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Health systems across the nation are recovering from massive financial and resource investments in electronic health record applications. In the midst of these recovery efforts, implementations of new care models, including accountable care organizations and population health initiatives, are underway. The shift from fee-for-service to fee-for-outcomes and fee-for-value payment models calls for care providers to work in new ways. It also changes how physicians are compensated and reimbursed. These changes necessitate that healthcare systems further invest in information technology solutions. Selecting which information technology (IT) projects are of most value is vital, especially in light of recent expenditures. Return-on-investment analysis is a powerful tool used in various industries to select the most appropriate IT investments. It has proven vital in selecting, justifying, and implementing software projects. Other financial metrics, such as net present value, economic value added, and total economic impact, also quantify the success of expenditures on information systems. This paper extends the concept of quantifying project value to include clinical outcomes and nonfinancial value as investment returns, applying a systematic approach to healthcare software projects. We term this inclusive approach Value of Investment. It offers a necessary extension for application in clinical settings where a strictly financial view may fall short in providing a complete picture of important benefits. This paper outlines the Value of Investment process and its attributes, and uses illustrative examples to explore the efficacy of this methodology within a midsized health system.

An environ is a within-system partition of the environment associated with each ecosystem component. The methodologies for calculating throughflow-based and storage-based environs have heretofore been considered quantitatively and qualitatively different. Below, we show, from the fundamental environ equations, that these two approaches are mathematically equivalent by proving the throughflow-storage-equivalence relationship, T E = S E . This implies that ecosystem flows of energy or matter to storage and throughflow are one and the same, differing only in storage delays (flow impedances) along the way in the storage case.

Network environ analysis is motivated by a desire to investigate ecosystems from a holistic perspective. It provides a quantitative measure of the integral (direct plus indirect) relationship between compartments and their within-system environments. In this analysis, each compartment within a system has an incoming interactive network that brings matter to it from the system's boundary inputs, and an outgoing network that takes matter from it to boundary outputs. These are, respectively, input and output environs. Methods described herein are used to compare environs from a time series of ecological networks for which the flow structure remains constant while flow quantities change. Observed differences in environs are analyzed with respect to: (a) differences between the steady-state seasonal ecosystem networks, and (b) which compartment receives the ‘analytical input’ in output-environ analysis. The Neuse River estuary is an ideal subject for this because a time series of 16 seasonal steady-state networks of nitrogen (N) storage and flow were constructed for the period spring 1985 through winter 1989 by Christian and colleagues. We explore two levels of analysis. The first is macro-level analysis of whole environs; total environ throughflow, an index of whole-environ activity, is computed and compared. The second is micro-level analysis which involves the individual intercompartmental flow and boundary output elements of output environs for two selected focal compartments, phytoplankton (x1-PN-Phyto) and nitrate/nitrite (x5-NOx). Our findings indicate that most of the observed variation in environs is being driven by differences in the seasonal networks analyzed. The macro-scale patterns observed for the environs show the same seasonal patterns evident in the whole steady-state network time series. These results support and extend the constancy of temporal indirect effects results reported by Borrett et al. [Borrett, S.R., Whipple, S.J., Patten, B.C., 2006. Indirect effects and distributed control in ecosystems: temporal variation of indirect effects in a seven-compartment model of nitrogen flow in the Neuse River estuary, USA—time series analysis. Ecol. Model. 194, 178–188]. When comparing different environs within the same season, the macro-scale patterns show that the environs are quantitatively very similar; however, when micro-scale patterns are observed, it is found that the environs are indeed unique. Macro-scale similarities are thought to be driven by high cycling indices and high network homogenization. A conclusion from the comparative analysis of the Neuse estuary networks is that these environs display a weak autonomy. We hypothesize that the individuality of the environs is suppressed by two characteristics of the Neuse estuary networks: they are strongly connected graphs, and they contain many linked autocatalytic cycles. Two aspects of our results provide a means to link ecological measures important to the Neuse River estuary and environ analysis. Environ throughflow ( T E ¯ T ) is driven by boundary inputs, which is equivalent to the ecological measure of N loading, with dominance of total T E ¯ T by DON (x4-DON), nitrate–nitrite (x5-NOx), and ammonium (x6-NH4), which receive the largest boundary inputs. Previous network analysis demonstrated that, on average, one-half of the nitrogen needs of phytoplankton are met by nitrogen that once resided in the sediments. Phytoplankton (x1-PN-Phyto) and sediment (x3-Sed), and ammonium (x6-NH4) had distinctly different T E ¯ T pattern than the steady-state TST pattern. The key ecological driver of the sediment source for N needs of phytoplankton is the release of N from sediments in the warmer months as ammonium which is taken up by phytoplankton; low T E ¯ T during winter for phytoplankton (x1-PN-Phyto), sediment (x3-Sed), and ammonium (x6-NH4) reflects the low levels of ammonium release and low growth rates; high T E ¯ T during summer corresponds to greater release of ammonium (x6-NH4) from the sediments (x3-Sed) and greater uptake of ammonium (x6-NH4) by phytoplankton (x1-PN-Phyto).

Network environ analysis (NEA), an application of ecological network analysis (ENA), is presented as a mathematical basis for developing insight into ecosystem interconnectivity for ecological engineering design. Two seven-compartment models of nitrogen flow in the Neuse River Estuary (Summer 1988 and Winter 1989) are used in developing quantitative metrics of indirect ecosystem relationships that are not observable based on empirically determined flows of nitrogen alone. Network total system throughflow was mathematically decomposed into microdynamic environ flows, which are presented as the basis for understanding and developing insight into the indirect relationships that develop in ecosystems over network pathways. Mathematical derivations also indicate that empirically determined, intercompartmental flows are constituted by the smaller scale environ flows. Results indicate that 43.5 and 45.9%, respectively, of total system throughflow for the Summer and Winter networks are generated by nitrogen flow over indirect network pathways. Implications of these results are that indirect effects from non-adjacent network relationships are indeed necessary considerations for ecosystem design and management and they can be quantified using ecological network analysis. Mathematical derivations and their significance in ecological design are presented and discussed.

Network environ analysis (NEA) was used to analyze a seven-compartment, steady-state model of nitrogen flow in the Neuse River Estuary, North Carolina, USA. Four perspectives for analyzing network properties emerge: (1) throughflow-specific oriented to output environs; (2) throughflow-specific oriented to input environs; (2) storage-specific oriented to output environs; (4) storage-specific oriented to input environs. Analysis of the model is based on a decomposition of total system throughflow, TST, into total environ throughflow, TET, for each of the seven compartments, and a further decomposition of total environ throughflow into compartmental boundary exchanges and environ flows generated by boundary inputs and outputs at individual compartments. The analysis provided a quantitative basis for the development of indirect effects between compartments and is the basis for analyzing the fate of nitrogen entering the system and the origin of nitrogen leaving the system. The decomposition of TST is also the basis for analyzing four network properties: pathway proliferation, ratio of indirect effects to direct effects, homogenization and amplification. Results indicate dominance of indirect effects within the model due to cycling. Moreover, insight about the interrelationship of all model compartments is developed quantitatively based on the NEA methodology.