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How Predictive Analytics Is Reshaping The Approach To Healthcare Staffing
With many issues facing the healthcare industry today, it is critical for healthcare managers to leverage partners and tools to ensure quality patient care. For example, the increase in job vacancies is one glaring concern for healthcare leaders. In 2014, the U.S. Bureau of Labor Statistics (BLS) reported the gap between healthcare job openings and job hires running at 30 percent. By 2016, that had risen to 50 percent. Over the last year, and still today, the unfilled healthcare jobs gap is higher than ever before, according to BLS data.
Provider organizations are feeling the effect of this shortage and are struggling to narrow the growing gap of unfilled job openings. Transcending any potential healthcare policy changes, the healthcare labor shortage is expected only to grow as the aging workforce of healthcare professionals nears retirement and an aging U.S. populace requires more healthcare services. Given these trends, healthcare leaders are urged to optimize the workforce they already have.
While the workforce shortage may initially be an alarming cause for concern, technology-enabled forecasting that has already been used successfully in many industries is gaining traction in healthcare. Early adopters of predictive analytics are seeing lower costs, higher staff morale, and improved patient care, achieved through filling the gaps in nurse staffing.
How Predictive Analytics Improves Healthcare Staffing
Ensuring hospital staff is in the right place at the right time is a challenging and time-consuming process for managers. Add in fluctuating patient volume, seasonality and acuity, this task includes a lot of guesswork.
Inaccurate projections of needs can lead to understaffed shifts, floating, unplanned overtime and incentive pay, last-minute schedule changes, and other issues that can stress staff, raise costs, and impair patient care quality. Adopting predictive analytics makes nurse scheduling and staffing a much more effective and efficient process.
As a technology-enabled solution, predictive analytics uses sophisticated computational techniques including data mining, algorithms, and computer modeling to analyze past data and make predictions about future patient volume and staffing needs. Such forecasting technology is already successfully used in other industries, such as manufacturing, transportation, and financial services, to save time and money through better resource management.
Early adopters of predictive analytics in healthcare are finding that it enables accurate forecasting of patient demand and staffing needs months in advance of the shift.
A lack of awareness among healthcare leaders and frontline managers seems to be the root of the slow uptake in the industry
When combined with advanced labor management strategies, major benefits extend to healthcare staff, patients, and the organization’s bottom line.
According to Fitch Ratings, workforce staffing represents 50-60 percent of budgetary expenditures for healthcare organizations. Therefore, efficiency resulting from predictive analytics can be significant. According to Avantas, an AMN Healthcare company that develops and provides predictive analytics, healthcare organizations that have implemented predictive analytics and advanced labor management have realized overall nurse labor spending reductions of four percent to seven percent.
In addition to cost savings, provider organizations employing predictive analytics are enjoying increased staff satisfaction scores and improved nurse retention – key assets as providers struggle to find and keep qualified nurses. Increased staff satisfaction also has been shown to correlate with improved patient satisfaction – a constant concern for healthcare providers.
For healthcare organizations that have implemented predictive analytics and advanced workforce management strategies, the results they have realized include:
• 97 percent accuracy in predicting staffing needs 30 days in advance of the shift
• 75 percent of open shift hours filled more than two weeks ahead of the shift
• 50 percent to 75 percent reductions in open shift and bonus shift incentives
• Increased from 18th to the 81st percentile in RN staffing satisfaction scoring
Why isn’t Predictive Analytics More Prevalent in Healthcare?
Surprisingly, despite such impressive numbers and benefits, predictive analytics has previously flown under the radar in healthcare staffing and scheduling. A lack of awareness among healthcare leaders and frontline managers seems to be the root of the slow uptake in the industry.
A 2016 survey by AMN Healthcare and Avantas, 'Predictive Analytics in Healthcare: Optimizing Nurse Staffing in an Era of Workforce Shortages' found that 80 percent of nurse managers are unaware of available technology that can accurately forecast patient demand and staffing needs. However, nearly 90 percent said that such technology would be helpful in the daily scheduling and staffing of nurses.
Even more shocking is how managers currently handle staff scheduling: 24 percent use paper-based scheduling and staffing tools, 19 percent use simple digital spreadsheets, and 23 percent don’t use any scheduling tools at all. The widespread use of antiquated scheduling tools, if any, is alarming given the availability of effective technology-enabled solutions.
Technology Offers Valuable Opportunities
While many healthcare leaders acknowledge that current approaches to staff scheduling and staffing are deficient in many ways, it is clear that many are unaware of this technological alternative that is fueled by predictive analytics. Steps should be taken to make healthcare leaders aware of the technology to accurately predict future demand and staffing needs and prompt them into action to learn more about it.
Possibly more significant than the labor cost reductions that result from the use of this technology is the time-savings opportunities. With the help of predictive analytics, managers and directors are delivered valuable time back in their work to devote to patient care and other essential clinical responsibilities. Shifting, scheduling, and staffing from guesswork to science, patients, staff, and organizations enjoy the benefits of predictive analytics.