area of assignment in nursing

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Development of a Nursing Assignment Tool Using Workload Acuity Scores

To determine a just and consistent practice for creating nursing assignments.

BACKGROUND:

Traditional methods of assigning patients to nurses may lead to unbalanced nursing workload. This article describes the ongoing, hospital-wide effort to evaluate and implement a nursing assignment tool based on electronic health record (EHR) functionality and auto-calculated nursing workload scores.

EHR records of individual patient workload scores from all hospital units were collected from August 2017 to June 2018. A nurse-specific total workload score was summed for each staff. Then, each hospital unit’s mean nurse workload score and standard deviation, along with the unit’s nurse-to-patient ratio, were used to calculate levels of high, medium, and low nursing workload measurement (NWM).

Mean patient-specific workload scores varied greatly across hospital units. Unit-specific nurse-to-patient ratios were factored into NWM scores to create ranges for assignments that were relatively consistent across the institution.

CONCLUSION:

The use of objective, electronically generated nursing workload scores, combined with traditional nurse-to-patient ratios, provides accurate real-time nurse staffing needs that can inform best practice in staffing. The confirmation of individual patient workload scores and an appreciation for the complexity of EHR vendor rules are necessary for successful implementation. Automation ensures patient safety, staff satisfaction, and optimal resource allocation.

The focus in healthcare has been to increase quality while maintaining costs. Donabedian’s model for improving quality is based on the triad of structure, process, and outcomes and is often used in current patient outcomes and value-based payment models. 1 Newer methodologies include the Quality Health Outcome Model, which uses pathways for associating nursing care and quality. Others focus on the National Database of Nursing Quality Indicators (NDNQI) to review nurse staffing and outcomes. 2 Research has shown that when administrators decrease staff in an effort to lower costs, quality decreases and adverse events increase. 3 , 4 Given that nurse staffing comprises 40% of hospital budgets, it is imperative that optimal nurse assignments continue to meet standards of quality care and improve patient outcomes. 3 , 4 The process of how nursing assignments are distributed in healthcare settings has evolved from uninformed to scientific. 5 Multiple factors, from budgeting and operations to staff satisfaction and patient safety, have driven this evolution. Nursing assignments are often based on room proximity, mandated nurse-to-patient ratio, patient’s medical diagnosis, and continuity of care from shift to shift. In reality, nursing activity will vary throughout a patient’s length of stay based on a combination of prescribed tasks including education, nursing interventions, and psychosocial needs, in addition to medical diagnosis. The NDNQI method for staff assignments uses the hours per patient day (HPPD) as a standard when evaluating staffing. 4 – 6 Managers take into account the average number of staff they have on a given unit and compute the assignment from that information. However, using the traditional methods of creating assignments without objective data may lead to unbalanced nurse workload; in other words, intensity of nursing care varies based on patient-specific needs and abilities. Ideally, assignments should take into account changes in any patient-related tasks, inclusive of psychosocial status, medical status, care transitions, and nursing plans of care. NDNQI has proven to be more accurate than HPPD in determining patient needs as it includes admission, discharge, transfer, and other activities that take up a nurse’s time. 7 Through appropriate documentation of patient-specific activity and utilization of a standard and reliable workload measurement system, nursing assignments become more equitable. 2 To create a process that takes the complexity of nursing care into consideration when making shift assignments, it is 1st necessary to assess the amount of nursing activity required by a single patient and translate into a workload score. 8 – 10

The 2nd step, and focus of the current article, is to sum the patient workload score attributed to each nurse on duty to plan nursing assignments and distribute the total work of the unit safely and equitably. Workload-based staffing technology satisfies an essential function that meets diverse patient needs when determining nursing assignments.

Calculating a workload score takes into account dynamic patient care demands that often change from shift to shift or even hour to hour. Historically, resource allocation and staff assignment did not take the ever-changing patient care requirements into consideration. 5 According to the American Nurses Association (ANA), “Greater benefit can be derived from staffing models that consider the number of nurses and/or the nurse-to-patient ratios and can be adjusted to account for unit and shift level factors.” 11 Using a workload score in combination with an electronic health record (EHR)–based assignment tool offers an opportunity for real-time patient-centered resource allocation. By leveraging existing documentation, the nursing workload measurement (NWM) allows for agility and accuracy in nurse staffing assignments.

