Alice F. Snyder, RN, MSN, MBA, NEA-BC, NMCC, FACHE Doctoral Student in Healthcare Administration
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1 Alice F. Snyder, RN, MSN, MBA, NEA-BC, NMCC, FACHE Doctoral Student in Healthcare Administration A. T. Still University College of Graduate Health Studies 1
2 Overview Quality care is paramount in healthcare organizations Healthcare cost reduction Optimized Reimbursement Nursing Sensitive Indicators Future Study 2
3 Literature Review Need for adequate nurse staffing ratios High quality outcomes Decreased mortality rates & hospital acquired infections Value Based Purchasing requirements Decrease healthcare costs Nurse to patient ratios mandated in some states Nursing Care Models Use of electronic software for support Dubois, C., D Amour, D., Tchouaket, E., Clarke, S., Rivard, M., & Blais, R. (2013); Fagerström, L., Lønning, K., & Andersen, M. H. (2014); Fitzpatrick, T., Anen, T., & Soto, E. M. (2013); Kavanaugh, K. T., Cimiotti, J. P., Abusalom, S., & Coty, M. (2012); Weston, M. J., Brewer, K. C., & Peterson, C. A. (2012) 3
4 Statement of the Problem Quality nursing care imperative Optimal staffing High quality outcomes Innovation for filling staff vacancies Reduce healthcare costs Patient Protection and Affordable Care Act (2010) Eldridge, G. N. (2011) 4
5 Purpose of the Study Quantitative approach Explore the use of a central staffing pool to fill shift vacancies Vacancies Immediate short term long term One region of a healthcare system Correlate staffing patterns with National Databank of Nursing Quality Indicators (NDNQI ) Hinshaw, P. (2011); Kavanaugh, K. T., Cimiotti, J. P., Abusalom, S., & Coty, M. (2012) 5
6 Hypotheses The Organizational Central Staffing Pool will achieve optimal nurse staffing ratios. Optimal nurse staffing achieves patient outcome measures established by NDNQI. Achieving patient outcome measures established by NDNQI improves the hospital value based purchasing (HBVP) incentive program. 6
7 Quantitative Research Design Research Design Quantitative correlational design discover the quality outcomes defined by NDNQI nursing sensitive indicators and reimbursement Longitudinal study for the period of one year Sample Pilot study will be in the Central Region of a healthcare system in the Midwest A cluster sampling will be used to gather data from each unit/facility to determine the effect the inter-system resource pool has on the outcome variables Locke, L. F., Silverman, S. J., & Spirduso, W. W. (2010) 7
8 Central Region Hospitals Facility Type Large, tertiary, academic Small 90 bed acute care, Medicare Dependent Number of Nursing Units 27 nursing departments 7 nursing departments Small 35 bed Critical Access Hospital Small 35 bed Critical Access Hospital 3 nursing departments 3 nursing departments 8
9 Quantitative Research Design Data Collection and Recording Procedures Data collected from Care Value Demand Manager tool from Clairvia Reports from NDNQI for the same period of time will be collected and evaluated HVBP report will be correlated to the NDNQI results for the same time period Data Analysis Procedures Results from the NDNQI, Clairvia, and HBVP reports merged and analyzed Minitab software Correlation Least squares regression Analysis of Variance Pearson s r Moore, D. S., McCabe, G. P., & Craig, B. A. (2014) 9
10 Validity and Reliability NDNQI Cerner ClairVia software HVBP outcomes Garcia, A. (2013); Montalvo, I. (2007) 10
11 Results and Outcomes TBD 11
12 Questions? 12
13 References Dubois, C., D Amour, D., Tchouaket, E., Clarke, S., Rivard, M., & Blais, R. (2013). Associations of patient safety outcomes with models of nursing care organization at unit level in hospitals. International Journal For Quality In Health Care, 25(2), Eldridge, G. N. (2011). Value-based purchasing: The evidence. American Journal of Managed Care, 17(8), e310-e313. Retrieved from 2d-c4f df-a1afb63e638d%40sessionmgr4002&vid=18&hid=4108 Fagerström, L., Lønning, K., & Andersen, M. H. (2014). The RAFAELA system: A workforce planning tool for nurse staffing and human resource management. Nursing Management - UK, 21(2), Retrieved from 2d-c4f df-a1afb63e638d%40sessionmgr4002&vid=44&hid=4108 Fitzpatrick, T., Anen, T., & Soto, E. M. (2013). Nurse staffing: The Illinois experience. Nursing Economic$, 31(5), Retrieved from 2d-c4f df-a1afb63e638d%40sessionmgr4002&vid=32&hid=
14 References Garcia, A. (2013). Regulatory readiness. A patient acuity checklist for the digital age. Nursing Management, 44(8), Hinshaw, P. (2011). The National Database of Nursing Quality Indicators (NDNQI): Linking nurse staffing with patient outcomes. Arizona Nurse, 64(2), 6. Retrieved from ort=relevance&inps=true&prodid=ppnu&usergroupname=morekirkcoll egeo&tabid=t004&searchid=r1&resultlisttype=result_list&contentsegm ent=&searchtype=advancedsearchform¤tposition=1&contentset=gal E A &&docId=GALE A &docType=GALE&role= Kavanaugh, K. T., Cimiotti, J. P., Abusalom, S., & Coty, M. (2012). Moving healthcare quality forward with nursing-sensitive value-based purchasing. Journal of Nursing Scholarship, 44(4), Locke, L. F., Silverman, S. J., & Spirduso, W. W. (2010). Reading and understanding research (3rd ed.). Los Angeles, CA: Sage. 14
15 References Montalvo, I. (2007). The National Database of Nursing Quality Indicators (NDNQI ). The Online Journal of Issues in Nursing, 12(3). Moore, D. S., McCabe, G. P., & Craig, B. A. (2014). Introduction to the practice of statistics (8th ed.). New York, NY: W.H. Freeman and Company Weston, M. J., Brewer, K. C., & Peterson, C. A. (2012). ANA principles: The framework for nurse staffing to positively impact outcomes. Nursing Economic$, 30(5), Retrieved from wer?sid= d-c4f dfa1afb63e638d%40sessionmgr4002&vid=26&hid=
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