Methods and Lessons Learned from a Current State Workflow Assessment following Transition to a New Electronic Health Record System.

The transition to a new electronic health record (EHR) system requires an understanding of how the new system addresses the needs, business processes, and current activities of a healthcare system. To address such requirements, a multidisciplinary team conducted a current state workflow assessment (CSWFA) of clinical and administrative functions to elicit and document business processes (via process diagrams), requirements, workarounds, and process issues (i.e., user interface issues, training gaps) at one healthcare facility. We provided a novel method of evaluating the implementation process to ensure that a CSWFA was documented with key stakeholders. In this analysis, we describe the CSWFA approach and expected outcomes with a specific emphasis on how a qualitative approach can be integrated to explore underlying patterns and relationships in the data. Overall, this methodology enables practitioners to deliver data-driven support initiatives that optimize EHR implementation while considering user experience, productivity, and patient safety.

Fuzzy signal detection theory: analysis of human and machine performance in air traffic control, and analytic considerations.

This paper applies fuzzy SDT (signal detection theory) techniques, which combine fuzzy logic and conventional SDT, to empirical data. Two studies involving detection of aircraft conflicts in air traffic control (ATC) were analysed using both conventional and fuzzy SDT. Study 1 used data from a preliminary field evaluation of an automated conflict probe system, the User Request Evaluation Tool (URET). The second study used data from a laboratory controller-in-the-loop simulation of Free Flight conditions. Instead of assigning each potential conflict event as a signal (conflict) or non-signal, each event was defined as a signal (conflict) to some fuzzy degree between 0 and 1 by mapping distance into the range [0, 1]. Each event was also given a fuzzy membership, [0, 1], in the set 'response', based on the perceived probability of a conflict or on the colour-coded alert severity. Fuzzy SDT generally reduced the computed false alarm rate for both the human and machine conflict systems, partly because conflicts just outside the conflict criterion used in conventional SDT, were defined by fuzzy SDT as a signal worthy of some attention. The results illustrate the potential of fuzzy SDT to provide, especially in exploratory data analysis, a more complete picture of performance in aircraft conflict detection and many other applications. Alternative analytic methods also using fuzzy SDT concepts are discussed.