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Introduction
This paper is to identifies and describe various decisions in Healthcare Management and Administration. It also carefully analyzes and identifies the data management system that used to apply the representative modeling technique. Apart from this; the paper includes Gantt chart for implementing the Healthcare Administration Decision Management System. Besides, it explains how the neural networks have been used in the health care sector.
In order to select or make the best decision, it is a prerequisite that one needs to have an idea about Project Management. Project Management has become one of the unavoidable parts or vital role in making or describing various decisions. Project Management consists of different goals. While taking a decision all these should meet. Clear cut understanding regarding the project goal definition is an important issue. Secondly, defining the tasks and activities in the decision process is also very important. Resource requirement is the next consideration. This includes people, time, money and related obsessions. Identifying various risks is the next important issue while taking the decision. Once these processes are over; next comes the development and execution of the decision process.
Decision in Healthcare Administration
Decision Support System in Healthcare Administration has got a very important role in taking decisions in the Healthcare sector. In order to make prompt and excellent decisions, a well coordinated project management is required. To describe the decision in Healthcare administration, a complete observation and analysis of various scenarios is very important. Different categories for decision making in the Healthcare sector and its process and objectives and their representation techniques are explained below. It helps in identifying and describing decision in Healthcare administration.
Optimization of problems with few alternatives
Here, the problems persisting in Healthcare Administration need to be resolved from the few alternatives available. It is required to find out the best solution from the available alternatives. For achieving this, it is required to seek the help of decision trees or tables. A decision tree is a graphic tool that represents conditions and their resulting actions. (Baker, 2010, para.8). In Healthcare System this is very important as it enhances or evaluate in finding the best option for the available alternatives. This is always useful in taking complex decision. In case of decision tables, it provides a matrix with each row carrying out various possible actions need to be carried out. Decision tables and decision trees are useful when you want to address questions such as what conditions influence various decisions? or what is the order in which the decision maker will check the conditions? (Sarkissian, 2010, para.2).
Optimization via Algorithm: This has got slight variation form the above. In this case, the best solution from available alternatives is selected by analyzing the step by step procedure. The technique employed is linear and other mathematical programming models and network models. This helps in an accurate decision making. This process allows to understand the reasons, the system may have had for a specific decision that may have been made. (Farukhi, n.d., para.20).
Optimization via Analytic Formula
This method finds out the best solution in just one step by using the formula. The technique used here is the Inventory Models. With the help of these inventory models, the best ever solution for taking decision in Healthcare administration has been made.
Simulation
This is one of the methods that require experiment in order to find out the best solution form a set of available alternatives. Various simulation methods are used here. In concise simulation has been used for modeling Healthcare System for over forty years. (Decision Support Systems in health care: Towards a simulated Healthcare, 2009, p.4).
Heuristics
This is a method which is based on certain rules. Based on rules this process will find out very good solution for decision making in Healthcare Administration. The technique employed here is Heuristic Programming Expert System. Otherwise it is called as Rule Based Expert System. One such system is oncocin. It was designed to assist physicians with the treatment of cancer patients receiving chemotherapy. (Decision Support Systems, 2010, para.14). This is said to be one of the first DSS which uses customized flowchart language.
Predictive Models
This model always forecast the future for a given scenario. It makes use of forecasting models or Markov analysis. Forecasting models have either an implicit or explicit error structure, where error is defined as the difference between the model prediction and the true value. (Arsham, 2009, para.2). Many performance measures are used in the forecasting models and most useful measures employed here is mean absolute deviation and variance.
Other Models
Here, the decisions are made based on some formulas. The employed technique is financial modeling and waiting lines.
Gantt Chart for Implementing Healthcare Administration Decision Support System
Gantt chart produces schedule or timeline of different activities that need to be carried out while implementing the system. Gantt chart can serve systematic approach for implementation of the system. Gantt allows team to identify the roles and activities each of them needs to carry out during the implementation of the system. A Gantt chart is in different formats. However, basic steps of Gantt chart includes description of tasks at left hand side followed by start date, number of days need to complete and finish date of the completion of the project.
Gantt chart:
How is Neural Networks Used in the Healthcare Field?
Neural networks have been used for assisted screening of Pap (cervical) smears [31], prediction of metastases in breast cancer patients [32], breast cancer diagnosis [33]. (Sordo, Vaidya & Jain, 2008, p.16). This has been achieved by using learning algorithm and other statistical models for survivals of breast cancer. Back Propagation Neural Network which is trained with robust supervised technique has been used in order to perform image processing operations. These operations include filtering and segmentation of brain MRIs that is magnetic resonance images. In order to improve resolutions in brain topographies; Cellular Neural Networks are used, which also can improve global frequency correction for the detection of micro calcifications in mammograms. (Sordo, Vaidya & Jain, 2008, p.16). Olmez and Dokur developed a method based on Neural Networks for classification of heart sounds. This uses genetic algorithms for classifying the Electrocardiogram (ECG) beats. Neural Networks is also used in enhancing eye images for diagnosing ophthalmopathy and also for preparing various endoscopy frames. Neural Networks have a wide range of other applications which provides much usefulness.
Conclusion
The present scenario suggests that Healthcare Administration Decision Support System has become an important part in health sector. The necessity for innovative systems with high decision making support is very decisive. The paper presented an overview of different categories of models and identified various decision process steps in Healthcare Administration. It also analyzed different modeling technique. The Gantt chart clearly explains plans for implementing the Healthcare Administration Decision Support System.
References
Arsham, H. (2009). Time-critical decision making for business administration: Combination of forecasts. EOF. Web.
Baker, D.R. (2010). A decision table based methodology for analysis of complex conditional actions. Martinig & Associates. Web.
Decision Support Systems. (2010). Openclinical. Web.
Decision Support Systems in Healthcare: Towards a simulated health system. (2009). European Journal of ePractice, p.4. Web.
Farukhi, F. (n.d.). Clinical Decision Supporting Systems. 2010. Web.
Sarkissian, A. (2010). Why are decision trees & tables important? eHow. Web.
Sordo, M., Vaidya, S., & Jain, L. C. (2008). Advanced computational intelligence paradigms in healthcare 3. Springer. Web.
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