It has been well documented that HPPD-based or diagnostic related group–based assignments do not accurately equate to perceived nursing workload. 7 The term workload is interpreted differently among healthcare professionals. Given that, according to Merriam-Webster, 12 the medical definition of workload is keenness of sense perception , it is vital to clarify this in relation to patient care. For this project, the operational definition of workload included the amount of nursing care needed, patient reliance on nursing, staff allocation, and workload measurement. 5 , 13 The term workload-based reflects an aggregate of medical- and nursing-related tasks, as well as other aspects, such as risk factors, admission, transfer, and discharge activities. 14 The intention of a patient-specific workload score generated by EHR documentation is to estimate the intensity of nursing work the patient will require in the upcoming shift. Unless a standard is applied to account for the intensity of nursing activity required for a patient during a shift, the process of distributing nursing assignments becomes biased.

Significance

Aiken et al 15 have led the battle regarding patient safety and the level of staffing needed to maintain this goal. There are currently no federal regulations to establish appropriate guidelines for safe patient care related to nurse staffing. The Safe Staffing for Nurse and Patient Safety Act of 2018 (S. 2446, H.R. 5052) proposes clear directives related to nurse staffing levels for hospitals that receive reimbursement from Medicare. 15 One such requirement is that minimum ratios are identified and adaptable based on “the level and variability of intensity of care required by patient under existing conditions.” 16 In this Act, Congress acknowledged the abundance of evidence supporting the correlation between safe nurse staffing and improved patient outcomes. The fact that this federal legislation has not passed should not negate its importance when addressing safe staffing. States are also actively addressing safe staffing legislation. Regulations are beginning to affect payments based on staffing models, and union contracts are demanding that healthcare organizations adopt workload-driven systems. 11 The proposed federal legislation acknowledged that Connecticut, Illinois, Nevada, Ohio, Oregon, Texas, and Washington have enacted this as recommended. 11 As stated in the Lippincott Blog: “14 states currently addressed nurse staffing in hospitals in law/regulations: CA, CT, IL, MA, MN, NV, NJ, NY, OH, OR, RI, TX, VT, and WA.” 17 California is the only state with unit-specific mandated minimum nurse ratios, whereas other states have developed committees and public disclosure of ratios. Massachusetts has written into law specific nurse-to-patient ratios for the ICU of 1:1 or 1:2. Man-dating a minimum nurse-to-patient ratio by no means restricts the ability of organizations to increase ratios according to need. 16

In this study, we are motivated by the current national discussion to provide insight on how to harness emerging EHR technologies to provide hospital-wide nurse staffing assignments based on real-time patient need. Our aim is to incorporate the ANA position on staffing, namely, that staffing should focus not only on ratio, and there is variation between nurse experience, hospitals, units, and shifts. 11 The current study integrates regularly captured patient workload scores with traditional nurse-to-patient ratios into an automated data nursing assignment tool (NAT).

Materials and Methods

In the fall of 2017, our organization, an approximately 400-bed tertiary care, rural academic medical center, located in New England, implemented an EHR-based workload tool that measures patient-specific nursing workload. The institutional review board granted exempt status to conduct this quality improvement work.

Prior to implementation, decisions were made by the organization to adapt EHR rules to a point value associated with each nursing task. There are 9 components that make up an individual patient score: assessments, medications, lines/drains/airways, risks, wounds, orders, activities of daily living, admission and transfer/discharge. The tool automates an individual patient workload score based on 300 available rules that look retrospectively and prospectively for certain elements within existing documentation as well as orders. The proprietary nature of the tool does not allow the authors to disclose the details of the rules that drive the workload score. The score is updated at the following times: 3:00 AM, 9:00 AM, 3:00 PM, and 9:00 PM. The times are set to allow for “filed status” of scores. It is important to note that the times were not set to allow for late documentation, but for the batch job to run. The next phase, and the focus of this article, was to use this individual patient-level EHR data as the driver to implement a patient-centered objective and automated NAT.

