Emergency department patient classification systems: A systematic review

Williams, Susan; Crouch, Robert

doi:10.1016/j.aaen.2006.06.002

Cited by 25 publications

(18 citation statements)

References 14 publications

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“…Bard and Purnomo [3] formulated and solved the nurse scheduling problem as a multi-objective problem which considered individual nurse's preference. In the past couple of decades, several patient classification systems and acuity systems were developed to aid determination of nursing care, staffing level, and schedule ahead of a shift [7,8,16,23,28,36,50]. It has to be noted that four levels of acuity were considered in this research depending upon the amount of care received by the patients in the north Texas hospital.…”

Section: Nurse Resource Planningmentioning

confidence: 99%

A data-integrated simulation-based optimization for assigning nurses to patient admissions

Sundaramoorthi

Chen

Rosenberger

et al. 2010

Health Care Manag Sci

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The health care system in the United States has a shortage of nurses. A careful planning of nurse resources is needed to ease the health care system from the burden of the nurse shortage and standardize nurse workload. An earlier research study developed a data-integrated simulation to evaluate nurse-patient assignments (SIMNA) at the beginning of a shift based on a real data set provided by a northeast Texas hospital. In this research, with the aid of the same SIMNA model, two policies are developed to make nurse-to-patient assignments when new patients are admitted during a shift. A heuristic (HEU) policy assigns a newly-admitted patient to the nurse who has performed the least assigned direct care among all the nurses. A partially-optimized (OPT) policy seeks to minimize the difference in workload among nurses for the entire shift by estimating the assigned direct care from SIMNA. Results comparing HEU and OPT policies are presented.

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Section: Nurse Resource Planningmentioning

confidence: 99%

A data-integrated simulation-based optimization for assigning nurses to patient admissions

Sundaramoorthi

Chen

Rosenberger

et al. 2010

Health Care Manag Sci

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“…Patient dependency on nursing care has been a conceptual determinant of nursing workload (Morris et al, ), being described as the source of nursing work (Jones, ) and as the most appropriate parameter to monitor nursing time (Needham, ). Accordingly, nursing workload has been assessed with dependency‐based patient classification systems in a large variety of settings (Brady, Byrne, Horan, Macgregor, & Begley, ; Williams, Harris, & Turner‐Stokes, ; Williams & Crouch, ). Studies showed that the nursing time taken to provide direct care for an individual patient was positively correlated with patient dependency (Crouch & Williams, ) and increased as a function of the dependency category (Brady et al, ; Williams et al, ).…”

Section: Methodsmentioning

confidence: 99%

“…WANE direct care metric was expected to yield similar results when tested against a valid and reliable dependency‐based classification system. Of the numerous dependency tools developed for use in ED settings, we chose for the JDT as it best met these criteria (Varndell, MacGregor, Gallagher, & Fry, ; Williams & Crouch, ). The JDT was validated to classify patients in categories conceived to differ by the amount of nursing interventions required per patient (O'Brien & Benger, ).…”

Section: Methodsmentioning

confidence: 99%

Development and validation of an assessment tool for nursing workload in emergency departments

Iordache

Elseviers

Cock

et al. 2019

Journal of Clinical Nursing

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Aims To develop the Workload Assessment of Nurses on Emergency (WANE) tool and to test its validity and reliability to measure nursing workload in the emergency departments. Background Ensuring safe nursing staffing in emergency departments is a worldwide concern. There is no valid tool to measure emergency nursing workload in order to determine the needed nurse staffing in the emergency departments. Design A two‐year, cross‐sectional, multicenter study. Methods Workload was operationalised as the time nurses spent with nursing activities, classified into direct and indirect care. A board of experts provided content validity. Construct validity was evaluated by examining the WANE's correlations and group‐discriminations patterns within the network of variables known to determine nursing workload. Reliability was assessed by the tool's ability to yield consistent results across repeated measurements. Reporting of this research adheres to STROBE guidelines. Results Seven emergency departments, including 3,024 patients, were involved in the first year and 18 emergency departments and 7,442 patients in the second year. Direct care time correlated positively and significantly with patient dependency on nursing care, age and length of emergency department stay and discriminated between the categories of dependency on nursing care, age and hospitalisation. Both direct and indirect care time discriminated between the emergency departments according to different patient care profiles and unit characteristics. WANE showed consistent results across measurements. Conclusions Results support the WANE's reliability and validity to measure emergency nursing workload. This tool could be used to determine, on patient and unit, a baseline nurse staffing and the nursing skill mix in the emergency departments. WANE is also an evidence‐based management tool for benchmarking purposes. Relevance to clinical practice The use of an evidence‐based workload tool in making staffing decisions in emergency departments is crucial to ensure safe patient care and prevent work overload in nursing staff.

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“…The operation processes are driven by resources such as medical specialists, mainly in charge of the medical treatment, researching and teaching with the aim of providing and developing new medical methods of health care services to increase the inhabitants' health quality of life [22] [10]. The individual caregivers such as doctors and nurses are patient-centred in their work, where different knowledge and experience has an impact in planning and scheduling incoming patients on top of the degree of complexity of the process itself [23]. The organizational processes are often divided in groups of clinical specialties focusing on single procedures or patient groups in order provide most effective care for the patients.…”

Section: Hospital Environmental Variablesmentioning

confidence: 99%

The Misalignment between Hospital Planning Frameworks and their Planning Environment – A Conceptual Matching Approach

Nguyen

Sommer

Steger-Jensen

et al. 2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract. Scientifically developed hospital planning frameworks have emerged during the last decade to support and prescribe planning and control practice in hospitals. The three presented frameworks are generally based on manufacturing planning and control frameworks attuned for manufacturing environments. This is critical to the adaptability of the frameworks, since manufacturing environments are focused on optimization of repetitive production processes whereas hospital processes are characterised by complex problem-solving processes with a high degree of uncertainty. In this paper, we identify and describe the misalignment from an environment variables perspective through analysis and comparison of three prominent hospital planning frameworks and hospital planning variables derived from empirical studies in existing research.Keywords: Hospital planning frameworks, hospital planning and control, planning and control frameworks, hospital environment, conceptual matching. IntroductionInspired from manufacturing industry, scholars from the field of operations management have proposed several frameworks for effective and efficient hospital planning. However, the planning environment of a hospital is different from the planning environment of a manufacturing company, and since research on hospital planning and control is a relatively new research area, little empirical evidence currently supports the applicability of planning and control frameworks in a hospital environment. Thus, the purpose of this paper is to conduct evaluations of adapted frameworks for hospital planning and control in relation to the planning environment of hospitals. Hence, the main focus of this paper is the relation between the planning environment and the planning framework used for hospital planning in practice. Jonsson and Mattson [1] used a similar analysis between planning environment and planning methods in manufacturing environments both empirically and conceptually. However, this paper will only focus on the conceptual matching between planning frameworks and planning environment, where the reasoning is that the planning environment and

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Emergency department patient classification systems: A systematic review

Cited by 25 publications

References 14 publications

A data-integrated simulation-based optimization for assigning nurses to patient admissions

A data-integrated simulation-based optimization for assigning nurses to patient admissions

Development and validation of an assessment tool for nursing workload in emergency departments

The Misalignment between Hospital Planning Frameworks and their Planning Environment – A Conceptual Matching Approach

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