Improving Patient Access to Chemotherapy Treatment

Improving Patient Access to Chemotherapy Treatment

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Because of cancer becoming to the leading cause of death, the demand of services in cancer institution is steady increasing in recent years. Certain of researchers indicated that patients spend much more time on waiting, either in waiting to range an appointment or waiting at cancer institutions. Therefore, the paper “Improving Patient Access to Chemotherapy Treatment at Duke Cancer Institute” (Woodall etc, 2013) is aim to improve to the patient flow in their institution, which also focus on to apply in other cancer institutions.

Initially, writers obtain some basic information about Duke Cancer Institute on the information flow among different departments. The departments include clinic, radiology, central laboratory, oncology treatment center (OTC) and pharmacy; the nurse’s types include full-time and part-time. In order to optimize and simulate the processes to meet patients demand and allocate resources, the researchers provide three models to achieve their objectives. As these three models are being analysis step-by-step and the later model is based on previous results.

The first model is ‘discrete-event simulation model’, which is aim to predict patient waiting time and acquire the information on resources utilization among different departments. Nevertheless, researchers identified that the most serious bottleneck is in OTC, because of nurses are unavailable during the treatment process. Therefore researchers decided to key focus on OTC, and made assumptions on maximum patients’ number for each nurse. As a result that OTC is subject to time variability, improve the shift time schedules and working length of nurses will be the better way to distribute nurse’s supply with demand of patients.

Based on the first mode, then they raised to using ‘mixed-integer programming model’ for nurse shifting, to release the bottleneck in OTC. The schedules shifting include daily, weekly and monthly, nurse type include full-time and part-time. This method is used to concentrate on predetermine the amount of nurses on weekly and monthly schedules. The full-time nurse type will be including 10-hours and 8-hours per day. The nurses’ allocation of weekly and monthly is based on daily patients’ demand. As a result, those analysts determine to change one or two full-time nurses into same ability levels of part-time nurses, which is more suitable for OTC peak time demand and reduce resources overuse when there don’t have too much patients.

The final model is based on the previous model to further relieve the bottleneck by optimize the start time and end time on daily nurse shifting. This method is focus on the daily nurse shifting.

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As a result, the optimization changing is increments half-hour times of start and end time during the day. This model releases the bottleneck at peak time during the day, and had greatest influence on patient waiting time.

Indicate the characteristics that a good OR/MS model should have

As the management science model is designed to describe the problems in simple way and using appropriate techniques and concepts to analysis the problems (Williams, 2008). A good model should include plenty of “good characteristics”. As for different model, the measurement standard will be focus on different level. Certain of researchers are summarise the characteristics of “good model” in different aspects. Classified the characters they summarized into some groups as follows: (Dennis and Dennis, 1991; Williams, 2008; Little, 1970; Daellenbach, etc. al., 2012)

• Simple and accurate. A simple model is easier to understand. However, the balance between simple and complicate of models, which also depends on the situation that why to build models. However even though design a simple model, analyst still need to obtain accurate data from the real situation. And not contain the information which is irrelevant from problems.
• Comprehensive. A model also should contain all the significant factors that will affect the measurement of implementation under their situation.
• Relevant to the problem. A model must be relevant to the situation, and produce relevant outputs information which is directly influence for decision-making process without need for further widespread transactions.
• Meet objectives. Designed a model must meet the objectives on the reason to design it. An objective should be based on client’s expectation and situation they face.
• Optimization. Among all the possible solutions, analysts should proving the final decision which is the best solution from any other possible alternatives. This optimization model(s) will have lowest possible cost, and during the time frame could have most effective decision-making process.
• Well structured. A model structure should have mathematical relationships or causality of among each decision variables, such as which factors could lead to what results and the reason to cause it etc.
• Formal. A model should be formal, means that the language in a model needs to be consistent, clear, and unambiguous in the greatest extent. The language need to explain explicit of the relationship among various decision variables.
• Easy to manipulate and communicate with. A good model should be easy for users and analysts to gain information, get prepare, modify, update and get results rapidly. During the process to get responses, don’t need irrelevant resources and effort to get involved.
• Could be adaptive in other situation. A model is established based on definitions of reality that can be manipulated into daily activities. A good model could also be applied into other situation without massive changes, only modify few factors.
• Could better understand the problem. Through a model process, problems should be simplified and users could have better understand on problem.

