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Abstract: 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. [Copyright &y& Elsevier]

Abstract: Purpose: We used ORA, a dynamic network analysis tool, to identify patient care unit communication patterns associated with patient safety and quality outcomes. Although ORA had previously had limited use in healthcare, we felt it could effectively model communication on patient care units. Methods: Using a survey methodology, we collected communication network data from nursing staff on seven patient care units on two different days. Patient outcome data were collected via a separate survey. Results of the staff survey were used to represent the communication networks for each unit in ORA. We then used ORA''s analysis capability to generate communication metrics for each unit. ORA''s visualization capability was used to better understand the metrics. Results: We identified communication patterns that correlated with two safety (falls and medication errors) and three quality (e.g., symptom management, complex self care, and patient satisfaction) outcome measures. Communication patterns differed substantially by shift. Conclusion: The results demonstrate the utility of ORA for healthcare research and the relationship of nursing unit communication patterns to patient safety and quality outcomes. [Copyright &y& Elsevier]

Summary: As part of ongoing research to investigate the impact of patient characteristics, organization characteristics and patient unit characteristics on safety and quality outcomes, we used a computational modeling program, OrgAhead, to model patient care units’ achievement of patient safety (medication errors and falls) and quality outcomes. We tuned OrgAhead using data we collected from 32 units in 12 hospitals in Arizona. Validation studies demonstrated acceptable levels of correspondence between actual and virtual patient units. In this paper, we report how we used OrgAhead to develop testable hypotheses about the kinds of innovations that nurse managers might realistically implement on their patient care units to improve quality and safety outcomes. Our focus was on unit-level innovations that are likely to be easier for managers to implement. For all but the highest performing unit (for which we encountered a ceiling effect), we were able to generate practical strategies that improved performance of the virtual units that could be implemented by actual units to improve safety and quality outcomes. Nurse managers have responded enthusiastically to the additional decision support for quality improvement. [Copyright &y& Elsevier]