To create an impartial assignment, the average workload scores on each unit were addressed. The authors felt this was important to compare unit scores so we would know if it was appropriate to use a universal assignment score, or whether this should be department specific. Having implemented the nursing workload tool, data were collected from August 2017 to June 2018. EHR-generated data were obtained using a web-based report of all patients and their workload numbers. We compiled the summary score of all patients assigned to one nurse, which is equivalent to the total workload score for that nurse. We examined the mean, SD, and median values to understand the distribution of the data. Nurse workload scores were aggregated at the department level and transformed into 3 categories indicating low, medium, and high workload, based on 1 SD from the mean department score. To set the ranges for these categories, the department level mean ± 1 SD was multiplied by each department-specific nurse-to-patient ratio. In some instances, fractional numbers were used to accommodate for units that have different nurse-to-patient ratios on the night shift. For example, a nurse-to-patient ratio of 3.5:1 was used for a unit with a 3:1 nurse-to-patient ratio on days and 4:1 nurse-to-patient ratio on nights. The result was department-specific NWM categories for nurse assignments that were represented with a color to indicate when the combined patient assignments for each nurse fell within a low, medium, or high range. The upper limit of the high range was determined by adding 200 to the lower limit of the high category. This value is only needed to program the ranges in the EHR, so it is somewhat arbitrary. However, after examining maximum values since August 2017, it is unlikely that this number will be exceeded.

The mean patient workload score varied greatly across departments, ranging from a mean score in pediatrics of 64 to a mean in ICU medical of 196 ( Table 1 ). Aggregated patient scores at the nurse level were summed across all units and compared. This aggregated number represents the NWM for a single nurse assignment having taken into account the unit’s nurse-to-patient ratio. The NWM score falls within the predefined ranges of low, medium, or high. For medium, the optimal NWM range in pediatrics with a nurse-to-patient ratio multiplier of 4 is 144 to 432, whereas in ICU medical, a nurse-to-patient ratio multiplier of 2 defines an optimal range of 272 to 512. As a visual indicator of the ranges, the NAT will be implemented with stoplight colors, with green representing the medium-level, or ideal, range. Yellow will indicate that the assignment is in the low range, indicating that a nurse still has capacity to care for additional patients, and red is in the high range relative to nursing workload. These categories will provide decision support to charge nurses and managers to determine nurse-to-patient ratios and assignments in real time, according to patient-centered needs.

Patient-Level and Nurse Assignment–Level Work Acuity Scores Across Departments in an Academic Hospital

All scores from Web Intelligence over 11.5 months (7/17 to 6/18 four times per day).

Nurse-to-patient ratio multiplier is an average in cases when a unit has different ratio standards for day and night shifts.

Strengths and Limitations

Because of the proprietary limitations of the EHR vendor, the direct application of ranges reported in our study cannot be generalized to other institutions. Nonetheless, the process of evaluating department-specific measures to derive appropriate ranges and staffing assignments can be universally adopted. Data were collected from a single academic center, which reduces the generalizability of our study. However, the sample size included 26,985 records and covered a 12-month period across all departments.

The major finding of this article demonstrates that patient workload scores, combined with minimum department-specific nurse-to-patient ratios, provide accurate patient needs to generate fair, hospital-wide staff assignments. As expected, patient workload scores varied by department. What was not expected were the higher scores observed in departments that were traditionally viewed as having lower patient care needs; that is, in the hospice unit, when we looked closer, scores were comparable to the ICU.