The characteristics match with case
As previous writers summarized the characteristics about a “good model” should have, here using them as a standard to evaluate the three models that designed for Duke Cancer institution. The aim of these three models is improve patient flow, and each model has very explicit separate objectives which also have closely relevant on what approach it should be focus to improve. So these three models have clearly objectives which are relevant to clients demand. They also provided better understand to users by split the problems into three models to solve, thus each model have specific focus and objectives.

Of these three models, even though the process on data classification and mathematical calculation are quite complicated. However, clearly data concentrate on each model guarantee analysts obtain accurate data and using in appropriate way. Then the models are simple for users because it gives accurate results on how to allocate nurses.

Moreover, the models also optimized the schemes and provide the best options that could maximum benefits and keep costs down, which also match the character that “good model” should give an optimize results.

The three models are running as separate process, therefore these three models have clearly boundary among them. On the other side about inside of each models, also could be recognized as well structured on each analysis level, such as clearly data collection level, primary research, model building, data analysis etc. Moreover they are using formal language to explain the process clearly, such as the reason on every steps, the results of every activities etc.

As writers mentioned in this paper, these three models could be applying in other cancer institution which want to improve their patient flow by optimize nurses schedule. Such as Cancer Center in Durham, and North Carolina are already adopted. Therefore these three models are adoptable in other organisation without massive changing.

As a result, according to the information that writers provided in this paper, the three models match most of the characteristics that previous researchers concluded. Therefore, these three models could be recognized as “good models” being applied.

Main issues encounter in case? How they are best managed?

The issues of problem structuring:
Rosenhead (1996) provide a definition on problem structuring:

“Problem structuring methods (PSMs) are a broad group of problem-handling approaches whose purpose is to assist in structuring problems rather than directly with solving them.” pp:117

Problem structuring is making sure the method could have useful contribution to solve problems, and meet the demand of clients. The purpose of problem structuring is structure the problems instead of solve them (Williams, 2008; Rosenhead, 1996).

However, during decision-making process generally will have multiple decision makers. Each decision maker’s perspective is different, separate objectives could lead to different approach and results, which also will become an issue on defining the problem. Moreover, the relationship between PSMs analysts and model users also could generate issue. Due to poor communication with clients could cause misunderstanding; thereby model’s objective might deviate from client’s expectation (Williams, 2008; Pidd, 2009; Mingers, and Rosenhead, 2004).

Another issues on problem structuring is that the results of problem structuring methods is not suitable of traditional OR method for wicked problems (Rosenhead, 1996). Moreover, currently have a large amount of PSMs in practice, which means that select a method(s) which is most closely related to a particular situation will be more difficult, especially when there have time limits during model choosing period. On the other side, some researchers also believe that technical could be recognised as an issue on problem structuring. Due to nowadays PMSs are always involved in numerous techniques to achieve, which might become a constraint on PSMs process. Finally, in actual process, some other actual objective and subjective factors which are involved in work could become an obstacle on PMSs; however it will depend on the nature of different work (Mingers, and Rosenhead, 2004; Pidd, and Woolley, 1980a; Pidd, and Woolley, 1980b).

Generally, these three model’s problem structuring process is running smoothly and well structured. However, as aforementioned, multiple decision makers will become an issue to obstacle PSMs process. What Duke Cancer institution had done is they made clearly decision within the organization before talk to analysts. Therefore in certain extent could be decrease the risk on misunderstanding the problem. On the other side, during the period to prepare problem structuring, analysts also done some research on previous models in other cancer center to learn about what they had done to help on problem structuring methods choosing. Consequently based on previous experience could narrow the options on what methods should be considering (Woodall etc., 2013).

Issues on data collection
Data collection is an essential section that will have great impact on the results. High quality data could predict precision error to analysts and increase their accurate rate. Generally data are collected from records, meetings, data banks, studies etc., however, some information will unable to collect under certain circumstance. Another issue on information collection is accessibility. Therefore, analysts have to predict the data when they don’t have accessibility to the information that they must be involved in. Moreover, another issue is that collect information from such a great amount of previous records will increase the difficulties for analysts to find appropriate information that can be analyzed and help to solve the problem (Dennis and Dennis, 1991; Xu, 2008; Halsey, 1981; Mitchell, 1993).

