Innovation management using logical framework in hospital-based healthcare units

Innovation is part and parcel of any service in today's environment, so as to remain competitive. Quality improvement in healthcare services is a complex, multi-dimensional task. This study proposes innovation management in healthcare services using a logical framework. A problem tree and an objective tree are developed to identify and mitigate issues and concerns. A logical framework is formulated to develop a plan for implementation and monitoring strategies, potentially creating an environment for continuous quality improvement in a specific unit. We recommend logical framework as a valuable model for innovation management in healthcare services. Copyright © 2006 Inderscience Enterprises Ltd.

Application of analytic hierarchy process for measuring and comparing the global performance of intensive care units

Purpose: To develop a model for the global performance measurement of intensive care units (ICUs) and to apply that model to compare the services for quality improvement. Materials and Methods: Analytic hierarchy process, a multiple-attribute decision-making technique, is used in this study to evolve such a model. The steps consisted of identifying the critical success factors for the best performance of an ICU, identifying subfactors that influence the critical factors, comparing them pairwise, deriving their relative importance and ratings, and calculating the cumulative performance according to the attributes of a given ICU. Every step in the model was derived by group discussions, brainstorming, and consensus among intensivists. Results: The model was applied to 3 ICUs, 1 each in Barbados, Trinidad, and India in tertiary care teaching hospitals of similar setting. The cumulative performance rating of the Barbados ICU was 1.17 when compared with that of Trinidad and Indian ICU, which were 0.82 and 0.75, respectively, showing that the Trinidad and Indian ICUs performed 70% and 64% with respect to Barbados ICU. The model also enabled identifying specific areas where the ICUs did not perform well, which helped to improvise those areas. Conclusions: Analytic hierarchy process is a very useful model to measure the global performance of an ICU. © 2005 Elsevier Inc. All rights reserved.

Using DEA and simulation in guiding operating units to improved performance

The potential for the use of DEA and simulation in a mutually supporting role in guiding operating units to improved performance is presented. An analysis following a three-stage process is suggested. Stage one involves obtaining the data for the DEA analysis. This can be sourced from historical data, simulated data or a combination of the two. Stage two involves the DEA analysis that identifies benchmark operating units. In the third stage simulation can now be used in order to offer practical guidance to operating units towards improved performance. This can be achieved by the use of sensitivity analysis of the benchmark unit using a simulation model to offer direct support as to the feasibility and efficiency of any variations in operating practices to be tested. Alternatively, the simulation can be used as a mechanism to transmit the practices of the benchmark unit to weaker performing units by building a simulation model of the weaker unit to the process design of the benchmark unit. The model can then compare performance of the current and benchmark process designs. Quantifying improvement in this way provides a useful driver to any process change initiative that is required to bring the performance of weaker units up to the best in class. © 2005 Operational Research Society Ltd. All rights reserved.

A DEA model for units with negative data

In this paper we propose an alternative method for measuring efficiency of Decision making Units, which allows the presence of variables with both negative and positive values. The model is applied to data on the notional effluent processing system to compare the results with recent developed methods; Modified Slacks Based Model as suggested by Sharp et al (2007) and Range Directional Measures developed by Silva Portela et al (2004). A further example explores advantages of using the new model.

A semi-oriented radial measure for measuring the efficiency of decision making units with negative data, using DEA

Data Envelopment Analysis (DEA) is a nonparametric method for measuring the efficiency of a set of decision making units such as firms or public sector agencies, first introduced into the operational research and management science literature by Charnes, Cooper, and Rhodes (CCR) [Charnes, A., Cooper, W.W., Rhodes, E., 1978. Measuring the efficiency of decision making units. European Journal of Operational Research 2, 429–444]. The original DEA models were applicable only to technologies characterized by positive inputs/outputs. In subsequent literature there have been various approaches to enable DEA to deal with negative data. In this paper, we propose a semi-oriented radial measure, which permits the presence of variables which can take both negative and positive values. The model is applied to data on a notional effluent processing system to compare the results with those yielded by two alternative methods for dealing with negative data in DEA: The modified slacks-based model suggested by Sharp et al. [Sharp, J.A., Liu, W.B., Meng, W., 2006. A modified slacks-based measure model for data envelopment analysis with ‘natural’ negative outputs and inputs. Journal of Operational Research Society 57 (11) 1–6] and the range directional model developed by Portela et al. [Portela, M.C.A.S., Thanassoulis, E., Simpson, G., 2004. A directional distance approach to deal with negative data in DEA: An application to bank branches. Journal of Operational Research Society 55 (10) 1111–1121]. A further example explores the advantages of using the new model.

