Technology and Decision Making Essay Example for Free

Technology and Decision Making Essay The quality of patient care, communication between health care staff, and the safety of patients has greatly improved since the onset of technology. Through the improvement of information technology, the ability to collect data and manage the decisions based on the data collected has enhanced in the clinical setting as well as in the business portion. Health care informatics incorporates theories from informational science, computer science, and cognitive science (Englebardt Nelson, 2002). This information helps to gather and process it in order to make an informed decision. Important information could be missed if the data is ignored. Some of the most recent technology which includes the internet and cell phones has made it possible to access information quickly in order to make the best decision for the patient in order to provide good quality care. Technology changes every day and it is important to keep up with these changes that will help support clinical decisions made by the caregivers. This paper on informatics will show the systems and information theories, the DIK model, and the role of the expert system in nursing care and medicine. System and information theories System. â€Å"A system is a set of related interacting parts enclosed in a boundary† (Englebardt Nelson, 2002, p.5). There are many types of systems which include but are not limited to: computer systems, school systems, health care systems, and people. Systems can be living or nonliving, open or closed. Closed systems do not act with the environment whereas open system have the ability to act with the environment. Open systems can be used to understand technology and those individuals associated with its use. This type of system takes input from the environment, processes it, and then returns it back to the environment as output, which serves as feedback. This theory can better help the individual understand the way people work with systems in the health care industry and allow for a visualization of the whole picture. A common term using in computer science is GIGO, â€Å"garbage in, garbage out†. This applies in the sense that a system is only as good as its user. If the user is inputting garbage, or poor quality data, the computer is likely to output the same. A system requires an accurate source in order for accurate material to be produced as a result. Open systems have three types of characteristics which include: purpose, functions, and structure (Englebardt Nelson, 2002). The purpose is the reason for the existence of the system or the program and is most often stated in the organization’s mission statement. This is true for health care organizations, churches, and schools. For example, the mission statement of the local public health department to promote health, prevent illness, and control communicable disease by providing quality services, health education, and environmental services for the community. Computer systems are often classified by their purpose and may have more than one purpose. By selecting a purpose that all individuals agree upon within the organization, a system can be chosen. It is important to take the time to identify the purpose with all those who will be using the system. Functions identify the methods in which the system will achieve its purpose. â€Å"Functions are activities that a system carries out to achieve its purpose† (Englebardt Nelson, 2002, p.6). When a computer system is chosen a list of functional specification must be put in writing to identify each function and how it will be performed. Systems are structured to allow the functions to be carried out. Some examples of structured systems include the nursing department. The nurse in charge will assign patients to the staff nurses with the purpose to provide care. The charge nurse will ensure that the team is functioning with the ability to provide the care the patient needs and deserves. Two different models can be used to visualize the structure of a system: hierarchical and web. In the hierarchical model, each computer is a part of the local area network (LAN) which in turn is part of a wide area network (WAN) that is connected to the mainframe computer system. The mainframe is the leader of the system or lead part. The web model functions much like that of a spider-web. It has the capability to pass information to many departments that may use it for different purposes. For example,  laboratory results may be sent to the pharmacy to calculate a medication dosage and patient vitals may be sent to another department for review and use. â€Å"A system includes structural elements from both the web and hierarchical model† (Englebardt Nelson, 2002, p.7). Everything living or nonliving are in a constant state of change. Six concepts are helpful in understanding the change process: 1)dynamic homeostasis, 2)entropy, 3)negentropy, 4) specialization, 5)reverberation, and 6)equifinality. Dynamic homeostasis consists of maintaining an equal balance within the system. At times, increased stress can throw off the balance and cause challenges to the organization. A health care informatics specialist’s job is to decrease the stress and restore the balance within the organization. Entropy can be best described as the tendency of the system to break down into parts. This can be the loss of some data when transmitted from one department to another. All systems, living or nonliving, reach a point where they are no longer repairable. When this point is reached, a system must be replaced. Negentropy is the opposite of entropy and is best described as the system’s ability to multiply and become more complex. As the size of the health car e industry grows, so do the health care information systems. Information technology. â€Å"Information technology has the potential to greatly streamline healthcare and greatly reduce the chance of human error. However, there is a growing literature indicating that if systems are not designed adequately they may actually increase the possibility of error in the complex interaction between clinician and machine in healthcare† (Borycki, E., Kushniruk, A., Brender, J., 2010, p. 714). The term information has more than one meaning and the term information theory refers to multiple theories. The two common theoretical theories of information theories are: Shannon and Weaver’s information-communication model and Blum’s model (Englebardt Nelson, 2002, p. 10). The information theory was presented as a formal theory in 1948 with a publication by Claude Shannon titled â€Å"A Mathematical Theory of Communication†. In this theory, the sender is the originator of the message and then the encoder converts the message into a code. A code can be a number, symbol, letters, or words. The decoder then converts the message to a format that can be recognized by the receiver. Shannon was a telephone engineer and explained this theory in a way that the decoder was the  telephone converting sound waves into a message the receiver could understand. â€Å"Warren Weaver, from the Sloan-Kettering Institute for Cancer Research, provided the interpretation for understanding the semantic meaning of a message† (Englebardt Nelson, 2002, p. 12). He used Shannon’s works to explain the interpretational aspects of communication as each individual perceives things different from the next. Different types of circumstances may occur causing a message to be interpreted wrong. For example, if a physician is using medical terminology that the patient cannot understand there is definitely a communication problem. If the patient cannot hear what is being said because the ear is not transmitting sound, then there is a different type of communication problem. The message must convey meaning and produce the intended result. Bruce L. Blum defined three types of health care computing applications called Blum’s Model. He grouped these applications in data, information, or knowledge. Data are those things such as height, weight, age, and name. Information is defined as data that has been processed. Knowledge is the relationship