The Epicenter of Effectiveness and Efficiency in Health Care Delivery: The Evolving U.S. Health Workforce

Washko, Michelle M.; Fennell, Mary L.

doi:10.1111/1475-6773.12662

Cited by 4 publications

(6 citation statements)

References 12 publications

(13 reference statements)

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“…assess the model with nurses as the only staff category and with and without patientand department-level confounders. Columns(3) and(4)include all staff categories, and column (3) excluding patient and department variables are included in column(4).Comparing the results in columns (1) and (2), we observed a change in the sign of the within nurse estimate.Without adjusting for patient and department characteristics (column (1)), the within estimate for nurses was positive (0.0129) and not statistically significant; more nurse hours per patient day increased LOS. This result conflicted with the literature5 and indicated endogeneity in the sense that departments were likely to adjust their staffing levels in periods where patient characteristics worsen.…”

mentioning

confidence: 80%

“…The within effect was a little smaller and showed a reduction in LOS of 21% (4 × 0.319/6) by an increase of one FTE nurse per patient day. From the estimates in column (4) in Table 4, we can see that an increase of one-half FTE nurses per patient day involved a reduction in mortality of 37%…”

Section: Comparison and Contrast To Other Studiesmentioning

confidence: 99%

“…The increased demand for health care due to recent demographic changes, combined with the predicted future shortage of labor in this market [1][2][3][4] emphasizes the need for a thorough understanding of the relationship between nurse staffing inputs and patients' health-care outcomes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Nurse Staffing and Patient Outcomes: Analyzing Within- and Between-Variation

Bjerregaard¹,

Hølge‐Hazelton²,

Kristensen

et al. 2020

SSRN Journal

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mentioning

confidence: 80%

Section: Comparison and Contrast To Other Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Nurse Staffing and Patient Outcomes: Analyzing Within- and Between-Variation

Bjerregaard¹,

Hølge‐Hazelton²,

Kristensen

et al. 2020

SSRN Journal

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“…1  Güvenlik: Sağlık Sağlık hizmetinin kalitesi ve sağlık hizmetine erişim fonksiyonel sağlık hizmeti için iki kritik sonuç niteliğindedir. 2 İnsan merkezli ve entegre sağlık hizmeti ulusal ve küresel olarak sağlıklı bir ortama ulaşmak için kritik bir öneme sahiptir. 3 Çünkü hastalar sağlık sisteminde hayati bir paydaş olarak yer almaktadır.…”

Section: Sağlıkunclassified

Türkiye’deki İBBS-1 bölgelerinin sağlık hizmeti sunum etkinliğinin veri zarflama analizi kullanılarak ölçülmesi

Özdemir

2020

Adıyaman Üniversitesi Sağlık Bilimleri Dergisi

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Measuring the of healthcare service delivery of Nomenclature of Territorial Units for Statistics-1 (NUTS-1) territories in Turkey. Materials and Methods: Data Envelopment Analysis was conducted on the twelve Nomenclature of Territorial Units for Statistics-1 (NUTS-1) territories using three input variables and three output variables. Results: The results of the analysis demonstrate that Western Blacksea, Eastern Marmara, Southeastern Anatolia and Northeastern Anatolia are efficient and, Mediterranean, Eastern Blacksea, Istanbul, Central Anatolia and Mideastern Anatolia are inefficient in terms of all models. Besides, it is determined that Western Anatolia, Western Marmara and Aegean are efficient only in terms of Banker, Charnes, Cooper (BCC) models. Conclusion: It was determined as Western Blacksea, Eastern Marmara, Southeastern Anatolia and Northeastern Anatolia are efficient and, Mediterranean, Eastern Blacksea, Istanbul, Central Anatolia and Mideastern Anatolia are inefficient. It is suggested that confirm in the fuzzy environment these results adding new input and output variables and, using new generation Data Envelopment Analysis techniques such as Network and Two-Stage Data Envelopment Analysis.

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“…Identified HCSC enablers of implementing emergency HCSC during COVID-19 outbreak Braithwaite et al (2018); Lapc ˇevi c et al(2019) Dynamic health workforceWashko and Fennell (2017),May et al …”

mentioning

confidence: 99%

Modeling the emergency health-care supply chains: responding to the COVID-19 pandemic

Hossain

Thakur

Mangla

2021

JBIM

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Purpose Due to the rapid surge in the number of COVID-19 cases in India, the health-care supply chain (HCSC) disruptions and uncertainties have increased manifold posing severe challenges to health-care facilities and significantly hampering the functioning of the health industry. This study aims to propose a hierarchical structural model of enablers of HCSC in the COVID-19 outbreak and identifies inter-relationships among them in the health-care market. Design/methodology/approach Enablers of emergency HCSC have been identified through extensive literature review and experts’ opinions. Subsequently, total interpretive structural modeling (TISM) and cross-impact matrix-multiplication (MICMAC) analysis have been implemented to determine the hierarchical inter-relationships among enablers and classify them according to their contribution to the overall system. Findings The research has identified and validated 15 enablers of the emergency supply chain in health-care businesses. The study resulted in a seven-level hierarchical structural model based on enabler’s driving and dependence powers. Further, the application of MICMAC analysis resulted in the classification of enablers into four groups, namely, autonomous, dependent, linkage and independent group. Research limitations/implications This study would help health professionals, policymakers and academia to implement the theoretical model constructed to alleviate the effect of COVID-19 by improving the HCSC performances in pandemic situations. This study has social and economic implications in terms of cost-effective and efficient delivery of care services in health emergencies. Originality/value The proposed theoretical model constructed is a new effort addressing the issues of HCSC in the COVID-19 crisis. Procedural implementation of TISM and MICMAC analysis in this study would help researchers to grasp concepts in a very lucid manner. The present study is one of the very few studies analyzing enablers in pandemic situations by implementing the TISM approach.

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The Epicenter of Effectiveness and Efficiency in Health Care Delivery: The Evolving U.S. Health Workforce

Cited by 4 publications

References 12 publications

Nurse Staffing and Patient Outcomes: Analyzing Within- and Between-Variation

Nurse Staffing and Patient Outcomes: Analyzing Within- and Between-Variation

Türkiye’deki İBBS-1 bölgelerinin sağlık hizmeti sunum etkinliğinin veri zarflama analizi kullanılarak ölçülmesi

Modeling the emergency health-care supply chains: responding to the COVID-19 pandemic

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