Automated electronic reminders to prevent miscommunication among primary medical, surgical and anaesthesia providers: A root cause analysis
Department of Anesthesiology, 1H247 Box 0048, University of Michigan Hospital, 1500 East Medical Center Drive, Ann Arbor, MI 48109, USABMJ quality & safety (Impact Factor: 3.99). 07/2012; 21(10):850-4. DOI: 10.1136/bmjqs-2011-000666
In this case report, the authors present an adverse event possibly caused by miscommunication among three separate medical teams at their hospital. The authors then discuss the hospital's root cause analysis and its proposed solutions, focusing on the subsequent hospital-wide implementation of an automated electronic reminder for abnormal laboratory values that may have helped to prevent similar medical errors.
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ABSTRACT: Study Objective To perform a frequency analysis of start minute digits (SMD) and end minute digits (EMD) taken from the electronic, computer-assisted, and manual anesthesia billing-record systems. Design Retrospective cross-sectional review. Setting University medical center. Measurements This cross-sectional review was conducted on billing records from a single healthcare institution over a 15-month period. A total of 30,738 cases were analyzed. For each record, the start time and end time were recorded. Distributions of SMD and EMD were tested against the null hypothesis of a frequency distribution equivalently spread between zero and nine. Main Results SMD and EMD aggregate distributions each differed from equivalency (P < 0.0001). When stratified by type of anesthetic record, no differences were found between the recorded and expected equivalent distribution patterns for electronic anesthesia records for start minute (P < 0.98) or end minute (P < 0.55). Manual and computer-assisted records maintained nonequivalent distribution patterns for SMD and EMD (P < 0.0001 for each comparison). Comparison of cumulative distributions between SMD and EMD distributions suggested a significant difference between the two patterns (P < 0.0001). Conclusion An electronic anesthesia record system, with automated time capture of events verified by the user, produces a more unified distribution of billing times than do more traditional methods of entering billing times.Journal of Clinical Anesthesia 06/2014; 26(4). DOI:10.1016/j.jclinane.2013.10.016 · 1.19 Impact Factor
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ABSTRACT: The US federal government has enacted legislation for a federal incentive program for health care providers and hospitals to implement electronic health records. The primary goal of the Meaningful Use (MU) program is to drive adoption of electronic health records nationwide and set the stage to monitor and guide efforts to improve population health and outcomes. The MU program provides incentives for the adoption and use of electronic health record technology and, in some cases, penalties for hospitals or providers not using the technology. The MU program is administrated by the Department of Health and Human Services and is divided into 3 stages that include specific reporting and compliance metrics. The rationale is that increased use of electronic health records will improve the process of delivering care at the individual level by improving the communication and allow for tracking population health and quality improvement metrics at a national level in the long run. The goal of this narrative review is to describe the MU program as it applies to anesthesiologists in the United States. This narrative review will discuss how anesthesiologists can meet the eligible provider reporting criteria of MU by applying anesthesia information management systems (AIMS) in various contexts in the United States. Subsequently, AIMS will be described in the context of MU criteria. This narrative literature review also will evaluate the evidence supporting the electronic health record technology in the operating room, including AIMS, independent of certification requirements for the electronic health record technology under MU in the United States.Anesthesia and analgesia 09/2015; 121(3):693-706. DOI:10.1213/ANE.0000000000000881 · 3.47 Impact Factor
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