As for this paper, the description from writers about problem structuring period is quite smoothly. This paper has four writers, two of them are the inside employees of Duke Cancer institution, therefore the data accessibility is quite high. The analysts obtain information is sample observation times in all departments of cancer center, include computer information systems, time studies, and interviews with oncologists, administrator, and nurse experts. However, the information that analysts need in Duke Cancer institution is not fully obtained from actual records, because some patients arrive time is unpredictable in various situation and nurse’s time to contribution on each patients are not been recorded. Therefore the analysts include some assumptions obtained from experts, in order to complete the data. The assumptions are such as the average assumption each nurse contribution time etc (Woodall etc., 2013).

Issues on information analysis

The issues could constraints analysis process could be classified into 4 groups (Hsiang, 2002; Mitchell, 1993). Firstly, comprehend preparation before analysis. The preparation work might be last for few days to several months, which depending on the objective and complexity of the situation. The analysts need to be first put data into spreadsheet for quick analysis, after this data will be display into a more convenience way to analysis.

Secondly, similar with choosing methods on problem structuring, during the process of data analysis generally will need particular mathematical and/or statistical methods to help analysis. However nowadays lots of methods been generated, it is more difficult to choose a method(s) as an analysis tools that most suitable to specific situation.

Thirdly, as aforementioned, management science need to provide optimization results, which increase the difficulty on analysts to calculate and arrange optimization result(s). However, in order to make sure the results of analysis is the best among other options, it will taking time and effort to done it. Moreover another issue for analysts is that they also need to consider the implementation ability during analysis period. Because if a model is optimization from other results of analysis, however due to some reasons that it cannot be implement in practice, then the results from analysts get from analysis process is inappropriate.

Same with PSMs choosing, analysts are using previous models as references to help them determine the most appropriate method that suit to their situation and problem. As previous mentioned in data collection, the data being collect in first model has part of assumption, which increase difficulty to analysis and get accurate results. In second model to manage nurse shifting is about establish optimize results that could capable to face all the situation. However analysts have to consider comprehensive on what will influence on practice. The third model face the same issue with second one that analysts have to contain all the relative factors (Woodall etc., 2013)

Implementation difficulties and assess the case
Difficulties with implementation

According to previous articles on the difficulties on MS/OR model implementation, put forward a series of reasons that could interference model implementation. Details as follows: (Dennis and Dennis, 1991; Daellenbach, etc. al., 2012; Randall and Dennis, 1983; Pidd, and Woolley, 1980).

• Personalities and motivation of various users could be different. Adopt a new procedure could cause mistrust and misunderstanding. Resistance from users that don’t want to change because it could change the interest balances among them.
• Age, background and level of education. Users from different background and have different education level, which will lead to various understanding on model. And various age groups will also have difference perspectives and techniques to apply models.
• Communication. During implementation, poor communications will hind model performing. Moreover, results feedback is necessary to reevaluate the process, delayed results feedback will hinder the model next stage implement.
• Cooperation. If problem users are not willing to cooperate with new activity process, which could impede implementation to proceed. On the other side, the less information that analysts obtain of the problems, the less cooperation from users.
• Influence on other work. Management science models will have some association with other working process in certain degree. If applying a new model in organization will have great impact on other activity normally running, this model will face negative motion and hard to apply. This will change to less cooperation issue.
• The implication ability of the organisation. Suppose that the model(s) is perfectly suit clients; however some departments doesn’t have enough ability to apply and next extra expenses to improve their ability. It will also constraint model applying.
• Document process. A model need to be well-documented, however prepare these documents is time consuming, could delay model implementation.
• Costs compare with benefits. The predictable change on costs and benefits after using a model, need to be within the scope of formally prescribed rules. If expenses of building a new model over than client’s expectation, there have possible that model might not be applied.

The implementation stage of case
There have three implementation stages on Duke Cancer Institution to apply these three models as previous mentioned. Firstly, Duke Cancer institution hired 4 part-time nurses to meet their daily peak time demand. Because the nature of part-time job is alternative. The time schedule of part-time nurses could be change based on daily patient amounts. Which is very convenience to optimize all the resources during peak time and poor time. Secondly is adjusted the beginning and the end time for all nurses, including original nurses and new part-time nurses. And arrange some existing nurses into half hour schedule. Half-hour schedule is suitable for the nurses which are taking 10-hour-shift. Finally, hired 1.75 additional on full-time nurses to decrease patients waiting time. Besides these three stages, analysts also indicated that combine using 10-hour and 8-hour shifting will benefit on patient average waiting time. After all timetable and nurses hired changing, longer shifts is not exist.

