Dataset Information

Development and use of an adjusted nurse staffing metric in the neonatal intensive care unit.

ABSTRACT:

Objective

To develop a nurse staffing prediction model and evaluate deviation from predicted nurse staffing as a contributor to patient outcomes.

Data sources

Secondary data collection conducted 2017-2018, using the California Office of Statewide Health Planning and Development and the California Perinatal Quality Care Collaborative databases. We included 276 054 infants born 2008-2016 and cared for in 99 California neonatal intensive care units (NICUs).

Study design

Repeated-measures observational study. We developed a nurse staffing prediction model using machine learning and hierarchical linear regression and then quantified deviation from predicted nurse staffing in relation to health care-associated infections, length of stay, and mortality using hierarchical logistic and linear regression.

Data collection methods

We linked NICU-level nurse staffing and organizational data to patient-level risk factors and outcomes using unique identifiers for NICUs and patients.

Principal findings

An 11-factor prediction model explained 35 percent of the nurse staffing variation among NICUs. Higher-than-predicted nurse staffing was associated with decreased risk-adjusted odds of health care-associated infection (OR: 0.79, 95% CI: 0.63-0.98), but not with length of stay or mortality.

Conclusions

Organizational and patient factors explain much of the variation in nurse staffing. Higher-than-predicted nurse staffing was associated with fewer infections. Prospective studies are needed to determine causality and to quantify the impact of staffing reforms on health outcomes.

SUBMITTER: Tawfik DS

PROVIDER: S-EPMC7080382 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Publications

Development and use of an adjusted nurse staffing metric in the neonatal intensive care unit.

Tawfik Daniel S DS Profit Jochen J Lake Eileen T ET Liu Jessica B JB Sanders Lee M LM Phibbs Ciaran S CS

Health services research 20191223 2

<h4>Objective</h4>To develop a nurse staffing prediction model and evaluate deviation from predicted nurse staffing as a contributor to patient outcomes.<h4>Data sources</h4>Secondary data collection conducted 2017-2018, using the California Office of Statewide Health Planning and Development and the California Perinatal Quality Care Collaborative databases. We included 276 054 infants born 2008-2016 and cared for in 99 California neonatal intensive care units (NICUs).<h4>Study design</h4>Repeat ...[more]

PMID: 31869865

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Project description:ImportanceQuality improvement initiatives demonstrate the contribution of reliable nursing care to gains in clinical and safety outcomes in neonatal intensive care units (NICUs); when core care is missed, outcomes can worsen.ObjectiveTo evaluate the association of NICU nurse workload with missed nursing care.Design, setting, and participantsA prospective design was used to evaluate associations between shift-level workload of individual nurses and missed care for assigned infants from March 1, 2013, through January 31, 2014, at a 52-bed level IV NICU in a Midwestern academic medical center. A convenience sample of registered nurses who provided direct patient care and completed unit orientation were enrolled. Nurses reported care during each shift for individual infants whose clinical data were extracted from the electronic health record. Data were analyzed from January 1, 2015, through August 13, 2018.ExposuresWorkload was assessed each shift with objective measures (infant-to-nurse staffing ratio and infant acuity scores) and a subjective measure (the National Aeronautics and Space Administration Task Load Index [NASA-TLX]).Main outcomes and measuresMissed nursing care was measured by self-report of omission of 11 essential care practices. Cross-classified, multilevel logistic regression models were used to estimate associations of workload with missed care.ResultsA total of 136 nurses provided reports of shift-level workload and missed nursing care for 418 infants during 332 shifts of 12 hours each. When workload variables were modeled independently, 7 of 12 models demonstrated a significant worsening association of increased infant-to-nurse ratio with odds of missed care (eg, nurses caring for ≥3 infants were 2.51 times more likely to report missing any care during the shift [95% credible interval, 1.81-3.47]), and all 12 models demonstrated a significant worsening association of increased NASA-TLX subjective workload ratings with odds of missed care (eg, each 5-point increase in a nurse's NASA-TLX rating during a shift was associated with a 34% increase in the likelihood of missing a nursing assessment for his or her assigned infant[s] during the same shift [95% credible interval, 1.30-1.39]). When modeling all workload variables jointly, only 4 of 12 models demonstrated significant association of staffing ratios with odds of missed care, whereas the association with NASA-TLX ratings remained significant in all models. Few associations of acuity scores were observed across modeling strategies.Conclusions and relevanceThe workload of NICU nurses is significantly associated with missed nursing care, and subjective workload ratings are particularly important. Subjective workload represents an important aspect of nurse workload that remains largely unmeasured despite high potential for intervention.

