Discuss about the Case Study for Nursing Decision making on Alternative Possibilities.
Decision making is an important activity in all sectors of work life or professional life. It is a cognitive process where people choose or decide a course of action after analysis of alternative possibilities. Problem analysis is the first step in decision making where the cause of the problem is deduced from sudden changes in the organization. Decision analysis is also an important part of clinical decision making. This report will evaluate the effectiveness of decision analysis to make nursing decisions more explicit. Nursing decision analysis is dependent on certain techniques to make decision making process more explicit. It involves breaking down the process into parts to analyze the benefits of using different action, observation, and probabilities. Based on this fact, the report will critically discuss the different techniques of clinical decision analysis and how it is utilized by nurses during their clinical practice. Based on my own experience of nursing practice, I will explain how decision tree helps in evaluating appropriate nursing intervention for patients.
Decision analysis is a discipline which involves philosophy, theory, systematic methods and professional practice to address important decisions. It utilizes different procedures, methods, and tools for evaluating important aspects of the decision. Various statistical tools like decision tree analysis, multivariate analysis, and probability forecasting are applied in the workplace to solve real world problems. The primary purpose of decision analysis is to identify the best alternatives to a given situation (Ishizaka & Nemery, 2013). Decision making in nursing practice is necessary to make intricate health decisions with insight into future consequences. Decision analysis tool allows nursing leaders to apply evidence-based clinical practice when faced with a complicated situation in clinical services. It assists them in making the best decisions in a complex situation where maximum uncertainty exists. It allows nurses to take appropriate action in the absence of physicians and take care of patient’s health. This decision analysis helps them in choosing the best nursing intervention for the patient to give them relief from their condition. The quality of decision is evaluated by expected consequences and the priority of the decision maker. The decision analysis process helps decision makers like nurses and physicians to think rationally and weigh all the aspects of the problem and their interrelationship (Neumann et al., 2014).
Decision analysis is used in various private and government organization for decision making. In business, it may be applied in Airline and hotel management, new project launching, oil exploration, credit and loan management and many others. Government utilizes decision analysis in emergency management, environmental risk management, research and development programs, choosing new energy resources, etc. It is also an important aspect of medical diagnosis and treatment and litigation (Slowiński, 2012).
Its scope lays in making people better decision makers. The set of tools used in the process further helps in making the optimizing the decision making process. Decision analysis relies heavily on people’s psychology to process information and access probabilities. Tools of decision analysis are beneficial in making complicated and risky decisions. Despite the availability of tools in decision analysis, it is necessary for decision makers to distinguish between a high quality decision and a lucky outcome. This is important because major decisions are made under uncertainty. People make a high quality decision based on thorough understanding of all aspects of the problem and poor decision relying on luck is made regardless of the quality of decision (Cioffi, 2012).
The main components of all decision making model are the decisions to be made, uncertain events, consequences of decision and objectives and preference of decision makers. Graphical paradigm is one of the decision making tools used to structure problems, and it uses graphs like decision trees and influence diagrams to come to a decision. A decision tree is a support tool in decision analysis which illustrates a tree-like a graph or models of decisions along with their possible outcome, cost, and utility. It is a flow chart of structure which shows internal nodes and its branches. An internal node represents a test on an attribute, and the branches represent the outcome of each test. This visual and analytical decision support tool is an efficient method for calculating the expected value of all alternative options in the decision (Larichev & Moshkovich, 2013).
Another tool used in decision analysis is Influence Diagram. It is an acyclic directed graph in which each node represents a single variable, and the arcs represent the relationship among variables. It is a compact representation of decision problems equivalent to decision trees. The primary principles of structuring decision according to influence diagram includes starting at the value node and coming back to decision nodes, drawing arcs in the easiest probability direction, using informational arcs to specify important events in the decision and ensuring that there are no cycles in the diagram. The strength of influence diagram lies in the fact that it allows decision makers to visualize complex problems in a compact graph that highlights the assumed dependence and independence of variables. This method can be useful in early stages of modeling problems in health care. It helps in discovering what is important in the decision and what is not, thus lead to a high quality decision with better outcome in the future (Cooper, 2013).