between data and information. Using these concepts, it is possible to identify different levels of computing and automated systems. Data, Information, and Knowledge (DIK) model Healthcare informatics can be explained using a model consisting of three parts: data, information, and knowledge (Georgiou, 2002). The three parts are demonstrated using a hierarchy pyramid. Data is the platform in the model, representing the foundation. Data is represented as facts and observations, but without supporting context, the data is irrelevant. Until the information is validated or manipulated the data is not significant, once it is manipulated, the data can provide value to the user. Information is the product of data once the data has been manipulated. The result of data and information is evidence-based knowledge. Evidence based knowledge can be used to support evidence based medicine. Some individuals feel that too much focus has been put on data, limiting the ability to practice medicine as a science. Instead, the use of data suggests that medicine is being practiced based on statistics instead of science. Yet, the same critics will use the same hierarchy of data, information and knowledge to treat a patient that develops a fever after hip surgery. The fever alone does not provide significant information but combined with information of a  recent surgery, a physician will test further for signs of infection. The end result is the knowledge of why the patient is feverish. Viewing informatics in the form of the decision-information-knowledge (DIK) model allows individuals to see the process as a whole. The data must be accurately representing what is occurring or the information will not be accurate. The statement, â€Å"dirty in, dirty out,† can be applied to the platform of the model. It is essential that clean data be entered into the system, allowing clean data and information to be produced. The product, knowledge, can then be substantiated through the evidence produced. Just as evidence is used to make clinical decisions, the DIK model is used, in conjunction with the scie ntific information, to build evidence based medicine. Health informatics involves spreading and distributing information as just one piece of the process of producing knowledge which is multifaceted (Georgiou, 2002). The role of expert system in nursing care and medicine Nurses and other health care professionals make decisions on a daily basis that affect patients’ care and treatment. Nurses and health care professionals are not expert in all areas of nursing care and medicine. Health care workers specialized in certain area or field of medicine or nursing are not always readily available to everyone. Expert systems have been developed to assist medical and health care providers with decisions about care and treatment of patient. An expert system is a knowledge-based computer program designed to â€Å"enhance the human ability to analyze, problem solve, treat, diagnose, and estimate prognosis of health-related conditions† (Englebardt Nelson, 2002, p. 114). â€Å"Nursing expert systems can improve the overall quality of care when designed for the appropriate end-user group and based on a knowledge base reflecting nursing expertise† (Courtney, Alexander, and Demiris, 2008, P. 697). Examples of expert systems include MYCIN, a system that advise physicians about antimicrobial selection for patients with meningitis or bacteremia and INTERNIST-1, a system that assist with diagnosing complex problems in general internal medicine (Shortliffe, 1986). Health care workers may not always have the knowledge base to diagnose and treat every condition or situation encountered. Expert systems are used to close the gap in knowledge providing effective, efficient, and  accurate care. The concept of expert system is driven by the desire to improve patient care, reduce cost, and disseminate expert knowledge. Expert systems are used just as x-rays and lab values are obtained to improve the human understanding of a patient’s condition. The human memory has limitations. Expert systems can be the answer to eliminating a large number of preventable medical mistakes. This system can alert health care workers about drug interactions and allergies, and provide preferable form o f treatment. Expert systems can assist in diagnostic suggestions, testing prompts, therapeutic protocols, and practice guidelines. The utilization of expert systems has an impact on the quality of care, economy, and medical education of staff. Expert systems, when used effectively can improve patient outcomes and decrease health care costs. Fewer mistakes lead to lower financial expenditures and increased profits. Improved quality of care result in improved patient satisfaction that leads to increased reimbursement from Medicare and Medicaid. Expert systems can also decrease the variation in medical practice emphasizing standardized and evidence-based practice of care. Along with expert systems, decision aids and decision support systems are used to improve patient care. The use of decision aids and decision support systems Clinical decision aids help to identify solutions to clinical situations. Decision aids can be either paper-form or electronic. The electronic decision aids can be accessed via recorded media or the Internet. Decision aids are utilized to facilitate shared decisions between the patient and interdisciplinary team taking care of them. They help the patient to think about the multiple decisions they must make in the course of their treatment regimen. An example is the Ottawa Patient Decision Aid. This decision aid helps to determine whether or not patients should seek antibiotics for bronchitis. Another example is a decision aid about whether or not someone should place his or her family in a long-term care facility for Alzheimer’s disease (Englebardt Nelson, 2002). A decision support system (DSS) is an interactive, flexible, and adaptable computer-based information system (CBIS), which was made to support decision-making as it relates to the solution of an individual problem. â€Å"A clinical decision support system (CDSS) is an automated decision support system (DSS) that  mimics human decision making and can facilitate the clinical diagnostic process, promote the use of best practices, assist with the development and adherence of guidelines, facilitate processes for improvement of care, and prevent errors† (Englebardt and Nelson, 2002, p. 116). Decision support systems utilize data and provide easy user interface that permit for the decision maker’s own insights. Four components of decision support systems are user interface, model library, model manager, and report writer. User interface makes communication between the executive and decision support system. Model library includes statistical, graphical, financial, and â€Å"what if† models. Model manager accesses available models. Report Writer generates written output (Englebardt Nelson, 2002). Four types of CDSS used in patient care decision-making are systems that use alerts to respond to clinical data, systems respond to decisions to alter care by critiquing decisions, systems suggest interventions at the request of care providers, and systems conduct retrospective quality assurance reviews. Examples of nursing-specific decision support systems are nursing diagnosis systems such as the Computer Aided Nursing Diagnosis and Intervention (CANDI) system, care planning systems such as the Urological Nursing Information System, symptom management systems such as the Cancer Pain Decision Support system, and nursing education systems such as the Creighton Online Multiple Modular Expert System (Courtney, Alexander, and Demiris, 2008). The uses of technology for patient and client management As Information Technology continues to have more presence in health care, patients, physicians, and staff are benefiting from on-demand access to information anyplace, anytime it is needed. Advances in technology provide healthcare organizations the ability to improve the quality of patient care. An ultimate goal of using technology is to improve the quality of care patients receive (Become a Meaningful User of Health IT, 2010). Technology can be found patient homes, clinics, extended care facilities, and hospitals, to name just a few. As the number of chronic diseases continues to increase technologies like telemedicine and video-conferencing can improve the quality of life of patients with chronic conditions, and reduce costs caused by these illnesses (Finkelstein Friedman, 2000). Improving quality, access, and client management is done by enhancing the  exchange of information between providers, institutions, and payers, allowing patients to receive uninterrupted continuity of care. For the people living in rural areas, the restrictions placed on services and specialists can be improved using technology (Smith, Bensink, Armfield, Stillman, Caffery, 2005). Telecommunications in the healthcare environment can provide patients and providers an opportunity to meet and even exceed expectations clients and the community have. A physician accessing a patients’ record from his home can provide treatment and develop a plan of care without sitting in his clinic to access the patients’ chart. Caregivers are no longer at the mercy of ongoing education provided at a variety of locations and cost. Learning management systems available via the Internet allow staff to review material and participate in competency testing. Tools are available through the advances in technology, which allow training by developing simulations of patients used for assessment training in virtual environments, assessing cognitive skills of providers (McGowan, 2008). As technologies in healthcare continue to improve, caregivers and patients will continue to experience changes in many areas.  Communication, teaching, and documenting will be affected, which change the way clinicians provide care and the way clients will receive it. Analysis of the effect of technology on health care and health status Prior to computers and digital equipment seen in today’s healthcare facilities, most of what was done for patients was done manually. Manual processes could be time consuming and the opportunity for human error, which could affect the quality of care a patient received, was real. In a recent report from the Institute of medical care, it was stated that humans are inherently imperfect, and error is frequent in medical car (Patton, 2001). Technologies affecting patient care and a person’s health status include improvements to imaging systems, documentation solutions, and scheduling systems. Modern medicine relies on technological systems coming together: the operating room, clinical laboratory, radiology department, and radiation oncology facility each incorporate interrelated networks of technologies (Patton, 2001). Surgeries that once required large incisions can be done through microscopic incisions resulting in shorter hospital stays. Early diagnosis and improved treatment plans have been inevitably affected by technology. Although technology allows healthcare to improve access to patient information allowing easier access that is current and up-to-date there are also disadvantages to this kind of access. Consumers and caregivers have large volumes of information, which can be accessed, not all of the information accessed will be understood or accurate. Society must be aware that not all sites will be able to monitor and ensure information being accessed is credible; it is inevitable some of the information provided and retrieved will be inaccurate. Worse yet information which are by law confidential, may also be accessed without the consent of the patient. In addition to the ability to monitor healthcare information, technology may also make it challenging for physicians to practice under complete autonomy. With the increase in the complexity of technology, physicians must agree on how components relate to one another, also known as standards (Patton, 2001). As a result, some physicians can be seen resisting the adoption of new processes, but with ongoing development of user-friendly systems, resistance can be overcome. References Become a Meaningful User of Health IT. (2010). HHN: Hospitals Health Networks, 84(12), 47. Borycki, E., Kushniruk, A., Brender, J. (2010). Theories, models and frameworks for diagnosing technology-induced error. Studies In Health Technology And Informatics, 160(Pt 1), 714-718. Finkelstein, J. J., Friedman, R. H. (2000). Potential Role of Telecommunication Technologies in the Management of Chronic Health Conditions. Disease Management Health Outcomes, 8(2), 57-63. Retrieved from EBSCOhost. Courtney, K. L., Alexander, G. L., Demiris, G. (2008). Information technology from novice to expert: implementation implications. Journal of Nursing Management, 16(6), 692-699. doi:10.1111/j.1365-2834.2007.00829.x Englebardt, S. P. Nelson, R. (2002).Health care informatics. An interdisciplinary approach. St. Louis, MO: Mosby Elsevier. Georgiou, A. (2002). Data information and knowledge: the health informatics model and its role in evidence-based medicine. Journal Of Evaluation In Clinical Practice, 8(2), 127-130. McGowan, J. J. (2008). The Pervasiveness of Telemedicine: Adoption With or Without a Research Base. JGIM: Journal of General Internal Medicine, 23(4), 505-507. doi:10.1007/s11606-008-0534-z Patton, G. (2001). The two-edged sword: how technology shapes medical practice. Physician Executive, 27(2), 42-49. Shortliffe, E. H. (1986). Medical Expert Systems- Knowledge Tool for Physicians. Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1307157/?page=2 Smith, A., Bensink, M., Armfield, N., Stillman, J., Caffery, L. (2005, October-December). Telemedicine and rural health care applications. Journal of Postgraduate Medicine, 51(4), 286.

Parental Conflict In Turtle Mo :: essays research papers fc

The Parental Conflict in Turtle Moon   Ã‚  Ã‚  Ã‚  Ã‚  For the average person, occasional inter-personal conflicts are a fact of life. Nowhere do these conflicts manifest themselves with greater tension than in the parent-adolescent relationship. Through their works, writers of fiction illuminate the sources of strain common to parent-child interactions. In the novel Turtle Moon, Alice Hoffman exemplifies this conflict in the relationship between Keith Rosen and his mother Lucy. There are several factors that contribute to this conflict and the work as a whole. The strife between Keith and his mother results from Keith’s desire to live in New York with his father, the lack of parental involvement, and the lack of communication between Keith and his mother.   Ã‚  Ã‚  Ã‚  Ã‚  The discord between Keith and his mother results from his preference to live with his father in New York. Keith has no choice in the decision and now he lives in Verity, a town he hates. This situation lies at the root of his rebellion against his mother. When he lives in New York he is never particularly well behaved, â€Å"but after eight months in Florida, he is horrid†(5). Through his rebellious actions Keith generates grief and worry in his mother Lucy. His backpack must be checked â€Å"for contraband everyday†(31), and he and his mother fight constantly. Because he is forced to live with his mother, Keith resents her. Keith is angry with Lucy because he feels as if he is trapped in Verity. â€Å"He wanted to live with his father, but who asked him?†(6). Keith deliberately disobeys Lucy and has no respect for her. He counts down the days until he can go back to New York and this ignites many arguments between them. Keith’s rebelli ous actions advance the novel’s theme of searching for identity and independence. McBane In addition to living in Verity, another source of the conflict between Keith and Lucy is her lack of parental involvement. Lucy and Keith grow more and more distant from each other because Lucy stays out of Keith’s life. In the same way Keith avoids his mother at every available opportunity. â€Å"He waits in bed until he’s sure she’s left, so he won’t have to see her and pretend to be normal or cheerful or whatever it is she wants him to be†(6). Because Lucy does not involve herself in Keith’s life she wonders what he is doing and tends to assume the worst about him.

