HUMAN FACTORS STUDY OF BUSINESS PERFORMANCE OF HOSPITAL SERVICES Buyukcekmece, Istanbul, Turkey

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1 To be presented in 4th International Symposium on Economy and Business, EB2005, September 12-17, Sunny Beach, 2005 HUMAN FACTORS STUDY OF BUSINESS PERFORMANCE OF HOSPITAL SERVICES Ali Turkyilmaz 1, Alexander Nikov 2, Selim Zaim 3, 1 Fatih University, Department of Industrial Engineering aturkyilmaz@fatih.edu.tr Buyukcekmece, Istanbul, Turkey 2 Fatih University, Department of Industrial Engineering nikov@fatih.edu.tr Buyukcekmece, Istanbul, Turkey 3 Fatih University, Department of Management szaim@fatih.edu.tr Buyukcekmece, Istanbul, Turkey ABSTRACT This study is aimed at determining the effect of managerial human factors on business performance of hospital services. For this purpose a neural networks-based model is employed. A checklist tool for evaluation of human factors from management viewpoint was applied. It consists of input checklist items like role of hospital top management, employee relations, process management and data collection; and an output checklist item measuring the business performance related to financial and non-financial hospital performance. By the checklist tool data from 70 top managers of Turkish hospitals was collected. They were processed by the neural networks-based model. The most important human factors determining the business performance of the hospitals were determined. Recommendations and measures for improving the hospital service were proposed. Keywords: Hospital service quality, business performance, neural networks 1 INTRODUCTION The role of human factors in services is widely recognized as being a critical determinant in the success and survival of an organization in today s competitive environment. Any decline in customer satisfaction due to poor service quality would be a matter of concern. Consumers are becoming increasingly aware of rising standards in service quality, prompted by competitive trends which have developed higher expectations [1]. In recent years, one of the fastest growing industries in the service sector is the healthcare industry. Here the rising level of technological developments as well as of quality standards have had an important impact on medical care, surgical techniques, drugs, equipment, and the organization and delivery of health care. Specifically, human factors have become an important issue in the healthcare sector after Based on these studies, a wide range of management issues, techniques, approaches, and systematic empirical investigation have been generated [2], [3], [4], [5]. Recently many artificial neural networks (NN) applications in health care industry are a better alternative to multivariate statistical methods, e.g. for predicting mortality rate in intensive care units, for evaluation of survival prediction accuracy in the intensive care units, and for measuring hospital performance or predicting health care costs [6], [7], [8]. There are many methods for hospital performance (HP) evaluation. Their drawback is that they do not provide recommendations for improving HP [9]. Here we propose a neural-networks-based (NNbased) approach for human factors evaluation of hospital service. By a case study this approach illustrates how could be assessed the hospital business performance by human factors and how could be done relevant improvements.

2 2 DESCRIPTION OF NN-BASED APPROACH On Figure 1 are shown the stages of neural networks-based approach. It aggregates the hospital service data to form quantitative evaluations and selects significant human factors evaluation checklist items for determining recommendations for HP improvements. Design of checklist 1 Collection of data 2 Learning of checklist weights 3 Analysis of shortages 4 Recommendations for improvements 5 Figure 1 Stages of NN-based approach Stage 1: Design of checklist The NN-based approach is using the modified tool of Saraph, Benson & Schroder [10] for identifying critical human factors in a business unit and further adapted by Raju, Lonial for use in the hospital industry [11]. The checklist consists of two parts. The input part contains 11 questions identifying the role of top management and quality policy, process management, quality data and reporting and employee relations. These questions are asked to managers of the hospital for measuring their perceptions of the service performance of the hospital. The second part of the checklist contains a single question regarding manager s overall evaluation of business performance. Each checklist item has relevant weight determined by experts.