Nurses know that their decision will have important implications for patient’s health outcomes. Experienced nurse are now increasingly cast in the role of active decision makers by policy makers and other members of health care team. For example in UK, Chief Nursing Officer has given nurses key responsibilities as part of National Health Service’s modernization agenda. It has broken down the artificial boundary between medicine and nursing. Therefore in the event of active involvement of nurse in decision making, it is important for nurse to access, appraise and evaluate their nursing judgment and decision making by use of appropriate decision analysis tools. It will require them to incorporate research evidence in their decision analysis. This paper will further explain the brief method of nursing decision analysis by use of decision tree. This information will enable nurse to apply this knowledge in their professional practice (Vaismoradi et al., 2013).
Decision analysis is essential in determining the feasibility and usefulness of a treatment procedure for patients amidst an atmosphere of uncertainty. It offers an explicit and systematic approach to decision making based on the rationality of decision. During decision analysis, health professionals like nurses incorporate many factors and analyze information to come to a structured decision on planning treatment in particular patients. Decision analysis in health care also helps nurses to maintain an ethical standard of care and implement decisions keeping patient’s optimal health in mind. Decision analysis process helps in uncovering the complexities in problem and acknowledging the choices available for solving the problem (Reuben & Tinetti, 2012). Identification of alternatives and their associated uncertainty is the valid step towards improving the relation between patients and nurses. Experienced nurse respects empirical data and patients values too to come to an integrated approach for treatment. They look after all the health report of particular patient before dealing with the patient. It enhances the autonomy of patients as they are also involved in it. Patient contributes their thought, and expectation to the doctors and nurses. The medicals staffs like nurses, physicians, etc make an informed decision based on the welfare of patients. It helps in calculating the highs and lows of a treatment procedure for particular patients. Therefore, health professional comes to a decision after reconciling both research evidence and preference of patients (Fayers & Machin, 2013).
In clinical decision analysis, healthcare team of nurses, doctors, surgeons, etc evaluates the choices and potential outcome of a procedure for a particular individual. They represent their decision in a decision tree. The decision is depicted in square nodes, and the chance of outcome is depicted in circular nodes. The line connecting the nodes illustrates the period of action. Two types of data are illustrated in decision tree-one is the probability of events according to evidence-based research and other is the expectation of the patients (Veroff et al., 2013). For example, in the case of deciding the treatment options for decreasing the risk of stroke in a patient with valvular atrial fibrillation, two choices are available. Nurses decide on giving either warfarin or aspirin to patients based on incidents of stroke and treatment side effects from different population studies. The decision analysis helps in eliciting implications of treatment in various patients with different health status. This approach of decision making process is useful in making treatment procedure tailored according to the needs of the patient (Giugliano et al., 2013).
In spite of several advantages of clinical decision analysis, several issues also exist in implementing the decision in different clinical context. One problem occurs in calculating patient utilities. The patient utility is calculated regarding preference of patients that expresses their moral, satisfaction and personal values. It is ranked by ordinal values but calculated by cardinal values. Therefore, the problem is to decide whether cardinal values will determine the strengths of preference in patients. To make future health decisions, health professionals assess different dimensions of outcomes in term of cardinal values such 0 indicating death and 1.0 indicating perfect health. Many people are not able to evaluate patient’s utilities, and an error occurs in putting a value on something which is not required in decision analysis. Nurses should also learn to calculate the utilities of patient which will provide better health outcome in the future (Pocock, 2013).
Health care team like nurses and specialized physicians also face problems in ethical considerations of decisions. Ethical values can be preserved in the decision by involving patients in decision making. But this is a complex process as it requires the transfer of responsibility from health professionals to patients. The nurse may need to make the patient understand why particular treatment is necessary for them. When patients are aware of the reasons for intervention, they can better respond to treatment and it also eliminates any nervousness about the procedure. But the ways of giving support to patients may differ, and bias may occur (Portney & Watkins, 2015). Decision making may also become difficult due to complexities in calculating utilities because of significant difference in outcome and impact of complications. It also becomes difficult when the patient is less likely to take a risk and gives more importance to the certain outcome of treatment. Many patients are afraid of certain aspects of treatment; it becomes very difficult for nurse to convince such patients.