Kyoto Protocol In Canada

The Kyoto Protocol has enormous implications on the greenhouse gas emissions scene in Canada and indeed all industrial countries. Its targets for reducing emissions has faced scepticism from both environmentalists who argue that it does not go far enough where as businesses and industry representatives complain over the enormous costs that will be endured in the process of achieving these targets.This essay gives a short description and background to the Kyoto protocol in the Canadian context. It then focuses on the benefits and advantages of the Kyoto protocol to Canada while the last section focuses on the disadvantages and potentially negative impact of the Kyoto protocol in Canada.BackgroundKyoto Protocol was signed in the Japanese city of Kyoto in the year 1997 between countries in order to decrease greenhouse emissions and counter climate change. The Protocol was signed a year later by Canada and formally ratified in late 2002 after a lengthy debate in the argument.The Liberal government in charge decided to decrease greenhouse emissions in the country by 6% below what they were in 1990. This was designed to occur over five years between 2008 and 2012.After the Conservative government came to power in early 2006, they called the Kyoto targets unrealistic as well as unachievable. In turn, the new government decided to focus on developing Canada’s own solutions to the problem, and decided to use the funds to improve the environment within Canada and not on global credits. It also decided to invest in the development of clean technologies.The Kyoto Protocol calls for these actions to be undertaken by national governments:Encourage Huge Final Emitter SystemAt the end of 2005 the government added greenhouse gases such as carbon dioxide and methane to the list of toxic substances. This was done under the umbrella of Canadian Environmental Protection Act in turn opening the doors to regulation.These regulations were published in 2006 as part of the Canada Gazette Part I and were followed by sector-specific greenhouse gas emissions targets. The deal was to decrease the total emissions by 45 mega tons in total.The Kyoto Promote Renewable Energy:This particular initiative offered the Wind Power Production Incentive as well as the Renewable Power Production Incentive. These initiatives included subsidy for producers of renewable energy of 1cent for ever Kwh of energy produced. These incentives were designed to decrease emissions by 15 mega tons in total.Promote Partnership FundDesigned to offer support to inter-government agreements, this fund offered cost sharing in order to sustain initiatives for reducing greenhouse gas emissions. Cash was directed towards aiding the province of Ontario to close coal-fired power plants which were among the worst emitters.This had the potential to offer 10% of the reductions promised as part of Canada’s Kyoto commitment of 6%. The Partnership Fund was also to offer financial support to Quebec f or executing its own climate change plan and also to help other provinces in decreasing their own emissions. These initiatives have the potential to reduce anywhere between 55 and 85 mega tons of greenhouse emissions.Promote ProgramsThis initiative has as part of it the Ener Guide program for homes and residential estates. It also promotes incentives for motorists to adopt more energy efficiency practices.As a result of the high success rate in the Ener Guide program, the government decided to channel in another $225 million in the program as part of budget in 2005 in order to increase 4 times the number of residential properties that had been retrofitted from 125,000 to half a million.One more initiative that found a lot of success was EGLIH (Ener Guide for Low Income Households) which was started in 2006. This program was designed to pay the full cost for energy efficiency upgrades to those found to qualify as low-income households. These programs are expected to result in a net d ecrease of 40 mega tons over a period of 5 years.Promote the One-Tonne ChallengeDesigned as a public education program, it called for all Canadians to reduce their annual emissions of greenhouse gases from five tons to four tons. The exception for this program is to reduce emission by a total of 5 mega tons.Promote the Climate FundThis fund was set up to establish a permanent institution that would buy emissions reduction as well as removal credits on behalf of the federal government. The Climate Fund was to buy credits from domestic as well as international sources which were recognized as well as approved under the Kyoto Protocol. This program is expected to result in a net decrease of 75 to 115 mega tons in emissions.Negative impact of Kyoto protocolThe federal government allocated a billion dollars in the year 2003 in order to phase in the Kyoto protocol and to reach the target of cutting emissions by eight percent of the total target. Compliance of the Kyoto agreement is admini stered by an institution called Environment Canada.This particular agency funded close to a hundred and fifty million dollars or roughly eighteen percent of the annual allocation of $841 million. By employing this as the standard, the cost to administer the Kyoto agreement was put at 1.18 billion dollars and this was to be funded by collecting taxes.

THE EFFECT OF FINANCIAL DISTRESS ON OPERATING CASH FLOWS - Free Essay Example

Sample details Pages: 17 Words: 5006 Downloads: 4 Date added: 2017/06/26 Category Finance Essay Type Cause and effect essay Did you like this example? This paper provides new evidence on the financial performance of Joint stock firms by emphasizing the role played by financial distress. The purpose of this paper is specify a model for early predication of financial distress that allows us to predict the specific nature of financial distress that can effect operating cash flow and which can lead the firm toward bankruptcy and to see the effect of financial distress on operating cash flows of companies listed on Karachi Stock Exchange. Financial distress is a situation when a firms assets value falls below some threshold. Don’t waste time! Our writers will create an original "THE EFFECT OF FINANCIAL DISTRESS ON OPERATING CASH FLOWS" essay for you Create order Firm starts to incur losses and it is not in a position to generate positive cash flows. A firm enters to financial distress before it goes bankrupt. We have studied 67 firms listed on Karachi Stock Exchange to see the effect on financial distress on their cash flows. Our sample includes financially distressed as well as financially health firms. We have incorporated financial data of consecutive four years (2003 to 2008) of 67 firms. In order to measure the financial distress we have used Modified Altman Z-Score as a proxy. Other independent variables, which have been used, are size of the firm, Working Capital, Working capital productivity and Operating Profit. By regressing these Five variables (Financial Distress, Working Capital, Size of Firm, Working capital productivity and Operating Profit) on Operating Cash Flows we have found that financial distress have a negative effect on corporate cash flows. However Size of Firm, Operating Profit and Working capital productivity have p ositive effect on Corporate Cash Flows. Working Capital has a negative effect on operating cash flows. We have estimated our model with the help of regression analysis. Our study is unique in a sense that there is a dearth of literature on financial distress with special reference to Pakistan. Keywords: Financial distress, Working capital, Working capital productivity, Bankruptcy, Altman