Optimization is defined as the action of making the best or most effective use of a situation’s resources. While generally applicable, this definition lacks specificity when it comes to complex processes such as staff scheduling. In industries with complex scheduling demands, optimization is more appropriately defined as finding an alternative that meets the organization’s goal for maximum coverage or lowest cost while meeting or exceeding quality expectations.
Goals Expected of Optimization
Optimization technology for complex scheduling has existed for at least two decades but is offered by no more than two scheduling systems on the market today. This technology has been shown to provide high quality results yet surprisingly is still not widely adopted by hospitals and healthcare systems in the United States. With few workforce management vendors offering optimization capability, few hospitals know much about the advantages that schedule optimization provides.
The most powerful optimization technologies use mathematical algorithms to produce schedules of best coverage or lowest cost by considering constraints such as staff availability, organizational staffing policies, regulatory standards, and staff preferences. The mathematics underlying schedule optimization do not create “perfect” schedules. Those of us who have managed complex schedules in 24/7/365 industries understand that a perfect schedule is a unicorn. Rather, optimization creates schedules that maximize essential coverage for patient care and affords managers the ability to easily minimize variances for the unit’s expected labor demand. In other words, robust optimization technology minimizes or can even eliminate the actual vs. expected staff variance each shift while producing the overall schedule at the lowest cost.
Business Case for Schedule Optimization in Healthcare
Hospitals are expected to ensure a sufficient number of appropriately licensed staff, namely Registered Nurses (RN), at the lowest achievable cost for the number of patients for whom they have to care. This is the charge that healthcare executives and nursing leaders frequently feel puts financial goals in conflict with the safety and quality goals. This difficult dance is complicated by a number of factors:
Staffing & Scheduling Functions Consume Significant Time: Anecdotal interviews with nursing leaders indicate that nursing managers spend between 20-40% of each day directly addressing staffing and scheduling needs. Schedules that are optimized on the front-end result in fewer necessary daily staffing adjustments being made on the back. The two to three hours per day nursing managers spend addressing daily staffing needs can then be used to drive improvements in other important organizational goals such as patient satisfaction, employee engagement, and quality initiatives.
Vacancy & Turnover Rate Impact Success: Recent RN vacancy and turnover rate data show that RN resources are in increasingly lower supply but increasingly higher demand, with an average “to fill” time for RN positions of 86 days (1). As competition for desirable jobs becomes more intense because of geographic location, specialty services offered, better schedules and consequently better work-life balance, the need for retaining nursing staff will become even more important than it is today. This importance is both qualitative and quantitative considering that each RN vacancy can cost the organization $47K to $96K per year in excess labor utilization, i.e. overtime and agency use (2).
Schedules are a Top Reason for RN Turnover: A study of the top reasons for nursing turnover indicates that “staffing & scheduling” is one of the higher ranked reasons RN’s leave their jobs (3). In reality, “staffing & scheduling” can be broken down into two primary elements. The first is “can I get the shifts I want”. Basic scheduling systems, even pen and paper, can sometimes effectively address this first element. The second element, what I will term the “nursing value proposition”, is the question of whether a sufficient number of qualified nursing staff exist on a given unit on a given shift. Too often, the answer to this question is no. In this way, poor scheduling and staffing can directly contribute to nursing turnover. Optimization’s greatest impact is in successfully addressing the “nursing value proposition” through limiting the number of shifts where RN’s work short.
Scheduling Technology Adoption is Lagging: In a 2013 survey of acute care hospitals, approximately 40% of survey respondents indicated that they did not use an automated staff scheduling system. When 40-60% of an organization’s total cost is directly related to its labor resources, can healthcare leaders afford to manage labor resources using antiquated methods? The short answer is no.