Another drawback in decision analysis is that patient’s attitude to risk is not explored during quantification of probability. This factor is rarely inserted in the decision tree, and it is assumed that all patients have the same risk in the treatment procedure. This issue is not included in utility measurement, and so error might occur in utility calculation as patient’s risk factor is missed. Many decision makers also revealed that it is a complex process to fill decision trees with accurate data. Data is derived from a selected sample study or review of the literature, so there is a minimum chance of accuracy of information. Therefore it emphasizes the need to work on those areas where evidence is not available. Clinical practice is a complex process involving many aspects of health care services so that heterogeneity might be an obstacle in practical decision analysis. For example, in the case of planning for the intervention of diabetes control, many decision nodes can be depicted such as severity of disease, morbidities like obesity, hypertension, and ischaemic heart disease. This is an obstacle in probability estimation for nurses and doctors. Therefore decision analysis is easier in a clinical setting with limited variability and challenging in cases where a lot of variables exist.
I will explain how decision analysis work from a relevant example from my nursing practice. I have to take many decisions under conditions of uncertainty. This is because individual patient’s response to treatment differs according to their health condition. It is also very unclear whether exacerbation of disease will repeat or whether patient will adhere to treatment regimen. My nursing decision is also dependent on how patient feels about the treatment. I generally try to analyze my decision by a decision tree and decide whether particular nursing intervention will be good for the patient or not.
Here is an example of my decision analysis while dealing with one patient in my nursing practice. Once I had to care for a patient with leg ulcer using compression bandage. The patient’s ulcer had healed but as a nurse I was worried it may recur. I thought I should keep on with compression treatment on patient so that it stops the ulcer from recurring. I had to review a number of nursing interventions with different level of compression. Another difficulty I faced was that during the initial stage of treatment, the patient faced a lot of difficulty with sticking to the compression bandage regime. So, I also wanted to discuss with the patient regarding the benefits and limitation of her going through compression treatment. I wanted to get her view regarding how she felt about the treatment to come to my final decision. My priority was to get the patient involved in my decision making process because as a nurse satisfying the patient through my care was my goal. I wanted the patient to be an informed partner in nursing care rather than a passive recipient of treatment procedure (Nursingtimes.net, 2016).
Three possible choices that I will have to make through my decision analysis are:
Based on appropriate nursing intervention of patient’s problem, I went about in finding the best remedy for preventing recurrence of ulcer in patient. Decision analysis is an approach to evaluate difficult decisions. In nursing practice, it is utilized at the individual level to decide appropriate treatment for patient and in a group level, it is utilized in developing nursing guideline and recommendation to upgrade the nursing service and maintain competency in health care. Major nursing decision analyses are done by drawing a decision tree. It is the easiest way to understand and estimate decisions. I also took the help of decision tree to evaluate my decision of determining the best nursing care for patients (Vickers, 2012).
My decision analysis began with the illustration of the clinical problem in a decision tree. It is a tool which gives structure to a decision. I constructed it according to treatment effectiveness, and I also included the complications of treatment in the decision tree. The decision tree consists of essential elements like nodes, set of branches representing the outcome of decision and time flow from left to right. Each branch in the tree is linked to a node, which represents an event. There is a square node which depicts the choice that the decision maker has to make after proper interpretation of all options available for treatment. The circle node is the chance node which shows uncertain events with multiple outcomes. The triangle node is used to represent final consequences of treatment. It is also the end point of the decision tree (Johansen & O’Brien, 2015, February).
My decision tree has a decision node depicting the optimal nursing intervention for leg ulcer. It has three branches emanating from it which is the chance node. The chance node depicts my option for treatment such as no treatment, high compression bandage or moderate compression bandage. Further branches from each node depict the probability and utility value for adherence to treatment and not adhering to treatment. The ultimate outcome will be either ulcer recurs in patient or no problem occurs. Each event has uncertainty associated with it. I will do probability analysis for each chance events in my decision tree (Kuhn et al., 2014).