Z-Score, Corporate Failure, Insolvency, Survival Analysis. Table of Contents 2 Abstract 2 Table of Contents 4 1. Introduction 5 2. Literature Review 8 3. Methodology 13 Data and Variables 13 Measurement of Variables 14 Operating Cash Flows (OCF) 14 Explanatory Variables 14 Financial Distress (FD) 14 Size of Firm (SZ) 15 Operating Profit (OP) 15 Working Capital (WC) 15 Working capital Productivity (WCP) 15 Hypotheses Testing 15 4. Empirical Framework 17 5. Results 17 Model Summary (b) 20 6. Discussion 22 Conclusion 23 Refererences 25 1. Introduction Financial Statements basically show the historical performance or record of the company at some previous point of time. By the time when financial statements are made public, changes are many economical areas such as market conditions, currency exchange rate and inflations can change the values of assets and liabilities. In this case there often exist discrepancies between book value of assets and their market values. In above case there might be companies that are healthy and many go through period of financial distress. In particular is the threat of not being able to meet debt obligations. The first Indication of financial distress is when firm does not have enough liquid assets (short-term assets) to cover (pay for) current liabilities (short-term liabilities) when this happen than firm ability to covering long-term liabilities is reduced resulting in creditors taking on more risk than the investment of loaning money to the firm is worth. When company is facing financi al distress, book value of company liabilities can become worth more than the market value of the same liabilities. If this happen, than firm is in danger of not meeting its obligations to creditors. In this case creditors may not be paid and in worst of financial distressed time, the creditors may receive nothing in interest or principal, if the firm files for bankruptcy. The importance of financial-decision making goals is to increase shareholders value and to keep them away from financial distress. The Predicting of financial distress is an early warning signal to keep investors from being loss. It has been more than 70 years, since Ramser Foster, and Fitzpatrich in 1931-1932, and 44 years, since Beaver (1966) but still they have not found the theory of financial distress ( Laclere M,2006). They were more statistical consideration then the intuitive models or fundamental causes of financial distress (Ooghe Prijcker, 2007; Balcean Ooghe, 2004). Since The Altmans model widely used among the investors, though it is not an intuitive model, once a firm is predicted having a financial distress next year, it has been treated as it has been financial distress currently (whtaker, 1999). This work aims at studying the effect of financial distress on operating cash flows of corporations. The interest in the area of financial distress has increased due to considerable number of corporate failures around the globe in recent years especially since the early 1990s. Notable failures include Global Crossing, Enron, Adelphia, Worldcom, HH Insurance, One Tel, and Ansert Airlines in 2001, and most recently FIN Corp in 2007. Financial distress is defined as a low cash flow state of a firm in which it incurs losses without being insolvent or financial distress is a term in Corporate Finance used to indicate a condition when promises to creditors of a company are broken or honored with difficulty. Financial distress is different from insolvency. Financially distressed companies have lower profitability, higher leverage, lower past excess returns and larger size compared to active companies. The failure or bankruptcy of financially distressed firms results in significant direct and indirect costs to many stakeholders; including shareholders, managers, employees, lenders and clients. For instance Shareholders lost nearly $11 billion when Enrons stock price, which hit a high of US$90 per share in mid-2000, plummeted to less than $1 by the end of November 2001. Failure of Australias second largest insurance company, HIH Insurance, in 2001 represents the 2nd largest corporate collapse in Australias history. The collapse of HIH entailed huge individual and social costs. The deficiency of the group was estimated to be $3.6 billion and $5.3 billion. The lineup of major corporate bankruptcies was capped by the mammoth filings of Conseco ($56.6 billion in liabilities), WorldCom ($ 46.0 billion), and Enron ($ 31.2 billion actually almost double this amo unt once you add in the enormous amount of off-balance liabilities making it the largest bankruptcy in the united states. Such costs may be avoided if financially distressed companies are identified well before failure. Then corrective measures can be taken to save the company from ominous bankruptcy. Much of the literary work on financial distress relates to failure prediction and survival analysis of firms. Some studies on financial distress have been made in the context of corporate risk management. Our study aims at studying the financial distress along with key performance indicators of the corporations to see how these indicators (profitability, Size of Firm, Working capital and Working capital productivity.) co-move with the financial distress. There is not sufficient literature on studying the effect of financial distress on corporate cash flows. Especially in Pakistan, the area has not been researched thoroughly. We estimate a linear model, which helps us in the measu rement of magnitude of effect of financial distress on the operating cash flows. Along with financial distress, we also measure the effect of size of firm, operating profits, working capital and working capital productivity on operating cash flows. We have included both financially distressed and financially healthy firms in our sample. Our findings provide evidence that financially distressed Pakistani firms face adverse cash flow problems. The remainder of this paper is organized as follows. Section 2 presents a review of literature in the area of financial distress. Section 3 describes Methodology and research design, i.e. data and variables used in the study. Section 4 describes the empirical framework (Model Description). Section 5 presents the results of the regression analysis. Section 6 Discussion and concludes the paper. 2. Literature Review The effect of financial distress on financial structure decisions is another conflicting point. According to the static trade-off theory, both the advantages of debt (tax shields) as well as its disadvantages (insolvency costs) have been traditionally considered in the capital structure literature. This trade-off between the benefits and costs of debt focuses on ex-ante insolvency costs, whose negative effect on leverage has been theoretically justified (Barnea et al., 1981) as well as empirically documented (Miguel Pindado, 2001). According to (Warner (1977), Altman (1984), Franks Touros (1989), Weiss (1990), Asquith, Gertner and Scharfstein (1994), Opler Titman (1994), Sharpe (1994), Denis Denis (1995), Gilson (1997) Financial distress has both direct and indirect costs. (Opler Titman (1994), (Shleifer Vishny (1992), Direct costs of distress, such as Litigation fees are relatively small. Indirect costs, such as loss of market share and inefficient asset sales are belie ved to be more important, but they are also much harder to quantify. The debate on financial distress started after the occurrence of corporate failures. Theorists and researchers emphasized on how to save a firm from being financially distressed. Opler Titman (1994) provide empirical evidence that financially distressed firms lose