What to Expect from Optimization
Labor resources are best managed through optimized scheduling and effective daily staffing management that is benchmarked against a productivity target. Gaps and errors in scheduling on the front-end create time-intensive problems to resolve on the back. (They can also yield significant worked and paid hour productivity variances.) Optimization minimizes these gaps, ensuring that units have balanced coverage, low variance from expected labor demand, and can ensure competent staff are available on each unit on each shift at costs in line with department budgets.
Based on the descriptions above, optimizing schedules may seem a daunting task. Schedule optimization using manual methods is certainly challenging because of the human and systems factors to be simultaneously considered. These factors include the work unit’s predicted labor demand, the organization’s budget, the organization’s staffing policies and procedures, regulatory standards (i.e. work hour limits), individual staff’s rates of pay, staff’s schedule preferences, and staff requests for time off. If leaders consider manual optimization a tool, they will find it extremely difficult because manual scheduling leaves many of the variables above to be judged on a subjective basis.
In contrast, computerized automation can be as simple as a single click of the mouse. Successful optimization systems build easy-to-use user interfaces on top of powerful science. The mathematics that drive optimization run efficiently in the background as they consider the complex sets of variables and constraints to render the best-fit solution. Recall, optimization does not produce “perfect” schedules. However, optimization will produce schedules with the minimum number of shifts with actual vs. expected labor variances (i.e. “short staffed shifts”). It is this process of producing the best schedule, considering all the system’s constraints, that inherently results in patients having the right number of RN staff to care for them, nursing managers having more time back in their day for value-added activities, and staff feeling more engaged because they work fewer shifts that are short-staffed. Through optimization, the ”nursing value proposition” problem can be successfully addressed.
Where Do We Go from Here
Optimized staff scheduling exists and is available in the workforce management marketplace today. Yet not all scheduling solution providers offer optimization capabilities. Some solution providers that sell “optimization” technology provide only the most rudimentary means to attempt it. Underpowered tools will not work in tomorrow’s healthcare and, unfortunately, there are those that provide the market with just that…underpowered tools. We need to change that.
Schedule optimization will be a valuable tool for healthcare organizations to successfully balance the quality needs so important to patients and families with each organization’s need to be good stewards of their resources. I believe that your organization deserves to leverage the direct and indirect benefits that schedule optimization provides.
In closing, in light of the cost pressures present in healthcare over the last decade, the low adoption rate of schedule optimization should surprise us all. Labor remains the largest expense of the healthcare organization. Managing our labor resources using outdated methods is outdated itself. We must aspire to provide sufficient patient care coverage each shift even when vacancy rates are higher than we like. We must balance employee and organizational schedule needs to the benefit of both. We must do these things while improving the quality of care provided to our patients. For us to meaningfully bend the healthcare cost curve and achieve the clinical results expected of us, we must continue to challenge the archaic ways we conduct our business and change ourselves to leverage today’s available technologies. Through optimization, we will.
If your organization is using a scheduling system that is underpowered and inhibiting your achievement of clinical, employee engagement and financial goals, I welcome the opportunity to schedule a time to share insights on how optimization may be right for you.
Please share your thoughts in the comments section below. I always learn from the discussions that follow and appreciate your interest, questions and expertise. Thank you.
ABOUT THE AUTHOR:
Ron Short is a 23-year experienced healthcare executive and Chief Operating Officer of Care Systems, Inc. Prior to joining Care Systems, Ron worked in a variety of roles at Good Shepherd Medical Center in Longview, Texas. A physical therapist by training, Ron has developed a unique passion for leadership development and the integration of advanced technologies to enhance clinical, operational and financial performance. Ron speaks nationally on topics including patient throughput improvements, use of technology to enhance clinical care, and leading change initiatives. He holds a Master of Physical Therapy degree from Boston University, a Master of Business Administration degree from the University of Texas at Tyler, and is a Fellow of the American College of Healthcare Executives.
1. National Healthcare RN Report 2017, NSI Nursing Solutions, Inc., http://www.nsinursingsolutions.com/Files/assets/library/retention-institute/NationalHealthcareRNRetentionReport2017.pdf