Project description:BackgroundIncreasing numbers of intensive care units (ICUs) are adopting the practice of nighttime intensivist staffing despite the lack of experimental evidence of its effectiveness.MethodsWe conducted a 1-year randomized trial in an academic medical ICU of the effects of nighttime staffing with in-hospital intensivists (intervention) as compared with nighttime coverage by daytime intensivists who were available for consultation by telephone (control). We randomly assigned blocks of 7 consecutive nights to the intervention or the control strategy. The primary outcome was patients' length of stay in the ICU. Secondary outcomes were patients' length of stay in the hospital, ICU and in-hospital mortality, discharge disposition, and rates of readmission to the ICU. For length-of-stay outcomes, we performed time-to-event analyses, with data censored at the time of a patient's death or transfer to another ICU.ResultsA total of 1598 patients were included in the analyses. The median Acute Physiology and Chronic Health Evaluation (APACHE) III score (in which scores range from 0 to 299, with higher scores indicating more severe illness) was 67 (interquartile range, 47 to 91), the median length of stay in the ICU was 52.7 hours (interquartile range, 29.0 to 113.4), and mortality in the ICU was 18%. Patients who were admitted on intervention days were exposed to nighttime intensivists on more nights than were patients admitted on control days (median, 100% of nights [interquartile range, 67 to 100] vs. median, 0% [interquartile range, 0 to 33]; P<0.001). Nonetheless, intensivist staffing on the night of admission did not have a significant effect on the length of stay in the ICU (rate ratio for the time to ICU discharge, 0.98; 95% confidence interval [CI], 0.88 to 1.09; P=0.72), ICU mortality (relative risk, 1.07; 95% CI, 0.90 to 1.28), or any other end point. Analyses restricted to patients who were admitted at night showed similar results, as did sensitivity analyses that used different definitions of exposure and outcome.ConclusionsIn an academic medical ICU in the United States, nighttime in-hospital intensivist staffing did not improve patient outcomes. (Funded by University of Pennsylvania Health System and others; ClinicalTrials.gov number, NCT01434823.).

Project description:BackgroundNursing care is a critical determinant of patient outcomes in the intensive care unit (ICU). Most studies of nursing care have focused on nursing characteristics aggregated across the ICU (eg, unit-wide nurse-to-patient ratios, education, and working environment). In contrast, relatively little work has focused on the influence of individual nurses and their characteristics on patient outcomes. Such research could provide granular information needed to create evidence-based nurse assignments, where a nurse's unique skills are matched to each patient's needs. To date, research in this area is hindered by an inability to link individual nurses to specific patients retrospectively and at scale.ObjectiveThis study aimed to determine the feasibility of using nurse metadata from the electronic health record (EHR) to retrospectively determine nurse-patient assignments in the ICU.MethodsWe used EHR data from 38 ICUs in 18 hospitals from 2018 to 2020. We abstracted data on the time and frequency of nurse charting of clinical assessments and medication administration; we then used those data to iteratively develop a deterministic algorithm to identify a single ICU nurse for each patient shift. We examined the accuracy and precision of the algorithm by performing manual chart review on a randomly selected subset of patient shifts.ResultsThe analytic data set contained 5,479,034 unique nurse-patient charting times; 748,771 patient shifts; 87,466 hospitalizations; 70,002 patients; and 8,134 individual nurses. The final algorithm identified a single nurse for 97.3% (728,533/748,771) of patient shifts. In the remaining 2.7% (20,238/748,771) of patient shifts, the algorithm either identified multiple nurses (4,755/748,771, 0.6%), no nurse (14,689/748,771, 2%), or the same nurse as the prior shift (794/748,771, 0.1%). In 200 patient shifts selected for chart review, the algorithm had a 93% accuracy (ie, correctly identifying the primary nurse or correctly identifying that there was no primary nurse) and a 94.4% precision (ie, correctly identifying the primary nurse when a primary nurse was identified). Misclassification was most frequently due to patient transitions in care location, such as ICU transfers, discharges, and admissions.ConclusionsMetadata from the EHR can accurately identify individual nurse-patient assignments in the ICU. This information enables novel studies of ICU nurse staffing at the individual nurse-patient level, which may provide further insights into how nurse staffing can be leveraged to improve patient outcomes.

Dataset Information

Development and use of an adjusted nurse staffing metric in the neonatal intensive care unit.

Objective

Data sources

Study design

Data collection methods

Principal findings

Conclusions

Publications

Development and use of an adjusted nurse staffing metric in the neonatal intensive care unit.

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