Determining nurse staffing needs: the workload intensity measurement system

Hoi, Shu Yin; Ismail, Norafida; Ong, Li Chern; Kang, Jasmine

doi:10.1111/j.1365-2834.2009.01045.x

Cited by 48 publications

(52 citation statements)

References 20 publications

(18 reference statements)

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“…The Professional Assessment of Optimal Nursing Care Intensity Level is another method to measure nurse intensity (Frilund & Fagerstrom, 2009). The Workload Intensity Measurement System uses nursing diagnosis to measure workload of nurses (Hoi, Ismail, Ong, & Kang, 2010). The Community Client Need Classification System was developed to measure workload of community nurses (Brady, Byrne, Horan, Macgregor, & Begley, 2008).…”

Section: Measuring Workloadmentioning

confidence: 99%

Measuring Workload of Nurses on a Neurosurgical Care Unit

Nelson

Valentino

Iacono

et al. 2015

Journal of Neuroscience Nursing

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Section: Measuring Workloadmentioning

confidence: 99%

Measuring Workload of Nurses on a Neurosurgical Care Unit

Nelson

Valentino

Iacono

et al. 2015

Journal of Neuroscience Nursing

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“…Seventeen of the instruments had been used in an acute care setting, [5,9,[11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] followed by one used in a psychiatric setting [26] and two in outpatient settings. [27,28] Few instruments in the studies had been designed based on a theoretical framework; three (15%) of the 20 instruments were based upon, respectively, Benner's NoviceExpert, the NIC, and Nursing Outcome Classification; [12] Horta's Basic Needs Theory; [13] and the Nursing Process Model.…”

Section: Survey Developmentmentioning

confidence: 99%

“…Duclos-Miller, 1996 [22] Labor, Delivery, Recovery, Postpartum Model (LDRP) (NI -2) 16 Hlusko & Nichols, 1996 [23] Not Available (NI -3) 17 Hoi et al, 2010 [24] Workload Intensity Measurement System (WIMS) (NI -4) 18 Karr & Fisher, 1997 [27] Not Available (NI -5) 19 Moore & Hastings, 2006 [28] Not Available (NI -6) 20 Chou et al, 2007 [25] Chinese Nursing Interventions in ICU (CNI-ICU) (NI -7)…”

mentioning

confidence: 99%

Nursing activities and factors influential to nurse staffing decision-making

Young

Lee

Sands

et al. 2015

JHA

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Objective: There is limited published research supporting the effectiveness of nursing workload measurement to comprehensively measure nursing workload and to formulate nurse resource need. Predictive accuracy is impaired due to variation in direct and indirect care-related activities across measurement instruments. This study aimed to (1) identify common nursing activities considered by nurse managers for staffing decision-making, (2) systematically review such nursing activities in relation to existing nursing workload instruments and Nursing Intervention Classification taxonomy, and (3) describe challenges perceived by managers in staffing decision-making. Methods: A survey was developed from an inclusive review of 20 nursing workload instruments collectively measuring 502 nursing activities. Nurse managers in 13 medical-surgical and two intensive care units at a Midwest healthcare organization identified nursing activities considered daily for staffing decision-making. Results: Twenty-one activities were commonly considered by at least 90 percent of managers (n = 13) for daily staffing decisionmaking, although none of the instruments reviewed included all 21 activities. Conclusions: Lack of a standardized framework for nursing workload measurement might have led to nurse managers' different perceptions about appropriate determinants of these measurements. A standardized approach for measuring nursing workload would facilitate benchmarking for estimating nurse resource need. Further research is needed to design a systematic infrastructure that ensures staffing to meet patient care need. A process is also needed to alleviate the challenges in staffing decision-making that nurse managers face, such as fluctuations in census and patient acuity, nurse competency-based patient assignments, and limited information resources for staffing estimation.

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“…They face a heavier workload with less than 16% of their time being spent on non-patient related activities such as completing and looking over patient paperwork and adequate staffing of emergency nurses continues to be a problem in many hospitals. [7][8][9] The belief is that to alleviate the overcrowding in the ED, one of the key issues is determining how to allocate resources in such a way that patient safety is not compromised by wait times. This problem has been partially addressed by standardizing patient care with triage systems such as the Emergency Severity Index (ESI) and with the implementation of fast-track systems to treat less acute patients.…”

Section: Introductionmentioning

confidence: 99%

Treatment allocation for the potential urgent patients in hospital emergency department to reduce length of stay – a simulation approach

Huang

2015

IJH

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Extended waiting in the emergency room continues to be a recognized problem; to alleviate this many hospitals have adopted triage systems which classify patients based on their priority level and fast track processes for low acuity patients. This paper aims to reduce the length of stay in Emergency Department (ED) by allocating urgent patients in either the traditional treatment or the fast track route without compromising care for higher acuity patients. This study focuses on the five-priority level system and evaluates the concept using simulation and classification techniques. Literature has reported that priority three patients, whose conditions are initially not life threatening but may progress to a critical level, are the majority and normally encounter the longest wait. The simulation was built based on a difined ED patient flow considering factors such as patient arrival patterns, treatment times, and resource capacity. Analysis of Variance was conducted to determine the impact of each factor to the decision on level 3 patient allocation. A regression model was constructed to estimate the cycle time savings. Decision tree was used to further provide ED a general guideline on the allocation of level 3 patients. The study concludes that the fast track route as an option for level 3 patients is adequate if the fast track has higher or similar capacity as the traditional treatment route especially for the rural EDs where level 3 patients are identified 61% of time and required longer treatment. The patient cycle time saving estimated as much as three hours.

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Determining nurse staffing needs: the workload intensity measurement system

Abstract: Workload predictions should de-link patient dependency with acuity status as both do not correlate, as evidenced by this study.

Cited by 48 publications

References 20 publications

Measuring Workload of Nurses on a Neurosurgical Care Unit

Measuring Workload of Nurses on a Neurosurgical Care Unit

Nursing activities and factors influential to nurse staffing decision-making

Treatment allocation for the potential urgent patients in hospital emergency department to reduce length of stay – a simulation approach

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