High-reliability organizations (HROs) demonstrate unique and consistent characteristics, including operational sensitivity and control, situational awareness, hyperacute use of technology and data, and actionable process transformation. System complexity and reliance on information-based processes challenge healthcare organizations to replicate HRO processes. This article describes a healthcare organization's 3-year journey to achieve key HRO features to deliver high-quality, patient-centric care via an operations center powered by the principles of high-reliability data and software to impact patient throughput and flow.
Managing patient flow can be an effective strategy to reduce idling hospital beds, thereby lowering the healthcare cost without sacrificing quality of care. However, improving patient flow can be a major challenge due to the complex patterns of communication across diverse hospital staff. To identify improvement opportunities, this paper investigates whether the Distributed Situation Awareness (DSA) framework can feasibly and meaningfully model Situation Awareness (SA) in patient flow. The investigation involved a case study on modeling the DSA of the admission phase in patient flow for a level 1 trauma center. A DSA model combining task, knowledge, and social networks was created, showing feasibility of the framework in depicting the distribution and transaction of knowledge across workers and information systems. Further, a true elective admission case was mapped onto the DSA model, verifying its practical merits.
i o Introdu,~tion new method [!] ~ developed by the author s for reducing A matrices with polynomial elements (hereafter referred to as lambda~matrices) to triangular form is presented° The method presented has been programmed on the IBM 704o The prc~ess may be used for various applications such as obtaining the characteristic equation of a constant matrix A~I ~I-A I = O~ determining the characteristic equation for generalized lambda=matrices such as those which arise in systems of linear differential equations with constant coefficients~ and providing a mathematical so_~ation in analytical form of such systems° Existing comp~ter programming methods for obtaining the characteristic equation of !ambda~matrices employ some variation of the method of determinants° It i~ well k~o~a~ that the computing time for such methods increases rapidly as the order of the matrix increases° The method presented requires far fewer operations than determinant methods and although it is equivalent to the division algorithm for ~lynomia!s discussed in [21~ it has an advantage over this method ~ that it does not require polynomial division and replaces general polynomial ~ltiplication with a trivial form of this operation° The paper is presented in three sections~ (1) the mathematical method employed in the reductions (2) some special programming techniques, and (3) a brief description of the scaling techniques used° 29 -I
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