As for this paper, the three implementation stages are not very simple to achieve. As for first stage, as aforementioned on it might be not easy to find four part-time nurses to get involved and might cause additional costs. Compare with first stage, second stage will be easier to achieve, but this schedule still need manager put lots of effort to rearrange the schedule after weekly/monthly changed. The final stage is aim decrease patients waiting time by hire additional nurses, to make sure even there have emergency, they still have enough nurses that won’t make patients dissatisfied. However the last stage also increasing the expenses on hired more nurses.

The main implementation issues of the case would be?
This project includes three management science models to improve patient flow. However even though the models are comprehensive and accurate as much as possible, still have some issues when applying them in practice.

This project is predicted patient’s treatment time and arrival time to make assumption on nurses’ amount. However is not all patients’ arrival on time, delayed arrival time will cause additional delay on other departments. The models are increasing part-time nurse amount in peak time, control average maximum waiting time within 90 minutes. However, in practice could have emergency happening, especially during peak time.

As a result of these models, is replacing 1 or 2 full time nurses with equal quality part time nurses. On the other sides, find nurses who have equal quality with original full time nurses and could accept shifting part time schedule is difficult. Nevertheless, original full time nurses might unwilling to leave and against the models implementation. Could also cause panic emotions among nurses until finally issued dismissal notice.

Additionally, recruit new part-time nurses from outside, they need time to get familiar with new circumstance. If cannot recruit equal quality nurses, might cause extra training costs. On the other side, nurse shifting weekly and monthly also will generate time consuming on get accustomed to new surroundings. Because every time shifting could cause time to develop new habits.


The three models build in this paper in order to improve patient flow in Duke Cancer institution had been achieved. The first model ‘discrete-event simulation model’ is aim to predict waiting time and resources distribution among all the department. Then ‘mixed-integer programming model’ is focus on nurses shifting in OTC department which has most serious bottle neck need to be release. The final model ‘novel simulation-optimization model’ is further release bottleneck in OTC by shifting nurse’s starting and end of time.

The three model are all match most of the characteristics on what ‘good model’ should have, even though they are not perfect, they still are very good model to applying in other cancer institutions in the future. And also have unavoidable series of issues during structuring problem, data collection, analysis and implementation. However, analysts put maximum efforts to manage most of the issues and reduce bad influence to minimum level.

Works Cited

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Daellenbach, H.G., McNickle, D.C. and Dye,S., (2012), “Management Science, decision-making through systems thinking, 2rd edition”, London, Palgrave Macmillan

Dennis, T.L. and Dennis, L.B., (1991), “Management Science”, West publishing company, St. Paul

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Little, J.D.C., (1970), “Models and managers: concepts of decision calculus”, Management Science, April

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Pidd, M., (2009), “Tools for Thinking Modelling in Management Science, 3rd edition”, Lancaster, John Wiley & Sons, Ltd

Pidd, M. and Woolley, R.N., (1980a), A Pilot Study of Problem Structuring, The Journal of the Operational Research Society, 31(12), pp. 1062-1068

Pidd, M., and Woolley, R. N., (1980b), Four Views on Problem Structuring, Interfaces 10(1):51-54. Available at: access at: 22/11/2013

Randall L. Schultz, Dennis P. Slevin, (1983) Implementation Exchange: The Implementation Profile, Interfaces, 13(1):87-92. Available at: access at: 22/11/2013

Rosenhead, J., (1996), What's the Problem? An Introduction to Problem Structuring Methods. Interfaces, 26(6), pp. 117-131. Available at: access at: 22/11/2013

Williams, T., (2008), “Management Science in Practice”, John Wiley & Sons Ltd, Chichester

Woodall, J.C., Boswell, T.G.A., Murr, M. and Denton, B.T., (2013), “Improving Patient Access to Chemotherapy Treatment at Duke Cancer Institute”, Interfaces, vol. 43, No. 5, pp: 449-461

Xu, Y., (2008), Methodological Issues and Challenges in Dta Collection and Analysis of Qualitative Meta-Synthesis, University of Nevada, 2 (3), pp. 173-183

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