Web App to Reduce Inefficiencies in Healthcare Staffing

Technology #15583

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Researchers
Patrick J. Tighe
Timothy E. Morey M.D.
Managed By
Zahara M. Jaffer
Assistant Director 352-392-8929

Optimizes Healthcare Staffing and Lowers Hospital Operating Costs

This web application enables efficient management of healthcare provider staffing in academic hospitals. Increasing healthcare costs are a driving force behind change in the medical goods and services industry, which has estimated costs above $2.8 trillion per year in the United States. Approximately 30 percent of hospital expenses – over $700 per inpatient per day – may be wasted funds associated with poorly managed provider staffing and other inefficiencies. The ability of a hospital department to accurately assess its staffing needs, using resource-based reimbursement parameters, is likely to be especially important in efforts to control spending and limit waste. Employing an anesthesiologist staffing model, researchers at the University of Florida have developed a web app that uses relative value units (RVUs) to predict changes in provider staffing needs for user-selected mixtures of medical services. This software will help academic anesthesia departments optimize faculty utilization and may be adapted to enable efficient staffing procedures in other healthcare environments in which RVUs are routinely used.

Application

Web app that determines the most optimal staffing of healthcare providers in a variety of treatment contexts

Advantages

  • Aids in proper staffing of healthcare providers, closely linking departmental cost structure with standard fee reimbursement schedules
  • Utilizes a variety of user-selected medical encounters as input, allowing administrators to predict staffing needs for a broad range of patient care scenarios
  • Demonstrates effectiveness using a model of anesthesia departments, but can be adapted to enable efficient staffing in other healthcare environments where RVUs are used

Technology

This web application is coded to demonstrate the impact of several financial variables on the staffing of academic anesthesiology departments. The web app inputs custom mixtures of nerve block/rounding encounter types, RVUs generated per event, and payer mixture of reimbursement per RVU. The web app then demonstrates changes in RVU/reimbursement based upon increasing numbers of blocks/encounters, utilizing the user-selected information. This allows a department to highlight areas of increased institutional support needs, as well as to determine whether the department is overstaffed or understaffed at any time.