Models of Intermediate Care Organization and Staffing at an Academic Medical Center: Considerations of an Inpatient Planning Committee.

Rationale: Geographic co-localization of patients and provider teams (geography) may improve care efficiency and quality. Patients requiring intermediate care present a unique challenge to the geographic model. Objective: Identify the best organizational and staffing model for intermediate care at our academic medical center. Methods: A modified nominal group technique was employed to assess the benefits and limitations of an existing model of intermediate care, identify and review potential alternative models, and choose a new model. Results: In addition to the institution's current model, the benefits and limitations of six alternative organizational and staffing models were characterized. The anticipated impact of each model on nurse: provider communication, maintenance of nursing competencies, nurse satisfaction, efficient utilization of technical and human resources, triage of patients to the unit, care continuity, and the impact on trainee education are described. After considering these features, stakeholders ranked a closed provider staffing model on a unit dedicated to intermediate care highest of the six alternative models. Important outcomes to monitor following transition to a closed staffing model included patient outcomes, nursing job satisfaction and retention, provider and trainee experience, unexpected patient transfers to higher or lower levels of care, and administrative costs. Conclusions: After considering six alternative staffing models for intermediate care, stakeholders ranked a closed provider staffing model highest. Further qualitative and quantitative comparisons to determine optimal models of intermediate care are needed.

The Use of Temperature and Pressure Data Loggers to Validate the Steam Sterilization of Category A Clinical Waste.

Introduction: Over the past decade, there have been outbreaks associated with high consequence infectious diseases such as Ebola virus disease, Lassa fever, and Monkeypox. The proper handling of clinical waste from patients infected with such pathogens is critical to ensure healthcare personnel and community safety. Methods: Mock clinical waste bags were created to simulate four distinct waste streams: personal protective equipment (PPE), dry linens, wet linens, and solidified liquids. Pressure and temperature data loggers were buried in the middle of simulated waste loads to record time at a sterilization temperature of 132°C (270°F) to reduce sterilization time. We also validated super rapid biological indicators (BIs) by embedding standard BIs (48 h), rapid BIs (3 h), and super rapid BIs (24 min) within each load. Cycles were validated over a 2-day period, using a total of 36 simulated waste bags (6 bags each for PPE, dry linen, and wet linen, and 18 bags for solidified liquids). Results: All waste bags achieved the target sterilization temperature, all BIs passed and cycle times were substantially decreased. For PPE waste processing, an estimated 15 h was saved for a 24-h period. Discussion: Default factory settings are inadequate to disinfect Category A clinical waste. Reliance on autoclave temperature readings may overestimate time at goal sterilization temperature for actual waste loads. Conclusions: The data provided by within bag data loggers and BIs allow for the optimization of autoclave parameters to increase throughput and enhance staff safety.