significant market share to their health competitors in industry downturns. Chevalier (1995) was of the view that financially distressed firm is likely to violate the debt covenants and these violations put heavy costs on the firm. Froot et al. (1993) established that financially distressed firms forego positive NPV projects. Researchers are of the view that a firm with a high leverage has an incentive to engage in hedging activities. The measurement of financial distress has also been debatable in the literary circles. Some researchers use leverage as a proxy for financial distress. Failure prediction models use firms distance to default as a prox y of the financial distress. Some models used accounting based measures of financial distress. Hill, Perry Andes(1996), Ward Foster(1997), DeYoung(2003), Nikitin(2003) and laitinen(2005) use only financial ratios as financial distress predictors; while Altman(1969), Ahrony, Jones and Swary(1980), Altman Brenner(1981), Broenstein Rose(1995) and Fama French(1995) used only market based covariance. Majority of researchers believe that financially distressed firms appear to exhibit lower profitability, lower historic excess returns and larger size than active companies. Beaver (1966) pioneered the development of model for corporate failure prediction. He found that the model can predict failed firms for at least five years before to failure. His model was based on financial ratios as single predictors of financial distress. Altman (1968) criticized the model and upheld that the model may give inconsistent and confusing classifications results for different ratios on the same fir m. Altman (1968) came up with his own model which can handle multiple financial ratios in predicting companys failure. In Altman (1968) study, five financial ratios include (1) working capital to total assets (2) retained earnings to total assets (3) earnings before interest and tax to total assets (4) market value of equity to par value to debt and (5) sales to total assets. His model found to be the best predictor of corporate bankruptcy. The model is very popular and is called Z Score model. The critics of this model say that it violates the assumption about the multivariate normal distribution of independent variables. Castagna Matolcsy (1981) pioneered the study of corporate financial distress and failure .In USA and Europeon countries, survival analysis techniques form the basis for a number of studies in financial distress research area. Cash flow is strongly related to financial distress. Henbry (1996) studied whether adding cash flow information will improve current ban k failure prediction models. Some researchers were of the view that combining market-driven variables with accounting ratios provide more accuracy to the financial distress models. Compartive studies have also been done in the area of financial distress. Rommer(2005) compared the financial distress predictors between French, Italian and Spanish firms using competing risk models. There are few research studies on financial distress in Asian context. For example, Honjo(2000) employs multiplicative hazards model for investigating business failure for new firms in Japanese manufacturing industry whereas Raj Rinastiti(2002) use Cox proportional hazards model to examine the failed banks in Asia during 1997 Asian crisis. Some of the prior corporate failure studies focus the analysis on specific industry sector. Chen and Lee (1993) focus the study on oil and gas industry. Similarly, Lee Urrutia(1996) have studied the property liability insurance industry. Researchers have establishe d that income capacity, operating efficiency and leverage are important factors in explaining corporate failure and financial distress.According to Hossari Rahman (2005), empirical investigation of corporate failure may be classified in to two categories; the studies that do not use financial data and those which use financial data which may be further classified in to those that use non ratio financial data and those that make use of financial ratios in modeling corporate collapse. The use of financial ratios to predict corporate failure has been well established since the original study of Beaver (1966). Most of the empirical research in this area has used financial ratios and have been successful in discriminating between failed and successful firms. However despite this success, financial ratio models have been criticized because of window dressing of figures on the part of the firm by use of creative accounting. Critics emphasize the use of market-based data along with fina ncial ratios. Many studies make use of market data for analyzing the financial distress of companies. Aharony, Jones and Swary (1980) find differences in the behavior of total and firm-specific variances in returns four years before formal bankruptcy is announced. Altman and Brenner (1981) suggest bankrupt firms experience deteriorating capital market returns for at least a year before to bankruptcy. Clark and Weinstein (1983) suggest that there is negative market return at least three years before to bankruptcy. Mossman et al. (1998), Shumway (2001) and Turetsky and McEwen (2001) also support that there is a relationship between market based variables and the likelihood of corporate financial distress. Company specific variables such as company age, size of the firm and squared size have also been used in the prediction of financial distress. Prior studies suggest that company age and size effect its endurance. The younger and smaller firms are more likely to fail than establ ished or bigger firms as they dont have sufficient experience in the business. Larger firms are expected to better manage and protect them from financial distress than smaller firms (Audretch Mahmood, 1995; Honjo, 2000). Small firms have a higher probability of entering financial distress because they are not resistant to the shocks they might encounter and the large firms have a high probability of entering financial distress because they might have inflexible organizations, problems with monitoring managers and employees and difficulties with providing efficient intra-firm communications. Researchers have also established that probability of financial distress is a decreasing function of firm size. Luoma Laitinen ( 1991) established that the symptoms of financial distress are observable from the deterioration of financial ratios or the effect of such ratios on corporate failure dont stay constant over time. Studies provide evidence that financial distress is not without costs . Financially distressed firms have to incur direct bankruptcy costs, higher contracting costs, the loss of tax shields and loss of valuable investment opportunities All the above studies provide us a solid base and give us idea regarding effect of financial and its components on operating cash flow. They also give us the results and conclusions of those researches already conducted on the same area for different countries and environment from different aspects. On basis of these researches this paper extends the previous research work done on financial distress. We have used modified Altman Z Score as a proxy for the financial distress. After including the financially distressed and financially healthy firms in our sample, we have seen the effect of financial distress on corporate cash flows. Prior to this work hardly any paper can be seen which studies the impact of financial distress on corporate cash flows, especially in Asian context. Our work adds to the literature in a sen se that it not only identifies the financially distressed firms but also measures the effect of financial distress on operating cash flows of the firms listed on Karachi Stock Exchange. Our work also contributes to the literature in establishing a fact that whether the model of financial distress developed by Altman is relevant in Pakistans Corporate Environment. 