge time from ED 0700–1530, 1531–2130, or 2131–0659; times classified according to the 3 working shifts of the setting

duration of stay in ED analyzed in units of minutes rather than blocks of minutes or in hours because the ED is a time-sensitive,precise environment; urgency of a patient’s condition demands accurate calculation of performance indicators

time first seen by ED 0700–1530, 1531–2130, or 2131–physician 0659; times classified according to

the 3 working shifts of the settingtime first seen by physician no, yesbeing within 1 h of shift change

wait time before an ED analyzed in units of minutes rather physician examined the than blocks of minutes due to the patient time-pressured nature of the ED

allocation of category on the urgency code categories 1–5; code Australasian Triage Scale identifies the time during which the

patient should be examined by an ED physician depending on urgency of condition

category 1: a life-threatening situation requiring immediate medical attention

category 2: requires critical treatment within 10 min of arrival

category 3: a potentially life-threaten-ing condition requiring assistance within 30 min

category 4: a potentially serious condition requiring assistance within 60 min

category 5: conditions considered nonurgent but requiring treatment within 2 h

ED = emergency department.

Appendix II. Classification of Medication Discrepancies

Categorya Description

1 no discrepancy noted2 new drug is started based on patient’s diagnosis or

clinical status3 medication omitted or changed in dose, frequency, or

route of administration based on clinical status at admission

4 similar or alternative drug is ordered based on patient’scondition upon hospitalization

5 omission of medication without clinical explanation6 new medication commenced without a clinical expla-

nation7 different dose, frequency, or route of administration

without changes in clinical status8 different medication in the same therapeutic class is

ordered, which differs from what patient was taking before the transfer, without clinical explanation for the change provided

aCategory 1 situations involve no discrepancy at all; category 2–4 sit-uations involve discrepancies that can be explained or justified; cat-egory 5–8 situations involve discrepancies that cannot be explained orjustified. The outcome (dependent) variable of consideration was de-fined to be binary and comprised patients who had no discrepanciesor explained medication discrepancies (categories 1–4) and patientswho had at least one unexplained medication discrepancy (category5–8).

21. Lau HS, Florax C, Porsius AJ, de Boer A. The completeness of medica-tion histories in hospital medical records of patients admitted to generalinternal medicine wards. Br J Clin Pharmacol 2000;49:597-603. DOI 10.1046/j.1365-2125.2000.00204.x

22. Field D. Discovering statistics using SPSS. 2nd ed. London: Sage, 2005.23. Miller SL, Miller S, Balon J, Helling TS. Medication reconciliation in a

rural trauma population. Ann Emerg Med 2008;52:483-91. Epub 12 Jun2008. DOI 10.1016.u.annemargmed.2008.03.021

24. Nickerson A, MacKinnon NJ, Roberts N, Saulnier L. Drug-therapy prob-lems, inconsistencies and omissions identified during a medication reconcil-iation and seamless care service. Healthcare Q 2005;8(Spec No):65-72.

25. Baillargeon J, Paar D, Giordano TP, et al. Emergency department usageby uninsured patients in Galveston County, Texas. Baylor UniversityMedical Center Proceedings 2008;21:236-42. www.pubmedcentral.nih.gov/ articlerender.fcgi?artid = 2446411 (accessed 2008 Dec 19).

26. Schachtner JM, Guharoy R, Medicis JJ, Newman N, Speizer R. Preva-lence and cost savings of therapeutic interchange among U.S. hospitals.Am J Health Syst Pharm 2002;59:529-33.

27. Apker J, Mallak LA, Gibson SC. Communicating in the “gray zone”:perceptions about emergency physician hospitalist handoffs and patientsafety. Acad Emerg Med 2007;14:192-6.

28. Spaite DW, Bartholomeaux F, Guisto J, et al. Rapid process redesign in auniversity-based emergency department: decreasing waiting time inter-vals and improving patient satisfaction. Ann Emerg Med 2002;39:168-77.

29. Rozich JD, Resar KR. Medication safety: one organization’s approach tothe challenge. J Clin Outcomes Manag 2001;8:27-34.

30. Joint Commission on Accreditation of Healthcare Organizations (JCAHO).National patient safety goals for 2007. www.jointcommission.org (ac-cessed 2008 Aug 31).

Uso de Medicamentos en Puntos de Transición del Departamento deEmergencia: Identificación de Factores Asociados conDiscrepancias en Medicamentos

E Manias, MF Gerdtz, TJ Weiland, y M Collins

Ann Pharmacother 2009;43:1755-64.

EXTRACTO

TRASFONDO: A medida que los pacientes se mueven a través de los puntosde transición de atención, es probable que ocurran discrepancias en eluso de medicamentos. En el departamento de emergencia, los pacientesson vulnerables a las discrepancias en el uso de medicamentos por queestán en un ambiente donde es necesario tomar decisiones rápidas encircunstancias donde hay altos niveles de tensión.

OBJETIVOS: Identificar los factores relacionados al paciente, al ambiente,y a los medicamentos asociados a las discrepancias de medicamentos queno pueden ser explicadas a través de puntos de transición en una sala deemergencia.

