Historically nurse scheduling was a function of assigning personnel on an equitable basis. These tasks were paper and pencil schedules with little accommodation for variation. The number of personnel assigned to a unit was based on historical patient occupancy rates. In the early 1970s several commercial systems containing a patient acuity system were available for use. As electronic health records (EHRs) developed, new applications for scheduling nurses became available. In addition to the scheduling functions, software began to provide workforce management functions. There has been a demand to provide minimum staffing levels, and some state laws mandate these levels. In today’s environment, there is an increasing need to prove value for nursing care and to use nursing skill to provide improved patient outcomes, manage costs of patient care, and ensure that the appropriate nursing skill is provided through credentialing techniques. This chapter will address some of the issues and provide a call to action to provide integrated evidence-based systems for nursing’s use. Historically nursing work commitment was a function of employment (nurses agreed to work required pattern of weekends, and rotating shifts). The function of credentialing was often handled by the human resources office and the scheduler had scant information about a specific nurses’ qualifications. The function of nurse scheduling was to try and provide an equitable number of nurses for an assigned shift and honor special requests if at all possible. Special requests were pieces of paper (or later sticky notes) handed to the nurse manager or schedule coordinator and the nursing schedules were completed using paper and pencil. Usually, allowing the staff nurses to self-schedule was too complicated to manage. Although there has been research on effective staffing, adoption and change in staffing methods and assignments have been slow (Kerfoot, 2018). The nursing supervisor accomplished shift changes in staffing for callouts, overstaffing, and understaffing. Oftentimes, temporary agency nurses were utilized to provide required patient care. In many instances, the use of agency personnel doubled the cost of nursing care. The concept of staffing to achieve more than just financial outcomes— patient, staff and organizational outcomes—has only just begun to be incorporated into the staffing processes as a result of the correlations with appropriate staffing and the achievement of these outcomes. In the early 1960s, Connor and others from Johns Hopkins University began to describe a nursing workload measurement system. This three-category classification was based on observable physical and emotional care requirements of patients. Based on the classification, an estimate of nursing care time requirements for the patients was assigned within each category. Other significant findings of the Connor study included the following: • Nursing workload was based on the number of patients in each category of care. • A wide variety in care requirements varied from day-to-day on the same ward or unit. • The number of class 3 or intensive patients was the main determinate of nursing workload. (Edwardson, p. 97) Nursing workload, nursing classification, patient acuity, and nursing intensity are among the terms used to differentiate the number of nurses required to provide optimal care to a group of patients. By the 1980s a number of computerized systems were developed to assist in the measurement of nursing workload. Patient profiles, nursing task documents, and critical indicators of care were used to develop these measurements. Early systems used time and motion measurement studies based on activities of daily living, e.g., how long to bathe, feed, and ambulate a patient. Some of the early computerized scheduling and nursing workload systems included GRASP, Medicus, PRN, ANSOS, Van Slyke, and the Navy Workload Management System (Edwardson & Giovanneti, 1994, p. 99.). Today, the predominant mode of scheduling and staffing continues to be on paper via spreadsheets and other similar methods. A recent survey by AMN Healthcare reported that 80% of the nurses involved in scheduling activities are not aware of technology-enabled solutions (Landi, 2016). Using this outdated method of staffing means critical employee information is likely kept in many disparate locations and databases. Without database integration, a WMS is not possible. An automated WMS should provide at a minimum the following: • Licensure information • Certification skills information such as PALS and ACLS • Current contact information • Employee work preferences • Competency to “float” or work on a variety of units • Ability to assess and match the competencies of the nurse with the needs of the patient • Prospective and predictive analytics and retrospective analytics for effective staffing • Integrating quality metrics with staff outcomes Credentialing is used here to refer to individual credentialing, such as state licensure for Registered Nurses (RN) and Licensed Practical or Vocational Nurses (LPN/LVN). The American Nurses Credentialing Center (ANCC) provides Board Certification for nurses in specific areas of practice such as medical-surgical nursing, and organizational credentialing, such as the Magnet recognition for healthcare organizations. ANCC also accredits providers of continuing education in nursing (American Nurses Credentialing Center, 2019). ANCC Board Certification provides recognition of study, work, and examination beyond nursing licensure. There are a variety of certifications including Cardio-Vascular, Medical-Surgical, and Nurse Executive. (American Nurses Credentialing Center, 2020). A professional nurse portfolio is a chronological, visual representation of your professional growth. The portfolio provides a detailed