The authors draw on their experience with the University of California, San Diego Medical Center's successful enterprise-level clinical telemedicine program to present a paradigm for other academic health centers (AHCs) that wish to develop such a program. They detail key telemedicine program elements, or "tele-ments," that they consider essential to the development of a centralized, structured telemedicine program and relevant to the development of smaller programs. These tele-ments include an overall organizational vision, a centralized telemedicine infrastructure, telemedicine-specific policies and procedures, medical record documentation, relationships between the AHC clinical hub and its remote (spoke) partners, identification of and training for specialty providers, a business plan based on service agreements and/or insurance billing, and licensure/privileging. They discuss the importance of delaying equipment purchases until a plan is in place for sustaining the telemedicine enterprise and of establishing measures to define success at the outset of program development. In addition, they detail the benefits and concerns associated with telemedicine, provide a comprehensive listing of the roles and responsibilities of providers and staff involved in all aspects of telemedicine, and share samples of their program's informed consent forms and workflow checklists. Their goal is to offer support and guidance to other AHCs entering the telemedicine arena, enabling them to replicate key elements of a successful, enterprise-wide telemedicine infrastructure.

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Prognostics and health management (PHM) systems in the photovoltaic (PV) marketplace are garnering increased attention and scrutiny as deployment soars and continues to accelerate. The foundation for any PHM system is the accurate prediction and subsequent comparison to measured data. For PV this is most frequently based on the output AC power. All PHM methods rely on in-field sensor readings to construct predictor variables to forecast expected power output-regardless of the solution method (regression, artificial neural networks, Bayesian, etc.)-and these observed quantities even in the ideal case constitute an incomplete basis set. This naturally gives rise to variations in derived coefficientsregardless of the model used to solve for them. These 'seasonal errors' incurred in this application are simply a statement that some dynamic features are ignored or not fully accounted for in the predictor-basis set. In this work, these variations are examined in the standard multiple linear regression framework in the "local" limit where the global variations are not known. In this paper, we examine the deviations between the observed and regression-expected AC power at a daily level as a function of the chosen polynomial basis.Index Terms -performance analysis, photovoltaic systems, predictive models, prognostics and health management, regression analysis.