The effect of data aggregation on estimations of nurse staffing and patient outcomes

Winter, Shira; Bartel, Ann P.; Cordova, Pamela B. de; Needleman, Jack; Schmitt, Susan; Phibbs, Ciaran S.

doi:10.1111/1475-6773.13866

Cited by 14 publications

(18 citation statements)

References 41 publications

(83 reference statements)

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“…This study makes contributions beyond those of the existing literature on the relationship between nurse staffing and patient outcomes in three respects: First, although the aggregation and omitted variable biases (due to aggregating different frequencies data into single frequency data) have attracted lots of attention regarding the estimation of the nurse staffing and patient outcomes relationship in the healthcare services research field [39], this study, for the first time, adopted the MF-VAR model proposed by Ghysel and his colleagues [47,48] to incorporate different frequencies data into the investigation of the relationships among PNR, ICQ, and real ICE per admission under the GBPS of Taiwan's NHI system over the period of 2015:Q1-2021:Q4. We illustrated that the MF-VAR model is superior to the LF-VAR (i.e., conventional VAR) model in terms of higher explanatory power (See Table 4).…”

Section: Discussionmentioning

confidence: 99%

“…Second, these studies fail to provide precautionary information in terms of the propagation mechanism of a nurse staffing policy shock across a period of time. Third, a recent study proposed by Winter and his colleagues cautioned against a potential data aggregation effect on estimations of the nurse staffing and patient outcomes relationship [39]. Moreover, nurse staffing and patient outcomes can be reported in different time frequencies.…”

Section: Introductionmentioning

confidence: 99%

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On the Relationships among Nurse Staffing, Inpatient Care Quality, and Hospital Competition under the Global Budget Payment Scheme of Taiwan’s National Health Insurance System: Mixed Frequency VAR Analyses

Chen

2022

Systems

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Background: Time series analyses on the relationship between nurse staffing and inpatient care quality are rare due to inconsistent frequencies of data between common observations of nurse-staffing (e.g., monthly) and inpatient care quality indicators (e.g., quarterly). Methods: In order to deal with the issue of mixed frequency data, this research adopted the MF-VAR model to explore causal relationships among nurse staffing, inpatient care quality, and hospital competition under the global budget payment scheme of Taiwan’s healthcare system. Results: Our results identified bi-directional causation between nurse staffing and patient outcomes and one-way Granger causality running between nurse staffing and reimbursement payments for inpatient care services. Impulse-response analyses found positive (negative) effects of the patient-to-nurse ratio on adverse patient outcomes (reimbursement payments) in all types of hospitals and detrimental effects of adverse patient outcomes on the patient-to-nurse ratio in medical centers and regional hospitals across a 12-month period. Conclusions: These findings suggest that nurse staffing is an essential determinant of both patient outcomes and reimbursement payments. Strategic policies such as direct subsidy and hospital accreditation for appropriate nurse staffing levels should be implemented for medical centers and regional hospitals to mitigate the harmful effects of adverse patient outcomes on nurse staffing.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the Relationships among Nurse Staffing, Inpatient Care Quality, and Hospital Competition under the Global Budget Payment Scheme of Taiwan’s National Health Insurance System: Mixed Frequency VAR Analyses

Chen

2022

Systems

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“…The weak correlation between FTE midwives and ward level CHPPD is interesting as we expected this to be more closely related. However, this is not an unusual finding as recent research from the USA has also discovered differences in the performance of staffing measures, and called for validated measures to be used[34] Winter et al[23] found that estimates of staffing and outcomes were larger and more precise when data is disaggregated. This has not been found in our study despite the expectation of gaining more clarity with localised data at a ward level.…”

Section: Discussionmentioning

confidence: 99%

“…We found no published studies examining the impact of staffing measured at a postnatal ward level, which is expected to provide a more accurate measurement of staffing exposure. A study by Winter et al [23] found that aggregation of data changes the magnitude of estimation when examining the association between staffing and outcomes. Point estimates for associations were larger in magnitude when more precise data on staffing was used.…”

Section: Introductionmentioning

confidence: 99%

Exploring the relationship between women’s experience of postnatal care and reported staffing measures: an observational study

