Failures in transition : learning from incidents relating to clinical handover in acute care

The appropriate handover of patients, whereby responsibility and accountability of care is transferred between healthcare providers, is a critical component of quality healthcare delivery. This paper examines data from recent incidents relating to clinical handover in acute care settings, in order to provide a basis for the design and implementation of preventive and corrective strategies. A sample of incidents (n = 459) relating to clinical handover was extracted from an Australian health service's incident reporting system using a manual search function. Incident narratives were subjected to classification according to the system safety and quality concepts of failure type, error type, and failure detection mechanism. The most prevalent failure types associated with clinical handover were those relating to the transfer of patients without adequate handover 28.8% (n = 132), omissions of critical information about the patient's condition 19.2% (n = 88), and omissions of critical information about the patient's care plan during the handover process 14.2% (n = 65). The most prevalent failure detection mechanisms were those of expectation mismatch 35.7% (n = 174), clinical mismatch 26.9% (n = 127), and mismatch with other documentation 24.0% (n = 117). The findings suggest the need for a structured approach to handover with a recording of standardized sets of information to ensure that critical components are not omitted. Limitations of existing reporting processes are also highlighted

Mapping the limits of safety reporting systems in health care : what lessons can we actually learn?

Objectives: To assess the utility of Australian health care incident reporting systems and determine the depth of information available within a typical system. Design and setting: Incidents relating to patient misidentification occurring between 2004 and 2008 were selected from a sample extracted from a number of Australian health services’ incident reporting systems using a manual search function. Main outcome measures: Incident type, aetiology (error type) and recovery (error detection mechanism). Analyses were performed to determine category saturation. Results: All 487 selected incidents could be classified according to incident type. The most prevalent incident type was medication being administered to the wrong patient (25.7%, 125), followed by incidents where a procedure was performed on the wrong patient (15.2%, 74) and incidents where an order for pathology or medical imaging was mislabelled (7.0%, 34). Category saturation was achieved quickly, with about half the total number of incident types identified in the first 13.5% of the incidents. All 43 incident types were classified within 76.2% of the dataset. Fifty-two incident reports (10.7%) included sufficient information to classify specific incident aetiology, and 288 reports (59.1%) had sufficient detailed information to classify a specific incident recovery mechanism. Conclusions: Incident reporting systems enable the classification of the surface features of an incident and identify common incident types. However, current systems provide little useful information on the underlying aetiology or incident recovery functions. Our study highlights several limitations of incident reporting systems, and provides guidance for improving the use of such systems in quality and safety improvement

Where failures occur in the imaging care cycle : lessons from the radiology events register

Adverse events contribute to significant patient morbidity and mortality on a global scale, and this has been documented in a number of international studies. Despite this, there is limited understanding of medical imaging’s involvement in such events. Incident reporting is a key feature of high-reliability organizations because, understandably, it is essential to know where things go wrong and why as the very first step informulating preventative and corrective strategies. Although anesthesiology has led the way, health care in general has been slow to adopt this technique, and this includes medical imaging. Knowledge as to where medical imaging incidents are initiated and detected, and why, is not well documented or appreciated, although this is critical information in relation to quality improvement. Using an online radiology reporting system, the authors therefore sought to gain further insight and also ascertain where failures are located in the imaging cycle, and whether different incidents sources provide different information. Last, the authors sought to examine the resilience of the imaging system using these incident data