Will 2018 be the year healthcare addresses its turnover problem?

As a new year begins, healthcare providers continue to confront profound unpredictability in their business outlook. Efforts to undermine the Affordable Care Act continue, and the individual mandate has been stripped away. The new tax law may trigger vast, automatic cuts to Medicare and Medicaid. Self-pay is a growing percentage of revenue, bringing a host of new revenue cycle and marketing challenges. And the shift to value-based payment and interoperability of medical records has been muddied by Trump administration delays and rescissions.

In this climate, cost efficiencies are existential needs. Many in the industry see consolidation as a ticket to economies of scale, which may be the case long-run, but lacks an immediate return. Others are focused on instant cuts, including in the labor force, without regard to the impact on morale and workflow.

A few organizations are looking at the same outlook and sensing opportunity. Already, innovations such as genomics, data analytics and machine learning are being applied to many areas of clinical care, including solving readmissions, treating cancer and preventing future disease. Cloud computing, especially with blockchain, offers scalability, flexibility and security in an array of projects in patient safety, telehealth and data-mining. Well-funded entrepreneurs are leaping into healthcare with disruptive innovations.

One of the most promising areas for innovation is to be found in the workforce – both in hiring and retention. For years healthcare has endured high turnover, and the trend seems to be accelerating. A study by Compdata Surveys of 11,000 healthcare employers with more than 11 million employees found the average turnover in healthcare jobs in 2017 was 20.6%, up from 15.6% in 2010, putting healthcare’s turnover second only to hospitality’s.

Nurses, whose work directly impacts safety, quality and patient satisfaction, also jump ship far too often. The turnover rate for bedside RNs in 2016 was 14.6%, according to a survey by NSI Nursing Solutions. This is an expensive problem: A study in the Journal of Nursing Administration found that it may cost anywhere from $97,216 to $104,440 in today’s dollars to replace a nurse, including pre-hire recruitment and aspects such as unstaffed beds, overtime and losses in productivity.

Despite the churn and the layoffs, the healthcare job market has never been more supply-driven. According to the U.S. Bureau of Labor Statistics, the healthcare unemployment rate hit 2.5% in April 2017 – the lowest level in more than 10 years.

The result is a restless workforce that can go anywhere it pleases at any time.

Organizations have tried an array of initiatives to keep employees in place. Using results of staff satisfaction surveys, they have reviewed compensation, provided professional development, and instituted programs such as flex time, awards programs, and retention bonuses, with little or no impact on turnover.

Those who do the hiring are often capable of understanding what’s needed for a position and sometimes are effective at eliciting the right information from candidates, but they are often ineffective at weighing the results and making the final choices. Instead, being human, they are easily distracted by a candidate’s appearance, reminders of themselves, or remarks on arbitrary topics – enabling subjective human bias to undo the work that went into establishing parameters for the job.

A few health systems are now using artificial intelligence to hire the right people for the right jobs, staff who are more likely to stay on the job and not need free pizza days to be satisfied. By collecting and analyzing vast amounts of experiential data on hiring, job performance and patient outcomes, customized predictions can be developed and can learn for each role in each department in each location that align most closely with success and longevity on the job. 

A meta-analysis of 17 studies of applicant evaluations, first published in 2013 in the Journal of Applied Psychology, found that a simple equation outperformed human decisions by at least 25%. Employees chosen based on data have higher supervisor ratings, more promotions and better ability to learn from training.

Despite the mixed messages coming out of Washington, the broken fee-for-service healthcare system still delivers too much care at too high a cost for too little value. We learned in December that total healthcare spending increased 4.3% to $3.3 trillion in 2016, accounting for 18% of GDP.

To make use of declining revenue, we still need to transition to population health management, which in turn will require a host of new roles that do not yet exist in many institutions, from care navigators to nutritionists to call center operators, community health workers and more.

Research shows there is great interest in these new roles, but those doing the hiring have little idea of what skills and background are best suited for them.

With enough unbiased data and the right equations, analytics can help here too, assuring that a new generation of healthcare workers will be the right fit and will stay on the job.

Michael Rosenbaum is CEO of Arena, which uses data and predictive analytics to transform healthcare organizations.

The views, opinions and positions expressed within these guest posts are those of the author alone and do not represent those of Becker's Hospital Review/Becker's Healthcare. The accuracy, completeness and validity of any statements made within this article are not guaranteed. We accept no liability for any errors, omissions or representations. The copyright of this content belongs to the author and any liability with regards to infringement of intellectual property rights remains with them.

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