Service Management Statistic of Vanderbilt Hospital - Statistics Project Example

Financial Management Inserts His/Her Inserts Inserts Introduction Vanderbilt Hospital has over the years grown to become one of the best hospitals in the country as well as in the world. In a recent poll that was conducted by U.S. News and World Report, the hospital was ranked as the 14th best in the United States (Vanderbilt, 2011). Due to its size, business strategy and organizational structure, the hospital has been able to attain a considerable competitive advantage over its rival in the health care industry. The Vanderbilt hospital has been selected for this project due to its position in the health care industry as well as the various issues that affect the health care industry in the country. The hospital gives us an opportunity to delve into a service industry that is plagued by several problems in the increasing competitive and unstable global economy. Problem Statement Dr. Stacey Boyd holds that the main problem in Vanderbilt Hospital has been the decline in international patients seeking healthcare in the hospital. Over the past few years, various government policies have been instituted creating several issues in the healthcare system. These policies have seen a decrease in the number of international in-coming patients to the hospital. Hospital policies that exist do not have the capability to tackle this situation and in reality, the hospital has been unable to analyze the problem so as to present a solution. In the past, many people were not overly concerned about the efficiency of hospitals as they usually do not follow traditional firm optimization behavior. Due to oppressive government mandates, changing consumer behavior and the large amount of resources that fund these institutions, there is a growing interest in assessing the efficiency of hospitals and optimizing there operations (Hollingsworth, Dawson & Maniadakis, 1999). This paper will seek to assess efficiency problems in Vanderbilt hospitals and provide recommendations based on the answers. Problem Modeling In this section, we shall utilize three models to the efficiency problems most hospitals are facing. The methods utilize include: Data Envelopment Analysis, Fishbone diagram, and House of Quality. Data Envelopment Analysis The Data Envelopment Analysis methodology defines efficiency as the ratio of the weighted sums of output of an object to its weighted sums of input (Smith, 1998). If we consider n outputs and m input, the efficiency (ho) of the given hospital will be given by: Maximize ho = Subject to j = 1, 2, …….., n Where = Quality of output r for hospital 0 Weight attached with output r, Quality of input I for hospital 0 Weight attached with input i, For the hospital under question, two input variables and three output variables we selected in this model. The input variables are international communication skills, and marketing initiatives oversees. For the input variables, the variables will be rated between 0 and 1, with 1 being very high and 0 being very low (non-existent). The output variable chosen are the number of international inpatients and outpatients, bed productivity and average turnover interval. Hospital Input    Output     Communication Skills Marketing Initiatives No. of International Patients served Bed Productivity Average Turnover interval             Vanderbilt 0.7 0.4 1320 3.06 2.07 We now introduce the formulae for relative efficiency incorporating multiple inputs and outputs. We have: This is written as The initial assumption is that this measure of efficiency requires a common set of weights to be applied across all units. This immediately raises the problem of how such an agreed common set of weights can be obtained (Tague, 2004). There can be two kinds of difficulties in obtaining a common set of weights. The first kind is the difficulties to value the inputs or outputs. For example in the department data the weights on the outputs presumably relate to the values or cost of producing the outputs but these costs or values are difficult to be measured. By applying the DEA model for the oncology department we get: Subject to: The u’s and v’s are variables of the problem and are constrained to be greater than or equal to some small positive quantity in order to avoid any input or output being totally ignored in determining the efficiency (Al-Shaeya, 2013). House of Quality The house of quality is one of the methods in Quality Function Deployment. The house of quality matrix is made up of different parts that can be broken down into a number of steps so as to assess the efficiency of different functions in an organization (Charnes & Rhodes, 1978). Customer requirements are usually the most important aspects of the House of Quality model (Shillito, 1994). The first step in the house of Quality is the voice of Customers. In Vanderbilt University Hospitals, data from customers was collected and recorded as displayed below. We then proceed to the planning matrix. This is regarded as the most important diagram that explains how an organization is viewed by its customers. When planning a given product, the company must assess how to sell that product to its customers (Senge et al. , 2008). This is done through taking feedback from the consumers, in our case, the international patients. The planning matrix will utilize a scale of 0 to 5, with 0 being the worst rating and 5 being the best. We shall compare the Vanderbilt University Hospital with the national average of the various characteristics connected to international in-patient and out-patient levels. For our customer requirement qualities, we shall utilize a grade system using the 0%- 49%, 50%-59%, 60%-69%, 70%-79%, 80%-89%, 90%-100% to equal 0, 1, 2 ,3 ,4, and 5 respectfully (Stacey, 2007). After this, we shall develop the relative weights of customer demand quality. We shall consider our quality requirements and show the relative weights of each quality. Each quality requirement shall be judged depending on its importance to consumers. We designated intercultural communication a weight of 100, thus allowing us to evaluate those of the other qualities. The next weight that was crucial was timely attendance to patients coupled with adequate description of procedures and medicine. For these two, we designated a weighted average of 65. The other qualities had low weighted averages with simplicity of payment procedures designated a weighted average of 30, acclimation to hospital was designated 10 and recognition of services