Dataset Information

Errors in medication history at hospital admission: prevalence and predicting factors.

ABSTRACT: BACKGROUND: An accurate medication list at hospital admission is essential for the evaluation and further treatment of patients. The objective of this study was to describe the frequency, type and predictors of errors in medication history, and to evaluate the extent to which standard care corrects these errors. METHODS: A descriptive study was carried out in two medical wards in a Swedish hospital using Lund Integrated Medicines Management (LIMM)-based medication reconciliation. A clinical pharmacist identified each patient's most accurate pre-admission medication list by conducting a medication reconciliation process shortly after admission. This list was then compared with the patient's medication list in the hospital medical records. Addition or withdrawal of a drug or changes to the dose or dosage form in the hospital medication list were considered medication discrepancies. Medication discrepancies for which no clinical reason could be identified (unintentional changes) were considered medication history errors. RESULTS: The final study population comprised 670 of 818 eligible patients. At least one medication history error was identified by pharmacists conducting medication reconciliations for 313 of these patients (47%; 95% CI 43-51%). The most common medication error was an omitted drug, followed by a wrong dose. Multivariate logistic regression analysis showed that a higher number of drugs at admission (odds ratio [OR] per 1 drug increase = 1.10; 95% CI 1.06-1.14; p < 0.0001) and the patient living in their own home without any care services (OR = 1.58; 95% CI 1.02-2.45; p = 0.042) were predictors for medication history errors at admission. The results further indicated that standard care by non-pharmacist ward staff had partly corrected the errors in affected patients by four days after admission, but a considerable proportion of the errors made in the initial medication history at admission remained undetected by standard care (OR for medication errors detected by pharmacists' medication reconciliation carried out on days 4-11 compared to days 0-1 = 0.52; 95% CI 0.30-0.91; p=0.021). CONCLUSIONS: Clinical pharmacists conducting LIMM-based medication reconciliations have a high potential for correcting errors in medication history for all patients. In an older Swedish population, those prescribed many drugs seem to benefit most from admission medication reconciliation.

SUBMITTER: Hellstrom LM

PROVIDER: S-EPMC3353244 | biostudies-other | 2012

REPOSITORIES: biostudies-other

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Errors in medication history at hospital admission: prevalence and predicting factors.

Hellström Lina M LM Bondesson Åsa Å Höglund Peter P Eriksson Tommy T

BMC clinical pharmacology 20120403

<h4>Background</h4>An accurate medication list at hospital admission is essential for the evaluation and further treatment of patients. The objective of this study was to describe the frequency, type and predictors of errors in medication history, and to evaluate the extent to which standard care corrects these errors.<h4>Methods</h4>A descriptive study was carried out in two medical wards in a Swedish hospital using Lund Integrated Medicines Management (LIMM)-based medication reconciliation. A ...[more]

PMID: 22471836

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Project description:Background: Hospital readmission rates are increasingly used as a measure of healthcare quality. Medicines are the most common therapeutic intervention but estimating the contribution of adverse drug events as a cause of readmissions is difficult. Objectives: To assess the prevalence and preventability of medication-related readmissions within 30 days after hospital discharge and to describe the risk factors, type of medication errors and types of medication involved in these preventable readmissions. Design: A cross-sectional observational study. Setting: The study took place across the cardiology, gastroenterology, internal medicine, neurology, psychiatry, pulmonology and general surgery departments in the OLVG teaching hospital, Netherlands. Participants: Patients with an unplanned readmission within 30 days after discharge from an earlier hospitalization (index hospitalization: IH) were reviewed. Measurements: The prevalence and preventability of medication-related readmissions were assessed by residents in multidisciplinary meetings. A senior internist and hospital pharmacist reassessed the prevalence and preventability of identified cases. Generalized estimating equation with logistic regression was performed to identify risk factors of potentially preventable medication-related readmissions. Results: Of 1,111 included readmissions, 181 (16%) were medication-related, of which 72 (40%) were potentially preventable. The number of medication changes at IH (Adjusted odds ratio [ORadj]: 1.14; 95% CI: 1.05-1.24) and having ≥3 hospitalizations 6 months before IH (ORadj: 2.11; 95% CI: 1.12-3.98) were risk factors of a preventable medication-related readmission. Of these preventable readmissions, 35% were due to prescribing errors, 35% by non-adherence and 30% by transition errors. Medications most frequently involved were diuretics and antidiabetics. Conclusion: This study shows that 16% of readmissions are medication-related, of which 40% are potentially preventable. If the results are confirmed in larger multicentre studies, this may indicate that more attention should be paid to medication-related harm in order to lower the overall readmission rates.

