Using lean "automation with a human touch" to improve medication safety: a step closer to the "perfect dose".

BACKGROUND: Virginia Mason Medical Center (Seattle) employed the Lean concept of Jidoka (automation with a human touch) to plan for and deploy bar code medication administration (BCMA) to hospitalized patients. METHODS: Integrating BCMA technology into the nursing work flow with minimal disruption was accomplished using three steps ofJidoka: (1) assigning work to humans and machines on the basis of their differing abilities, (2) adapting machines to the human work flow, and (3) monitoring the human-machine interaction. Effectiveness of BCMA to both reinforce safe administration practices and reduce medication errors was measured using the Collaborative Alliance for Nursing Outcomes (CALNOC) Medication Administration Accuracy Quality Study methodology. Trained nurses observed a total of 16,149 medication doses for 3,617 patients in a three-year period. RESULTS: Following BCMA implementation, the number of safe practice violations decreased from 54.8 violations/100 doses (January 2010-September 2011) to 29.0 violations/100 doses (October 2011-December 2012), resulting in an absolute risk reduction of 25.8 violations/100 doses (95% confidence interval [CI]: 23.7, 27.9, p < .001). The number of medication errors decreased from 5.9 errors/100 doses at baseline to 3.0 errors/100 doses after BCMA implementation (absolute risk reduction: 2.9 errors/100 doses [95% CI: 2.2, 3.6,p < .001]). The number of unsafe administration practices (estimate, -5.481; standard error 1.133; p < .001; 95% CI: -7.702, -3.260) also decreased. CONCLUSION: As more hospitals respond to health information technology meaningful use incentives, thoughtful, methodical, and well-managed approaches to technology deployment are crucial. This work illustrates how Jidoka offers opportunities for a smooth transition to new technology.

Implementation of a custom alert to prevent medication-timing errors associated with computerized prescriber order entry.

PURPOSE: The implementation of a custom alert to prevent medication-timing errors associated with the use of a computerized prescriber order-entry (CPOE) system is described. METHODS: In early 2013, Virginia Mason Medical Center began work to modify the CPOE system to make it impossible for a medication-timing error to occur. A visual control, a custom alert that forced prescribers to "self-inspect with pause" (a soft-stop override) before signing an order that may result in a medication-timing error, was developed. The prescriber could choose to modify the order to skip the next dose or change the start date or time of the order, cancel the order, or continue without altering the order. The custom alert was designed to fire only for medications that are administered once or twice daily. The effectiveness of the intervention was evaluated with interrupted time series before-and-after analysis of medication-timing errors. Outcomes measured included the proportion of errors that were corrected by the prescriber before signing the order and the number of medication-timing errors that reached patients. RESULTS: The proportion of orders where a prescriber modified the order as a result of the alert increased from 12% before the intervention to 29% postintervention (p > 0.001), and this percentage continued to increase at five months postintervention (p < 0.001). The percentage of medication-timing errors that reached the patient decreased by 50% in the postintervention period. CONCLUSION: Medication-timing errors associated with the use of a CPOE system were corrected through implementation of a customized alert.