John F Schnelle1, L Dale Schroyer2, Avantika A Saraf3, Sandra F Simmons4. 1. Vanderbilt University, School of Medicine, Division of Geriatrics, Center for Quality Aging, Nashville, TN; Geriatric Research Education and Clinical Center (GRECC), Tennessee Valley Healthcare System Veterans Affairs, Nashville, TN. Electronic address: john.schnelle@vanderbilt.edu. 2. ProModel Corporation, Life Sciences Vertical, Allentown, PA. 3. Vanderbilt University, School of Medicine, Division of Geriatrics, Center for Quality Aging, Nashville, TN; Vanderbilt University Medical Center, Division of Cardiovascular Medicine, Nashville, TN. 4. Vanderbilt University, School of Medicine, Division of Geriatrics, Center for Quality Aging, Nashville, TN; Geriatric Research Education and Clinical Center (GRECC), Tennessee Valley Healthcare System Veterans Affairs, Nashville, TN.
Abstract
BACKGROUND: Nursing aides provide most of the labor-intensive activities of daily living (ADL) care to nursing home (NH) residents. Currently, most NHs do not determine nurse aide staffing requirements based on the time to provide ADL care for their unique resident population. The lack of an objective method to determine nurse aide staffing requirements suggests that many NHs could be understaffed in their capacity to provide consistent ADL care to all residents in need. Discrete event simulation (DES) mathematically models key work parameters (eg, time to provide an episode of care and available staff) to predict the ability of the work setting to provide care over time and offers an objective method to determine nurse aide staffing needs in NHs. OBJECTIVES: This study had 2 primary objectives: (1) to describe the relationship between ADL workload and the level of nurse aide staffing reported by NHs; and, (2) to use a DES model to determine the relationship between ADL workload and nurse aide staffing necessary for consistent, timely ADL care. DESIGN: Minimum Data Set data related to the level of dependency on staff for ADL care for residents in over 13,500 NHs nationwide were converted into 7 workload categories that captured 98% of all residents. In addition, data related to the time to provide care for the ADLs within each workload category was used to calculate a workload score for each facility. The correlation between workload and reported nurse aide staffing levels was calculated to determine the association between staffing reported by NHs and workload. Simulations to project staffing requirements necessary to provide ADL care were then conducted for 65 different workload scenarios, which included 13 different nurse aide staffing levels (ranging from 1.6 to 4.0 total hours per resident day) and 5 different workload percentiles (ranging from the 5th to the 95th percentile). The purpose of the simulation model was to determine the staffing necessary to provide care within each workload percentile based on resident ADL care needs and compare the simulated staffing projections to the NH reported staffing levels. MEASURES: The percentage of scheduled care time that was omitted was estimated by the simulation model for each of the 65 workload scenarios using optimistic assumptions about staff productivity and efficiency. RESULTS: There was a low correlation between ADL workload and reported nurse aide staffing (Pearson = .11; P < .01), which suggests that most of the 13,500 NHs were not using ADL acuity to determine nurse aide staffing levels. Based on the DES model, the nurse aide staffing required for ADL care that would result in a rate of care omissions below 10% ranged from 2.8 hours/resident/day for NHs with a low workload (5th percentile) to 3.6 hours/resident/day for NHs with a high workload (95th percentile). In contrast, NHs reported staffing levels that ranged from an average of 2.3 to 2.5 hours/resident/day across all 5 workload percentiles. Higher workload NHs had the largest discrepancies between reported and predicted nurse aide staffing levels. CONCLUSIONS: The average nurse aide staffing levels reported by NHs falls below the level of staffing predicted as necessary to provide consistent ADL care to all residents in need. DES methodology can be used to determine nurse aide staffing requirements to provide ADL care and simulate management interventions to improve care efficiency and quality.
BACKGROUND: Nursing aides provide most of the labor-intensive activities of daily living (ADL) care to nursing home (NH) residents. Currently, most NHs do not determine nurse aide staffing requirements based on the time to provide ADL care for their unique resident population. The lack of an objective method to determine nurse aide staffing requirements suggests that many NHs could be understaffed in their capacity to provide consistent ADL care to all residents in need. Discrete event simulation (DES) mathematically models key work parameters (eg, time to provide an episode of care and available staff) to predict the ability of the work setting to provide care over time and offers an objective method to determine nurse aide staffing needs in NHs. OBJECTIVES: This study had 2 primary objectives: (1) to describe the relationship between ADL workload and the level of nurse aide staffing reported by NHs; and, (2) to use a DES model to determine the relationship between ADL workload and nurse aide staffing necessary for consistent, timely ADL care. DESIGN: Minimum Data Set data related to the level of dependency on staff for ADL care for residents in over 13,500 NHs nationwide were converted into 7 workload categories that captured 98% of all residents. In addition, data related to the time to provide care for the ADLs within each workload category was used to calculate a workload score for each facility. The correlation between workload and reported nurse aide staffing levels was calculated to determine the association between staffing reported by NHs and workload. Simulations to project staffing requirements necessary to provide ADL care were then conducted for 65 different workload scenarios, which included 13 different nurse aide staffing levels (ranging from 1.6 to 4.0 total hours per resident day) and 5 different workload percentiles (ranging from the 5th to the 95th percentile). The purpose of the simulation model was to determine the staffing necessary to provide care within each workload percentile based on resident ADL care needs and compare the simulated staffing projections to the NH reported staffing levels. MEASURES: The percentage of scheduled care time that was omitted was estimated by the simulation model for each of the 65 workload scenarios using optimistic assumptions about staff productivity and efficiency. RESULTS: There was a low correlation between ADL workload and reported nurse aide staffing (Pearson = .11; P < .01), which suggests that most of the 13,500 NHs were not using ADL acuity to determine nurse aide staffing levels. Based on the DES model, the nurse aide staffing required for ADL care that would result in a rate of care omissions below 10% ranged from 2.8 hours/resident/day for NHs with a low workload (5th percentile) to 3.6 hours/resident/day for NHs with a high workload (95th percentile). In contrast, NHs reported staffing levels that ranged from an average of 2.3 to 2.5 hours/resident/day across all 5 workload percentiles. Higher workload NHs had the largest discrepancies between reported and predicted nurse aide staffing levels. CONCLUSIONS: The average nurse aide staffing levels reported by NHs falls below the level of staffing predicted as necessary to provide consistent ADL care to all residents in need. DES methodology can be used to determine nurse aide staffing requirements to provide ADL care and simulate management interventions to improve care efficiency and quality.