Project description:ObjectiveMedication reconciliation (MedRec) is a relevant safety procedure in medication management at transitions of care. The aim of this study was to evaluate the impact of MedRec, including a best possible medication history (BPMH) compared with a standard medication history in patients admitted to an internal medicine ward.DesignProspective interventional study. Data were analysed using descriptive statistics followed by univariate and multivariate Poisson regression models and a zero-inflated Poisson regression model.SettingInternal medicine ward in a secondary care hospital in Southern Switzerland.ParticipantsThe first 100 consecutive patients admitted in an internal medicine ward.Primary and secondary outcome measuresMedication discrepancies between the medication list obtained by the physician and that obtained by a pharmacist according to a systematic approach (BPMH) were collected, quantified and assessed by an expert panel that assigned a severity score. The same procedure was applied to discrepancies regarding allergies. Predicting factors for medication discrepancies were identified.ResultsThe median of medications per patient was 8 after standard medication history and 11 after BPMH. Total admission discrepancies were 524 (5.24 discrepancies per patient) with at least 1 discrepancy per patient. For 47 patients, at least one discrepancy was classified as clinically relevant. Discrepancies were classified as significant and serious in 19% and 2% of cases, respectively. Furthermore, 67% of the discrepancies were detected during the interview conducted by the pharmacist with the patients and/or their caregivers. The number of drugs used and the autonomous management of home therapy were associated with an increased number of clinically relevant discrepancies in a multivariable Poisson regression model.ConclusionEven in an advanced healthcare system, a standardised MedRec process including a BPMH represents an important strategy that may contribute to avoid a notable number of clinically relevant discrepancies and potential adverse drug events.

Project description:Multimorbidity and polypharmacy are risk factors for drug-related hospital admissions (DRAs) in the ageing population. DRAs caused by medication errors (MEs) are considered potentially preventable. The STOPP/START criteria were developed to detect potential MEs in older people. The aim of this study was to assess the detectability of MEs with a STOPP/START-based in-hospital medication review in older people with polypharmacy and multimorbidity prior to a potentially preventable DRA. Hospitalised older patients (n = 963) with polypharmacy and multimorbidity from the intervention arm of the OPERAM trial received a STOPP/START-based in-hospital medication review by a pharmacotherapy team. Readmissions within 1 year after the in-hospital medication review were adjudicated for drug-relatedness. A retrospective assessment was performed to determine whether MEs identified at the first DRA were detectable during the in-hospital medication review. In total, 84 of 963 OPERAM intervention patients (8.7%) were readmitted with a potentially preventable DRA, of which 72 patients (n = 77 MEs) were eligible for analysis. About half (48%, n = 37/77) of the MEs were not present during the in-hospital medication review and therefore were not detectable at that time. The pharmacotherapy team recommended a change in medication regimen in 50% (n = 20/40) of present MEs, which corresponds to 26% (n = 20/77) of the total identified MEs at readmission. However, these recommendations were not implemented. MEs identified at readmission were not addressed by a prior single in-hospital medication review because either these MEs occurred after the medication review (~50%), or no recommendation was given during the medication review (~25%), or the recommendation was not implemented (~25%). Future research should focus on optimisation of the timing and frequency of medication review and the implementation of proposed medication recommendations. ClinicalTrials.gov identifier: NCT02986425. December 8, 2016. European Union HORIZON 2020, Swiss State Secretariat for Education, Research and Innovation (SERI), Swiss National Science Foundation (SNSF).

Project description:Current approaches to understanding medication ordering errors rely on relatively small manually captured error samples. These approaches are resource-intensive, do not scale for computerized provider order entry (CPOE) systems, and are likely to miss important risk factors associated with medication ordering errors. Previously, we described a dataset of CPOE-based medication voiding accompanied by univariable and multivariable regression analyses. However, these traditional techniques require expert guidance and may perform poorly compared to newer approaches. In this paper, we update that analysis using machine learning (ML) models to predict erroneous medication orders and identify its contributing factors. We retrieved patient demographics (race/ethnicity, sex, age), clinician characteristics, type of medication order (inpatient, prescription, home medication by history), and order content. We compared logistic regression, random forest, boosted decision trees, and artificial neural network models. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). The dataset included 5,804,192 medication orders, of which 28,695 (0.5%) were voided. ML correctly classified voids at reasonable accuracy; with a positive predictive value of 10%, ~20% of errors were included. Gradient boosted decision trees achieved the highest AUROC (0.7968) and AUPRC (0.0647) among all models. Logistic regression had the poorest performance. Models identified predictive factors with high face validity (e.g., student orders), and a decision tree revealed interacting contexts with high rates of errors not identified by previous regression models. Prediction models using order-entry information offers promise for error surveillance, patient safety improvements, and targeted clinical review. The improved performance of models with complex interactions points to the importance of contextual medication ordering information for understanding contributors to medication errors.

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Errors in medication history at hospital admission: prevalence and predicting factors.

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Errors in medication history at hospital admission: prevalence and predicting factors.

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