Classifying and Predicting Errors of Inpatient Medication Reconciliation

Pippins, Jennifer R.; Gandhi, Tejal K.; Hamann, Claus; Ndumele, Chima D.; Labonville, Stephanie; Diedrichsen, Ellen K.; Carty, Marcy G.; Karson, Andrew S.; Bhan, Ishir; Coley, Christopher M.; Liang, Catherine; Turchin, Alexander; McCarthy, Patricia C.; Schnipper, Jeffrey L.

doi:10.1007/s11606-008-0687-9

Cited by 337 publications

(357 citation statements)

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“…1,3,12,14,15,23,25 Several factors we found to be associated with medication reconciliation errors, such as older age and number of preadmission medications, were confirmed by others. 11,12,14,15 However, in one study fewer unintentional medication discrepancies were associated with age over 85 compared to age less than 50. 12 Herrero-Herrero et al found that the number of preadmission medications predicted unjustified discrepancies (i.e.…”

Section: Discussionsupporting

confidence: 76%

“…26 Furthermore, Pippins et al showed that 72% of all potentially harmful discrepancies in admission or discharge orders were due to PAML errors. 12 Similarly, we discovered that PAML errors and medication changes made during hospitalization predicted discharge medication errors.…”

Section: Discussionmentioning

confidence: 87%

“…11,12,14,15 However, in one study fewer unintentional medication discrepancies were associated with age over 85 compared to age less than 50. 12 Herrero-Herrero et al found that the number of preadmission medications predicted unjustified discrepancies (i.e. medication errors) at discharge.…”

Section: Discussionmentioning

confidence: 93%

“…These factors had been demonstrated in prior research to predict medication reconciliation errors. 12,14 …”

Section: Secondary Analysesmentioning

confidence: 99%

“…11 Little research has examined how patient characteristics and other factors affect the medication reconciliation process-those that have are generally small, single site studies (or two sites within one hospital system). [11][12][13][14][15] Also few studies have examined how errors in the medication history-taking process (PAML errors) affect the likelihood of discharge errors.…”

Section: Introductionmentioning

confidence: 99%

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Effect of Patient- and Medication-Related Factors on Inpatient Medication Reconciliation Errors

et al. 2012

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BACKGROUND:Little research has examined the incidence, clinical relevance, and predictors of medication reconciliation errors at hospital admission and discharge. OBJECTIVE: To identify patient-and medication-related factors that contribute to pre-admission medication list (PAML) errors and admission order errors, and to test whether such errors persist in the discharge medication list. DESIGN, PARTICIPANTS: We conducted a crosssectional analysis of 423 adults with acute coronary syndromes or acute decompensated heart failure admitted to two academic hospitals who received pharmacistassisted medication reconciliation during the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL-CVD) Study. MAIN MEASURES:Pharmacists assessed the number of total and clinically relevant errors in the PAML and admission and discharge medication orders. We used negative binomial regression and report incidence rate ratios (IRR) of predictors of reconciliation errors. KEY RESULTS: On admission, 174 of 413 patients (42%) had ≥1 PAML error, and 73 (18%) had ≥1 clinically relevant PAML error. At discharge, 158 of 405 patients (39%) had ≥1 discharge medication error, and 126 (31%) had ≥1 clinically relevant discharge medication error. Clinically relevant PAML errors were associated with older age (IRR=1.46; 95% CI, 1.00-2.12) and number of pre-admission medications (IRR=1.17; 95% CI, 1.10-1.25), and were less likely when a recent medication list was present in the electronic medical record (EMR) (IRR=0.54; 95% CI, 0.30-0.96). Clinically relevant admission order errors were also associated with older age and number of pre-admission medications. Clinically relevant discharge medication errors were more likely for every PAML error (IRR=1.31; 95% CI, 1.19-1.45) and number of medications changed prior to discharge (IRR=1.06; 95% CI, 1.01-1.11). CONCLUSIONS: Medication reconciliation errors are common at hospital admission and discharge. Errors in preadmission medication histories are associated with older age and number of medications and lead to more discharge reconciliation errors. A recent medication list in the EMR is protective against medication reconciliation errors.

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Section: Discussionsupporting

confidence: 76%

Section: Discussionmentioning

confidence: 87%

Section: Discussionmentioning

confidence: 93%

“…These factors had been demonstrated in prior research to predict medication reconciliation errors. 12,14 …”

Section: Secondary Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effect of Patient- and Medication-Related Factors on Inpatient Medication Reconciliation Errors

et al. 2012

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Effect of an Electronic Medication Reconciliation Application and Process Redesign on Potential Adverse Drug Events

Schnipper¹,

Hamann²,

Ndumele³

et al. 2009

Arch Intern Med

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Hospital-Based Medication Reconciliation Practices

Mueller

Sponsler²,

Kripalani³

et al. 2012

Arch Intern Med

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447

356

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Background Medication discrepancies at care transitions are common and lead to patient harm. Medication reconciliation is a strategy to reduce this risk. Objectives To summarize available evidence on medication reconciliation interventions in the hospital setting and identify the most effective practices. Data Sources Medline (1966 through February 2012) and hand search of article bibliographies. Study Selection 26 controlled studies. Data Extraction Data were extracted on study design, setting, participants, inclusion/exclusion criteria, intervention components, timing, comparison group, outcomes, and results. Data Synthesis Studies were grouped by type of medication reconciliation intervention: pharmacist-related, information technology (IT), or other, and assigned quality ratings utilizing U.S. Preventative Services Task Force criteria. Results 15 of 26 studies reported on pharmacist-related interventions, 6 evaluated IT interventions, and 5 studied other interventions. 6 studies were classified as good quality. The comparison group for all studies was usual care, with no direct comparisons of different types of interventions. Studies consistently demonstrated a reduction in medication discrepancies (17/17 studies), potential adverse drug events (5/6 studies), and adverse drug events (2/3 studies), but showed inconsistent reduction in post-discharge healthcare utilization (improvement in 2/8 studies). Key aspects of successful interventions included intensive pharmacy staff involvement and targeting the intervention to a ‘high-risk’ patient population. Conclusions There is a paucity of rigorously designed studies comparing different inpatient medication reconciliation practices and their effects on clinical outcomes. Available evidence supports medication reconciliation interventions that heavily utilize pharmacy staff and focus on patients at high-risk for adverse events. Higher quality studies are needed to determine the most effective approaches to inpatient medication reconciliation.

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Classifying and Predicting Errors of Inpatient Medication Reconciliation

Cited by 337 publications

References 22 publications

Effect of Patient- and Medication-Related Factors on Inpatient Medication Reconciliation Errors

Effect of Patient- and Medication-Related Factors on Inpatient Medication Reconciliation Errors

Effect of an Electronic Medication Reconciliation Application and Process Redesign on Potential Adverse Drug Events

Hospital-Based Medication Reconciliation Practices

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