More often than not, project implementation does not proceed in the exact manner envisioned by the implementer. There are many factors that could positively or negatively influence the project in unexpected ways. Such a scenario also leads to digressions from best practice. Thus, it is important to monitor the implementation, establish the root cause, and proactively manage arising issues to ensure success. During the evaluation, opportunities for improvement should be explored with regard to managing implementation issues.
Issues in Implementation and Data Gathering
Financial considerations are a very real threat to implementation. There are costs associated with in-service education, learning needs assessment surveys, documentation, evaluation, and mentoring itself. Without a cost-benefit analysis that compares the costs and savings associated with the mentorship program to the costs of turnover, there is a chance that management will not approve the project. Hence, the economics side of change should be a component of program planning. This was overlooked during the planning phase. In relation to data gathering, there was no central source of readily available data pertaining to staff attrition. Turnover rate was computed by obtaining raw data from the human resources department with disaggregation into senior, junior, and new staff. As such there is a need for better data management that will facilitate monitoring and evaluation.
Cause and Meaning of Issues
If management failed to allocate funding for the mentorship program, it would be unlikely to be implemented. The root cause of the absence of a cost-benefit analysis is the lack of skills and discomfort in conducting it. There is often a misconception that it is not the responsibility of nurses to engage in fiscal matters. However, it is an integral part of leadership as health care reforms call for evidence-based practices wherein cost-efficiency is an element of the evidence (Fulton, Lyon & Goudreau, 2010). On the other hand, efficient monitoring and evaluation necessitates data management that generates timely and accurate information on outcomes (Sylvia & Terhaar, 2014). It facilitates the evaluation of effectiveness. Similarly, the lack of data management skills among HR personnel was reason behind the absence of readily available data on nurse turnover and reflects the need for collaboration in creating a data management tool.
Variances with Best Practice
The lack of cost-efficiency data was raised by Buddeberg-Fischer & Herta (2007) in their systematic review of formal mentorship programs in the medical profession and reflects the increasing expectation for projects to include an economic analysis. In an evaluation of the California Nurse Mentor Project, a nursing cost model was developed by the project’s Advisory Board. The model estimated the average cost of nursing turnover and potential savings with a 3% reduction in turnover in a 500-bed facility with 500 nurses working full-time and a 60-40 general nurse and specialty nurse mix (Mills & Mullins, 2008). The analysis was used as a criterion in establishing project effectiveness. In addition, a clinical mentor care delivery model was implemented in a hospital to improve the quality of patient care (Burritt et al., 2007). Both clinical outcome measures and fiscal outcomes were employed to evaluate program effectiveness after implementation and for several years after.
In nursing research, data management and centralized storage has been shown to provide immediate access to information, minimize errors in data entry, and ensure data accuracy (Musick et al., 2011). Data management has also been useful in reducing the likelihood of data errors and missing data, thus ensuring the integrity of quality improvement project results (Needham et al., 2009). Van den Berg, Frenken & Bal (2009) suggest that a simple standardized spreadsheet using Microsoft Excel is sufficient in improving data management.
Summary of the Evaluation
Overall, the project has been successful. As it was a first-time experience in participatory change planning and implementation for many of the nurses, feedback has been very positive both among the senior and junior staff. Senior nurses continue to affirm their commitment in the mentor role. There has been an increase in the number of nurses reporting high levels of job satisfaction in the latest survey. So far, the loss of nursing staff has been due entirely to retirement, but monthly turnover rates will be monitored to obtain quantitative data on effectiveness. If given the opportunity to redo the project, I will make sure to include a cost-benefit analysis. I will include cost-efficiency in the outcome measures as well. Last, I will collaborate with the HRD and the informatics nurse to create an electronic data management template on monthly turnover rates.
References
Buddeberg-Fischer, B., & Herta, K.D. (). Formal mentoring programmes for medical students and doctors – a review of the Medline literature. Medical Teacher, 28(3), 248-257. doi: 10.1080/01421590500313043.
Burritt, J.E., Wallace, P., Steckel, C., & Hunter, A. (2007). Achieving quality and fiscal outcomes in patient care: The clinical mentor care delivery model. Journal of Nursing Administration, 37(12), 558-563. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/18090519
Fulton, J.S., Lyon, B.L., & Goudreau, K.A. (2010). Clinical nurse specialist practice. Danvers, MA: Springer Publishing Company.
Mills, J.F., & Mullins, A.C. (2008). The California Nurse Mentor Project: Every nurse deserves a mentor. Nursing Economics, 26(5), 310-315. Retrieved from http://www.medscape.com/viewarticle/582650
Musick, B.S., Robb, S.L., Burns, D.S., Stegenga, K., Yan, M., McCorkle, K.J., & Haase, J.E. (2011). Development and use of a web-based data management system for a randomized clinical trial of adolescents and young adults. Computers, Informatics, Nursing, 29(6), 337-343. doi: 10.1097/NCN.0b013e3181fcbc95.
Needham, D.M., Sinopoli, D.J., Dinglas, V.D., Berenholtz, S.M., Korupolu, R., Watson, S.R., Pronovost, P. (2009). Improving data quality control in quality improvement projects. International Journal for Quality in Health Care, 21(2), 145-150. doi: 10.1093/intqhc/mzp005.
Sylvia, M., & Terhaar, M. (2014). An approach to clinical data management for the doctor of nursing practice curriculum. Journal of Professional Nursing, 30(1), 56-62. Retrieved from http://dx.doi.org/10.1016/j.profnurs.2013.04.002
van den Berg, M., Frenken, R., & Bal, R. (2009). Quantitative data management in quality improvement collaboratives. BMC Health Services Research, 9(175), 1-11. doi:10.1186/1472-6963-9-175.
