Faculty Bibliography

Objective: This study sought to determine whether infusion device event logs could support accident investigation. Methods: An incident reporting database was searched for information about log file use in investigations. Log file data from devices in clinical use were downloaded and electronically searched for characteristics (signatures) matching specific function queries. Different programming sequences were simulated, and device logs were downloaded for analysis. Results: Database reports mentioned difficulties resolving log file data to the incident report and used log file data to confirm programming failures. Log file search revealed that, aside from alarm types and times, the devices were unable to adequately satisfy functional queries. Different simulated programming scenarios could not be easily differentiated by log file analysis. Conclusions: The device logs we studied collect data that are poorly suited to accident investigation. We conclude that infusion device logs cannot function as black boxes do in aviation accidents. Logs would be better applied to assist routine operations

The flow of technical work in acute healthcare varies unpredictably, in patterns that occur regularly enough that they can be managed. Acute care organizations develop ways to hedge resources so that they are available if they are needed. This pragmatic approach to the distribution of work among and across groups shows how rules can be used to manage a response to irregular demands for care. However, no rule set can be complete enough to cover this setting’s variety of care demands. Expertise is also needed to tie together the loose ends of conflicts that remain where rules no longer suffice. Many informal solutions to systemic problems go unnoticed unless they are the subjects of study. Naturalistic decision making (NDM) methods such as observational study, interviews, and process tracing reveal the activities of workers in their natural settings. Results of findings from such explorations of technical work can improve understanding of large scale work processes and, ultimately, patient safety. We have explored how practitioners cope with the demands that the system presents to them. While not all succeed, successful initiatives workers have developed demonstrate how their solutions create resilience at large scale

Information technology (IT) systems have been described as brittle and prone to automation surprises. Recent reports of information system failure, particularly computerized physician order entry (CPOE) systems, shows the result of such IT failure in actual practice. Such mismatches with healthcare work requirements require improvement to IT research and development. Resilience is the feature of some systems that makes it possible for them to respond to sudden, unanticipated demands for performance and return to normal operation quickly, with minimum decrement in performance. Workers create resilience at healthcare's sharp end by daily confronting constraints and obstacles that need to be surmounted in order to accomplish results. The resident sign-out sheet is described as an example of resilience. Efforts to develop successful IT systems for healthcare's sharp end must incorporate properties that reflect work domain traits, as the sign-out sheet shows.

Objectives: Automated piggybacks are purported to make drug administration safer and more reliable. We evaluated the human factors of piggyback infusion, investigated the practice in our institution, and analyzed incidents from an anonymous database to better characterize the practice and substantiate these assertions. Methods: To find examples of problems with piggyback, or secondary infusions, we searched the Food and Drug Administration's on-line incident database for incidents involving piggybacks. As part of a task analysis, 19 senior nurses each programmed 2 of 4 different pumps for a simulated piggyback infusion. To characterize infusion practice, we evaluated data logs from 55 infusion devices used in our institution. Results: Incidents from the database provided strong evidence that potential problems existed with piggyback infusions. Nurse behaviors suggested mismatches between the task, user, and devices that can lead to adverse events. Log files showed piggybacks were a common practice, and that available safeguards were not used. Conclusions: Our multiple data sources suggest automated piggyback infusion practice is neither simple nor safe. Incident report analysis suggests these findings contribute to adverse events. Further study is needed to understand and improve the safety of this practice

Healthcare demonstrates the same properties of risk, complexity, uncertainty, dynamic change, and time-pressure as other high hazard sectors including aviation, nuclear power generation, the military, and transportation. Unlike those sectors, healthcare has particular traits that make it unique such as wide variability, ad hoc configuration, evanescence, resource constraints, and governmental and professional regulation. While healthcare's blunt (management) end is more easily understood, the sharp (operator) end is more difficult to research the closer one gets to the sharp end's point. Understanding sharp end practice and cognitive work can improve computer-based systems resilience, which is the ability to perform despite change and challenges. Research into actual practice at the sharp end of healthcare will provide the basis to understand how IT can support clinical practice. That understanding can be used to develop computer-based systems that will act as team players, able to support both individual and distributed cognitive work at healthcare's sharp end.

The advent of fast-acting drugs has made the infusion pump the most pervasive electronic medical device in the acute care (hospital) environment. Despite the importance of its correct operation, incident reports in the US Food and Drug Administration (FDA) database implicate interface programming as a significant aspect of adverse outcomes. This article describes a study of infusion pump-programming performance by experienced healthcare professionals in a major urban teaching hospital. Early findings indicate that practitioner experience with device programming does not increase proficiency. This suggests that a complex menu structure ("menuspace") makes programming difficult and inefficient in ways that impede practitioner development of mental models that are sufficient for reliable device operation. This causes operators to become disoriented in the interface structure, or "lost in menuspace." We relate these findings to the current study of the USFDA adverse events reports and indicate directions for further research.