Editors Prof. Amos DAVID & Prof. Charles UWADIA

Transcription

1 1 Editors Prof. Amos DAVID & Prof. Charles UWADIA

2 Decision Support System to assist Health Service Administrators using the concepts of Observatory and Competitive Intelligence BREMANG Appah Department of Compter Science African University of Science and Technology (AUST), Abuja, Nigeria. DAVID Amos University of Lorraine, Nancy, France. Abstract. The problem of inadequate funding of medical resources has triggered the need for strategic planning and decision making among health administrators. Health service administrators application of decision support system will play an important role in promoting efficiency in the health care delivery. The decision support system uses the concept of observatory both at the data source for building the data warehouse and at output visualization in a strategic process of decision support system. The competitive intelligence concept is employed to enhance the model execution for best alternative decision in the context of scarce resources. The visualization will appear on a dashboard for mangers to observe the resultant graphical displays for decision making purposes. Keywords: competitive intelligence, observatory, data mining, knowledge discovery in databases, health service, decision support system. 157

3 1. Introduction Decision support systems (DSS) are designed to support the decision making process of managers to improve their effectiveness and thereby efficiency of the organization. They are based on the premise that managerial judgment cannot be replaced by any computer based solution. However, by offering the support of data and models, it is possible to improve the decision making process even in the case of semi-structured and unstructured problems. This DSS will give flexibility to the mangers of health service to decide the input data, tool of analysis, depth of analysis and reliance on the outcome of the analysis for decision making. This system will offer interactive environment to users in order to permit manager to experiment data and models to develop optimal decision making strategy in a given situation. 1.1 Features of the Health Service Administrators (HSA) DSS The health service administrators decision support system would have the following features that make it distinct from other types of information systems: 1. It would not aim at any specific type of decisions. It will have the flexibility of use in various unexpected decision situations concerning service delivery. The concept of competitive intelligence will help in the evaluation of a larger number of alternatives because strategic decisions are major choices of action and influence a major part of the organization. 2. The user friendly interface of DSS will appear like executive dashboard for observatory different from other types of information systems. Once a manager has used a HSA Decision Support System for some time, its irregular use will not adversely affect the ease of use. 3. The report generators and graphic facilities in the Health Service Administrators DSS provide better ways of representing the information generated by use of models in DSS. These facilities 158

4 therefore add value to the information. For example, a head of a section can get information regarding patient diseases and treatment. In this case the visualization will appear on an executive dashboard for observatory and hence interpretation for intended purpose. 4. It will offer any user complete control over the system. The input method of processing and output are controlled by the end user where changes would be made to variables, or relationships among variables, and observes the resulting changes in the values of other variables. 4. Literature Review Decision support systems (DSS) is divided into five broader categories: 1) Communication-driven; 2) Data-driven; 3) Documentdriven; 4) Knowledge-driven and; 5) Model-driven decision support systems, (Velmurugan and Narayanaswamy 2008). There have been management decision support systems that assisted production and marketing managers for marketing and coordinating with other departments. The knowledge driven DSS can suggest or recommend actions to managers. DSS can be seen as person-computer system with specialized problem solving that consists of knowledge about a particular domain or understanding of problems within that domain and solving some of these problems (Power, 2006). Quite recently, information technology, and computer software in particular, have extended the scope of their activities to healthcare industry management, planning and administration. The digital communication technologies which the internet is the most visible paradigm are opening up capabilities to the actors of healthcare delivery including patients and the general public (Telemedicine, 2002). Enhancement of effective communication between patients, physicians, nurses, pharmacists and other healthcare professionals is vital for improved healthcare. The current communication mechanisms which are based on paper records and prescriptions are outmoded, inefficient, and unreliable in 159

