Nursing Workload Enables Data Driven Decision Making

Data driven decision making is important in life and in business, including healthcare. Nurses use data in all aspects of providing patient care; and decisions regarding resource management and patient assignments should be no different. Nursing workload is the most important data needed to determine long and short term staffing requirements, skill mix, and to manage labor resources.

So, what comprises nursing workload? Nursing workload data is a combination of:

1) Patient workload, based on the patients’ needs for care

2) Complexity of care, a measure of the patient’s needs for professional care

3) Patient admission, transfer and discharge care needs

4) Patient activity workload, including caring for patients in an off unit environment

5) Staff experience and competency

6) Environmental and practice factors, such as unit geography, support services, care delivery model, and technology.

Data driven decisions are based on having data that is valid, reliable, and transportable, meaning that you need to have and maintain workload data that provides the same results across units and organizations. Your patient classification system needs to be evidence-based, re-validated on a regular basis (preferably by the vendor), provide benchmarking data that is acuity-adjusted for both internal and external data comparison, and you need an established monitoring program to insure that staff are using your measurement tool correctly and consistently. Transparent classification, which is classification as a by-product of electronic documentation improves the completeness of classification in addition to the validity of the data.

SEE ALSO: Nursing Informatics and Technology



Data Driven Decisions

Daily:

 

A workload measurement system used daily, provides recommended staffing that incorporates the components of nursing workload-providing the data required for making data-driven decisions across the organization to match nursing workload and staffing resources. Nursing workload incorporates the five Rights of Staffing.

1)Having the right number of staff

2)With the right skill level

3)In the right location (nursing unit)

4)At the right time

5)With the right assignment.

Staffing effectiveness is improved when workload measurement is consistent across the organization, and data is available real time to capture the changes in patients’ needs for care. We know that volume provides us with some of the information needed to determine staffing, but not all. Patients are unique and so are their needs for care. Workload is a combination of patient needs for care and the number of patients. For example, in Figure 1, both Unit A and Unit B have 25 patients, and both units are medical units. When looking at the patients by patient type (I through VI), and the resulting workload – it becomes apparent that the needs of the patients are different between the units, and therefore the number of staff required to meet the patients’ needs is not the same. Using a value of 5.5 hours of care per unit of workload, the required hours of care for Unit A are 182.5 hours and the required hours of care on Unit B are 157.3 hours. This is a difference of 24.8 hours. To provide the same level of care based on nursing workload, Unit A needs an additional 24.8 hours of staff.

In addition to nursing workload, using a Complexity of Care measure adds another dimension of the data. Workload provides the hours our care required based on the patients’ needs for care; complexity of care provides a relative measure of the professional care required to meet the patient’s needs for care. In essence, how much RN care is needed? The higher the complexity of care measure, the greater the patients’ need for professional care. This data is valuable in the distribution of your RN staffing and in the process of making assignments. In Figure 2, Unit A and Unit B have the same number of patients and the same total workload – yet the Complexity of Care is different. Data for making decisions about the distribution of RN staff across nursing units.

Patient Type

UNIT

I

II

III

IV

V

VI

Census

Acuity

WI

Unit A

6

8

6

4

1

25

1.34

33.1

Unit B

8

10

4

3

25

1.14

28.6

Figure 1: Comparison of worload, acuity, and census
Workload and Complexity of Care also provide the data you need to make efficient and effective staff -patient assignments. Incorporating workload in the assignment process leads to balanced assignments- based on workload, not patient census. One RN can be assigned 4 patients and another 3 patients; and the RN with 3 patients can actually have a higher workload assignment. Workload-based assignments result in the equitable distribution of workload, data is not limited to patient volume. The Complexity of Care measure provides additional data to match experienced RN staff to the patients with more complex care needs.

Figure 2: Complexity of Care Measures

Cen

Acuity

WI

HPWI

Req Hours

CM

% RN

Unit A

29

1.33

39

5.5

215

3.1

70

Unit B

29

1.33

39

5.5

215

2.3

60

Additionally, the Complexity of Care data is valuable in making assignments for new staff; including new graduate nurses and nurses new to the clinical population of patients. The assignments for the new RNs can start with patients that have a lower complex of care, and provide documentation across the orientation period of the nurse’s progress in providing care to patients with greater complexity of care needs. This supports the novice to expert concept. Figure 3 shows a sample assignment for a unit with 20 patients. The RNs with patients shaded green, indicates that this nurse cared for the patient yesterday or most recently, providing data to include continuity of care in the decision making process of creating staff assignments. The (*) indicates a staff competency, that will display when hovered over and on the print preview or printed copy of the assignment. Automating the assignment process provides data on continuity of care across time, analysis to determine if assignments are correlated to staff overtime, and to document the equitable distribution of workload.

Data Analysis to Support the Daily Decision Making Process

Analysis of workload data provides a framework for making multiple decisions, such as determining core staffing, the need for standard and non-standard shifts, and the impact of patient arrival and departure patterns. The ability to analyze workload data by day of week and hour of day provides the data needed to determine core staffing patterns. Gone are the days when core staff is the same for every day of the week or even the same across an entire shift. For example, a surgical unit on has an average workload of 30 on Monday at 7 a.m., and at 11 a.m. the workload is 34. An analysis of patient arrival times shows post-operative patients start arriving at 11 a.m., resulting in an increase in workload. Data can be evaluated based on hourly workload or in intervals of workload such as 4, 8 or 12 hour blocks of time.

Figure 3: Sample Assignments
Green hading indicates continuity of care. Patients assigned to RN who cared for
them previously.

New, Nurse Camel, Cynthia Green, Gloria
01 Transparent Classification 05 Trans2 02 ANCC, Margaret
03 New Arrival <I>07 Pulmon, Airy 04 Fractured, Hip
13 Calcu, Lations <I> 08 Cardiac, Event
19 Red, White, and Blue 12 Complete, Care
Rec 3.40 Rec 7.23 Rec 7.23
WI 0.6 WI 1.3 WI 1.3
Complexity Units 2.0 Complexity Units 4.6 Complexity Units 4.3

Core staffing is then aligned with workload, enhancing efficient and effective utilization of staff – and determines the number of staff needed for staff schedules. Fluctuations in workload from the core staffing patterns can be used to fund a central staffing pool. The fluctuations in workload are translated to staff hours and the associated dollars fund a central staffing pool. The central staffing pool is used to meet the staff requirements associated with workload fluctuations. Analysis of the patient arrival and departure data by hour of day and day of week can identify if there is an opportunity to match a resource to meet the workload associated with patient arrivals and departures. The data may identify the opportunity for non-standard shifts to meet this workload.

Moving beyond the daily decisions related to staffing, workload driven decisions can include:

  • Acuity-based labor budgeting
  • Management of variance analysis based on workload
  • Acuity-adjusted benchmarking, internal and external
  • Evaluation of the impact of workload based staffing on patient outcomes
  • Workload based costing of nursing care
  • Strategic planning, for modeling new units and/or new practice modalities

The potential for decision making and change is limitless!

Heather Wood is AcuityPlus Product Manager of QuadraMed Corporation in Reston, Virginia.

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