MÉTODOS: Usando un diseño de revisión retrospectiva de expedientes, seobtuvo una muestra estratificada aleatoria de datos en un período de 12meses en un hospital de Australia. La información se obtuvo de un bancode datos administrativo electrónico y de expedientes médicos a medidaque los pacientes realizaron una transición del departamento de emergenciaa otro punto de atención. Se definió como discrepancia cualquier diferenciaentre los medicamentos que aparecían en el expediente cuando el pacientefue dado de alta del departamento de emergencia y los que aparecían enel expediente cuando fue admitido al hospital. Las discrepancias de medi-camentos se clasificaron en 2 grupos: ninguna discrepancia o discrepanciasadecuadamente explicadas y discrepancias que no tuvieron una explicaciónadecuada (inexplicables).

RESULTADOS: Doscientos doce pacientes fueron seleccionados aleatoria-mente para el período de estudio, de los cuales 73 (34.8%) tuvieron por

lo menos una discrepancia inexplicable. El modelo de regresión logísticabinaria reveló 4 factores que fueron estadísticamente significativos endeterminar la incidencia de por lo menos una discrepancia de medica-mentos inexplicada. Pacientes que tenían una tarjeta de beneficios delseguro del gobierno de Australia tuvieron 3.73 veces mayor probabili-dad de experimentar una discrepancia de medicamentos inexplicada(95% IC 1.72 y 8.07; p = 0.001). Aquellos pacientes que le fueronprescritos 5 o más medicamentos al ser dados de alta del departamentode emergencia, tuvieron 12.22 mayor probabilidad de tener por lo menosuna discrepancia de medicamento inexplicada (95% IC 5.52 y 27.08; p< 0.001). Los pacientes que fueron evaluados por un médico en laprimera hora de cambio de jornada laboral tuvieron 3.70 mayor proba-bilidad de tener una discrepancia de medicamentos inexplicada (95% IC1.67 y 8.18; p = 0.001). Por cada minuto adicional de espera por unmédico, la probabilidad de tener una discrepancia de medicamentoinexplicada aumentó en 1.01 (95% IC 1.00 a 1.01; p = 0.042).

CONCLUSIONES: Existen factores relacionados al paciente, al ambiente, ya los medicamentos que contribuyen a aumentar el riesgo dediscrepancias de medicamentos a través de los puntos de transición deun departamento de emergencia.

Traducido por Homero A Monsanto

Utilisation des Médicaments à Travers Différents Points deTransition d’un Département de l’Urgence: Identification desFacteurs Associés à des Inexactitudes au Niveau des Médicaments

E Manias, MF Gerdtz, TJ Weiland, et M Collins

Ann Pharmacother 2009;43:1755-64.

RÉSUMÉ

MISE EN CONTEXTE: A mesure que les patients progressent dans le systèmede soins, le risque d’inexactitudes au niveau des médicaments s’accroit.Au département d’urgence, les patients sont plus vulnérables à ce typed’erreurs puisqu’ils sont dans un environnement dans lequel les décisionssont prises rapidement sous un haut niveau de stress.

OBJECTIFS: Identifier les patients, l’environnement, et les médicamentspouvant causer des inexactitudes au niveau des médicaments lors d’unpassage à l’unité d’urgence.

MÉTHODES: En utilisant une revue rétrospective des dossiers médicaux,un échantillon stratifié pris au hasard a été constitué durant une périodede 12 mois. L’information a été obtenue à partir d’une banque dedonnées électronique et des dossiers médicaux des patients lorsqu’ils sedéplaçaient d’un point de soins à un autre. Les inexactitudes au niveaudes médicaments ont été classées en 2 groupes: aucune inexactitude ouinexactitude justifiée et un groupe avec des inexactitudes sansjustification valable.

RÉSULTATS: Durant la période de 12 mois, 210 patients ont été choisis auhasard et 73 d’entre eux (34.8%) avaient au moins une inexactitude. Uneanalyse de régression logistique binaire a démontré que 4 facteurspouvaient déterminer de façon statistiquement significative l’incidenced’au moins une inexactitude. Les titulaires d’une carte de soins avaientun risque 3.73 fois plus élevé d’avoir une inexactitude non justifiée (IC95% 1.72 à 8.07; p = 0.001). Les patients ayant reçu une prescriptionpour 5 ou plus de médicaments avaient un risque 12.22 fois plus élevé(IC 95% 5.52 à 27.08; p = 0.001). Les patients ayant été vus par unmédecin dans la première heure suivant le changement de quart detravail avaient un risque 3.70 fois plus élevé (IC 95% 1.67 à 8.18; p =0.001). Pour chaque minute additionnelle d’attente pour un médecin, lerisque augmentait d’un facteur de 1.01 (IC 95% 1.00 à 1.01; p = 0.042).

CONCLUSIONS: Les patients, l’environnement, et les médicaments utiliséssont tous des facteurs pouvant contribuer au risque d’inexactitudes auniveau des médicaments à travers différents points de soins dans undépartement d’urgence.

Traduit par Nicolas Paquette-Lamontagne

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