Healthcare recruiters are increasingly relying on big data to improve the talent acquisition process.
More and more healthcare organizations are using predictive analysis and data metrics to streamline workflows, save time and money and improve return on investment. Those same methods also can be valuable in recruiting talent to fill nursing shortages.
To meet hiring challenges, more than one-third of human resources departments – including those in healthcare – use analytics for recruiting, hiring and managing employees, according to the 2017 Deloitte Global Human Capital Trends report. Major hospitals have sophisticated human resource management systems for employee records and talent management, while small facilities and clinics often rely on free analytics tools such as job sites, email clients and search engines.
Big data and analytics may sound intimidating to individuals who are not technologically savvy, but the information that is collected and analyzed often is quite simple.
“That a nurse has earned a BSN – that’s a piece of big data,” said Brittney Wilson, BSN, RN, an informatics expert from Nashville, Tenn., who owns a popular blog called The Nerdy Nurse. Other data that may be collected and analyzed include length of employment, nursing certifications, volunteer activity and leadership experience, she added.
Big-data collection methods
In a recruiting context, big data is any and all information that can be collected from social media platforms, consumer data, public records and other sources, in addition to a hospital’s personnel data.
Specific data points can be merged and analyzed in order to identify, segment and score candidates or employees. Analysts can create algorithms or statistical models to predict variables such as turnover, performance and workplace culture fit.
In a hypothetical example, a review of workforce records may lead a hospital to suspect that prospective job seekers who live in certain ZIP codes are more likely to be short-timers because they have a lengthy commute. As a result, recruiters may view those applicants less favorably.
The U.S. Equal Employment Opportunity Commission warns there are risks to essentially profiling employees and job seekers. Excluding applicants from a particular ZIP code may reduce turnover, but also may open the hospital to charges of racial bias if that ZIP code has a large minority population. Big data must be prudently harnessed by data scientists and HR management to avoid such practices, experts say.
Large healthcare organizations aren’t the only ones that can benefit from using big data and analytics to help in making good hiring decisions. Smaller clinics or health systems with limited budgets may also find these techniques to be valuable in attracting quality talent.
One low-cost way to conduct data-driven recruiting is to determine common keywords, such as BSN, and highlight them on applications in a PDF format, Wilson said. “Better yet, use scrape software, which searches for keywords, and then send an email to a list of interested nurses inviting them to call you,” Wilson said. “Or send an email and just call back people who click on it or open it.”
A robust human capital platform with workforce analysis capabilities makes it easier to identify candidates internally. Say, for example, a nurse manager needs to hire an assistant nurse manager with a master’s degree and a specific number of years of experience. Rather than posting the job broadly, the manager could look through the company’s internal database for candidates in house, Wilson said. “Grooming someone internal for a bigger role increases nurse engagement,” she added.
Gathering and analyzing data in advance also saves a recruiter time during the actual job interview, according to Wilson.
“Make sure that you get to ask real questions, critical thinking questions, cultural fit questions,” she said. “No matter how hard you look at the data, it usually can’t tell you that. For example, is this person a volunteer? That’s a good clue to their cultural fit.”
Not every recruitment team is comfortable working with data, according to Dana Cates, SPHR, SWP, talent acquisition consultant with Plymouth, Mich.-based LEAN Human Capital by Healthcare Source.
“Marketers measure metrics on their organization’s website constantly, but somehow that mindset doesn’t always translate to recruiting,” Cates said.
While everyone should work to improve their analytic skills, that might not be enough without deep expertise in data management. She suggests recruiters and sourcing staff receive more training in analytics.
“I was fortunate to have a data scientist on my team,” Cates said of her previous position as director of talent and acquisition and strategic workforce planning at Texas Health Resources. “That’s why we were more successful than most departments in looking at analytics, trends, and telling the right, credible story to senior leadership.”
Healthcare finance team members may question the validity of numbers and data when evaluating resources for recruitment.
“If you have a smart data scientist on your team, it’s much easier to present figures and make a case for recruiting,” Cates said. “You can go deep into the data and see wait times, how long it takes a manager to hire, and to predict turnover. You can also partner with the strategic planning department to understand growth objectives in a particular area.”
Tracking metrics also makes it possible to measure personal and departmental recruiting performance against local benchmarks. Cates’ professional online profile provides an excellent example of metrics usage. With a team of 30, she filled 7,000 requisitions annually, delivered a vacancy rate of less than 4%, a time-to-fill average of 40 days, and achieved a satisfaction rating of 4.5 on a 5-point scale.
Freelance writer Jebra Turner contributed to the writing and research of this article.