The next step is to assign probabilities and numerical estimate value to the decision tree. Two types of estimate exist in decision analysis-one is probability value and other is outcome values. Chance nodes are given probability values, and terminal nodes are given outcome values. The more is the accuracy of the estimates, better is the validity of decision analysis. This section is about how to come to outcome values in nursing decision analysis. The proper prediction of probabilities helps in assigning outcome values at the end of branches of the decision tree.
Utility means the degree of possibility of a health outcome and it measures the quality of life in values of 0 to 1.0. Generally in health scenarios, two possible utilities are death and good health. Death is given a utility value of 1.0 while good health is given a utility value of 1.0. Thus utility value summarizes the positive and negative aspects of health outcome. Direct measurement of utilities is an expensive and time-consuming process (Shaban, 2015). So relying on expert consensus and searching relevant literature is an alternative option that decision makers can take. To determine the accuracy of my estimates, I researched much scientific literature to get the idea about the effectiveness of each treatment options. These systematic reviews help in studying credible research which is based on high-quality trials and evidence-based studies. It helped me determine efficacy and degree of risk involved in each treatment options (Holt et al., 2016). But the problem that decision makers face is that they may not always get high quality studies with valid information. In that, they have to look for other sources to get information such as expert opinion or by conducting a survey. The values given to probability range from 0 meaning impossible to 1.0 meaning absolute certainty of events.
The number associated with each branch of chance node of my decision tree represents the degree of uncertainty in my nursing decision. I came across this value after studying experience of hundreds of patients with ulcer and who has undergone similar treatment. It has an estimate of likelihood of ulcer without treatment which is 64%. The chance of patient adhering to treatment with high compression bandage is 57% and that with moderate compression bandage is 82%. The chance of ulcer recurring after adherence to treatment was 32% in high compression treatment and 34 % in moderate compression treatment. This probability values has been shown in decimal in my decision tree. The number at the end of each branch is the utility value for each outcome. It represent the weightage of each treatment option by the patient or how they feel about it. Utility value 0 is associated with worse health outcome while utility value 100 is associated with best possible health outcome (Nursingtimes.net, 2016).
Figure 1: Example of decision tree to determine optimal nursing intervention for leg ulcer. (2016). Nursingtimes.net. Retrieved 24 July 2016, from https://www.nursingtimes.net/download?ac=1257006
I have given much preference to the utilities of patient regarding how patient feel about different outcomes. The patient preference is mostly determined by societal-level evaluations like quality adjusted life year (QALY). In my nursing decision tree, I have given occurrence of ulcer and having pain a value of 0.35. It is a calculated average value and not based on individual patient.
The next for me was to determine which treatment option has the highest weightage or utility value. Based on that only, I finalized my decision to give appropriate treatment to the patient with leg ulcer. To calculate the value of each chance outcome, I multiplied the probability value with utility value and then added them up. All the value associated with each chance ooutcome has been shown in the above diagram of my decision tree. To calculate and estimate my decisions, I decided to interpret my decisions from right to left of the decision tree. This method of coming to a decision is called rolling back the tree. Ultimately the value at each terminal node depicts the final decision which is the best possible treatment option for patients (Ashby et al., 2014). Therefore after backward calculation, I got the following value for my decision option:
Therefore, after interpreting my decision option through decision tree, I finally came to my final decision. I decided that treating the patient with moderate compression hosiery will be an optimal nursing decision. Now I was sure that the patient will benefit from this nursing intervention and she will have minimum chance of recurrence of leg ulcer.