5 any serious healthcare facility facing competition from its competitors (Telemedicine, 2002). A healthcare delivery model based on mobile technology as an information transmission tool between rural patients and centrally located providers using trained intermediaries as a local facilitators for health activists (Tiwari, 2010). A clinical decision support system for healthcare practitioners in rural areas for young infants is in existence to help support healthcare delivery (Gray and Watson, 1998). A healthcare system which collects diagnosis patterns classifies them into normal and emergencies terms and declares emergency by comparing the two data groups and suggests methods to analysis and model patterns of patients normal and emergency status (Telemedicine, 2002). Autonomous, reactive and proactive intelligent agents provide an opportunity to generate end user oriented, packaged, value added decision support or strategic planning services for healthcare professionals as managers (Zaidi, Syed Abidi and Manickam, 2002). There is another system that improves quality and delivery of healthcare services called strategic healthcare decision support services which is a synergy between knowledge management and data mining techniques (Rajalakshmi, Mohan and Babu, 2011). Literature review has revealed that another popular heuristics approach is data mining techniques which identify pattern or rules about various quality problems. DSS is therefore built based on the data that are derived by data mining techniques and is effective in reducing cost incurred by the healthcare by preventing adverse medical events and improving quality of care (Ahmad, 2000). 5. Intelligence technique in DSS Artificial Intelligence techniques (AI) have been applied to decision support system and this systems are normally called Intelligent Decision Support System, IDSS (Bidgoli, 1998). The IDSS is developed to help decision makers during different phases of decision making by integrating modeling tools and human knowledge. IDSS, as its name implies, is used to support decision making and not intended to replace the decision maker s task. In addition, IDSS works under an assumption that the decision maker is familiar with the problem to be 160

6 solved. In that case, IDSS gives full control to the user regarding information acquisition, evaluation and making the final decision. IDSS is an interactive system, flexible, adaptable, and specifically developed to support the solution of a non-structured management problem for improved decision making (Quintero et al., 2005). Most researchers agree that the purpose of IDSS is to support the solution of a nonstructured management and enable knowledge processing with communication capabilities (Qian et al., 2004; Quintero et al., 2005). IDSS can incorporate specific domain knowledge and perform some types of intelligent behaviours, such as learning and reasoning, in order to support decision-making processes (Qian et al., 2004; Viademonte and Burstein, 2006). Most of the applications are specific to problem domain in that area. For example, in business industries, IDSS is used for sales prediction (Baba and Suto, 2000), stock trading forecasting (Kuo et al., 2001), financial investment (Palmados-Reis and Zahedi, 1999). An IDSS is more cognitive rather than a technological system, the fundamental difference is that even basic characteristics of intelligence cannot be captured in mechanistic (Malhotra et al., 2003). 6. Observatory in the Process of DSS The concept of observatory is an emerging field being considered in scientific research disciplines. In all research work that needs credible data, there is always the problem of finding reliable archived data. In order to do any meaningful works require the need to identify what kind of data exist, the location, and the details of the file naming convention and access methods. In order to resolve the complexity of this phenomenon requires the assembling of data acquired through observations and correlate spatial and on-hand observations to facilitate the research. In the area of DSS in engineering sciences, the concept of an observatory is adopted just as it has been done in the field of astronomical sciences (Glenn et al., 2000). Here the observatory data archives and catalog is structured into a uniform repository of information to build DSS model for hospital administrators. For 161

7 instance a database archive is built containing observational data from past records, the present, and the future that lead to a new approach as to how DSS are modelled and utilized. The virtual observatory will in no small way help to provide a bridge that will straddle any gaps in data collection and this will be accessible to all those concerned with DSS modeling. An Observatory implemented in a DSS for hospital administrators will serve patients and experts in the field of health service so that medical records or historical data can always be analyzed for research purposes. In the field of information science, the concept of observatory could be designed and model for Decision Support Systems to serve various capacities. After the observatory for the primary data source, a well-structured data warehouse is constructed to consolidate and standardize information from different operational databases in the health service in order for the information to be used for analysis and decision-making. The observers of the DSS now apply analysis, statistics and visualization for display purposes for observation and hence decision making. The visualization appears on an executive dashboard (see figure1) for observatory and decision making where end user makes changes to variables, or relationships among variables, and observes the resulting changes in the values of other variables. The HSA DSS complex data and interactive threedimensional forms such as charts, graphs, and maps will be simplified using data visualization system like executive dashboard. Data visualization system tools help users to interactively sort, sub-divide, combine, and organize data whilst it is in its graphical form (Friendly, 2008). 162

