Variability in Acuity in Acute Care

Trajectories of nursing hours over the course of hospitalization and estimated additional nurse staffing requirements to reduce the length of stay

PURPOSE

The aims of this study are to examine the trajectories of nursing hours per patient day (NHPPD) over the course of hospitalization according to the patient's length of stay (LOS) and to estimate changes in the total nursing hours during hospitalization, average NHPPD, and the number of nurses additionally required when the LOS was reduced by 1 day.

DESIGN

This retrospective longitudinal study analyzed patient data collected from a tertiary university hospital located in Seoul, South Korea. The study sample included 11,316 inpatients who were discharged between September 1 and October 31, 2022.

METHODS

NHPPD over the course of each patient's hospitalization was estimated using the total score of the Korean Patient Classification System-1 (KPCS-1), which nurses evaluated and recorded every day from admission to discharge. The NHPPD trajectories were examined using linear mixed models to analyze repeated KPCS-1 measurements and control for the effects of patient characteristics. The changes in the average NHPPD when LOS was reduced by 1 day were estimated using maximum and minimum estimations. The impact of a 1-day reduction in LOS on staffing requirements was calculated as the number of nurses additionally required to work each shift and to be hired.

FINDINGS

The average LOS was 5.6 days, and the short (1-6 days) and medium (7-14 days) LOS groups accounted for 78.9% and 14.3% of patients, respectively. The NHPPD trajectories showed a "rise-peak-decline" pattern. Patients in the short LOS group received the most NHPPD on day 1 (day of admission) or day 2, whereas the NHPPD for patients in the medium LOS group peaked on days 3-6. After peaking, the NHPPD tended to decrease toward the end of hospitalization, with the least NHPPD on the day of discharge, followed by the day before discharge. When LOS was reduced by 1 day, the average NHPPD was estimated to increase by 7.7-50.0% in the maximum estimation, and 0.9-12.5% in the minimum estimation. In response to a 1-day reduction, 1.10-7.44 nurses were additionally required to care for 100 patients each shift and 5.28-35.70 additional nurses needed to be hired in the maximum estimation. In the minimum estimation, these values were 0.13-1.85 additional nurses per shift and 0.65-8.90 additional nurses to be hired, respectively.

CONCLUSIONS

Since NHPPD exhibited a "rise-peak-decline" trajectory, reducing the LOS by 1 day was estimated to increase the average NHPPD and lead to additional staffing requirements. The additional nurse requirement for a 1-day reduction was not constant; instead, it increased with each day subtracted from an already shorter LOS.

CLINICAL RELEVANCE

Sufficient nurse staffing is necessary to provide increased NHPPD as a result of shortened LOS. Changes in the LOS should be considered when determining nurse staffing requirements.

Coincidence Analysis: A Novel Approach to Modeling Nurses' Workplace Experience

OBJECTIVES

The purpose of this study is to identify combinations of workplace conditions that uniquely differentiate high, medium, and low registered nurse (RN) ratings of appropriateness of patient assignment during daytime intensive care unit (ICU) work shifts.

METHODS

A collective case study design and coincidence analysis were employed to identify combinations of workplace conditions that link directly to high, medium, and low RN perception of appropriateness of patient assignment at a mid-shift time point. RN members of the study team hypothesized a set of 55 workplace conditions as potential difference makers through the application of theoretical and empirical knowledge. Conditions were derived from data exported from electronic systems commonly used in nursing care.

RESULTS

Analysis of 64 cases (25 high, 24 medium, and 15 low) produced three models, one for each level of the outcome. Each model contained multiple pathways to the same outcome. The model for "high" appropriateness was the simplest model with two paths to the outcome and a shared condition across pathways. The first path comprised of the absence of overtime and a before-noon patient discharge or transfer, and the second path comprised of the absence of overtime and RN assignment to a single ICU patient.

CONCLUSION

Specific combinations of workplace conditions uniquely distinguish RN perception of appropriateness of patient assignment at a mid-shift time point, and these difference-making conditions provide a foundation for enhanced observability of nurses' work experience during hospital work shifts. This study illuminates the complexity of assessing nursing work system status by revealing that multiple paths, comprised of multiple conditions, can lead to the same outcome. Operational decision support tools may best reflect the complex adaptive nature of the work systems they intend to support by utilizing methods that accommodate both causal complexity and equifinality.

Factors That Influence Nurse Staffing Levels in Acute Care Hospital Settings

PURPOSE

To identify which patient and hospital characteristics are related to nurse staffing levels in acute care hospital settings.

