Case descriptionA 71-year-old female accidentally received thiothixene (Navane), an antipsychotic, instead of her anti-hypertensive medication amlodipine (Norvasc) for 3 months. She sustained physical and psychological harm including ambulatory dysfunction, tremors, mood swings, and personality changes. Despite the many opportunities for intervention, multiple health care providers overlooked her symptoms.DiscussionErrors occurred at multiple care levels, including prescribing, initial pharmacy dispensation, hospitalization, and subsequent outpatient follow-up. This exemplifies the Swiss Cheese Model of how errors can occur within a system. Adverse drug events (ADEs) account for more than 3.5 million physician office visits and 1 million emergency department visits each year. It is believed that preventable medication errors impact more than 7 million patients and cost almost $21 billion annually across all care settings. About 30% of hospitalized patients have at least one discrepancy on discharge medication reconciliation. Medication errors and ADEs are an underreported burden that adversely affects patients, providers, and the economy.ConclusionMedication reconciliation including an ‘indication review’ for each prescription is an important aspect of patient safety. The decreasing frequency of pill bottle reviews, suboptimal patient education, and poor communication between healthcare providers are factors that threaten patient safety. Medication error and ADEs cost billions of health care dollars and are detrimental to the provider–patient relationship.

Background and objectiveSevere adverse events such as cardiac arrest and death are often heralded by abnormal vital signs hours before the event. This necessitates an organized track and trigger approach of early recognition and response to subtle changes in a patient’s condition. The Modified Early Warning System (MEWS) is one of such systems that use temperature, blood pressure, pulse, respiratory rate, and level of consciousness with each progressive higher score triggering an action. Root cause analysis for mortalities in our institute has led to the implementation of MEWS in an effort to improve patient outcomes. Here we discuss our experience and the impact of MEWS implementation on patient care at our community academic hospital.MethodsMEWS was implemented in a protocolized manner in June 2013. The following data were collected from non-ICU wards on a monthly basis from January 2010 to June 2014: 1) number of rapid response teams (RRTs) per 100 patient-days (100PD); 2) number of cardiopulmonary arrests ‘Code Blue’ per 100PD; and 3) result of each RRT and Code Blue (RRT progressed to Code Blue, higher level of care, ICU transfer, etc.). Overall inpatient mortality data were also analyzed.ResultsSince the implementation of MEWS, the number of RRT has increased from 0.24 per 100PD in 2011 to 0.38 per 100PD in 2013, and 0.48 per 100PD in 2014. The percentage of RRTs that progressed to Code Blue, an indicator of poor outcome of RRT, has been decreasing. In contrast, the numbers of Code Blue in non-ICU floors has been progressively decreasing from 0.05 per 100PD in 2011 to 0.02 per 100PD in 2013 and 2014. These improved clinical outcomes are associated with a decline of overall inpatient mortality rate from 2.3% in 2011 to 1.5% in 2013 and 1.2% in 2014.ConclusionsImplementation of MEWS in our institute has led to higher rapid response system utilization but lower cardiopulmonary arrest events; this is associated with a lower mortality rate, and improved patient safety and clinical outcomes. We recommend the widespread use of MEWS to improve patient outcomes.

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