Turner,

Ball,

Culliford

et al. 2022

Preprint

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BackgroundWomen have reported dissatisfaction with care received on postnatal wards and this area has been highlighted for improvement. Studies have shown an association between midwifery staffing levels and postnatal care experiences, but so far, the influence of registered and support staff deployed in postnatal wards has not been studied. This work is timely as the number of support workers has increased in the workforce and there has been little research on skill mix to date.MethodsCross sectional secondary analysis including 13,264 women from 123 postnatal wards within 93 hospital Trusts. Staffing was measured at organisational level as Full Time Equivalent staff, and at ward level using Care Hours Per Patient Day. Women’s experiences were assessed using four items from the 2019 national maternity survey. Multilevel logistic regression models were used to examine relationships and adjust for maternal age, parity, type of birth, medical staff and number of births per year in the Trust.ResultsTrusts with higher levels of midwifery staffing had higher rates of women reporting positive experiences of postnatal care. However, when staffing was measured at a ward level, there was no evidence of an association between registered staffing and patient experience. Wards with higher levels of support worker staffing were associated with higher rates of women reporting they had help when they needed it and were treated with kindness and understanding.ConclusionThe relationship between reported registered staffing levels on postnatal wards and women’s experience is uncertain. Further work should be carried out to examine why relationships observed at an organisational level were not replicated closer to the patient, at ward level. It is possible that reported staffing levels do not reflect staff as deployed if midwives are floated to cover delivery units. This study highlights the potential contribution of support workers in providing quality care on postnatal wards.

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“…1 In minutes2 Categorical variables (units, days, and months) were coded using sum contrasts. Each level in the table reflects deviation from the grand-mean.…”

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confidence: 99%

A Novel Use of Bar Code Medication Administration Data to Assess Nurse Staffing and Workload

et al. 2022

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Objective To introduce an innovative use of bar code medication administration (BCMA) data, medication pass analysis, that allows for examination of nurse staffing and workload using data generated during regular nursing workflow Methods Using one year (October 1, 2014 to September 30, 2015) of BCMA data for 11 acute-care units in one Veterans Affairs Medical Center, we determined the peak time for scheduled medications and included medications scheduled for and administered within two hours of that time in analyses. We established for each staff member their daily peak-time medication pass characteristics (number of patients, number of peak-time scheduled medications, duration, start-time), generated unit-level descriptive statistics, examined staffing trends, and estimated linear mixed-effects models of duration and start-time. Results As the most frequent (39.7%) scheduled medication time, 9:00 was the peak-time medication pass; 98.3% of patients (87.3% of patient-days) had a 9:00 medication. Use of nursing roles and number of patients per staff varied across units and over time. Number of patients, number of medications, and unit-level factors explained significant variability in registered nurse (RN) medication pass duration (conditional R2 = 0.237; marginal R2 = 0.199; Intraclass correlation = 0.05). On average, an RN and a licensed practical nurse (LPN) with four patients, each with six medications, would be expected to take 70 and 74 minutes, respectively, to complete the medication pass. On a unit with median 10 patients per LPN, the median duration (127 minutes) represents untimely medication administration on more than half of staff days. With each additional patient assigned to a nurse, average start time was earlier by 4.2 minutes for RNs and 1.4 minutes for LPNs. Conclusion Medication pass analysis of BCMA data can provide health systems a means for assessing variations in staffing, workload, and nursing practice using data generated during routine patient care activities.

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The effect of data aggregation on estimations of nurse staffing and patient outcomes

Abstract: Objective: To examine how estimates of the association between nurse staffing and patient length of stay (LOS) change with data aggregation over varying time periods and settings, and statistical controls for unobserved heterogeneity.

Cited by 14 publications

References 41 publications

On the Relationships among Nurse Staffing, Inpatient Care Quality, and Hospital Competition under the Global Budget Payment Scheme of Taiwan’s National Health Insurance System: Mixed Frequency VAR Analyses

On the Relationships among Nurse Staffing, Inpatient Care Quality, and Hospital Competition under the Global Budget Payment Scheme of Taiwan’s National Health Insurance System: Mixed Frequency VAR Analyses

Exploring the relationship between women’s experience of postnatal care and reported staffing measures: an observational study

A Novel Use of Bar Code Medication Administration Data to Assess Nurse Staffing and Workload

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