DESIGN

A cross-sectional design was used for this study.

METHODS

The sample comprised 1,004 patients across 10 hospitals in the Andalucian Health Care System (southern Spain) in 2015. The sampling was carried out in a stratified, consecutive manner on the basis of (a) hospital size by geographical location, (b) type of hospital unit, and (c) patients' sex and age group. Random criteria were used to select patients based on their user identification in the electronic health record system. The variables were grouped into two categories, patient and hospital characteristics. Multilevel linear regression models (MLMs) with random intercepts were used. Two models were fitted: the first was the null model, which contained no explanatory variables except the intercepts (fixed and random), and the second (explanatory) model included selected independent variables. Independent variables were allowed to enter the explanatory model if their univariate association with the nurse staffing level in the MLM was significant at p < .05.

RESULTS

Two hierarchical levels were established to control variance (patients and hospital). The model variables explained 63.4% of the variance at level 1 (patients) and 71.8% at level 2 (hospital). Statistically significant factors were the type of hospital unit (p = .002), shift (p < .001), and season (p < .001). None of the variables associated with patient characteristics obtained statistical significance in the model.

CONCLUSIONS

Nurse staffing levels were associated with hospital characteristics rather than patient characteristics.

CLINICAL RELEVANCE

This study provides evidence about factors that impact on nurse staffing levels in the settings studied. Further studies should determine the influence of patient characteristics in determining optimal nurse staffing levels.

Predicting healthcare-associated infections, length of stay, and mortality with the nursing intensity of care index

OBJECTIVES

The objectives of this study were (1) to develop and validate a simulation model to estimate daily probabilities of healthcare-associated infections (HAIs), length of stay (LOS), and mortality using time varying patient- and unit-level factors including staffing adequacy and (2) to examine whether HAI incidence varies with staffing adequacy.

SETTING

The study was conducted at 2 tertiary- and quaternary-care hospitals, a pediatric acute care hospital, and a community hospital within a single New York City healthcare network.

PATIENTS

All patients discharged from 2012 through 2016 (N = 562,435).

METHODS

We developed a non-Markovian simulation to estimate daily conditional probabilities of bloodstream, urinary tract, surgical site, and Clostridioides difficile infection, pneumonia, length of stay, and mortality. Staffing adequacy was modeled based on total nurse staffing (care supply) and the Nursing Intensity of Care Index (care demand). We compared model performance with logistic regression, and we generated case studies to illustrate daily changes in infection risk. We also described infection incidence by unit-level staffing and patient care demand on the day of infection.

RESULTS

Most model estimates fell within 95% confidence intervals of actual outcomes. The predictive power of the simulation model exceeded that of logistic regression (area under the curve [AUC], 0.852 and 0.816, respectively). HAI incidence was greatest when staffing was lowest and nursing care intensity was highest.

CONCLUSIONS

This model has potential clinical utility for identifying modifiable conditions in real time, such as low staffing coupled with high care demand.

Critical care nursing service costs: Comparison of the top-down versus bottom-up micro-costing approach in Brazil

AIM

To estimate the nursing service costs using a top-down micro-costing approach and to compare it with a bottom-up micro-costing approach.

BACKGROUND

Accurate data of nursing cost can contribute to reliable resource management.

METHOD

We employed a retrospective cohort design in an adult intensive care unit in São Paulo. A total of 286 patient records were included. Micro-costing analysis was conducted in two stages: a top-down approach, whereby nursing costs were allocated to patients through apportionment, and a bottom-up approach, considering actual nursing care hours estimated by the Nursing Activities Score (NAS).

RESULTS

The total mean cost by the top-down approach was US$1,640.4 ± 1,484.2/patient. The bottom-up approach based on a total mean NAS of 833 ± 776 points (equivalent to 200 ± 86 hr of nursing care) yielded a mean cost of US$1,487.2 ± 1,385.7/patient. In the 268 patients for whom the top-down approach estimated higher costs than the bottom-up approach, the total cost discrepancy was US$4,427.3, while for those costed higher based on NAS, the total discrepancy was US$436.9. The top-down methodology overestimated costs for patients requiring lower intensity of care, while it underestimated costs for patients requiring higher intensity of care (NAS >100).

CONCLUSIONS

The top-down approach may yield higher estimated ICU costs compared with a NAS-based bottom-up approach.

IMPLICATIONS FOR NURSING MANAGEMENT

These findings can contribute to an evidence-based approach to budgeting through reliable costing methods based on actual nursing workload, and to efficient resource allocation and cost management.