From the above decision tree, it is seen that any decision analysis is dependent on values entered into the decision model. There is high uncertainty associated with it. To identify how uncertainty affects the final result, I also decided to do carry out a sensitivity analysis. In my nursing decision, there was great uncertainty regarding the chance of ulcer recurring without treatment. After reviewing several literature and studies on this topic, I found that probability range from 26 to 69% after one year of treatment (Dowding et al., 2012). I also felt that patient will have different value as they are the one living with the ulcer and suffering every day. So, this sensitivity analysis was essential to determine optimal option in different context. It then helps in identifying factors were change in value changes the best treatment option. In sensitivity analysis I found that high compression hosiery compliance rate is 10 to 90%. So identifies threshold value to be 77%. It meant if compliance rate is 77% then I will go for high compression treatment but if it is lower than that, then I will give moderate compression hosiery the final preference. The utility value for high compression hosiery in my decision tree was 70% which was less than 77%. So, based on that I finalized my decision to carry forward moderate compression hosiery treatment on patient. I finally came to the conclusion that for my current patient with leg ulcer, my treatment option was not affected by change in patient preference (Nursingtimes.net, 2016).
This method of nursing decision analysis has great implications in my nursing career. It helped me in clarifying any doubts and coming to a confirmed decision which will be the best for patients. Many a times it has helped me solving complex nursing problems. Decision tree is a n effective tool that aids nurse in their clinical problem. It allows the application of valuable research evidence to individual patient situations. Though till now decision analysis has been mainly used in medical practice, but in exceptional cases nurse is also utilizing it to examine the appropriate nursing intervention for patients with different health complications. It is also uses as a teaching aid to teach new nurse trainee how to take better decision in their actual nursing practice. It is suitable for those nursing decision where there is choice between alternative treatments, nurse has enough time to contemplate different choices, using research evidence and when patient views are considered (Polit & Beck, 2013). However, it is dependent on accurate probability estimates. It probability is not assigned by research evidence, and then expert opinion is taken.
The validity of nursing decision analysis may not always be credible. There is a chance of biases when the nurses do not consider high quality relevant literature for taking out information related to the nursing decision. There is also a high degree of uncertainty with each probability of events because what happens, in reality, is also unpredictable. It might be possible that patient might react differently in spite of evidence showing a different outcome. In this situation, sensitivity analyses are essential where a range of probabilities is considered in an analytical manner. It involves repeatedly folding back a decision tree (Ishizaka & Nemery, 2013). If any event is associated with a high degree of uncertainty, then decision remain uncertain until high quality evidence is available for a particular event. Therefore, a sensitivity analysis plays a vital role in minimizing gaps in nursing practice and accurately addresses any vagueness in the study. In my nursing decision also, I found very few evidence to prove effectiveness rate of leg ulcer treatment. So I had to put extra efforts by taking the opinion of expert physicians regarding best option to stop recurrence of ulcer in patient. I also realized that in the case of decisions which are sensitive to poor evidence, wide angle of research will be needed in making decisions (Eiselt & Sandblom, 2013).
This type of decision analysis is common in health care service. This approach of decision analyses was first used by physicians to decide appropriate treatment options for individual patients. Now it is widely used by nurse too to teach junior nurse how to make appropriate nursing care decisions. Health care team and policy makers now actively involve nurses in decision making. This factor made it more necessary for nurse to know the technique of decision analysis to come to a decision. It helped them in critically analyzing all the available options for treatment and then planning a treatment regimen for particular patients. It is also used by health care economist to evaluate and implement health care programs according to the needs of the community. It helps in identifying the gaps in health care infrastructures such as lack of medical staff, inadequate medical equipment, low competency, poor quality of health care, etc (Seo & Sakawa, 2012).
The example of decision tree above is a simplified example, but in reality, many more complexities may exist. The different patient may have a different response to nursing intervention. For instance in the above example, the patient may get relieve from ulcer for some time, but there was great possibility that her leg ulcer pain will recur if she leaves the treatment regimen. Many expert nurses utilize software to simulate a large data revealing what has happened in patients receiving a particular treatment. It is dependent on Markov state transition models that will allow realistic representation of real events (Vickers, 2012).