3 Stage 2: Collection of data During this stage the managers evaluations of the single checklist items at the lowest first layer of the checklist hierarchy are determined. Stage 3: Learning of checklist weights The initial weights of checklist hierarchy are learned. Because of the strong nonlinear correlation between checklist items we chose a nonlinear evaluation model: the backpropagation (BP) algorithm. [12], [13], [14], [15]. It carries out supervised learning of neural network weights using training data as inputs and known output minimizing the mean square error. In this study, managers perception related to pre-specified human factors checklist item is paired with overall HP evaluation. During the learning process, the neural network learns the relationship between output and input checklist items. With each neuron input a weight is associated. It represents its relative importance in the set of neurons inputs. The inputs of each neuron from other neurons are aggregated. Its net value represents a weighted combination of the neuron inputs. The hierarchy is coded in a hierarchical neural network, where each neuron corresponds to a checklist item. The single checklist items correspond to the network inputs. The complex checklist items correspond to the neurons at hidden layer and to the output neuron. The net function of the neural network is used as an evaluation function. As a gradient algorithm the BP algorithm minimizes the average square error between the current output and the target value by modification of the network weights. A neural network with 11 inputs (single checklist items), four hidden neurons (complex checklist items) and an output neuron (business performance) is created. Stages 4 & 5: Analysis of shortage and recommendations for improvements The NN-based approach is aimed at supporting the shortage analysis and defining relevant improvement recommendations. For this the most important single checklist items at input layer whose improvement will lead to the most significant increase of the business performance are determined. The shortage analysis consists in sequential computation of HP indices for the checklist items of each network layer and appropriate decisions at stage 5 regarding relevant improvement recommendations. A team of experts discusses the results at the previous stage. Thus, improvement recommendations are generated (cf. Figure 2). 3 CASE STUDY Using random sampling, a total of 200 Istanbul hospitals were studied. In distributing the checklist, a key person of the respondent hospital was identified and interviewed. A total of 70 useable responses were obtained and subsequently analyzed. Each checklist item was rated on a seven-point Likert scale anchored at the verbal statement Strongly Disagree to which is associated a value of 1and the verbal statement Strongly Agree valued at 7. For extracting the dimensions underlying the construct exploratory factor analysis with varimax rotation on the human factors checklist items was performed. The factor analysis of the 30 initial single checklist items yielded four factors explaining 84% of total variance. Only eleven of the thirty items loaded on these four factors and, based on the items loading on each factor, the factors were labeled " Role of top management and quality policy (Factor 1), Process management (Factor 2), Quality data and reporting (Factor 3), Employee relations (Factor 4). The factors loading scores of these eleven items are shown on Table 1. Business performance is computed as an average score of financial and non-financial performance. Since Cronbach s alpha measures for each factor are above the traditionally acceptable value of 0.70, all of the factors were accepted as being reliable. The BP checklist item is based on executive s perception of how the organization is performing relative to the competition.

