Risk factors for medication errors at admission in preoperatively screened patients

Which surgical patients require shared care?

Most hospitalized surgical patients have significant medical comorbidity and are treated with a considerable number of drugs and/or experience significant complications. Shared care (SC) is the shared responsibility and authority in managing hospitalized patients. In this article, we discuss whether patients should be selected for SC or not. The various selection criteria are not an exact science nor are they easy to apply. Furthermore, they may leave out many patients who may be good candidates for SC. Perioperative management is essential for preventing postoperative mortality. Failure to rescue (in-hospital mortality secondary to postoperative complications) is the main factor linked to in-hospital surgical mortality and can affect any patient regardless of age, comorbidity, or type of surgery. The component that most reduces failure to rescue is the presence of internists in surgical wards. We believe that all patients hospitalized in surgery departments should receive SC.

Medication reconciliation: time to save? A cross-sectional study from one acute hospital

PURPOSE

Medication errors during transitional care are an important patient safety issue. Medication reconciliation is an established intervention to reduce such errors. Current evidence has not demonstrated an associated reduction in healthcare costs, however, with complexity and resource intensity being identified as issues. The aims of this study were to examine an existing process of medication reconciliation in terms of time taken, to identify factors associated with additional time, and to determine if additional time is associated with detecting errors of clinical significance.

METHODS

A cross-sectional study was conducted. Issues arising during medication reconciliation incurring a time burden additional to the usual process were logged and quantified by pharmacists. Regression analyses investigated associations between patient characteristics and clinically significant errors and additional time. Cost for additional time in terms of hospital pharmacist salary was calculated.

RESULTS

Eighty-nine patients were included. Having a personal record of medication at admission (OR 3.30, 95% CI: (1.05 to 10.42), p = 0.004) was a significant predictor of additional time. No significant associations were found between the occurrence of clinically significant error and additional time (p > 0.05). The most common reason for additional time was clarifying issues pertaining to primary care medication information. Projected annual 5-year costs for the mean additional time of 3.75 min were €1.8-1.9 million.

CONCLUSIONS

Spending additional time on medication reconciliation is associated with economic burden and may not yield benefit in terms of capturing clinically significant errors. There is a need to improve communication of medication information between primary and secondary care.

Prospective Validation of a Risk Prediction Model to Identify High-Risk Patients for Medication Errors at Hospital Admission

Background: Pharmacy-led medication reconciliation in elective surgery patients is often performed at the preoperative screening (POS). Because of the time lag between POS and admission, changes in medication may lead to medication errors at admission (MEAs). In a previous study, a risk prediction model for MEA was developed. Objective: To validate this risk prediction model to identify patients at risk for MEAs in a university hospital setting. Methods: The risk prediction model was derived from a cohort of a Dutch general hospital and validated within a comparable cohort from a Dutch University Medical Centre. MEAs were assessed by comparing the POS medication list with the reconciled medication list at hospital admission. This was considered the gold standard. For every patient, a risk score using the risk prediction model was calculated and compared with the gold standard. The risk prediction model was assessed with receiver operating characteristic (ROC) analysis. Results: Of 368 included patients, 167 (45.4%) had at least 1 MEA. ROC analysis revealed significant differences in the area under the curve of 0.535 ( P = 0.26; validation cohort) versus 0.752 ( P < 0.0001; derivation cohort). The sensitivity in this validating cohort was 66%, with a specificity of 40%. Conclusion and Relevance: The risk prediction model developed in a general hospital population is not suitable to identify patients at risk for MEA in a university hospital population. However, number of medications is a common risk factor in both patient populations and should, thus, form the basis of an adapted risk prediction model.