Workforce Planning for Embedded Mental Health Care in the U.S. Navy.

INTRODUCTION: Embedding mental health providers directly into operational units provides opportunities for holistic individual and population focused mental health support. To effectively provide clinical mental health care to a large number of Sailors and Marines while supporting the larger command, it is crucial to arrive at an optimal number of mental health (MH) care staff. In response to an increasing demand for MH care by operational units distributed globally, the U.S. Navy (USN) critically analyzed the current MH staffing levels, estimated future demand for MH care providers, and evaluated several staffing options. The following article illustrates a case study of workforce planning for the USN's embedded MH delivery model. MATERIALS AND METHODS: Several existing data sources were used to calculate current number of MH care staff across all USN platforms and to estimate demand for MH care. An open source Linear Programming application was used to estimate staffing solutions that meet business requirements in the most efficient manner possible. RESULTS: Results suggested different conclusions for embedded mental health staffing across USN communities. Depending on existing staffing levels and the number of Sailors or Marines anticipated to require care, the Linear Programming algorithm estimated needed staffing levels to address demand. CONCLUSION: The current project represents the first systematic workforce planning initiative designed to help staff the USN's global demand for community focused MH care. The results of this project have identified areas where additional embedded mental health resources should be made available. By systematically documenting all services and capabilities and carefully examining the operational demands of each community, the current solution was able to identify precisely what type of MH resources should be allocated to a given community.

Mahalanobis-Taguchi System to Identify Preindicators of Delirium in the ICU.

This paper was designed to determine if the Mahalanobis-Taguchi System (MTS) applied to the delirium-evidence-based bundle could detect medical patterns in retrospective datasets. The methodology defined the evidence-based bundle as a multidimensional system that conformed to a parameter diagram. The Mahalanobis distance (MD) was calculated for the retrospective healthy observations and the retrospective unhealthy observations. Signal-to-noise ratios were calculated to determine the relative strength of detection of 23 delirium preindicators. This study discovered that the sufficient variation in the CAM-ICU assessment, the standard for delirium assessment, would benefit from knowledge of how different the MD is from the healthy average. The sensitivity of the detection system was 0.89 with a 95% confidence interval of between 0.84 and 0.92. The specificity of the detection system was 0.93 with a 95% confidence interval between 0.90 and 0.95. The MTS applied to the delirium-evidence-based bundle could detect medical patterns in retrospective datasets. The implication of this paper to a biomedical research is an automated decision support tool for the delirium-evidence-based bundle providing an early detection capability needed today.