4 Figure 2 Determining of improvement/redesign recommendations

5 In the present checklist the original eight critical human factors were reduced to four. Consequently, three critical factors, namely role of quality department, training, and product and service design were excluded from the checklist. A fourth critical factor, supplier quality management, was also omitted since the Turkish Ministry of Health requires hospitals to award contracts to vendors who are the lowest bidders as long as they satisfy certain specifications. Variables Factors Extent to which top executives assume responsibility for quality performance Extent to which top management has objectives for quality performance Extent to which top management has developed and communicated a vision for quality as part of a strategic vision of the organization Amount of preventive equipment maintenance Amount of inspection, review, or checking of work Clarity of work or process instructions given to employees Availability of quality data (mortality, morbidity) Extent to which quality data are used as tools to manage quality Scope of the quality data includes clinical performance Extent to which quality awareness building among employees is on-going Extent to which employees are recognized for superior quality performance Table 1: Factor Analysis for human factors checklist items 3.1 Shortage analysis and determining of improvement recommendations The current business performance of Istanbul hospitals was analyzed according to stages 4 and 5. Relevant improvement and redesign recommendations were determined. The business performance was assessed in two stages. At the first stage complex checklist items were evaluated and ranked from most important to less important based on their weights. At the second stage considering the complex checklist items, single criterion scores were calculated. Subsequently, improvement plans were developed. In health care industry, successes of human factors applications depend on a strong leadership that must be initiated by the top management. Human factors improvement plans proposed by several gurus emphasize primarily the commitment of top management. At the first stage, according to the evaluation of the complex checklist items, the role of top management and quality policy with a score of 100 was found to be the most important human factors criterion. Top management of the hospitals determines an appropriate organization culture, vision, and quality policy. Managers of health care organizations should determine objectives, and set specific measurable goals to satisfy customer expectations and improve their organizations performance. On the other hand, the top managements must provide adequate resources to the implementation of human factors efforts. This model implies that the managers have a very important role on the business performance of the hospitals. Employee relations factor with a score of was found the second important human factors criterion related to business performance. In this model, employee relations have two variables. The first one is building quality awareness among employees; the second variable is recognition of employees for superior business performance. Hospitals must develop formal reward and recognition systems to encourage employee involvement, and support teamwork.

6 Human factors data and reporting factor with a score of was the third important criterion. There are many purposes for gathering data in quality management. Data can be collected to determine mortality and morbidity rate in hospitals to understand current processes. Moreover, data provides inspection, various test results and verification records. Data also are used to analyze the process using various types of statistical process control tools such as control charts, Pareto charts, cause and effect diagrams, check sheet, histograms, scatter diagram, and so on. These traditional quality tools are very useful in monitoring and measuring human factors progress and business performance. The process management with a score of 41.07, which includes such sub-factors as process monitoring, supervision, and preventive equipment maintenance, was found as the forth important factor related to BP in this model. As mentioned above, there is not any significant difference between employee relations, human factors data and reporting and process management. After determining important complex and related single checklist items one needs to develop an improvement plan to increase business performance. If the performance of the above mentioned single checklist items is enhanced, the overall performance of the hospital will be improved. 4 CONCLUSIONS An approach for evaluation of hospital service performance is proposed. It is based on a checklist tool using a neural networks-based evaluation approach. By this approach, the most important checklist items can be determined and recommendations for improving the hospital service performance can be generated. This is illustrated and validated by a study in 70 small- and medium-sized hospitals in Istanbul. Based on the approach, the most critical factors that affect business performance in the hospital industry in Istanbul were determined. Relevant recommendations and measures for improving the hospital service were proposed. We are aware of several limitations and hence opportunities for further research of this study. First, sample size is just sufficient and needs to be increased. Therefore, we plan to extend the data collection to several large cities in Turkey. Second, we employed subjective evaluations of the hospitals top managers only, and we believe objective performance indicators should also be employed. REFERENCES [1] Yavas, U., Shemwell, D. J. (2001) Modified importance-performance analysis: an application to hospitals, International Journal of Health Care Quality Assurance, Vol 14 No 3, pp [2] Eggli. Y., & Halfon P. (2003). A conceptual framework for hospital quality management. International Journal of Health Care Quality Assurance, 16(1), [3] Kunst, P., & Lemmink J. (2000). Quality management and business performance in hospitals: a search for success parameters. Total Quality Management, 11(8), [4] Meyer, S.M., & Collier, D. A. (2001). An empirical test of the causal relationships in the Baldrige Health Care Pilot Criteria. Journals of Operations Management, 19, [5] Yang, C.C. (2003). The establishment of a TQM system for the health care industry. The TQM Magazine, 15(2), 9. [6] Goss, E.P. & Vozikis, S.G. (2002). Improving health care organizational management through neural network learning. Health Care Management Science 5, [7] Goss, E.P. & Ramchandani, H. (1998). Survival prediction in the intensive care unit: a Comparison of neural networks and binary regression. Socio Economy and Planning Science, 32(3),

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