8 Figure1:Dashboard( 7. Competitive Intelligence in the Process of DSS The concept of a DSS is indeed very important for every organization in one way or the other given that competition in resources management for quality health service delivery is of utmost urgency. Research in this area believe competitive intelligence has the following characteristics: It is an art of collecting, processing, and storing information to be made available to people at all levels of the organization to help shape its future and protect it against current competitive threat. It should be legal and ethical. It involves a transfer of knowledge from the environment to the organization within established rules. Although information is at the center of the concept of competitive intelligence, covers wider objectives than just the gathering of this information. Fuld (1995) describes intelligence basically as analyzed information and not realms of database print-outs or is not necessarily thick, densely-written reports and most certainly not spying, stealing, or bugging. 163

9 Figure 2: Intelligence Pyramid Competitive intelligence requires knowing precisely the differences between information and intelligence since its intelligence, not information, is what managers need in order to make decisions. In relation to health service administrators decision support system, competitive intelligence looks at how a hospital s scarce resource can be allotted as and when needed based on competitive demand and supply. Going up the pyramid in figure 2, it moves from quantity (huge amounts of data and information available to everyone) to quality for intelligence leading to specific decisions and actions which can ensure competitive intelligence for efficiency in the institution. 8. Techniques in Health Service Administrators DSS Application Data Warehouse The data warehousing technique is considered in this work because it is optimized for answering complex queries from direct users (decision makers) and applications. The concept of data warehousing is particularly useful for developing decision support systems. A data warehouse is typically a read-only dedicated database system created 164

10 by integrating data from multiple databases and other information sources (Gray and Watson, 2008). The data warehouse helps set the stage for Knowledge Discovery in Database (KDD) in two important ways: (1) data cleaning and (2) data access. With the data cleaning, organizations are forced to think about a unified logical view of the wide variety of data and databases they possess, they have to address the issues of mapping data to a single naming convention, uniformly representing and handling missing data, and handling noise and errors when possible. Data access give room for uniform and well-defined methods to be created for accessing the data and providing access paths to data that were historically difficult. For example, once organization in the health service have solved the problem of how to store and access their data, the natural next step is the question, what else do we do with all the data? This is where opportunities for knowledge discovery in database naturally arise. Data warehouse in this context (that is health service) will be used to store medical records of the patients and the resources that would be used in the treatment of patients who visit the hospital. Knowledge Discovery in Data Warehouse for Health Service Application Knowledge Discovery in Data warehouse (KDD) and data mining are approaches that are now receiving great attention and are being recognized as a newly emerging analysis tool (Tso and Yau, 2007). Data mining has given considerable attention to the information industry and in society as a whole recently. This is due to the wide accessibility of enormous amounts of data and the important need for turning such data into useful information and knowledge (Han and Kamber, 2006). Computer application such as DSS that interfaces with data analysis tool can help administrators to make more informed and objective decisions and help managers retrieve, summarize and analysis decision related data to make prudent and more informed decisions. The data analyses problem in this work is generally categorized as association, classification, clustering and prediction. Data mining in the 165