Evaluation of Electronic Health Record-Generated Work Intensity Scores and Nurse Perceptions of Workload Appropriateness

Electronic health record-generated work intensity scores represent state-of-the art functionality for dynamic nursing workload estimation in the hospital setting. In contrast to traditional stand-alone patient classification and acuity tools, electronic health record-based tools eliminate the need for dedicated data entry, and scores are automatically updated as new information is entered into patient records. This paper summarizes the method and results of evaluation of electronic health record-generated work intensity scores on six hospital patient care units in a single academic medical center. The correlation between beginning-of-shift work intensity scores and self-reported registered nurse rating of appropriateness of patient assignment was assessed using Spearman rank correlation. A weak negative correlation (-0.09 to -0.23) was observed on all study units, indicating that nurse appropriateness ratings decrease as work intensity scores increase. Electronic health record-generated work intensity scores provide useful information that can augment existing data sources used by charge nurses to create equitable nurse-patient assignments. Additional research is needed to explain observed variation in nurses' appropriateness ratings across similar work intensity point ranges.

Acuity, nurse staffing and workforce, missed care and patient outcomes: A cluster-unit-level descriptive comparison

AIM

To compare the patient acuity, nurse staffing and workforce, missed nursing care and patient outcomes among hospital unit-clusters.

BACKGROUND

Relationships among acuity, nurse staffing and workforce, missed nursing care and patient outcomes are not completely understood.

METHOD

Descriptive design with data from four unit-clusters: medical, surgical, combined and step-down units. Descriptive statistics were used to compare acuity, nurse staffing coverage, education and expertise, missed nursing care and selected nurse-sensitive outcomes.

RESULTS

Patient acuity in general (medical, surgical and combined) floors is similar to step-down units, with an average of 5.6 required RN hours per patient day. In general wards, available RN hours per patient day reach only 50% of required RN hours to meet patient needs. Workforce measures are comparable among unit-clusters, and average missed nursing care is 21%. Patient outcomes vary among unit-clusters.

CONCLUSION

Patient acuity is similar among unit-clusters, while nurse staffing coverage is halved in general wards. While RN education, expertise and missed care are comparable among unit-clusters, mortality, skin injuries and risk of family compassion fatigue rates are higher in general wards.

IMPLICATIONS FOR NURSING MANAGEMENT

Nurse managers play a pivotal role in hustling policymakers to address structural understaffing in general wards, to maximize patient safety outcomes.

Subtle cues: Qualitative elicitation of signs of capacity strain in the hospital workplace

Through everyday care experiences, nurses develop expertise in recognition of capacity strain in hospital workplaces. Through qualitative interview, experienced nurses identify common activity changes and adaptive work strategies that may signal an imbalance between patient demand and service supply at the bedside. Activity change examples include nurse helping behaviors across patient assignments, increased volume of nurse calls from patient rooms, and decreased presence of staff at the nurses' station. Adaptive work strategies encompass actions taken to recruit resources, move work in time, reduce work demands, or reduce thoroughness of task performance. Nurses' knowledge of perceptible signs of strain provides a foundation for future exploration and development of real-time indicators of capacity strain in hospital-based work systems.

Predicting patient acuity according to their main problem

AIM

To assess the ability of the patient main problem to predict acuity in adults admitted to hospital wards and step-down units.

BACKGROUND

Acuity refers to the categorization of patients based on their required nursing intensity. The relationship between acuity and nurses' clinical judgment on the patient problems, including their prioritization, is an underexplored issue.

METHOD

Cross-sectional, multi-centre study in a sample of 200,000 adults. Multivariate analysis of main problems potentially associated with acuity levels higher than acute was performed. Distribution of patients and outcome differences among acuity clusters were evaluated.

RESULTS

The main problems identified are strongly associated with patient acuity. The model exhibits remarkable ability to predict acuity (AUC, 0.814; 95% CI, 0.81-0.816). Most patients (64.8%) match higher than acute categories. Significant differences in terms of mortality, hospital readmission and other outcomes are observed (p < .005).

CONCLUSION

The patient main problem predicts acuity. Most inpatients require more intensive than acute nursing care and their outcomes are adversely affected.

IMPLICATIONS FOR NURSING MANAGEMENT

Prospective measurement of acuity, considering nurses' clinical judgments on the patient main problem, is feasible and may contribute to support nurse management workforce planning and staffing decision-making, and to optimize patients, nurses and organizational outcomes.