Models and methods of work system design need to be developed and implemented to advance research in and design for patient safety. In this paper we describe how the Systems Engineering Initiative for Patient Safety (SEIPS) model of work system and patient safety, which provides a framework for understanding the structures, processes and outcomes in health care and their relationships, can be used toward these ends. An application of the SEIPS model in one particular care setting (outpatient surgery) is presented and other practical and research applications of the model are described. Most errors and inefficiencies in patient care arise not from the solitary actions of individuals but from conflicting, incomplete, or suboptimal systems of which they are a part and with which they interact. To improve the design of these systems, the US Institute of Medicine (IOM) has proposed the application of engineering concepts and methods-in particular, human factors and systems engineering. 1-3Emphasis on system design was promoted in a recent report by the National Academy of Engineering and the IOM: ''… it is time to… establish a vigorous new partnership between engineering and health care and hasten a transition to a patient-centered 21st century health care system''.4 Our research program, the Systems Engineering Initiative for Patient Safety (SEIPS, http:// www2.fpm.wisc.edu/seips/), originally funded by the Agency for Healthcare Research and Quality, meets this challenge through a novel integration of human factors and healthcare quality models and proposes the SEIPS model of work system 5-7 and patient safety. Patient safety researchers clearly recognize the need for human factors engineering and systems approaches to patient safety research, analysis, and improvement. However, noticeably missing from the patient safety literature are models to guide studies to empirically examine system design in relation to patient safety and medical errors. The model described by Reason, 8 often referred to as the ''Swiss cheese'' model, is probably the most well known system model used within the patient safety community. Vincent et al 9 have expanded Reason's model and described seven categories of factors that influence clinical practice, such as organizational and management factors, work environment, team factors, task factors and patient characteristics. The Haddon model, which is used commonly in epidemiology and injury prevention, has been proposed for use in quality and safety. A comparison of the strengths and weaknesses of the SEIPS model, the Reason/Vincent model, and Donabedian's quality model is shown in table 1. The SEIPS model explains how the design of the work system can impact not only the safety of patients but also employee and organizational outcomes. Employee outcomes include safety, health, satisfaction, stress and burnout; organizational outcomes include rates of turnover, injuries and illnesses, and organizational health (profitability).In this paper we describe the SEIPS model and its research and pra...

The study demonstrates that perceived usefulness, perceived ease of use, subjective norm, and healthcare knowledge together predict most of the variance in patients' acceptance and self-reported use of the web-based self-management technology.

No abstract

Purpose Contemporary big data initiatives in health care will benefit from greater integration with nursing science and nursing practice; in turn, nursing science and nursing practice has much to gain from the data science initiatives. Big data arises secondary to scholarly inquiry (e.g., ‐omics) and everyday observations like cardiac flow sensors or Twitter feeds. Data science methods that are emerging ensure that these data be leveraged to improve patient care. Organizing Construct Big data encompasses data that exceed human comprehension, that exist at a volume unmanageable by standard computer systems, that arrive at a velocity not under the control of the investigator and possess a level of imprecision not found in traditional inquiry. Data science methods are emerging to manage and gain insights from big data. Methods The primary methods included investigation of emerging federal big data initiatives, and exploration of exemplars from nursing informatics research to benchmark where nursing is already poised to participate in the big data revolution. We provide observations and reflections on experiences in the emerging big data initiatives. Conclusions Existing approaches to large data set analysis provide a necessary but not sufficient foundation for nursing to participate in the big data revolution. Nursing's Social Policy Statement guides a principled, ethical perspective on big data and data science. There are implications for basic and advanced practice clinical nurses in practice, for the nurse scientist who collaborates with data scientists, and for the nurse data scientist. Clinical Relevance Big data and data science has the potential to provide greater richness in understanding patient phenomena and in tailoring interventional strategies that are personalized to the patient.

Patient-centered care is valued in nursing. However, until recently, nurse-researchers have focused on testing the effects of standardized rather than patient-centered interventions (PCIs). The latter are those interventions that are altered to address selected patient characteristics (e.g., beliefs, habits, or goals). PCIs have been well received, and in some studies they have been associated with improved health outcomes. In this article we describe briefly the concept patient centered, summarize the development of research on PCIs, discuss kinds of PCIs, provide examples of PCIs and how they have been derived and implemented, and raise issues for theory and future research.