3. Methodology The purpose of this research is to contribute towards a very important aspect of financial management known as financial distress effects on operating cash flow with reference to Pakistan. Here we will see the relationship between financial distress effect on profitability of 64 Pakistani Joint stock firms listed on Karachi stock Exchange for a period of six years from 2003 2008. This section of the article discusses the firms and variables included in the study, the distribution patterns of data and applied statistical techniques regression analysis in investigating the relationship between financial distress and operating cash flow. Data and Variables Secondary data has been used in this study. The financial data of 67 companies listed on Karachi Stock Exchange has been compiled. The source of data is Statistics Department, State Bank of Pakistan. We have used financial data of 67 companies for four consecutive years i.e. from 2003 to 2008. We have selected 67 companies from different sectors such as Fuel and Energy, Cement, transport and communication, Engineering, Sugar, Chemical, Paper and Board and Miscellaneous sectors. Our sample consists of financially healthy as well as financially distressed companies. In this study we have operating cash flows as dependent variable and Financial Distress as independent variable. Along with financial distress we have used four other variables; firm size, operating profit working capital and working capital productivity. Measurement of Variables Operating Cash Flows (OCF) OCF has been arrived at by adding depreciation and current liabilities to the operating profit and deducting the accounts receivables there from have measured OCF. OCF is a dependent variable in this study. Explanatory Variables Financial Distress (FD), Size of Firm (SZ), Working Capital (WC), Working capital productivity (WCP) and Operating Profit are explanatory variables. Financial Distress (FD) In order to measure financial distress we have used modified Altman Z-Score model. It has been calculated as follows Altman Z Score= EBIT/Total Assets + Sales/Total Assets + 1.4*Retained Earnings/Total Assets + 1.2*Working Capital/Total Assets Where EBIT stands for earnings before income tax and interest. If Altman Z-Score is 3 or greater than 3, firm is said to be in good financial health. If Altman Z Score is greater than 2 but less than 3 firms has some risk of entering financial distress. And if firm has Altman Z Score of less than 2, it means that firm has entered financial distress and it may become bankrupt. Size of Firm (SZ) We have measured the size of firm (SZ) by taking the natural logarithm of the total sales of the firm. Operating Profit (OP) Operating profit means the profit associated with the core operations of the business. Working Capital (WC) Working Capital has been measured by deducting current liabilities from current assets. WC= Current Assets Current Liabilities Working capital Productivity (WCP) Working capital productivity is an expression of how effectively a company spends its available funds compared with sales or turnover, the working capital productivity figure helps to establish a clear relationship between its financial performance and process improvement. Higher will be the figure better would be working capital productivity. Working capital productivity = Sales à · (Current assets Current liabilities) Hypotheses Testing Since the aim of this study is to examine the relationship between financial distress and operating cash flow, the study makes a set of testable hypothesis {the Null Hypotheses H0 versus the Alternative ones H1}. Hypothesis 1 The first hypothesis of this study: H01: There is positive effect of financial distress on operating cash flow of Pakistani firms. H11: There is a negative effect of financial distress on operating cash flow of Pakistani firms. Hypothesis 2 The second hypothesis of the study is: H02: There is positive effect of operating profit on operating cash flow of Pakistani firms. H12: There is negative effect of operating profit on operating cash flow of Pakistani firms Hypothesis 3 The Third hypothesis of the study is: H03: There is positive effect of size of firms on operating cash flow of Pakistani firms. . H13: There is negative effect of size of firms on operating cash flow of Pakistani firms. Hypothesis 4 The Fourth hypothesis of the study is: H04: There is positive effect of working capital on operating cash flow of Pakistani firms. H14: There is negative effect of working capital on operating cash flow of Pakistani firms. Hypothesis 5 The Fourth hypothesis of the study is: H05: There is positive effect of working capital productivity on operating cash flow of Pakistani firms. H15: There is negative effect of working capital productivity on operating cash flow of Pakistani firms. 4. Empirical Framework Our estimated model, which shows the effect of financial distress on corporate cash flows, is as under: OCF = B B1FD + B2 SZ + B3 OP -B4 WC + B5WCP In this equation: OCF = Operating Cash Flows B= Constant Term or intercept of the equation B1= Slope of the variable financial distress (FD) FD= Financial Distress B2= Slope of the size variable SZ= Size of the firm B3= Slope of the operating profit variable OP= Operating Profit B4= Slope of the working capital WC= Working Capital B5= Slope of the working capital productivity WCP= Working capital productivity 5. Results The model shows that variable FD has a negative coefficient, which means that with the FD has a negative effect on the operating cash flows. Variable Size (SZ) has a positive coefficient which means that greater the size of the firm, the more cash flows for the firm from operations. Operating Profit (OP) has a positive coefficient, which means that OP has robust effect on Operating cash flows. Working capital has negative coefficient, which means that it is negatively related to cash flows from operations and working capital productivity (WCP) has a positive coefficient, which means Sales growing faster than the resources required to generate them is a clear sign of efficiency. B in this equation is intercept of the model or constant term. Let us see some descriptive statistics of our analysis. The table shows the mean values of OCF, FD, OP, SZ, WC and WCP. Descriptive Statistics Mean Std. Deviation N OCF 4525.2953 12646.70110 67 FD (Altman Z-Score) 1.926 1.5573 67 Firm Size 7.45587 2.162929 67 Working Capital 882.35 2587.491 67 Working Capital Productivity Operating Profit 6.75426 1348.82373 1.876545 5619.621546 67 67 Let us see the correlation matrix of the dependent and explanatory variables. The matrix shows that OCF is negatively related to FD while it is positively related to SZ, WC, and OP. It shows that FD is negatively related to OCF and OP while positively related to SZ and WC. Firm Size (SZ) is positively related to all variables. Similarly WC is negatively related to WCP and positively related to positive correlation with all other variables. Operating Profit (OP) has negative correlation with FD while positive correlations with OCF, SZ, WC and WCP .Working Capital Productivity (WCP) is negatively related to WC and positively related to all other variables. Correlations OCF FD(Altman Z-Score) Firm Size Working Capital Working Capital Productivity Operating Profit Pearson Correlation OCF 1.000 -.110 .443 .645 .387 .928 FD(Altman