also designated 10. After developing the planning matrix, we develop the relationship matrix. This matrix is meant to show the relationship between Quality requirements and quality characteristics. The relationship between the requirements and the characteristics are shown by values representing a strong relationship, moderate relationship and weak relationship which are given values corresponding to 9, 3, and 1, respectively. In developing the technical properties and targets, we determine the target value, difficulty value, importance weight and relative weight. Our first step was to set the target value for the hospital with respect to customer requirements. We wanted the 100% of the patients in the hospital to be treated by experienced staff, to be treated respectfully, to be given adequate and precise information with regards to the treatment options and to have access to consultation. After this was done, we determined the weight of importance. The weight of importance was calculated by taking the relative weights of the quality requirements and multiplying them with the values for the relationship between quality requirements and the characteristics. A relative weight of 65 for timely attendance to patients and a relationship value of 3 will yield a weight of importance equals to 195. The weight of importance is then measured against a value of 100% to give us the relative weight. Direction of improvement             Product Comparison   Characteristic (Hows) Experience of Staff Staff explain information clearly Respect to patients Staff follow correct procedures Consultation personnel available to patients Customer Imortance (p1) Present Value National Average Target Value (P2) inter-cultural communication expertise 9 1 1 0 9 5 2 1 5 Simplicity of payment procedures (international transfers) 3 9 0 0 9 3 1 0 5 Adequate description about procedures or medication 9 9 1 3 0 4 2 3 5 Acclimation to hospital 3 3 9 9 0 2 3 3 5 Timely attendance to patients 9 0 1 9 0 4 5 4 5 Recognitions of services by patients 0 9 3 1 0 3 2 3 4 Weight Factor (P1) 132 101 40 69 72 414       Weight factor % 31.88% 24.396% 9.662% 16.67% 17.39% 100%       Weight Factor (P2) 165 146 72 109 90 582       Weight factor % 28.35% 25.086% 12.37% 18.73% 15.46% 100%       Selections X X     X         The relative weight explains which of the customer characteristics will have the highest priority with regards to our problem. The highest relative weight is the characteristics that should have the highest quality in our organization during instituting changes. From our model, the selection that are of most importance is the experience of the staff, that staff explain details clearly, and consultation personnel available to staff. Fishbone Analysis A typical cause and effect diagram such as the fishbone diagram seeks to identify all possible causes for a given problem and not just the obvious ones (Fitzsimmons & Fitzsimmons, 2013). It seeks to find out the root of a given problem through systemic analysis. In developing the Fishbone Diagram for Vanderbilt hospital, we first began by collecting data using an open question approach in determining what happened, how did it happened, why it happened, and what solutions can be developed. Model Results From the models portrayed above, we have identified the various aspects in healthcare that result in low admission of international patients in Vanderbilt hospital. The Vanderbilt hospital has grown in stature and size such that it can accommodate and offer various medical services to both local and international patients. However, the hospital has been unable to maintain an adequate number of international patients each year. Using the Fishbone method, we have identified the various reasons than may cause low admission which can be confirmed with the House of Quality model which identified Lack of international translators or staff who can be able to communicate to patient not fluent in English as the main problem. The hospital should hire interpreters so as to ease communication problems. It has also been noted that the marketing of the hospital in foreign countries is low. The Hospital should thus engage in aggressive marketing campaigns in selected countries. Conclusion The modern healthcare system has progressed in suh a way that a large percentage of patients come from international countries that do not have the resources or skill to tackle a particular medical problem. The Vanderbilt University Medical Center is a leader in the provision of health services and as such should integrate mechanisms to improve the level of international in and out—patients. The project has developed model for analyzing the efficiency of the hospital with respect to this subject and provided sufficient evidence of the causes of this problem as well as possible solutions. Future Research and Discussion For future research, the trend of international patients should be carried in an universal setting, that is, investigations should be carried out with regards to people from foreign countries seeking help in American hospitals. We should consider how, American health policies affect this number and how other factors affect the number of international patients in American hospitals. References Al-Shayea, A. (2013). Measuring Hospital’s Units Efficiency: A data environment analysis approach. International Journal of Engineering and Technology, 11(6): 7-20 Charnes, W. W. & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 8(2): 429–441. Fitzsimmons, J. & Fitzsimmons, M. (2013). Service Management. New Jersey: Irwin/McGraw Hill Hollingsworth B, Dawson P.J & Maniadakis N. (1999) Efficiency measurement of healthcare: A review of non-parametric methods and applications, Health Care Management Science, 15(2): 23-39 Senge, P., Smith, B., Kruschwitz, N., Laur, J., & Schley, S. (2008). The necessary revolution: How individuals and organizations are working together to create a sustainable world. New York: Doubleday. Shillito, Larry M. (1994). Advanced QFD Linking Technology to Market and Company Needs. John Wiley & Sons, Inc. Smith P.C. (1998) Data envelopment analysis in health care: An introductory note. Centre for Health Economics: University of York. Stacey, R. (2007). Strategic management and organizational dynamics: The challenge of complexity (5th ed.). Essex, England: Pearson Education Limited. Tague, N. (2004). The Quality Toolbox, New York: ASQ Quality Press Vanderbilt University Medical Center. (2011). Factsheet. Available at [Accessed 3 April 2014] Read More