Design considerations for future radionuclide aerosol monitoring systems.

Pacific Northwest National Laboratory (PNNL) staff developed the Radionuclide Aerosol Sampler Analyzer (RASA) for worldwide aerosol monitoring in the 1990s. Recently, researchers at PNNL and Creare, LLC, have investigated possibilities for how RASA could be improved, based on lessons learned from more than 15 years of continuous operation, including during the Fukushima Daiichi Nuclear Power Plant disaster. Key themes addressed in upgrade possibilities include having a modular approach to additional radionuclide measurements, optimizing the sampling/analyzing times to improve detection location capabilities, and reducing power consumption by using electrostatic collection versus classic filtration collection. These individual efforts have been made in a modular context that might constitute retrofits to the existing RASA, modular components that could improve a manual monitoring approach, or a completely new RASA. Substantial optimization of the detection and location capabilities of an aerosol network is possible and new missions could be addressed by including additional measurements.

Value Driven Outcomes (VDO): a pragmatic, modular, and extensible software framework for understanding and improving health care costs and outcomes.

OBJECTIVE: To develop expeditiously a pragmatic, modular, and extensible software framework for understanding and improving healthcare value (costs relative to outcomes). MATERIALS AND METHODS: In 2012, a multidisciplinary team was assembled by the leadership of the University of Utah Health Sciences Center and charged with rapidly developing a pragmatic and actionable analytics framework for understanding and enhancing healthcare value. Based on an analysis of relevant prior work, a value analytics framework known as Value Driven Outcomes (VDO) was developed using an agile methodology. Evaluation consisted of measurement against project objectives, including implementation timeliness, system performance, completeness, accuracy, extensibility, adoption, satisfaction, and the ability to support value improvement. RESULTS: A modular, extensible framework was developed to allocate clinical care costs to individual patient encounters. For example, labor costs in a hospital unit are allocated to patients based on the hours they spent in the unit; actual medication acquisition costs are allocated to patients based on utilization; and radiology costs are allocated based on the minutes required for study performance. Relevant process and outcome measures are also available. A visualization layer facilitates the identification of value improvement opportunities, such as high-volume, high-cost case types with high variability in costs across providers. Initial implementation was completed within 6 months, and all project objectives were fulfilled. The framework has been improved iteratively and is now a foundational tool for delivering high-value care. CONCLUSIONS: The framework described can be expeditiously implemented to provide a pragmatic, modular, and extensible approach to understanding and improving healthcare value.