Markov model is a decision tree used for problems that have continued risk involved in it such as screening for colorectal cancer. In such cases representing events with conventional decision tree is difficult, so the Markov model is utilized in such situations. It assumes the patient to be at discrete health states and events are a transition from one form of health to another. State-transition modeling is a flexible approach to computer-based analytical modeling. It includes Markov Model cohort simulation and individual-based simulation. State transition model is one of the most popular techniques for clinical decision analysis, evaluation of health service and assessment of medical treatment and technology. It is utilized in different population and disease studies and applied in making appropriate public health care strategies. It is a valuable tool for risk assessment, intervention screening, treatment methods and procedures and disease management programs (Solomon et al., 2014).
State transition model is utilized by nurses to compare and evaluate the various clinical responses. In the case of primary prevention strategies, it is used to determine the risk of developing a particular disease in particular areas. It investigates the risk factors before the development of disease by conducting a study of individual living in that area. This model is also useful in evaluating screening strategies for newborns or for preventing HIV or cancer in patients. It covers many aspects of screening such as type and routine of test used the interval of screening, diagnostic plan and the age of patients at the start of screening. It is also a useful method for identifying diagnostic strategies for individuals with a chronic disease or those with early signs and symptoms of the disease. In the case of a person who already suffers from the disease, this process reveals the natural history of illness, effect of possible treatment and expected prognosis routine in the absence of intervention (Siebert et al., 2012).
This section will describe a method used by nurse to estimate the cost-effectiveness of nursing intervention that reduces hospital readmission among old patient. They utilized cost-effectiveness model to estimate the health benefit of any intervention planned. The model was dependent on data collected from randomized controlled trial in an Australian hospital. First the nurse tried to identify the risk associated with acute medical admission for old patients. They found that patient above 65 years of age had risk of multiple co morbidities and impaired function due to readmission. Some also were at risk due to poor social support and history of depression. The intervention was related to comprehensive nursing assessment which suggested individually tailored program of exercise strategy for old patients. It was also planned to give necessary care to older patient in their home. Therefore nurse will visit the patient’s home and patient will follow-up via telephonic conversation. Major diagnosis will commence in hospital but nursing care will continue after discharge till 24 weeks (Graves, 2010).
The purpose of this decision was to improve the cost of outcomes in patient. It would mean decreasing cost of nursing intervention and improving health benefit in such patients. They measured health benefit according to Quality Adjusted Life Year (QALY). They compared this intervention with existing nursing practice to determine the efficacy of the intervention. They describe the progress of all patients using Markov State Transition Model. The advantage of this model is that it helps in quantifying a decision problem that has risk over time. The timing of event is important in this case and it also considers the chance of events getting repeated which was not considered in decision tree analysis. It is also important to know that when time moves on, patient may consider staying at home or moving to a community care facility or home nursing service for treatment (Solomon et al., 2014).
When they studied change in total cost and quality adjusted life years for group of older patients over 24 weeks, they found that cost and quality adjusted life year remarkably improved in all patients. The mean net health cost benefit for all individual with the new intervention compared to usual care routine increased for patient within the period of 24 weeks. Therefore considering the benefit to patient, they found that adopting this nursing intervention will be highly beneficial for elderly hospitalized patients (Jo et al., 2015).
Effective decision analysis is difficult because uncertainty exists regarding what will happen if decision makers do not follow the standard course of action. This problem is also faced in nursing practice. However, the process can be improved by following the process honestly. Nurses can question themselves regarding accuracy of their decision by questions like-
Whether all standard strategies were followed or not?
Whether all important outcomes of events were covered?
Was appropriate model applied for decision analysis?
Whether values were assigned to outcome in a systematic manner?
Whether the impact of probability values was evaluated efficiently or not?
Conclusion
From the detailed report on clinical decision analysis, it can be concluded that quality of decision analysis depends on the effort of nurses. The report gave information about decision analysis process and types of a tool utilized for decision analysis. It explained with the example of a patient with leg ulcer that how nurses comes to a decision for appropriate treatment based on decision making tree. It described the complete structure of a decision tree and how a nurse can calculate utility value to come to a decision. It explained the drawbacks in the decision tree and also suggested how to improve it by use of Markov state transition model. This was explained with a relevant example. Finally, it also revealed the ways by which nurses and other health staff can reduce limitation in decision analysis.
