Application of failure mode and effect analysis in a radiology department.

With increasing deployment, complexity, and sophistication of equipment and related processes within the clinical imaging environment, system failures are more likely to occur. These failures may have varying effects on the patient, ranging from no harm to devastating harm. Failure mode and effect analysis (FMEA) is a tool that permits the proactive identification of possible failures in complex processes and provides a basis for continuous improvement. This overview of the basic principles and methodology of FMEA provides an explanation of how FMEA can be applied to clinical operations in a radiology department to reduce, predict, or prevent errors. The six sequential steps in the FMEA process are explained, and clinical magnetic resonance imaging services are used as an example for which FMEA is particularly applicable. A modified version of traditional FMEA called Healthcare Failure Mode and Effect Analysis, which was introduced by the U.S. Department of Veterans Affairs National Center for Patient Safety, is briefly reviewed. In conclusion, FMEA is an effective and reliable method to proactively examine complex processes in the radiology department. FMEA can be used to highlight the high-risk subprocesses and allows these to be targeted to minimize the future occurrence of failures, thus improving patient safety and streamlining the efficiency of the radiology department.

Implementation of online radiology quality assurance reporting system for performance improvement: initial evaluation.

PURPOSE: To evaluate an online system developed and implemented for reporting and managing quality assurance (QA) events occurring in a radiology department. MATERIALS AND METHODS: This HIPAA-compliant study had institutional review board approval; informed consent was not required. Using repeated plan-do-study-act cycles, a radiology quality management team applied a 10-step process to implement an online reporting system. The system permits remote submission of cases by staff members. The number of weekly submissions to the system over a 9-month period was evaluated and compared with that for the preceding 6 months by using the Mann-Whitney test. Sources and nature of data, actions initiated, and final outcomes were also analyzed. Recorded data included forum of discussion, dimension of care, action items, monitoring of follow-up and compliance, and notification status. RESULTS: During the first 9 months of implementation, 605 cases were submitted (mean, 21.4 cases per week), a significant increase (P < .005) compared with the preceding 6 months (mean, 3.2 cases per week). Cases, which were submitted by residents (121 cases [20.0%]), fellows (94 cases [15.5%]), faculty members (319 cases [52.7%]), or technologists (54 cases [8.9%]), reported technical (33.1%) or administrative (8.0%) issues. The 329 clinical cases (54.4%) included 60 errors in communication (18.2%), 67 errors in interpretation (20.4%), 78 diagnostic delays (23.7%), 99 missed diagnoses (30.1%), and 54 procedural complications (16.4%); some cases were in more than one category. Twenty-three cases (3.8%) resulted in submission-related QA projects, and 69 cases (11.4%) resulted in individuals or sections of the hospital undergoing additional training. CONCLUSION: A secure online QA reporting system promotes reporting of QA events and serves as a database for identifying and managing trends, initiating performance improvement projects, and providing feedback to staff members who submit cases.