Measuring the operational performance of intensive care units using the analytic hierarchy process approach

Purpose - The purpose of this study is to develop a performance measurement model for service operations using the analytic hierarchy process approach. Design/methodology/approach - The study reviews current relevant literature on performance measurement and develops a model for performance measurement. The model is then applied to the intensive care units (ICUs) of three different hospitals in developing nations. Six focus group discussions were undertaken, involving experts from the specific area under investigation, in order to develop an understandable performance measurement model that was both quantitative and hierarchical. Findings - A combination of outcome, structure and process-based factors were used as a foundation for the model. The analyses of the links between them were used to reveal the relative importance of each and their associated sub factors. It was considered to be an effective quantitative tool by the stakeholders. Research limitations/implications - This research only applies the model to ICUs in healthcare services. Practical implications - Performance measurement is an important area within the operations management field. Although numerous models are routinely being deployed both in practice and research, there is always room for improvement. The present study proposes a hierarchical quantitative approach, which considers both subjective and objective performance criteria. Originality/value - This paper develops a hierarchical quantitative model for service performance measurement. It considers success factors with respect to outcomes, structure and processes with the involvement of the concerned stakeholders based upon the analytic hierarchy process approach. The unique model is applied to the ICUs of hospitals in order to demonstrate its effectiveness. The unique application provides a comparative international study of service performance measurement in ICUs of hospitals in three different countries. © Emerald Group Publishing Limited.

Elementary units of cortical activity? The rise and fall of the cortical column

The roots of the concept of cortical columns stretch far back into the history of neuroscience. The impulse to compartmentalise the cortex into functional units can be seen at work in the phrenology of the beginning of the nineteenth century. At the beginning of the next century Korbinian Brodmann and several others published treatises on cortical architectonics. Later, in the middle of that century, Lorente de No writes of chains of ‘reverberatory’ neurons orthogonal to the pial surface of the cortex and called them ‘elementary units of cortical activity’. This is the first hint that a columnar organisation might exist. With the advent of microelectrode recording first Vernon Mountcastle (1957) and then David Hubel and Torsten Wiesel provided evidence consistent with the idea that columns might constitute units of physiological activity. This idea was backed up in the 1970s by clever histochemical techniques and culminated in Hubel and Wiesel’s well-known ‘ice-cube’ model of the cortex and Szentogathai’s brilliant iconography. The cortical column can thus be seen as the terminus ad quem of several great lines of neuroscientific research: currents originating in phrenology and passing through cytoarchitectonics; currents originating in neurocytology and passing through Lorente de No. Famously, Huxley noted the tragedy of a beautiful hypothesis destroyed by an ugly fact. Famously, too, human visual perception is orientated toward seeing edges and demarcations when, perhaps, they are not there. Recently the concept of cortical columns has come in for the same radical criticism that undermined the architectonics of the early part of the twentieth century. Does history repeat itself? This paper reviews this history and asks the question.

Ranking efficient decision-making units in data envelopment analysis using fuzzy concept

This paper introduces a new mathematical method for improving the discrimination power of data envelopment analysis and to completely rank the efficient decision-making units (DMUs). Fuzzy concept is utilised. For this purpose, first all DMUs are evaluated with the CCR model. Thereafter, the resulted weights for each output are considered as fuzzy sets and are then converted to fuzzy numbers. The introduced model is a multi-objective linear model, endpoints of which are the highest and lowest of the weighted values. An added advantage of the model is its ability to handle the infeasibility situation sometimes faced by previously introduced models.

Selective functionalisation of Re6 cluster anionic units:from hexa-hydroxo [Re6Q8(OH)6]4− (Q = S, Se) to neutral trans-[Re6Q8L4L′2] hybrid building blocks

The reaction of [Re6Q8(OH)6]4- (Q = S, Se) with p-tertbutylpyridine (TBP) in water leads to neutral trans-[Re6Q8(TBP)4(OH)2] whose hydroxyl reactivity with carboxylic acid and TBP exchange reaction with functional pyridine have been investigated.

HbA1c - changing units

Measurement of glycated haemoglobin A (HbA) provides an indication of longer-term glycaemic control. Standardisation of this test between laboratories is difficult to achieve, and most assays are currently calibrated to the values used in the Diabetes Control and Complications Trial (DCCT-aligned). With the availability of more specific reference standards it is now proposed that HbA is expressed as mmol HbA per mol of non-glycated haemoglobin. An HbA of 7% is approximately equal to 53 mmol/mol.

Fuzzy assessment of performance of a decision making units using DEA:a non-radical approach

Data Envelopment Analysis (DEA) is recognized as a modern approach to the assessment of performance of a set of homogeneous Decision Making Units (DMUs) that use similar sources to produce similar outputs. While DEA commonly is used with precise data, recently several approaches are introduced for evaluating DMUs with uncertain data. In the existing approaches many information on uncertainties are lost. For example in the defuzzification, the a-level and fuzzy ranking approaches are not considered. In the tolerance approach the inequality or equality signs are fuzzified but the fuzzy coefficients (inputs and outputs) are not treated directly. The purpose of this paper is to develop a new model to evaluate DMUs under uncertainty using Fuzzy DEA and to include a-level to the model under fuzzy environment. An example is given to illustrate this method in details.