Staffing for Patients, Not Beds
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BY AMANDA MEWBORN, ROGER GRUNEISEN, JARED DAVIS AND JACK LIN
INDUSTRIAL ENGINEER – FEBRUARY 2013; VOLUME 45; NUMBER 2
A hospital needed industrial engineering expertise to quantify what staffing levels were required to serve the actual number of patients in the hospital. In the hospital, supply consists of two components: beds and staff to care for patients in beds. Demand consists of patients. The goal was to ensure that patients received proper care while nursing resources were used efficiently. The problem is solved for three time horizon: 1. 3 to 12 months from current: solution involved predicting patient census and identifying how many full time equivalents (FTEs) are required to staff for that census. 2. Approximately 1 to 12 weeks from current: solution involved scheduling staff members to cover the expected demand over the next three months. 3. The next 36 hours: solution involved dispersing scheduled resources based on the actual number of patients in the hospital. There are five types of staff: management, registered nurses (RNs), licensed practical nurses (LPNs), patient care technicians (PCTs) and psychiatry technicians (psychtechs). Quantifying the number of staffing resources working on each unit at any given time was not feasible because the hospital lacked a time and attendance system for personnel. Without an accurate tracking method for defining worked hours, an interim data collection tool was implemented, and each unit recorded staffing information for each shift in an accurate and standardized fashion.There are also three types of demand: 1. Capacity represents the number of operational beds on a unit. 2. Census represents the number of patients on a unit. 3. Demand represents the number of appropriate patients on a unit. The acuity distribution of patients, or the workload associated with caring for patients, was measured by unit to determine staffing requirements. An analysis and review of literature helped determine the best type of acuity model for the hospital. The patient census for each unit was converted into a plan for scheduling staff to meet the predicted demand. Over and understaffing were balanced and staffing flexibility was considered. The unit management determined it was reasonable to “find” one extra nurse when patient census increases. There was no real time mechanism for determining the number of staff currently on hand, staff needed or the current patient census. The staffing analysis was very comprehensive, quantifying many aspects often ignored in such studies. Five potential strategies are identified to align staffing with patient census: 1. Increase patient volumes or acuity, thereby using the existing staff in a more productive manner. 2. Shift staff members to another area of the hospital where they are needed. 3. Supplement another program (e.g., a medical home model) and shift excess staff to support that program. 4. Reduce staff through lay-offs and attrition. 5. Do nothing.
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