To broadly examine the potential health and financial benefits of health information technology (HIT), this paper compares health care with the use of IT in other industries. It estimates potential savings and costs of widespread adoption of electronic medical record (EMR) systems, models important health and safety benefits, and concludes that effective EMR implementation and networking could eventually save more than $81 billion annually--by improving health care efficiency and safety--and that HIT-enabled prevention and management of chronic disease could eventually double those savings while increasing health and other social benefits. However, this is unlikely to be realized without related changes to the health care system.

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Health information technology (HIT) could save $81-$162 billion or more annually while greatly reducing morbidity and mortality. However, gaining these benefits requires broad adoption, effective implementation, and associated changes in health care processes and structures. The policy options that could speed the adoption of HIT and the realization of these benefits include incentives to promote standard-based electronic medical record (EMR) system adoption; subsidies to develop information-exchange networks; and programs to measure, report, and reward performance. Investments in these and other identified policy options should pay for themselves while also laying the foundation for needed transformation of the U.S. health care system.

BackgroundAdvanced prosthetic knees allow for more dynamic movements and improved quality of life, but payers have recently started questioning their value. To answer this question, the differential clinical outcomes and cost of microprocessor-controlled knees (MPK) compared to non-microprocessor controlled knees (NMPK) were assessed.MethodsWe conducted a literature review of the clinical and economic impacts of prosthetic knees, convened technical expert panel meetings, and implemented a simulation model over a 10-year time period for unilateral transfemoral Medicare amputees with a Medicare Functional Classification Level of 3 and 4 using estimates from the published literature and expert input. The results are summarized as an incremental cost effectiveness ratio (ICER) from a societal perspective, i.e., the incremental cost of MPK compared to NMPK for each quality-adjusted life-year gained. All costs were adjusted to 2016 U.S. dollars and discounted using a 3% rate to the present time.ResultsThe results demonstrated that compared to NMPK over a 10-year time period: for every 100 persons, MPK results in 82 fewer major injurious falls, 62 fewer minor injurious falls, 16 fewer incidences of osteoarthritis, and 11 lives saved; on a per person per year basis, MPK reduces direct healthcare cost by $3676 and indirect cost by $909, but increases device acquisition and repair cost by $6287 and total cost by $1702; on a per person basis, MPK is associated with an incremental total cost of $10,604 and increases the number of life years by 0.11 and quality adjusted life years by 0.91. MPK has an ICER ratio of $11,606 per quality adjusted life year, and the economic benefits of MPK are robust in various sensitivity analyses.ConclusionsAdvanced prosthetics for transfemoral amputees, specifically MPKs, are associated with improved clinical benefits compared to non-MPKs. The economic benefits of MPKs are similar to or even greater than those of other medical technologies currently reimbursed by U.S. payers.Electronic supplementary materialThe online version of this article (10.1186/s12984-018-0405-8) contains supplementary material, which is available to authorized users.

Reverse convex programs generally have disconnected feasible regions. Basic solutions are defined and properties of the latter and of the convex hull of the feasible region are derived. Solution procedures are discussed and a cutting plane algorithm is developed. IntroductionA constraint h ( x ) >1 0 is called a reverse convex constraint if h is pseudo-convex. Optimization problems with several such constraints generally have disconnected feasible regions. To our knowledge, problems of this form were first studied by Rosen [13], in a control theoretic setting, and subsequently, in an engineering design setting, by Avriel and Williams [1,2]. These authors developed a procedure for finding a Kuhn-Tucker point and this procedure, in a more general setting, has been proven to converge to such a point by Meyer [12] from whom the term "reverse convex" is taken. As shown by Avriel and Williams, such constraints arise in geometric programming when some coefficients of product terms are negative, a likely occurrence in engineering design problems. Bansal and Jacobsen [4,5] and Hillestad [9] show that such problems arise when there are b u d g e t constraints which reflect economies-of-scale. It is also of interest to note that problems with 0-1 restrictions can be cast into the reverse convex form. For instance, the constraint x i = 0 or 1 can be rewritten as -x i + x~ >t 0, 0 < x i • 1. Ueing [ 16] has developed a combinatorial procedure for reverse convex problems and this procedure is expanded upon in this paper.

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This paper describes a finite procedure for locating a global minimum of a problem with linear objective constraints except for one nonlinear constraint, which is of the “reverse convex” variety; that is, the direction of the inequality is the opposite of that requited for a convex constraint. Budget constraints in which the cost functions are subject to economies of scale are typically of this form. An illustrative example of the procedure is provided.

Abstract. A constraint g ( x )>1 0 is said to be a reverse convex constraint if the function g is continuous and strictly quasi-convex. The feasible regions for linear programs with an additional reverse convex constraint are generally non-convex and disconnected. It is shown that the convex hull of the feasible region is a convex polytope and, as a result, there is an optimal solution on an edge of the polytope defined by only the linear constraints. The only possible edges which can contain such an optimal solution are characterized in relation to the best feasible vertex of the polytope defined by only the linear constraints. This characterization then provides a finite algorithm for finding a globally optimal solution.