Our work demonstrates that a NAT allows the person responsible for making nursing assignments, usually the charge nurse, to quickly assess and adjust a nurse’s workload. The cumulative NWM score is translated into a visual indicator using color and a slide bar. The colors change based on a range of scores customized to each unit. When developing our approach, research into other organizations’ strategy to develop the ranges for the NAT yielded sparse results. It was determined that a descriptive statistical approach would be utilized to define and maintain each unit’s optimal range. Nurse managers were presented with the proposed ranges and educated on the logic behind the process and development of the tool. Work is ongoing to fully implement this assignment tool into everyday practice at the institution to ensure staff assignments are fair and unbiased. Most managers responded positively and are eager to use this tool when available. However, there was some reluctance to using patient workload scores as a basis for a staff assignment tool. The inpatient psychiatric unit staff initially did not feel this tool would be applicable to their care model. The range of scores for this unit was 30 to 90, with an outlier of 205. Data revealed that outliers in the psychiatric unit were dramatically visible and could be directly attributed to increased patient care needs, which we believe reinforced the reliability of the workload scores.

The next phase of developing an improved practice of assigning staff will require that staff schedules are batch uploaded to the EHR and into the NAT. The availability of the daily nurse schedule is a vital component for successful implementation; however, it was outside the scope of the current project. Once implemented, the staff responsible for assigning patients will drag and drop a patient’s name to the assigned nurse. A bar under the staff nurse’s name will fill with the color to indicate the current status of his/her assigned workload. The patient’s workload score will be automatically updated 4 times per day to adjust to real-time documentation and upcoming orders. As the score is dependent on nursing documentation, complete and real-time documentation of patient care will produce the most reliable score ( Figure 1 ).

Nursing assignment tool workflow.

There will be ongoing monitoring of this tool to ensure usability following implementation. Some nurse managers expressed concern with the stoplight color scheme and have suggested that a gradation of a single color may be more useful. The middle range is the optimal assignment. An assignment classified as red may be construed as precarious or undesirable. Color scheme changes will be considered pending feedback after implementation. Continued review of the ranges will also be necessary as documentation standards change or updates to the EHR are made that may lead to breakage of rules used to calculate scores.

Patient safety issues are rightly at the center of concern regarding ineffective staffing models. Studies have drawn a direct line between nursing workload and staffing ratios and avoidable deaths. 6 , 10 , 16 Patient safety is only one of the concerns that can be addressed by utilizing a NAT for staffing decisions. Other areas of concern that may be addressed include staff retention, burnout, and work satisfaction. 8 Identifying and remediating workload disparities will allow managers to allocate staffing resources appropriately, including using flexible staff when needed. 13 “Fixed staffing numbers or ratios only identify minimum staffing levels and do not adjust for the ever-changing nature of patient care needs.” 3

The national conversation continues to reflect positively on agile nursing assignment processes that flex with patient needs. 9 , 18 , 19 However, there are logistic and cultural barriers to implementation. Another challenge to the adoption of this technology may be the geography related to specific patient locations in the hospital unit. Adjusting nursing assignments based strictly on nursing workload may fail to take location of patients into consideration. Some departments currently base assignment on room location, as there are physical barriers in the unit design. Changing the status quo of the process to assign patients to nurses may be challenging in some units. One unit manager reported that they assign nurses up to 24 hours in advance, making the every 6-hour update to the nurse workload score less valuable and less sensitive to acuity and condition changes, as well as nurse competence. Clearly, each institution will require adjustments that can be easily managed from the back end of this flexible product. Engagement with operational leaders is a vital component of implementation. Such systems that leverage EHR technology have the potential to impact excellence in nursing practice.

Future versions of this tool will allow a charge nurse to quickly match patients to nurses based on continuity of care, expertise, and location. Coordinating care at this level of granularity will help ensure the patient is paired with the right nurse for the current phase of care to achieve patient safety, staff satisfaction, and optimal resource allocation. The use of objective, data-driven, electronically generated NWM scores based on actual patient workload, combined with nurse-to-patient ratios, provides accurate real-time nurse staffing needs that can lead to best practice in staffing. The validation of workload scores and an appreciation for the complexity of vendor rules are necessary for successful implementation.

Acknowledgments

The authors acknowledge Geoffrey Tarbox, MBA, RN, for his work on the Excel spreadsheets; and Petrice DiDominic, MSN, RNC-OB, for her help with the Workload Acuity Tool.

R.T.E. was supported by award number UL1TR001086 from the National Center for Advancing Translational Sciences of the National Institutes of Health.

The authors declare no conflicts of interest.