11 pass has used various techniques including statistics, neural network, decision tree, genetic algorithm, and visualization techniques. Data mining also has been applied in many fields such as health care, customer relationship, finance, manufacturing, marketing and many more. For example, prediction application that use data mining in health service includes estimating the probability that a patient will survive given the results of a set of diagnostic tests, predicting consumer demand for a new product as a function of advertising expenditure, and predicting time series where the input variables can be time-lagged versions of the prediction variable (U. Fayyad et all, 1996). The HSA DSS Proposed Model The suggested health service administrators DSS uses technology that is term as building blocks. This HSA DSS technological model is presented in figure 3. However the data warehouse will not be too much different of those of a database project. Specific to the data warehouse for HSA DSS is the fact that it will be built through an iterative process with the concept of observatory which will consists identification of health service business requirements in relation to the patients medical records processing, and the hospital resources distribution, development of solution in accordance with this requirements and implementation of the data warehouse architecture. The model creation or management component will handle representation of events, facts or situations. The user interface management component will be dashboard visualization for observatory when the model is executed based on competitive intelligence for decision making in the organization. 166

12 Figure 3: Schematic view of HSA DSS The proposed HSA DSS framework also has these components: 1. Knowledge discovery in data warehouse (KDD) technique will be used to develop the predictive model in order to find out the possible patterns and rules from the database system. The health service databases relating to problems like patients response to treatment prediction, the medical records database can be used to find answers. Here, the relevant data will be transformed into useful knowledge for predicting trends in the records through predictive modeling. 2. Model based system for model management is in place to store the constructed model to be used for decision making process. 3. There will be knowledge base system facts and information about data mining techniques. 9. Discussion HSA as a decision support system application would play important role to assist hospital administrators in discharging their duties. A quality healthcare delivery is a major issue troubling health service providers. This is because it consumes healthcare resources and huge sums of money if care is not taken to arrest the situation. This challenge affects patients and medical practitioners in delivering quality health 167

13 care in their various sections. The HSA DSS will use executive dashboard for visualization to be observed by the end users to allow faster decision making, identification of negative trends and better allocation of health service resources. The DSS model that has been proposed will enable hospital administrators or mangers to be supported with dashboard views of progress, analysis and forecasting future events. The dashboard would help each manager to track individual patients as well as their entire panel of administrators utilizing the dashboard feature of the DSS. This dashboard views would be observed by managers of the hospital to make prudent analysis and predictions on medical equipment acquisition and distribution based on the concept of competitive intelligence to help save lives, time, and reduce cost in the hospital. The information can therefore be utilized by both the section manager as well as the health service organization to determine the utility of patients medical records based on scarce resources. 10. Conclusion In this work, it is shown how useful the application of the concept of observatory, competitive intelligence and data mining techniques in health service administration decision support system can be of help to the management of the hospital scarce resources. Here, the visualization on the dashboard to observe the patients medical records in order to obtain interesting information in a more efficient and faster way to facilitate improved health care delivery. The health service administration DSS application would accrue benefits such as decision quality, improved communication, cost reduction, time savings and improved patient and health service efficiency and satisfaction. The system will bring necessary information for future strategic decisions in addition to enabling comparative analysis, studying trends and forecasting purposes. The data warehouse conceptual model would be established in accordance with the health service demand and the storage technology chosen will decide the way the data will be accessed. 168

