Hospital Nurses’ Perceptions of Human Factors Contributing to Nursing Errors

Roth, Cheryl K.; Wieck, K. Lynn; Fountain, Rebecca; Haas, Barbara K

doi:10.1097/nna.0000000000000196

Cited by 19 publications

(15 citation statements)

References 5 publications

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“…These terms include work intensification, 13 operational failures, 14 missed nursing care, 15 workplace decompensation, 11 and swamping. 16 For the purposes of this study, RN strain is defined as temporal phenomenon in which service demand exceeds RN capacity, necessitating intervention to avoid degradation in quality of patient care or RN work experience.…”

Section: Background and Significancementioning

confidence: 99%

Registered Nurse Strain Detection Using Ambient Data: An Exploratory Study of Underutilized Operational Data Streams in the Hospital Workplace

et al. 2020

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Background Registered nurses (RNs) regularly adapt their work to ever-changing situations but routine adaptation transforms into RN strain when service demand exceeds staff capacity and patients are at risk of missed or delayed care. Dynamic monitoring of RN strain could identify when intervention is needed, but comprehensive views of RN work demands are not readily available. Electronic care delivery tools such as nurse call systems produce ambient data that illuminate workplace activity, but little is known about the ability of these data to predict RN strain. Objectives The purpose of this study was to assess the utility of ambient workplace data, defined as time-stamped transaction records and log file data produced by non-electronic health record care delivery tools (e.g., nurse call systems, communication devices), as an information channel for automated sensing of RN strain. Methods In this exploratory retrospective study, ambient data for a 1-year time period were exported from electronic nurse call, medication dispensing, time and attendance, and staff communication systems. Feature sets were derived from these data for supervised machine learning models that classified work shifts by unplanned overtime. Models for three timeframes —8, 10, and 12 hours—were created to assess each model's ability to predict unplanned overtime at various points across the work shift. Results Classification accuracy ranged from 57 to 64% across three analysis timeframes. Accuracy was lowest at 10 hours and highest at shift end. Features with the highest importance include minutes spent using a communication device and percent of medications delivered via a syringe. Conclusion Ambient data streams can serve as information channels that contain signals related to unplanned overtime as a proxy indicator of RN strain as early as 8 hours into a work shift. This study represents an initial step toward enhanced detection of RN strain and proactive prevention of missed or delayed patient care.

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Section: Background and Significancementioning

confidence: 99%

Registered Nurse Strain Detection Using Ambient Data: An Exploratory Study of Underutilized Operational Data Streams in the Hospital Workplace

et al. 2020

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“…The BSN groups’ SAQ scores also reflected the influence an organization can have on their patient safety attitudes. There are implications here because work environment and culture have both been shown to influence adverse events and improve patient outcomes (Aiken et al, 2011; Aiken et al, 2008; Roth, Wieck, Fountain, & Haas, 2015). It would be important to consider that if a group of RNs is relying on the organization to enable them to keep patients safe, while another group is conceding and “mak[ing] it work,” regardless of the organization, then the risk for the illusion of shared meaning, miscommunication, and error increases.…”

Section: Discussionmentioning

confidence: 99%

Patient Safety Communication Among Differently Educated Nurses: Converging and Diverging Meaning Systems

2017

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Studies that suggest an increased number of bachelor's prepared nurses (BSNs) at the bedside improves patient safety do not stratify their samples into traditional bachelor's and associates (ADN) to BSN graduates. This qualitative study investigated potential differences in patient safety meaning among BSNs and ADN to BSN graduates. Guided by the theory of Language Convergence/Meaning Divergence, interview data from eight BSN and eight ADN to BSN graduates were analyzed. Findings indicate there are two meaning levels or systems, the local level and the systemic level. At the local level, the meaning of patient safety is focused at the patient's bedside and regulated by the nurse. The systemic level included the notion that health system factors such as policies and staffing are paramount to keeping patients safe. More frequently, ADN to BSN graduates' meaning of patient safety was at the local level, while BSNs' meaning centered at the systemic level.

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“…Much of the research about nurses’ attitudes has focused on job satisfaction 23,24 or work environment. 25 Although updated safety models, such as, human factors and safety science, are becoming increasingly common for examining adverse health care outcomes, 2627 research has yet to examine nurses’ safety practices in terms of the competency framework of knowledge, skills and attitudes. Attitudes impact nurses’ clinical decision making; nurses continuously prioritize work importance based on their attitudes.…”

Section: Discussionmentioning

confidence: 99%

Nurses' Perceived Skills and Attitudes About Updated Safety Concepts

Armstrong

Dietrich

Norman

et al. 2017

Journal of Nursing Care Quality

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Approximately one-quarter of medication errors in the hospital occur at the administration phase, which is solely under the purview of the bedside nurse. The purpose of this study was to assess bedside nurses’ perceived skills and attitudes about updated safety concepts, and examine their impact on medication administration errors (MAEs) and adherence to safe medication administration practices. Findings support the premise that MAEs result from an interplay among system-, unit- and nurse-level factors.

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Hospital Nurses’ Perceptions of Human Factors Contributing to Nursing Errors

Abstract: Managers must provide a way for nurses to feel empowered to intervene on error situations they believe are most likely, common, and important.

Cited by 19 publications

References 5 publications

Registered Nurse Strain Detection Using Ambient Data: An Exploratory Study of Underutilized Operational Data Streams in the Hospital Workplace

Registered Nurse Strain Detection Using Ambient Data: An Exploratory Study of Underutilized Operational Data Streams in the Hospital Workplace

Patient Safety Communication Among Differently Educated Nurses: Converging and Diverging Meaning Systems

Nurses' Perceived Skills and Attitudes About Updated Safety Concepts

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