Detecting adverse events for patient safety research: a review of current methodologies <s> Cognitive frameworks for monitoring and detecting adverse events for patient safety research <s> The authors describe HFMEA, a five-step process used to proactively evaluate a health care process, and provide examples of a team's forms and actions regarding prostate-specific antigen testing. <s> BIB001 </s> Detecting adverse events for patient safety research: a review of current methodologies <s> Cognitive frameworks for monitoring and detecting adverse events for patient safety research <s> Article-at-a-Glance Background In February 2001 Good Samaritan Hospital in Dayton, Ohio, conducted a Failure Mode and Effect Analysis (FMEA) on the blood transfusion process to reduce the risk of problems inherent in the procedure. Developing the FMEA The major steps of the analysis were to identify problems (failure modes), define their causes, and surmise the effects if failures occurred. Numerical scores were assigned for the likelihood of failure occurrence, the severity of the effects, and the possibility that the failure would escape detection. These scores were multiplied and reported as a risk priority number (RPN) for each failure mode. Solutions (process redesign actions) and monitoring plans (design validation) were developed to address the failure modes with the highest RPNs. Presenting the FMEA In March 2001 the FMEA document was presented to the Safety Board, which approved design changes such as use of a blood barrier system that restricts access to the blood until a patient-specific code is dialed. Results Measures were developed to analyze results, and rapid-cycle Plan-Do-Study-Act methodology was used to test and document redesign changes; most became the standard operating procedure. The new process accomplished its purpose of preventing serious, avoidable errors. No outcome errors occurred from March 2001 through June 2001 or in the 8 months following housewide implementation on June 18, 2001. Discussion FMEA was a valuable tool in error-trapping the blood transfusion process. Yet the FMEA process was time-consuming, tedious, and difficult and should be reserved for an organization's highest-priority processes. <s> BIB002
In light of the growing awareness of the role of human errors in widely publicized incidents such as airline accidents and complications of medical procedures, understanding of the cognitive, social, and information sciences have become crucial in order to make any significant contributions to the study and prevention of human errors. Medical decision-making is constrained by human cognitive limitations, as well as socio-cultural and organizational factors, and errors often arise due to poor and flawed memory, inexperience and poor training, as well as faulty reasoning and ambiguous communication. To improve patient safety, technology should be designed that take into account these limitations. Several industries (e.g., aviation and nuclear power plants) have been very successful in monitoring and preventing human errors through the careful design of user interfaces and use of systems theory. A key issue with many detection systems is that they may not describe errors or events in sufficient detail to make it possible to get to the root of the problem, and make appropriate systems changes, which will often involve interface design. Many organizations are beginning to apply traditional engineering methodologies to the study of patient safety concerns, such as failure mode and effects analysis (FMEA) and root cause analysis (RCA). While these approaches have produce some notable successes BIB001 BIB002 , in general, far too often errors and adverse events are simply accumulated in healthcare, and the data are not effectively used. A recent report from the Institute of Medicine supports the importance of systems design by suggesting that most medical errors are related to inadequacies in process rather to incompetence of health workers, and that information technology can play a particularly important role in preventing or minimizing the consequences of such errors  . Examples include the computer-based verification of dosing information at the time that a drug regimen is ordered, or improved access to (and legibility of) pertinent clinical information that may prevent decision-making errors before they occur. However, there remain major challenges in the implementing and integrating such facilities into clinical environments and even technology that is designed to improve safety could result in unintended errors if improperly designed for the user. Because errors are fundamentally cognitive, a cognitive framework is necessary to identify and specify the underlying mechanisms that cause human error. Two ways in which cognitive factors play important roles in medical errors are through the cognitive properties of interactions between human and technology (user interfaces) and the dynamics of information processing in distributed cognitive systems involving groups of people and technology (groupware and distributed cognition). A cognitive foundation for the system is essential for a complete and in-depth understanding of medical errors. Once a cognitive foundation is developed, we can classify medical errors according to their underlying cognitive mechanisms, which we may be able to automate for adverse event monitoring and detection.