Z-Score) -.110 1.000 .174 .020 .225 -.044 Firm Size Working Capital Working Capital productivity Operating Profit .443 .421 .645 .928 .174 .225 .020 -.044 1.000 1.500 -.043 .309 .343 .348 1.000 .752 .174 -.100 2.50 .285 .309 1.032 .752 1.000 Sig. (1-tailed) OCF . .189 .000 .000 .000 .000 FD(Altman Z-Score) .189 . .079 .437 .000 .363 Firm Size .000 .079 . .002 .079 .005 Working Capital Working Capital Productivity .000 .000 .437 .072 .002 . .387. . .000 Operating Profit .000 .363 .005 .000 .005 . N OCF 67 67 67 67 67 67 FD(Altman Z-Score) 67 67 67 67 67 67 Firm Size 67 67 67 67 67 67 Working Capital Working Capital Productivity 67 67 67 67 67 67 67 67 67 67 67 67 Operating Profit 67 67 67 67 67 67 Variables Entered/Removed (b) Model Variables Entered Variables Removed Method 1 Operating Profit, FD(Altman Z-Score), Firm Size, Working Capital(a) Working Capital Productivity . Enter a. All requested variables entered. b. Dependent Variable: OCF Consider the Model Summary of our Estimated Regression Model. Model Summary (b) Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson 1 .954(a) .911 .905 3892.72617 2.145 a. Predictors: (Constant), Operating Profit, FD (Altman Z-Score), Firm Size, Working Capital (WC), Working Capital Productivity (WCP) b. Dependent Variable: OCF Coefficient of determination (R Square) or Model Fit is 0.911 which means that explanatory variables are capable of explaining 91% variations in the dependent variable i.e. Operating cash flows OCF. The ANOVA Table shows us the F-statistics. F-Statistics shows the overall strength of the model. F Value is 158.653 which is quite high. Hence we reject the null hypothesis that explanatory variables have positive effect on operating cash flows and we establish that Financial distress (FD) has a negative effect on operating cash flows (OCF). ANOVA shows that our model is quite good to estimate the effect of financial distress (FD), Size of the Firm, Operating Profit, Wor king Capital and Working Capital Productivity on Operating Cash Flows. ANOVA (b) Model Sum of Squares df Mean Square F Sig. 1 Regression 9616471554.991 4 2404117888.748 158.653 .000(a) Residual 939505654.596 62 15153317.010 Total 10555977209.586 66 a. Predictors: (Constant), Operating Profit, FD (Altman Z-Score), Firm Size, Working Capital, Working Capital Productivity b. Dependent Variable: OCF Consider the table which shows the t-values for our variables. The table shows that the size of the firm (SZ), operating profit and Working Capital Productivity (WCP) are statistically significant to affect the operating cash flows. If we ignore the sign FD is statistically significant to affect the corporate cash flows. Coefficients (a) Model Standardized Coefficients t Sig. Correlations Beta Zero-order Partial Part 1 (Constant) -3.051 .003 FD(Altman Z-Score) -.101 -2.605 .011 -.110 -.314 -.099 Firm Size .214 5.192 .000 .443 .550 .197 Working Capital Working Capital Productivity -.165 .245 -2.818 5.428 .006 .000 .645 .389 -.337 .500 -.107 .187 Operating Profit .982 16.916 .000 .928 .907 .641 a. Dependent Variable: OCF Coefficient Correlations (a) Model Operating Profit FD(Altman Z-Score) Firm Size Working Capital Working Capital Productivity 1 Correlations Operating Profit 1.000 .105 -.100 -.724 -.200 FD(Altman Z-Score) .105 1.000 -.187 -.046 -.185 Firm Size -.100 -.187 1.000 -.165 -.285 Working Capital Working Capital Productivity -.724 1.500 -.046 -.187 -.165 -0.45 1.000 -.058 -0.56 1.000 Co-variances Operating Profit .017 4.327 -3.154 -.027 -2.564 FD(Altman Z-Score) 4.327 98915.750 -14174.525 -4.175 -12178.252 Firm Size -3.154 -14174.525 58048.854 -11.340 58045.85 Working Capital Working Capital Productivity -.027 -3.254 -4.175 -12175.252 -11.340 4.327 .082 -.028 -4.585 .958 a. Dependent Variable: OCF Case wise Diagnostics (a) Case Number Std. Residual OCF Predicted Value Residual 56 3.892 11960.00 -3190.8577 15150.85766 62 4.706 27198.30 8880.1328 18318.16716 a. Dependent Variable: OCF Residuals Statistics (a) Minimum Maximum Mean Std. Deviation N Predicted Value -7234.8931 94892.6719 4525.2953 12070.79593 67 Residual -6178.19580 18318.16797 .00000 3772.92117 67 Std. Predicted Value -.974 7.486 .000 1.000 67 Std. Residual -1.587 4.706 .000 .969 67 a. Dependent Variable: OCF 6. Discussion Analysis on financial distress prediction model with modified Altman-Z Score results shows that our model is robust in explaining the variations in dependent variable i.e. Operating Cash Flows (OCF). Our estimated model shows that the variable Financial Distress (FD) is negatively related to corporate cash flows. However Firm Size (SZ) Operating Profit (OP) and Working Capital Productivity (WCP) are positively related to FD. In this study we found another negative relationship between Working Capital (WC) and operating cash flow. This study shows that financial distress negatively affects the operating cash flow of firm and if firm would be big in case of size than effect of financial distress on operating cash flow would not be as negative as this will be in case of small firm and positive effect of Working capital productivity and operating cash flow shows that how effectively a company spends its available funds compared with sales or turnover, the working capital productivity f igure helps to establish a clear relationship between its financial performance ,process improvement and operating cash flow. Negative effect of working capital on operating cash flow is obvious because it shows that capital not being put to work properly is being wasted, which is certainly not in investors best interests. Conclusion Our results show that our model is robust in explaining the variations in dependent variable i.e. Operating Cash Flows (OCF). We have used the financial data of 67 firms, half of which were facing financial distress. We measured the effect of Financial Distress (FD) on the Operating cash flows. Our estimated model shows that the variable Financial Distress (FD) is negatively related to corporate cash flows. However Firm Size (SZ) Operating Profit (OP) and Working Capital Productivity (WCP) are positively related to FD. The notion that large firms in Size have more probability of entering financial distress has not been substantiated by our study. Rather our study shows that the larger the size of the firm, the more the operating cash flows and company effectively spends its available funds compared with sales or turnover, the working capital productivity figure helps to establish a clear relationship between its financial performance and process improvement and therefore less chanc es of being financially distressed. Another important finding of the study is negative relationship between working capital (WC) and Operating Cash Flows (OCF). It means the more working capital we have, the less operating cash flows we have. Actually greater working capital means we have more funds tied up which have not been gainfully utilized in the business. This may be as a result of an error of estimating cash for business requirements on the part of the management. Huge working capital has its opportunity cost and that cost may be in the shape of less operating cash flows and less profitability. Our analysis strongly supports that higher operating profits result in higher operating cash flows for the firm; and this is true for small firms as well as for large firms in size. Summing up we can say that by using this model, on large data set we can obtain more generalize ability of the results. Refererences Altman E. (1968). Financial Ratios, Discriminant Analysis and the prediction of Corporate Bankruptcies. 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