Nurse-patient assignment models considering patient acuity metrics and nurses' perceived workload

Area of Science:

• Healthcare Management
• Nursing Informatics
• Operations Research

Background:

• Patient classification systems (PCSs) are widely used for nursing workload assessment and staffing.
• Current PCSs may not fully capture the nuances of perceived nursing workload.
• Accurate workload assessment is crucial for effective nurse-patient assignments and staff allocation.

Purpose of the Study:

• To test the hypothesis that existing PCSs inaccurately capture nursing workload.
• To develop and evaluate an optimization model that integrates nurse-perceived workload into patient assignment.
• To improve the balance and efficiency of nurse-patient assignments.

Main Methods:

• Conducted a survey study with 45 nurses in oncology and surgery units.
• Nurses rated the impact of patient acuity indicators on their perceived workload using a Likert scale.
• Developed optimization models using PCS acuity scores and survey-based workload scores for balanced nurse-patient assignments.

Main Results:

• The proposed models achieved a more balanced patient assignment compared to PCS-only assignments.
• Integration of survey-based workload scores led to a lower overall perceived workload.
• An improvement in perceived workload of upwards of five percent was observed.

Conclusions:

• Existing patient classification systems may underestimate true nursing workload.
• Incorporating nurses' perceptions of workload into assignment models enhances assignment balance.
• This approach offers a more accurate and efficient method for nurse-patient assignments, improving perceived workload.