Reference
Ashby, R. L., Gabe, R., Ali, S., Saramago, P., Chuang, L. H., Adderley, U., … & Kang’ombe, A. R. (2014). Cost-effectiveness analyses methods.
Cioffi, J. (2012). Expanding the scope of decision-making research for nursing and midwifery practice. International journal of nursing studies, 49(4), 481-489.
Cooper, G. F. (2013). A method for using belief networks as influence diagrams. arXiv preprint arXiv:1304.2346.
Decision tree. Reproduction of clinical reality observe | Open-i. (2016). Openi.nlm.nih.gov. Retrieved 20 July 2016, from https://openi.nlm.nih.gov/detailedresult.php?img=PMC2626578_1478-7547-6-21-1&req=4
Dowding, D. W., Turley, M., & Garrido, T. (2012). The impact of an electronic health record on nurse sensitive patient outcomes: an interrupted time series analysis. Journal of the American Medical Informatics Association, 19(4), 615-620.
Eiselt, H. A., & Sandblom, C. L. (2013). Decision analysis, location models, and scheduling problems. Springer Science & Business Media.
Fayers, P. M., & Machin, D. (2013). Quality of life: the assessment, analysis and interpretation of patient-reported outcomes. John Wiley & Sons.
Giugliano, R. P., Ruff, C. T., Braunwald, E., Murphy, S. A., Wiviott, S. D., Halperin, J. L., … & Ruzyllo, W. (2013). Edoxaban versus warfarin in patients with atrial fibrillation. New England Journal of Medicine, 369(22), 2093-2104.
Graves, N., Courtney, M., Edwards, H., Chang, A., Parker, A., & Finlayson, K. (2010). Cost-Effectiveness of an Intervention to Reduce Emergency Re-Admissions to Hospital among Older Patients.Plos ONE, 4(10), e7455. https://dx.doi.org/10.1371/journal.pone.0007455
Hemphill, J. C., Greenberg, S. M., Anderson, C. S., Becker, K., Bendok, B. R., Cushman, M., … & Scott, P. A. (2015). Guidelines for the management of spontaneous intracerebral hemorrhage a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke, 46(7), 2032-2060.
Holt, I. G., Green, S. M., & Nelson, E. A. (2016). Oral nutritional supplements for treating venous leg ulcers. The Cochrane Library.
Ishizaka, A., & Nemery, P. (2013). Multi-criteria decision analysis: methods and software. John Wiley & Sons.
Ishizaka, A., & Nemery, P. (2013). Multi-criteria decision analysis: methods and software. John Wiley & Sons.
Jo, Y., Loghmanpour, N., & Rosé, C. P. (2015, October). Time Series Analysis of Nursing Notes for Mortality Prediction via a State Transition Topic Model. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (pp. 1171-1180). ACM.
Johansen, M. L., & O’Brien, J. L. (2015, February). Decision making in nursing practice: a concept analysis. In Nursing forum.
Kuhn, L., Page, K., Ward, J., & Worrallâ€ÂCarter, L. (2014). The process and utility of classification and regression tree methodology in nursing research.Journal of advanced nursing, 70(6), 1276-1286.
Larichev, O. I., & Moshkovich, H. M. (2013). Verbal decision analysis for unstructured problems (Vol. 17). Springer Science & Business Media.
Neumann, I., Akl, E. A., Vandvik, P. O., Agoritsas, T., Alonso-Coello, P., Rind, D. M., … & Bates, S. (2014). Clinical Practice Guidelines and Decision Analyses. TITLE: Clinical Practice Guidelines: Facilitating Their Use and Enhancing Their Trustworthiness, 9.
Pocock, S. J. (2013). Clinical trials: a practical approach. John Wiley & Sons.
Polit, D. F., & Beck, C. T. (2013). Essentials of nursing research: Appraising evidence for nursing practice. Lippincott Williams & Wilkins.
Portney, L. G., & Watkins, M. P. (2015). Foundations of clinical research: applications to practice. FA Davis.
