Midlevel practitioner workforce analysis at a university-affiliated teaching hospital

Area of Science:

• Healthcare Management
• Medical Staffing Optimization
• Clinical Operations

Background:

• Determining optimal midlevel practitioner (MLP) staffing is crucial for efficient healthcare delivery.
• Existing staffing models may not adequately account for patient care volume, complexity, and resident duty hour regulations.
• Accurate MLP staffing is essential for maintaining quality of care and operational efficiency.

Purpose of the Study:

• To develop and validate a quantitative methodology for assessing midlevel practitioner (MLP) staffing requirements.
• To align MLP staffing levels with patient care demands and Accreditation Council for Graduate Medical Education (ACGME) 80-hour workweek limitations.
• To provide a data-driven approach for optimizing MLP deployment in a teaching hospital setting.

Main Methods:

• Data were extracted from hospital information systems, financial budgets, census reports, and clinical schedules of MLPs, residents, and fellows.
• Patient care activity volume and complexity were assessed using verified self-reporting and defined by attending physician service and patient care location.
• Coverage and workload staffing efficiency indices were calculated to evaluate current staffing levels and identify needs.

Main Results:

• The developed methodology, utilizing coverage and workload staffing efficiency indices, identified four clinical services that would benefit from additional midlevel practitioners (MLPs).
• Analysis revealed specific areas where current MLP staffing levels were insufficient to meet patient care demands within duty-hour constraints.
• The quantitative approach provided actionable insights for targeted MLP recruitment and deployment.

Conclusions:

• A robust, quantitative methodology for determining MLP staffing needs has been established.
• This approach effectively integrates patient volume, care complexity, and the availability of residents and fellows.
• The findings support data-driven decision-making for optimizing MLP staffing to enhance patient care and operational efficiency.