Stop training nurses for other hospitals! How proactive recruiting improves retention

By David Murdock

I have recently heard from three of my Clients, all Chief Nursing Officers (CNO), about their frustration in a too often recurring scenario. Their organizations are training new graduates or less experienced nurses only to have a significant number of them leave shortly after becoming a fully functioning member of the clinical team. These CNOs are leading nursing teams in diverse locales (rural South, Midwest, and Eastern urban city centers), so the phenomenon is not attributed only to geographic factors. Our clinical and HR consultants recently worked with an organization that, as a result of new hire turnover, had new graduate RNs sign a contract to repay orientation costs should they leave within a certain time period from the date hired.

Turnover rates for bedside nurses have increased from 11.2% to 16.4% in the past 4 years (reported by NSI Nursing Solutions in their January 2015 survey). Lower-tenured nurse turnover has been reported to be comparatively high with 17.2% of newly-licensed nurses leaving their first job within the first year (according to Policy Politics & Nursing Practice, August 2014). Reducing turnover rates is known to have positive financial benefit and should be expected to be highly correlated with improved staff satisfaction. These factors underscore the importance of making effective candidate selection choices.

Now back to my client CNOs – each of the organizations had invested in orientation and onboarding training programs and were keeping their nurse recruiters busy working to fill critical openings with both graduate nurses and experienced nurses where available. The operative word here is “critical.” There was often such an acute need to fill schedules and open shifts that very little proactive recruiting was able to occur.

It is my opinion – backed by conversations with my clients – that when organizations are rushing to fill critical openings, they often are making hiring decisions more influenced by filling an opening than identifying the best fit candidate. This scenario can result in there being insufficient consideration for factors that will influence the long-term retention of the candidates. The nursing “brain drain” then occurs when the nurses (after having been trained on the hospital’s proverbial dime) that were looking solely to gain clinical experience and move on, find another opportunity. And thus, another critical job opening is created for the recruiter to begin trying to fill.

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So how do we limit the occurrences of this cycle of brain drain? Predictive analytics is an area of study that uses historical data and statistical analysis to model trends and performance patterns. This methodology has been used in areas such as actuarial science and credit scoring and is gaining some momentum in healthcare. In particular, it can be applied to help reduce the nursing brain drain by providing nursing leadership and their recruiting partners within HR with data to improve their recruiting efforts.

Acquiring such data that is readily available and processing it with predictive modeling methods can enable recruiters to take a proactive approach to recruiting. More effective and predictive recruiting provides hiring managers with the ability to be more selective in choosing candidates from a larger pool that was methodically built with specific requirements related to organizational and unit “fit” versus the “we need somebody now” driven candidate pool.

Proactive Recruiting Models are built using some of the foundational elements used in core and flexible staffing analysis: historical census data, staffing grids, competency requirements, and full time versus contingent staffing targets. In addition, in order to complete the proactive staffing model analytical engine, we need to account for factors that specifically impact recruiting. These factors include staff turnover trends (seasonal and/or monthly by unit), orientation and training requirements by unit (new graduates and experienced hires), recruiting lead time, and local employment market factors (e.g., the timing of RN graduations).

An effective model will produce a proactive recruiting schedule that empowers the recruiters with the ability to give hiring managers the luxury of time. This additional time increases the organization’s ability to scrutinize the applicant pool for the right fit and not just availability to start when needed. While use of predictive analytics in proactive recruiting models will not stem all of the tide of nursing “brain drain,” it is an effective tool to improve retention rates and reduce costs associated with turnover and premium pay resulting from the need to fill critical shifts.

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