Puterman, M. L. (2014). Markov decision processes: discrete stochastic dynamic programming. John Wiley & Sons.
Reuben, D. B., & Tinetti, M. E. (2012). Goal-oriented patient care—an alternative health outcomes paradigm. New England Journal of Medicine,366(9), 777-779.
Seo, F., & Sakawa, M. (2012). Multiple criteria decision analysis in regional planning: concepts, methods and applications (Vol. 10). Springer Science & Business Media.
Shaban, R. (2015). Theories of clinical judgment and decision-making: A review of the theoretical literature. Australasian Journal of Paramedicine,3(1).
Siebert, U., Alagoz, O., Bayoumi, A. M., Jahn, B., Owens, D. K., Cohen, D. J., … & ISPOR-SMDM Modeling Good Research Practices Task Force. (2012). State-transition modeling: a report of the ISPOR-SMDM modeling good research practices task force-3. Value in Health, 15(6), 812-820.
Slowiński, R. (Ed.). (2012). Fuzzy sets in decision analysis, operations research and statistics (Vol. 1). Springer Science & Business Media.
Solomon, D. H., Patrick, A. R., Schousboe, J., & Losina, E. (2014). The Potential Economic Benefits of Improved Postfracture Care: A Costâ€ÂEffectiveness Analysis of a Fracture Liaison Service in the US Healthâ€ÂCare System. Journal of Bone and Mineral Research, 29(7), 1667-1674.
Solomon, D. H., Patrick, A. R., Schousboe, J., & Losina, E. (2014). The Potential Economic Benefits of Improved Postfracture Care: A Costâ€ÂEffectiveness Analysis of a Fracture Liaison Service in the US Healthâ€ÂCare System. Journal of Bone and Mineral Research, 29(7), 1667-1674.
Vaismoradi, M., Turunen, H., & Bondas, T. (2013). Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study. Nursing & health sciences, 15(3), 398-405.
van Walsem, A., Pandhi, S., Nixon, R. M., Guyot, P., Karabis, A., & Moore, R. A. (2015). Relative benefit-risk comparing diclofenac to other traditional non-steroidal anti-inflammatory drugs and cyclooxygenase-2 inhibitors in patients with osteoarthritis or rheumatoid arthritis: a network meta-analysis.Arthritis research & therapy, 17(1), 1.
Veroff, D., Marr, A., & Wennberg, D. E. (2013). Enhanced support for shared decision making reduced costs of care for patients with preference-sensitive conditions. Health Affairs, 32(2), 285-293.
Vickers, A. J. (2012). Decision analysis for the evaluation of diagnostic tests, prediction models, and molecular markers. The American Statistician.
Vickers, A. J. (2012). Decision analysis for the evaluation of diagnostic tests, prediction models, and molecular markers. The American Statistician.
Wouterse, B., Huisman, M., Meijboom, B. R., Deeg, D. J., & Polder, J. J. (2013). Modeling the relationship between health and health care expenditures using a latent Markov model. Journal of health economics,32(2), 423-439.
Yu, J., Shah, B. M., Ip, E. J., & Chan, J. (2013). A Markov model of the cost-effectiveness of pharmacist care for diabetes in prevention of cardiovascular diseases: evidence from Kaiser Permanente Northern California. Journal of Managed Care Pharmacy, 19(2), 102-114.
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$8How Our Essay Writing Service Works
First, you will need to complete an order form. It's not difficult but, in case there is anything you find not to be clear, you may always call us so that we can guide you through it. On the order form, you will need to include some basic information concerning your order: subject, topic, number of pages, etc. We also encourage our clients to upload any relevant information or sources that will help.
Complete the order form
Once we have all the information and instructions that we need, we select the most suitable writer for your assignment. While everything seems to be clear, the writer, who has complete knowledge of the subject, may need clarification from you. It is at that point that you would receive a call or email from us.
Writer’s assignment
As soon as the writer has finished, it will be delivered both to the website and to your email address so that you will not miss it. If your deadline is close at hand, we will place a call to you to make sure that you receive the paper on time.
Completing the order and download