look at your professional growth. It is a useful tool as you apply for advancement within the organization, or for a new employment opportunity (Schneider, 2016). Nurses have varying skills, experience, competency, abilities, and preferences to care for different types of patients. In traditional staffing systems there often is not much information available about the nurse to effectively match those competencies with the needs of the patient. Expiration dates for licensure, required certifications, and other expiration dates can sometimes be found but are often stored in another system, such as the human resources system, and are not communicated to staffing managers or those responsible for matching patients to caregivers. Likewise, certifications such as chemotherapy, pediatric advanced life support (PALS), and advanced cardiovascular life support (ACLS) and required yearly educational modules might be found in information stored on the unit or in a learning management system that is also kept separate from, and not communicated to, the staffing system. Some healthcare information systems base the credentialing process on clinical ladders and a differentiated practice model, and clinical ladders. Without a fully integrated and dynamic system that makes this information available at the point of assignment of the nurse to the patient, the scheduling process happens without the necessary information to make a good assignment. Bad assignments can be detrimental to an organization’s bottom line, not just from a safety standpoint but also from a quality-care-measurement standpoint. Manually identifying critical employee information stored on separate systems takes time and can be difficult, making it impossible to accomplish in a timely manner. If efficiency is the goal, scheduling managers, who are often nursing department leaders, should be spending less time on staffing and scheduling and more time on patient care. Evidence is mounting about the effect nurse fatigue has on patient care. For example, the likelihood of a nurse making an error is three times higher after working a shift lasting more than 12 hours (Rogers, Hwang, Scott, & Aiken, 2004). More hours of RN time spent on direct patient care result in shorter length of patient stays and fewer failures to rescue (Kane, Shamliyan, Mueller, Duval, & Wilt, 2007). For each full-time equivalent (FTE) nurse per patient day, the absolute risk of mortality decreases by 0.25% (Shilling, Campbell, Englesbe, & Davis, 2010). Intelligent staffing systems track the number of hours a nurse has worked and sends alerts to prevent scheduling a nurse approaching excessive hours, which will create patient safety problems. Financially, costs can be reduced by systems that alert scheduling managers to prevent unnecessary overtime by scheduling a nurse who, if scheduled, will be in overtime to the exclusion of one who is not given similar competencies. These systems can also ensure that equitable assignments are made by balancing the workload of all the people in a unit by using the information from the patient classification system and nurse competencies. It is not uncommon to see new nurses given difficult and heavy assignments while experienced nurses are under-utilized and not assigned to the patients they can care for best. Nurse satisfaction is tightly tied to assignments. Without data about the competencies of the nurse to effectively make equitable and safe assignments, nurses will more than likely become less than satisfied over time. Without question nurses who are unhappy effect patient satisfaction and engagement. Nurse turnover is an expensive item in the budget, with replacement costs amounting to as much as 100% to 250% of their salary depending on factors such as the cost and length of RN replacements, the cost of the recruitment process, etc. Turnover also accounts for major quality issues. Patient assignments are extremely important when the goal is to generate the best quality and financial outcomes and best nurse satisfaction and retention rates. In this metrics heavy, care quality–focused market, the ultimate goal of staffing and scheduling should be to match the needs of the patient with the abilities of the nurse in order to create excellence in clinical outcomes and operational excellence. Staffing by ratios or matrixes does not capture the unique needs of the individual patient. For example, the needs of an 80-year-old with pancreatitis differ greatly from that of a 40-year-old with the same diagnoses. The 80-year-old needs help with ambulation, has a complicated medication regime, and has a risk of falls and pressure ulcers. Without clearly knowing the needs of the patient, staffing systems fail to keep the needs of the patient as the central focus of staffing and scheduling. Now acuity systems can pull information directly from the medical record to calculate hours of care, eliminating the variation between nurses in the manual system. This integrated technology also saves significant time on the scheduling process as a whole. In 2015 Alavare Health LLC prepared a white paper for the American Nurses Association titled Optimal Nurse Staffing to Improve Quality of Care and Patient Outcomes. This paper in part stated: “appropriate nurse staffing is associated with improved patient outcomes. With the increased focus on valuebased care, optimal nurse staffing will be essential to delivering high-quality, cost-effective care. Implementation of a legislative model will help set basic staffing standards and encourage transparency of action through public reporting and imposing penalties on institutions that fail to comply with minimal standards.” (Avalare Health, LLC, 2015 p. 5) In addition to knowing the needs of patients for nursing care, and matching that with the competencies of the