Using Cognitive Work Analysis to fit decision support tools to nurse managers' work flow

摘要

Purpose: To better understand the environmental constraints on nurse managers that impact their need for and use of decision support tools, we conducted a Cognitive Work Analysis (CWA). A complete CWA includes system analyses at five levels: work domain, decision-making procedures, decision-making strategies, social organization/collaboration, and worker skill level. Here we describe the results of the Work Domain Analysis (WDA) portion in detail then integrate the WDA with other portions of the CWA, reported previously, to generate a more complete picture of the nurse manager's work domain. Methods: Data for the WDA were obtained from semi-structured interviews with nurse managers, division directors, CNOs, and other managers (n = 20) on 10 patient care units in three Arizona hospitals. The WDA described the nurse manager's environment in terms of the constraints it imposes on the nurse manager's ability to achieve targeted outcomes through organizational goals and priorities, functions, processes, as well as work objects and resources (e.g., people, equipment, technology, and data). Constraints were identified and summarized through qualitative thematic analysis. Results: The results highlight the competing priorities, and external and internal constraints that today's nurse managers must satisfy as they try to improve quality and safety outcomes on their units. Nurse managers receive a great deal of data, much in electronic format. Although dashboards were perceived as helpful because they integrated some data elements, no decision support tools were available to help nurse managers with planning or answering "what if questions. The results suggest both the need for additional decision support to manage the growing complexity of the environment, and the constraints the environment places on the design of that technology if it is to be effective. Limitations of the study include the small homogeneous sample and the reliance on interview data targeting safety and quality.