http://hdl.handle.net/10027/7378 Fri, 24 May 2013 20:28:01 GMT 2013-05-24T20:28:01Z http://hdl.handle.net/10027/8210 Balancing Influence Between Actors in Healthcare Decision Making Kaplan, Robert M.; Babad, Yair M. Background: Healthcare costs in most developed countries are not clearly linked to better patient and public health outcomes, but are rather associated with service delivery orientation. In the U.S. this has resulted in large variation in healthcare availability and use, increased cost, reduced employer participation in health insurance programs, and reduced overall population health outcomes. Recent U.S. healthcare reform legislation addresses only some of these issues. Other countries face similar healthcare issues. Discussion: A major goal of healthcare is to enhance patient health outcomes. This objective is not realized in many countries because incentives and structures are currently not aligned for maximizing population health. The misalignment occurs because of the competing interests between “actors” in healthcare. In a simplified model these are individuals motivated to enhance their own health; enterprises (including a mix of nonprofit, for profit and government providers, payers, and suppliers, etc.) motivated by profit, political, organizational and other forces; and government which often acts in the conflicting roles of a healthcare payer and provider in addition to its role as the representative and protector of the people. An imbalance exists between the actors, due to the resources and information control of the enterprise and government actors relative to the individual and the public. Failure to use effective preventive interventions is perhaps the best example of the misalignment of incentives. We consider the current Pareto efficient balance between the actors in relation to the Pareto frontier, and show that a significant change in the healthcare market requires major changes in the utilities of the enterprise and government actors. Summary: A variety of actions are necessary for maximizing population health within the constraints of available resources and the current balance between the actors. These actions include improved transparency of all aspects of medical decision making, greater involvement of patients in shared medical decision making, greater oversight of guideline development and coverage decisions, limitations on direct to consumer advertising, and the need for an enhanced role of the government as the public advocate. © 2011 Kaplan and Babad; license BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/license/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The original version is available through BioMed Central at DOI: 10.1186/1472-6963-11-85. Tue, 19 Apr 2011 05:00:00 GMT http://hdl.handle.net/10027/8210 2011-04-19T05:00:00Z http://hdl.handle.net/10027/8153 Measuring Efficiency under Fixed Proportion Technologies Barnum, Darold T.; Gleason, John M. Data Envelopment Analysis (DEA) applications frequently involve nonsubstitutable inputs and nonsubstitutable outputs (that is, fixed proportion technologies). However, DEA theory requires substitutability. In this paper, we illustrate the consequences of nonsubstitutability on DEA efficiency estimates, and we develop new efficiency indicators that are similar to those of conventional DEA models except that they require nonsubstitutability. Then, using simulated and real-world datasets that encompass fixed proportion technologies, we compare DEA efficiency estimates with those of the new indicators. The examples demonstrate that DEA efficiency estimates are biased when inputs and outputs are nonsubstitutable. The degree of bias varies considerably among Decision Making Units, resulting in substantial differences in efficiency rankings between DEA and the new measures. And, over 90 percent of the units that DEA identifies as efficient are, in truth, not efficient. We conclude that when inputs and outputs are not substituted for either technological or socio-economic/legal reasons, conventional DEA models should be replaced with models that account for nonsubstitutability. Post print version of article may differ from published version. The original publication is available at springerlink.com; DOI: 10.1007/s11123-010-0194-y. Wed, 01 Jun 2011 05:00:00 GMT http://hdl.handle.net/10027/8153 2011-06-01T05:00:00Z http://hdl.handle.net/10027/7669 Measuring Hospital Efficiency with Data Envelopment Analysis: Nonsubstitutable vs. Substitutable Inputs and Outputs Barnum, Darold T.; Walton, Surrey M.; Shields, Karen L.; Schumock, Glen T. There is a conflict between Data Envelopment Analysis (DEA) theory’s requirement that inputs (outputs) be substitutable, and the ubiquitous use of nonsubstitutable inputs and outputs in DEA applications to hospitals. This paper develops efficiency indicators valid for nonsubstitutable variables. Then, using a sample of 87 community hospitals, it compares the new measures’ efficiency estimates with those of conventional DEA measures. DEA substantially overestimated the hospitals’ efficiency on the average, and reported many inefficient hospitals to be efficient. Further, it greatly overestimated the efficiency of some hospitals but only slightly overestimated the efficiency of others, thus making any comparisons among hospitals questionable. These results suggest that conventional DEA models should not be used to estimate the efficiency of hospitals unless there is empirical evidence that the inputs (outputs) are substitutable. If inputs (outputs) are not substitutes, efficiency indicators valid for nonsubstitutability should be employed, or, before applying DEA, the nonsubstitutable variables should be combined using an appropriate weighting scheme or statistical methodology. The post print version of this article may differ from the published version. The original publication is available at www.springerlink.com at DOI: 10.1007/s10916-009-9416-0. Tue, 15 Dec 2009 06:00:00 GMT http://hdl.handle.net/10027/7669 2009-12-15T06:00:00Z http://hdl.handle.net/10027/7655 Lower bounds on sample size in structural equation modeling Westland, Christopher J. Computationally intensive structural equation modeling (SEM) approaches have been in development over much of the 20th century, initiated by the seminal work of Sewall Wright. To this day, sample size requirements remain a vexing question in SEM based studies. Complexities which increase information demands in structural model estimation increase with the number of potential combinations of latent variables; while the information supplied for estimation increases with the number of measured parameters times the number of observations in the sample size – both are non-linear. This alone would imply that requisite sample size is not a linear function solely of indicator count, even though such heuristics are widely invoked in justifying SEM sample size. This paper develops two lower bounds on sample size in SEM, the first as a function of the ratio of indicator variables to latent variables, and the second as a function of minimum effect, power and significance. The algorithm is applied to a meta-study of a set of research published in five of the top MIS journals. The study shows a systematic bias towards choosing sample sizes that are significantly too small. Actual sample sizes averaged only 50% of the minimum needed to draw the conclusions the studies claimed. Overall, 80% of the research articles in the meta-study drew conclusions from insufficient samples. Lacking accurate sample size information, researchers are inclined to economize on sample collection with inadequate samples that hurt the credibility of research conclusions. Guidelines are provided for applying the algorithms developed in this study, and companion software encapsulating the paper’s formulae is made available for download. Post print version of article may differ from published version. The definitive version is available through Elsevier at DOI: 10.1016/j.elerap.2010.07.003 Mon, 01 Nov 2010 05:00:00 GMT http://hdl.handle.net/10027/7655 2010-11-01T05:00:00Z