14 List of references Baba, N., & Suto, H. (2000). Utilization of artificial neural networks and the TD-learning method for constructing intelligent decision support system. European Journal of Operational Research, 122(2), D.J. Power, A Brief History of Decision Support Systems, Version 4.1. Fayyad, U. M.; Piatetsky-Shapiro, G.; and Smyth, P From Data Mining to Knowledge Discovery:An Overview. In Advances in Knowledge Discovery and Data Mining, eds. U. Fayyad, G. PiatetskyShapiro, P. Smyth, and R. Uthurusamy, Menlo Park, Calif.: AAAI Press. Han, J., & Kamber, M. (2006). Data Mining: Concepts and Techniques. San Francisco: Morgan Kaufmann Publisher. I. Ahmad, Data warehousing in construction organizations, ASCE Construction Congress-6, February 2000, Orlando, FL. J. Ang, T.S.H. Teo, Management issues in data warehousing: insight from the housing and development board, Decision Support Systems 29(2000) K. Rajalakshmi, S.C. Mohan and S.D.Babu, Decision Support System in Healthcare Industry, International Journal of computer applications ( ), Volume 26- No.9, July Kuo, R. J., Chen, C. H., & Hwang, Y. C. (2001). An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network. Fuzzy Sets and Systems, 118(2), M. Senthil Velmurugan, Kogilah Narayanaswamy, Application of Decision Support System in E-Commerce, Communications of the IBIMA, Vol Malhotra, P., Burstein, F., Fisher, J., McKemmish, S., Anderson, J., & Manaszewicz, R. (2003). Brest Cancer Knowledge On-Line Portal: An Intelligent Decision Support System Perspective. Paper presented at the 14th Australasian Conference on Information System, Perth. Michael Friendly, (2008). Milestones in the history of thematic, cartography, statistical graphics and data visualization. Palma-dos-Reis, A., & Zahedi, F. M. (1999). Designing personalized intelligent financial support systems. Decision Support System, 26(1), P.G. Gray, H.J. Watson, Decision Support in the Data Warehouse, Practice-Hall, New Jersey, 1998.Priyesh Tiwari, Providing Services in Rural India:Innovative application of Mobile 169

15 Technology, Health Care and Information Review Online, 2010, 14(2), pg3-9, published online at Qian, Z., Huang, G. H., & Chan, C. W. (2004). Development of an intelligent decision support system for air pollution control at coalfired power plants. Expert System with Applications, 26(3), Quintero, A., Konare, D., & Pierre, S. (2005). Prototyping an Intelligent Decision Support System for improving urban infrastructures management. European Journal of Operational Research, 162(3), Syed Zahid Hassan Zaidi, Syed Sibte Raza Abidi and Selvakumar Manickam, Distributed Data Mining from heterogeneous healthcare data repositories: Towards an Intelligent Agent based Framework, Proceedings of the 15 th IEEE Symposium on Computer based medical systems 2002 IEEE. Telemedicine and Information Society Research Division, The e- Health development Framework in Spain, Carlos III Institute of Health, Tso, G. K. F., & Yau, K. K. W. (2007). Predicting electricity energy comsumption: A comparison of regression analysis, decision tree and nerural networks. Energy, 32, Viademonte, S., & Burstein, F. (2006). From Knowledge Discovery to computational Intelligent: A Framework for Intelligent Decision Support System. London: Springer London. 170

16 Some scientific fields that are currently receiving more attention both from scientific communities and in the general public are competitive intelligence, smart city (intelligent city), and territorial intelligence. Common to all these fields are the concepts of information, information systems, knowledge, intelligence, decisionsupport systems, ubiquities, etc. The advantages for industries (production and service industries) and governments (federal, state and local governments) cannot be overemphasized. This resurgence is due to the impact of technologies for dematerialization of objects and human activities. Since the term intelligence is central for the theme of this conference, there is need to specify its meaning that we are using for the conference. Intelligence is a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience. It is not merely book learning, a narrow academic skill, or test-taking smarts. Rather, it reflects a broader and deeper capability for comprehending our surroundings "catching on," "making sense" of things, or "figuring out" what to do. Individuals differ from one another in their ability to understand complex ideas, to adapt effectively to the environment, to learn from experience, to engage in various forms of reasoning, to overcome obstacles by taking thought. Although these individual differences can be substantial, they are never entirely consistent: a given person's intellectual performance will vary on different occasions, in different domains, as judged by different criteria. From this definition, it is obvious that intelligence in a way or the other rely on the process of observation (comprehending our surroundings) and ensuring that the observation is transformed into knowledge ("catching on," "making sense of things, or "figuring out what to do ). Editors Prof. Amos DAVID & Prof. Charles UWADIA