nurse, the availability of the right nurse at the right time is paramount. With the best technology available, the quality of patient care will be compromised with a shortage of RN direct care hours. Blouin and Podjasek (2019) note that with increasing amounts of published research on the impact of patient outcomes on direct care hours, there is a clear case for the adequacy of nursing resources. These researchers note delayed, unfinished, or missed care, adverse patient outcomes, patient readmissions, and poor patient experience as impacts on the financial and reputational risks as well as the impact on the caregivers. Nursing turnover correlated with inadequate staffing is noted to increase costs and compromise staffing. In addition, these researchers note the predicted loss of experienced nurses due to retirements and the effect on patient outcomes. Fenush in 2017 defined predictive analytics as the practice of extracting information from existing data sets to determine patterns and predict future outcomes and trends. It leverages processes and staffing data and then sorts the data with a standard algorithm. This creates a forecasting model that’s validated over time. When combined with a technology solution, the data assists nurse leaders in staffing and scheduling by working behind the scenes to ensure a clinical nurse is placed at a patient’s bedside at the right time. (Fenush, 2017, p. 27) Effective staffing and scheduling systems depend on information from a variety of sources such as projected admissions, acuity of patients, and expected length of stay. By integrating and analyzing information from the sources of data that effect staffing and scheduling, it is possible to predict staffing needs and create better and more effective processes. The amount of associated staffing information that is collected and stored is very large. With the modern technology that is designed to analyze large data sets, we now have the opportunity to turn that data into predictable and actionable information. For years, nurses have worked diligently to develop quality metrics to measure and report the effect of nursing care on patient outcomes. Nursing sensitive indicators in major nursing databases include the National Database of Nursing Quality Indicators (NDNQI), the California Nursing Outcomes Coalition (CalNOC), the Military Nursing Outcomes Database (MilNOD), and the Veteran’s Administration Nursing Outcomes Database (VaNOD). However, effective quality and outcomes measurements in nursing have changed very little in the past 40 plus years. Quality metrics with staff outcomes are also affected by funding and consensus among nurses and nurse administrators. (Jones, 2016). With the ability to integrate quality data from several sources, and pull it into a staffing system, it is now possible to integrate quality data with the staffing process. Technology allows us to see in advance what kind of nursing care a particular patient needs, based on an assessment pulled from the documentation in the medical record. By integrating all of this data safer, more effective care is provided. In today’s market, where quality standards are given equal measure against financial goals, the clinical and financial sides of the business must work together, else long-term sustainability will suffer. Gartner Research notes five trends in workforce management for the near future. Automating the manager experience, creating a positive employee experience, managing a more flexible workforce, adding Virtual Assistants, and developing cloud solutions will be part of our future. The challenge will be to ensure the integration of quality metrics with the outcomes of staffing to ensure we can truly see the inputs and outputs of effective staffing (Pang, Ranadip, Hanscome, & Grinter, 2018). The multiple influences of quality performance initiatives, performance targets for patient quality, nurse satisfaction, budget/financial controls and unanticipated events reflect the ever-increasing complexity of the healthcare system now and in the future. The principles and realities of complexity and complex adaptive systems provide an excellent framework upon which to determine strategies and processes in a rapidly advancing digital world. The recognition of the unpredictable nature of scheduling and staffing as normative, and the importance of the multiple relationships among nurses, staffers, and informaticists supporting computerized systems, can only enhance the effectiveness of future systems. Achieving long-term financial sustainability in this new marketplace will be impossible for organizations tied to outdated and inefficient workforce scheduling, staffing, and assignment systems. There is an acute need to recognize that a transformational workforce is necessary to meet the needs of the next generation of healthcare. For the better part of the last 50 years the healthcare industry adhered to a simple, easy to measure, fee-for-service model. Today, the focus is moving toward a value-based model that relies heavily on metrics tied to high-quality outcomes. Technology such as the EHR can play a significant role in ensuring patients are not only receiving the highest quality care, but the right care and that information can be linked to a WMS.
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Nurse Scheduling and Credentialing Systems
ABSTRACT
INTRODUCTION
NURSING WORKLOAD MANAGEMENT
CREDENTIALING, CERTIFICATION, AND A PROFESSIONAL NURSING PORTFOLIO
Assessing and Matching the Competencies of the Nurse
Assessing the Needs of the Patient
Ensuring Appropriate Staffing
Prospective/Predictive Analytics
Integrating Quality Metrics with Staffing Outcomes
VALUES-BASED HEALTHCARE AND THE CHALLENGE OF STAFFING
Moving from Volume to Value
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